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Get Healthy Get Active: Prevention is better than care
GEORGE JON SANDERS
A thesis submitted in partial fulfilment of the requirements of Edge Hill University for the
degree of Doctor of Philosophy.
July 2018
1
Statements
The presented research programme evaluated the effectiveness of Sport England’s
Get Healthy Get Active physical activity intervention on older adults’ physical activity
levels. The research presented within this thesis including project design, data
collection, and data analyses was funded by Sefton Metropolitan Borough Council
and was conducted solely at Edge Hill University.
2
Acknowledgements
This thesis is dedicated to my father, mother and brother whom have provided me
with the necessary life skills and mental fortitude to complete this thesis. Your love
and support motivates me more than I could ever describe. I couldn’t have done it
without you.
I would like to thank my Director of Studies, Professor Stuart Fairclough for his
continued support and guidance throughout my PhD. I would also like to thank
Professor Brenda Roe and Dr Axel Kaehne for their insightful advice, support and
feedback provided every step of the way. The knowledge and expertise from this
supervisory team has enabled me to continually learn, improve and progress
throughout this project. It has been a fantastic experience and this is in part down to
this incredible supervisory team.
Thank you also to the Active Lifestyles team at Sefton Metropolitan Borough Council
whom were instrumental in the research process and provided me with full access to
the Get Healthy Get Active sessions. Thank you for being infinitely patient with the
endless ideas and subsequent measures thrust upon participants throughout the
sessions. You kept me grounded throughout this project and completion of this
thesis would simply not have been possible without your full support.
3
A massive thank you also goes out to all the participants and Edge hill University
students who gave their time to participate in the project. I must also thank Dr Andy
Sparks, Dr Whitney Curry, Professor Zoe Knowles, and Dr Lynne Boddy for their
expertise and input throughout the project. Michael and Sarah, it was brilliant going
through this process with you both and I can only hope that future colleagues are as
patient as you were with the constant smell of coffee, chicken and rice that lingered
throughout our office for the past three years.
In conclusion, what a fantastic experience this has been!
4
Abstract
Low levels of physical activity (PA) and high levels of sedentary behaviour (SB) among
older adults, carry considerable burdens to physical (e.g., premature mortality,
chronic diseases and all-cause dementia risk) and psychosocial (e.g., self-perceived
quality of life, wellbeing and self-efficacy for exercise) health. Numerous large scale
interventions designed to engage physically inactive older adults have shown the
potential that interventions guided by theoretical frameworks, consider
implementation at scale across levels of the socioecological model and are designed,
implemented and delivered in close partnership with stakeholders can have among
this population. This thesis aimed to investigate the effectiveness of Sport England’s
Get Healthy Get Active (GHGA) PA intervention. GHGA was delivered by Sefton
Metropolitan Borough Council (SMBC) and was designed to engage inactive older
adults in PA for at least once a week for 30 minutes.
The purpose of Chapter 3 was to elicit subjective views of older adults about
perceived facilitators and barriers to PA participation and to inform the design,
delivery and recruitment strategies of Sport England’s GHGA PA intervention.
Analyses revealed time of day, cost and social support to be key predictors in
promoting PA. Sessions that avoid taking place in the early morning or late
afternoon, are free of charge, and promote social interaction were also significant
predictors of older adults’ PA participation. Wrist- and hip-based accelerometers are
now common in assessing PA in population-based studies, however no raw
acceleration cutpoints for moderate-to-vigorous PA (MVPA) and SB exist for older
5
adults. Chapter 4 was the first to test a laboratory-based protocol using activities
representative of older adults’ PA behaviours, to generate behaviourally valid,
population specific wrist- and hip-based raw acceleration cutpoints for SB and MVPA
in older adults. These cut-points were subsequently applied within Chapter 5, along
with self-reported measures of SB, PA and health indicators, to investigate time
spent in MVPA and SB, and measures of quality of life (QoL), self-rated health (SRH),
self-assessment of physical fitness (SAPF), and self-efficacy for exercise (SEE).
Chapter 6 addressed the main objective of the thesis which was to assess the
effectiveness of the GHGA PA intervention on PA levels among inactive older adults ≥
65 years of age. The GHGA PA intervention was effective in increasing QoL, SRH,
SAPF, and SEE scores over time after adjustment for covariates. However, the
intervention was ineffective in both reducing time spent in SB and increasing time
spent in MVPA. As a measure of intervention fidelity, Chapter 7 evaluated whether
the GHGA multi-component PA intervention was implemented as intended. Results
from both deliverer interviews and session observations revealed that a high degree
of intervention fidelity was maintained throughout the GHGA PA sessions within five
core domains including: Study Design, Provider Training, Intervention Delivery,
Intervention Receipt and Enactment.
This thesis contributes to the understanding of feasible and acceptable PA strategies
in older adults. Future research is needed to establish whole system-oriented multi-
component community-based interventions that are effective at increasing PA levels
in older adults.
6
Contents
Acknowledgements 3
Abstract 5
List of Tables 11
List of Figures 12
List of Abbreviations 13
Chapter 1. Introduction 15
1.1. The Research Problem 16
1.2. Conceptual Framework 19
1.3. Organisation of Thesis 23
1.4. Original contribution to knowledge 24
1.5. Aims and objectives 25
Chapter 2. Literature Review 29
2.1. Guidelines 30
2.2. The Health Benefits of Physical Activity Participation 33
2.3. Physical Activity Levels 36
2.4. Sedentary Behaviour 38
2.5. Physical Activity Measurement 43
2.6. Correlates of Physical Activity and Sedentary Behaviour 50
2.7. Community-Based Physical Activity Interventions 53
2.8. Community-Based Intervention Process Evaluation 57
2.9. Summary of literature 60
Thesis Study Map 62
7
Chapter 3 (Study 1). Using formative research with older adults to inform a
community physical activity programme: Get Healthy, Get Active. 63
3.1. Introduction 64
3.2. Methods 65
3.3. Findings and Discussion 70
3.4. Strengths and Limitations 82
3.5. Conclusions 84
Thesis Study Map 86
Chapter 4 (Study 2). Evaluation of wrist and hip sedentary behaviour and
moderate-to-vigorous physical activity raw acceleration cutpoints in older adults.
88
4.1. Introduction 89
4.2. Methods 91
4.3. Results 98
4.4. Discussion 101
4.5. Conclusion 106
Thesis Study Map 107
Chapter 5 (Study 3). Physical activity, sedentary behaviour, perceived health and
fitness, and psychosocial wellbeing among community-dwelling older adults. 109
5.1. Introduction 110
5.2. Methods 112
5.3. Results 121
5.4. Discussion 129
5.5. Strengths and Limitations 133
8
5.6. Conclusions 134
Thesis Study Map 136
Chapter 6 (Study 4). A pragmatic evaluation of the Get Healthy Get Active physical
activity programme for community-dwelling older adults. 139
6.1. Introduction 140
6.2. Methods 143
6.3. Results 153
6.4. Discussion 158
6.5. Strengths and Limitations 164
6.6. Conclusions 167
Thesis Study Map 169
Chapter 7 (Study 5). Intervention fidelity of the Get Healthy Get Active physical
activity programme for community-dwelling older adults. 172
7.1. Introduction 173
7.2. Methods 177
7.3. Results 185
7.4. Discussion 197
7.5. Strengths and Limitations 204
7.6. Conclusions 206
Thesis Study Map 207
Chapter 8. Synthesis of Findings, Recommendations and Conclusions 210
8.1. Synthesis of Findings 211
8.2. Strengths and Limitations 221
8.3. Recommendations 224
9
8.4. Conclusions 229
References 230
Appendices 275
Appendix 1. Ethical Approval 276
Appendix 2. Accelerometer Instructions 290
Appendix 3. Associated Publications 293
10
List of Tables
Table 4.1. Description of the sixteen structured activities. 93
Table 4.2. Study sample characteristics. 99
Table 4.3. Mean (SD) accelerometer output from GA and AG (mg) during each activity performed by older adults.
99
Table 4.4. Calibration cutpoints and cross-validation % agreement, kappa (k) and se and sp. 101
Table 5.1. Descriptive characteristics of the participants. 122
Table 5.2. GENEActiv wrist-worn accelerometer data descriptives. 123
Table 5.3. Self-reported physical activity and psychosocial outcome measures. 125
Table 5.4. GENEActiv SB and physical activity outcomes. 127
Table 6.1. Exercise typical of a GHGA session. 145
Table 6.2. Descriptive baseline characteristics of the participants. 155
Table 6.3. Unadjusted self-reported physical activity and psychosocial outcome measures. 156
Table 6.4. Crude multilevel model analyses of the outcome measures at three, six and 12-months follow-up. 158
Table 6.5. Adjusted multilevel model analyses of the outcome measures at three, six and 12-months follow-up. 159
Table 6.6. Significant intervention subgroup interactions. 160
Table 7.1. Frequency counts and descriptives. 198
11
List of Figures
Figure 1.1. Precede-Proceed model of health programme design, implementation, and evaluation (Green & Kreuter, 2005).
21
Figure 3.1. Predisposing correlates of physical activity participation among older adults. n = Individual mentions per person (multiple mentions not included); Fn = Focus group number; Pn = Participant number. 73
Figure 3.2. Enabling correlates of physical activity participation among older adults. n = Individual mentions per person (multiple mentions not included); Fn = Focus group number; Pn = Participant number. 77
Figure 3.3. Reinforcing correlates of physical activity participation among older adults. n = Individual mentions per person (multiple mentions not included); Fn = Focus group number; Pn = Participant number.
80
Figure 6.1. Flow of participants through the study. 146
12
List of abbreviations
6MWT Six-minute Treadmill Walk Test
AG ActiGraph
ANCOVA Analysis of Covariance
BCC Behaviour Change Consortium
BMI Body mass index
CDC Centers for Disease Control and Prevention
CONSORT Consolidated Standards of Reporting Trials
EIT Evidence Integration Triangle
EE Energy Expenditure
ENMO Euclidean norm minus one
GA GENEActiv
GHGA Get Healthy Get Active
IMD Indices of Multiple Deprivation
LPA Light physical activity
MANCOVA Multivariate Analysis of Covariance
MET Metabolic equivalent
MPA Moderate physical activity
MVPA Moderate to vigorous physical activity
13
NIH National Institute of Health
NHANES National Health and Nutrition Examination Survey
NHS National Health Service
ONS Office for National Statistics
OR Odds Ratio
PA Physical activity
PAR-Q Physical Activity Readiness Questionnaire
RCT Randomised Controlled Trial
RMR Resting Metabolic Rate
RR Relative Risk
RTM Regression to the Mean
SB Sedentary behaviour
SD Standard deviation
SEF Standard Evaluation Framework
SES Socioeconomic status
US United States
UK United Kingdom
VPA Vigorous physical activity
WHO World Health Organisation
14
Chapter 1. Introduction
15
1.1. The Research Problem
The physical and psychological health benefits of PA are widely acknowledged
(Vahlberg, Cederholm, Lindmark, Zetterberg, & Hellström, 2017). PA is defined as
any bodily movement produced by skeletal muscles that results in energy
expenditure (EE) (Caspersen, Powell and Christenson, 1985). The term encompasses
exercise, sport, recreation, walking, active transport (e.g., cycling, running,
skateboarding), occupational activity, and domestic activity such as gardening and
cleaning (Caspersen et al., 1985). Research has explored health benefits in relation to
PA intensities including light-PA (LPA), moderate-PA (MPA), moderate-to-vigorous PA
(MVPA), and vigorous-PA (VPA) (Barone Gibbs et al., 2017; Biswas et al., 2015;
McPhee et al., 2016; Windle, Hughes, Linck, Russell, & Woods, 2010). Older adults
are said to typically engage in LPA (Ku, Fox, Liao, Sun, & Chen, 2016) consisting of
activities including carrying light objects, walking slowly, and housework (e.g.,
washing up and hoovering) (Public Health England, 2017). Opportunities for older
adults to be physically active exist in many different settings and contexts such as at
home, at recreation facilities, through active commuting, and within local community
spaces (Milligan et al., 2015; Gardiner, Geldenhuys and Gott, 2016).
The chronological age of 65 years is the accepted definition of an older person in the
United Kingdom (Age UK, 2018). Guidelines issued by the United Kingdom (UK) Chief
Medical Officers and the United States (US) Surgeon Generals recommend that older
adults (≥65 years) engage in at least 150 minutes of MPA (or 75 minutes of VPA) per
week in bouts of at least 10 minutes, with muscle-strengthening and balance
16
activities included on at least two of those days (Department of Health, 2011a;
Centers for Disease Control and Prevention (CDC), 2015). Despite overwhelming
evidence outlining the benefits of regular PA on both physical (Zhu et al., 2017) and
psychosocial (Devereux-Fitzgerald, Powell, Dewhurst, & French, 2016; Franco et al.,
2015; Greaney, Lees, Blissmer, Riebe, & Clark, 2016) determinants of health at older
ages (Lehne & Bolte, 2017), less than 12% of older adults globally perform PA on a
daily basis (CDC, 2016). Objective summaries of PA levels among older adults show
that only 15 per cent of males and ten percent of females within the UK, and 9.5% of
males and 7% of females within the US meet the recommended PA guidelines
(Tucker, Welk & Beyler, 2011; Jefferis et al., 2014). Large scale cohort studies have
shown that PA further declines with increasing age, among females, those of lower
socioeconomic status (SES), and among individuals with lower levels of perceived
health status and self-efficacy (Lehne & Bolte, 2017; Murtagh et al., 2015; Smith,
Gardner, Fisher, & Hamer, 2015). Given that current PA guidelines are the same for
both adults (18-64 years) and older adults (≥65 years), such high levels of inactivity
suggests that current PA guidelines may be too demanding for the latter population
(Booth & Hawley, 2015).
Accumulating evidence suggests that prolonged and continuous bouts of SB (defined
as waking behaviours in a sitting, reclining or lying posture with EE ≤1.5 metabolic
equivalents (MET) (Tremblay et al., 2017) have similar physical (e.g., premature
mortality, chronic diseases and all-cause dementia risk) and psychosocial (e.g., self-
perceived QoL, wellbeing and SEE) risk factors to those associated with physical
17
inactivity (Wilmot et al., 2012; Edwards & Loprinzi, 2016; Falck, Davis & Liu-Ambrose,
2016; Kim, Im & Choi, 2016). In fact, SB is now an identifiable risk factor independent
of other PA behaviours (Tremblay et al., 2017). Spending on average 80% of their
time in a seated posture, and with 67% sedentary for more than eight and a half
hours per day (Shaw et al., 2017), older adults are the most sedentary segment of
society and seldom engage in MVPA (Chastin et al., 2017).
PA is a complex behaviour influenced by various individual and environmental
factors (Devereux-Fitzgerald et al., 2016; Phoenix & Tulle, 2017). Identification of
modifiable correlates and a comprehensive understanding of the influence of these
factors over time on older adults’ PA are imperative in enabling policymakers and
healthcare professionals to develop and implement successful interventions
(Banerjee et al., 2015; Devereux-Fitzgerald et al., 2016; Greaney et al., 2016).
Intervention research in the field of PA in this population has primarily focused on
pre- and post-intervention measurements and less on longer term follow-up
measurements after intervention completion (McMahon et al., 2017). Follow-up
measures post-intervention are critical for understanding implementation
sustainability and maintenance patterns (McMahon et al., 2017). To improve
population health, efficacious PA interventions in controlled research settings must
be scaled up to reach broader populations across multiple settings (Milat et al.,
2016). A recent PA intervention (Choose to Move) designed to engage physically
inactive (e.g., not meeting current PA guidelines) older adults has shown the
potential for interventions that are designed, implemented and delivered in close
18
partnership with stakeholders and across multiple settings (McKay, Sims-Gould,
Nettlefold, Hoy, & Bauman, 2017).
1.2. Conceptual Framework
As previously discussed, PA is complex and many PA correlates exist among older
adults (Devereux-Fitzgerald et al., 2016; Phoenix & Tulle, 2017). Consequently,
behaviour change is complex to achieve and maintain. Theoretical frameworks are
vital in explaining and predicting health behaviour, and theory-based interventions
are more efficacious than atheoretical approaches at changing many health
behaviours, including PA (Plotnikoff et al., 2014). According to Michie and colleagues
(2007), theories can specify causal relations between potential correlates and proffer
implications for designing interventions to promote health. Previous literature now
includes many cross-sectional associations and longitudinal relationships between
demographic, biological, psychological, social environmental, and physical
environmental variables (commonly referred to as correlates) (Bauman et al., 2002;
Bauman et al., 2012; Bryan et al., 2007; Trost et al., 2002). Such findings have
emphasised the fact that regular PA yields numerous health benefits among all age
groups (Haskell et al., 2007). However, the current evidence base examining the
effects of PA among older adults is inconsistent and at times, contradictory
(Livingston et al., 2014) and thus, research should move beyond individual factors
and instead adopt multilevel socio-ecological models of health behaviour change
(Plotnikoff et al., 2014). Given that various factors can be influential to
19
implementation, the use of multi-level socio-ecological frameworks to design PA
promotion strategies are warranted (Plotnikoff et al., 2014).
Within health behaviour and promotion research the use of a socio-ecological model
as a theoretical underpinning has been widely adopted since it was originally
proposed (McLeroy, Bibeau, Steckler, & Glanz, 1988; Simplican, Leader, Kosciulek, &
Leahy, 2015). Levels of influence within the socio-ecological model include the
intrapersonal, interpersonal, organisational, community, environmental, and policy
levels of behaviour, whilst incorporating physical and psychological influences in an
attempt to better predict PA behaviour and ensure a supportive social and
community environment (Kerr et al., 2012; Sallis, Owen & Fisher, 2008). Indeed, this
combination of psychosocial and environmental factors is significantly related to
older adults’ PA (Carlson et al., 2012). A review of the literature indicates that
interventions targeting PA determinants at different levels of the socio-ecological
model, including the social and organisational/built environment levels, have the
highest potential to increase overall PA in older adults (Plotnikoff et al., 2014). For
encouraging older adults to be physically active and less sedentary, socio-ecological
models such as the PRECEDE-PROCEED model of health programme design,
implementation, and evaluation (Green & Kreuter, 2005; Figure 1.1), are well suited
for studying PA because participation occurs in specific locations such as leisure
centres and church halls. Ecological models direct attention towards the
characteristics of locations including the broader political and environmental factors
which either facilitate or hinder participation (Sallis et al., 2006). Framing an
20
intervention within a socio-ecological model has been favourably received previously
(Haggis et al., 2013; Kerr et al., 2012) and consequently, such a model is adopted in
the current thesis in order to ascertain the predictive power of a PA intervention in
positively affecting a broad range of intrapersonal, interpersonal, organisational,
community, environmental, and policy level contexts of PA behaviour among older
adults.
Figure 1.1. Precede-Proceed model of health programme design, implementation, and evaluation (Green & Kreuter, 2005).
An approach that holds relevance for PA intervention design, and promises further
understanding of the processes leading to sustained motivation and optimal
functioning/well-being in promoting PA is the PRECEDE-PROCEED model of health
programme design, implementation, and evaluation (Green & Kreuter, 2005). This
model provides a comprehensive structured assessment of health and health needs,
through the design and implementation of health promotion programmes to meet
21
those emerging needs. The PRECEDE-PROCEED model has been considered to be
among the 10 best planning models on usefulness for research and practice (Linnan
et al., 2005) and could therefore potentially increase the sustainability of a PA
intervention among older adults. The PRECEDE-PROCEED model (Green & Kreuter,
2005) is a social-ecological framework which allows enabling, predisposing and
reinforcing factors associated with PA to be acknowledged. The model systematically
considers the social and situational circumstances of a target group, relevant
epidemiological data, environmental and behavioural (lifestyle) factors, and factors
that influence these behaviours and the environments in which they occur (Green &
Kreuter, 2005). The PRECEDE phase represents the process that precedes the
intervention and is an acronym for predisposing, reinforcing and enabling constructs
(Atun et al., 2010; Yeo et al., 2007; Yuan et al., 2010). Predisposing factors are the
motivators for PA participation and include the knowledge, attitudes and beliefs that
motivate behaviour prior or during the intervention (Yuan et al., 2010). Enabling
factors include skill development and access to resources that facilitate change and
participation in the intervention (Yuan et al., 2010). Reinforcing factors are the
positive and negative factors that result as a consequence of behaviour change and
includes the social support and rewards and/or incentives for the PA behaviour
(Atun et al., 2010; Yuan et al., 2010). Manipulation of any one of these factors has
been found to result in behaviour change that is sustainable over time (Green &
Kreuter, 1999; Yuan et al., 2010). The PROCEED (Policy, Regulatory and
Organisational Constructs in Educational and Environmental Development) phase
aids in the implementation and evaluation of programmes. The last step
accommodates intervention planning based on available resources and potential
22
barriers. There are nine key phases in the model, five for assessment, one for
implementation, and three related to evaluation. The PRECEDE-PROCEED model
allows for participation of participants in the evaluation process so that they can
determine their behaviour and health outcomes by voluntary active involvement
(Green, Kreuter, Deeds, & Partidge, 1980). By involving the target population to
assess their own needs and barriers, the participants' compliance to a tailored
intervention programme is more likely to be successful and sustainable (Cole &
Horacek, 2009; Lean, Lara & Hill, 2007). Predisposing, enabling and reinforcing
factors will be based on relevant components of the ecological model of behaviour
change (Stokols, 1992). The model has been used extensively in health promotion
planning and evaluation among older adults (Banerjee et al., 2015; Gagliardi,
Faulkner, Ciliska, & Hicks, 2015; Jancey et al., 2008) and other populations
(Makintosh et al., 2011; Emdadi, Hazavehie, Soltanian, Bashirian, & Heidari
Moghadam, 2015; Susan, Mallan, Callaway, Daniels, & Nicholson, 2017) and thus,
was appropriate for adoption.
1.3. Organisation of Thesis
The central theme of the thesis is increasing PA levels of inactive community-
dwelling older adults aged ≥65 years. A review of the literature is provided in
Chapter 2. The key topics addressed are the impact of PA and SB on health, older
adults’ PA and SB levels, correlates of PA and SB, measurement of PA and SB, and
the effects of interventions on older adults’ PA levels and SB. The review critiques
the current literature, and highlights gaps, which provide a rationale for the current
23
research. Chapter 3 presents a formative study: Using formative research with older
adults to inform a community PA programme: Get Healthy, Get Active. Wrist-based
accelerometers are now common in assessing PA in population-based studies, but no
raw acceleration cutpoints for moderate-to-vigorous PA and SB exist for older adults.
This issue is addressed in Chapter 4. Older adults’ PA, SB and health indicators prior
to exposure to the GHGA intervention are reported in Chapter 5. Chapter 6
evaluated the impact of the GHGA intervention on older adults’ PA levels, SB, and
health indicators. As a measure of intervention fidelity, Chapter 7 evaluated whether
the GHGA multi-component PA intervention was implemented as intended. The final
chapter (Chapter 8) synthesises the key findings of the thesis and discusses the
overall strengths and limitations of the research programme. Recommendations for
future research and practice and conclusions are then presented in the final section
of Chapter 8.
1.4. Original contribution to knowledge
Original contributions to knowledge will be made through the design,
implementation and evaluation of a bespoke PA intervention aimed at increasing PA
among inactive community-dwelling older adults. Through the adoption of focus
groups, Chapter 3 will provide valuable insights into current knowledge and attitudes
towards PA, as well as perceived barriers, facilitators and opportunities for PA
participation among older adults living in the community. Chapter 4 will be the first
to test a laboratory-based protocol using intermittent activities representative of
older adults’ PA behaviours to generate behaviourally valid, population specific
24
wrist- and hip-based raw acceleration cutpoints for SB and MVPA in older adults.
These novel cut-points will be applied within Chapter 5, along with self-reported
measures of SB, PA and health indicators, to investigate time spent in MVPA and SB,
and measures of QoL, SRH, SAPF, and SEE. Chapter 6 will assess the effectiveness of
a PA intervention in increasing inactive community-dwelling older adults’ PA levels
and psychosocial health statuses through self-reported measures. Chapter 7 will add
to the limited intervention fidelity literature by evaluating whether a PA intervention
was implemented as intended. Decisions throughout the programme of work will be
informed by the PRECEDE-PROCEED model of health programme design,
implementation, and evaluation (Green & Kreuter, 2005) in order to ascertain the
predictive power of a PA intervention in positively affecting a broad range of
intrapersonal, interpersonal, organisational, community, environmental, and policy
level contexts of PA behaviour among older adults.
1.5. Aims and Objectives
The main aim of the thesis is to assess the effectiveness and implementation of Sport
England’s Get Healthy Get Active (GHGA) intervention on inactive community-
dwelling older adults’ PA levels. A detailed description of the intervention can be
found in Chapter 6, but a brief summary is provided here. The GHGA intervention
was aimed at engaging inactive community-dwelling older adults in PA at least once
a week for 30 minutes, via a PA intervention. The project was funded by Sport
England and delivered by SMBC. Findings from a comprehensive meta-analysis
suggest that interventions designed to increase PA behaviour among adults aged 65
25
and older can be effective (Chase, 2015). Increased effectiveness has been observed
in interventions which are guided by theoretical frameworks, consider
implementation at scale across levels of the socioecological model and are designed,
implemented and delivered in close partnership with stakeholders (Harris et al.,
2015; Harris et al., 2018; Sink et al., 2015). Resultantly, if proven to be effective, it is
proposed that the GHGA PA intervention will lead to bigger, sustainable, national
level research projects whose results would have policy ramifications and inform the
thought and practice of professionals in PA, social work and care settings.
Five studies were conducted to address the following objectives:
Study 1 objectives.
1. To explore current knowledge and attitudes towards physical activity, as well
as perceived barriers, facilitators and opportunities for physical activity
participation among older adults living in the community.
2. To use these data to subsequently inform the design, delivery and
recruitment strategies of Sport England’s national GHGA initiative.
Study 2 objectives.
3. To test a laboratory-based protocol to generate behaviourally valid,
population specific wrist- and hip-based raw acceleration cutpoints for
sedentary behaviour and moderate-to-vigorous physical activity in older
adults.
26
4. To apply these cut-points to subsequently analyse physical activity data for
Sport England’s GHGA physical activity intervention.
Study 3 objectives.
5. To investigate gender, age, and socio-economic status differences in older
adults’ sedentary behaviour, physical activity and self-reported health
indicators.
6. To examine associations between sedentary behaviour and physical activity
with self-reported health indicators.
Study 4 objective.
7. To evaluate the effectiveness of Sport England’s GHGA physical activity
intervention on older adults physical activity, sedentary behaviour and self-
reported health indicators.
Study 5 objectives.
8. To evaluate whether the GHGA multi-component intervention was
implemented as intended.
9. To evaluate sustainability of the GHGA multi-component intervention in
terms of its feasibility and acceptability of being implemented and
incorporated in the long-term.
27
These objectives are aligned to the following Research Questions:
1. What are the current knowledge and attitudes towards PA among older adults
living in the community, as well as perceived barriers, facilitators and opportunities
for PA participation? (Objective 1).
2. What are the most appropriate wrist- and hip-worn raw acceleration cutpoints for
SB and MVPA activity in the GHGA sample of older adults? (Objective 3).
3. Are there any gender, age, and socio-economic status differences in older adults’
SB, PA and self-reported health indicators? (Objective 5).
4. What are the associations between SB and PA with self-reported health
indicators? (Objective 6).
5. Is Sport England’s GHGA PA intervention effective in increasing community-
dwelling older adults PA levels? (Objective 7).
6. Was the GHGA PA intervention implemented as intended? (Objective 8).
28
Chapter 2. Literature Review
29
2.1. Guidelines
2.1.1. Physical Activity Guidelines
PA can be classified and measured by the intensity continuum of effort required,
ranging from LPA, MPA and VPA (Butte, Ekelund, & Westerterp, 2012). A fact sheet
incorporating information from the four home countries (Department of Health in
England, the Scottish Office, the Welsh Office and the Department of Health in
Northern Ireland) provides information on experiencing PA at differing intensities,
and associated example activities for each intensity for older adults ≥65 years
(Department of Health, 2011a). LPA includes activities that take little effort and
cause older adults to breathe a little harder than normal such as carrying light things,
walking slowly and housework (e.g., washing up and hoovering). Moderate intensity
activities will cause older adults to breathe harder and their hearts to beat faster,
but they should still be able to carry on a conversation. Examples of moderate
intensity activities include brisk walking, climbing stairs and gardening. Whilst the
effects of vigorous activities are similar to those of moderate activities, these
30
activities will cause older adults to breathe much harder and their hearts to beat
rapidly, making it more difficult to carry on a conversation. Examples of vigorous
intensity activities include heavy lifting, aerobics and fast cycling (Department of
Health, 2011a). Activities to strengthen muscles and balance include body weight
exercises or activities such as dancing, chair aerobics, Tai Chi, and Yoga (Department
of Health, 2011a).
Combating physical inactivity requires an emphasis on encouraging older adults to
achieve PA guidelines (Edwards & Loprinzi, 2016). These are to engage in at least 150
minutes of MPA (or 75 minutes of VPA) per week in bouts of at least 10 minutes,
with muscle-strengthening and balance activities included on at least two of those
days (CDC, 2015; Department of Health, 2011a; Lillo, Palomo-Vélez, Fuentes, &
Palomo, 2015; Wullems, Verschueren, Degens, Morse, & Onambélé, 2016).
However, recent research notes that the same physical and psychosocial health
benefits associated with 10 minute bouts of MVPA can be achieved through total
accumulated 1 second bouts of MVPA in older adults (Jefferis et al., 2016; Sparling,
Howard, Dunstan, & Owen, 2015). Consequently, revised PA guidelines soon to be
published in the US (Office of Disease Prevention and Health Promotion, 2018) now
recognise that any amount of time spent in MVPA counts toward meeting PA
recommendations. It is also acknowledged that some older adults might not be
capable of meeting these recommendations due to poor functional ability or health.
For these older adults, it is recommended that they should complete as much PA as
they can do. In other words, even though they might not meet current PA guidelines,
31
there are still health benefits related to PA at lower levels (Warburton & Bredin,
2016). In fact, it has been suggested that two sessions per week of light-to-
moderate intensity PA each of a minimum of 45 minutes duration are optimal for
improving self-reported physical and psychosocial outcomes in older adults (Windle
et al., 2010). Although less than the recommended PA guidelines, fewer sessions of a
lower intensity are more realistic for encouraging long-term adherence to PA in older
adults regardless of gender, age and SES-group status (Kuosmanen et al., 2016). In
addition, older adults with poor mobility are advised to conduct PA that will improve
their balance and prevent falls on at least 3 days a week (World Health Organisation
(WHO), 2011). Chair-based PA interventions have been shown to be effective at
increasing balance (Lewis, Peiris & Shields, 2017), mobility (Kato, Islam, Koizumi,
Rogers, & Takeshima, 2018; Oestergaard et al., 2018) and reducing falls incidence in
frail older adults (Furtado et al., 2016). PA guidelines are now well established across
all age-groups globally (Hallal, Andersen, Bull, Guthold, Haskell, & Ekelund, 2012).
2.1.2. Sedentary Behaviour Guidelines
Compared to the research area of PA, research on SB is a relatively new scientific
field and consequently, countries on a global scale have only just started to provide
recommendations on SB for health, either by incorporating them into their PA
guidelines or by issuing specific SB guidelines (Leitzmann, Jochem & Schmid, 2017).
Existing SB recommendations mainly target children and young people with SB
guidelines for adults and older adults either absent (e.g., Australia, Austria, Belgium,
Canada, Ireland and the US) or extremely vague and simply focusing on reducing
32
prolonged periods of SB (e.g., Germany, New Zealand, Japan, and the UK). Prolonged
periods of SB are defined as ≥8 hours/day (Copeland, Clarke & Dogra, 2015). UK SB
guidelines state that older adults should minimise the amount of time spent being
sedentary (sitting) for extended periods (Department of Health, 2011b). The high
prevalence of SB and its public health significance in older adults warrants further
research in order to obtain more specific national and international
recommendations on SB for public health in this population (Leitzmann et al., 2017).
2.2. The Health Benefits of Physical Activity Participation
Performing sufficient PA is a primary modifiable determinant of health (Birkel et al.,
2015) and recent research has identified PA to be an integral contributor to a healthy
lifestyle which can provide both short- and long-term health benefits (Vahlberg et
al., 2017). Specifically, PA has the potential to benefit an array of physical (Zhu et al.,
2017) and psychosocial (Devereux-Fitzgerald et al., 2016; Franco et al., 2015;
Greaney et al., 2016) determinants of health in older adults. Favourable relationships
are evident between PA and health indicators including incident cardiovascular
disease (e.g., coronary heart disease, heart disease and stroke) (Li & Siegrist, 2012;
Lim et al., 2017), hypertension (Shaltout et al., 2017), osteoporotic fractures (de Kam
et al., 2009), depression (Potter, Ellard, Rees, & Thorogood, 2011), QoL (Potter et al.,
2011), and wellbeing (Wu et al., 2015). Frailty is a common condition among the
older population (Landi et al., 2010) and is described as a biological status in which
resistance to stressors including the immune, endocrine, musculoskeletal and
33
nervous system are reduced (Walston et al., 2006). Frailty leads to a state of high
vulnerability to adverse health outcomes and is associated with worsening of
physical functioning and falls, and higher rates of admissions to hospital, co-
morbidity and mortality (Landi et al., 2010). The relationship between PA, MVPA,
and frailty has been well explored (Mañas, del Pozo-Cruz, García-García, Guadalupe-
Grau, & Ara, 2018) and a negative association between MVPA and frailty among
older adults is now well established (Blodgett, Theou, Kirkland, Andreou, &
Rockwood, 2015; Peterson et al., 2009).
There is also evidence to suggest that among older adults PA can slow down the
progression of cognitive impairment (Sofi et al., 2011) and reduce symptoms of
dementia, delay its progression, and even prevent its occurrence (Forbes et al., 2015;
Lautenschlager, Cox and Kurz, 2010; Middleton & Yaffe, 2009). A recent meta-
analysis by Blondell, Hammersley-Mather & Veerman (2014) found significant
negative associations between PA and both cognitive decline and dementia (overall
effects of relative risk (RR) 0.65, 95% CI 0.55-0.76 and RR 0.86, 95% CI 0.76-0.97,
respectively). Results also showed that higher levels of PA (more than one 30 minute
session of MPA per week), versus lower levels of PA, were associated with a 14%
reduction in the risk of dementia (RR 0.86, 95% CI 0.76-0.97). Further support for
such findings is provided by Elwood et al. (2013) who reported PA to be a greater
predictor of cognitive impairment (Odds Ratio (OR) 0.64 95% CI 0.41, 0.92; P<0.04)
and dementia (OR 0.41 95% CI 0.22, 0.77; P<0.005), than for any other identified
cardiovascular risk or lifestyle factor including body mass index, eating fruits and
34
vegetables, smoking, and alcohol consumption. In fact, it has been estimated that 3
million cases of dementia could be averted globally, with a 10-25% shift in modifiable
risk factors including; cardiovascular risk factors such as hypertension, diabetes, the
metabolic syndrome, obesity and smoking (Cyarto et al., 2012; Molinuevo, Valls-
Pedret & Rami, 2010; WHO, 2012), and lifestyle factors such as PA (Erickson,
Weinstein & Lopez, 2012; Plassman et al., 2010). Such findings suggest a significant
and consistent protection for all levels of PA against cognitive decline in older adults
(Sofi et al., 2011). This is important given that the prevalence and financial
implications of dementia are such that small reductions in cognitive decline may
have a large impact on healthcare costs and overall individual burden (Forbes et al.,
2015).
Given the recommended PA guidelines much research has concentrated on the
effects of MVPA (Barone Gibbs et al., 2017). Specifically looking at studies conducted
in the UK, objectively-assessed MVPA has been related to various health indicators in
older adults, with low levels of MVPA associated with a greater likelihood of a
diagnosis of chronic illnesses and all-cause mortality (Fox et al., 2015), poorer
physical well-being (Withall et al., 2014) and number of falls (Barone Gibbs et al.,
2017; Simmonds et al., 2014). Lower levels of MVPA are also associated with
decreased SRH (Kuosmanen et al., 2016; Ramires et al., 2017), QoL (Lok, Lok &
Canbaz, 2017) and SEE (Dionigi, 2007; French et al., 2015).
35
PA guidelines relate to MVPA and most evidence shows positive associations
between MVPA and health (Mañas et al., 2018). There has also been an emergence
of interest in the health benefits of LPA, owing to the development of accelerometry
techniques in epidemiological studies (Lee & Shiroma, 2014; Shephard & Tudor-
Locke, 2016). Even though several studies have confirmed the potential health
benefits of LPA (van Baal, Hoogendoorn and Fischer, 2016; McMahon et al., 2017),
studies have often been cross-sectional and based upon self-reported LPA
(Autenrieth et al., 2011; Huerta et al., 2016). Objective assessment can record more
detailed and accurate patterns of personal daily activity (Jefferis et al., 2016;
Shephard & Tudor-Locke, 2016). A recent systematic review by Amagasa et al. (2018)
assessed whether objectively measured LPA was associated with health outcomes
after adjustment for MVPA. Results from 24 cross-sectional and 6 longitudinal
studies revealed that LPA was inversely associated with all-cause mortality risk and
associated favourably with the cardiometabolic risk factors of waist circumference,
triglyceride levels, insulin, and presence of metabolic syndrome. Some evidence of
the benefits of LPA on cognitive function (Johnson et al., 2016) and psychosocial
well-being has also been reported (Thraen-Borowski, Trentham-Dietz, Edwards,
Koltyn, & Colbert, 2013). These findings are important considering that many older
adults actually prefer LPA over MVPA, as LPA may be more achievable and
appropriate for this age-group (McMahon et al. 2017). Although current global PA
guidelines recommend only MVPA, promoting LPA may confer additional health
benefits and consequently, further longitudinal randomised controlled trials (RCTs)
are required to establish causality between LPA and physical and psychosocial health
outcomes.
36
2.3. Physical Activity Levels
Despite the recognised evidence of health benefits associated with regular PA,
objective measures show that compliance with the recommended PA guidelines is
poor in the general population and even worse in older adults (Troiano et al., 2008).
Results across 122 countries revealed that overall prevalence of adults reporting
three or more days per week of MVPA was only 31.4% (Hallal et al., 2012). Marked
differences were detected across regions including Africa (38.0%), Americas (24.6%),
Eastern Mediterranean (43.2%), Europe (25.4%), South-East Asia (43.2%), and
Western Pacific (35.3%) (Hallal et al., 2012). Objectively assessed levels of MVPA
have also been observed in older adults on a global scale with findings indicating
even poorer results. In the US, results from a large scale study among 3459 older
adults revealed that only 2.5% of participants met PA guidelines (Berkemeyer et al.,
2016). Similarly, a study of American older adults by Loprinzi (2013) found
participants engaged in objectively-assessed MVPA for only 10.0 minutes/day. A
study of 971 Brazilian older adults by Ramires et al. (2017) reported that men and
women engaged in only 40.5 and 22.5 minutes/week of MVPA in ≥10 minute bouts
(Ramires et al., 2017). A Swedish study by Hagströmer, Troiano, Sjöström, and
Berrigan (2010) reported that among 217 older adults participants spent 130
minutes/week engaged in MVPA. However, results are confounded by differing
methods of PA measurement (e.g., differing montiors such as the GENEActiv (GA)
and ActiGraph (AG) GT3X+ and GT9X) and monitor placement (e.g., wrist- versus hip-
worn accelerometer placement).
37
A national survey by the National Health Service (NHS) (2015) showed that in the UK,
only 20% of men and 17% of women aged 65 to 74 years meet recommended PA
guidelines. This contrasts with 49 % of men and 35 % of women aged 25 to 34 years
(NHS, 2015). More recent UK-based studies objectively assessing PA in older adults
have reported similar findings (Harris et al., 2018; Withall et al., 2014). Withall et al.
(2014) reported that older adults spent 93 minutes of their waking hours/week
engaged in MVPA. Similarly, Harris et al. (2018) found older adults engaged in 94
minutes/week of MVPA in ≥10 minute bouts. It is widely acknowledged that
increasing age leads to decreased time spent in PA (Arnardottir et al., 2013;
Berkemeyer et al., 2016; Harvey, Chastin & Skelton, 2014; Martinez-Gomez et al.,
2017; Wullems et al., 2016). When comparing those between 66-69 years old with
those aged 80 years and older engagement in MVPA decreased from 16.2
minutes/day to 10.7 minutes/day (Buman et al., 2010). In fact, engagement in MVPA
has been found to steadily decrease after retirement age (Martinez-Gomez et al.,
2017; Strain et al., 2016). Gender differences with time spent in LPA have also been
reported (Amagasa et al., 2017; Ramires et al., 2017). Ramires et al. (2017) reported
older men to spend an average of 127.6 minutes/day enaged in objectively
measured total accumulated LPA, whilst older women spent 136.2 minutes/day
(Ramires et al., 2017). Amagasa et al. (2017) reported even greater gender
differences with older males enagaging in an average of 263.1 min/day of objectively
measured total accumulated LPA, whilst females accumulated 365.3 minutes/day.
Time spent in LPA has also been shown to vary dependent upon age, with males and
females aged 80 years or more spending on average 45 and 65 minutes/day less in
38
objectively measured total accumulated LPA when compared to those aged 60 to 69
years old, respectively (Ramires et al., 2017). Overall, older adults on a global scale
are not meeting current PA guidelines and thus, it is important to understand which
factors are affecting PA participation so that successful interventions can be
developed and implemented.
2.4. Sedentary Behaviour
2.4.1. Prevalence of Sedentary Behaviour
Successful aging is a big concern in western societies. Globally, the older adult
population has dramatically increased worldwide in the last two decades, and it is
estimated that by 2015 the older population will represent approximately 22% of the
world’s population (Scully, 2012). In the last decade, SB has emerged as a new risk
factor for health (Chastin et al., 2017; Mañas et al., 2017). SB is characterised as any
waking behaviours in a sitting, reclining or lying posture with EE ≤1.5 METs (Tremblay
et al., 2017). Typical SBs among older adults are television viewing, reading and
sitting time (Pate, O'neill & Lobelo, 2008). Compared with other age groups, older
adults are the most sedentary segment of society (Chastin et al., 2017; Shaw et al.,
2017). Findings from studies in the US and Europe have reported that older adults
spend approximately 80% of their awake time engaged in SB which represents eight
to 12 hours/day (Chastin et al., 2017). Similarly, Hallal et al. (2012) conducted a
global assessment in more than 60 countries and found that the elderly had the
highest prevalence of reporting a minimum of four hours of sitting time daily.
Similarly, Davis et al. (2014) found a substantial amount of time in objectively-
39
assessed SB (71.3%) compared to other objectively-assessed PA categories (e.g., LPA:
9.0%, MVPA: 1.5%) in older adults. When objective data from a number of studies
are weighted and pooled, older adults spend a mean of 9.4 hours/day (ranging from
8.5 to 10.7 hours/day) engaged in SB (Harvey et al., 2014). From the available
studies, UK and US older adults record the highest levels of SB at approximately 11
hours/day (Bann et al., 2015; Hamer, Kivimaki & Steptoe, 2012; Sartini et al., 2015;
Withall et al., 2014).
2.4.2. Sedentary Behaviour Health Outcomes
Given the high prevalence of time spent in SB among older adults, identifying health
outcomes of objectively assessed SB in this population seems to be crucial in the
promotion of successful aging (Mañas et al., 2017). SB is an identifiable risk factor
affecting physical (e.g., premature mortality, chronic diseases and all-cause dementia
risk) and psychosocial (e.g., self-perceived QoL, wellbeing and self-efficacy)
determinants of health (Edwards & Loprinzi, 2016; Falck et al., 2016; Lewis et al.,
2017) independent of PA (Tremblay et al., 2017). Older adults who report sitting less
tend to age more successfully, report better QoL, have less dizziness, and have better
balance (Balboa-Castillo, Leon-Munoz, Graciani, Rodriguez-Artalejo, & Guallar-
Castillon, 2011; Dogra & Stathokostas, 2012; Van Uffelen et al., 2012). A recent
systematic review of objectively measured SB reported associations with health
outcomes relating to physical performance, frailty and mortality (Mañas et al., 2017).
Negative associations between SB and physical performance, regardless of MVPA
has also been reported (Rosenberg et al., 2015). Likewise, Fleig et al., (2016) and
40
Cooper, Simmons, Kuh, Brage, & Cooper (2015) found a negative association
between time spent in SB and the Timed Up and Go physical performance test
(Ikezoe, Asakawa, Shima, Kishibuchi, & Ichihashi, 2013; Podsiadlo & Richardson,
1991). Similarly, a recent study by Rosenberg et al. (2015) showed that objectively
measured SB is associated with worse physical function measured using the Short
Physical Performance Battery (Guralnik et al., 1994), balance task scores, 400 m walk
time, chair stand time, and gait speed.
Increased time spent in objectively measured SB has also been negatively associated
with physical function and frailty in older adults, regardless of participation in MVPA
(Gennuso, Thraen-Borowski, Gangnon, & Colbert, 2016; Song et al., 2015). Despite
all the potential benefits of PA in relation to frailty, frail older adults spend up to 10
hours (84.9%) of their daily time engaged in SBs (Jansen et al., 2015). Evidence
indicates that physically inactive individuals who have lower levels of functional
disability (Tremblay, Kho, Tricco, & Duggan, 2010), and those individuals who have
high levels of SB are more likely to be frail (Peterson et al., 2009). A British cohort
study by Cooper et al. (2015) found that even in young old age (60–64 years), time
spent sedentary is associated with frailty, lower grip strength and lower timed up
and go speed. Examination of large health survey data and objective monitoring
suggests those most sedentary older adults have higher levels of frailty, high activity
of daily living disability, and have higher healthcare usage (Blodgett et al., 2015).
Findings have also reported that objectively measured time spent in SB, after
controlling for MVPA, is related to metabolic syndrome (Bankoski et al., 2011),
41
cancer (Lynch et al., 2011) and mortality (Koster et al., 2012). It is also worth
emphasising that, even when people are physically active, prolonged sedentary
periods can still have a negative impact on health (Biswas et al., 2015). These
findings highlight the need to separate SB from insufficient MVPA patterns and for SB
to be identified as a modifiable risk factor independent of PA (Mañas et al., 2017).
Heightened amounts of time engaged in SB is also an independent risk factor for
psychosocial health (Biswas et al., 2015a; de Rezende, Rey-López, Matsudo, & do
Carmo Luiz, 2014; Withall et al., 2014). Psychosocial health includes psychological
and social psychological outcomes, interlinked with socioeconomic factors
(Leitzmann et al., 2017). It is broadly defined as the mental (e.g., values, attitudes,
beliefs), social (e.g., interacting with others, social support), and emotional (e.g., self-
esteem, mood and anxiety) dimensions of what it means to be healthy (Biddle,
1995). Systematic reviews have shown that sitting, television time and screen time
have all been associated with lower psychological well-being and depression
(Rhodes, Mark & Temmel, 2012), mood disorder and sense of belonging to
community (Dogra & Stathokostas, 2012). Prolonged periods of sitting are associated
with depression and social isolation (de Rezende et al., 2014). While PA has been
positively related to QoL, higher levels of SB have been associated with poorer QoL
(Balboa-Castillo et al., 2011; Meneguci, Sasaki, Santos, Scatena & Damião, 2015). The
combination of increased PA and decreased SB is related to better QoL compared to
those who were are active and more sedentary (Bampton, Johnson & Vallance, 2015;
Hart, 2016). Furthermore, Buman et al. (2010) demonstrated that sedentary time
42
was negatively associated with psychosocial well-being (β -0.03; 95% CI −0.05 - -
0.01); p < 0.001). SB has also been viewed as a positive determinant of anxiety and
depression in older adults (Fernandez-Alonso, Muñoz-García, & Touche, 2016;
Teychenne, Costigan, & Parker, 2015). Anxiety is defined as being “excessively
fearful, anxious, or avoidant of perceived threats in the environment (e.g., social
situations or unfamiliar locations) or internal to oneself (e.g., unusual bodily
sensations)” (Craske & Stein, 2016, p1). Anxiety symptoms have been reported to be
prevalent in 3.2% to 15.4% in older adults (Wolitzky Taylor, Castriotta, Lenze,‐
Stanley, & Craske, 2010). Given that anxiety has been associated with health-related
factors in older adults, further research exploring the associations between anxiety
and PA as well as SB are warranted. SB is extremely prevalent in community-
dwelling older adults and the poor long-term health outcomes of those engaged in
prolonged periods of SB are clear and independent of PA (Copeland et al., 2017).
More work identifying the most efficient methods of reducing time spent in SB is
needed in the older population (Dogra et al., 2017).
2.5. Physical Activity Measurement
2.5.1. Self-Report Measures of Physical Activity
Parallel with the concern to promote and increase PA levels among older adults, how
to most accurately measure PA has drawn the attention of researchers (Lewis et al.,
2017). As described in the behavioural epidemiological framework (Sallis, Owen &
Fotheringham, 2000), accurate measurements of SB and PA are needed to detect
potential correlates; identify relationships between such behaviours and associated
43
health outcomes; and evaluate the efficacy of intervention strategies (Lewis et al.,
2017). SB and PA levels have traditionally been measured via subjective self-report
questionnaires in older adults (Kowalski, Rhodes, Naylor, Tuokko, & MacDonald,
2012). Questionnaire-based methods of data collection have been adopted as they
are relatively cheap to conduct and have the potential to reach a large number of
participants (Aguilar-Farías, Brown, Olds, & Peeters, 2015; Celis-Morales et al., 2012;
Chastin, Culhane, & Dall, 2014; Healy et al., 2011). Several PA questionnaires have
been used and validated for older adults including the Physical Activity Scale for the
Elderly (PASE; Washburn, Smith, Jette, & Janney, 1993; Mudrak, Stochl, Slepicka, &
Elavsky, 2016) and the International Physical Activity Questionnaire for the Elderly
(IPAQ-E; Hurtig-Wennlöf, Hagströmer & Olsson, 2010), which is the questionnaire
used in the work presented in this thesis as required by the funder. The IPAQ-E is
based on the short version of the IPAQ (www.ipaq.ki.se) and assesses time spent
sitting, walking in bouts of 10 minutes or more, MPA in bouts of 10 minutes or more,
and VPA during the previous 7 days. The categorical outcome from IPAQ-E assigns
the participants into one of three PA categories (e.g., low, moderate, or high-PA).
The IPAQ-E provides favourable levels of both direct and indirect levels of criterion
validity for sitting (Spearman r = 0.28, P <0.05), walking (Spearman r = 0.35, P <0.01),
MPA (Spearman r = 0.40, P <0.01), and VPA (Spearman r = 0.37, P <0.01) (Hurtig-
Wennlöf et al., 2010). However, varying levels of test-retest reliability (intraclass
correlation ranging from 0.30 to 0.82) have also been reported (Tomioka et al.,
2011).
44
Even though these self-report measures are tailored to and validated for older
adults, the ubiquitous presence of total accumulated and sporadic PA in older adults
makes it difficult to recall in questionnaire surveys (Washburn, 2000), though such
behaviours may be of particular importance, especially for older adults who tend to
perform shorter duration exercises (Amagasa et al., 2017; Jefferis et al., 2016;
Sparling et al., 2015). A study by van Uffelen, Heesch, Hill, & Brown (2011) also
showed that older adults had difficulties remembering the frequency of sitting and
the scope of sitting activities. Consequently, recall bias is a probability (Barnett, van
den Hoek, Barnett, & Cerin, 2016) given evidence that such methods of data
collection can lead to underestimations of SB (Aguilar-Farías et al., 2015; Chastin &
Granat, 2010; Harvey et al., 2014) and overestimations of time spent engaged in LPA,
MPA and VPA (Tucker et al., 2011).
2.5.2. Objective Measures of Physical Activity
To overcome the associated limitations of self-reported questionnaires, the adoption
of research-grade objective measures including pedometers and accelerometers
have been adopted in older adults (Amagasa et al., 2017; Harris et al., 2018; Ramires
et al., 2017). Pedometers are mechanical counters that record the number of steps
in response to a vertical acceleration of the body (Hensley, Ainsworth & Ansorge,
1993). These devices are lightweight, portable, low cost, and are based on horizontal
hip movement inherent in the swing phase of a step in humans (Ewald, McEvoy &
Attia, 2010). Accelerometers are motion sensors that are sensitive to changes in
acceleration of the body in one or all three axes and are able to provide a more
45
direct measurement of the frequency, intensity, and duration of the movements
related to the activity performed (Doherty et al., 2017).
Pedometer-based interventions have been found to increase PA levels both in the
short- (Hobbs et al., 2013) and long-term (Harris et al., 2018). Consequently,
pedometers continue to be adopted in large-scale community-based PA studies
among older adults given their low cost and non-invasive nature (Harris et al., 2018;
Kerr et al., 2018). Pedometers do however have major disadvantages. They are not
sensitive to engagement in SBs, isometric exercise, activities involving the arms, and
they are also not resistant to water (Kerr et al., 2018). Furthermore, pedometers
have been found to underestimate walking at low speeds and overestimate walking
at higher speeds (Husted & Llewellyn, 2017). Consequnelty, accelerometer usage is
now common among all age-groups (Gorman et al., 2014; Healy et al., 2011) and
accelerometers have been tested within large population based surveillance systems
in a number of developed countries (Hallal et al., 2012). Accelerometers are
particularly appropriate for assessing PA in older adults as these devices require no
input from the participant over the data collection period, and superior wearer
compliance has been demonstrated among older adults when compared to younger
age groups (Doherty et al., 2017). Consequntly, they can provide accurate ways of
estimating the frequency, duration and intensity of both SB and PA (Mathie, Coster,
Lovell, & Celler, 2004; Prince et al., 2008) and are preferable when measuring older
adults’ SB and PA (Murphy, 2009). A development in accelerometer-based SB and PA
research is the move toward raw acceleration signal processing. This advance in
46
accelerometer-based PA monitoring, which has traditionally used accelerometer
output reduced to dimensionless activity “counts” per user-specified period of time
or epoch (Fairclough et al., 2016) is likely to provide greater methodological
transparency in post-data collection analytical processes and improve comparability
of data between different accelerometer models (Hildebrand, Van Hees, Hansen, &
Ekelund, 2014). Devices such as the GA (Activinsights, Cambs, United Kingdom) and
AG GT3X+ and GT9X (ActiGraph, Pensacola, FL) are capable of collecting and
recording raw unfiltered accelerations, which can then be subject to researcher-
driven data processing procedures (Welk, McClain & Ainsworth, 2012).
One of the main decisions to be made by researchers using either raw acceleration
or count-based outcomes is monitor placement location (de Almeida Mendes, da
Silva, Ramires Reichert, Martins, & Tomasi, 2017). The hip has been the conventional
attachment site for accelerometers because of its proximity to the centre of mass
(Troiano, McClain, Brychta, & Chen, 2014; Van Hees et al., 2011). However, recent
accelerometer studies have suggested that the wrist may be a preferable attachment
site as it can more accurately capture the arm motions of non-ambulatory based
activities such as household chores (Evenson et al., 2015; Landry, Falck, Beets, & Liu-
Ambrose, 2015), and is less influenced by atypical gait patterns and walking speed
variability, which are both commonly observed in older adults (Ko, Jerome,
Simonsick, Studenski, & Ferrucci, 2018). Wrist-worn accelerometers have
demonstrated excellent validity against energy expenditure as the criterion measure,
and in comparison to hip-worn monitors (Esliger et al., 2011). Furthermore, wrist-
47
and hip-worn accelerometers have demonstrated comparable free-living MVPA
classification accuracy (Hargens, et al., 2017). Superior wearer compliance has also
been reported for accelerometers worn on the wrist versus the hip in large
population-based studies including the National Health and Nutrition Examination
Survey (NHANES), Dallas Heart Study, and the UK Biobank project adopting wrist-
worn accelerometer protocols (Doherty et al., 2017; Lakoski & Kozlitina, 2014;
Troiano et al., 2014). Specifically, wrist-worn data from the 2011 to 2012 cycle of
NHANES showed that 70–80% of participants provided at least six days of data with
at least 18 hours of wear. This contrasts with 40–70% of participants who provided
at least six days of hip-worn accelerometer data with at least 10 hours of wear in the
2003 to 2004 cycle of NHANES (Troiano et al., 2014). Considering the superior wear
compliance associated with wrist-worn devices in older adults (Doherty et al., 2017),
this attachment site may be the most suitable location during free-living protocols.
However, a limitation of both wrist- and hip-worn accelerometers in studies
involving older adults (Copeland & Esliger, 2009; Taylor et al., 2014) is that most
commonly used cutpoints applied to data to classify PA intensity have been
calibrated for younger adults (Falck, Davis, & Liu-Ambrose, 2016). Specifically,
existing raw acceleration cutpoints include SB cutpoints for GA wrist-worn (46 mg)
and AG hip-worn (47 mg) accelerometers (Hildebrand et al., 2016), and MVPA
cutpoints for GA wrist-worn accelerometers in adults (93 mg; Hildebrand et al.,
2014) and older adults (100 mg; Menai et al., 2017), and AG hip-worn
accelerometers in adults (69.1 mg; Hildebrand et al., 2014). As the energy
48
expenditure associated with a given MET activity intensity threshold is lower in older
adults compared to younger adults (Hall, Howe, Rana, Martin, & Morey, 2013), using
accelerometer cutpoints developed in young adults would likely result in an
underestimation of time spent in MVPA (Barnett et al., 2016). Given the shift
towards objective accelerometry in the measurement of PA, future research should
adopt research grade accelerometers such as the GA and AG triaxial accelerometers
to develop older adult specific wrist- and hip-worn acceleration cutpoints for SB and
MVPA.
Despite objective accelerometry providing a seemingly more accurate report of SB
and PA, there are also limitations. Although accelerometers provide objective
measures, accelerometer site placement (e.g., wrist and hip) affects reliability as
during certain types of activities they cannot accurately detect postural information
(e.g., standing versus sitting) or capture some types of PA (e.g., bicycling), which may
influence estimations of SB and PA and cause some misclassification of time spent in
SB and PA intensity (Shephard & Tudor-Locke, 2016). Emerging techniques including
pattern recognition and machine learning have been found to outperform traditional
cut-off point based algorithms through being robust for individual's physiological and
non-physiological characteristics, more accurate and showing acceptable accuracies
for all activity intensities among older adults (Wullems et al., 2017). Additional
advantages of pattern recognition is that it does not require subgroup-specific
calibrations and/or specific accelerometer body part positioning, is capable of
recognising actual human activities, and works independent of accelerometer
49
brand/settings (Wullems et al., 2017). However, a disadvantage of pattern
recognition research is that pattern data is not directly transferable (Wullems et al.,
2017). For example, algorithms obtained from accelerometer counts at the wrist
would not be directly transferable to raw acceleration data obtained from a differing
accelerometer device. Consequently, large-scale studies are now needed to further
explore the applicability of pattern recognition techniques in distinguishing SB and
differing types and intensities of PA in older adults.
The adoption of wearable devices to monitor personal PA levels has also dramatically
increased through smartphones, wrist- or body-worn devices, and mobile apps. Such
devices offer opportunities for increasing PA (Harris et al., 2018) and new, easy ways
to measure steps (Marshall et al., 2009). Small short-term studies in adults and older
adults have demonstrated that mobile PA apps can increase PA self-monitoring and
engagement in regular PA (Turner-McGrievy et al., 2013; Cadmus-Bertram, Marcus,
Patterson, Parker, & Morey, 2015) and that body-worn consumer fitness trackers
(e.g., Fitbit) can increase time spent in MVPA (Cadmus-Bertram et al., 2015).
However, despite new PA monitoring opportunities, it is important not to ignore
robust, evidence on effective and cost-effective pedometer- and accelerometer-
based interventions (Harris et al., 2018). At present, there is an absence of an
instrument that meets all the advantages desired and thus, adopting both self-report
and objective measures is recommended in order to provide information about the
intensity and duration, as well as the specific types of activities that are engaged in
(Healy et al., 2011; Skender et al., 2016).
50
2.6. Correlates of Physical Activity and Sedentary Behaviour
Factors that are associated with participation in PA are typically referred to as the
study of PA determinants or correlates (Chastin et al., 2017). Correlates will be used
from this point on, as many correlates may not be true determinants, as studies
often show associations yet are unable to conclude causality (National Institute for
Health and Clinical Excellence, 2007). PA is a complex behaviour, influenced by a
number of correlates, which affect the frequency, intensity, duration and type of
older adults’ activity (Sallis & Patrick, 1994). Identifying correlates of PA and SB and
in particular those that are modifiable are imperative in developing successful
interventions (Chastin et al., 2015). Socio-ecological models suggest that behaviours
such as PA have multiple levels of influences, often including intrapersonal (e.g.,
biological and psychological), interpersonal (e.g., social and cultural), organisational,
community, physical environmental, and policy (Sallis et al., 2008). This perspective
proposes that understanding the multiple and interacting determinants of health
behaviours is essential when attempting to change behaviour (Sallis et al., 2008).
Intrapersonal correlates have been the research focus of a majority of studies to
date, either by quantifying motivators or limiters, or through interventions that
attempt to change these intrapersonal correlates (Li et al., 2005). Intrapersonal
correlates of PA include age, health or fitness status, intention to exercise, outcome
expectations, perceived behavioural control, self-efficacy, and perceived fitness
(Choi, Lee, Lee, Kang, & Choi, 2017). Interpersonal correlates relate to dimensions
51
such as how the participant interacts with their friends and family, their level of
social engagement and social cohesion, and the ambience of the social setting
(Berger-Schmitt, 2000). Also important are the support systems available through
general practitioners, community workers, and wellness centre staff. The
environmental level includes the built environment, such as access to local
recreational areas, facilities, and neighbourhood improvements to support activity,
such as footpaths and bike trails. Specifically, intrapersonal including biological (e.g.,
younger age and male sex), psychosocial (e.g., favourable health status, increased
self-determination for PA, perceived greater autonomy support, increased self-
efficacy for exercise, and higher levels of both self-determined motivation and
psychological need satisfaction), and demographic (e.g., higher education) correlates
are all related to time spent in PA among community-dwelling older adults (Bauman
et al., 2012; Chad & Reeder, 2005; Fisher et al., 2018; Hall & McAuley, 2010;
Murtagh et al., 2015; Ng, Ntoumanis & Thøgersen Ntoumani, 2014; Teixeira,‐
Carraça, Markland, Silva, & Ryan, 2012; Thøgersen-Ntoumani, Cumming, Ntoumanis,
& Nikitaras, 2012). In an earlier review of longitudinal studies Koeneman¸
Verheijden, Chinapaw, & Hopman-Rock (2011) also reported that the biological
correlates of general physical functioning and absence of disease were associated
with PA participation. Self-efficacy has consistently been evaluated as the clearest
correlate to PA (Bauman et al., 2012; Choi et al., 2017). According to Bandura’s Social
Cognitive Theory (Bandura, 1986), self-efficacy functions both directly and indirectly
with outcome expectations and other constructs and consequently has a role as a
mediating factor of social support in health behavior (Duncan & McAuley, 1993;
McNeill, Wyrwich, Brownson, Clark, & Kreuter, 2006). Interpersonal correlates of PA
52
have revolved around social support, with increased social support from friends and
family associated with increased levels of PA (Murtagh et al., 2015). Environmental
correlates of PA have included population density, crime rate, geographical location,
perceived neighbourhood safety, perceptions of a PA-conducive physical
environment (e.g., benches available throughout the community), and SES (Murtagh
et al., 2015).
Among possible correlates of SB assessed, age is most frequently associated with
sedentary time (Chastin et al., 2015). In general, age has been positively related to
SB, whether self-reported or measured objectively, and across different countries or
regions (Chastin et al., 2015). Education is also a consistent correlate of SB, with an
inverse association in European populations but not in studies from Asia, suggesting
a possible cultural factor. Health status (Ekelund, Brage, Besson, Sharp, & Wareham,
2008), body mass index (Van Der Berg, 2014), social isolation (Chastin, Fitzpatrick,
Andrews, & DiCroce, 2014), transportation options (Godfrey, Lord, Mathers, Burn, &
Rochester, 2014), SES (Barnett, Van Sluijs, Ogilvie, Wareham, 2014), and the
presence of cultural facilities in the environment and perceived neighborhood safety
are further correlates of SB among community-dwelling older adults (Van
Cauwenberg et al., 2014).
2.7. Community-Based Physical Activity Interventions
53
The growing evidence base surrounding the positive effects of increased PA in older
adults has led to increased implementation of community-based PA interventions
(Bauman, Merom, Bull, Buchner, & Singh, 2016). Such interventions have the
potential to reduce age-related morbidity and declines in activities of daily living,
maintain muscle strength and mass, improve QoL, and thus reduce the primary and
total health care costs associated with SB and physical inactivity among this
population (Bauman et al., 2016). A number of strategies to enhance PA levels in
community-dwelling older adults have been explored in the literature with
interventions delivered in a variety of settings, such as at home, in the community,
and in primary care facilities (Chase, 2015; Neidrick, Fick, & Loeb, 2012. Reviews of
the literature have indicated that interventions can be equally effective in increasing
PA levels regardless of delivery setting (Chase, 2015; Zubala et al., 2017). In terms of
intervention specific components, effective interventions typically utilise
behavioural, motivational and/or cognitive-type components as opposed to health
education or instruction alone (McCluskey & Lovarini, 2005). Furthermore, providing
information on consequences of behaviour, instructing where and when to practice,
and providing ongoing individualised feedback on progress are promising strategies
for PA promotion in this population (Zubala et al., 2017). However, considerations
towards costing and sustainability are needed with such interventions, for example
with expensive equipment and the use of external facilitators to implement the
sessions.
54
In terms of effectiveness, findings from a comprehensive meta-analysis suggest that
interventions designed to increase PA behaviour among adults 65 and older are
effective (Chase, 2015). Specifically, the overall mean effect size for two-group
posttest comparisons was 0.18 (p <.001), equivalent to a difference of 620 more
steps per day or 73 more minutes of PA per week for treatment over control groups
(Chase, 2015). Similar findings were demonstrated in prior meta-analyses studying
younger populations (Dishman & Buckworth, 1996) and healthy adults (Conn,
Hafdahl & Mehr, 2011). There are indications that purely cognitive strategies and
behavioural change techniques (BCTs) might be less suitable for older adults than
motivators more meaningful to them, including social and environmental support,
and enjoyment coming from being physically active. Consequently, a whole system-
oriented multi-component approach is required that is tailored to meet the needs of
older adults and aligned with social, individual and environmental factors (Zubala et
al., 2017).
The Lifestyle Interventions and Independence for Elders (LIFE) pilot study is an
example of an effective whole system-oriented multi-component approach
conducted in comparison with a health-education group (Sink et al., 2015). This
intervention involved a structured, moderate-intensity PA programme that included
walking, strength, flexibility, and balance training. It is recommended that
community-based programmes within this population should consider centre- and
home-based delivery settings in combination rather than isolation (Bauman et al.,
2016). The LIFE intervention involved participants attending two centre-based PA
55
sessions per week, and also performing homebased PA three to four times per week.
An important aspect of this intervention was the flexibility of session content
allowing for increased difficulty of exercises as participants progressed which serves
to allow better tailoring of the intervention to individual needs and the local context
(Lawton, Mceachan, Jackson, West, & Conner, 2014). The ultimate goal was for
participants to progress towards achieving 30 minutes of walking at moderate
intensity, 10 minutes of primarily lower-extremity strength training with ankle
weights, and 10 minutes of balance training and large muscle group flexibility
exercises per week (Sink et al., 2015). The health-education group attended weekly
60 to 90 minute sessions of interactive and didactic presentations, facilitator
demonstrations, guest speakers, or field trips. Sessions included approximately 10
minutes of group discussion and interaction and 5 to 10 minutes of upper extremity
stretching and flexibility exercises (Sink et al., 2015). The intervention resulted in
increases in self-reported PA level from baseline to 24-months (mean difference,
130.4 minutes/week [95% CI, 116.7 to 144.1 minutes/week]) compared with the
health education group (mean difference, 30.5 minutes/week [95% CI, 18.9 to 42.1
minutes/week]; P <.001) (Sink et al., 2015).
Brisk walking in older adults can increase step-counts and MVPA in ≥10 minute
bouts. The Pedometer Accelerometer Consultation Evaluation (PACE)-Lift Cluster RCT
assessed whether a primary care nurse delivered whole-system oriented multi-
component intervention increased objectively measured step-counts and MVPA at
three-, 12-months and four years post-baseline (Harris et al., 2015; Harris et al.,
56
2018). Intervention participants (n=150; control n=148) received four primary care
nurse PA consultations over 3 months, incorporating behaviour change techniques,
pedometer step-count and accelerometer PA intensity feedback, and an individual
PA diary and plan. The addition of individualised support was important as this is
reported to be a key motivator toward PA behaviour change in older adults (Brown
et al., 2015). Results at 3-months revealed that both average daily step-counts and
weekly MVPA in ≥10 minute bouts were significantly higher in the intervention than
control group: by 1,037 (95% CI 513–1,560) steps/day and 63 (95% CI 40–87)
minutes/week, respectively. At 12-months corresponding differences were 609 (95%
CI 104–1,115) steps/day and 40 (95% CI 17–63) minutes/week (Harris et al., 2015).
At 4-years post-baseline versus control results revealed sustained intervention
effects resulting in: +407 (95% CI: −177±992, p =0.17) steps/day, and +32 (95% CI:
5±60, p =0.02) minutes/week MVPA in bouts in the intervention compared to the
control group, respectively. The PACE-LIFT study shows the potential that whole-
system oriented multi-component interventions can have in community-dwelling
older adults. Strategies to implement such interventions may be dependent on the
intervention delivery and its resources. Now more than ever, policy and community
priorities should focus on raising awareness of relationships between PA and health
in older adults, as well as providing better facilities and sustainable PA programmes
in the community which are individually tailored, provide personalised activity goals,
and are delivered according to the evidence-based needs of older adults (Franco et
al., 2015). To maintain intervention effects, gradual transition to less-intensive
programmes along with some type of remote supervision is recommended to avoid
relapse (Bauman et al., 2016). Better methods are needed to set behavioural goals,
57
increase self-monitoring, and provide feedback using new technologies in real time
in the home or community setting (Bauman et al., 2016).
2.8. Community-Based Intervention Process Evaluation
Although the different multi-component interventions discussed in this chapter were
able to have a positive effect on PA levels, research has showed that multi-
component interventions may not always be successful at increasing PA in older
adults (Olanrewaju, Kelly, Cowan, Brayne, & Lafortune, 2016; Richards, Hillsdon,
Thorogood, & Foster, 2013; Zubala et al., 2017). Multi-component interventions can
be difficult to successfully implement and consequently, an accurate interpretation
of either positive or negative outcomes is dependent on having an understanding of
which aspects of an intervention was delivered and how so that they can be further
integrated into ‘real world’ community settings (Bellg et al., 2004; Oakley et al.,
2006; Craig et al., 2008). A large-scale community-based PA intervention study found
that facilitators delivered only around 44% of the specified intervention techniques
across four key sessions (Hardeman et al., 2008). It is recommended, therefore, that
process evaluations of intervention fidelity become an integral part of the conduct
and evaluation of all health behaviour intervention research (Castillo, Wang, Daye,
Shum, & March, 2017). Fidelity is the degree to which an intervention is
implemented as intended by its developers and ensures that the intervention
maintains its intended effects (Carroll et al., 2007). Whether community-based multi-
component interventions succeed at positively impacting PA levels or not, it is
important to understand how they have been implemented in practice, so that the
58
potential for long-term implementation and scaling up to inform policy and practice
of professionals in PA, social work, and care settings can be assessed. This
assessment of implementation is crucial to ensure intervention results are truly
attributable to the programme (internal validity) and that the results are
generalisable to other study populations (external validity) (Frank, Coviak, Healy,
Belza, & Casado, 2008).
The process evaluation of interventions is now advocated by the Standard Evaluation
Framework for PA interventions (SEF), which deems it to be an essential part of
designing and testing multi-component interventions (National Obesity Observatory,
2012). However, approximately only one-third of PA intervention studies report on
process evaluation (Antikainen & Ellis, 2011). This is concerning given that public
health impact is dependent on the extent to which efficacious PA interventions are
disseminated with fidelity into ‘real world’ settings, maintained, and institutionalised
(Lewis et al., 2017). If an intervention is not implemented as directed and no effect is
found, then one cannot be sure whether this is due to lack of efficacy of the
intervention or simply that it has not been implemented correctly (Hasson, 2010).
Despite recommendations for process evaluation research, recent research outlines
that there is considerable heterogeneity and variability in the conceptualisation and
measurement of intervention fidelity in the quality of measurement of delivery
fidelity in interventions promoting PA (Breckon, Johnston & Hutchison, 2008;
Lambert et al., 2017; Quested, Ntoumanis, Thøgersen-Ntoumani, Hagger, Hancox,
59
2017). Consequently, research needs to move toward adopting a common process
evaluation framework which outlines the core variety of fidelity factors consistent in
the literature that affect treatment integrity and treatment differentiation of PA
interventions in community-dwelling older adults (Borrelli, 2011; Calsyn, 2000;
Moncher & Prinz, 1991; Pérez, Van der Stuyft, del Carmen Zabala, Castro, & Lefèvre,
2015). Comprehensive treatment fidelity frameworks specifically developed to
provide guidance for the assessment, enhancement and monitoring of fidelity for
tailored health behaviour interventions include the reach, effectiveness, adoption,
implementation, and maintenance (RE-AIM) framework (Glasgow, Vogt & Boles,
1999), and the National Institutes of Health’s (NIH) Behaviour Change Consortium
(BCC) framework which is adopted in the current thesis (Bellg et al., 2004). The BCC
framework conceptualises fidelity across five core domains: Study Design, Provider
Training, Intervention Delivery, Intervention Receipt and Enactment. Study design is
concerned with whether a study adequately tests its hypotheses in relation to its
underlying theoretical and clinical processes. Provider training involves standardising
training between providers and ensuring they are trained to clear criteria and
monitored over time. Intervention delivery involves assessing and monitoring
differentiation (e.g., differences between the intervention and any comparison
treatments), competency (e.g., skills set of provider), and adherence (e.g., delivery of
intended components). Intervention Receipt refers to whether the intervention was
understood and received by participants and enactment refers to intervention
sustainability and in particular, whether participants used intervention related skills
in day to day settings (Bellg et al., 2004; Borrelli, 2011). Assessing all these elements
enables more accurate inferences to be made about programme effectiveness and
60
any implications for wider roll out/implementation (Dane & Schneider, 1998). The
model has been previously adopted among health behaviour interventions (Chiang,
Seman, Belza, & Tsai, 2008; Nes, van Dulmen, Brembo, & Eide, 2018) and provides a
set of guidelines for translating research into practice and improving the successful
implementation of interventions into ‘real world’ settings (Demiris, Parker Oliver,
Capurro, & Wittenberg-Lyles, 2014).
2.9. Summary of literature
The literature review has highlighted the importance of increasing PA and reducing
SB in community-dwelling older adults, and the potential benefits that this can have
on physical and psychosocial health outcomes. It is also outlined that older adults
both within the UK and on a global level are not meeting current PA guidelines and
thus, interventions to increase PA levels are warranted. The literature suggests that
intervention environment is key for PA promotion in older adults, and that
community-based interventions which are multi-component and target not only
intrapersonal, but also contextual factors such as interpersonal, environmental and
policy level correlates according to socio-ecological models of health behaviour hold
the most promise for positively effecting PA levels. Furthermore, interventions which
are low cost and do not warrant specialist equipment are necessary in order for
them to be sustainable. The literature also highlighted that both self-reported
questionnaires and objective accelerometers are valid methodologies for evaluating
PA levels and SB in older adults and subsequently the effectiveness of interventions.
Moreover, raw data in particular should be analysed. Finally, it is important that
61
interventions are studied in terms of their implementation and fidelity, as this
process evaluation research can improve understanding of how interventions have
been implemented in practice, and subsequently improve the successful
implementation of interventions into ‘real world’ settings.
62
Thesis Study Map
The thesis study map is presented at the beginning and end of each research study
chapter to illustrate the objectives and key findings from the five studies presented
in this programme of work. The thesis study map introduces the next study and
provides a concise summary of the completed study.
Study Objectives and Key Findings
Study 1. Using formative research with older
adults to inform a community physical activity
programme: Get Healthy, Get Active.
Objectives
To explore current knowledge and attitudes
towards physical activity, as well as perceived
barriers, facilitators and opportunities for physical
activity participation among older adults living in
the community.
Use these data to subsequently inform the design,
delivery and recruitment strategies of Sport
England’s national Get Healthy, Get Active
initiative.
Study 2. Evaluation of wrist and hip sedentary behaviour and moderate-to-vigorous physical activity raw
acceleration cutpoints in older adults.
Study 3. Physical activity, sedentary behaviour, perceived health and fitness, and psychosocial wellbeing
among community-dwelling older adults.
Study 4. A pragmatic evaluation of the Get Healthy Get Active physical activity programme for community-
dwelling older adults.
Study 5. Implementation fidelity of the Get Healthy Get Active physical activity programme for community-
dwelling older adults.
63
Chapter 3
Study 1: Using formative research with older adults to inform a community physical activity
programme: Get Healthy, Get Active.
64
This study has been published in the Journal of Primary Health Care Research &
Development and can be found in Appendix 3.
Sanders, G. J., Roe, B., Knowles, Z. R., Kaehne, A., & Fairclough, S. J. (2018). Using
formative research with older adults to inform a community physical activity
programme: Get Healthy, Get Active. Journal of Primary Health Care Research &
Development. 1-10. doi: 10.1017/S1463423618000373
3.1. Introduction
In the UK there are over 11 million older adults aged 65 years and over who make up
18 per cent of the population (UK Office for National Statistics (ONS), 2017). Aligning
with the US and other developed countries (United Nations, 2015) this proportion is
projected to increase to at least 24 per cent by 2039 (UK ONS, 2017). Although
prolongation of life remains an important public health goal, of even greater
significance is that extended life should involve preservation of the capacity to live
independently and function well (Rejeski et al., 2013). The purpose of this formative
descriptive study was to explore current knowledge and attitudes towards PA, as
well as perceived barriers, facilitators and opportunities for PA participation among
older adults living in the community. The findings were used to inform the design,
delivery, and recruitment strategies of an ongoing three-year community PA
intervention project, GHGA, which forms part of Sport England’s national GHGA
programme (Sport England, 2012).
Aligned to objectives 1 and 2 of the thesis, the purpose of this formative study was
to 1. Explore current knowledge and attitudes towards PA, as well as the perceived
65
barriers, facilitators and opportunities for PA participation among older adults living
in the community who had agreed to take part in an ongoing PA programme; and 2.
Use this data to inform the design, delivery and recruitment strategies of an ongoing
community PA intervention programme, as well as international PA interventions
among this population. Given the purpose and objectives outlined, the Evidence
Integration Triangle (EIT) (Glasgow, Green, Taylor, & Stange, 2012) was adopted as
the overarching theoretical framework. Through the prompt identification of success
and failures across individual-focused and patient–provider interventions, as well as
health systems and policy-level change initiatives, the framework allows for the
exploration of the three main evidence-based components of intervention
program/policy, implementation processes, and measures of progress. Hence, this
framework enabled a steep learning cycle through an initial 12 week pilot GHGA
programme delivered by the Metropolitan Borough Council within the chosen local
authority. Results and analysis from this pilot were fed back to Sport England as the
funder, as well as deliverers and participants in order to assess, evaluate and
promptly inform adapted future iterations of the GHGA programme.
3.2. Methods
3.2.1. Participants and procedures
A descriptive formative study was undertaken from March to June 2016. Participants
were recruited from one local authority in North West England recognised as having
the highest percentage of inactive older adults (80%) compared to the UK national
average, and the highest national health costs associated with physical inactivity
66
(Active People Survey, 2014; Sport England’s Local Profile Tool, 2015). The first
author facilitated six, mixed-gender focus groups. Representative of the uptake of
participants within the target GHGA initiative, a homogenous purposive sample of 28
community-dwelling white, British older adults (five male) participated in five of the
focus groups, with an additional convenience pragmatic sub-sample of six
participants (three male) recruited from an assisted living retirement home,
participating in the sixth focus group. In total, 34 older adults (eight male), aged 65
to 90 years (mean age of participants =78, SD=7 years), participated across the six
sessions. Four focus groups involved a group size of six to ten participants, and two
involved three participants (mean focus group size=6 participants, SD=5). Previous
focus groups in PA studies have been conducted effectively with as many as 12
(Moran et al., 2015), and as few as four (Schneider et al., 2016) participants. Focus
groups took place in two church halls, an assisted living retirement home lounge,
and a theatre. All locations were free from background noise, and participants could
be overlooked but not overheard. The inclusion criterion set out by Sport England as
funders of the GHGA programme were that participants must be 65 years of age or
over, reside within one local authority in North West England, and could provide
written informed consent to participate.
GHGA is an ongoing three-year project which seeks to increase the number of
inactive older adults participating in PA at least once a week for 30 minutes, via a 12
week PA intervention delivered by the Metropolitan Borough Council within the
assigned local authority. Participants due to participate in GHGA received a covering
letter, participant information sheet, and consent form. Prior to the commencement
67
of the study, institutional ethical approval was received (#SPA-REC-2015-329) and
written informed consent was obtained for all participants prior to participation. All
focus groups utilised the PRECEDE stage of the PRECEDE-PROCEDE model (Green &
Kreuter, 2005) within their design allowing for the exploration of predisposing,
enabling and reinforcing correlates of PA participation. To maximise the interaction
between participants, focus group questions were reviewed by the project team for
appropriateness of question ordering and flow. Subsequent minor additions were
made to questions on social isolation and PA advertisement. The semi-structured
discussion guide included open ended questions structured to prompt discussion
with equal chance for participants to contribute (Stewart & Shamdasani, 2014).
Focus groups were led by a trained facilitator and with an observer/note taker also
present. Questions addressed knowledge, attitudes and beliefs towards PA as well as
views on barriers and opportunities for PA participation. An example question from a
section exploring barriers to PA was: “Can you tell me about what stops you from
participating in physical activity?” Questions therefore demonstrated aspects of face
validity as they were transparent and relevant to both the topic and target
population (French et al., 2015).
3.2.2. Data Coding and Analysis
Focus groups lasted between 20 and 45 minutes (mean focus group length =29
minutes, SD=12), were audio recorded, and later transcribed verbatim, resulting in
66 pages of raw transcription data with Arial font, size 12 and double-spaced.
Verbatim transcripts were read and re-read to allow familiarisation of the data and
68
then imported into the QSR NVivo 11 software package (QSR International Pty Ltd.,
Doncaster, Victoria, Australia, 2017).
Previous research within this population has adopted analytical procedures including
thematic analysis (Van Dyck et al., 2017), content analysis (Middelweerd, Mollee,
van der Wal, Brug, & te Velde, 2014) and used specialist qualitative data analysis
packages, such as NVivo (Warmoth et al., 2016). In supporting new methodologies
and data representation within qualitative research (Orr & Phoenix, 2015), the
current study followed the pen profiling protocol. The pen profile approach has been
used in recent child PA research (Mackintosh et al., 2011; Boddy et al., 2012;
Knowles et al., 2013; Noonan et al., 2016b) and presents findings from content
analysis via a diagram of composite key emerging themes. In summary, data were
initially analysed deductively via content analysis (Braun & Clarke, 2006), using the
PRECEDE component of the PRECEDE-PROCEED model (Green & Kreuter, 2005) as a
thematic framework which reflects the underlying study purpose. Inductive analysis
then allowed for emerging themes to be created beyond the pre-defined categories.
Data were then organised schematically to assist with interpretation of the themes
(Aggio et al., 2016). As akin to more traditional qualitative research, verbatim
quotations were subsequently used to expand the pen profiles, provide context, and
verify participant responses. Previous studies have demonstrated this method’s
applicability in representing analysis outcomes within PA research (Mackintosh et al.,
2011; Boddy et al., 2012; Knowles et al., 2013; Noonan et al., 2016a) making it
accessible to researchers who have an affinity with both quantitative and qualitative
69
backgrounds (Knowles et al., 2013; Noonan et al., 2016a). Recent findings suggest
that the discrepancy between objective isolation and felt loneliness may be
associated with undesirable health outcomes such as cognitive dysfunction.
Three pen profiles were developed to display themes within the data aligned to the
PRECEDE component of the PRECEDE-PROCEED model (Green & Kreuter, 2005).
Quotations were labelled by focus group number (Fn) and subsequent participant
number (Pn) within that focus group. Characterising traits of this protocol include
details of frequency counts and extracts of verbatim quotes to provide context to
the themes. A minimum threshold for theme inclusion was based upon comparable
participant numbers within previous research adopting a pen profiling approach
(Boddy et al., 2012; Noonan et al., 2016a) and hence, was set as ≥n = 6, with n
representing individual mentions per participant. However, multiple ‘mentions’ by
the same participant were only counted once. Methodological rigour was
demonstrated through a process of triangular consensus (Hawley- Hague et al.,
2016) between the authors. This offered transparency, credibility, and
trustworthiness of the results, as the data were critically reviewed using a reverse
tracking process from pen profiles to verbatim transcripts, providing alternative
interpretations of the data (Smith & Caddick, 2012). The process was repeated
through cross verification and discussion until subsequent agreement on data
themes in relation to verbatim extracts was reached (Aggio et al., 2016).
70
3.3. Findings and Discussion
3.3.1 Predisposing Correlates
Figure 3.1 displays the predisposing correlates of PA participation. In agreement with
previous research (Gray, Murphy, Gallagher, & Simpson, 2015; Kosteli, Williams &
Cumming, 2016), the most highly cited theme of motivation (n=29) was perceived to
be both a facilitator (n=15) and barrier (n=14) to PA participation throughout. Some
participants were proactive in seeking out opportunities for PA.
“I’m a lung cancer survivor and I just ran a mile last month and I raised £550.” (Focus group number (Fn) 1, Participant number (Pn) 2, Line 76).
Contrastingly, others expressed disinterest in PA altogether believing that they
would not derive any health benefit.
“I’ve pushed these [PA] classes to lots and lots of friends and they still ignore it, they will not come to anything like this.” (Fn1, Pn3, Lines 98-100).
Participants also reported laziness or apathy to prevent participation.
“It’s [lack of PA] apathy, just apathy, people can’t be bothered.” (Fn4, Pn3, Line 43).
The importance of pre-intervention intrinsic motivation (e.g., participating for
enjoyment) among older adults is key for both initial adoption and maintenance of
PA participation (Gray et al., 2015). Hence, future interventions could promote
intrinsic motivation for PA through the adoption of socioemotional selectivity theory
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(Carstensen, Isaacowitz & Charles, 1999). Recent findings support this theory’s
notion that motivation for PA is more effectively promoted when paired with
positive messages about the benefits of PA rather than with negative messages
about the risks of inactivity (Notthoff et al., 2016).
The theme of age (n=20) was identified as a key barrier (n=13) to PA participation
throughout.
“They [older adults] get to a certain age and just give up.” (Fn1, Pn7, Line 110).
Social norms and cultural misconceptions often influence not only the type of PA in
which older adults engage, but whether they participate at all (Greaney et al., 2016).
Moreover, participants noted that lifestyle (n=20) often affects individual views
regarding ageing stereotypes, and therefore PA participation. Some participants felt
that physically active older adults were more likely to be habituated to PA
engagement over many years.
“Well if you’ve kept healthy, kept fit all your life, you can keep doing it.” (Fn1, Pn4,
Line 83).
Conversely, it was felt that inactive older adults were reluctant to start exercising.
“You see the ones who haven’t been doing it [PA] are not going to be able to start and do it now.” (Fn2, Pn1, Lines 121-122).
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Previous research has also reported prior PA behaviours (e.g., being sedentary or
active) to be key correlates affecting older adults’ current PA participation levels
(Franco et al., 2015). Additionally, ageing is associated with a decrease in the size of
social networks and hence, older adults are at increased risks of isolation (Devereux-
Fitzgerald et al., 2016; Greaney et al., 2016). Corroborating with prior research
(Greaney et al., 2016), participants throughout perceived isolation (n=15) to be a key
barrier (n=14) to PA participation.
“It’s so easy to get trapped inside and not go out. People sit in front of the television from the moment they wake up to when they go to bed.” (Fn6, Pn5, Lines 79-81).
Isolation is associated with decreased social and psychological wellbeing (Owen et
al., 2010; Milligan et al., 2015) and increased SB among older adults (Nicholson,
2012). Certain targeted intervention strategies can reduce isolation by providing an
opportunity for older adults from differing socio-economic areas to take part in PA
within local community spaces (e.g., parks, leisure centres and churches), that
promote social networking by encouraging camaraderie, adaptability, and
productive engagement, without the pressure to perform (Milligan et al., 2015;
Gardiner et al., 2016). Given that SB is an independent and modifiable behavioural
target for interventions (Lewis et al., 2017), opportunities to replace SB with health-
enhancing behaviours such as moderate-to-vigorous PA (Prince, Saunders, Gresty, &
Reid, 2014), light PA (McMahon et al., 2017; Phoenix & Tulle, 2017) and standing
(Healy et al., 2015) should be promoted. However, none of the participants in the
current study noted negative health effects of prolonged sitting, or the importance
of breaks in sedentary time. Previous research has noted that older adults are not
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yet familiar with the concept of SB and hence, are not motivated to reduce such
behaviours (Van Dyck et al., 2017). Hence, it is first crucial to increase knowledge
about the negative health consequences of SB independent from PA among both
older adults and other populations (Van Dyck et al., 2017).
Participants also emphasised the importance of having a wide range of choice and
opportunities for PA (n=22), and in general their perceptions of community provision
were positive (n=16).
“Yes it’s quite a good place [the local authority where the study took place]. There are a lot of different physical activity sessions to try.” (Fn2, Pn1, Lines 133-135).
However, in line with recent research (Baert et al., 2016; Träff, Cedersund and Nord,
2017), key barriers noted by the participants within the assisted living group included
a lack of advertisement regarding PA opportunities, and few opportunities to take
part in PA within the assisted living facility itself.
“It’s hard to know what is on if you don’t read the noticeboards and to be honest most of us have even stopped looking at that [noticeboard] because there is never anything on it.” (Fn3, Pn3, Lines 49-51).
Further research into the most effective advertisement strategies to engage older
adults in assisted living facilities is warranted (Hildebrand and Neufeld, 2009).
Regardless of living status, participants noted a strong preference not to engage with
online and/or social media channels for advertising and awareness-raising.
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“A lot of people our age don’t like that technology stuff at all. I would not know where to start.” (Fn5, Pn2, Lines 331-332).
These results suggest educational strategies outlining the potential benefits of
technology in aiding PA participation are needed (Bird et al., 2015). This is especially
salient given that recent research has shown technology-based interventions to have
good adherence and provide a sustainable means of reducing SB and promoting PA
participation among older adults (Garcia et al., 2016; Skjæret et al., 2016).
Figure 3.1. Predisposing correlates of physical activity participation among older adults. n = Individual mentions per person (multiple mentions not included); Fn = Focus group number; Pn = Participant number.
3.3.2 Enabling Correlates
Figure 3.2 displays the enabling correlates of PA participation. Consistent with
previous research findings (Franco et al., 2015; Borodulin et al., 2016), cost (n=21)
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was perceived to be a key barrier (n=12) to PA participation exclusively among the
community-dwelling participants who were either unable, or unwilling to pay the
perceived high costs associated with both attending and travelling to such
programmes.
“Money is the big bug bear [barrier to PA participation] isn’t it.” (Fn2, Pn5, Line 406).
Examples of competing programmes were also noted, with free and lower cost
programmes taking precedence over the more expensive.
“We like it [a local chair-based PA programme] because it’s free.” (Fn4, P3, Line 392).
Thus, to effectively increase PA participation within this population, health-
promotion strategies should go further than merely educating and raising awareness
about potential health benefits, and should also advocate for the provision of low-
cost, and easy reachable PA opportunities regardless of financial status (Petrescu-
Prahova, Belza, Kohn, & Miyawaki, 2015; Borodulin et al., 2016). It is worth noting
that for the participants recruited from the assisted living retirement home, any PA
sessions delivered were included within the cost of the overall living fee, and hence
lack of financial resources was rejected as a potential barrier for PA participation
(Baert et al., 2016).
Participants’ views on the theme of location (n=11) centered on neighbourhood
safety. Declining health and physical impairments associated with ageing increase
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the time spent in ones’ neighbourhood and thus, neighbourhood environmental
factors such as, PA provision, proximity, traffic volume, and overall neighbourhood
safety are considered to be important correlates affecting older adults’ PA
participation (Greaney et al., 2016). Perceived neighbourhood safety was identified
as a barrier (n=7) to PA participation exclusively among the community-dwelling
older adults.
“You wouldn’t go out on your own at night around here.” (Fn1, Pn5, Line 203).
Participants from the assisted living retirement home did not view neighbourhood
safety to be either a barrier to or facilitator of PA. This neighbourhood environment
was perhaps viewed as the norm and therefore they did not associate safety
concerns so acutely (Moran et al., 2015). This association could have also affected
results obtained for the theme time/day of the week as such participants did not
recognise this to be a barrier to PA participation either.
“Time of day wouldn’t make much difference [to PA participation]. To be fair you aren’t doing much at the weekend so day of the week isn’t going to make much difference [to PA participation] either.” (Fn3, Pn1, Lines 403-405).
Conversely, community-dwelling participants reported time/day of the week to be a
barrier (n=15), with early morning or early evening sessions identified as reducing PA
participation, especially during the winter months when daylight hours are more
limited. These findings could have been further amplified by the neighbourhood
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safety concerns also identified by this group (Hoppmann et al., 2015; Prins & van
Lenthe, 2015).
The theme of transportation (n=14) has been extensively reported to be both a
barrier and facilitator to PA participation among older adults (Bouma, van Wilgen &
Dijkstra, 2015; Haselwandter et al., 2015; Kosteli et al., 2016; Van Dyck et al., 2017).
Within the current study transportation was identified as a barrier (n=10) restricting
access to PA sessions regardless of living status.
“I would like to go to the baths [swimming pool] but it’s difficult to get there and back so I just don’t bother.” (Fn4, Pn5, Lines 302-303).
Transport is especially important for those lacking the ability to be more
independently mobile as it allows individuals to bridge larger distances than they
could by walking alone (Van Cauwenberg et al., 2016). Thus, lack of access to a car
and inadequate availability, frequency and reliability of affordable public transport
are all associated with decreased PA participation (Newitt, Barnett & Crowe, 2016).
Additionally, being dependent upon others (e.g., family, friends and peers) for
transportation has been identified as a barrier to PA participation within this
population (Baert et al., 2015). This was also noted in the current study.
“I think the worst thing is having to rely on somebody else to take you [to a PA session] as anything can happen in your own life let alone somebody else’s.” (Fn5, Pn2, Lines 266-267).
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Prior research suggests the promotion of walking for transportation to PA sessions
among physically independent older adults (Chudyk et al., 2017). However, given the
neighbourhood safety concerns noted by participants, and the varying levels of
functional ability among this population, further research examining access to PA
sessions including walking facilities (e.g., path and crossing quality), traffic safety,
and safety from crime is warranted (Van Cauwenberg et al., 2016).
Figure 3.2. Enabling correlates of physical activity participation among older adults. n = Individual mentions per person (multiple mentions not included); Fn = Focus group number; Pn = Participant number.
3.3.3 Reinforcing Correlates
Figure 3.3 displays the reinforcing correlates of PA participation. Peer support is
associated with PA adherence in older adults (Brown et al., 2015), and was identified
as a key theme (n=18) and subsequent facilitator (n=13) to PA participation in the
current study.
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“I’ve got to know everybody now and I’m used to you all. I feel more comfortable and I don’t feel anxious or anything.” (Fn3, Pn6, Lines 354-355).
Unsurprisingly, in light of the above several participants reported peers to be a
barrier to PA participation (n=5) because of an unwillingness to attend other PA
sessions due to anxieties about meeting new people.
“I wouldn’t like to go somewhere else as I wouldn’t like to walk in on a crowd of new people.” (Fn3, Pn6, Lines 366-367).
Although group-based activities offer older adults the chance to gain a sense of
belonging, enjoyment and establish friendships, designing sustainable exit routes in
order to retain the provision of group activities which continue to facilitate, build
and retain social bonds post-intervention should be considered by PA programmers
and policymakers (Wu et al., 2015).
In line with recent research (Devereux-Fitzgerald et al., 2016; Smith et al., 2017),
family members were identified as being both barriers (n=2) and facilitators (n=4) to
PA participation. Specifically, a barrier often reported is overprotectiveness, in which
family members may not allow older adults to participate in PA out of concern for
their safety or health (Greaney et al., 2016). Participants among the community-
dwelling groups also noted this.
“My sons in for a shock that we’re coming to this as he’s like, ‘no long walks, no boat rides’, he goes ‘you’re past it.” (Fn6, Pn2, Lines 441-442).
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Such results suggest a need to educate family members on the importance and
benefits of PA among older adults. Educational resources such as the older adults PA
guidelines infographics for the, UK (Reid & Foster, 2016), Canada (Canadian Society
for Exercise Physiology, 2016), Australia (Australian Government Department of
Health and Ageing, 2013), New Zealand (Ministry of Health, 2013), and the US (CDC,
2008) are appropriate tools advocating for older adults to be active safely, and can
be understood by family members plus health care providers. Furthermore, the
adoption of local/national mass media messages may be a cost effective educational
solution at a time when there is a growing ageing population (United Nations, 2015;
UK ONS 2017). However, given the resistance to technology-based PA noted in the
current study, further educational strategies promoting enjoyable, easy-to-use
technology within a family environment are needed for community-dwelling older
adults (Bird et al., 2015). Participants within the assisted living group did not
perceive family members to be either barriers or facilitators to PA participation and
thus, further research is needed to identify approaches to involve family members as
additional facilitators of PA participation within this group.
Participants viewed the theme of perceived health benefits (n=23) to be both a
facilitator (n=14) and barrier (n=9) to PA participation regardless of living status.
Participants were knowledgeable regarding the potential benefits of PA for their
physical health.
“It [PA] loosens all your limbs up.” (Fn2, Pn2, Line 123).
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Participants also noted the potential benefits of PA for their psychological health.
“The wellbeing [from PA participation] makes you feel better.” (Fn1, Pn3, Line 49).
Despite the irrefutable evidence demonstrating the benefits of PA among older
adults (CDC, 2015; Reid & Foster, 2017; WHO, 2017), participants also noted health
to be a potential barrier (n=14) to PA participation due to doubts about their
capabilities, or fear of causing themselves harm, particularly if they were unfamiliar
with it.
“People have to be sure they can come to PA sessions because my sister had a heart attack… and she can’t do a lot of these exercises.” (Fn1, Pn5, Lines 177-178).
To overcome such perceptions, educational strategies at a population level should
focus on communicating the role of PA in gaining health benefits for all as well as
how well-designed PA programmes can aid in the management of common
comorbidities specific to this age group (Gillespie et al., 2012; Hamer, Lavoie &
Bacon, 2013).
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Figure 3.3. Reinforcing correlates of physical activity participation among older adults. n = Individual mentions per person (multiple mentions not included); Fn = Focus group number; Pn = Participant number.
Taken together with the findings of recent qualitative studies examining correlates of
PA participation among older adults living in both assisted living (Baert et al., 2016;
Träff et al., 2017) and community-dwelling older adults (Fisher et al., 2018; Phoenix
& Tulle, 2017), results from this formative research study have been used to inform
the design, delivery and recruitment strategies of an ongoing community PA
intervention project. Specifically, changes implemented to programme design have
included the introduction of, increased intervention duration from six to 12-weeks,
maintenance sessions post-initial 12-week intervention, tea and coffee after each
session to promote social interaction, and a reduction of early morning and late
afternoon sessions. Changes to programme delivery have included the introduction
of, participant choice in session activities, videoing participants at week one and
week 12 to show participants their progression, and signposting participants to other
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local PA programmes. Finally, changes implemented to recruitment strategies have
included, improved relationships with general practitioners to enable them to refer
participants onto the programme, leafleting in church halls and charity shops, and
deliverers attending and subsequently advertising the programme at several Older
Peoples' Forums. Such methods could also be adopted throughout similar
community PA programmes elsewhere in order to increase programme fidelity,
representativeness and effectiveness.
3.4. Strengths and Limitations
Methodological strengths include the exploration of consensus and associated
discussion through the focus groups and subsequent analysis process which allowed
insight into the predisposing, enabling and reinforcing correlates of PA participation
among older adults. Consistency of themes, data credibility, transferability, and
dependability were achieved through the triangulation consensus of data between
authors and methods. While this study reiterates important insights into the
perceived barriers, facilitators and opportunities for PA participation among both
community-dwelling and assisted living older adults, value outside of this to the
wider research community may be limited due to programme funding which only
allowed for formative research strategies to recruit participants who had agreed to
take part in an ongoing PA programme. Consequently, sampling bias is a potential
issue as it could be assumed that a high proportion of the participants were already
inclined to be and/or currently physically active given the positive predisposing
comments with regard to motivation towards PA and current lifestyle choices
(Costello et al., 2011). This is especially important given that motivators and barriers
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toward regular PA vary among currently active and inactive adults across the age
range (Costello et al., 2011; Hoare et al., 2017). Considering that less than 10% of
older adults (≥ 65 years of age) meet the recommended PA guidelines (Jefferis et al.,
2014), future research should seek to identify barriers and facilitators among larger
sample sizes of currently inactive older adults living within both the community and
assisted living facilities.
Additionally, a small convenience pragmatic sub-sample of participants from one
assisted living facility were recruited and hence results cannot be considered
representative. Furthermore, men tend to decrease participation in leisure-time PA
as they get older; whereas this dose-response is not seen among women (Amagasa
et al., 2017). Consequently, there is the possibility of gender bias given the higher
number of female participants recruited. However, the sample size, participants’
ages and gender distribution are comparable to those reported in two recent studies
examining barriers and facilitators to PA participation among older adults (Baert et
al., 2015; Moran et al., 2015). Within these two studies the total number of
participants was 15 (five male) and 40 (13 male) and the mean age of the
respondents was 74 years, and 84 years, respectively. This compares to a total
number of thirty-four participants (eight male) with a mean age of 78 years in the
current study. Nevertheless, as well as exploring correlates of PA participation in
relation to gender, functional status and age differences between the young-old (60-
69 years), old-old (70-79 years), and oldest-old (80+ years) (Heo et al., 2017), future
research should obtain additional participant characteristic data prior to the
intervention including, participants’ current sedentary time and PA levels, history of
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PA, family history of PA, ethnicity, employment status, and educational
achievements as such have been shown to potentially affect the perceived barriers
and facilitators to PA participation among older adults (Greaney et al., 2016; Keadle
et al., 2016).
3.5. Conclusions
Older adults acknowledged the benefits of PA, not only for health but also those
relating to socialising, enjoyment, relaxation, and physical and psychological
wellbeing. The themes of opportunities and awareness for PA participation, cost,
transport, location and season/weather varied dependent upon living status. These
findings suggest current living status to be a separate correlate of PA participation
among older adults. This data can be used to further strengthen the design, delivery
and recruitment strategies of both the target GHGA PA intervention programme and
international PA intervention programmes among older adults. Future interventions
should consider educational strategies to communicate the role of PA in gaining
health benefits for all, reducing SB, and countering the negative implicit attitudes
that may undermine PA within this population. Given the small sample of
participants in the current study, further comparative research exploring the barriers
and facilitators between assisted living and community-dwelling, and active and
inactive older adults on both national and international levels is warranted.
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Thesis Study Map
Study Objectives and Key Findings
Study 1. Using formative research with older
adults to inform a community physical activity
programme: Get Healthy, Get Active.
Objectives
To explore current knowledge and attitudes
towards physical activity, as well as perceived
barriers, facilitators and opportunities for physical
activity participation among older adults living in
the community.
Use these data to subsequently inform the design,
delivery and recruitment strategies of Sport
England’s national Get Healthy, Get Active
initiative.
Key Findings:
Older adults acknowledged the benefits of
physical activity, not only for health but also those
relating to socialising, enjoyment, relaxation, and
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physical and psychological wellbeing regardless of
socioeconomic status.
The themes of opportunities and awareness for
physical activity participation, cost, transport,
location and season/weather varied between
assisted living and community-dwelling older
adults.
Study 2. Evaluation of wrist and hip sedentary
behaviour and moderate-to-vigorous physical
activity raw acceleration cutpoints in older
adults.
Objectives
To test a laboratory-based protocol to generate
behaviourally valid, population specific wrist- and
hip-based raw acceleration cutpoints for
sedentary behaviour and moderate-to-vigorous
physical activity in older adults.
Apply these cut-points to subsequently analyse
physical activity data for Sport England’s Get
Healthy Get Active physical activity intervention.
Study 3. Physical activity, sedentary behaviour, perceived health and fitness, and psychosocial wellbeing
among community-dwelling older adults.
Study 4. A pragmatic evaluation of the Get Healthy Get Active physical activity programme for community-
dwelling older adults.
Study 5. Implementation fidelity of the Get Healthy Get Active physical activity programme for community-
dwelling older adults
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Chapter 4
Study 2: Evaluation of wrist and hip sedentary
behaviour and moderate-to-vigorous physical
activity raw acceleration cutpoints in older
adults.
89
This study is currently under peer-review in the Journal of Sports Sciences.
Sanders, G. J., Boddy, L.M., Sparks, A. S., Curry, W.B., Roe, B., Kaehne, A., &
Fairclough, S. J. (In Review). Evaluation of wrist and hip sedentary behaviour and
moderate-to-vigorous physical activity raw acceleration cutpoints in older adults.
4.1. Introduction
Chapter 3 (Study 1) established that older adults acknowledge the benefits of PA,
not only for health but also those relating to socialising, enjoyment, relaxation, and
physical and psychological wellbeing. Findings were also supportive of previous
research which has highlighted current living status (assisted living versus
community-dwelling) to be a separate correlate of PA participation among older
adults (Baert et al., 2016; Träff et al., 2017). As described in the behavioural
epidemiological framework (Sallis, et al., 2000), accurate measurements of SB and
PA are needed to detect potential correlates; identify relationships between such
90
behaviours and associated health outcomes; and evaluate the efficacy of
intervention strategies (Lewis et al., 2017). Accelerometers are particularly
appropriate for assessing PA in older adults as these devices require no input from
the participant over the data collection period, and superior wearer compliance has
been demonstrated when compared to younger age groups (Doherty et al., 2017).
Objective measurement methods such as accelerometry also eliminate self-report
questionnaire bias related to subjective recall of past events which is an ability that
can decline with ageing (Barnett et al., 2016). Consequently, accelerometry is now
commonly adopted for monitoring older adults’ SB and PA levels (Mañas et al., 2017;
Oguma et al., 2017; Wullems et al., 2017; Zhu et al., 2017).
A further development in accelerometer-based SB and PA research is the move
toward raw acceleration signal processing. This advance in accelerometer-based PA
monitoring, which has traditionally used accelerometer output reduced to
dimensionless activity “counts” per user-specified period of time or epoch
(Fairclough et al., 2016) is likely to provide greater methodological transparency in
post-data collection analytical processes and improve comparability of data between
different accelerometer models (Hildebrand et al., 2014). Devices such as the GA
(Activinsights, Cambs, United Kingdom) and AG GT3X+ and GT9X (ActiGraph,
Pensacola, FL) are capable of collecting and recording raw unfiltered accelerations,
which can then be subject to researcher-driven data processing procedures (Welk et
al., 2012). Interunit reliability is acceptable for both brands (Esliger et al., 2011;
Santos-Lozano et al., 2012). Although time spent in SB and light, moderate and/or
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vigorous intensity PA can be quantified from raw acceleration data (Matthew, 2005),
currently, no raw acceleration cutpoints for SB and MVPA exist for older adults.
Therefore, in line with objective 3 of the thesis, this study aimed to determine
laboratory-based wrist-worn GA and hip-worn AG GT3X+ raw acceleration cutpoints
for SB and MVPA in older adults. The obtained cutpoints will then be adopted to aid
in answering objectives 5 and 6.
4.2. Methods
4.2.1. Study Population
A homogenous purposive sample of 34 community-dwelling white, British older
adults (10 male), aged 60 to 86 years (mean number of participants =70, SD=8 years)
were recruited through leaflets distributed at local fitness centers/gyms and
community centers, as well as through word of mouth referrals. A sample size of 30
participants was targeted so as to be comparable with recent calibration studies in
older adults (Landry et al., 2015; Wullems et al., 2017). Individuals interested in
participating in the study were pre-screened for inclusion criteria which set out that
participants must be (1) ≥60 years of age and be physically cleared for exercise using
the modified Physical Activity Readiness Questionnaire (Modified PAR-Q; Cardinal,
Esters & Cardinal, 1996; Cardinal & Cardinal, 2000), (2) have the ability to walk
briskly on a treadmill without assistance, and (3) not be taking any medications that
would influence EE or their ability to perform ambulatory activity. Participants were
excluded if they had a medical condition precluding them from exercise, were unable
to wear a portable indirect calorimeter during testing, or had limited mobility such
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that they could not walk on a treadmill independently. Prior to the commencement
of the study, institutional ethical approval was received (SPA-REC-2016-343) and all
participants provided written informed consent prior to their inclusion.
4.2.2. Anthropometrics
Participants’ body mass (Seca mechanical scales, Birmingham, UK) and standing
height (Holtain Limited stadiometer, Crymych, UK) were measured in light clothing
without shoes. Resting blood pressure was measured upon arrival and immediately
prior to commencement of the accelerometer calibration protocol using a Boso
Medicus Prestige blood pressure monitor (Boso Bosch + Sohn, Germany).
4.2.3. Study Protocol
Participants completed two separate laboratory data collection visits (separated by
one week) at the university site. Visit 1 was an initial familiarization of the protocol
structure, equipment, and laboratory. Participants also provided written informed
consent and completed anthropometric measures, and a six-minute treadmill walk
test (6MWT; ATS Committee on Proficiency Standards for Clinical Pulmonary
Function Laboratories, 2002) to establish maximal walking speed. Participants then
completed a laboratory-based protocol consisting of six sedentary activities, six
stationary activities and four physical activities of varying intensity (see Table 4.1 for
protocol description). All activities were performed in a standardised order.
Participants were provided with standardised instructions from a predetermined
script delivered by the first author prior to beginning each activity. The activity
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protocol only was repeated at visit 2. Visit 1 lasted ∼80 minutes whilst visit 2 lasted
∼60 minutes as participants did not need to repeat the familiarization components
and 6MWT.
Table 4.1. Description of the sixteen structured activities.
Activity Description of activity
Lying down
Reclined
Reclined
Sitting
Sitting
Sitting
Standing
Standing
Standing
Washing
Shopping
Mopping
Stepping
Walking
Walking
Walking
Lying in supine position awake, with arms at the side, avoiding bodily movement.
Reclined in supine position reading newspaper turning the page every 20 seconds.
Reclined in supine position using a mobile phone.
Sitting in a chair with hands on knees, avoiding bodily movement.
Sitting in a chair reading newspaper turning the page every 20 seconds.
Sitting in a chair using a mobile phone.
Standing upright, with arms at the side, avoiding bodily movement.
Standing upright reading newspaper turning the page every 20 seconds.
Standing upright using a mobile phone.
Standing upright at a sink washing (20 seconds) and drying (20 seconds) five items.
Standing placing five items in cupboard, then remove with the opposite hand.
Standing upright dry mopping the floor (marked out area).
Stepping up and down at 69 beats per minute on a 23cm high step.
Walking on a treadmill at 65% maximal speed individually calibrated from the 6MWT.
Walking on a treadmill at 75% maximal speed individually calibrated from the 6MWT.
Walking on a treadmill at 85% maximal speed individually calibrated from the 6MWT.
6MWT, six-minute walk test.
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Sedentary, stationary and light physical activities were performed for three minutes
each, whilst the stepping and walking activities representing MVPA were performed
for two minutes each. All activities were separated by a 1 minute transition period.
To ensure consistency across measurement sessions, the first author was trained
and led on all aspects of the measurement protocol. The start and end of each
activity was timed by the first author with a stopwatch (Fastime, Leicestershire, UK).
All instruments were synchronised to the same clock to ensure that criterion
measurements for a given period of activity were matched with time-stamped
accelerometer data for precisely the same duration of the activity. This allowed for
appropriate data comparisons to be made across all recording devices.
4.2.4. Accelerometers
Participants wore GA (ActivInsights Ltd., Kimbolton, Cambridgeshire, UK) and AG
GT3X+ (ActiGraph, Pensacola, FL) triaxial accelerometers on the non-dominant wrist
and left hip, respectively. Both monitors were set to collect raw triaxial accelerations
at 60 Hz. Participants also wore an activPAL (PAL Technologies Ltd., Glasgow, UK)
accelerometer on the left anterior thigh as the criterion measure for SB (Chastin,
Culhane & Dall, 2014; Varela Mato, Yates, Stensel, Biddle, & Clemes, 2017). The
activPAL uses proprietary algorithms to classify activity into periods spent sitting,
standing and stepping. acitvPAL data were collected at a sampling frequency of 20
Hz. Participants wore the same monitors (matched by serial number) for both visits.
A total of six different GA and AG, and four different activPAL monitors were used
throughout the study. All devices were used and calibrated as per the manufacturer
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instructions and initialised approximately 10 minutes before the start of each
session.
Immediately after testing, the activity monitors were removed and the data
downloaded to a single, secured computer. The GA data were downloaded using GA
PC software version 2.9 and saved in raw format as binary files, whilst the AG data
were downloaded using ActiLife version 3.13.3, and saved in raw format as .gt3x files
and converted to time-stamped .csv files to facilitate raw data processing. ActivPAL
data were downloaded using activPAL3 version 7.2.32, saved as .datx files and
converted to .csv “Event” files for processing. Signal processing of raw GA .bin files
and raw AG .csv files was completed offline using R-package GGIR version 1.5
(https://cran.r-project.org/web/packages/GGIR/) (van Hees et al., 2013). This R-
package facilitates data cleaning and the extraction of user-defined acceleration
levels, which can then be set to reflect the intensity thresholds as derived in this
study. Concurrent with previous studies (Hildebrand et al., 2014; Fairclough et al.,
2016; Menai et al., 2017; Rowlands, Yates, Davies, Khunti, & Edwardson, 2016), the
Euclidean Norm Minus One (ENMO) (van Hees et al., 2013b) was adopted to quantify
acceleration relative to gravity (1 mg = 0.00981 m/s -2), after which negative values
were rounded to zero. Raw data were further reduced by averaging the ENMO
values over 1 second epochs. All resulting values are expressed in milli (10 -3) gravity-
based acceleration units (mg), where 1g = 9.81 m/s2. Although the ENMO metric can
be sensitive to poor calibration (van Hees, et al., 2013b), GGIR autocalibrates the raw
triaxial accelerometer signal in order to reduce such calibration error (van Hees et
96
al., 2014). Where insufficient non-movement periods were available for auto-
calibration, back-up calibration coefficients derived from free-living data collected
with the same accelerometer units were used (Rowlands, Mirkes, Yates, Clemes,
Davies, Khunti, & Edwardson, 2017). The activPAL “Event” files provided the exact
time in seconds that posture change occurred and each second was classified as
sedentary, standing, or stepping. These files were then expanded using an Excel
formula to obtain second-by-second data, with each second subsequently classified
as either sedentary or not sedentary. These second-by-second activPAL files were
synchronised with the 1 s ENMO values from GA and AG. To exclude any transitional
movements, the middle two minutes of data from each three minute activity were
extracted and subsequently utilised for analysis. The full two minutes for each of the
stepping and walking activities were used.
4.2.5. Energy Expenditure
As the criterion measure for MVPA, oxygen consumption (VO2; ml·kg·min-1) was
measured with a portable indirect calorimetry system (MetaMax 3B-R2, CORTEX
Biophysik GmbH, Leipzig, Germany). The Metamax interface and breathing mask
(7600 Series V2, Hans Rudolph, Kansas) were set up and fitted as per the
manufacturer instructions. VO2 was measured using breath-by-breath mode and in
order to match time periods across devices, data were stored in second-by-second
intervals. These measurements were used to determine EE, which was then used to
classify activity intensity in METs. Resting EE was measured at visit 2 during the first
three lying/recumbent activities of the protocol. The observed mean resting EE was
2.89 ml·kg·min-1, which was used to define 1 MET. This value is comparable with
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previous calibration studies in older adults (Barnett et al., 2016; Evenson et al., 2015;
Sergi et al., 2010; Siervo et al., 2014), and is consistent with the expected decrease in
resting metabolic rate (RMR) associated with ageing (Byrne, Hills, Hunter, Weinsier,
& Schutz, 2005; Kwan, Woo & Kwok, 2004). MVPA was defined as an intensity of 3
METs and above (e.g., ≥8.68 ml·kg·min-1) (Shephard, 2011).
4.2.5. Energy Expenditure
4.2.5.1. Cutpoint Calibration
A randomly counter-balanced sample of 12 female and five male participants from
visits 1 and 2 provided the calibration data. Descriptive statistics for all devices were
calculated for each activity in the protocol. The activPAL sedentary events and 3 MET
VO2 values were used as the criterion reference standards for SB and MVPA,
respectively. SB and MVPA were each coded as either 0 or 1, where 1 represented
the behaviour occurring and 0 represented the behaviour not occurring. Receiver
Operating Characteristic (ROC) curve analyses (Jago, Zakeri, Baranowski, & Watson,
2007) were used to determine SB and MVPA cutpoints. Area under the curve (AUC)
was calculated for each analysis as a measure of diagnostic accuracy. AUC values of;
≥ 0.90 are considered excellent, 0.80–0.89 good, 0.70–0.79 fair, and < 0.70 poor
(Metz, 1978). For each device two different pairs of cutpoints were generated by
analyzing combinations of sensitivity (Se) and specificity (Sp) on the ROC curves. Our
aim was to determine a cutpoint that accurately captured SB and MVPA (Se) whilst
limiting misclassification of SB and MVPA (Sp). Two approaches were adopted to
achieve this. Firstly, ENMO values that maximised both Se and Sp (Youden index)
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(Perkins & Schisterman, 2006) were identified as one set of cutpoints (SB Youden and
MVPAYouden). The Youden index can however result in low positive predicted values
(Evenson et al., 2015) and it is recommended that researchers consider the relative
importance of Se and Sp (Welk, Laurson, Eisenmann, & Cureton, 2011), and
implications of the selected cutpoints on the biobehavioural impact on the outcome
variables (Mackintosh, Fairclough, Stratton, & Ridgers, 2012). Secondly, cutpoints
were determined that emphasised Se over Sp for SB cutpoints (SBSe) to minimise the
likelihood of classifying SB as PA, with Sp emphasised over Se for MVPA cutpoints
(MVPASp) to reduce the likelihood of misclassifying light PA as MVPA. Both cutpoints
reflected recommendations that the lower Se or Sp values should be ≥60% (Lugade,
Fortune, Morrow, & Kaufman, 2014). This prioritization approach minimises the risk
of individuals being misclassified in the target behaviour and is common in
accelerometer calibration (Landry et al., 2015; Mackintosh et al., 2012; Nero, Wallén,
Franzén, Ståhle, & Hagströmer, 2015) and fitness standards research (Welk et al.,
2011).
4.2.5.2. Cross-validation
To be consistent with good practice guidelines suggested by Welk (2005), SBYouden and
MVPAYouden, and SBSe and MVPASp cutpoints were cross-validated. The remaining 12
female and five male participants from visits 1 and 2 not included in the calibration
sample provided the cross-validation data. Two-by-two (2x2) contingency tables
were used to check classification agreement. The criterion measure and ENMO data
were first categorised into sedentary/not sedentary and MVPA/not MVPA binary
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codes. Computed Se and Sp, Cohen’s kappa coefficients, and percentage agreement
between classifications were also assessed. Statistical analyses were performed
using IBM SPSS Statistics, version 24 (IBM, Armonk, NY), with the level of statistical
significance set at p ≤ 0.05.
4.3. Results
4.3.1. Participant characteristics
Among the 34 participants who completed the study, the mean (SD) height, weight,
and body mass index were 164.3 (1) cm, 71.7 (17.5) kg, and 26 (4.7) kg -1·m-2,
respectively. Mean (SD) maximal walking speed and blood pressure were 4.3 (1.5)
km·h-1, and 146/85 (22/11) mmHg, respectively. Further sample characteristics are
presented in Table 4.2. The mean (SD) accelerometer output from GA and AG
accelerometers (mg) are provided in Table 4.3 for each of phase of the laboratory
protocol.
Table 4.2. Study sample characteristics.
Sample Characteristics(n = 34)
Age (years)
Body Mass (kg)
Body Height (cm)
69.6 (8.0)
71.7 (17.5)
164.3 (1)
BMI (kg-1·m-2)
Blood Pressure (mmHg )
Maximal Walking Speed (km·h-1)
26.3 (4.7)
146/85 (22/11)
4.3 (1.5)
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Values represent arithmetic mean (SD). SD, standard deviation; BMI, body mass index; mmHg, millimeters of mercury.
Table 4.3. Mean (SD) accelerometer output from GA and AG (mg) during each activity performed by older adults.
Lying Sitting Standing Household Stepping 65% Walk 75% Walk 85% Walk
GA Wrist
AG Hip
5.41 (1.47)
2.32 (2.15)
6.92 (5.61)
4.07 (2.10)
8.97 (4.34)
6.97 (1.90)
68.92 (18.30)
15.71 (4.50)
98.71 (78.21)
108.20 (19.22)
112.63 (88.10)
86.10 (19.80)
127.01 (86.45)
88.94 (31.50)
166.32 (97.40)
105.40 (34.90)
4.3.2. ROC Curve Analysis
ROC curve analysis revealed an AUC for the GA of 0.88 (95% CI: 0.87-0.88; P < 0.001)
for SB and 0.88 (95% CI: 0.87-0.88; P < 0.001) for MVPA. For the AG the AUC for SB
was 0.90 (95% CI: 0.90-0.91; P < 0.001), and 0.94 (95% CI: 0.94-0.95; P < 0.001) for
MVPA.
4.3.3. Cutpoint generation
The GA cutpoints which maximised both Se and Sp using the Youden Index were
SBYouden: 20 mg (Se = 94%, Sp = 72%) and MVPAYouden: 32 mg (Se = 88%, Sp = 77%). AG
cutpoints were 6 mg (Se = 85%, Sp = 82%) for SBYouden and 19 mg (Se = 86%, Sp = 92%)
for MVPAYouden. The cutpoints optimizing Se and Sp for GA were 57 mg (Se = 99%, Sp =
60%) for SBSe and 104 mg (Se = 60%, Sp = 89%) for MVPASp. Respective AG cutpoints
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for SBSe and MVPASp were 15 mg (Se = 98%, Sp = 60%) and 69 mg (Se = 60%, Sp =
99%). Table 4.4 displays all cutpoints generated for both the GA and AG
accelerometers.
4.3.4. Cross-validation
The classification agreement, sensitivity, specificity and kappa coefficients between
calibration and cross-validation data for SB and MVPA cutpoints are shown in Table
4.4. GA SBYouden (Se = 47%, Sp = 92%) and MVPAYouden (Se = 76%, Sp = 76%) cutpoints
demonstrated moderate percentage agreement (73.1–76.2%) and moderate kappa
scores (0.42–0.52). AG SBYouden (Se = 47%, Sp = 92%) and MVPAYouden (Se = 76%, Sp =
76%) cutpoints demonstrated high percentage agreement (83.3–87.3%) and
moderate to substantial kappa scores (0.59–0.75). Comparatively, lower percentage
agreement and kappa scores were observed for both GA SBSe and MVPASp (67.2-
68.9%, k = 0.38-0.36), and AG SBSe and MVPASp (73.2-80.4%, k = 0.46-0.60) cutpoints,
respectively.
Table 4.4. Calibration cutpoints and cross-validation % agreement, kappa (k) and se and sp.
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GA,.GENEActiv; AG, Actigraph; MVPA, moderate-to-vigorous physical activity; Se, sensitivity; Sp, specificity.
4.4. Discussion
This is the first study to determine GA wrist- and AG hip-worn raw acceleration
cutpoints for SB and MVPA in older adults. ROC curve analyses revealed that wrist
GA and hip AG accelerometer raw acceleration cutpoints provide good and excellent
discriminations of SB and MVPA, respectively. Results indicated that the SBYouden and
MVPAYouden cutpoints of 20 mg and 32 mg for GA, and 6 mg and 19 mg for AG yielded
the greatest classification accuracy. Such cutpoints are low compared to existing
ENMO adult SB cutpoints for GA wrist- (45.8 mg) and AG hip-worn (47.4 mg)
accelerometers (Hildebrand et al., 2016), and MVPA cutpoints for GA wrist-worn
accelerometers in adults (93 mg; Hildebrand et al., 2014) and older adults (100 mg;
Menai et al., 2017), and AG hip-worn accelerometers in adults (69.1 mg; Hildebrand
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Cutpoint (mg)Cross-Validation
% Agreement
Cross-Validation
Kappa
Cross-Validation
Se (%)
Cross-Validation
Sp (%)
GA
SBYouden
MVPAYouden
≤ 20
≥ 32
73.1
76.2
0.42
0.52
47
76
92
76
AG
SBYouden
MVPAYouden
GA
SBSe
MVPASp
AG
SBSe
MVPASp
≤ 6
≥ 19
≤ 57
≥ 104
≤ 15
≥ 69
83.3
87.3
67.2
68.9
73.2
80.4
0.59
0.75
0.38
0.36
0.46
0.60
62
86
43
81
48
94
93
89
99
65
97
74
et al., 2014). Consequently, the SBYouden and MVPAYouden cutpoints may underestimate
SB and overestimate light PA when applied in free-living environments, resulting in
participants being falsely classified as being physically active when they are more
likely to be sedentary.
The alternative SBSe and MVPASp cutpoints were more comparable to values reported
previously (Hildebrand et al., 2014; Hildebrand et al., 2016; Menai et al., 2017). The
Se values of 99% for GA SBSe and 98% for AG SBSe cutpoints ensure that almost all
older adults who are sedentary have ENMO values below the established cutpoints,
and therefore have a very low risk of being misclassified as being physically active.
The de-emphasis on Sp (e.g., % of older adults correctly identified as not being
sedentary) acknowledges the risk that a proportion (up to 40%) of older adults who
are physically active may be classified as sedentary. However, SB is an identifiable
risk factor affecting physical (e.g., premature mortality, chronic diseases and all-
cause dementia risk) and psychosocial (e.g., self-perceived QoL, wellbeing and self-
efficacy) determinants of health (Edwards & Loprinzi, 2016; Falck et al., 2016; Lewis,
Napolitano, Buman, Williams, & Nigg, 2017) independent of PA (Tremblay et al.,
2017). Such misclassification is likely to be beneficial if already physically active older
adults were offered further opportunities to take part in interventions to reduce SB
and increase PA levels (Chastin et al., 2017). Conversely, the higher Sp values for the
GA MVPASp cutpoint (89%) and AG MVPASp cutpoint (99%) ensures that older adults
not engaged in MVPA (e.g., ENMO values below the established cutpoints) are not
falsely classified as being in MVPA and are correctly identified and targeted for PA-
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promoting interventions (Lyons, Swartz, Lewis, Martinez, & Jennings, 2017). The de-
emphasis on Se suggests that up to 40% of older adults who are not in MVPA could
have ENMO values above this cutpoint (due to the lower true positive rate, relative
to the true negative rate (Sp)), and therefore their behaviour could be misclassified
as MVPA (Nero et al., 2015). However, it is more likely to be harmful for an older
adult to be wrongly classified as active rather than asking active older adults to take
part in additional MVPA. Hence, the goal of the MVPA cutpoint to identify older
adults who may have increased health risks due to being below this cutpoint (by
favouring Sp over Se) appears to be justified (Nero et al., 2015; Welk, Going,
Morrow, & Meredith, 2011).
Given that acceleration magnitudes are significantly lower for the AG GT3X+ relative
to the GA (John, Sasaki, Staudenmayer, Mavilia, & Freedson, 2013; Rowlands et al.,
2015; Rowlands et al., 2016), the higher wrist-worn cutpoints relative to the hip-
worn cutpoints were consistent with those observed previously (Rowlands et al.,
2015; Stiles, Griew & Rowlands, 2013). Our protocol was comparable to previous
calibration studies implemented in controlled settings (de Almeida Mendes et al.,
2017). However, laboratory calibration protocols rely on small deliberate increases in
PA intensities and movement patterns within a limited period of time, compared to
free-living activities over extended periods (van Hees, Golubic, Ekelund, & Brage,
2013a). Such protocols cannot fully reflect daily SB and PA patterns, and this may
limit the accuracy of the SB and MVPA thresholds obtained for wrist- and hip-worn
devices when they are applied in free-living environments (Van Hees et al., 2013a).
105
One of the main decisions to be made by researchers using either raw acceleration
or count-based outcomes is monitor placement location (de Almeida Mendes et al.,
2017). After comparing SB and PA estimates from wrist- and hip-worn monitors with
EE, Rosenberger et al. (2013) concluded that SB and MVPA classification accuracy
was superior for the hip-worn devices. Our cross-validation results support this due
to the superior percent agreement and kappa scores for the hip-worn AG over the
wrist-worn GA in classifying both SB and MVPA. However, our results also
demonstrate that wrist-worn accelerometers can provide accurate estimates of SB
and MVPA and the subsequent cutpoints performed reasonably well at
discriminating both SB and MVPA (Troiano et al., 2014). Indeed, a recent systematic
review of raw acceleration calibration studies reported no evidence of meaningful
differences in the accuracy of wrist- and hip-worn accelerometers (de Almeida
Mendes et al., 2017). Considering the superior wear compliance associated with
wrist-worn devices (Doherty et al., 2017), this attachment site may be the most
suitable location during free-living protocols. Consequently, the wrist-worn GA SBSe
and MVPASp cutpoints will be adopted in Chapter 5 (Study 3) to aid in answering
objectives 5 and 6 of the thesis.
Several strengths and limitations should be noted when interpreting the results of
this study. A main strength was the use of raw acceleration data from two commonly
used devices positioned at wrist and hip wear sites. These cutpoints will be of utility
to researchers using the raw data capabilities of the GA and current AG
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accelerometers to study SB and PA in older adults. Rigorous best practice analytical
procedures were also adopted to calibrate and cross-validate the cutpoints (Welk,
2005), which were specific to adults aged 60 years and over. Resting EE was directly
measured to allow a sample-specific interpretation of 3 METs as the MVPA
threshold, and used a validated separate criterion measure for SB (Kim, Barry &
Kang, 2015). There were also a number of limitations. The sample may not have
been representative of the wider older adult population in respect of their fitness
status and motivation to engage in PA, as recruitment included a convenience
sample of healthy older adults who answered advertisements and showed an
interest in the study representing a broad age range. Furthermore, specific older
adult populations (e.g., those with chronic diseases and impaired mobility) may
require different SB and PA cutpoints (Landry et al., 2015) that reflect differences in
RMR and energy cost during ambulatory PA across this age group (Miller, Strath,
Swartz, & Cashin, 2010). Moreover, activities that replicate everyday movements
and tasks performed by older adults were incorporated. However, it is recognised
that the laboratory setting limits the ecological validity of the resultant data
(Hildebrand et al., 2016). Lastly, cross-validation was performed using the same
laboratory protocol rather than using data collected from a free-living or a simulated
free-living protocol (Welk, 2005). It was felt that the challenges associated with
having the participants wear the gas analysis system for an extended period in free-
living situations were too great to warrant taking this approach. Consequently, the
cutpoints obtained should be further cross-validated with independent samples,
ideally from other settings and within free-living environments (Welk, 2005).
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4.5. Conclusion
In conclusion, cutpoints varied dependent upon attachment site, with the wrist-worn
GA cutpoints higher than those for the hip-worn AG. The identified GA and AG SBSe
and MVPASp cutpoints can enable researchers to classify older adults as engaging in
SB or not engaging in MVPA with an acceptable degree of confidence. Further cross-
validation research is needed to test the utility of these cutpoints in independent
samples within free-living environments.
Thesis Study Map
Study Objectives and Key Findings
Study 1. Using formative research with older
adults to inform a community physical activity
programme: Get Healthy, Get Active.
Objectives
To explore current knowledge and attitudes
towards physical activity, as well as perceived
barriers, facilitators and opportunities for physical
activity participation among older adults living in
the community.
Use these data to subsequently inform the design,
delivery and recruitment strategies of Sport
England’s national Get Healthy, Get Active
initiative.
Key Findings:
Older adults acknowledged the benefits of
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physical activity, not only for health but also those
relating to socialising, enjoyment, relaxation, and
physical and psychological wellbeing regardless of
socioeconomic status.
The themes of opportunities and awareness for
physical activity participation, cost, transport,
location and season/weather varied between
assisted living and community-dwelling older
adults.
Study 2. Evaluation of wrist and hip sedentary
behaviour and moderate-to-vigorous physical
activity raw acceleration cutpoints in older
adults.
Objectives
To test a laboratory-based protocol to generate
behaviourally valid, population specific wrist- and
hip-based raw acceleration cutpoints for
sedentary behaviour and moderate-to-vigorous
physical activity in older adults.
Apply these cut-points to subsequently analyse
physical activity data for Sport England’s Get
Healthy Get Active physical activity intervention.
Key Findings
When optimizing Sensitivity for sedentary
behaviour and Specificity for moderate-to-
vigorous physical activity, wrist-worn GENEActiv
accelerometer cutpoints of 57 mg and 104 mg
were generated for sedentary behaviour and
moderate-to-vigorous physical activity,
respectively.
For the hip-worn ActiGraph GT3X+ the cutpoints
were 15 mg and 69 mg for sedentary behaviour
and moderate-to-vigorous physical activity,
respectively.
The resultant cutpoints can enable researchers to
classify older adults as engaging in sedentary
behaviour or not engaging in moderate-to-
vigorous physical activity with an acceptable
degree of confidence.
Study 3. Physical activity, sedentary
behaviour, perceived health and fitness, and
psychosocial wellbeing among community-
Objectives
To investigate gender, age, and socio-economic
status differences in older adults’ sedentary
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dwelling older adults. behaviour, physical activity and self-reported
health indicators.
To examine associations between sedentary
behaviour and physical activity with self-reported
health indicators.
Study 4. A pragmatic evaluation of the Get Healthy Get Active physical activity programme for community-
dwelling older adults.
Study 5. Implementation fidelity of the Get Healthy Get Active physical activity programme for community-
dwelling older adults
Chapter 5
Study 3: Physical activity, sedentary behaviour, perceived health and fitness, and psychosocial
wellbeing among community-dwelling older adults.
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5.1. Introduction
Chapter 4 (Study 2) determined laboratory-based wrist-worn GA and hip-worn AG
GT3X+ raw acceleration cutpoints for SB and MVPA in older adults. The current study
progresses the work of Chapter 4 (Study 2) by implementing the wrist-worn GA
cutpoints within free-living environments.
Performing sufficient PA is a primary modifiable determinant of health (Birkel et al.,
2015) and recent research has demonstrated its potential to benefit an array of
physical (Zhu et al., 2017) and psychosocial (Devereux-Fitzgerald et al., 2016; Franco
et al., 2015; Greaney et al., 2016) determinants of health in older adults. There is
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also growing public health interest in the amount of time spent in SB; defined as
waking behaviours in a sitting, reclining or lying posture with EE ≤1.5 METs (Tremblay
et al., 2017). Sociodemographic attributes including gender (Greaney et al., 2016),
age (Heo, Chun, Kim, Ryu, & Lee, 2017) and SES (Gray et al., 2015) have been shown
to affect both SB and PA levels in older adults. Accumulating evidence suggests that
prolonged episodes of SB have similar physical (e.g., premature mortality, chronic
diseases and all-cause dementia risk) and psychosocial (e.g., QoL and SEE) risk
factors to that of physical inactivity (Edwards & Loprinzi, 2016; Falck et al., 2016; Kim
et al., 2016; Lewis et al., 2017). Consequently, physical inactivity in combination with
prolonged periods of SB further compound negative physical (Haywood et al., 2018)
and psychosocial (Biswas et al., 2015; Pulsford, Stamatakis, Britton, Brunner, &
Hillsdon, 2015) health outcomes. The high levels of SB and low levels of MVPA in this
population are concerning given their negative associations with self-reported health
(Beyer, Wolff, Warner, Schüz, & Wurm, 2015), fitness (Kuosmanen et al., 2016) and
psychosocial outcomes such as QoL and SEE (French, Olander, Chisholm, &
McSharry, 2014; Greaney et al., 2016; Kim et al., 2016; Olson et al. 2016).
Interventions should focus with not only the single effect of either SB or PA level, but
with the balance of both (ten Brinke et al., 2015). To achieve this requires accurate
and reliable measurement of SB and PA patterns over time (Greaney et al., 2016).
Health comprises not only physical, but also psychological and social components,
and it is therefore important to take self-rated measurements into consideration
when evaluating health status (Kuosmanen et al., 2016). Self-report questionnaires
concerning SRH and SAPF are suitable ways of obtaining important information
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about a person’s overall health and functional status (Kuosmanen et al., 2016) and
are strongly correlated with objective assessments of health and fitness (Meng, Xie &
Zhang, 2014; Wu et al., 2013). Among older adults both concepts have been found to
be influenced by sociodemographic attributes including gender, age, and SES (Bamia
et al., 2017; Meyer, Castro-Schilo & Aguilar-Gaxiola, 2014), as well as being positively
associated with PA level (Beyer et al., 2015; Haywood et al., 2018), and negatively
associated with SB (Haywood et al., 2018). Specifically, those who are physically
active, male, younger-old (60 to 69 years), and of higher SES are more likely to report
favourable ratings of health and physical fitness (Bamia et al., 2017; Kuosmanen et
al., 2016). Further research in more sociodemographically diverse older populations
is warranted to improve understanding of the relationship between gender, age, and
SES and, self-reported physical and psychosocial outcomes, as well as the
independent factors affecting them such as SB and PA levels (Kuosmanen et al.,
2016). This study examined SB and PA levels assessed by self-report and
accelerometry. The aims related to thesis objectives 5 and 6 were firstly to;
investigate gender, age, and socio-economic status differences in older adults’ SB, PA
and self-reported health indicators, and secondly to examine associations between
SB and PA with self-reported health indicators.
5.2. Methods
5.2.1. Participants and procedures
This cross-sectional study was undertaken between January 2016 to December 2017
as the baseline phase of Sport England’s GHGA PA programme. GHGA was a three-
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year project aimed at engaging inactive older adults in PA at least once a week for 30
minutes, via a 12 week PA intervention. The project was funded by Sport England
and delivered by Sefton Borough Council. A full outline of the GHGA programme is
presented in Chapter 6.
A homogenous purposive sample of 380 older adults were approached whilst
attending the GHGA sessions throughout Sefton Borough in north-west England.
Sefton was appropriate for participant recruitment because compared to the UK
national average it is recognised as having a higher percentage of male and female
older adults over 65 (30% of the total population), and over 85 (6% of the total
population), and the highest percentage of inactive over 65 year olds (80%) (ONS,
2017). Furthermore, Sefton has the highest National Health Service costs associated
with physical inactivity (Sport England, 2014; Sport England, 2015). Sefton is also
characterised by large differences in SES according to the Indices of Multiple
Deprivation (IMD) (Public Health England, 2017). A total of 318 older adults
consented to take part (83.7% recruitment rate), with 207 older adults (164 female;
43 male; 54.5% participation rate), aged 65 to 102 years (mean age of participants
=77.8, SD =7.7) meeting the inclusion criteria determined by Sport England as
funders of the GHGA programme. These criteria required participants to reside
within Sefton Borough, be ≥65 years of age, be without physical and/or intellectual
disabilities which prevented written informed consent being provided, and be
physically inactive as indicated by the Single Item Physical Activity Question
(Donaldson, 2004). The Single Item Physical Activity Question was a mandatory Sport
114
England screening tool for the GHGA programme to determine participants’ activity
levels. This question asks participants,
“In the past week, on how many days have you done a total of 30 minutes or more PA, which was enough to raise your breathing rate? This may include sport, exercise and brisk walking or cycling for recreation or to get to and from places, but should not include housework or PA that may be part of your job.”
Only individuals who answered “0 days” (inactive) were deemed to have met this
inclusion criterion. The Single Item Physical Activity Question has demonstrated
sound psychometric properties (Milton, Bull & Buamn, 2010) including good test-
retest reliability agreement (kappa = 0.63, 95% confidence interval = 0.54 to 0.72),
and modest concurrent validity (r = 0.53) against the Global Physical Activity
Questionnaire (Armstrong & Bull, 2006).
Participants invited to participate in the programme received a covering letter,
participant information sheet, and consent form. Before the study commenced,
institutional ethical approval was received (#SPA-REC-2015-329) and written
informed consent was obtained for all participants prior to participation.
Participation was voluntary with no incentives provided.
5.2.2. Primary Outcome Measures
5.2.2.1. International Physical Activity Questionnaire for the Elderly
To assess SB and PA levels, the IPAQ-E (Hurtig-Wennlöf et al., 2010) was adopted as
required by the funder. IPAQ-E is based on the short version of the IPAQ
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(www.ipaq.ki.se) and assesses time spent sitting, walking in bouts of 10 minutes or
more (labelled as Walk10min herein), MPA in bouts of 10 minutes or more (labelled as
MPA10min herein), and VPA in bouts of 10 minutes or more (labelled as VPA10min herein)
during the previous 7-days. The categorical outcome from IPAQ-E assigns the
participants into one of three PA categories (e.g., low, moderate, or high-PA). The
IPAQ-E provides favourable levels of both direct and indirect levels of criterion
validity for sitting (Spearman r = 0.28, P < 0.05), Walk10min (Spearman r = 0.35, P <
0.01), MPA10min (Spearman r = 0.40, P < 0.01), and VPA10min (Spearman r = 0.37, P <
0.01) (Hurtig-Wennlöf et al., 2010). However, varying levels of test-retest reliability
(intraclass correlation ranging from 0.30 to 0.82) have also been reported (Tomioka
et al., 2011).
5.2.2.1. Wrist-based Accelerometer (GA)
A sample of 101 participants (75 female; 26 male), aged 65 to 90 years (mean age
=77, SD=7.1) wore a triaxial GA accelerometer (ActivInsights Ltd., Kimbolton,
Cambridgeshire, United Kingdom) on their non-dominant wrist for seven days, 24
hours per day. Objective measures of PA such as accelerometers are commonly
adopted as methods of monitoring older adults’ SB and PA levels (Evenson et al.,
2015; Mañas et al., 2017; Parsons et al. 2017; Thornton et al. 2017; Wullems et al.,
2017). Accelerometers are particularly appropriate for assessing PA in older adults as
these devices require no input from the participant over the data collection period,
and superior wearer compliance has been demonstrated when compared to younger
age groups (Doherty et al., 2017). Given the complexity of SB and PA constructs, and
the increasing adoption of accelerometers within surveillance, epidemiology, clinical,
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and intervention research, it is recommended that research in this population adopts
both objective and self-report measures to investigate and provide evidence for
participants’ SB and PA. Adopting both methods enables researchers to more fully
characterise individuals SB and PA patterns and identify potential intervention
targets for increasing activity (Shiroma, Schrack & Harris, 2018).
The GA is a water-proof device which measures raw accelerations expressed in
gravitational equivalent units at a range of -8 g and 8 g. Acceleration values are
digitised by a 12-bit analog-to-digital converter and the devices were set to record
accelerations at a frequency of 60 Hz. The GA accelerometer has demonstrated
excellent technical reliability as well as criterion and concurrent validity in adult
populations (Esliger et al., 2011). Participants were provided with a leaflet outlining
how and when to wear the devices prior to participation. The first author was
responsible for attaching and collecting the accelerometers at a time and location
most suitable to the participant. A log sheet was provided for each participant to
record any times that the device was removed and subsequently replaced. GA data
were downloaded using GA PC software version 2.9 and saved in raw format as
binary files. Signal processing of raw GENEActiv.bin files was completed offline using
R-package GGIR version 1.5 (https://cran.r-project.org/web/packages/GGIR/) (van
Hees et al., 2013). This R-package facilitates data cleaning and the extraction of user-
defined acceleration levels, which can then be set to reflect the intensity levels.
Concurrent with previous studies (Hildebrand et al., 2014; Fairclough et al., 2016;
Menai et al., 2017; Rowlands et al., 2016), the Euclidean Norm Minus One (ENMO)
metric (e.g., signal vector magnitude of the three axes; g = √x2 + y2 + z2 -1 g) (van
117
Hees et al., 2013) was adopted to quantify acceleration relative to gravity, after
which negative values were rounded to zero. Raw data were further reduced by
averaging the ENMO values expressed as over 1 s epochs. All resulting values were
expressed in milli (10-3) gravity-based acceleration units (mg), where 1g = 9.81 m·s2.
Although the ENMO metric can be sensitive to poor calibration (van Hees et al.,
2013), GGIR autocalibrates the raw triaxial accelerometer signal in order to reduce
such calibration error (van Hees et al., 2013). A valid day was defined as at least 10
hours wear-time during waking hours, with at least 3 valid days required for
inclusion in the analysis. Waking hours are not significantly different between the
young-old, middle-old and old-old (Valenti, Bonomi & Westerterp, 2017) and were
defined as being between 07:00 and 23:00. For all participants with usable data,
mean daily time spent in total accumulated 1 second bouts of SB and MVPA were
established by applying ENMO cutpoints of ≤57 mg (SB/light-PA) and ≥104 mg
(MVPA) generated from a previously conducted calibration study in older adults as
part of the GHGA project (unpublished data). Given that both US (CDC, 2015) and UK
(Department of Health, 2011a) derived PA guidelines recommend that over a week,
older adults should accumulate up to at least 150 minutes of MVPA10min, daily mean
MVPA derived only from bouts of at least 10 minutes above the MVPA cutpoint were
also reported. Total accumulated 1 second bouts of SB and MVPA, MVPA10min, and
mean accelerations (mg·day-1) were the main outcomes in the GA analyses.
5.2.3. Secondary Outcome Measures
5.2.3.1. Self-Assessment of Physical Fitness
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To assess self-perceived fitness, the SAPF (Weening-Dijksterhuis, de Greef, Krijnen, &
van der Schans, 2012) was adopted. The questionnaire asks three questions
including: How do you rate your strength?; How do you rate your aerobic
endurance?; and How do you rate your balance? The SAPF uses a rating method from
0 (indicating the lowest rating) to 10 (indicating the highest rating) for each of the
three items. Sound psychometric properties have been demonstrated for the SAPF
scale among frail older adults with acceptable internal consistency (Cronbach alpha
=0.71) (Weening-Dijksterhuis et al., 2012), one week test-retest validity (0.70), and
moderate concurrent validity against the Groningen Fitness Test for the Elderly
(Lemmink, 1996).
5.2.3.2. Kemp Quality of Life Scale
To assess QoL, the single item Kemp Quality of Life Scale (Kemp & Ettelson, 2001)
was adopted. Following the question: Thinking about the good and bad things that
make up your quality of life, how would you rate the quality of your life as a whole? ,
respondents are asked to tick the box next to the answer that best describes their
QoL on a 7-item scale anchored by “so good, it could not be better” (score of 1) to
“so bad, it could not be worse” (score of 7). Although there is no published
information about the distribution or error of data obtained using this scale, the
scale is interval level, can provide data for parametric and non-parametric analysis,
and has been adopted previously in older adult populations (Siebens, Tsukerman,
Adkins, Kahan, & Kemp, 2015; Roe et al., 2011).
5.2.3.3. Self-Rated Health Questionnaire
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The SRH (Sargent-Cox, Anstey & Luszcz, 2010) is a 3-item questionnaire assessing
global, age-comparative, and self-comparative health status (HS). Global HS is
measured via the question: How would you rate your overall health at the present
time?. Participants answer on a five point scale (1 = excellent; 5 = poor). Age-
comparative HS is measured on a three-point scale via the question: Would you say
your health is: (1) better, (2) about the same, or (3) worse than most people your
age?. Self-comparative HS is also measured on a three point scale via the question:
Is your health now… (1) better, (2) about the same, or (3) not as good as it was 12
months ago?. Previous research has shown good prognostic validity for mortality,
and high internal reliability (α =.75) (Ferraro & Wilkinson, 2013).
5.2.3.3. Self-Efficacy for Exercise Scale
The SEE (Resnick & Jenkins, 2000) is an 11-item scale designed to assess confidence
to continue exercising in the face of perceived barriers. The SEE scale consists of 11
situations that might affect participation in exercise. Items are rated from 0 (not
confident) to 10 (very confident). The mean score of numerical ratings from each
response indicates the strength of efficacy expectations. The SEE scale has
demonstrated excellent internal consistency (Cronbach α =0.92) and significant
predictive validity of exercise activity in older adults when controlled for age and
gender (F = 78.8; p < 0.05) (Resnick & Jenkins, 2000; Resnick, Luisi, Vogel, &
Junaleepa, 2004).
All measures were administered by the first author to each participant whilst
attending the GHGA sessions. Measures took no longer than 30 minutes to complete
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per participant. Regular breaks were incorporated if needed, to reduce fatigue.
Additional visits were also arranged if needed, to aid completion of a full dataset due
to participant time constraints. All participants recorded their age, gender and home
postal code. Ages were categorised as 65 to 69 years, 70 to 79 years, and ≥80 years.
Postal codes were used to estimate SES by generating Indices of IMD (Department
for Communities and Local Government, 2015) using an online conversion tool
(http://imd-by-postcode.opendatacommunities.org/). The IMD is a UK government
metric used to rank area-level deprivation within and between different
communities. The IMD scores rank each super output area in England from 1 (most
deprived area) to 32,844 (least deprived area). Super output areas were then split
into 3 equal groups to represent low (1 to 10,947), middle (10,948 to 21,895), and
high-SES (21,896 to 32,844). Questionnaire data were input manually into Microsoft
Excel 2013 and checked for missing data. All 207 participants who consented to take
part were included in the final analytical sample. Confidentiality and data storage
procedures were adhered to as is set out in Edge Hill University’s research data
management (Edge Hill University, 2017) and code of practice for the conduct of
research guidelines (Edge Hill University, 2017). All participant data was anonymised
and coded to prevent identification, and securely stored using password-protected
files on the Edge Hill University computing network. The server hosting the files was
backed up every four hours and nightly to tape, to ensure data attrition was
minimised. Only research team members had access to the anonymised data. This
was shared between the team strictly for the purposes of research.
5.2.4. Statistical Analysis
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Primary outcome measures were self-reported IPAQ outcomes and GA wrist-based
accelerometer-derived SB and MVPA. To address objective 5, multivariate analysis of
covariance (MANCOVA) assessed differences between the IPAQ outcomes and the
independent variables of gender, age category (e.g., 65 to 69, 70 to 79 and ≥80
years), and SES-group (e.g., low-SES, middle-SES and high-SES). Season of data
collection (e.g., spring/summer and autumn/winter) was included as a covariate.
Analysis of covariance (ANCOVA) assessed differences in accelerometer outcomes
between the independent variables. Accelerometer wear time (min‧d-1) was included
as a covariate for SB only, with season of data collection (spring/summer and
autumn/winter) included as a covariate. The secondary outcomes were QoL, SRH,
SAPF and SEE. ANCOVAs examined gender, age category, and SES-group differences
in each secondary outcome and season of data collection was included as a covariate
in each analysis (Prins & van Lenthe, 2015). To address objective 6, stepwise multiple
regression analyses were used to examine associations between secondary
outcomes with each of the primary outcomes. Effects were considered significant at
the p < 0.05 level. All data analyses were performed using IBM SPSS Statistics for
Windows version 22.0 (IBM Corp, Armonk, NY).
5.3. Results
Among the 318 older adults who consented to participate in the study, 111 (97
female; 14 male) were identified as being “active” as per the single item PA
screening measure (Donaldson, 2004). There was no missing data among the
remaining participants. The resulting final analytical sample consisted of 207
participants (mean age= 77.8, SD =7.7; 65.1% compliance rate). Among the sample of
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111 participants wearing a GA accelerometer, 10 participants (eight female; two
male) did not satisfy the wear time criteria and so were excluded from the analysis.
The resulting final analytical sample consisted of 101 participants (mean age= 77, SD
=7.1 years; 91% compliance rate). Characteristics of the questionnaire- and
accelerometer-wearing samples were comparable in terms of gender (male = 20.3%;
23.7% men), age (65 to 69 years =15.5%; 18.8%, 70 to 79 years =44.7%; 45.5%, ≥80
years =39.8%; 36.6%) and SES-group (low-SES =35.9%; 31.6%, middle-SES =24.3%;
21.8%, high-SES =39.8%; 46.5%) splits, respectively. Missing data among the
accelerometer-wearing sample was accrued through a variety of reasons including
skin irritation (three participants), concern over device impact on general health
(two participants) and forgetting to wear the monitor after removing it before
bedtime (five participants). There were no significant differences for any of the
measured variables between participants included in the analyses and those
excluded. Descriptive statistics are shown in Tables 5.1 and 5.2 for the questionnaire-
and accelerometer-wearing samples, respectively.
Table 5.1. Descriptive characteristics of the participants
Self-report data Total sample(n =207)
Age years (SD)
Gender n (%)
Female
Male
Age Category n (%)
60-69 yrs
77.8 (7.7)
165 (79.7)
42 (20.3)
32 (15.5)
123
70-79 yrs
80+ yrs
92 (44.7)
83 (39.8)
Season of data collection n (%)
Autumn/Winter
Spring/Summer
SES Group n (%)
Low-SES
Mid-SES
High-SES
86 (41.7)
121 (58.3)
74 (35.9)
51 (24.3)
82 (39.8)
SD = Standard Deviation; SES = Socioeconomic status.
Table 5.2. GENEActiv wrist-worn accelerometer data descriptives.
GENEActiv data Total Sample (n =101)
Age (years)
Gender n (mean age ± SD)
Female
Male
Age Category n (%)
60-69 yrs
77.0 (7.1)
77 (76.2)
24 (23.8)
18 (17.8)
124
70-79 yrs
80+ yrs
46 (45.5)
37 (36.6)
Season of data collection n (%)
Autumn/Winter
Spring/Summer
SES Group n (%)
Low-SES
Mid-SES
High-SES
49 (48.5)
52 (51.5)
32 (31.6)
22 (21.8)
47 (46.5)
SD = Standard Deviation; SES = Socioeconomic status.
5.3.1. Primary Outcomes
Table 5.3 shows time spent in self-reported sitting, PA behaviours, as well as
secondary outcome data for the questionnaire sample. No significant differences
between gender, age category and SES-group were present. Men spent more time
sitting (3073.1 vs 2835.3 min‧w-1) and engaged in more MPA10min (151.9 vs 116.2 min‧
w-1) than women, respectively. Women spent more time Walk10min (333.5 vs. 235.8
min‧w-1) than men and reported spending more time in MVPA10min per day than men
(66.4 vs 48.1 min‧d-1). There was no significant difference between older adults
achieving PA guidelines (57% of women; 47.6% of men) and those not achieving PA
guidelines.
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126
Total sample(n =207)
Female (n =165)
Male (n =42)
Young-old (n =32)
Middle-old (n =93)
Old-old (n =82)
Low-SES (n =75)
Mid-SES (n =50)
High-SES (n =82)
Sitting (min‧w-1) 2883.7 (1137.2)
2835.3 (1118.0)
3073.1 (1204.3)
2677.2 (1028.4)
3048.8 (1188.3)
2793 (11110.9)
2951.8 (1118.5)
2728.3 (1075.7)
2932.3 (1195.7)
Walk10min (min‧w-1) 313.5 (483.5) 333.5 (512) 235.8 (345.1) 257.8 (375.1) 335.7 (473.0) 316.5 (534.2) 368.8 (639.2) 274.7 (315.7) 293.1 (396.7)
MPA10min (min‧w-1)
MVPA10min (min‧d-1)
Meeting Guidelines n (%)
YES
NO
123.5 (334.9)
62.7 (92.2)
114 (55.3)
92 (44.7)
116.2 (326.8)
66.4 (95.4)
94 (57)
71 (43)
151.9 (367.4)
48.1 (77.7)
20 (47.6)
22 (52.4)
85.6 (171.8)
31.9 (33.3)
6 (18.8)
26 (81)
131.6 (362)
68.3 (94.7)
15 (16.3)
77 (83.7)
127.6 (350.9)
68.3 (102.8)
14 (16.9)
69 (83.1)
84.9 (224.1)
66.6 (105)
57 (77)
17 (23)
86 (223.2)
46.2 (67)
9 (17.6)
42 (82.4)
180.2 (449.9)
69.2 (92.9)
15 (18.3)
67 (81.7)
Quality of Life 3.2 (.77) 3.2 (.77) 3.4 (.78) 3 (.78) 3.2 (.80) 3.2 (.75) 3.3 (.76) 3 (.71) 3.1 (.80)
Self-Assessment of Physical Fitness
9.8 (3.48) 9.4 (3.4) 11.3 (4) 9.8 (3.7) 9.6 (3.6) 10.1 (3.2) 8.8 (3.5) 10.7 (3.2) 10.2 (3.4)
Self-Rated Health 8.1 (1.5) 8.2 (1.4) 8.3 (1.2) 8.3 (1.4) 8.2 (1.3) 8.2 (1.4) 8.2 (1.3) 8.1 (1.3) 8.2 (1.4)
Self-Efficacy for Exercise
29.8 (10.1) 28.8 (10) 33.6 (9.56) 29.7 (11.1) 28.7 (10.1) 30.9 (9.7) 28.2 (9.4) 30.7 (10.7) 30.6 (10.3)
Table 5.3. Self-reported physical activity and psychosocial outcome measures.
*.= Significant difference; Data are mean (SD) unless otherwise stated; Young-old = 65-69 years; Middle-old = 70-79 years; Old-old = 80+ years; Walk 10min = Walking in ≥ 10-minute bouts; MPA10min = Moderate-physical activity in ≥ 10-minute bouts; MVPA10min = Moderate-to-vigorous physical activity in ≥ 10-minute bouts. Note. Lower score = more favourable result for Quality of Life and Self-Rated Health.
127
Table 5.4 shows time spent in SB and PA for the accelerometer-wearing sample.
Mean total wear time was 1244.7 min‧d-1 in men and 1222.5 min‧d-1 in women. Time
spent in SB (771.7 vs. 773.9 min‧d-1), LPA (113.7 vs. 113.3 min‧d-1), MVPA (74.7 vs.
72.8 min‧d-1), MVPA10min (7.8 vs 8.4 min‧d-1) and mean acceleration (26.3 vs 25.4 mg‧d-
1) did not differ between women and men, respectively. Overall, there was no
significant difference in the proportion of older adults recorded as achieving PA
guidelines from total accumulated 1 second bouts of MVPA (89.6% of women, 79.2%
men) and MVPA10min (11.7% of women, 16.7% of men).
No significant gender, age category, and SES-group differences were observed
between IPAQ outcomes and accelerometer-derived SB and PA.
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Table 5.4. GENEActiv SB and physical activity outcomes.
Total sample(n =101)
Female (n =77)
Male (n =24)
Young-old (n =18)
Middle-old (n =46)
Old-old (n =37)
Low-SES (n =32)
Mid-SES (n =22)
High-SES (n =47)
Total wear time (min‧d-1)
Mean acceleration (mg‧d-1)
SB (min‧d-1)
1227.8 (142.6)
26.1 (7.9)
772.0 (69.0)
1222.5 (138.8)
26.3 (7.5)
771.7 (69.7)
1244.7 (155.9)
25.4 (9.3)
773.9 (67.9)
1206.7 (194.6)
24.5 (7.7)
773 (52.5)
1256.7 (114.7)
24.6 (7.8)
779.1 (68.6)
1202.1 (141.9)
28.6 (7.7)
763.3 (76.8)
1227.9 (143.4)
23.1 (7.4)
763.4 (62.2)
1187.8 (175.3)
29.1 (7.4)
784.9 (72.7)
1246 (123.2)
26.7 (7.9)
772.3 (72)
LPA (min‧d-1)
MVPA (min‧d-1)
113.5 (36.2)
74.2 (39.4)
113.7 (37.5)
74.7 (39.3)
113.3 (32.3)
72.8 (40.6)
118 (35.6)
69 (27.5)
110.4 (35.9)
70.5 (38.6)
115.3 (37.6)
81.4 (44.8)
118.8 (34.2)
77.8 (37.1)
104.5 (36.7)
70.6 (42.1)
114.3 (37.3)
73.4 (40.3)
6.2 (8.1)
41 (87.2)
6 (12.8)
4 (8.5)
43 (91.5)
MVPA10min (min‧d-1)
Meet MVPA Guidelines n (%)
YES
NO
Meet MVPA10min n (%)
YES
NO
7.7 (9.1)
88 (87.1)
13 (12.9)
13 (12.9)
88 (87.1)
7.8 (9.3)
69(89.6)
8 (10.4)
9 (11.7)
68 (88.3)
8.4 (8.9)
19 (79.2)
5 (20.8)
4 (16.7)
20 (83.3)
3.1 (4.4)
17 (94.4)
1 (5.6)
0 (0)
18 (100)
9.9 (9.8)
40 (87)
6 (13)
8 (17.4)
38 (82.6)
7.9 (9.4)
31 (83.8)
6 (16.2)
5 (13.5)
32 (86.5)
9.9 (10.4)
29 (90.6)
3 (9.4)
5 (15.6)
27 (84.4)
9 (9.2)
18 (81.8)
4 (18.2)
4 (18.2)
18 (81.8)
*.= Significant difference; Data are mean (SD) unless otherwise stated; Young-old = 65-69 years; Middle-old = 70-79 years; Old-old = 80+ years; SB = Sedentary behaviour; LPA = Light physical activity; MVPA = moderate-to-vigorous physical activity; MVPA10min = Moderate-to-vigorous physical activity in ≥ 10-minute bouts.
129
5.3.2. Secondary Outcomes
Mean ± SD scores for QoL (3.2 ± 0.77 vs 3.1 ± 0.78), SAPF (9.4 ± 3.4 vs 11.3 ± 3.53),
SRH (8.2 ± 1.38 vs 8.3 ± 1.22), and SEE (28.8 ± 10 vs 33.6 ± 9.56) did not differ
significantly between women and men, respectively. Significant gender, age category
and SES-group (F(4, 188) =3.440, p =0.01) differences were observed with QoL, and
gender and age category (F(2, 188) =3.899, p =0.022) differences with SRH. No
significant post-hoc pairwise comparisons were present for these results. SAPF score
was 17.6% higher for males compared to females (F(1, 201) =7.893, p =0.005) and
26.5% and 11% higher for the middle-SES group when compared to the low- and
high-SES groups, respectively (F(2, 201) =5.449, p =0.005). Post-hoc analysis revealed
significant interactions between low- and middle-SES groups (p =0.009) and low- and
high-SES groups (p =0.047). SEE scores were 16.4% higher for males compared to
females (F(1, 205) =8.137, p =0.005).
5.3.3. Regression Analysis
After adjusting for gender, age category, SES-group, and season, there was a
significant negative association between sitting (min‧w-1) and SAPF (β =-97.187, p
<0.001) and SEE (β =-17.819, p <0.029). Significant positive associations between
MPA10min (min‧w-1) and SEE (β =7.340, p =0.002), and MVPA10min and SRH (β =13.176, p
=0.005) and SAPF (β =5.762, p =0.002) were also observed. No significant
associations were obtained from self-reported sitting, MPA10min and MVPA10min.
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5.4. Discussion
This study investigated gender, age and SES differences in older adults’ SB, PA and
self-reported physical and psychosocial health outcomes, and explored associations
between SB and PA with self-reported physical and psychosocial health outcomes.
No significant gender, age category and SES-group differences were observed
between self-reported and accelerometer-derived SB and PA. These findings are
counter to those in previous studies which have noted that MVPA10min is lower
among those who are female and older (Amagasa et al., 2017; Lohne-Seiler, Hansen,
Kolle & Anderssen, 2014; Ramires et al., 2017; Shiroma et al., 2018) due to
difficulties in mobility, general health status, and lower levels of self-efficacy
(Ramires et al., 2017). Men are more physically active than women in almost every
country throughout the adult and older adult age range when evaluated based on
current PA guidelines (Hallal et al., 2012; Sallis et al., 2016; Sun, Norman & While,
2013). Participants were recruited whilst attending the GHGA PA sessions and
therefore, both men and women across the age range were likely more inclined to
be active. Moreover, gender bias in the sample could have further affected any
potential gender and age category differences between SB and PA. Previous studies
have reported significantly lower levels of MVPA10min in older adults with low-SES
compared to those with middle and high-SES (Mendoza-Vasconez et al., 2016). A
variety of barriers including cost, transport, lower levels of education (e.g,, lack of
knowledge about PA benefits) and neighbourhood safety have been suggested as
reasons for these disparities (Buckman et al., 2014; Greaney et al., 2016; Lindgren,
Borjesson, Ekblo, Bergstrom, Lappas & Rosengren, 2016; Mendoza-Vasconez et al.,
131
2016; Xiao, Keadle, Berrigan, & Matthews, 2018). Participants in the current study
were recruited whilst attending PA programme sessions which were free to attend,
with free transport (local taxis) provided to and from each PA session and hence,
barriers associated with SES-group were not apparent as the PA sessions largely
eliminated cost, transport and neighbourhood safety barriers noted in previous
research (Baert et al., 2016).
In support of findings outlined in Chapter 3 (Study 1), significant gender, age
category and SES-group differences were observed with QoL, gender, and age
category differences with SRH, gender and SES-group differences with SAPF, and
gender differences with SEE. Older adults who are male, younger-old (60 to 69
years), and of higher SES are more likely to report favourable ratings of self-reported
physical and psychosocial health (Bamia et al., 2017; Kuosmanen et al., 2016).
Negative associations between SB and SAPF and SEE, and positive associations
between MVPA10min and SRH and SAPF were also observed. A number of self-
reported physical conditions including number of falls, balance, pain interference,
and lower-extremity function have been shown to be associated with time spent in
SB and MVPA (Haywood et al., 2018; de Rezende et al., 2014). Previous studies have
also noted negative associations of SB, and positive associations of MVPA on QoL,
wellbeing, depression, and self-efficacy (Ku et al., 2016; Withall et al., 2014).
No significant effects of Walk10min on secondary outcomes was found. Conversely,
previous studies have demonstrated significant positive effects of time spent in LPA
behaviours such as walking on SRH and SAPF (Buman et al., 2010; Kuosmanen et al.,
132
2016; Loprinzi et al., 2013), and psychosocial outcomes including increased QoL,
social well-being, socialization, and reduced stress (Ku et al., 2016; Sun et al., 2013).
Among older adults, the physical health benefits (e.g., number of falls, balance and
strength) of LPA are as beneficial as those for total accumulated MVPA (Ku et al.,
2016), and greater than all forms of other activity, including MVPA10min, in terms of
benefits to psychosocial well-being (Buman et al., 2010). It has been suggested that
two sessions per week of ‘light-to-moderate’ intensity PA each of a minimum of 45
minutes duration are optimal for improving self-reported physical and psychosocial
outcomes in older adults (Windle et al., 2010). Although less than the recommended
PA guidelines, fewer sessions of a lower intensity are more realistic for encouraging
long-term adherence to PA in older adults regardless of gender, age and SES-group
status (Kuosmanen et al., 2016). Further longitudinal studies in older adults are
warranted examining the effects of replacing sedentary behaviours not just with
MVPA, but also LPA (Chastin et al., 2018; Jefferis et al., 2016; McMahon et al., 2017;
Phoenix & Tulle, 2017).
Self-report and accelerometer assessed total time spent in SB (411.9 vs 772 min‧d-1)
and MVPA10min (62.7 vs 7.7 min‧d-1) is comparable to recent self-report and
accelerometer assessed SB and PA studies in older adults by López-Rodríguez et al.
(2017) and Amagasa et al. (2017), respectively. Within these two studies the total
number of participants was 80 (mean age =72, SD =5.5) and 450 (mean age =74.3, SD
=2.9) respectively, and total time spent in SB was 407.4 and 548.3 min‧d-1, and
MVPA10min was 27.4 and 17.9 min‧d-1, respectively. Compared to GA wrist-based
accelerometer-derived MVPA10min (cutpoint of ≥104.mg), self-reported MVPA10min
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levels were higher by 58 min‧d-1 and 40 min‧d-1 for women and men, respectively. A
recent study by Menai et al. (2017) reported similar results when comparing
accelerometer-assessed and self-reported MVPA10min. Self-reported levels of
MVPA10min were 19.3 min‧d-1 higher than GA accelerometer-assessed MVPA10min after
applying an MVPA cutpoint of ≥100 mg. These findings further confirm the inherent
limitation of recall bias within self-report measures (Barnett et al., 2016). The
ubiquitous presence of total accumulated and sporadic PA in older adults makes it
difficult to recall in questionnaire surveys (Washburn, 2000), though such behaviours
may be of particular importance, especially for older adults who tend to perform
shorter duration exercises (Amagasa et al., 2017; Jefferis et al., 2016; Sparling et al.,
2015). Consequently, this population tend to misreport time spent in such activities
when compared with objective measures such as accelerometry (Ku et al., 2016).
The use of raw accelerometry presents many advances, such as transparency in the
analytical process and enhanced comparability between data collected from
different devices; however, there are still only limited triaxial wrist-based
acceleration data to compare current results to, owing to this attachment site only
becoming more commonly used in very recent studies (Menai et al., 2017; Ramires
et al., 2017; Troiano et al., 2014; Wijndaele et al., 2015). Total accumulated 1 second
bouts of MVPA were 74.7 and 72.8 min‧d-1, MVPA10min were 7.8 and 8.4 min‧d-1, and
mean acceleration were 26.3 and 25.4 mg‧d-1 for women and men, respectively.
These results are comparative to a recent study in older adults which reported total
accumulated 1 second bouts of MVPA to be 56.7 and 64.6 min‧d-1, MVPA10min to be
4.5 and 9.5 min‧d-1, and mean acceleration to be 21.5 and 22 mg‧d-1 for women and
men, respectively (Ramires et al., 2017). A study by Sun et al. (2013) reported that
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under half (47.6%) of older adults accumulated at least one 10 minute bout of MVPA
per day. This figure was even lower in the current study (32.5%). PA guidelines (CDC,
2015; Department of Health, 2011a), were achieved by 88.1% and 12.9% of
accelerometer wearing participants when total accumulated 1 second bouts and 10
minute bout criteria were applied, respectively. The large differences between total
accumulated 1 second bouts of MVPA and MVPA10min outlines the considerable effect
that the use of different bout criteria has on the final estimate of MVPA in older
adults (Menai et al., 2017; Ramires et al., 2017). These differences might be even
more pronounced compared to other age groups since older adults are less likely to
sustain MVPA for longer periods (Amagas et al., 2017). Recent research notes that
the same physical and psychosocial health benefits associated with MVPA10min can be
achieved through total accumulated 1 second bouts of MVPA in older adults (Jefferis
et al., 2016; Sparling et al., 2015). Consequently, revised PA guidelines soon to be
published in the US (Office of Disease Prevention and Health Promotion, 2018) now
recognise that any amount of time spent in MVPA counts toward meeting PA
recommendations.
5.5. Strengths and Limitations
Strengths of the study included the use of both questionnaire- and accelerometer-
assessed SB and PA. Furthermore, high wearer compliance was observed among the
accelerometer-wearing sample. Previous research has also demonstrated superior
wearer compliance in older adults when compared to younger age groups (Doherty
et al., 2017). Some limitations of this study need to be considered when interpreting
the results. The cross-sectional design is a limitation, precluding causal inferences
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and any associations (or lack of associations) between SB and PA with self-reported
health outcomes. Participant recruitment strategies increased the risk of sampling
bias and affected external validity as it is possible that those who were already
inclined to be active were more likely to participate. Furthermore, men tend to
decrease participation in leisure-time PA as they get older; whereas this dose-
response is not seen among women (Amagasa et al., 2017). Consequently, there is
the possibility of gender bias given the higher number of female participants
recruited across both samples, especially given the relatively even gender split of
older adults across Sefton Borough (43.5% men) (ONS, 2017). Due to the limited
number of available GA devices during the period of contact with the older adults’
population, uneven sample sizes occurred. Although accelerometers provide
objective measures, they cannot accurately detect postural information (e.g.,
standing vs. sitting) and capture some types of PA (e.g., bicycling), which may have
influenced estimations of SB and PA and caused some misclassification of time spent
in SB and LPA (Shephard & Tudor-Locke, 2016). Furthermore, only self-reported
physical and psychosocial health outcomes were adopted and no objective health
indicators were assessed. Consequently, recall bias is a probability (Barnett et al.,
2016). Lastly, although IMD is commonly adopted as a measure of SES (Ramsay et al.,
2015), it refers to street level deprivation rather than individual level SES. Hence, the
results may not be truly representative of the entire socioeconomic spectrum of
older adults across the three areas of differing SES participants were recruited from.
5.6. Conclusions
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No significant gender, age category or SES-group differences were observed between
self-reported and accelerometer-derived SB and PA outcomes. There were
significant gender, age category and SES-group differences between QoL, SRH, SAPF,
and SEE. Results also provided evidence of a negative association of self-report SB,
and positive association of self-reported MPA10min and MVPA10min with physical and
psychosocial health outcomes. Large differences in participants achieving PA
guidelines were noted both between self-report and accelerometer-assessed
samples, and between accelerometer-assessed total accumulated 1 second bouts of
MVPA and MVPA10min. Future longitudinal studies are warranted to further confirm
these baseline findings and to further explore the most reliable methods to
successfully decrease SB and increase PA among older adults.
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Thesis Study Map
Study Objectives and Key Findings
Study 1. Using formative research with older
adults to inform a community physical activity
programme: Get Healthy, Get Active.
Objectives
To explore current knowledge and attitudes
towards physical activity, as well as perceived
barriers, facilitators and opportunities for physical
activity participation among older adults living in
the community.
Use these data to subsequently inform the design,
delivery and recruitment strategies of Sport
England’s national Get Healthy, Get Active
initiative.
Key Findings:
Older adults acknowledged the benefits of
physical activity, not only for health but also those
relating to socialising, enjoyment, relaxation, and
physical and psychological wellbeing regardless of
socioeconomic status.
The themes of opportunities and awareness for
physical activity participation, cost, transport,
location and season/weather varied between
assisted living and community-dwelling older
adults.
Study 2. Evaluation of wrist and hip sedentary
behaviour and moderate-to-vigorous physical
activity raw acceleration cutpoints in older
adults.
Objectives
To test a laboratory-based protocol to generate
behaviourally valid, population specific wrist- and
hip-based raw acceleration cutpoints for
sedentary behaviour and moderate-to-vigorous
physical activity in older adults.
Apply these cut-points to subsequently analyse
physical activity data for Sport England’s Get
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Healthy Get Active physical activity intervention.
Key Findings
When optimizing Sensitivity for sedentary
behaviour and Specificity for moderate-to-
vigorous physical activity, wrist-worn GENEActiv
accelerometer cutpoints of 57 mg and 104 mg
were generated for sedentary behaviour and
moderate-to-vigorous physical activity,
respectively.
For the hip-worn ActiGraph GT3X+ the cutpoints
were 15 mg and 69 mg for sedentary behaviour
and moderate-to-vigorous physical activity,
respectively.
The resultant cutpoints can enable researchers to
classify older adults as engaging in sedentary
behaviour or not engaging in moderate-to-
vigorous physical activity with an acceptable
degree of confidence.
Study 3. Physical activity, sedentary
behaviour, perceived health and fitness, and
psychosocial wellbeing among community-
dwelling older adults.
Objectives
To investigate gender, age, and socio-economic
status differences in older adults’ sedentary
behaviour, physical activity and self-reported
health indicators.
To examine associations between sedentary
behaviour and physical activity with self-reported
health outcomes.
Key Findings
No significant gender, age category or
socioeconomic status differences were observed
between self-reported and accelerometer-derived
sedentary behaviour and physical activity
outcomes.
Significant gender, age category and
socioeconomic status differences between self-
reported quality of life, self-rated health, self-
assessment of physical fitness, and self-efficacy
for exercise were observed.
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Study 5. Implementation fidelity of the Get Healthy Get Active physical activity programme for community-dwelling older adults
A negative association of self-reported sedentary
behaviour, and positive association of self-
reported moderate and moderate-to-vigorous
physical activity with health indicators was also
evident.
Study 4. A pragmatic evaluation of the Get
Healthy Get Active physical activity
programme for community-dwelling older
adults.
Objectives
To evaluate the effectiveness of Sport England’s
Get Healthy Get Active physical activity
intervention on older adults physical activity,
sedentary behaviour and self-reported health
indicators.
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Chapter 6
Study 4: A pragmatic evaluation of the Get
Healthy Get Active physical activity programme
for community-dwelling older adults.
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6.1. Introduction
Chapter 5 (Study 3) established that objectively assessed PA levels of older adults
were low, thus confirming that interventions targeting increased PA among this
population were warranted. These findings are also supportive of previous research
which has highlighted the need for PA promotion strategies among this population
(Plotnikoff et al., 2014; ten Brinke et al., 2015). The current study progresses the
work of Chapter 5 (Study 3) by evaluating the effectiveness of Sport England’s GHGA
PA intervention on older adults PA, SB and self-reported health indicators. Despite
the benefits associated PA (Devereux-Fitzgerald et al., 2016; Greaney et al., 2016;
Zhu et al., 2017), less than 12% of older adults globally perform PA on a daily basis
(Centers for Disease Control and Prevention, 2016). Large scale repeated measures
studies have shown that PA further declines with increasing age, among females,
those of lower SES, and among individuals with lower levels of self-reported health
status and SEE (Lehne & Bolte, 2017; Murtagh et al., 2015; Smith et al., 2015).
There is also growing public health interest in the amount of time spent in SB
(Tremblay et al., 2017). Older adults are the most sedentary segment of society
(Chastin et al., 2017). Observational studies show that more than 60% of an adults
non-sleeping hours are spent in SB, corresponding to around ten hours per day
(Dunstan, Howard, Healey, & Owen, 2012). Epidemiological evidence indicates that
time spent in SB is associated with physical (e.g., premature mortality, chronic
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diseases and all-cause dementia risk) and psychosocial (e.g., self-reported QoL,
wellbeing and SEE) risk factors, even among individuals who meet current PA
guidelines (Meneguci et al., 2015; Thorp, Owen, Neuhaus, & Dunstan, 2011). Time
spent in SB further increases with increasing age, decreased MVPA levels, lower SES,
and on days with lower temperature, less sunshine, inclement weather, and fewer
daylight hours (Diaz et al., 2016; Eisinga, Franses & Vergeer, 2011).
The high levels of SB and low levels of PA among older adults are concerning given
their negative associations with self-reported health (Beyer et al., 2015), fitness
(Kuosmanen et al., 2016) and psychosocial outcomes including QoL and self-efficacy
(French et al., 2014; Greaney et al., 2016; Kim et al., 2016; Olson et al. 2016). SB
represents a unique and clinically important aspect of an individual’s overall activity
profile and is no longer considered simply to be the extreme low end of the PA
continuum (Dunstan et al., 2012). Consequently, safe, effective, inclusive, and
sustainable interventions are needed to address not only the single effect of either
SB or PA level, but a balance of both (ten Brinke et al., 2015).
A recent systematic review noted that the successful reduction of SB and promotion
of PA among community-dwelling older adults requires a whole system-oriented
approach tailored to meet the needs of older adults and aligned with social,
individual and environmental factors (Zubula et al., 2017). Interventions also need to
be adaptable and offer choice in order to be suitable to all, including those with
mobility restrictions, disabilities, or other limiting health conditions, and those in
different socio-demographic groups (Zubula et al., 2017). Such multi-modal and
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multi-component interventions have had positive effects on reducing SB and
increasing levels of PA, SEE, and QoL in older adults (Gardner, Smith, Lorencatto,
Hamer, & Biddle, 2016; Zubula et al., 2017). However, effects on maintenance
beyond post-intervention remain unclear due to a lack of high quality longitudinal
studies (Olanrewaju et al., 2016; Richards et al., 2013).
Older adults' engagement in PA may also benefit from key facilitators of participation
in PA such as enjoyment, access, timing, and social support (Olanrewaju et al., 2016).
Social support is particularly relevant to ageing populations (Franco et al., 2015;
Sanders, Roe, Knowles, Kaehne, & Fairclough, 2018; Warner, Wolff, Ziegelmann,
Schwarzer, & Wurm, 2016), and is associated with PA adherence and maintenance in
older adults (Brown et al., 2015). Overall, participant centred, personalised
interventions starting with professional and tailored guidance and providing ongoing
support throughout and beyond the intervention have been found to lead to the
greatest improvements in PA levels in community-dwelling older adults (Zubula et
al., 2017). The characteristics of the GHGA PA intervention were in line with the
Consolidated Standards of Reporting Trials (CONSORT) checklist for pragmatic
evaluations (Zwarenstein et al., 2008) and thus, a pragmatic approach was adopted.
Pragmatic evaluations aim to inform a policy decision by providing evidence for
adoption of the intervention into ‘real-world’ practice (Schwartz & Lellouch, 1967).
Features include the recruitment of investigators and participants, the intervention
and its delivery, follow-up, and the determination and analysis of outcomes (Ford &
Norrie, 2016). In line with objective 7 of the thesis objectives, the aim of the current
study was to evaluate the effectiveness of Sport England’s GHGA PA intervention on
144
older adults PA, SB and self-reported health indicators. If effective, intentions were
for the GHGA programme to be scaled up and lead to bigger, sustainable, national
level research projects whose results would have policy ramifications and inform the
thought and practice of professionals in PA, social work and care settings.
6.2. Methods
6.2.1. Participants and procedures
This study protocol was prepared according to the CONSORT checklist of items for
reporting pragmatic trials (Zwarenstein et al., 2008). Between January 2016 and
December 2017, a quasi-experimental study with repeated follow-ups was
undertaken. All 207 participants who provided informed consent and met the
eligibility criterion for study three were invited to take part in the current study. Data
were collected from participants at 3-months (n =193; attrition =6.8%), 6-months (n
=168; attrition =18.8%), and 12-months (n =118; attrition =43%) post-baseline.
Participants invited to participate in the programme received a covering letter,
participant information sheet, and consent form, and provided written informed
consent prior to participation at baseline. Before the study commenced, institutional
ethical approval was received (#SPA-REC-2015-329). Participation was voluntary with
no incentives provided. Details of the flow of participants through the study from
baseline to follow-up are displayed in Figure 6.1.
Figure 6.1. Flow of participants through the study.
145Baseline
Total number of participants approached = 380
Total number of participants providing written informed consent and meeting inclusion
criteria = 207
6.2.2. Intervention
GHGA was a three-year project aimed at engaging inactive older adults in PA at least
once a week for 30 minutes, via a 12 week PA intervention. The project was funded
by Sport England and delivered by SMBC employees trained in delivering PA sessions
among this population. These trained employees are referred to as deliverers herein.
The intervention was implemented throughout Sefton Borough within differing
locations (e.g., leisure centres, a church hall, a theatre, a retirement homes, and a
library) with each 12 week PA intervention implemented at the same venue.
Deliverers were required to incorporate exercises outlined in table 6.1 which
targeted five core aspects of fitness (balance, endurance, flexibility, resistance, and
strength exercises), whilst also incorporating a warm-up and a cool-down.
Exercise component Example exercises
Warm-up
Balance
Seated stretching of all major muscle groups
Standing walking on the spot
Standing knee raises
Single limb stance
Single limb stance with arm
Side leg raise
Back leg raise
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Follow-up 3 (12-months post baseline)Participants lost from baseline = 89 (74 missed due to data collection ending, 15 out of reach)Total number of participants assessed and analysed = 118
Table 6.1. Exercises typical of a GHGA PA session.
Endurance
Flexibility
Resistance (with exercise band)
Strength
Cool-down
Walking heel to toe
Seated jumping jacks
Arm circles
Bicycle kicks
Spine twist
Shoulder rolls
Ankle circles
Head circles
Overhead stretch
Seated bicep curl
Seated chest press
Seated shoulder press
Seated leg press
Standing side deltoid raise
Seated dumbbell curls
Seated weighted ankle circles
Standing weighted ankle circles
Standing squats
Standing calf raises
Seated stretching of all major muscle groups
In line with the Evidence Integration Triangle (Glasgow et al., 2012), the exploration
of the three main evidence-based components of intervention programme/policy,
implementation processes, and measures of progress were achieved via a 12 week
pilot GHGA programme delivered by SMBC throughout Sefton Borough from
September to December 2015. A total of 281 older adults took part in individual
interviews. Data regarding current trends of inactivity, barriers to participation, and
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preferred sports and venues were collected via informal focus groups and individual
interviews. Results and analysis from this pilot, along with relevant intervention
literature and findings from Chapter 3 (Study 1) of the current thesis (Sanders et al.,
2018), were fed back to Sport England as the funder, as well as SMBC in order to
assess, evaluate and promptly inform adapted future iterations of the GHGA PA
intervention. Through adopting key elements of the ecological model of behaviour
change (Stokols, 1992), the GHGA PA intervention targeted individual (e.g., session
components tailored to individual needs), interpersonal (e.g., social networking),
organisational (e.g., advertising through Older Peoples' Forums throughout Sefton
Borough), and community (e.g., advertising through general practitioner surgeries
and leisure centres throughout Sefton Borough) level factors. PA sessions consisted
of chair-based PA, including a wide range of progressive exercise modalities, at
differing intensities. Sessions were designed to have no financial cost to the
participants and hence, were free to attend with transport to and from each session
provided if necessary. Sessions lasted for one hour and consisted of a ten minute
warm-up, forty minutes of varying aerobic, endurance, strength, flexibility, and
balance exercises, and a ten minute cool-down undertaken by each individual within
a group setting.
6.2.3. Primary Outcome Measures
6.2.3.1. International Physical Activity Questionnaire for the Elderly
To assess SB and PA levels, the IPAQ-E (Hurtig-Wennlöf et al., 2010) was adopted as
required by funder. IPAQ-E is based on the short version of the IPAQ
(www.ipaq.ki.se) and assesses time spent sitting, walking in bouts of 10 minutes or
148
more, MPA in bouts of 10 minutes or more, and VPA in bouts of 10 minutes or more
during the previous 7 days. The categorical outcome from IPAQ-E assigns the
participants into one of three PA categories (e.g., low, moderate, or high-PA). The
IPAQ-E provides favourable levels of both direct, and indirect levels of criterion
validity for sitting (Spearman r =0.28, p <0.05), walking (Spearman r =0.35, p <0.01),
MPA (Spearman r =0.40, p <0.01), and VPA (Spearman r =0.37, p <0.01) (Hurtig-
Wennlöf et al., 2010). However, varying levels of test-retest reliability (intraclass
correlation ranging from 0.30 to 0.82) have also been reported (Tomioka, Iwamoto,
Saeki, & Okamoto, 2011).
6.2.4. Secondary Outcome Measures
6.2.4.1. Self-Assessment of Physical Fitness
To assess self-reported levels of fitness, the SAPF (Weening-Dijksterhuis et al., 2012)
was adopted. The questionnaire asks three questions including: How do you rate
your strength?; How do you rate your aerobic endurance?; and How do you rate your
balance? The SAPF uses a rating method from 0 (indicating the lowest rating) to 10
(indicating the highest rating) for each of the three items. Sound psychometric
properties have been demonstrated for the SAPF scale among frail older adults with
acceptable internal consistency (Cronbach alpha =0.71) (Weening-Dijksterhuis et al.,
2012), one week test-retest validity (0.70), and moderate concurrent validity against
the Groningen Fitness Test for the Elderly (Lemmink, 1996).
6.2.4.2. Kemp Quality of Life Scale
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To assess QoL, the single item Kemp Quality of Life Scale (Kemp & Ettelson, 2001)
was adopted. Following the question: Thinking about the good and bad things that
make up your quality of life, how would you rate the quality of your life as a whole? ,
respondents are asked to tick the box next to the answer that best describes their
QoL on a 7-item scale anchored by “so good, it could not be better” (score of 1) to
“so bad, it could not be worse” (score of 7). Although there is no published
information about the distribution or error of data obtained using this scale, the
scale is interval level, can provide data for parametric and non-parametric analysis,
and has been adopted previously in older adult populations (Siebens et al., 2015;
Roe et al., 2011).
6.2.4.3. Self-Rated Health Questionnaire
The SRH (Sargent-Cox et al., 2010) is a 3-item questionnaire assessing global, age-
comparative, and self-comparative HS. Global HS is measured via the question: How
would you rate your overall health at the present time?. Participants answer on a five
point scale (1= excellent; 5= poor). Age-comparative HS is measured on a three-point
scale via the question: Would you say your health is: (1) better, (2) about the same,
or (3) worse than most people your age?. Self-comparative HS is also measured on a
three point scale via the question: Is your health now… (1) better, (2) about the
same, or (3) not as good as it was 12 months ago?. Previous research has shown
good prognostic validity for mortality, and high internal reliability (α =.75) (Ferraro &
Wilkinson, 2013).
6.2.4.4. Self-Efficacy for Exercise Scale
150
The SEE (Resnick & Jenkins, 2000) is an 11-item scale designed to assess confidence
to continue exercising in the face of perceived barriers. The SEE consists of 11
situations that might affect participation in exercise. Items are rated from 0 (not
confident) to 10 (very confident). The mean score of numerical ratings from each
response indicates the strength of efficacy expectations. The SEE scale has
demonstrated excellent internal consistency (Cronbach α =0.92) and significant
predictive validity of exercise activity in older adults when controlled for age and
gender (F =78.8; p <0.05) (Resnick & Jenkins, 2000; Resnick et al., 2004).
All measures were administered by the first author to each participant whilst they
attended the GHGA sessions. Measures took no longer than 30 minutes to complete
per participant. Regular breaks were incorporated if needed, to reduce fatigue.
Additional visits were also arranged if needed, to aid completion of a full dataset due
to participant time constraints. All participants recorded their age, gender, and home
postal code. Ages were categorised as 65 to 69 years, 70 to 79 years, and ≥80 years.
Postal codes were used to estimate SES by generating IMD (Department for
Communities and Local Government, 2015) using an online conversion tool
(http://imd-by-postcode.opendatacommunities.org/). The IMD is a UK government
metric used to rank area-level deprivation within and between different
communities. The IMD scores rank each super output area in England from 1 (most
deprived area) to 32,844 (least deprived area). Super output areas were then split
into 3 equal groups to represent low (1 to 10,947), middle (10,948 to 21,895), and
high-SES (21,896 to 32,844). Questionnaire data were input manually into Microsoft
Excel 2013 and checked for missing data. Confidentiality and data storage
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procedures were adhered to as is set out in Edge Hill University’s research data
management (Edge Hill University, 2017) and code of practice for the conduct of
research guidelines (Edge Hill University, 2017). All participant data were
anonymised and coded to prevent identification, and securely stored using
password-protected files on the Edge Hill University computing network. The server
hosting the files was backed up every four hours and nightly to tape, to ensure data
attrition was minimised. Only research team members had access to the anonymised
data. This was shared between the team strictly for the purposes of research.
6.2.5. Statistical Analysis
Descriptive statistics were calculated for the outcomes of all participants at baseline
and follow-up. Multilevel modelling was performed using MLwiN Version 3.00
(Centre for Multilevel Modelling, University of Bristol, UK) (Rabesh, Charlton,
Browne, Healy, & Cameron, 2009) to determine the effects of the intervention.
Multilevel modelling was appropriate for use in this study design where data
collection time points (e.g., observations) are clustered within older adults (Twisk,
2006). Therefore, a 2-level data structure was used with data collection time point
defined as the first level of analysis, and participant ID as the second level of analysis.
An issue of longitudinal studies is missing data. Previous literature has suggested
imputation of missing data to obtain a ‘complete dataset’ (Little & Rubin, 2014).
However, imputation may result in over- or underestimation of the importance of
covariates, underestimation of random coefficient and effect variance corresponding
to time-varying covariates with missing values, and conflation of within-person and
152
between-person effects (Enders, Mistler & Keller, 2016; Grund, Lüdtke & Robitzsch,
2016; Lüdtke, Robitzsch & Grund, 2017; van Buuren, 2011). Applying multilevel
analysis to an incomplete longitudinal dataset is even better than applying
imputation methods (Twisk, 2013) due to its ability to analyse the variance of
random intercept and regression coefficients regardless of individual missing values
(Twisk, 2006).
Continuous outcome variables were sitting, MVPA, QoL, SRH, SAPF and SEE.
Additionally, dichotomous outcome variables of achieving/not achieving MVPA
guidelines as well as PA level (low and moderate/high active (according to the IPAQ-
E MET min‧w-1 scoring protocol; IPAQ Research Committee, 2005) were studied.
Initially, ‘crude’ analyses were conducted with only the outcome variables at three,
six and 12-months included in the model (Twisk, 2006). Potential confounding
covariates were then added to construct the ‘adjusted’ models. These potential
confounding covariates were selected based on previous research which has
deemed them to be influential the dependent outcomes. For sitting these covariates
included gender (Greaney et al., 2016), age (Diaz et al., 2016; Heo et al., 2017), SES
(Diaz et al., 2016; Gray et al., 2015), season (Diaz et al., 2016), QoL (Kim et al., 2016),
SRH (Beyer et al., 2015; Kuosmanen et al., 2016) and MVPA (Beyer et al., 2015). For
the PA level categorical outcome the covariates included gender (Amagasa et al.,
2017), age (Amagasa et al., 2017), SES (Ku, Steptoe, Liao, Sun, & Chen, 2018), season
(Parsons et al., 2016), QoL (Buman et al., 2010), SAPF (Ku et al., 2016), SRH (Ku et al.,
2016), and MVPA (Barone Gibbs et al., 2017). For MVPA and achieving/not achieving
153
MVPA guidelines, confounding covariates of gender (Amagasa et al., 2017; Shiroma
et al., 2018), age (Doherty et al., 2017; Ramires et al., 2017), SES (Mendoza-Vasconez
et al., 2016), season (Prins & van Lenthe, 2015), QoL (Lok, Lok & Canbaz, 2017), SRH
(Kuosmanen et al., 2016; Ramires et al., 2017) and PA self-efficacy (Dionigi, 2007;
French et al., 2014) were included. Confounding covariates for the health indicators
of QoL, SRH, SAPF, and SEE included gender (Bamia et al., 2017; Kirchengast &
Haslinger, 2008; Moreno et al., 2017; Overdorf, Coker & Kollia, 2016), age (Bamia et
al., 2017; Hong, 2015; Langan & Marotta, 2000; Overdorf et al., 2016), sitting (Ku et
al., 2016; Kuosmanen et al., 2016), and MVPA (Beyer et al., 2015; Ku et al., 2016).
Regression coefficients from the models were assessed for significance using the
Wald statistic and the following equation, (regression coefficient/standard error)2.
Statistical significance was set at p <0.05. The evaluation of potential effect
modification was also carried out on the gender, age category, SES-group, and
season to determine whether the intervention effects were different for these
subgroups. Interaction terms were added to the models, consisting of a
multiplication of the main determinant (intervention) and the potential effect
modifier (Twisk, 2006). Due to the reduced power which interaction terms have,
statistical significance for these analyses was set at p <0.1 (Twisk, 2006).
6.3. Results
A total of 318 participants were recruited for participation in this study. Two
hundred and seven participants (mean age =77.8, SD =7.7; 65.1% compliance rate)
met the inclusion criterion, provided written informed consent, and were
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subsequently included in the analysis. Table 6.2 highlights baseline characteristics of
the participants and Table 6.3 highlights unadjusted self-reported PA and
psychosocial outcome measures at baseline and follow-up time points.
Table 6.2. Descriptive baseline characteristics of the participants.
SD, Standard Deviation; SES, Socioeconomic status.
Table 6.3. Unadjusted self-reported physical activity and psychosocial outcome measures.
Baseline(n =207)
3-months (n =193)
6-months (n =168)
12-months (n =118)
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Baseline(n =207)
Age (years)
Gender n (%)
Female
Male
Age Category n (%)
65-69 yrs
70-79 yrs
80+ yrs
Season of data collection n (%)
Autumn/Winter
Spring/Summer
SES Group n (%)
Low-SES
Mid-SES
High-SES
77.8 (7.7)
165 (79.7)
42 (20.3)
32 (15.5)
92 (44.7)
83 (39.8)
86 (41.7)
121 (58.3)
74 (35.9)
51 (24.3)
82 (39.8)
Sitting (min‧w-1) 2883.7 (1137.2)
3348 (916.4)
3435 (886.9)
3735.5 (895.1)
MVPA (min‧d-1)
Meeting PA Guidelines n (%)
YES
NO
62.7 (92.2)
114 (55.3)
92 (44.7)
62.5 (63.2)
49 (25.4)
144 (74.6)
57.6 (49)
38 (22.6)
130 (77.4)
61.7 (44.1)
29 (24.6)
89 (75.4)
IPAQ-E Activity Category n (%)
Low
Moderate
High
118 (57)
41 (19.8)
48 (23.2)
67 (34.7)
64 (33.7)
60 (31.6)
60 (35.7)
64 (38.1)
44 (26.2)
46 (39)
36 (30.5)
36 (30.5)
Quality of Life 3.2 (.77) 3.4 (.77) 2.8 (.7) 2.8 (.8)
Self-Assessment of Physical Fitness
9.8 (3.48) 11.7 (4) 12.3 (3.4) 12 (3.3)
Self-Rated Health 8.1 (1.5) 6.9 (2) 6.6 (1.9) 6.7 (2)
Self-Efficacy for Exercise 29.8 (10.1) 37.9 (12.3) 39.9 (12.4) 40.1 (13 )
6.3.1. Intervention Effects
Tables 6.4 and 6.5 show the results of the crude and adjusted multilevel analyses,
respectively. In the adjusted models, significant increases in sitting time of 526, 650
and 974 minutes per week were observed at three, six and 12-months compared to
baseline, respectively. Results also indicated decreases in MVPA of 12.95, 21.85 and
18.99 minutes per day at three, six and 12-months, respectively, although three (p
=0.16) and 12-months (p =0.1) did not reach significance. Significant odds of meeting
MVPA guidelines were 9.09, 11.1 and 9.09 times lower across the follow-up time
points compared to baseline. Finally, significant odds at three and six-month follow-
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up time points indicated participants were 5.88 times more likely to be classed as
low active compared to baseline.
Significant changes in health indicator scores were also observed across all follow-up
time points. At 12-months, favourable changes in QoL (β =-0.09 (95% CI =-0.36, 0.18),
p< .001), SRH (β =-1.41 (95% CI =-2.02, -0.8), p <.001), SAPF (β =2.97 (95% CI =1.71,
4.23), p <.001), and SEE (β =11.57 (95% CI =7.7, 15.44), p <.05) scores were observed
compared to baseline.
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Table 6.4. Crude multilevel model analyses of the outcome measures at three, six and 12-months follow-up.
3-months 6-months 12-months
β or OR 95% CI p β or OR 95% CI p β or OR 95% CI p
Sitting (min‧w-1) 402.68 c 244.49 to 560.87 <0.001 599.4 c 434.04 to 764.76 <0.001 919.02 c 732.41 to 1105.63 <0.001
MVPA (min‧d-1)
Meeting PA Guidelines (No)
IPAQ-E Activity Category (Low)
Quality of Life
Self-Assessment of Physical Fitness
-0.58 c
3.7* d
1.85* d
-0.36 c
2.04 c
-10.99 to 9.83
3.27 to 4.13
1.55 to 2.15
-0.49 to -0.23
1.49 to 2.59
0.91
<0.001
<0.001
<0.001
<0.001
-7.18 c
4.35* d
1.85* d
-0.37 c
2.28 c
−18.1 to 3.71
3.9 to 4.8
1.53 to 2.17
-0.5 to -0.24
1.7 to 2.86
0.2
<0.001
<0.001
<0.001
<0.001
0.62 c
3.85* d
1.61* d
-0.44 c
2.3 c
−11.69 to 12.93
3.35 to 4.35
1.24 to 1.98
-0.59 to -0.29
1.65 to 2.95
0.92
<0.001
0.01
<0.001
<0.001
Self-Rated Health -1.1 c -1.4 to -0.8 <0.001 -1.48 c −1.79 to -1.17 <0.001 -1.4 c −1.75 to -1.05 <0.001
Self-Efficacy for Exercise 9.01 c 7.22 to 10.8 <0.001 10.07 c 8.2 to 11.95 <0.001 10.82 c 8.7 to 12.94 <0.001
Values reflect the intervention effects (e.g., within group differences) between baseline and follow-up time points. Use of bold denotes beta significant intervention effects (p < 0.05). * Significant odds ratios <1 have been inverted (Osborne, 2006). c β value. d OR. OR, odds ratio; CI, confidence interval; PA, physical activity; MVPA, Moderate-to-vigorous physical activity. IPAQ-E; International Physical Activity Questionnaire for the Elderly; Quality of Life and Self-Rated Health are negatively scored (e.g., lower score = better outcome). Note. Lower score = more favourable result for Quality of Life and Self-Rated Health.
158
3-months 6-months 12-months
β or OR 95% CI p β or OR 95% CI p β or OR 95% CI p
Sitting (min‧w-1) 526.91 c 242.69 to 811.13 <0.001 650.09 c 339.93 to 960.25 <0.001 974.41 c 616.47 to 1332.36 <0.001
MVPA (min‧d-1)
Meeting PA Guidelines (No)
IPAQ-E Activity Category (Low)
Quality of Life
Self-Assessment of Physical Fitness
-12.95 c
9.09* d
5.88* d
-0.37 c
2.59 c
−30.92 to 5.02
8.16 to 10.02
2.12 to 9.64
-0.58 to -0.17
1.62 to 3.56
0.16
<0.001
0.01
<0.001
<0.001
-21.85 c
11.1* c
5.88* d
-0.40 c
2.11 c
-41.08 to -2.62
10.07 to 12.13
1.78 to 9.98
-0.17 to -0.62
1.04 to 3.18
0.03
<0.001
0.02
<0.001
<0.001
-18.99 c
9.09* d
1.63 d
-0.09 c
2.97 c
-41.56 to 3.58
8.01 to 10.17
-1.04 to 4.3
-0.36 to 0.18
1.71 to 4.23
0.1
<0.001
0.72
<0.001
<0.001
Self-Rated Health -0.89 c -1.37 to -0.41 <0.001 -1.19 c -1.71 to -0.67 <0.001 -1.41 c -2.02 to -0.8 <0.001
Self-Efficacy for Exercise
9.89 c 6.93 to 12.85 <0.001 9.92 c 6.63 to 13.21 <0.001 11.57 c 7.7 to 15.44 <0.001
Table 6.5. Adjusted multilevel model analyses of the outcome measures at three, six and 12-months follow-up.
159
Values reflect the intervention effects (e.g., within group differences) adjusted for confounding covariates at baseline, between baseline and follow-up time points. Values in bold denote beta (95% CI) and significance values of outcomes with significant intervention effects (p < 0.05). * Significant odds ratios <1 have been inverted (Osborne, 2006). c β value. d OR. OR, odds ratio; CI, confidence interval; PA, physical activity; MVPA, Moderate-to-vigorous physical activity; IPAQ-E; International Physical Activity Questionnaire for the Elderly. Note. Lower score = more favourable result for Quality of Life and Self-Rated Health.
160
6.3.2. Interaction Analyses
Subgroup interaction analyses (see Table 6.6) revealed that improvement in SAPF
score at 12-months was significant among males (β =3.38 (95% CI =2.01, 4.75), p
<.001), and non-significant among females (β =1.51 (95% CI =-0.89, 3.91), p =.22). No
other significant subgroup interaction effects were evident.
Table 6.6. Significant intervention subgroup interactions.
Values in bold denote beta (95% CI) and significance values of crude and adjusted intervention outcomes with significant subgroup effect modifiers (p < 0.1). CI, confidence interval.
6.4. Discussion
This study aimed to evaluate the impact of Sport England’s GHGA PA intervention on
time spent in SB and MVPA, as well as health indicators. After accounting for
confounding variables, significant increases in sitting time of 526, 650 and 974
minutes per week at three, six and 12-months, respectively were observed. Given
the decreasing levels of MVPA noted, increased sitting time was to be expected as
lower amounts of time spent in MVPA has been associated with greater total
sedentary time (Diaz et al., 2016). This result further confirms the need for
interventions to go beyond simply increasing MVPA levels, and to actively seek
methods of decreasing SB. It is suggested that interventions should concentrate not
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Interactions SAPF Score
β 95% CI p
Adjusted model x gender
Male x 12-months
Female x 12-months
3.38
1.51
2.01, 4.75
-0.89 to 3.91
<0.001
0.22
only on the single effect of either SB or PA level, but a balance of both (ten Brinke et
al., 2015), as older adults have been found to compensate for increased MVPA levels
by decreasing LPA and increasing SB during the remainder of the day (Barone Gibbs
et al., 2017). Concurrently, results of the current study showed significant increased
odds of participants being classed as low active at three and six-month follow-up
time points compared to baseline.
Across all follow-up time points participants spent on average 8.3 hours of waking
time per day sitting. This is comparative to previous research suggesting that older
adults spend up to 8.5 hours of waking time per day sedentary regardless of PA level
(Shaw et al., 2017). Individuals aged 65 to 74 and 75 years or older have a 2-fold and
4-fold greater likelihood of exhibiting prolonged SB compared to individuals aged 45
to 54 years (Diaz et al., 2016). Hence, targeting less SB might be a successful
approach to increase whole day PA in older adults who may have limited mobility,
motivation, or self-efficacy for more intense or prolonged bouts of exercise
(Gardiner, Eakin, Healy, & Owen, 2011; Manns, Dunstan, Owen, & Healy, 2012). To
date however, interventions aimed at both promoting PA and reducing SB have
reported mixed results (Martin et al., 2015; Prince et al., 2014). Results from a recent
meta-analysis by Prince et al. (2014) identified that on average, interventions
targeting both PA and SB resulted in significant, but modest reductions in time spent
engaged in SB (standardised mean difference (SMD) =−0.37 [95% CI =−0.69, −0.05])
equating to a mean difference of approximately 35 min‧d-1 less sedentary time in the
intervention groups compared with the controls. However, a more recent systematic
review by Martin et al. (2015) concluded that there was no evidence that combined
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PA and SB interventions reduced SB. Interventions solely targeting SB seem
promising given meta-analysis results indicating significant and large reductions of
up to 91 min‧d-1 of sedentary time in intervention groups compared with the
controls (Prince et al., 2014). To further build evidence-based approaches for
reducing SB, there is a need to understand the factors that influence patterns of
prolonged SB (Diaz et al., 2016). Studies exploring the barriers and facilitators of
reducing SB are warranted in order to inform future interventions.
Results indicated a significant decrease in MVPA of 22 min‧d-1 at six months follow-
up, and non-significant decreases in MVPA of 13 and 19 min‧d-1 at three and 12-
months, respectively. These results are in contrast to a recent meta-analysis of 53
exercise intervention studies in community-dwelling older adults which reported a
pooled effect equivalent to a 73 min‧w-1 increase in MVPA when comparing
intervention with control groups (Chase, 2015). Similarly, Barone Gibbs et al. (2017)
observed increases of both self-reported (67 min‧w-1) and objectively assessed MVPA
(75 min‧w-1) compared to baseline following a 12-week PA intervention in older
adults. The GHGA PA sessions aimed to provide sustainable PA sessions which would
lead to PA maintenance among inactive older adults. Adult and older adult
guidelines advise ≥150 minutes of MVPA weekly, or 75 minutes of vigorous PA, or a
combination, in ≥10 minute bouts (Centers for Disease Control and Prevention, 2015;
Department of Health, 2011a), but any increase in PA for inactive people is valuable
(Sparling et al., 2015). Fewer PA sessions of a lighter intensity are more realistic for
encouraging long-term PA adherence in older adults regardless of gender, age and
SES-group status (Kuosmanen et al., 2016; McMahon et al. 2017). Furthermore, LPA
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is reported to be optimal for improving self-reported physical and psychosocial
outcomes in older adults (Windle et al., 2010). Given such results, Chapter 7 (Study
5) will evaluate whether or not the GHGA multi-component intervention was
implemented as intended. Process evaluation of intervention delivery is deemed to
be particularly important in multi-component interventions delivered across varying
locations where the same intervention may be implemented and received in
different ways (Koorts et al., 2018).
Significant increases in QoL, SAPF, SRH and SEE scores were observed throughout the
three, six and 12-month follow-up time points, providing further evidence for the
benefits of PA interventions in this population. Although type of PA does not seem to
influence effectiveness, there is an indication that light-to-moderate intensity PA
interventions may be preferred in older adults (Zubula et al., 2017). Among older
adults it is reported that the physical health benefits of LPA are as beneficial as those
for MVPA (Ku et al., 2016), and greater than all forms of other activity in terms of
benefits to psychosocial well-being (Buman et al., 2010). Significant favourable
changes in all health indicators were observed throughout the follow-up time points.
A recent meta-analysis also reported significant increases in self-reported QoL at six
and 12-months (Chase, 2015), suggesting that PA interventions among community-
dwelling older adults can be effective in prompting long-term increases in
psychosocial health indicators.
Research has also explored the relationship between SB and health indicators in
older adults, subsequently highlighting the detrimental effects that increased time
164
spent in SB can have on self-reported physical and psychosocial outcomes including
SRH (Beyer et al., 2015; Kuosmanen et al., 2016) and QoL (Kim et al., 2016).
Moreover, social support has been recognised as an important social determinant of
health and studies have demonstrated a relationship between social support and
QoL (Siedlecki, Salthouse, Oishi, & Jeswani, 2014), SRH (Dai, Zhang, Zhang, Li, Jiang,
& Huang, 2016), and SEE (Warner, Ziegelmann, Schüz, Wurm, & Schwarzer, 2011).
Resultantly, the thirty minutes set aside at the end of each weekly GHGA PA session
to allow participants to socialise could have further emphasised the long-term health
indicator increases observed. Given the decrease in MVPA, increase in sitting time,
and significant increases in health indicator scores observed, further research
exploring the individual effects of PA, SB and social support on health indicators, and
the interactions between them are warranted.
Subgroup analysis of this study highlighted that the GHGA intervention had a
significant positive effect on males SAPF score at 12-months, but this effect was not
significant for females. Previous studies also highlight a strong positive relationship
between gender and self-reported physical health indicators (Bamia et al., 2017;
Overdorf et al., 2016). A recent meta-analysis by Bamia et al. (2017) examining
confounding covariates of SRH scores among 424,791 European and US older adults
(aged ≥60 years) noted gender (male) to be a long-term confounding covariate
favourably associated with increased SRH score. Additional covariates included age
(younger-old), education (high), marital status (married/cohabiting), PA (active),
body mass index (non-obese), alcohol consumption (low to moderate), and previous
morbidity (absence). Similarly, Overdorf et al. (2016) reported gender (male) and age
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(younger-old) to be confounding covariates favourably associated with physical self-
perception (PSPP; Fox & Corbin, 1989). A contributing factor to the gender
differences noted previously is that self-esteem is highly correlated with masculinity
and self-efficacy but not with femininity (Delignières, Marcellini, Brisswalter, &
Legros, 1994). Furthermore, it has been suggested that the masculine-role
endorsement could have a major influence on physical self-worth and hence, males
do consider themselves to be of higher levels of fitness and health (Delignières et al.,
1994).
Results of the current study also showed that regardless of gender, age and SES, the
GHGA intervention had significant effects on SB, MVPA and health indicators. These
findings are counter to those in previous studies which have noted that time spent in
SB is higher among those who are male, older and of low-SES compared to those
with middle and high-SES (Bellettiere et al., 2015; Shaw et al., 2017). MVPA is noted
as being lower among those who are female, older and of low-SES compared to
those with middle and high-SES (Lehne & Bolte, 2017; Mendoza-Vasconez et al.,
2016; Murtagh et al., 2015; Smith et al., 2015). Men are more physically active than
women in almost every country throughout the adult and older adult age range
when evaluated based on current PA guidelines (Hallal et al., 2012; Sallis et al., 2016;
Sun et al., 2013). Among self-reported health indicators, older adults who are male,
younger-old (60-69 years), and of higher SES are more likely to report favourable
ratings of self-reported physical and psychosocial health (Bamia et al., 2017;
Kuosmanen et al., 2016). Despite repeated requests by previous systematic reviews
(Richards et al., 2013; Vijay, Wilson, Suhrcke, Hardeman, & Sutton 2016) and
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guidelines (National Institute of Health and Care Excellence, 2014) for interventions
to be conducted with longer follow-up periods and objective PA measures, there
remains a lack of data from interventions assessed using objectively measured PA
levels beyond 12-months (Harris et al., 2018). In order to progress this field of
research, future interventions targeting PA in older adults should be designed and
implemented based upon the requirements of recognised intervention guidelines
such as the SEF for PA interventions (National Obesity Observatory, 2012), and
intervention effect should be assessed via objective measures.
6.5. Strengths and Limitations
The GHGA PA intervention had several strengths. The repeated measures design at
three follow-up time points post-intervention is a strength, as well as the statistical
analysis which took into account these differing follow-up time points. In line with
the SEF for PA interventions (National Obesity Observatory, 2012), the design,
delivery and recruitment strategies were developed through prior formative
research (Sanders et al., 2018) and were theoretically underpinned by conceptual
behaviour change models (McLeroy et al., 1988; Stokols, 1992). Additionally, GHGA
provided a rare opportunity for participants to participate in 12 weeks of PA sessions
free of charge. Due to high engagement throughout the initial 12 week PA sessions,
and high demand from participants for the continuation of the GHGA PA sessions,
paid maintenance sessions were set up by SMBC. This demand regardless of cost,
suggests that the GHGA PA intervention can be self-sustained by SMBC beyond the
initial funding by Sport England. However, the decreases in MVPA and increases in
sitting time across all follow-up time points suggests that the potential for long-term
167
implementation throughout Sefton Borough and scaling up of the intervention to
reach broader populations across multiple settings is not warranted.
This study also acknowledges several limitations. The major limitation of the current
study was the pre-post design. This design results in lowered levels of causal validity
due to the uncontrollable effects of regression to the mean (RTM), maturation,
history and test effects (Marsden & Torgerson, 2012). The absence of a control group
is also a limitation. A methodological review of studies of psychological, educational
and behavioural treatments (Lipsey & Wilson, 1993) showed that pre-post designs
consistently overestimate effectiveness by an average of 61% compared with studies
with a control group. However, the feasibility of a clustered controlled research
design was rejected by SMBC due to both low GHGA deliverer and participant
numbers. Coverage and sampling errors are also limitations due to the modest
sample size and non-randomisation of participants. Consequently, selection bias is
an issue (Marsden & Torgerson, 2012). However, participant non-randomisation was
justified given the pragmatic approach adopted (Thomas et al., 2006) and the low
number of participants recruited from intact groupings (Sefton’s Older People Forum
and care homes) throughout Sefton Borough. Furthermore, minimal initial
participant characteristic data is also a concern further inhibiting causal validity.
Future studies should obtain additional participant characteristic data including
height and weight, current sedentary time and PA levels, history of PA, family history
of PA, ethnicity, employment status, and educational achievements as such are
reported as confounding covariates of older adults SB and PA levels (Greaney et al.,
2016; Keadle et al., 2016).
168
An additional limitation is the lack of objective measures of sitting time and/or PA
level. Recall bias is an inherent limitation of self-report measures, particularly for
routine and sedentary activities that are not encoded in memory as discrete events
(Altschuler et al., 2009). Given that recall is an ability that can decline with ageing,
recall bias is a possibility (Barnett et al., 2016). Given the complexity of PA
constructs, and the variety of applications available for their measurement in
surveillance, epidemiology, clinical, and intervention research, it is recommended
that future repeated measures research among this population adopt objective
measures such as accelerometry in order to provide the most accurate evidence for
intervention effect on PA levels and SB (Martin et al., 2015). Objective measurement
in the form of GA accelerometry was adopted as a measure of PA level and SB to
examine baseline participant characteristics prior to participation in the GHGA PA
intervention. Due to time constraints and a limited number of available GA
accelerometer devices during the period of contact with the older adults’
population, this was not possible in the current study.
Systematic attrition is a limitation of repeated measure research designs and causes
biases in all results that are influenced by these variables (Asendorpf, Van De Schoot,
Denissen, & Hutteman, 2014). Consequently, nonresponse bias is a concern given
attrition levels of 6.8, 18.8 and 43% compared to baseline at three, six and 12-month
follow-up time points, respectively. However, comparable attrition rates of 15.7,
20.2 and 47.2% at three, six and 12-month follow-up time points were reported in a
recent study examining the impact of a PA intervention among older adults
169
(Eggenberger, Theill, Holenstein, Schumacher, & de Bruin, 2015). Furthermore,
multilevel analysis is flexible to missing data and has been shown to be more
effective at analysing incomplete datasets than applying imputation methods (Twisk,
2013). However, examining correlates of attrition and attempting to understand
reasons for attrition is a vital step in the research process that needs further
exploring (McDonald, Haardoerfer, Windle, Goodman, & Berg, 2017). Finally,
implementation bias is a limitation as the GHGA PA sessions were delivered by
differing SMBC staff, and session content varied according to participant feedback
and group capabilities. Gaining an accurate and objective record of session content is
important in determining intervention suitability and overall effectiveness. However,
providing older adults with both choice and a wide range of PA components is a
facilitator of both initial adoption and maintenance of PA engagement and so
content variation was justified (Petrescu-Prahova et al., 2015; Sanders et al., 2018).
6.6. Conclusions
The GHGA PA intervention resulted in favourable changes in QoL, SAPF, SRH and SEE
scores throughout all follow-up time points. These results add further support for
the effectiveness of PA interventions to impact upon self-reported physical and
psychosocial health indicators in older adults. However, significant increases in
sitting time across all follow-up time points were observed, as well as a significant
decrease in time spent in MVPA at the six-month follow-up time point. The odds of
meeting MVPA guidelines also decreased significantly across the three follow-up
time points. Results from this pragmatic evaluation indicate that the potential for
long-term implementation of the GHGA programme throughout Sefton Borough, and
170
scaling up of the intervention to inform the thought of policy and practice of
professionals in PA, social work and care settings is not warranted in its current
capacity. After taking into account the barriers and facilitators of PA participation
outlined in Chapter 3 (Study 1), future research should continue to explore the
feasibility of interventions targeting PA among inactive older adults. It is
recommended that future research studies explore the potential of PA promotion
interventions to effect sustained improvements. Large scale longitudinal projects
with follow-up beyond two years are needed to identify the interventions capable of
achieving long-term results and establishing maintained PA engagement post-
intervention.
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Thesis Study Map
Study Objectives and Key Findings
Study 1. Using formative research with older
adults to inform a community physical activity
programme: Get Healthy, Get Active.
Objectives
To explore current knowledge and attitudes
towards physical activity, as well as perceived
barriers, facilitators and opportunities for physical
activity participation among older adults living in
the community.
Use these data to subsequently inform the design,
delivery and recruitment strategies of Sport
England’s national Get Healthy, Get Active
initiative.
Key Findings:
Older adults acknowledged the benefits of
physical activity, not only for health but also those
relating to socialising, enjoyment, relaxation, and
physical and psychological wellbeing regardless of
socioeconomic status.
The themes of opportunities and awareness for
physical activity participation, cost, transport,
location and season/weather varied between
assisted living and community-dwelling older
adults.
Study 2. Evaluation of wrist and hip sedentary
behaviour and moderate-to-vigorous physical
activity raw acceleration cutpoints in older
adults.
Objectives
To test a laboratory-based protocol to generate
behaviourally valid, population specific wrist- and
hip-based raw acceleration cutpoints for
sedentary behaviour and moderate-to-vigorous
physical activity in older adults.
Apply these cut-points to subsequently analyse
physical activity data for Sport England’s Get
Healthy Get Active physical activity intervention.
Key Findings
When optimizing Sensitivity for sedentary
172
behaviour and Specificity for moderate-to-
vigorous physical activity, wrist-worn GENEActiv
accelerometer cutpoints of 57 mg and 104 mg
were generated for sedentary behaviour and
moderate-to-vigorous physical activity,
respectively.
For the hip-worn ActiGraph GT3X+ the cutpoints
were 15 mg and 69 mg for sedentary behaviour
and moderate-to-vigorous physical activity,
respectively.
The resultant cutpoints can enable researchers to
classify older adults as engaging in sedentary
behaviour or not engaging in moderate-to-
vigorous physical activity with an acceptable
degree of confidence.
Study 3. Physical activity, sedentary
behaviour, perceived health and fitness, and
psychosocial wellbeing among community-
dwelling older adults.
Objectives
To investigate gender, age, and socio-economic
status differences in older adults’ sedentary
behaviour, physical activity and self-reported
health indicators.
To examine associations between sedentary
behaviour and physical activity with self-reported
health outcomes.
Key Findings
No significant gender, age category or
socioeconomic status differences were observed
between self-reported and accelerometer-derived
sedentary behaviour and physical activity
outcomes.
Significant gender, age category and
socioeconomic status differences between self-
reported quality of life, self-rated health, self-
assessment of physical fitness, and self-efficacy
for exercise were observed.
A negative association of self-reported sedentary
behaviour, and positive association of self-
reported moderate and moderate-to-vigorous
physical activity with health indicators was also
173
evident.
Study 4. A pragmatic evaluation of the Get
Healthy Get Active physical activity
programme for community-dwelling older
adults.
Objectives
To evaluate the effectiveness of Sport England’s
Get Healthy Get Active physical activity
intervention on older adults physical activity,
sedentary behaviour and self-reported health
indicators.
Key Findings:
The Get Healthy Get Active physical activity
intervention was effective in increasing quality of
life, self-rated health, self-assessment of physical
fitness, and self-efficacy for exercise scores over
time after adjustment for covariates.
There was no significant intervention effect on
time spent in moderate-to-vigorous physical
activity.
The intervention also led to a significant increase
in sitting time across all three follow-up time
points.
Study 5. Implementation fidelity of the Get
Healthy Get Active physical activity
programme for community-dwelling older
adults
Objectives:
To evaluate whether or not the GHGA multi-
component intervention was implemented as
intended.
To evaluate sustainability of the GHGA multi-
component intervention in terms of its feasibility
and acceptability of long-term implementation
across multiple settings.
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Chapter 7
Study 5: Implementation fidelity of the Get
Healthy Get Active physical activity programme
for community-dwelling older adults.
175
7.1. Introduction
Results from Chapter 6 (Study 4) revealed that Sport England’s GHGA PA
intervention was ineffective at increasing MVPA levels across all three follow-up time
points. Significant increases in sitting time were also observed across the follow-up
time points. The current study provides quantitative and qualitative data to assess
the implementation fidelity of the GHGA PA intervention. It is important to
understand how the GHGA intervention was implemented in practice so that
changes in the behaviours of intervention participants can be attributed to the novel
remedy that the intervention represents rather than to variations in the delivery and
receipt of the intervention. Efficacious interventions can then be scaled up in order
to inform the thought of policy and practice of professionals in PA, social work and
care settings.
Intervention research in the field of PA in older adults has primarily focused on pre-
and post-intervention measurements and less on longer term follow-up
measurements after intervention completion (McMahon et al., 2017). Follow-up
measures post-intervention are critical for understanding implementation
sustainability and maintenance patterns (McMahon et al., 2017). Home-based,
group-based, community-based, and educational whole-system oriented multi-
component PA interventions can result in both short- and long-term increases in PA
(McMahon et al., 2017). Findings from a recent Pedometer Accelerometer
Consultation Evaluation (PACE)-Lift Cluster RCT (Harris et al., 2015; Harris et al.,
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2018) showed at 3-months that both average daily step-counts and weekly MVPA in
≥10 minute bouts were significantly higher in the intervention than control group: by
1,037 (95% CI 513–1,560) steps/day and 63 (95% CI 40–87) minutes/week,
respectively. At 12-months corresponding differences were 609 (95% CI 104–1,115)
steps/day and 40 (95% CI 17–63) minutes/week (Harris et al., 2015), and at 4-years
post-baseline versus control results revealed sustained intervention effects resulting
in: +407 (95% CI: −177±992), p = 0.17 steps/day, and +32 (95% CI: 5±60), p = 0.02
minutes/week MVPA in ≥10 minute bouts in the intervention compared to the
control group, respectively. A systematic review of reviews by Zubala et al. (2017)
found that PA interventions among community-dwelling older adults often resulted
in sustained improvements in PA over the study period, typically at 12 months.
However, effects on maintenance beyond 12 months remains unclear, due to a lack
of high quality longitudinal studies (Olanrewaju et al., 2016; Richards et al., 2013;
Zubala et al., 2017). Consequently, only a minority of interventions move from
research into practice, and those that do provide limited information on
sustainability or institutionalisation within routine practice (Reis et al., 2016). Across
three decades of PA intervention research, the majority of publications have been
efficacy/effectiveness trials and only 3% comprised dissemination studies
(Gottfredson et al., 2015). This continued lack of evidence for the successful
institutionalisation of PA interventions in real-world settings, combined with
unacceptably high levels of PA inactivity worldwide (Hallal et al., 2012; Kohl et al.,
2012) makes addressing the research-to-practice gap a significant public health
priority (Koorts et al., 2018). It is recommended therefore, that process evaluations
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of intervention implementation and fidelity become an integral part of the conduct
and evaluation of all health behaviour intervention research (Castillo et al., 2017).
Intervention fidelity is the degree to which an intervention is implemented as
intended by its developers and ensures that the intervention maintains its intended
effects (Carroll et al., 2007). When research is inattentive to fidelity, changes in the
behaviours of intervention participants can be attributed to variations in the delivery
and receipt of the intervention just as plausibly as they can be credited to the novel
remedy that the intervention represents (Bellg et al., 2004). It is now widely
acknowledged that it is important that interventions are studied in terms of their
implementation and fidelity, as this process evaluation research can improve
understanding of how interventions have been implemented in practice, so that they
can be further integrated into ‘real world’ community settings (Bellg et al., 2004;
Oakley et al., 2006; Craig et al., 2008). Process evaluation of intervention delivery is
deemed to be particularly important in multi-component interventions delivered
across varying locations where the same intervention may be implemented and
received in different ways (Koorts et al., 2018).
Assessing intervention fidelity has been identified to be a key challenge for health
behaviour interventions (Koorts et al., 2018). Public health impact is dependent on
the extent to which efficacious PA interventions are disseminated with fidelity into
real world settings, then maintained, and institutionalised (Lewis et al., 2017). If an
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intervention is not implemented as directed and no effect is found, then one cannot
be sure whether this is due to lack of efficacy of the intervention or simply that it has
not been implemented correctly (Hasson, 2010). When analysing fidelity of a large-
scale community-based PA intervention study, Hardeman et al. (2008) found that
facilitators delivered only around 44% of the specified intervention techniques
across four key sessions. It is recommended, therefore, that evaluations of
intervention fidelity become an integral part of the conduct and evaluation of all
health behaviour intervention research (Castillo et al., 2017). The process evaluation
of interventions is advocated by the SEF, which deems it to be an essential part of
designing and testing multi-component interventions (National Obesity Observatory,
2012). Assessment of fidelity requires a mixed methods approach, using quantitative
and qualitative methods to understand processes which influence implementation,
and their variation across contexts (Moore et al., 2015).
Assessing the degree of fidelity for intervention design and implementation is a
critical feature in translating research-based studies with positive outcomes into
successful programmes (Frank et al., 2008). Despite this recommendation, there has
been considerable heterogeneity and variability in the conceptualisation and
measurement of intervention fidelity in the quality of measurement of delivery
fidelity in interventions promoting PA (Lambert et al., 2017; Quested et al., 2017).
Fidelity assessment includes appraisal of the intervention itself, by addressing core
elements of treatment integrity and treatment differentiation (Calsyn, 2000;
Moncher & Prinz, 1991). The characteristics and actions of the deliverer(s) also need
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to be scrutinised. These include adherence (accuracy in delivering the components of
an intervention) and competence (the ability of the deliverer to engage the
participants effectively) (Santacroce, Maccarelli & Grey, 2004). Increasingly
sophisticated conceptual models of fidelity measurement have been developed and
tested (Pérez et al., 2015; Moore et al., 2015). A comprehensive treatment fidelity
framework specifically developed to provide guidance for the assessment,
enhancement and monitoring of fidelity for tailored health behaviour interventions
is the NIH BCC framework (Bellg et al., 2004). The BCC framework conceptualises
fidelity across five core domains: Study Design, Provider Training, Intervention
Delivery, Intervention Receipt and Enactment. Assessing all these elements enables
more accurate inferences to be made about programme effectiveness, and (if
appropriate) any implications for wider roll out/implementation (Dane & Schneider,
1998). The model has been previously adopted among health behaviour
interventions in older adults (Chiang et al., 2006; Quijano et al., 2007) and provides a
set of guidelines for translating research into practice and improving the successful
implementation of interventions into real world settings (Demiris et al., 2014). For
these reasons the NIH BCC framework (Bellg et al., 2004) was adopted in the current
study to assess programme fidelity of the Get Healthy Get Active (GHGA) multi-
component PA intervention. In line with thesis objectives 8 and 9, this study aimed;
to evaluate whether the GHGA multi-component intervention was implemented as
intended, and evaluate sustainability of the GHGA multi-component intervention in
terms of its feasibility and acceptability of being implemented and incorporated in
the long-term across multiple settings.
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7.2. Methods
The GHGA PA intervention was a pragmatic, quasi-experimental study with repeated
follow-ups, delivered from January 2016 and July 2018 and conducted in various
locations throughout Sefton Borough in the North West of England. The intervention
aimed to engage inactive older adults in PA at least once a week for 30 minutes, via a
12 week PA intervention. The project was funded by Sport England and delivered by
SMBC. The characteristics of the GHGA PA intervention were in line with the
CONSORT checklist for pragmatic evaluations (Zwarenstein et al., 2008). A detailed
description of the intervention can be found in Chapter 6 (Study 4), but a brief
summary is provided here. Funded by Sport England and delivered by SMBC, the
GHGA PA intervention was a three-year project aimed at engaging inactive older
adults in PA at least once a week for 30 minutes, via a 12 week PA intervention. The
intervention was implemented throughout Sefton Borough within differing locations
(e.g., leisure centres, a church hall, a theatre, a retirement homes, and a library) with
each 12 week PA intervention implemented at the same venue. However, deliverer
retention throughout the entirety of each 12 week PA intervention was not possible
due to competing time demands, holidays and sickness of deliverers. The potential,
therefore, for the fidelity of the intervention to vary considerably both across
separate locations, and within the same location was high.
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Given the varying levels of functional ability and physical and psychosocial health
among this population (Van Cauwenberg et al., 2016), the exercises within each
session were designed to be flexible (e.g., variations of the same exercise each with a
differing difficulty) in order to meet the needs of all participants and the varying
needs of deliverers themselves (e.g., if a deliverer was injured and could not perform
certain activities). Consequently, the precise standardisation of the delivery of the
intervention was neither desirable nor feasible. Thus, it was predicted that there
would be variation in both the delivery of the intervention (by deliverers) and the
response to the intervention (by participants), and therefore the fidelity of the
intervention. This study draws upon various quantitative and qualitative session
observation and qualitative interview data sources to provide a comprehensive
exploration of GHGA PA intervention fidelity. A total of 43 mixed-gender GHGA
session observations, and 21 GHGA deliverer semi-structured interviews were
completed by the author.
7.2.1. Design
A mixed-methods research design was adopted as adopting both quantitative and
qualitative methods in combination can enable a deeper understanding of
programme implementation and maintenance to be gained (Moore et al., 2015).
7.2.2. Measures
7.2.2.1. Session Observations
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Due to the nature of the intervention and logistical and time constraints of the
research staff, randomisation of sessions was not possible and consequently, a
convenience sample of 43 mixed-gender GHGA session observations took place
between June 2016 and December 2017. Given the demographically and
geographically local nature of the intervention sessions, this type of sampling is
acceptable in qualitative research (Robinson, 2014). Observations adhered to
Merriam’s framework for session observations (Merriam, 1998) and were completed
in a contrived (non-natural setting), non-disguised, human, direct, structured
manner. Merriman’s framework conceptualises session observations across four
core domains. Firstly, frequency counts of the physical environment (e.g., number of
participants, size of the space, number and type of equipment available, and number
and type of activities performed) are noted. Participants’ and deliverers’ perceptions
of delivery, organisation, venue, facilities, timings and engagement are also recorded
by the first author who was knowledgeable and experienced in the requirements of
delivering a successful GHGA PA session. Activities and interactions between the
participants and the deliverer(s) including the intervention itself (e.g., delivery,
content and structure), the deliverer (e.g., competence, adherence to
objectives/exercise content, consistency and enthusiasm), and perceived participant
enjoyment are then taken into account. Finally, frequency and duration of any other
subtle factors (e.g., unplanned activities, symbolic meanings, nonverbal
communication, physical clues, and what should happen that has not happened) are
noted. A total of ~60 hours of sessions observations were aggregated and analysed
in Microsoft Word (version 2013, Microsoft Corporation, Redmond, WA, US)
resulting in 215 pages of typeset data with Arial font, size 12, double-spaced. All
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participant and deliverer data were anonymised and coded throughout the
transcripts to ensure confidentiality. Session observations served to triangulate
interview findings and thus, aid the latter stages of the analysis by exploring further
intervention fidelity.
7.2.2.2. Deliverer Interviews
Due to time constraints of both the GHGA deliverers and the first author, a
convenience sample of seven GHGA intervention deliverers participated across 21
interviews, with all seven deliverers participating in at least one interview.
Specifically, one deliverer took part in six, one deliverer in four, one deliverer in
three, and finally four deliverers in two interviews, respectively. Convenience
sampling has been adopted in older adult interview research previously (Van
Cauwenberg et al., 2018) and thus, the current study extends the applicability of this
method within this population. Interviews took place between June 2016 and
November 2017. Deliverer interviews were conducted using a semi-structured
interview guide including open- and closed-ended items. Interviews included 12
questions with probes and follow-up questions used as needed. Guide development
was informed by Merriam’s framework for session observations (Merriam, 1998) and
consequently, elicited information in line with the four core domains of, physical
environment, participants and deliverer perceptions, activities and interactions
between both the participants and the deliverer(s), and the frequency and duration
of any other subtle factors. An example question was: ‘Are the facilities appropriate
for what you need in order to fully complete the session? Please explain.’ To
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maximise the interaction between the deliverer and the first author, interview
questions were reviewed by the project team for appropriateness of question
ordering and flow prior to the deliverer interviews. All interviews were led by the
first author. An example question from a section exploring interactions between
participants and deliverers was: “How would you rate participant engagement with
this session out of 10? Please explain.” Questions therefore demonstrated aspects of
face validity as they were transparent and relevant to both the topic and target
population (French et al., 2015). Interviews took place within multiple venues,
multiple times including two leisure centres (x5 interviews), a church hall (x8
interviews), a theatre (x3 interviews), a retirement home (x2 interviews), and a
library (x3 interviews). All locations were free from background noise, and deliverers
could be overlooked but not overheard. Interviews averaged 14 minutes in duration
(ranging from 11 to 23 minutes), were digitally recorded, and transcribed verbatim.
Transcribed data was aggregated and analysed in Microsoft Word (version 2013,
Microsoft Corporation, Redmond, WA, US) resulting in 70 pages of typeset data with
Arial font, size 12, double-spaced. The text for each interview was sequentially
labelled with numbers to identify the sentences that belonged to the participant or
interviewer (Silverman, 1994). All data were anonymised and coded throughout the
transcripts to ensure confidentiality.
Verbatim transcripts were read and re-read to allow familiarisation of the data.
Participants of the GHGA PA intervention received a covering letter, participant
information sheet, and consent form. Prior to the commencement of the study,
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institutional ethical approval was received (#SPA-REC-2015-329) and written
informed consent was obtained for all participants prior to participation.
7.2.3. Data Coding and Analysis
Previous research within this population has adopted analytical procedures including
thematic analysis (Van Dyck et al., 2017), content analysis (Middelweerd et al., 2014)
and used specialist qualitative data analysis packages, such as NVivo (Warmoth et al.,
2016). Deductive content analysis (Braun & Clarke, 2006) was initially adopted to
categorise session observation and interview data into a priori themes from
Merriam’s session observation framework (Merriam, 1998). Inductive analysis then
allowed for emerging themes to be created beyond the pre-defined categories.
As well as being used as a tool to shape treatment fidelity plans in intervention
development (Sineat et al., 2017), the NIH BCC framework (Bellg et al., 2004) has
also been adopted to assess and evaluate treatment fidelity of PA interventions in
both older adults (Frank et al., 2008) and adults (Lambert et al., 2017). Consequently,
to exemplify operationalisation of the NIH BCC framework, relevant a priori and
emergent themes obtained from both interview and session observation data were
retrospectively applied into the five core fidelity domains of: Study Design, Provider
Training, Intervention Delivery, Intervention Receipt and Enactment. Study design is
concerned with whether a study adequately tests its hypotheses in relation to its
underlying theoretical and clinical processes. Quality criteria includes delivery and
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adherence to the study protocol, and ensuring an environment where the protocol
can be fully operationalised (Lambert et al., 2017). Provider training involves
standardising training between providers and ensuring they are trained to clear
criteria and monitored over time (Bellg et al., 2004). Quality criteria includes both
adherence to treatment components and adherence to process (e.g., interactional
style) (Lambert et al., 2017). Intervention delivery involves assessing and monitoring
deliverer differentiation (differences between the intervention and any comparison
treatments), competency (skills set of deliverer), and adherence (delivery of
intended components) to treatment components and competence to deliver the
treatment in the manner specified (Frank et al., 2008). Assessment of non-specific
treatment effects (e.g., infrastructure) should also be noted (Lambert et al., 2017).
Intervention Receipt refers to whether the intervention was understood and
‘received’ by participants (Bellg et al., 2004). Assessment of participant receipt can
involve pre-post tests and verbal/non-verbal cues (Lambert et al., 2017). Fidelity to
treatment enactment refers to intervention sustainability and in particular, whether
participants used intervention related skills in day to day settings (Bellg et al., 2004;
Borrelli, 2011; Borrelli et al., 2005). Assessment includes objective observations and
psychometric properties (Lambert et al., 2017). Verbatim quotations were
subsequently used to provide context and verify participant responses. Quotations
relating to the deliverer interviews were labelled by interview number (In), and
subsequent deliverer number (Dn) within that interview. Characterising traits of this
protocol include details of frequency counts of the physical environment, and
extracts of verbatim quotes to provide context to the a priori and emergent themes.
The pen profile approach adopted in Chapter 3 (Study 1) was not adopted for the
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current study as the minimum threshold for theme inclusion criteria required for this
approach would have severely reduced the already limited data available.
Methodological rigour was demonstrated through a process of interrater reliability
(McHugh, 2012) whereby coding checks were undertaken for a 10% random sample
of all data collected from observations (n =5) and interview transcripts (n =2)
independently by a member of the project team. This involved cross-checking
placement of data into the four main themes, within 19 sub-themes obtained from
Merriam’s framework for session observations (Merriam, 1998), and then into the
five core themes associated with the NIH BCC framework (Bellg et al., 2004). A total
of nine emergent themes were identified which were placed within the appropriate
main theme within Merriam’s framework for session observations (Merriam, 1998),
and then if relevant, within the appropriate domain within the NIH BCC framework
(Bellg et al., 2004). Any omissions and discrepancies with coding analysis were
identified and discussed until subsequent agreement on data themes in relation to
verbatim extracts was reached. Agreement for coding of themes from the data
ranged from 84% to 95% across the seven complete data sets. 80% and above
agreement is considered acceptable (McHugh, 2012). This process ensured
transparency, credibility and trustworthiness of the results (Smith & Caddick, 2012).
7.3. Results
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In total, seven GHGA intervention deliverers and 388 older adults (54 male)
participated across the 21 GHGA deliverer semi-structured interviews (mean number
of participants per session =1.1, SD= 0.3) and 43 session observations (mean number
of participants per session= 9.02, SD =4.08), respectively.
7.3.1. Fidelity to study design
7.3.1.1. Number and type of exercises included
Although deliverers often did not have a specific session plan outlining specific
exercises for each individual session, deliverers noted that they had to include
exercises which targeted five core aspects of fitness including balance, endurance,
flexibility, resistance, and strength exercises, whilst also incorporating a warm-up
and a cool-down.
“So there is a warm-up, endurance, resistance, balance, strength, flexibility, and then a cool-down.” (In12, Dn3, Lines 69-70).
Table 7.1 shows that 93.1% of GHGA sessions delivered the required five core
components, and the associated warm-ups and cool-downs. Deliverers also noted
that they were given flexibility to introduce essential exercise components of varying
difficulty, thus allowing all participants regardless of ability, to engage with the
entirety of the session protocol.
“Yeh it’s nice to see people moving onto more difficult exercises or even just getting out of the chair if they couldn’t do it before coming.” (In6, Dn7, Lines 54-55)
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“…there are different progressions people can do like increasing or decreasing
resistance in the exercise bands or holding onto the chair or not during the balance
exercises and again they (GHGA participants) know to go at their own pace and stop
if anything starts to hurt or anything.” (In9, Dn5, Lines, 51-54)
7.3.1.2. Environment
Deliverers emphasised the usefulness of the pilot-phase of the project in aiding with
session set-up, timings, adherence to essential protocol elements, and overall
session environment.
“Yes we had practice sessions before we fully started which has made the running of the actual sessions much smoother.” (In1, Dn2, Lines 4-5).
7.3.2. Fidelity of provider training
7.3.2.1. The intervention itself
Deliverers noted that they were trained by SMBC employees who were experienced
in delivering chair-based PA sessions.
“I only started on the programme around 6 months ago and didn’t have any training on how to deliver chair based exercises but in only 8 weeks I was trained and now I am delivering classes around Sefton on my own.” (In11, Dn7, Lines 3-5).
“That’s the good thing about having set exercises is that in only a month or so I went from not teaching anything to being fully trained and running sessions on my own. It is scary but enjoyable now at the same time.” (In8, Dn7, Lines 103-105).
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Deliverers also said how much participants were progressing which meant that
deliverers were soon expected to devise sessions of greater difficulty in order to
keep participants engaged and subsequently benefitting from the sessions.
“At the beginning (of the GHGA PA intervention delivery) we were told we would just be doing chair based exercises in care homes but it is very different to that and we now do walking and balance and more advanced things which I’m not really trained for.” (In10, Dn5, Lines 83-85).
7.3.2.2. Deliverer experience
A limited number of deliverers were trained and subsequently conducted the
sessions throughout the programme’s entirety. Consequently, all deliverers were
experienced in all aspects of session delivery.
“I have session plans for all of them but because I have done the sessions for a long time now, I know them off the back of my hand, but there is a set thing we have to do.” (In13, Dn3, Lines 68-69).
However, it was noted that the limited number of deliverers available had an impact
on the number of participants that could attend each session.
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“I can only have a limited number of participants because I wouldn’t be able to observe them all carefully enough if we had too many… there is only me delivering we need more deliverers.” (In13, Dn3, Lines 95-97).
7.3.2.3. Rapport
Deliverers noted the importance of building rapport with the participants and in
particular how much they enjoyed delivering the sessions.
“Yes, it is a good group! It is an engaged group there is a lot of feedback from the group and we have a laugh and a joke. Good group to engage with, one of the best I would say.” (In14, Dn1, Lines 33-35).
“…after the first 12 weeks we actually did satisfaction forms with them and each one of them pretty much rated it excellent which is a good sign. They were all anonymous so they could of wrote anything down and we wouldn’t know who said what but they all came back said it was positive, and you can tell by their body language as when they come they all love to see each other and love having a little gathering seeing what has happened over the week. So I do generally think they are positive and because they are coming back as some of them have been there for 6 months now and they come back every week that has to be a good sign.” (In15, Dn3, Lines 130-137).
7.3.2.4. Motivation
Deliverers talked about the freedom they were given to introduce a variety of
exercises of varying levels of difficulty. This kept deliverers themselves engaged and
motivated.
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“I try to do different things (exercises) each week so it’s not the same it keeps me excited as I’m not doing the same thing over and over and it keeps people (GHGA participants) working different areas as for some this will be the only exercise they do all week!” (In5, Dn1, Lines 44-46).
“Everybody is really pushing themselves in this class and although there are lots of different levels of ability and balance and flexibility in the class… they are moving more freely and their balance has improved a lot as I keep making it harder for them!” (In8, Dn7, Lines 19-23).
7.3.3. Fidelity of treatment delivery
7.3.3.1. Delivery
Many deliverers talked about the different ways in which they adapted the exercises
to fit the characteristics and needs of the participants whilst still adhering to the
GHGA PA intervention protocol.
“In any group you will have people who perhaps can’t do certain exercises and that is mainly due to us having such varying levels of ability of participants turning up so we try and cater for all as there are always lesser and harder variations of exercises we can do to keep all involved.” (In4, Dn4, Lines 19-22).
Some also noted the importance of knowing the health history of class members to
make sure exercises are appropriate and safe.
“We have to do health checks with them all to see if there is anything restricting them in exercise, certain medication can also affect the exercises they can do but we
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will always give adaptions (of exercises) for them so regardless of what is going on we can give adaptions”. (In15, Dn3, Lines 169-172).
7.3.3.2. Venue
Deliverers noted how the on the whole the venues used were suitable in both size
and location for the target population and allowed for the sessions to be completed
fully.
“Yes, I like it, it’s good! The space is accessible and its quite central in the area, I don’t know the area too much but everyone seems to know it and how to get here.” (In14, Dn1, Lines 22-23).
“Yes I think it’s perfect really it’s a nice cheap room it’s plenty big enough for what we need it for.” (In1, Dn6, Lines 30-31).
Table 7.1 shows that there was sufficient room space to fully complete required
GHGA session components 93.1% of the time. Some venues however were deemed
to be too small and inaccessible, which impacted upon session delivery, especially
the elements which required more space such as the walking balance exercises.
“We were in a small room for the last couple of weeks… so it wasn’t good enough, wasn’t big enough, couldn’t actually operate the session very well.” (In6, Dn7, Lines 7-9).
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“…it’s (the venue) a little dark and it’s upstairs.” (In9, Dn5, Line 56).
7.3.3.3. Facilities and Equipment
Deliverers spoke highly of the facilities and equipment available for each session.
“…there are plenty of chairs in the room and also the weight box of dumbbells which is good and also the good speakers in the corner of the room.” (In10, Dn5, Lines 59-60).
Table 7.1 shows that sufficient equipment was available for 95% of the sessions.
Kitchen facilities were also noted as being important given the strong focus of
socialisation portrayed by GHGA and were available throughout 88% of the sessions.
“…the kitchen is brilliant for making the teas and coffees at the end (of the session).” (In8, Dn7, Lines 50-51).
“I think that everything is there to be honest because they have got the kitchen as well, the kitchen is a good idea for when we are doing tea and coffee and it’s obviously got the kettle facilities and stuff.” (In15, Dn3, Lines 96-98).
7.3.4. Fidelity of receipt of treatment
7.3.4.1. Confidence
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Participant confidence in performing everyday tasks was observed via the timed up
and go test (Podsiadlo & Richardson, 1991) at baseline, and at 12-weeks post-
baseline assessment.
“We do an assessment at the beginning (first session of the 12-week GHGA PA intervention) and after 12-weeks and each participant has progressed massively in both confidence and the physical element of it… I think the majority of them improved by about 7 seconds on the walking (based on the timed up and go test; Podsiadlo & Richardson, 1991) and their ability to actually get up out the chair improved massively, they’re all quite confident now to get up out of the chair.” (In15, Dn3, Lines 86-92).
7.3.4.2. Perceived participant engagement and enjoyment
As well as being physically tested, participants noted their progress to deliverers
verbally, allowing deliverers to further assess their understanding and progress.
Specifically, deliverers talked about how the flexibility of the session protocol and
their delivery styles themselves allowed participants of all abilities to perform,
understand and enjoy the entirety of each session.
“Nobody is left behind or left with nothing to do as almost every exercise can be adapted so all can be involved… We describe the exercise and show it before the participants do it so all know exactly what to do and can get involved.” (In10, Dn5, Lines 127-129).
“When people actually come to the session more often than not they keep coming as they enjoy it so much!” (In7, Dn6, Lines 88-89).
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Table 7.1 shows participant fidelity to receipt of treatment on a scale from 1-10, with
10 representing full participant engagement and 1 representing no engagement.
Deliverers reported average participant engagement throughout the sessions to be
8.1. Furthermore, participants themselves rated overall session enjoyment (based
upon the four main themes outlined in Merriam’s session observation framework)
on a scale from 1-10, with 10 representing excellent enjoyment and 1 representing
no enjoyment. Data collected from 388 older adults revealed an overall mean score
of 8.6 (SD 2.21).
7.3.4.3. Retention
Deliverers also talked about the high rates of participant retention throughout the
12-week GHGA PA sessions.
“Yes the same participants keep coming back which is good and we are slowly getting more participants coming. The sessions are really motivating and you can see the benefits to the participants even after just a few short weeks.” (In10, Dn5, Lines 98-99).
“I can really push participants and they thrive off the energy. Participants seem hooked once they have been to one session as the same faces keep coming back… people just don’t believe how much it actually helps them.” (In1, Dn2, Lines 52-56).
7.3.5. Fidelity to treatment enactment
7.3.5.1. Benefits to health
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Deliverers noted that GHGA measured falls efficacy, falls management, and falls
control using self-report scales at baseline and 12-weeks post-baseline.
“From the falls questionnaires we can see participants are increasing mobility reducing falls and improving their flexibility and you know their strength as well which is fantastic as it means they will be able to stay independent for longer and that is kind of the aim of these sessions.” (In7, Dn6, Lines 18-20).
Deliverers also talked about how participants themselves recognised the benefits
that the sessions were having and how this was helping participants retain their
independence outside of the sessions and consequently, reducing burden on
healthcare services.
“…one participant said they had a problem with their ankle but that is now getting better after coming to these sessions and keeping people healthy puts less pressure on the NHS.” (In14, Dn1, Lines 46-47).
7.3.5.2. Socialising
The social aspect of the GHGA PA sessions was noted by deliverers as being a key
aspect by both deliverers and participants.
“Yes definitely I’d like to think they (GHGA participants) enjoy the sessions and the social aspect which is the tea and coffee after which everyone stays for and brings biscuits and things. Participants have made new friends which they keep in touch with outside of these sessions which is fantastic.” (In9, Dn5, Lines 40-43).
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Table 7.1. Frequency counts and descriptives.
7.3.5.3. Sustainability
Deliverers talked about wanting to set up paid-maintenance sessions after the initial
free 12-week block of sessions as despite being given leaflets and DVDs outlining the
GHGA session components, participants were not utilising these outside of the
sessions themselves.
“I mean a lot of them (GHGA participants) don’t use the bands or do the exercises at home even though we give out DVDs and booklets explaining and showing the exercises we do so it’s important to keep these sessions going if we can do especially as these participants are willing to pay for the sessions.” (In7, Dn6, Lines 29-32).
Session Observation
Number
Number of
participants (male)
Sufficient room space
to fully complete required
GHGA session
components
Sufficient equipment available
for a complete session
(e.g., chairs, exercise bands)
Kitchen Facilities
Core components completed
(%)
Perceived participant
engagement by GHGA deliverers
(/10)
Perceived participant
session fidelity (/10)
1
2
3
4
5
6
7
8
9
10
11
23 (3)
20 (0)
14 (1)
12 (1)
16 (0)
11 (3)
8 (0)
10 (2)
3 (0)
3 (0)
5 (1)
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
No
No
No
Yes
Yes
No
No
Yes
80%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
7
7
8
7
8
7
7
7
8
9
9
8
8
7
9
10
8
7
8
8
10
8
199
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
7 (2)
7 (3)
8 (1)
4 (0)
4 (0)
9 (0)
11 (3)
7 (2)
10 (1)
10 (1)
7 (2)
11 (2)
9 (1)
9 (1)
7 (0)
7 (2)
11 (2)
8 (1)
9 (1)
5 (1)
10 (2)
3 (0)
9 (1)
7 (2)
9 (1)
8 (1)
7 (2)
7 (2)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
80%
80%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10
10
10
7
8
7
7
9
9
9
8
9
8
8
9
7
7
7
8
7
8
9
7
7
8
7
10
8
9
9
9
8
10
10
9
8
8
9
7
9
9
10
10
10
7
9
9
9
9
8
9
10
10
8
8
7
200
40
41
42
43
7 (2)
12 (2)
9 (0)
15 (2)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
100%
100%
100%
100%
10
10
8
8
9
7
8
9
7.4. Discussion
Through the adoption of a comprehensive treatment fidelity framework developed
by the BCC for tailored health behaviour interventions (Bellg et al., 2004), this study
aimed to evaluate whether the GHGA multi-component PA intervention was
implemented as intended with a view of better understanding the results outlined in
Chapter 6 (Study 4).
Study design fidelity ensures procedures are put in place to ensure equivalent
content both within and across conditions, as well as creating plans to deal with
possible setbacks during implementation (Frank et al., 2008). Results revealed that
although 93.1% of GHGA sessions adhered to and included a set structure of
exercises targeting the five core aspects of fitness (endurance, resistance, balance,
strength, and flexibility), variation of session content and session exercises were also
built into the sessions. This resulted in varying numbers, types and timings of
exercise components in every session, even between sessions delivered by the same
GHGA deliverers. However, given the varying levels of functional ability and physical
and psychosocial health associated with community-dwelling older adults (Van
Cauwenberg et al., 2016), these results support the idea that a one size fits all
201
approach to multi-component interventions is not appropriate (Hawe, Shiell & Riley,
2004). A strict protocol consisting of the same exercises for all participants regardless
of ability may have resulted in decreased participant engagement, motivation and
subsequent retention (Rose, 2018). Consequently, the core components of each
session were included but with a degree of variation whereby the same exercise
could be delivered at differing difficulties to enable all participants to successfully
take part regardless of ability. Previous research has also advocated for certain levels
of flexibility and progressions in session content based upon participant requests and
levels of ability given that such serves to allow better tailoring of the intervention to
the local context (Lawton et al., 2014). However, results from Chapter 6 (Study 4)
showed that despite the varied and flexible sessions, the GHGA PA intervention was
ineffective at increasing MVPA levels. Hence, delivering a block of sessions with
consistent content and delivery techniques may better allow researchers to
understand the approaches that are both effective and ineffective in eliciting PA
behaviour change among this population (Chase, 2014). Incorporating both
quantitative and qualitative measures of intervention fidelity through
comprehensive frameworks such as Merriam’s framework for session observations
(Merriam, 1998) and NIH BCC’s framework (Bellg et al., 2004) can allow future
researchers to accurately measure both session content and delivery consistency
and consequently, whether the intervention is efficacious to PA behaviour change.
Fidelity to provider training ensures that deliverers are satisfactorily trained to
deliver the intervention to participants (Frank et al., 2008). Training practitioners to
202
faithfully deliver multi-component interventions is a major challenge and thus,
assessment and ongoing evaluation of those who implement the programme is a key
element of fidelity as this ensures that deliverers have been satisfactorily trained to
deliver the programme as intended to participants (Bellg et al., 2004). GHGA
deliverers noted that they were trained by SMBC employees who were experienced
in delivering chair-based exercise sessions. This ensured that deliverers had baseline
knowledge of exercise science and safety, which they then built upon further whilst
gaining experience delivering the GHGA sessions themselves. Session observation
findings provided further agreement for fidelity to provider training given the sound
knowledge and descriptions of each exercise provided by GHGA deliverers during the
sessions. Effective deliverers as noted by both intervention participants and the first
author were those who; provided clear, concise instructions both before and during
each exercise, demonstrated each exercise both verbally and visually, performed the
exercises with the participants and therefore provided a reference for required
speed and intensity, and set out a target for participants during each set (i.e. number
of reps, time). GHGA deliverers also received a GHGA instructor manual, detailing
the exercises to be included within the sessions. A DVD outlining the exercises was
also available for the deliverers. A previous evidence-based group exercise
intervention for older adults (EnhanceFitness) also noted that providing deliverers
with detailed scripts, descriptions, and guidelines for each intervention component
could increase fidelity to provider training (Quijano et al., 2007).
203
The importance of deliverer engagement and motivation has also been identified as
a key determinant affecting fidelity to provider training (Schoenwald et al., 2011).
Session observations revealed that all deliverers were fully engaged and motivated
to deliver GHGA sessions due to their strong beliefs in the potential benefits of the
GHGA intervention to participant’s physical and psychosocial health. Consequently,
fidelity to provider training was further ensured as those who believe in the value of
the intervention are more likely to fully engage with the training (Castillo et al.,
2017).
Fidelity to treatment delivery is considered the ‘heart of fidelity assessment in
behavioural interventions’ (Gearing et al., 2011, p.82) but has historically been
insufficiently considered (Miller & Rollnick, 2014). Treatment delivery is crucial to
ensure intervention results are truly attributable to the programme (internal validity)
and that the results are generalizable to other study populations (external validity)
(Frank et al., 2008). In line with previous interventions in older adults (Quijano et al.,
2007; Tennstedt et al., 1998), GHGA monitored treatment delivery through onsite
observations of new deliverers throughout the 12 weeks by senior SMBC staff
experienced in the design and structure of the GHGA sessions. Staff involved in
GHGA from the pilot stage were not observed. However, participant satisfaction with
delivery was assessed for all deliverers after 12 weeks through a written programme
evaluation, which asked about satisfaction with the programme and the deliverer. It
is to be expected that deliverers potentially became more proficient in delivery with
increased experience throughout the 12 week intervention and consequently, future
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process evaluations should assess participant satisfaction across time (e.g., baseline,
three-, six- and 12-weeks) (Thompson, Lambert, Greaves, & Taylor, 2018).
Infrastructure is also a key aspect of treatment fidelity and includes venue location
and size, availability of equipment and materials, and session timing (Petrescu-
Prahova et al., 2015). GHGA sessions were implemented throughout Sefton Borough
within several differing locations (e.g., leisure centres, a church hall, a theatre, a
retirement homes, and a library). Deliverers noted that on the whole the venues
adopted were in locations that were suitable for participants, and 93.1% were of a
size which allowed for the full completion of the required GHGA session
components. Session observations provided further agreement for the size of
venues, however participants noted that locations were often not suitable to be
reached by public transport. This could have affected session numbers as frequency
and reliability of affordable public transport are all associated with decreased PA
participation (Newitt et al., 2016). Treatment fidelity was further ensured through
the sufficient availability of equipment at each venue which was either provided by
the venue itself (e.g., chairs and music systems) or by SMBC (e.g., fitness bands,
ankle weights and dumbbells).
Fidelity related to receipt of treatment concerns both documenting participant
exposure to the treatment and the ability of participants to understand and perform
treatment-related activities and strategies during treatment delivery. In GHGA,
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participant confidence in performing everyday tasks was observed via the timed up
and go test (Podsiadlo & Richardson, 1991) at baseline, and at 12-weeks post-
baseline. This measure, along with participants informally noting their progress to
deliverers verbally at the end of each session, ensured participants were able to
comprehend and perform the exercises as instructed (Frank et al., 2008). Session
observations provided further evidence of participant receipt and engagement. As is
recommended in the NIH BCC guidelines (Bellg et al., 2004; Lambert et al., 2017),
GHGA deliverers demonstrated each exercise both verbally and visually throughout
the sessions in order to ensure participant comprehension of each exercise.
Participant confidence in performing the behaviours and success in meeting goals
was also assessed during classes through videotapes of participants at baseline and
12 weeks post-baseline (Lambert et al., 2017). Concurrent with recent PA
intervention research in older adults, (Lyons et al., 2017), trained GHGA deliverers
monitored “dose” by tracking older adults’ participation in programme activities
(e.g., attendance) and those who discontinued (e.g., dropped out) to assess level of
receipt. The subsequent high rates of participant retention throughout the 12 week
GHGA PA sessions further solidified the efficacy of receipt of treatment.
Fidelity to treatment enactment concerns the participants’ ability to implement the
learned skills and activities in real world settings (Frank et al., 2008). The repeated
measures design of the research element of the GHGA PA intervention allowed for
fidelity strategies to track participants’ implementation of the learned behaviours,
skills, and cognitive strategies presented in the GHGA PA sessions in relevant real
206
world settings at three-, six- and 12-months post-baseline via self-reported physical
and psychosocial outcomes (see Chapter 6). Additionally, falls efficacy, falls
management, and falls control were measured by GHGA deliverers using self-report
scales at baseline and 12 weeks post-baseline. Session observations also revealed
that participants themselves recognised the benefits that the sessions were having
on their physical health and how this was helping them retain their independence
outside of the sessions. Participants also noted that they had made friends for life
which they continued seeing outside of the GHGA PA sessions. A recent systematic
review of qualitative studies of PA in older age highlighted social influences in its
thematic synthesis of findings, identifying social interactions as important facilitators
for PA sustainability, and hence fidelity to treatment enactment, in this age group
(Franco et al., 2015).
Based upon NIH BCC’s treatment fidelity framework assessment criteria (Bellg et al.,
2004), this process evaluation has provided a comprehensive review of fidelity
relating to the GHGA PA intervention for community-dwelling older adults. Results
derived from this post-hoc fidelity analysis can be used to conceptualise best
practices as a process for planning future interventions that will be appropriate
within this setting and population (Green, 2001).
7.5. Strengths and Limitations
207
A strength of the current study was the comprehensive assessment of intervention
fidelity using multiple sources of data based upon NIH BCC’s treatment fidelity
framework assessment criteria (Bellg et al., 2004). The triangulation of data, utilising
multiple methods of qualitative data alongside quantitative data is a further strength
which enhanced understanding of intervention implementation and subsequently,
overall intervention fidelity (Farquhar, Ewing & Booth, 2011). Adherence to
intervention components and content delivery were both self-assessed by deliverers
and by an independent observer thus, increasing the validity of the data. Study
limitations are also noted. The subjective nature of the data is a limitation, as is the
presence of self-selection bias which resulted from the convenience sampling
methods adopted. Furthermore, as was noted in Chapter 3 (Study 1), men tend to
decrease participation in leisure-time PA as they get older; whereas this trend is not
seen among women (Amagasa et al., 2017). Consequently, gender bias is a possibility
given the large discrepancy between male (n =54) and female (n =334) attendees
noted throughout the session observations. With regard to the deliverer interviews,
not all GHGA deliverers participated in the study and thus, results may not be
representative of the larger group of GHGA deliverers. Interviews did however
reflect a broad range of experiences and opinions encompassing a spectrum of
implementation factors. One of the key benefits of assessing treatment fidelity is to
allow for the early detection of errors to prevent protocol deviations from becoming
widespread and long lasting before their implementation into real world settings
(Borrelli, 2011). Consequently, the post-hoc analysis design is a limitation (Lawton et
al., 2014). However, when a multi-component intervention is being tested within
‘real world’ settings there is a much greater blurring of the boundaries between
208
evaluations of efficacy and effectiveness (Glasgow et al., 2003). Consequently, it is
entirely appropriate to measure fidelity and to use this information to explain
variations in effectiveness as the intervention is being delivered in ‘real world’
settings (Craig et al., 2008). This allows for more informed decision making about the
commissioning and roll out of the intervention in any subsequent settings (Craig et
al., 2008). Furthermore, post-hoc fidelity analysis has been adopted previously in
multi-component PA interventions in older adults (McMahon et al., 2017; Vidovich et
al., 2015) and thus, was suitable for adoption in the current study.
7.6. Conclusions
Through the adoption of a comprehensive fidelity framework, GHGA PA intervention
fidelity was assessed under ‘real world’ settings. Subsequent analysis of the factors
influencing fidelity to delivery, provide valuable evidence to aid interpretation of
overall programme findings and effectiveness. Results from both deliverer interviews
and session observations revealed that a high degree of intervention fidelity was
maintained throughout the GHGA PA sessions, across all venues and deliverers.
Although the GHGA PA sessions were implemented as intended, findings reported in
Chapter 6 revealed that the programme was ineffective in increasing PA, and
decreasing time spent in SB. Consequently, the potential for long-term
implementation of the GHGA programme throughout Sefton Borough, and scaling up
209
of the intervention to inform the thought of policy and practice of professionals in
PA, social work and care settings is not warranted in its current capacity.
Results can be used to further strengthen the design, delivery and recruitment
strategies of future community-based PA interventions in older adults. However,
understanding of the optimal formatting and content of multi-component
interventions, of relevance for implementation in older adults remains
underdeveloped (Mc Sharry, Olander & French, 2014; Nigg & Long, 2012), and
further process evaluations of multi-component community-based PA interventions
in older adults are warranted to guarantee future success.
Thesis Study Map
Study Objectives and Key Findings
Study 1. Using formative research with older
adults to inform a community physical activity
programme: Get Healthy, Get Active.
Objectives
To explore current knowledge and attitudes
towards physical activity, as well as perceived
barriers, facilitators and opportunities for physical
activity participation among older adults living in
the community.
Use these data to subsequently inform the design,
delivery and recruitment strategies of Sport
England’s national Get Healthy, Get Active
initiative.
Key Findings:
Older adults acknowledged the benefits of
physical activity, not only for health but also those
relating to socialising, enjoyment, relaxation, and
210
physical and psychological wellbeing regardless of
socioeconomic status.
The themes of opportunities and awareness for
physical activity participation, cost, transport,
location and season/weather varied between
assisted living and community-dwelling older
adults.
Study 2. Evaluation of wrist and hip sedentary
behaviour and moderate-to-vigorous physical
activity raw acceleration cutpoints in older
adults.
Objectives
To test a laboratory-based protocol to generate
behaviourally valid, population specific wrist- and
hip-based raw acceleration cutpoints for
sedentary behaviour and moderate-to-vigorous
physical activity in older adults.
Apply these cut-points to subsequently analyse
physical activity data for Sport England’s Get
Healthy Get Active physical activity intervention.
Key Findings
When optimizing Sensitivity for sedentary
behaviour and Specificity for moderate-to-
vigorous physical activity, wrist-worn GENEActiv
accelerometer cutpoints of 57 mg and 104 mg
were generated for sedentary behaviour and
moderate-to-vigorous physical activity,
respectively.
For the hip-worn ActiGraph GT3X+ the cutpoints
were 15 mg and 69 mg for sedentary behaviour
and moderate-to-vigorous physical activity,
respectively.
The resultant cutpoints can enable researchers to
classify older adults as engaging in sedentary
behaviour or not engaging in moderate-to-
vigorous physical activity with an acceptable
degree of confidence.
Study 3. Physical activity, sedentary
behaviour, perceived health and fitness, and
psychosocial wellbeing among UK community-
dwelling older adults.
Objectives
To investigate gender, age, and socio-economic
status differences in older adults’ sedentary
behaviour, physical activity and self-reported
211
health indicators.
To examine associations between sedentary
behaviour and physical activity with self-reported
health outcomes.
Key Findings
No significant gender, age category or
socioeconomic status differences were observed
between self-reported and accelerometer-derived
sedentary behaviour and physical activity
outcomes.
Significant gender, age category and
socioeconomic status differences between self-
reported quality of life, self-rated health, self-
assessment of physical fitness, and self-efficacy
for exercise were observed.
A negative association of self-reported sedentary
behaviour, and positive association of self-
reported moderate and moderate-to-vigorous
physical activity with health indicators was also
evident.
Study 4. A pragmatic evaluation of the Get
Healthy Get Active physical activity
programme for community-dwelling older
adults.
Objectives
To evaluate the impact of Sport England’s Get
Healthy Get Active physical activity intervention
on older adults physical activity, sedentary
behaviour and self-reported health indicators.
Key Findings:
The Get Healthy Get Active physical activity
intervention was effective in increasing quality of
life, self-rated health, self-assessment of physical
fitness, and self-efficacy for exercise scores over
time after adjustment for covariates.
There was no significant intervention effect on
time spent in moderate-to-vigorous physical
activity.
The intervention also led to a significant increase
in sitting time throughout the follow-up time
points.
Study 5. Implementation fidelity of the Get Objectives:
212
Healthy Get Active physical activity
programme for community-dwelling older
adults
To evaluate whether or not the GHGA multi-
component intervention was implemented as
intended.
To evaluate sustainability of the GHGA multi-
component intervention in terms of its feasibility
and acceptability of long-term implementation
across multiple settings.
Key Findings:
A high degree of intervention fidelity was
maintained throughout the GHGA PA sessions
within the five core domains of: Study Design,
Provider Training, Intervention Delivery,
Intervention Receipt and Enactment.
Chapter 8. Synthesis of Findings,
Recommendations and Conclusions
213
8.1. Synthesis of findings
Overwhelming evidence shows that regular PA is among the most important
modifiable determinants for maintenance of physical (Zhu et al., 2017) and
psychosocial (Devereux-Fitzgerald et al., 2016; Franco et al., 2015; Greaney et al.,
2016) health at older ages (Lehne & Bolte, 2017). Government recommendations
state that older adults (≥65 years) should engage in at least 150 minutes of MPA (or
75 minutes of VPA) per week in bouts of at least 10 minutes, with muscle-
strengthening and balance activities included on at least two of those days
(Department of Health, 2011; CDC, 2015). Objectively collected data indicates that
only 15 per cent of males and ten percent of females within the UK, and 9.5% of
males and 7% of females within the US are meeting the recommended PA guidelines
(Tucker et al., 2011; Jefferis et al., 2014). To improve population health, efficacious
214
PA interventions in controlled research settings must be scaled up to reach broader
populations across multiple settings (Milat et al., 2016).
The overarching aim of the research programme was to assess the effectiveness and
implementation of Sport England’s GHGA intervention with community-dwelling
older adults. This aim was assessed through the following Research Questions:
1. What are the current knowledge and attitudes towards PA among older adults
living in the community, as well as perceived barriers, facilitators and opportunities
for PA participation?
2. What are the most appropriate wrist- and hip-worn raw acceleration cutpoints for
SB and MVPA activity in the GHGA sample of older adults?
3. Are there any gender, age, and socio-economic status differences in older adults’
SB, PA and self-reported health indicators?
4. What are the associations between SB and PA with self-reported health
indicators?
5. Is Sport England’s GHGA PA intervention effective in increasing community-
dwelling older adults PA levels?
6. Was the GHGA PA intervention implemented as intended?
215
The programme of work firstly aimed to explore older adults’ current knowledge and
attitudes towards PA, as well as perceived barriers, facilitators and opportunities for
PA participation among older adults living in the community. Secondly, a laboratory-
based protocol to generate behaviourally valid, population specific wrist-based GA
and hip-based AG raw acceleration cutpoints for SB and MVPA in older adults was
conducted. Thirdly, based upon the GA cutpoints generated, gender, age, and SES
differences in older adults’ SB, PA, and self-reported health outcomes were explored.
Associations between SB and PA with self-reported health outcomes were also
investigated. Fourthly, a mixed-methods evaluation was adopted in order to test the
feasibility, acceptability and effectiveness of Sport England’s GHGA PA intervention.
Finally, implementation fidelity of the GHGA PA intervention was assessed. The
studies conducted as part of this thesis were theoretically underpinned by two
conceptual models.
The programme of work was grounded in the socio-ecological model (McLeroy et al.,
1988) and the PRECEDE-PROCEED model of health programme design,
implementation, and evaluation (Green & Kreuter, 2005). Use of these frameworks
ensured that the studies considered a range of multidimensional interacting
influences on older adults’ PA, including intrapersonal, interpersonal, organisational,
community, environmental, and policy levels. This then enabled the community-
based PA strategies which were appropriate for use. Few other community-based PA
interventions targeting older adults’ PA have been underpinned by appropriate
theory (Chase, 2015). Interventions which are guided by theoretical frameworks,
216
consider implementation at scale across levels of the socioecological model, and are
designed, implemented and delivered in close partnership with stakeholders are
warranted among this population (Harris et al., 2015; Harris et al., 2018; Sink et al.,
2015).
This final chapter of the thesis will summarise the findings of each study and
synthesise them in relation to each other and the existing literature base. The overall
strengths and limitations of the thesis will then be discussed. Finally,
recommendations are outlined to inform future practice and research and overall
thesis conclusions are presented.
Several social (e.g., social awkwardness and peer/family support), behavioural (e.g.,
ageing stereotypes and lack of time), physical (e.g., improved balance and flexibility),
and environmental (e.g., transport and neighbourhood safety) correlates of PA
among older adults have been noted in previous formative (van Schijndel-Speet et
al., 2014; Banerjee et al., 2015) and qualitative research (Franco et al., 2015;
Devereux-Fitzgerald et al., 2016; Phoenix & Tulle, 2017). Such findings are a first step
in enabling policymakers and healthcare professionals to implement effective PA
interventions and promote active ageing (Franco et al., 2015).
Chapter 3 was the first to adopt a pen profiling protocol in order to analyse the
barriers and facilitators to PA among both assisted living and community dwelling
older adults. Results from this chapter revealed a variety of predisposing, enabling
217
and reinforcing correlates of PA participation. Consistent with previous research
(Gray et al., 2015; Kosteli et al., 2016), motivation was the most highly cited
predisposing correlate of PA participation and was perceived to be both a facilitator
and barrier to PA participation. The importance of pre-intervention intrinsic
motivation (e.g., participating for enjoyment) among older adults is key for both
initial adoption and maintenance of PA participation (Gray et al., 2015). A key
enabling correlate of PA behaviour was financial cost which was viewed as being a
barrier to PA participation (Franco et al., 2015; Borodulin et al., 2016). Community-
dwelling participants were either unable, or unwilling to pay the perceived high costs
associated with both attending and travelling to PA intervention sessions.
Consequently, future research is warranted to source ways that can sustain low-cost,
and easy reachable PA opportunities (Petrescu-Prahova et al., 2015; Borodulin et al.,
2016). An additional correlate of PA adherence in community-dwelling older adults
noted in previous literature is peer support (Brown et al., 2015). This reinforcing
correlate of PA participation was also identified to be a key facilitator (n=13) to PA
participation in Chapter 3. Hence, sustainable exit routes in order to retain the
provision of group activities which continue to facilitate, build and retain social
bonds post-intervention should be considered by PA programmers and policymakers
when designing PA interventions in this population (Wu et al., 2015).
In answering Research Question 1, Chapter 3 revealed that older adults
acknowledged the benefits of PA, not only for health but also those relating to
socialising, enjoyment, relaxation, and physical and psychological wellbeing. This
218
confirmed the need for efficacious PA interventions among this population. These
data were used to strengthen the design, delivery and recruitment strategies of the
GHGA PA intervention which was implemented and evaluated in Chapters 6 and 7.
Chapter 4 was the first to test a laboratory-based protocol to generate behaviourally
valid, population specific wrist-based GENEActiv (GA) and hip-based Actigraph GT3X+
(AG) raw acceleration cutpoints for SB and MVPA in older adults. ROC curve analyses
revealed that both wrist-based GA and hip-based AG accelerometer raw acceleration
cutpoints provide good and excellent discriminations of SB and MVPA, respectively.
This study was also the first to test the effect of both Youden and Se/Sp-based
cutpoints among older adults in order to make an informed decision as to which may
be most suitable once the data were analysed. In answering Research Question 2, GA
cutpoints of 57 mg and 104 mg were generated for SBSe and MVPASp, respectively.
For AG the cutpoints were 15 mg and 69 mg for SB Se and MVPASp thresholds,
respectively. These results are comparable to values reported previously for SB and
MVPA (Hildebrand et al., 2014; Hildebrand et al., 2016; Menai et al., 2017). Cross-
validation analysis revealed moderate agreement for GA and AG SB cutpoints, and
fair to substantial agreement for GA and AG MVPA cutpoints, respectively. Therefore
future interventions should seek to further demonstrate the utility of these cutpoints
in this population.
Based on the novel GA cut-points obtained in Chapter 4, Chapter 5 investigated
objectively measured time spent in MVPA and SB, as well as self-reported SB and PA
219
of community-dwelling older adults. Health comprises not only physical, but also
psychological and social components, and it is therefore important to take self-rated
measurements into consideration when evaluating health status (Kuosmanen et al.,
2016). Consequently, self-reported health indicators including QoL, SRH, SAPF, and
SEE, and their associations with SB and PA were also explored. Among older adults,
health indicators have been found to be influenced by sociodemographic attributes
including gender, age, and SES (Bamia et al., 2017; Meyer et al., 2014), as well as
being positively associated with PA level (Beyer et al., 2015; Haywood et al., 2018),
and negatively associated with SB (Haywood et al., 2018).
Comparable to recent self-report and accelerometer assessed SB and PA studies in
older adults (Amagasa et al., 2017; López-Rodríguez et al., 2017), results from
Chapter 5 revealed self-report and accelerometer assessed total time spent in SB
and MVPA in at least 10 minute bouts to be 411.9 vs 772 min‧d-1 and 62.7 vs 7.7 min‧
d-1, respectively. The lower levels of self-reported SB, and higher levels of self-
reported MVPA in at least 10 minute bouts reported when compared to objective GA
accelerometer-assessed SB and MVPA further confirms the inherent limitation of
recall bias within self-report measures (Barnett et al., 2016). The ubiquitous
presence of total accumulated and sporadic PA in older adults makes it difficult to
recall in questionnaire surveys (Washburn, 2000), though such behaviours may be of
particular importance, especially for older adults who tend to perform shorter
duration exercises (Amagasa et al., 2017; Jefferis et al., 2016; Sparling et al., 2015).
Consequently, this population tend to misreport time spent in such activities when
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compared with objective measures such as accelerometry (Ku et al., 2016). In
answering Research Question 3, results revealed no significant gender, age or SES
differences between self-reported and accelerometer-derived SB and PA. These
findings are counter to those in previous studies which have noted that MVPA is
lower among those who are female and older (Amagasa et al., 2017; Lohne-Seiler et
al., 2014; Ramires et al., 2017; Shiroma et al., 2018) due to difficulties in mobility,
general health status, and lower levels of self-efficacy (Ramires et al., 2017).
Participants were recruited whilst attending the GHGA PA sessions and therefore,
both men and women across the age range were likely more inclined to be active.
Moreover, gender bias in the sample could have further affected any potential
gender and age category differences between SB and PA.
Results from answering Research Question 4 revealed significant gender, age
category and SES-group differences between QoL, SRH, SAPF, and SEE. Concurrently,
previous research has noted that older adults who are male, younger-old (60-69
years), and of higher SES are more likely to report favourable ratings of self-reported
physical and psychosocial health (Bamia et al., 2017; Kuosmanen et al., 2016).
Further results from Chapter 5 also provided evidence of a negative association of
self-reported SB, and positive association of self-reported MVPA in at least 10
minute bouts with health indicators. A number of self-reported physical conditions
including number of falls, balance, pain interference, and lower-extremity function
are associated with time spent in SB and MVPA (Haywood et al., 2018; Rezende et
al., 2014). Previous studies have also noted negative associations of SB, and positive
221
associations of MVPA on QoL, wellbeing, depression, and self-efficacy (Ku et al.,
2016; Withall et al., 2014).
The findings of Chapter 5 provided baseline information of participants due to
participate in the GHGA PA intervention. Chapter 6 evaluated the overall
effectiveness of Sport England’s GHGA PA intervention on older adults’ PA, SB and
self-reported health indicators. A major strength of this study was the 2-level data
structured multilevel modelling statistical analysis which took into account the three
follow-up time points and multiple covariates associated with the primary and
secondary outcome measures. Results indicated a significant decrease in self-
reported MVPA at six months follow-up. The odds of meeting MVPA guidelines
decreased significantly across all three follow-up time points. These results contrast
with a recent meta-analysis of 53 exercise intervention studies in community-
dwelling older adults which reported a positive pooled effect equivalent to a 73
minute per week increase in MVPA when comparing intervention with control
groups (Chase, 2015). Results also revealed significant increases in self-reported
sitting time across all three follow-up time points. However, given the decreasing
levels of MVPA noted, increased sitting time was to be expected as lower amounts of
time spent in MVPA has been associated with greater total sedentary time (Diaz et
al., 2016). In line with recent research (Zubula et al., 2017), significant increases in
QoL, SAPF, SRH and SEE health indicator scores were observed throughout the three,
six and 12-month follow-up time points. Previous research has demonstrated
positive associations between social support and QoL (Siedlecki et al., 2014), SRH
222
(Dai et al., 2016), and SEE (Warner et al., 2011). Resultantly, the thirty minutes set
aside at the end of each weekly GHGA PA session to allow participants to socialise
could explain the increases in health indicator scores despite the lower levels of
MVPA, and increased levels of sitting observed. In answering Research Question 5,
these results revealed that Sport England’s GHGA PA intervention was ineffective in
increasing community-dwelling older adults’ PA levels.
Gaining an accurate and objective record of session content is important in
determining intervention suitability and overall fidelity (Moore et al., 2015).
Consequently, Research Question 6 was answered in Chapter 7 through the
assessment of implementation fidelity of the GHGA PA intervention. The prominent
strength of Chapter 7 was the use of qualitative and quantitative data sources. This
approach is advocated when conducting implementation fidelity research as it allows
for a comprehensive understanding of the processes which influence
implementation, and their variation across contexts to be gained (Moore et al.,
2015). Through the adoption of a comprehensive treatment fidelity framework
developed by the NIH BCC for tailored health behaviour interventions (Bellg et al.,
2004), results from both deliverer interviews and session observations revealed that
a high degree of session content fidelity was maintained throughout the delivery of
the GHGA PA intervention within the five BCC core domains of: Study Design,
Provider Training, Intervention Delivery, Intervention Receipt and Enactment.
Despite such results, the GHGA PA intervention was ineffective at increasing MVPA
levels. Consequently, further exploration of the most suitable advertising, session
223
content and delivery strategies to sustain increases in PA among older adults are
warranted and hence, should remain a nationwide public health priority.
The results associated with this thesis underline the importance of conducting
process evaluations prior to intervention roll out in ‘real world’ settings. The GHGA
PA sessions were predominantly chair-based sessions aiming to engage inactive
older adults in PA at least once a week for 30 minutes. A major limitation of the
current thesis was the IPAQ-E (Hurtig-Wennlöf et al., 2010) which was adopted as
the measure of PA as required by the funder. The IPAQ-E lacks a measure of LPA, a
PA activity which is most associated with older adults (McMahon et al., 2017) and
one which was most associated with the GHGA PA sessions. Consequently, any
changes in LPA as a consequence of the programme were not captured by the self-
reported outcome measure of PA. A more specific aim (e.g., a certain PA intensity
such as MVPA, or an aim anchored to current PA guidelines) would have provided
more specificity over the required outcome measures required. Specifically, national
governing bodies, stakeholders and key partners should work together prior to
intervention implementation in ‘real world’ settings’. Clear intervention aims and
subsequent intervention measures can then be decided upon in order to avoid null
intervention effects.
8.2. Strengths and Limitations
224
Given that study-specific strengths and limitations have already been discussed in
each chapter, this section will discuss in more detail the main strengths and
limitations that were consistent across the whole programme of research.
8.2.1. Physical activity measurement
Both self-report and objective measures of PA were adopted throughout the
programme. PA levels have traditionally been measured via subjective self-report
questionnaires in older adults (Kowalski et al., 2012) as they are relatively cheap to
conduct and have the potential to reach a large number of participants (Aguilar-
Farías et al., 2015; Celis-Morales et al., 2012; Chastin et al., 2014; Healy et al., 2011).
Specifically, the IPAQ-E (Hurtig-Wennlöf et al., 2010) was adopted as required by the
funder. The IPAQ-E is tailored to and validated for older adults and hence was
appropriate for use throughout programme. However, the ubiquitous presence of
total accumulated and sporadic PA in older adults makes it difficult to recall in
questionnaire surveys (Washburn, 2000), though such behaviours may be of
particular importance, especially for older adults who tend to perform shorter
duration exercises (Amagasa et al., 2017; Jefferis et al., 2016; Sparling et al., 2015).
Consequently, recall bias is a probability (Barnett et al., 2016) given evidence that
such methods of data collection can lead to underestimations of SB (Aguilar-Farías et
al., 2015; Chastin & Granat, 2010; Harvey et al., 2014) and overestimations of time
spent engaged in PA of all intensities (Tucker et al., 2011). A further limitation of the
IPAQ-E is that there is no measure for LPA, a PA activity which is most associated
with older adults (McMahon et al., 2017). Objective assessment such as
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accelerometers can record more detailed and accurate patterns of personal daily
activity (Jefferis et al., 2016; Shephard & Tudor-Locke, 2016). Accelerometers can
precisely obtain this type of information. Therefore, the adoption of both
questionnaire- and accelerometer-assessed SB and PA in Chapter 5 further
strengthened the overall study outcome. Most commonly used outputs from
accelerometers are counts which are dependent on internal proprietary algorithms
(Welk et al., 2012). Uncertainties of pre-processed count data include the possibility
that signal filtering methods can alter study results (Freedson, Bowles, Troiano, &
Haskell, 2012; Peach, Van Hoomissen, & Callender, 2014). Consequently, the
adoption of raw accelerations in Chapter 4 provided greater methodological
transparency in post-data collection analytical processes (Hildebrand et al., 2014).
However, the use of raw data is still in its infancy and the increased control which
researchers can have over this form of data means that there is a lack of consensus
over the procedures. A major limitation of Chapter 6 was the lack of objective
measures of sitting time and/or PA level, especially given the recall bias associated
with older adults’ assessment of PA and SB when compared to objective
accelerometry which resulted in Chapter 5. Due to time constraints and a limited
number of available GA accelerometer devices during the period of contact with the
older adult population, this was not possible in the current study. The GHGA PA
sessions were predominantly chair-based sessions aiming to engage inactive older
adults in PA at least once a week for 30 minutes. A more specific aim (e.g., a certain
PA intensity such as MVPA, or an aim anchored to current PA guidelines) would have
provided more specificity over the required outcome measures required.
Considering the large variation in participant abilities, sessions were relatively low
226
intensity and consequently, a major limitation of Chapter 6 was that any changes in
LPA as a consequence of the programme, were not captured by the self-reported
outcome measure of PA which was adopted as a requirement of the funder in
Chapters 5 and 6.
The repeated measures design at three follow-up time points post-intervention is a
major strength, in light of recommendations for longer-term post-intervention
follow-ups among PA interventions (McMahon et al., 2017). The multi-level analysis
which took into account the clustered nature of the design, with measures clustered
in the differing follow-up time points, was also a strength. The major limitations of
the study included the pre-post design and absence of a control group. A clustered
RCT research design was initially advocated but was rejected by SMBC due to both
low GHGA deliverer and participant numbers.
8.2.2. Methodological approach
The mixed methods approach adopted across the five chapters further strengthens
the overall results of the thesis. The GHGA PA intervention employed a
methodological approach that enabled researchers to capture PA data from
participants and contextual data from all key stakeholders (e.g., GHGA session
deliverers) in order to comprehensively assess both effectiveness and
implementation fidelity. The triangulation of data sources in Chapter 1 and interrater
reliability (McHugh, 2012) procedures in Chapter 7 allowed for the comparison and
confirmation of data, which provided the study with a high degree of credibility and
227
methodological rigour. Quantitative methodologies are most useful for testing
effectiveness but cannot identify mistakes, limitations or unintended consequences
of PA strategies which can influence effectiveness outcomes (Beltran-Carrillo, Ferriz,
Brown, & Gonzalez-Cutre, 2017). Learning from the target population (older adults)
and incorporating evaluation techniques based on the qualitative approaches
undertaken in Chapters 1 and 7 can attend to this gap in evaluative knowledge. The
insight provided from qualitative data is an important complimentary contribution to
the research base particularly when combined with quantitative methodologies
(Beltran-Carrillo et al., 2017). This approach allows for a review of not just overall
intervention effectiveness, but also implementation fidelity. Therefore, programme
suitability, feasibility and long-term sustainability in ‘real world’ settings can be
reviewed. The results of the current programme of research advocates the further
use of mixed methodologies in community-based PA intervention research to better
understand strategies which are both effective and also feasible.
8.2.3. Sampling
The formative research strategies adopted to recruit participants throughout the
chapters is a limitation and introduces a level of sampling and selection biases into
the results. A major limitation of Chapter 6 is the pre-post design. This design results
in lowered levels of causal validity due to the uncontrollable effects of regression to
the mean, maturation, history, and test effects (Marsden & Torgerson, 2012). The
absence of a control group is also a limitation. Coverage and sampling errors are also
limitations due to the modest sample size and non-randomisation of participants.
228
Consequently, selection bias is an issue (Marsden & Torgerson, 2012). However,
participant non-randomisation was justified given the pragmatic approach adopted
(Thomas et al., 2006) and the low number of participants recruited from intact
groupings (Sefton’s Older People Forum and care homes) throughout Sefton
Borough. However, the project was limited by the resources available, for example
access to accelerometers, research staff to collect data and also time available.
Subsequently, the modest sample sizes of Chapter 5 and 6 are recognised as
limitations of this research. Given that statistical power increases as the number of
participants increases and overall sample size is extremely influential on power,
some of the analyses may have lacked sufficient power to detect significant changes
in outcomes (Thomas, Silverman, & Nelson, 2015). Gender bias is also a concern
because of the disproportionate male to female participation ratio throughout the
five chapters. This is of particular concern in Chapter 5 and could have affected any
potential gender and age category differences between SB and PA. There was a lack
of characteristic data collected throughout the programme due to the formative
research strategies adopted to recruit the participants. Such have been shown to
affect the perceived barriers and facilitators to PA participation among older adults
(Greaney et al., 2016; Keadle et al., 2016) and thus, future research should obtain
data including, participants’ current sedentary time and PA levels, history of PA,
family history of PA, ethnicity, employment status, and educational achievements.
8.3. Recommendations
229
As a result of the findings presented from this programme of research, various
recommendations for future work are proposed. These are separated into
recommendations for research and practice.
8.2.1: Recommendations for practice
Community-based PA interventions should explore the effects of delivering
blocks of sessions with consistent content and delivery techniques in order to
allow for a better understanding of the approaches that are both effective
and ineffective in eliciting both short- and long-term PA behaviour change
among this population.
Community-based PA interventions should take into account location and
timing of sessions. Locations should be easy to access by both public
transport and by car, and sessions should avoid taking place during either
morning or afternoon rush hour periods.
Health-promotion strategies should advocate for the provision of low-cost,
and easy reachable PA opportunities.
If adopting self-report questionnaires, studies should include assessments of
LPA as well as MPA and VPA. This is especially applicable to older adults
whose PA participation is sporadic and often of a lower intensity.
The adoption of local/national mass media messages may be a cost effective
educational solution at a time when there is a growing ageing population.
Post-hoc fidelity analysis can be adopted to conceptualize best practices as a
process for planning future interventions that will be appropriate within
specific settings and populations.
230
8.2.2: Recommendations for future research
Future researchers should work in partnership with funders and key
stakeholders prior to intervention delivery to establish outcome measures
which are fit for purpose for each specific project.
Future research should seek to identify barriers and facilitators among
diverse samples (e.g., community/assisted living and young-old, middle-old
and old-old participants) that are more representative of the older adult
population.
Future interventions targeting PA in older adults should be designed and
implemented based upon the requirements of the SEF for PA interventions
(National Obesity Observatory, 2012) to enhance implementation consistency
across settings.
PA research among this population should use accelerometers to provide
more accurate estimates of PA levels and SB. Wrist-worn, 24-hour protocols
are also recommended to optimise compliance.
The raw acceleration wrist-worn GA and hip-worn AG SB and MVPA cutpoints
obtained in Chapter 4 should be further cross-validated with independent
samples, ideally from other settings and within free-living environments.
It is recommended that local funders and commissioners of research obtain
participant characteristic data even when it is not central to the intervention
outcomes as these data are confounding covariates of older adults SB and PA
levels.
231
Examining correlates of attrition and initial non-participation in older adults is
a vital step in the research process that needs further exploring.
Further research with more socio-demographically diverse older populations
is warranted to improve understanding of the relationship between gender,
age, and SES and, self-reported physical and psychosocial outcomes, as well
as the independent factors affecting them such as SB and PA levels.
Future research should make efforts to collect qualitative data from older
adults across differing locations and SES-strata in order to understand
perceived feasibility and acceptability of PA strategies. The triangulation of
data and utilising qualitative data alongside quantitative data can enhance
understanding.
Process evaluations of intervention implementation and fidelity based upon
comprehensive frameworks such as the NIH BCC (Bellg et al., 2004) should
become an integral part of the conduct and evaluation of all health behaviour
intervention research. Such will improve understanding of how interventions
have been implemented in practice, so that they can be further integrated
into ‘real world’ community settings and contribute to overall public health.
8.4. Conclusions
The overall aim of this thesis was to assess the effectiveness and implementation of
Sport England’s GHGA intervention on inactive community-dwelling older adults’ PA
levels. Although a high degree of intervention fidelity was maintained throughout
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the GHGA PA sessions, across all venues and deliverers, the intervention was
ineffective in reducing time spent in SB and increasing time spent in MVPA across
three, six and 12-month follow-up time points. However, the GHGA intervention was
effective in increasing QoL, SRH, SAPF, and SEE scores at 12-months post-baseline
measurement after adjustment for covariates. These results indicate that the
potential for long-term implementation and scaling up of the GHGA programme
throughout Sefton Borough is not warranted in its current format. Major facilitators
and barriers to PA participation were first identified in order to inform the design,
delivery and recruitment strategies of the intervention. Facilitators for PA included
motivation, enjoyment, health benefits and social support. Barriers for PA
participation included age, isolation, opportunities and awareness for physical
activity participation, cost, transport, location, season/weather, and time of day.
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Appendices
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Appendix 1. Ethical Approval
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Appendix 1.1. Cover Letter
George Sanders Department of Sport and Physical Activity Edge Hill University St Helens Road Ormskirk Lancs, UK L39 4QP
E: [email protected] T: 01695 657 344
22nd January 2016
Dear Professor McNaughton
Please find enclosed an application for ethical approval by the Department of Sport and Physical Activity REC for a study entitled Get Healthy Get Active. The project has confirmed funding by, and will be conducted in collaboration with Sefton Metropolitan Borough Council.
This project aims to increase physical activity levels in adults with intellectual disabilities and older adults (over 65s) with or at risk of dementia within Sefton Borough. By providing the target groups with the opportunity to access bespoke sport and physical activity programmes, the project aims to increase physical activity levels and in doing so reduce the health inequalities currently experienced by these populations. A major strength of the project is its inclusivity and great potential for generalisation. Almost all people with varying abilities will be able to participate in the programme and potentially obtain physical and mental health benefits from it.
The project supervisory team involves individuals with specific knowledge and research experience of both the proposed target groups and methodologies and includes:
Professor Stuart Fairclough - Dept. Sport and Physical ActivityProfessor Brenda Roe - Faculty of Health and Social CareDr Axel Kaehne - Faculty of Health and Social Care
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This project has already been presented to; the REC Chair for the Faculty of Arts and Sciences, the REC Chair for the Faculty of Health and Social Care, and a selection of professors and Edge Hill University academic staff with specific knowledge of the both the proposed target groups and methodologies. This presentation was positively received and the project subsequently obtained professional indemnity and insurance cover by Edge Hill University. This was approved by the Director of the Research Office, Dr Nikki Craske.
This application for ethical approval is upon recommendation from the Social Care Research Ethics Committee (SCREC), and follows an unsuccessful application for ethical approval by SCREC. Feedback from SCREC highlighted that:
There was a lack of clarity between the activity of the ‘Get Healthy Get Active’ programme and the PhD research project.The Chief Investigator (Mr George Sanders) has insufficient experience of undertaking research with the proposed target groups.There were no breaching confidentiality statements on any of the Participant Information Sheets. The Committee recommended the phrase: ‘Everything you say/report is confidential unless you tell us something that indicates you or someone else is at risk of harm. We would discuss this with you before telling anyone else.’The language used within the consultee consent forms was not appropriate as the forms implied that the consultee was giving proxy consent on behalf of a person lacking capacity to consent. The use of the Mini-Mental State Examination and Short Portable Mental Status Questionnaire as screening tools were inappropriate due to their clinical connotations and American wording.
These points have now been addressed, and following the recommendation of SCREC to apply for ethical approval through the Department of Sport and Physical Activity at Edge Hill University, please find enclosed all relevant details of the project and additional materials. My academic supervisors and I look forward to hearing the outcome of the submission.
Yours faithfully,
George Sanders
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Appendix 1.2. Participant Invitation Letter
Dept. Sport and Physical ActivityEdge Hill UniversitySt. Helens RoadOrmskirkL39 4QP
January 2016
Dear Sir/Madam,
In partnership with Sefton Metropolitan Borough Council we are conducting a
research project called Get Healthy Get Active. The aim of the project is to increase
physical activity levels in older adults aged 65 years and above. The information
gathered from the project will help us to know whether a programme such as this
can be of benefit to individual’s physical activity levels, fitness, and health.
I am writing to enquire whether you would like to take part in this project. To take
part in the project you need to complete and return the participant consent form to
a project team member.
Your participation in this project is really important to us and as a way of saying
thank you, the project team will be giving all participants a free Sefton Metropolitan
Borough Council ‘Choices’ card, which provides discounted access for participants to
all Active Sefton Leisure facilities. In addition, discounted gym/swim memberships at
just £21 per month will be available. Should you wish to take part please complete
and return the project participant consent form.
Kind regards,
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George Sanders
PhD Researcher
Sport and Physical Activity Department
Edge Hill University
Email: [email protected]
Tel: 01695 657 344
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Appendix 1.3. Participant Information Sheet
George Sanders Department of Sport and Physical Activity Edge Hill University St Helens Road Ormskirk Lancs, UK L39 4QP
T: 01695 657 344
Date:
Dear Sir/Madam,
You are being invited to take part in a research project to evaluate Sefton’s ‘Get Healthy Get Active’ programme. Before you decide whether or not to take part, it is vital that you understand why the research is being conducted and what will be required of you should you choose to participate. Please read the following information carefully and ask a researcher involved with the study for assistance if you have any further questions or queries.
Before taking part
This participant information sheet is intended to help you make an informed decision about whether or not to take part in the project.
If you decide to take part, it would be useful if you could inform us before the start of the project of a person that you might want to be contacted should you need support at any point. Whilst the research has been designed to avoid upsetting you, this is always a wise precaution in case the project unintentionally leads to any kind of physical or psychological harm.
What is the purpose of the study?
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The purpose of the study is to increase physical activity levels in older adults over 65 years of age.
Who is organizing and funding the research and why?
This study is being funded and supported by Sport England, Sefton Metropolitan Borough Council, and Edge Hill University in order to promote physical activity levels and well-being.
The main researcher is George Sanders, who is completing this project with support and supervision from;
- Professor Stuart Fairclough (Edge Hill University) - Dr Axel Kaehne (Edge Hill University) - Professor Brenda Roe (Edge Hill University)
Are there any exclusion criteria?
To be included within the project we ask that you:
Are over 65 years of ageBe without physical disabilities which prevent participation in physical activities
Do I have to take part?
No. There is no obligation on you to take part. This information sheet is designed to help you make an informed decision about whether or not to do so.
What will happen if I agree to take part?
Should you choose to participate, you will be enrolled upon a six/twelve week sport and physical activity programme. Both the programme content and length are dependent upon your current health status and capabilities and will be decided by the research team should you wish to participate. The full programme is as follows:
Participation in a six/twelve week sport and physical activity programme. Participation in three short follow-up sessions 3, 6, and 12 months after the start of the sport and physical activity programme.
At the start of the sport and physical activity programme, and at the three short follow-up sessions the participant will be asked to complete various questionnaires that assess; (i) physical activity levels (ii) falls risk (iii) general health (iv) quality of life (v) self-confidence during sport and physical activity, and (vi) thoughts about the programme. The participant will also be asked to wear an activity monitor on their wrist for 7-days at the start of the programme, and at each of the three follow-up sessions.
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There will also be opportunities to take part in:
A focus group study discussing your current preferences and attitudes towards physical activity, as well as the provision of services across Sefton Borough.A laboratory-based study assessing physical activity and sedentary time in older adults. During this study you will be asked to wear an activity monitor on your wrist and also to complete a questionnaire assessing your physical activity levels.
Once I take part, can I change my mind?
Yes. Should you wish to withdraw from the study, you are able to do so at any time, for any reason, and you will not be asked to explain your reasons for withdrawing.
However, once the results of the study have been submitted (expected to be by November 2018), it will not be possible to withdraw your individual data from the research.
How long will it take to complete the project?
Including the follow-up period, each participant will be in the study for 12 to 14 months.
What personal information will be required from me?
Information regarding: (i) age (ii) gender (iii) height (iv) weight and (v) current physical activity levels will be required.
Are there any risks in participating?
As with any sport or physical exercise, there is a possibility of both psychological (e.g. anxiety, stress) and physical (e.g. exertion, injury) distress. These risks should be minimal but could occur before, during or after the project. If you do decide to take part in the project, the most important thing is that you feel safe. Therefore, the project team are all specifically trained in delivering sessions that are individually tailored to each participant’s confidence, ability, and skill levels.
The project team has also been given guidance on how to work sensitively and supportively with anyone who might experience upset or distress before, during or after the project.
What are the benefits of taking part?
Taking part offers you the opportunity to access a bespoke sport and physical activity programme, which aims to increase sport and physical activity levels, and might improve your physical and psychological health and well-being, self-confidence for exercise, and general quality of life.
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By participating in the project you could also help us influence national guidelines regarding physical activity in older adults.
Expenses and payments
As a way of saying thank you, the team will be giving all participants a free Sefton Metropolitan Borough Council ‘Choices’ card, which provides discounted access to all Active Sefton Leisure facilities.
In addition, discounted gym/swim memberships at just £21 per month will be available.
Will the participant’s identity be kept anonymous and confidential throughout the study?
Yes. Any information provided will remain strictly confidential and if you do decide to take part in the project, no names of participants will appear in any work we might publish. All participant and consultee information will be kept safely on a University computer to which only the researchers have access.
Everything you say/report is confidential unless you tell us something that indicates you or someone else is at risk of harm. We would discuss this with you before telling anyone else.
It is entirely your choice about whether to take part. We don’t want to put pressure on anyone.
What will happen to the information?
The information will be collected in order to assist in the completion of:
Sport England’s ‘Get Healthy, Get Active’ initiative, details of which can be found at the link below: http://www.sportengland.org/media/388302/Get-Healthy-Get-Active-Prospectus-FINAL.pdfA PhD research project at Edge Hill University evaluating the results of the ‘Get Healthy Get Active’ programme.
Any information collected will be anonymised and coded to prevent identification, and securely stored using password-protected files on the Edge Hill University computing network. The information will only be used for the purposes of academic research and only research team members will have access to such information.
Upon completion of the study a summary copy of the finalized research study will be made available to you upon request.
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What happens when the project stops?
Upon completion of the programme, you will be asked to participate in three short follow-up sessions 3, 6, and 12 months after the start of the programme. Should you regret disclosing anything, or want to withdraw your contribution in part or whole throughout this time period, please contact the lead researcher, George Sanders. His contact details can be found on this information sheet. You will need to let the lead researcher know by November 2018 at the latest and he will ensure that any data you have contributed that you want to withdraw does not appear in any academic journals or media reports.
If you later feel distressed about something you have contributed, you might find it helpful to discuss it with a person you trust, and you and/or that person could get in touch with a member of the research team and/or project staff and we will try to do what you advise or negotiate with you to resolve any upset. (See also below in case this might not satisfy you).
What will happen to the results of the project?
We will provide all those helping with the project with a summary of the key findings upon request. The project team also hopes to publish the results in the media, and in professional and academic journals.
We would also hope to keep the anonymous data from the project - securely archived - for up to ten years so that we can compare it with any new findings from other research.
I have some more questions; who should I contact?
Should you have any further questions regarding any part of the research process, please contact the lead researcher, George Sanders, via the contact details provided below:
Email: [email protected]
Telephone number: 01695 657 344
What if I am not happy with how the research was conducted?
If you have a complaint or concern about any aspect of the project, you should contact a member of the research team and/or project staff who will do their best to answer your questions. If they are unable to resolve any concern or you wish to make a complaint regarding the project, please contact Ms Joanne Morris, Edge Hill Universities Research Ethics Secretary at:
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Ms J Morris Research Office The Lodge Edge Hill University St Helens Road Ormskirk Lancs, UK L39 4QP
Tel: 01695 650925.
Email: [email protected]
The University also has a policy relating to Research Misconduct which is available online at: https://www.edgehill.ac.uk/research/files/2012/05/Strategy-Output-Code-of-Practice-for-the-Investigation-of-Research-Misconduct-RO-GOV-02.pdf
Who has approved the project?
This project has been approved by Edge Hill Universities Department of Sport and Physical Activity Research Ethics Committee.
Further information is available from:
George Sanders Department of Sport and Physical Activity Edge Hill University St Helens Road Ormskirk Lancs, UK L39 4QP
01695 657 344
Thank you for taking the time to read this participant information sheet.
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Appendix 1.4. Participant Consent Form
Project Title: Get Healthy Get Active
Lead Researcher Name: Mr George Sanders, Edge Hill University
PARTICIPANT CONSENT FORM
Thank you for considering taking part in this project aiming to increase physical activity levels in Older Adults (over 65 years of age). If you have any questions at all, please ask a member of the project team and/or the lead researcher before you decide whether or not to take part. You will be given a copy of this consent form to keep, which you can refer to at any time.
Please tick one of the relevant ‘Yes’ or ‘No’ boxes below each question.
The purpose and details of this project have been explained to me. I understand that this project is designed to further scientific knowledge and that all procedures have been approved by Edge Hill Universities Department of Sport and Physical Activity Research Ethics Committee.
Yes No
I confirm that I have read and understood the participant information sheet for the project titled ‘Get Healthy Get Active’ dated ............................, and have had the opportunity to consider the information, ask questions and have had these answered satisfactorily.
Yes No
I understand that my participation in this project is voluntary and will have no effect on the care that I receive.
Yes No
I understand that I have the right to withdraw from this project at any stage for any reason without my care or legal rights being affected, and that I will not be required to explain my reasons for withdrawing.
I understand that if I withdraw from the project before November 2018, any data contributed that I want to withdraw will not appear in
Yes
Yes
No
No
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any academic journals or media reports upon request.
I understand that all the information I provide will be treated in strict confidence and will be kept anonymous and confidential to the research team at Edge Hill University unless (under the statutory obligations of the agencies which the researchers are working with), it is judged that confidentiality will have to be breached for the safety of the participant or others.
Yes No
I understand that the research team may use my telephone/ mobile number to call/ text message regarding the Get Healthy Get Active project and for no other reason.
Yes No
I agree to participate in this project. Yes No
Name of participant ________________________________(please print)
Participant signature ________________________________
Date ________________________________
Name of researcher _________________________________(please print)
Signed __________________________________
Date __________________________________
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Appendix 1.5. Ethical Approval Letter
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Appendix 2. Accelerometer Instructions
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Appendix 2.1. GENEActiv Accelerometer Instructions
HOW TO WEAR THE ACTIVITY MONITOR
As part of the Get Healthy Get Active project, we will be investigating how active you are during one week (7 days). To measure your activity you will wear a GENEactiv accelerometer. It is a wrist-worn lightweight monitor that detects activity by sensing movement.
How do I wear it?Wear the monitor around your non-dominant wrist, just like a watch.
Adjust the GENEactiv wrist strap so that it is tight enough so that the monitor does not move when you are being active
When do I wear it?
Please wear the monitor for 24 hours
The monitor is waterproof, so there is no need to remove it before showering or swimming
You should wear the monitor for at least 10 hours each day
When and how do I give the monitor back?George Sanders will arrange collection of the monitor after you have worn it for 7 days.The research team may use your mobile number to call/ send text message reminders to wear the physical activity monitor and for no other reason.
If you have any questions you can contact George by telephone: 01695 657 344 or by email: [email protected]
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ACTIVITY MONITOR DIARY Monitor Number: ___________
In the table below write down the times that you put the monitor on and take it off during each day. The first row is an example for you to see
how to fill it out.
If you take the monitor off for more than 5 minutes, please record when you take it off and put it back on.
My name is: ____________________________________________________________
Day: Time periods when activity monitor was taken off
Reason activity monitor was taken off
Signature
Example:Wednesday
8:00am-8:15am Getting changed Mrs Smith
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Appendix 3. Associated Publications
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Primary Health Care Research & Development
cambridge.org/phc
ResearchCite this article: Sanders GJ, Roe B, Knowles ZR, Kaehne A, Fairclough SJ. (2018) Using formative research with older adults to inform a community physical activity programme: Get Healthy, Get Active. Primary Health Care Research & Developmentpage 1 of 10. doi: 10.1017/S1463423618000373
Received: 2 November 2017Revised: 18 April 2018Accepted: 1 May 2018
Key words:ageing; community groups; formative; physical activity; primary care
Author for correspondence:George J. Sanders, Department of Sport and Physical Activity, Edge Hill University,St Helens Road, Ormskirk L39 4QP, UK.E-mail: [email protected]
Using formative research with older adults to inform a community physical activity programme: Get Healthy, Get Active
George J. Sanders1, Brenda Roe2,3, Zoe R. Knowles4, Axel Kaehne2 and Stuart J. Fairclough1,5
1Physical Activity and Health Research Group, Department of Sport and Physical Activity, Edge Hill University, Ormskirk, UK, 2Faculty of Health & Social Care, Edge Hill University, Ormskirk, UK, 3Personal Social Services Research Unit, University of Manchester, Manchester, UK, 4The Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK and 5Department of Physical Education and Sports Sciences, University of Limerick, Ireland
Introduction
In the United Kingdom there are over 11 million older adults aged 65 years and over who make up 18% of the population (UK Office for National Statistics, 2017). Aligning with the United States and other developed countries (United Nations, 2015) this proportion is pro- jected to increase to at least 24% by 2039 (UK Office for National Statistics, 2017). Although prolongation of life remains an important public health goal, of even greater significance is that extended life should involve preservation of the capacity to live independently, function well and quality of life (Rejeski et al., 2013). The purpose of this formative descriptive study was to explore current knowledge and attitudes towards physical activity (PA), as well as perceived barriers, facilitators and opportunities for PA participation among older adults living in the community. The findings were used to inform the design, delivery and recruit- ment strategies of an ongoing three-year community PA intervention project, Get Healthy, Get Active (GHGA), which forms part of Sport England’s national GHGA programme (Sport England, 2012).
© Cambridge University Press 2018.
Abstract
Aim: The purpose of this formative study was to explore current knowledge and attitudes towards physical activity, as well as perceived barriers, facilitators and opportunities for physical activity participation among older adults living in the community. The findings have subsequently informed the design, delivery and recruitment strategies of a local community physical activity intervention programme which forms part of Sport England’s national Get Healthy, Get Active initiative. Background: There is a growing public health concern regarding the amount of time spent in sedentary and physical activity behaviours within the older adult population. Methods: Between March and June 2016, 34 participants took part in one of six focus groups as part of a descriptive formative study. A homogenous purposive sample of 28
community-dwelling white, British older adults (six male), aged 65–90 years (M = 78, SD = 7
years) participated in one of five focus group sessions. An additional convenience pragmatic sub-sample of six participants (three male), aged 65–90 years (M = 75, SD = 4 years), recruited from an assisted living retirement home participated in a sixth focus group. Questions for focus groups were structured around the PRECEDE stage of the PRECEDE– PROCEDE model of health programme design, implementation and evaluation. Questions addressed knowledge, attitudes and beliefs towards physical activity, as well as views on
barriers and opportunities for physical activity participation. All data were transcribed verbatim. Thematic analysis was then conducted with outcomes represented as pen
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BackgroundGuidelines issued by the UK Chief Medical Officers and the US Surgeon Generals recommend that older adults (⩾65 years) engage in at least 150 min of moderate (or 75 min of vigorous) PA per week in bouts of at least 10 min, with muscle-strengthening and balance activities
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included on at least two of those days (Department of Health, 2011; Centers for Disease Control and Prevention (CDC), 2015). Despite the recognised evidence base for the benefits of regular PA (CDC, 2015; Reid and Foster, 2016; World Health Organi- zation (WHO), 2017), objective summaries of PA levels among older adults show that only 15% of males and 10% of females within the United Kingdom, and 9.5% of males and 7% of females within the United States meet the recommended PA guidelines (Tucker et al., 2011; Jefferis et al., 2014). Given that current PA guidelines remain the same for both adults (18–64 years) and older adults (⩾65 years), such high levels of inactivity suggests that PA guidelines appear too demanding for the latter popula- tion (Booth and Hawley, 2015).Accumulating evidence suggests that prolonged and con- tinuous bouts of sedentary behaviours [SB; defined as waking behaviours in a sitting, reclining or lying posture with energy expenditure ⩽1.5 metabolic equivalents (Tremblay et al., 2017)] have similar physical (eg, premature mortality, chronic diseases and all-cause dementia risk) and psychosocial (eg, self-perceived quality of life, well-being and self-efficacy) risk factors to that of physical inactivity (Wilmot et al., 2012; Edwards and Loprinzi, 2016; Falck et al., 2016; Kim et al., 2016). In fact, SB is now an identifiable risk factor independent of other PA behaviours (Tremblay et al., 2017). Spending on average 80% of their time in a seated posture, and with 67% being sedentary for more than8.5 h/day (Shaw et al., 2017), older adults are the most sedentary segment of society and seldom engage in moderate-to-vigorous PA (Chastin et al., 2017).Several social (eg, social awkwardness and peer/family support), behavioural (eg, ageing stereotypes and lack of time), physical (eg, improved balance and flexibility) and environmental (eg, transport and neighbourhood safety) correlates of PA among older adults have been noted in recent formative (van Schijndel-Speet et al., 2014; Banerjee et al., 2015) and qualitative research (Franco et al., 2015; Devereux-Fitzgerald et al., 2016; Phoenix and Tulle, 2017). Such findings are a first step in enabling policymakers and health care professionals to implement effective PA interventions and promote active ageing (Franco et al., 2015). Given the potential benefits associated with PA outlined, such interventions have the potential to reduce, age-related morbidity and declines in activities of daily living, maintain muscle strength and mass, improve quality of life, and thus reduce the primary and total health care costs associated with SB and physical inactivity among this population (Bauman et al., 2016).Prior research notes that interventions aimed at promoting PA participation should adopt an appropriate conceptual health promotion model to prioritise the key assets of the target group (Plotnikoff et al., 2014). The PRECEDE–PROCEED model of health programme design, implementation and evaluation (Green and Kreuter, 2005) provides the target population with a com- prehensive and structured assessment of their own needs and barriers to a healthy lifestyle. The PRECEDE component of the model comprises of, predisposing, enabling and reinforcing fac- tors has previously been used as a formative framework to guide PA intervention content and design (Mackintosh et al., 2011; Banerjee et al., 2015). This model has also been adopted as a method for the identification of perceived PA barriers and facilitators among older adults (Banerjee et al., 2015; Gagliardi et al., 2015) and other populations (Mackintosh et al., 2011; Emdadi et al., 2015; Susan et al., 2017).The purpose of this formative study was to (i) explore current knowledge and attitudes towards PA, as well as the perceived
barriers, facilitators and opportunities for PA participation among older adults living in the community who had agreed to take part in an ongoing PA programme; and (ii) use this data to inform the design, delivery and recruitment strategies of an ongoing com- munity PA intervention programme, as well as international PA interventions among this population. Given the purpose and aims outlined, the Evidence Integration Triangle (Glasgow et al., 2012) was adopted as the overarching theoretical framework. Through the prompt identification of success and failures across individual-focussed and patient–provider interventions, as well as health systems and policy-level change initiatives, the framework allows for the exploration of the three main evidence-based components of intervention program/policy, implementation processes and measures of progress. Hence, this framework enabled a steep learning cycle through an initial 12-week pilot GHGA programme delivered by the Metropolitan Borough Council within the chosen local authority. Results and analysis from this pilot were fed back to Sport England as the funder, as well as deliverers and participants in order to assess, evaluate and promptly inform adapted future iterations of the GHGA programme.
Methods
Participants and proceduresA descriptive formative study was undertaken from March to June 2016. Participants were recruited from one local authority in North West England recognised as having the highest percentage of inactive older adults (80%) compared to the UK national average, and the highest national health costs associated with physical inactivity (Active People Survey, 2014; Sport England’s Local Profile Tool, 2015). The first author facilitated six, mixed- gender focus groups. Representative of the uptake of participants within the target GHGA initiative, a homogenous purposive sample of 28 community-dwelling white, British older adults (five male) participated in five of the focus groups, with an additional convenience pragmatic sub-sample of six participants (three male) recruited from an assisted living retirement home, parti- cipating in the sixth focus group. In total, 34 older adults (eightmale), aged 65–90 years (M = 78, SD = 7 years), participated across the six sessions. Four focus groups involved a group size of six to ten participants, and two involved three participants (mean focus group size of 6 ± 5 participants). Previous focus groups inPA studies have been conducted effectively with as many as 12 (Moran et al., 2015), and as few as four (Schneider et al., 2016) participants. Focus groups took place in two church halls, an assisted living retirement home lounge, and a theatre. All loca- tions were free from background noise, and participants could be overlooked but not overheard. The inclusion criterion set out by Sport England as funders of the GHGA programme were that participants must be 65 years of age or over, reside within one local authority in North West England, could provide written informed consent to participate.GHGA is an ongoing three-year project which seeks to increase the number of inactive older adults participating in PA at least once a week for 30 min, via a 12-week PA intervention delivered by the Metropolitan Borough Council within the assigned local authority. Participants due to participate in GHGA received a covering letter, participant information sheet, and consent form. Prior to the commencement of the study, institu- tional ethical approval was received (#SPA-REC-2015-329) and
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written informed consent was obtained for all participants prior to participation. All focus groups utilised the PRECEDE stage of the PRECEDE–PROCEDE model (Green and Kreuter, 2005) within their design allowing for the exploration of predisposing, enabling and reinforcing correlates of PA participation. To maximise the interaction between participants, focus group questions were reviewed by the project team for appropriateness of question ordering and flow. Subsequent minor additions were made to questions on social isolation and PA advertisement. The semi-structured discussion guide included open ended questions structured to prompt discussion with equal chance for partici- pants to contribute (Stewart and Shamdasani, 2014). Focus groups were led by a trained facilitator and with an observer/ note taker also present. Questions addressed knowledge, attitudes and beliefs towards PA as well as views on barriers and opportunities for PA participation. An example question from a section exploring barriers to PA was: ‘Can you tell me about what stops you from participating in physical activity?’ Questions therefore demonstrated aspects of face validity as they were transparent and relevant to both the topic and target population (French et al., 2015).
Data coding and analysisFocus groups lasted between 20 and 45 min (M = 29, SD = 12), were audio recorded, and later transcribed verbatim, resulting in 66 pages of raw transcription data with Arial font, size 12 and double-spaced. Verbatim transcripts were read and re-read to allow familiarisation of the data and then imported into the QSR NVivo 11 software package (QSR International Pty Ltd., Doncaster, Victoria, Australia, 2017).Previous research within this population has adopted analytical procedures including thematic analysis (Van Dyck et al., 2017), content analysis (Middelweerd et al., 2014) and used specialist qualitative data analysis packages, such as NVivo (Warmoth et al., 2016). In supporting new methodologies and data representation within qualitative research (Orr and Phoenix, 2015), the current study followed the pen profiling protocol. The pen profile approach has been used in recent child PA research (Mackintosh et al., 2011; Boddy et al., 2012; Knowles et al., 2013; Noonan et al., 2016b) and presents findings from content analysis via a diagram of composite key emerging themes. In summary, data were initially analysed deductively via content analysis (Braun and Clarke, 2006), using the PRECEDE component of the PRECEDE–PROCEED model (Green and Kreuter, 2005) as a thematic framework which reflects the underlying study purpose. Inductive analysis then allowed for emerging themes to be created beyond the pre-defined categories. Data were then organised schematically to assist with interpretation of the themes (Aggio et al., 2016). As akin to more traditional qualitative research, verbatim quotations were subsequently used to expand the pen profiles, provide context and verify participant responses. Previous studies have demonstrated this method’s applicability in representing analysis outcomes within PA research (Mackintosh et al., 2011; Boddy et al., 2012; Knowles et al., 2013; Noonan et al., 2016a) making it accessible to researchers who have an affinity with both quantitative and qualitative backgrounds (Knowles et al., 2013; Noonan et al., 2016a). Recent findings suggest that the discrepancy between objective isolation and felt loneliness may be associated with undesirable health outcomes such as cognitive dysfunction.Three pen profiles were developed to display themes within the data aligned to the PRECEDE component of the
PRECEDE–PROCEED model (Green and Kreuter, 2005). Quo- tations were labelled by focus group number (Fn) and subsequent participant number (Pn) within that focus group. Characterising traits of this protocol include details of frequency counts and extracts of verbatim quotes to provide context to the themes. A minimum threshold for theme inclusion was based upon com- parable participant numbers within previous research adopting a pen profiling approach (Boddy et al., 2012; Noonan et al., 2016a)and hence, was set as ⩾n = 6, with n representing individual mentions per participant. However, multiple ‘mentions’ by thesame participant were only counted once. Methodological rigour was demonstrated through a process of triangular consensus (Hawley- Hague et al., 2016) between the authors. This offered transparency, credibility and trustworthiness of the results, as the data were critically reviewed using a reverse tracking process from pen profiles to verbatim transcripts, providing alternative inter- pretations of the data (Smith and Caddick, 2012). The process was repeated through cross-verification and discussion until subsequent agreement on data themes in relation to verbatim extracts was reached (Aggio et al., 2016).
Findings and discussion
Predisposing correlatesFigure 1 displays the predisposing correlates of PA participation. In agreement with previous research (Gray et al., 2015; Kosteli et al., 2016), the most highly cited theme of motivation (n = 29) was perceived to be both a facilitator (n = 15) and barrier (n = 14) to PA participation throughout. Some participants were proactivein seeking out opportunities for PA.I’m a lung cancer survivor and I just ran a mile last month and I raised£550.
(Focus group (F) 1: Participant (P) 2)
Contrastingly, others expressed disinterest in PA altogether believing that they would not derive any health benefit.I’ve pushed these [PA] classes to lots and lots of friends and they still ignore it, they will not come to anything like this.(F1: P3)
Participants also reported laziness or apathy to prevent participation.It’s [lack of PA] apathy, just apathy, people can’t be bothered.(F4: P3)
The importance of pre-intervention intrinsic motivation (eg, participating for enjoyment) among older adults is key for both initial adoption and maintenance of PA participation (Gray et al., 2015). Hence, future interventions could promote intrinsic motivation for PA through the adoption of socio-emotional selectivity theory (Carstensen et al., 1999). Recent findings sup- port this theory’s notion that motivation for PA is more effec- tively promoted when paired with positive messages about the benefits of PA rather than with negative messages about the risks of inactivity (Notthoff et al., 2016).The theme of age (n = 20) was identified as a key barrier (n = 13) to PA participation throughout.They [older adults] get to a certain age and just give up.
(F1: P7)Social norms and cultural misconceptions often influence not only the type of PA in which older adults engage, but whether they participate at all (Greaney et al., 2016). Moreover,
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Figure 1. Predisposing correlates of physical activity participation among older adults. n = Individual mentions per person (multiple mentions not included); Fn = focus group number; Pn = participant number.
participants noted that lifestyle (n = 20) often affects individual views regarding ageing stereotypes, and therefore PA participa- tion. Some participants felt that physically active older adults were more likely to be habituated to PA engagement over many years.Well if you’ve kept healthy, kept fit all your life, you can keep doing it.(F1: P4)
Conversely, it was felt that inactive older adults were reluctant to start exercising.You see the ones who haven’t been doing it [PA] are not going to be able to start and do it now.(F2: P1)
Previous research has also reported prior PA behaviours (eg, being sedentary or active) to be key correlates affecting older adults’ current PA participation levels (Franco et al., 2015). Additionally, ageing is associated with a decrease in the size of social networks and hence, older adults are at increased risks of isolation (Devereux-Fitzgerald et al., 2016; Greaney et al., 2016).Corroborating with prior research (Greaney et al., 2016), parti- cipants throughout perceived isolation (n = 15) to be a key barrier (n = 14) to PA participation.
It’s so easy to get trapped inside and not go out. People sit in front of the television from the moment they wake up to when they go to bed.(F6: P5)
Isolation is associated with decreased social and psychological well-being (Owen et al., 2010; Milligan et al., 2015) and increased SB among older adults (Nicholson, 2012). Certain targeted intervention strategies can reduce isolation by providing an opportunity for older adults from differing socio-economic areas to take part in PA within local community spaces (eg, parks, leisure centres and churches), that promote social networking by encouraging camaraderie, adaptability and productive engage- ment, without the pressure to perform (Milligan et al., 2015; Gardiner et al., 2016). Given that SB is an independent and
modifiable behavioural target for interventions (Lewis et al., 2017), opportunities to replace SB with health-enhancing beha- viours such as moderate-to-vigorous PA (Prince et al., 2014), light PA (McMahon et al., 2017; Phoenix and Tulle, 2017) and standing (Healy et al., 2015) should be promoted. However, none of the participants in the current study noted negative health effects of prolonged sitting, or the importance of breaks in sedentary time. Previous research has noted that older adults are not yet familiar with the concept of SB and hence, are not motivated to reduce such behaviours (Van Dyck et al., 2017). Hence, it is first crucial to increase knowledge about the negative health consequences of SB independent from PA among both older adults and other populations (Van Dyck et al., 2017).Participants also emphasised the importance of having a wide range of choice and opportunities for PA (n = 22), and in general their perceptions of community provision were positive (n = 16).Yes it’s quite a good place [the local authority where the study took place]. There are a lot of different physical activity sessions to try.(F2: P1)
However, in line with recent research (Baert et al., 2016; Träff et al., 2017), key barriers noted by the participants within the assisted living group included a lack of advertisement regarding PA opportunities, and few opportunities to take part in PA within the assisted living facility itself.It’s hard to know what is on if you don ’t read the noticeboards and to be honest most of us have even stopped looking at that [noticeboard] because there is never anything on it.(F3: P3)
Further research into the most effective advertisement strate- gies to engage older adults in assisted living facilities is warranted (Hildebrand and Neufeld, 2009). Regardless of living status, participants noted a strong preference not to engage with online and/or social media channels for advertising and awareness- raising.
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A lot of people our age don’t like that technology stuff at all. I would not know where to start.(F5: P2)
These results suggest educational strategies outlining the potential benefits of technology in aiding PA participation are needed (Bird et al., 2015). This is especially salient given that recent research has shown technology-based interventions to have good adherence and provide a sustainable means of reducing SB and promoting PA participation among older adults (Garcia et al., 2016; Skjæret et al., 2016).
Enabling correlatesFigure 2 displays the enabling correlates of PA participation. Consistent with previous research findings (Franco et al., 2015; Borodulin et al., 2016), cost (n = 21) was perceived to be a key barrier (n = 12) to PA participation exclusively among the com- munity dwelling participants who were either unable, or unwillingto pay the perceived high costs associated with both attending and travelling to such programmes.Money is the big bug bear [barrier to PA participation] isn’t it.(F2: P5)
Examples of competing programmes were also noted, with free and lower cost programmes taking precedence over the more expensive.We like it [a local chair-based PA programme] because it’s free.(F4: P3)
Thus, to effectively increase PA participation within this population, health-promotion strategies should go further than merely educating and raising awareness about potential health benefits, and should also advocate for the provision of low-cost, and easy reachable PA opportunities regardless of financial status (Petrescu-Prahova et al., 2015; Borodulin et al., 2016). It is worth
noting that for the participants recruited from the assisted living retirement home, any PA sessions delivered were included within the cost of the overall living fee, and hence lack of financial resources was rejected as a potential barrier for PA participation (Baert et al., 2016).Participants’ views on the theme of location (n = 11) centred on neighbourhood safety. Declining health and physical impairmentsassociated with ageing increase the time spent in ones’ neighbour- hood and thus, neighbourhood environmental factors such as, PA provision, proximity, traffic volume and overall neighbourhood safety are considered to be important correlates affecting older adults’ PA participation (Greaney et al., 2016). Perceived neigh-bourhood safety was identified as a barrier (n = 7) to PA partici- pation exclusively among the community-dwelling older adults.You wouldn’t go out on your own at night around here.(F1: P5)
Participants from the assisted living retirement home did not view neighbourhood safety to be either a barrier to or facilitator of PA. This neighbourhood environment was perhaps viewed as the norm and therefore they did not associate safety concerns so acutely (Moran et al., 2015). This association could have also affected results obtained for the theme time/day of the week as such participants did not recognise this to be a barrier to PA participation either.Time of day wouldn’t make much difference [to PA participation]. To be fair you aren’t doing much at the weekend so day of the week isn’t going to make much difference [to PA participation] either.(F3: P1)
Conversely, community-dwelling participants reported time/ day of the week to be a barrier (n = 15), with early morning or early evening sessions identified as reducing PA participation, especially during the winter months when daylight hours are more limited. These findings could have been further
Figure 2. Enabling correlates of physical activity participation among older adults. n = Individual mentions per person (multiple mentions not included); Fn = focus group number; Pn = participant number.
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Figure 3. Reinforcing correlates of physical activity participation among older adults. n = Individual mentions per person (multiple mentions not included); Fn = focus group number; Pn = participant number.
amplified by the neighbourhood safety concerns also identified by this group (Hoppmann et al., 2015; Prins and van Lenthe, 2015).The theme of transportation (n = 14) has been extensively reported to be both a barrier and facilitator to PA participation among older adults (Bouma et al., 2015; Haselwandter et al., 2015; Kosteli et al., 2016; Van Dyck et al., 2017). Within the currentstudy transportation was identified as a barrier (n = 10) restricting access to PA sessions regardless of living status.I would like to go to the baths [swimming pool] but it’s difficult to get there and back so I just don’t bother.(F4: P5)
Transport is especially important for those lacking the ability to be more independently mobile as it allows individuals to bridge larger distances than they could by walking alone (Van Cauwenberg et al., 2016). Thus, lack of access to a car and inadequate availability, frequency and reliability of affordable public transport are all associated with decreased PA participation (Newitt et al., 2016). Additionally, being dependent upon others (eg, family, friends and peers) for transportation has been iden- tified as a barrier to PA participation within this population (Baert et al., 2015). This was also noted in the current study.I think the worst thing is having to rely on somebody else to take you [to a PA session] as anything can happen in your own life let alone somebody else’s. (F5: P2)
Prior research suggests the promotion of walking for trans- portation to PA sessions among physically independent older adults (Chudyk et al., 2017). However, given the neighbourhood safety concerns noted by participants, and the varying levels of functional ability among this population, further research exam- ining access to PA sessions including walking facilities (eg, path and crossing quality), traffic safety and safety from crime is warranted (Van Cauwenberg et al., 2016).
Reinforcing correlatesFigure 3 displays the reinforcing correlates of PA participation. Peer support is associated with PA adherence in older adults (Brown et al., 2015), and was identified as a key theme (n = 18) and subsequent facilitator (n = 13) to PA participation in the current study.I’ve got to know everybody now and I’m used to you all. I feel more comfortable and I don’t feel anxious or anything.(F3: P6)
Unsurprisingly, in light of the above several participants reported peers to be a barrier to PA participation (n = 5) because of an unwillingness to attend other PA sessions due to anxieties about meeting new people.I wouldn’t like to go somewhere else as I wouldn’t like to walk in on a crowd of new people.(F3: P6)
Although group-based activities offer older adults the chance to gain a sense of belonging, enjoyment and establish friendships, designing sustainable exit routes in order to retain the provision of group activities which continue to facilitate, build and retain social bonds post-intervention should be considered by PA programmers and policymakers (Wu et al., 2015).In line with recent research (Devereux-Fitzgerald et al., 2016; Smith et al., 2017), family members were identified as being both barriers (n = 2) and facilitators (n = 4) to PA participation. Specifically, a barrier often reported is overprotectiveness, in which family members may not allow older adults to participatein PA out of concern for their safety or health (Greaney et al., 2016). Participants among the community-dwelling groups also noted this.My sons in for a shock that we’re coming to this as he’s like, ‘no long walks, no boat rides’, he goes ‘you’re past it’.(F6: P2)
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Such results suggest a need to educate family members on the importance and benefits of PA among older adults. Educational resources such as the older adults PA guidelines infographics for the, United Kingdom (Reid and Foster, 2016), Canada (Canadian Society for Exercise Physiology, 2016), Australia (Australian Government Department of Health and Ageing, 2013), New Zealand (Ministry of Health, 2013) and the United States (CDC, 2008) are appropriate tools advocating for older adults to be active safely, and can be understood by family members plus health care providers. Furthermore, the adoption of local/national mass media messages may be a cost effective educational solution at a time when there is a growing ageing population (United Nations, 2015; UK Office for National Statistics, 2017). However, given the resistance to technology-based PA noted in the current study, further educational strategies promoting enjoyable, easy-to-use technology within a family environment are needed for community-dwelling older adults (Bird et al., 2015). Participants within the assisted living group did not per- ceive family members to be either barriers or facilitators to PA participation and thus, further research is needed to identify approaches to involve family members as additional facilitators of PA participation within this group.Participants viewed the theme of perceived health benefits (n = 23) to be both a facilitator (n = 14) and barrier (n = 9) to PA participation regardless of living status. Participants were knowledgeable regarding the potential benefits of PA for their physical health.
It [PA] loosens all your limbs up.(F2: P2)
Participants also noted the potential benefits of PA for their psychological health.The wellbeing [from PA participation] makes you feel better.(F1: P3)
Despite the irrefutable evidence demonstrating the benefits of PA among older adults (CDC, 2015; Reid and Foster, 2016; WHO, 2017), participants also noted health to be a potential barrier (n = 14) to PA participation due to doubts about their capabilities, or fear of causing themselves harm, particularly if they were unfamiliar with it.People have to be sure they can come to PA sessions because my sister had a heart attack … and she can’t do a lot of these exercises.(F1: P5)
To overcome such perceptions, educational strategies at a population level should focus on communicating the role of PA in gaining health benefits for all as well as how well-designed PA programmes can aid in the management of common comorbid- ities specific to this age group (Gillespie et al., 2012; Hamer et al., 2013).Taken together with the findings of recent qualitative studies examining correlates of PA participation among older adults living in both assisted living (Baert et al., 2016; Träff et al., 2017) and community-dwelling older adults (Fisher et al., 2017; Phoenix and Tulle, 2017), results from this formative research study have been used to inform the design, delivery and recruitment strategies of an ongoing community PA intervention project. Specifically, changes implemented to programme design have included the introduction of, increased intervention duration from 6 to 12-weeks, maintenance sessions post-initial 12-week intervention, tea and coffee after each session to promote social interaction, and a reduction of early morning and late afternoon sessions. Changes to programme delivery have
included the introduction of, participant choice in session activ- ities, videoing participants at week 1 and week 12 to show par- ticipants their progression, and signposting participants to other local PA programmes. Finally, changes implemented to recruit- ment strategies have included, improved relationships with gen- eral practitioners to enable them to refer participants onto the programme, leafleting in church halls and charity shops, and deliverers attending and subsequently advertising the programme at several Older Peoples’ Forums. Such methods could also be adopted throughout similar community PA programmes else- where in order to increase programme fidelity, representativeness and effectiveness.
Strengths and limitations
Methodological strengths include the exploration of consensus and associated discussion through the focus groups and sub- sequent analysis process which allowed insight into the predis- posing, enabling and reinforcing correlates of PA participation among older adults. Consistency of themes, data credibility, transferability, and dependability were achieved through the triangulation consensus of data between authors and methods. While this study reiterates important insights into the perceived barriers, facilitators and opportunities for PA participation among both community-dwelling and assisted living older adults, value outside of this to the wider research community may be limited due to programme funding which only allowed for formative research strategies to recruit participants who had agreed to take part in an ongoing PA programme. Consequently, sampling bias is a potential issue as it could be assumed that a high proportion of the participants were already inclined to be and/or currently physically active given the positive predisposing comments with regard to motivation towards PA and current lifestyle choices (Costello et al., 2011). This is especially important given that motivators and barriers towards regular PA vary among currently active and inactive adults across the age range (Costello et al., 2011; Hoare et al., 2017). Considering that less than 10% of older adults ( ⩾ 65 years of age) meet the recommended PA guidelines (Jefferis et al., 2014), future research should seek to identify barriers and facilitators among larger sample sizes of currently inactive older adults living within both the community and assisted living facilities.Additionally, a small convenience pragmatic sub-sample of participants from one assisted living facility were recruited and hence results cannot be considered representative. Furthermore, men tend to decrease participation in leisure-time PA as they get older; whereas this dose-response is not seen among women (Amagasa et al., 2017). Consequently, there is the possibility of gender bias given the higher number of female participants recruited. However, the sample size, participants’ ages and gender distribution are comparable to those reported in two recent studies examining barriers and facilitators to PA participation among older adults (Baert et al., 2015; Moran et al., 2015). Within these two studies the total number of participants was 15 (five male) and 40 (13 male), and the mean age of the respondents was 74 years, and 84 years, respectively. This compares to a total number of 34 participants (eight male) with a mean age of 78 years in the current study. Nevertheless, as well as exploring correlates of PA participation in relation to gender, functional status and age differences between the young–old (60–69 years), old–old (70–79 years) and oldest–old (80 + years) (Heo et al., 2017), future research should obtain additional participant
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characteristic data prior to the intervention including, partici- pants’ current sedentary time and PA levels, history of PA, family history of PA, ethnicity, employment status and educational achievements as such have been shown to potentially affect the perceived barriers and facilitators to PA participation among older adults (Greaney et al., 2016; Keadle et al., 2016).
Conclusions
Older adults acknowledged the benefits of PA, not only for health but also those relating to socialising, enjoyment, relaxation, and physical and psychological well-being. The themes of opportu- nities and awareness for PA participation, cost, transport, location and season/weather varied dependent upon living status. These findings suggest current living status to be a separate correlate of PA participation among older adults. This data can be used to further strengthen the design, delivery and recruitment strategies of both the target GHGA PA intervention programme and international PA intervention programmes among older adults. Future interventions should consider educational strategies to communicate the role of PA in gaining health benefits for all, reducing SB, and countering the negative implicit attitudes that may undermine PA within this population. Given the small sample of participants in the current study, further comparativeresearch exploring the barriers and facilitators between assisted living and community-dwelling, and active and inactive older adults on both national and international levels is warranted.
Acknowledgements. The authors express their deepest gratitude to all the participants involved. Edge Hill University institutional ethical approval number: # SPA-REC-2015-329.
Conflicts of Interest. No potential conflict of interest was reported by the authors.
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