Calender and Biological Age

8/3/2019 Calender and Biological Age

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Occupational Medicine 2010;60:2128

Published online 10 August 2009 doi:10.1093/occmed/kqp113

Predictors of the discrepancy between calendar and

biological age

Gabriele Freude1, Olga Jakob2, Peter Martus2, Uwe Rose1 and Reingart Seibt3

Background The rate of ageing can differ considerably between individuals. This might result in major differences

between calendar age (CA) and biological age (BA).

Aims To identify work- and health-related predictors of the discrepancy between CA and BA.

Methods The sample analysed in this study consisted of 371 subjects of different occupational groups (teachers,

office workers, nursery school teachers and managers). BA was measured with the vitality measuring

station, which recorded 45 vitality indicators of physical, mental and social functions.

Work ability index, effortreward imbalance and relaxation inability were measured to determine

work- and health-related predictors.

Results The greatest discrepancy between CA and BA (9 years) was found for the subgroup of managers,

followed by female teachers (5 years). Managers showed also the best results in work ability, the effortreward balance and relaxation ability.

By means of multiple regression analysis, particularly mental attitudes and resources towards work,

occupational reward and the body fat percentage were identified as relevant predictors for the dis-

crepancy between CA and BA.

Conclusions Our study indicates that not only health- but also work-related factors are associated with vitality and

BA of employees. We assume that measures focused on promoting of health (healthy diet and physical

activities) and improving working conditions (e.g. job satisfaction and social support and stress

prevention) may also affect the ageing process positively.

Key words Biological age; calendar age; health- and work-related predictors; vitality; work ability.

Introduction

There is no doubt that the demographic development

will affect the age structure of workers in the workplace.

All forecasts assume a rise in the average age of employ-

ees [1]. Since ageing is associated with changes in func-

tional capacity, suitable measures for maintaining and

promoting health and work ability of ageing workers will

be of increasing importance for occupational safety and

health.

Biological age (BA) that refers to the human functional

capacity can differ considerably between individuals

[24] of the same calendar age (CA). Individuals might

be healthier, more vital and biologically younger thanmight be expected from their CA and vice versa. On

the one hand, genetic intrinsic factors that can hardlybe influenced contribute to the interindividual differen-

ces; on the other hand, extrinsic factors act on the ageing

process, which can affect a persons functional capacity

and its changes with age both positively and negatively.

Within the conceptual framework of successful ageing

[5,6], the effectiveness of extrinsic factors that counteract

the intrinsic age-accelerating factors is assumed. Losses in

function with advancing age can be avoided or at least

restricted [7] by protective extrinsic factors.

The influence of work-related factors on the ageing

process has hardly been studied to date. In general, there

is no doubt that a high level of engagement with life in-

cluding productive engagement in occupation [8,9] isa strong predictor of successful ageing. Barnes-Farrell

and Pietrowski [10] studied the relation between work

stress and workers self-perceptions of the ageing process.

The reported presence of stressors and the extent of strain

were positively correlated with feeling older than ones

CA. Hacker [11] postulated that poor working conditions

might induce work-induced ageing, e.g. working condi-

tions that impair physical and mental functionality can

lead to premature ageing.

1Department of Mental Health and Cognitive Capacity, Federal Institute for

Occupational Safety and Health, Berlin, Germany.

2Institute for Biometry and Clinical Epidemiology, Charite, Berlin, Germany.

3Institute and Clinicof Occupationaland Social Medicine, Technical University of

Dresden, Dresden, Germany.

Correspondence to: G. Freude, Department of Mental Health and Cognitive

Capacity, Federal Institute for Occupational Safety and Health, Berlin, Germany.

e-mail: [email protected]

The Author 2009. Published by Oxford University Press on behalf of the Society of Occupational Medicine.All rights reserved. For Permissions, please email: [email protected]


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There are different approaches to evaluate the rate of

ageing on basis of BA measurements [4,12]. BA cannot

be measured directly but merely approximated using

certain vitality and age indicators [4].

Data of BA analysed in this study were obtained using

the so-called vitality measuring station (VMS) [13]. This

methodological approach is based on the concept of

vitality [13], which defines the construct of vitality as

an individuals age-typical whole-organism functionality

in their unity of physical, mental and social efficiency.

Efficiency in the different functional domains was deter-

mined on basis of 45 vitality indicators implemented in

the VMS approach.

The aim of this study was to examine the discrepancy

between CA and BA in employees of different occupa-

tional groups, which were assumed to differ in work abil-

ity and other work-related factors. We were interested in

analysing the BACA discrepancy in relation to work- and

health-related factors. Furthermore, we tested which of

these factors are relevant predictors for the discrepancy

between CA and BA. We hypothesized that protectivework- and health-related indicators are related to BA that

is lower than CA, i.e. to positive discrepancies between

CA and BA.

Methods

Data for this analysis were pooled from three studies

[1418] conducted with identical questionnaires [relaxa-

tion inability (RI), Work ability index (WAI) and effort

reward imbalance (ERI)] and measurements (VMS). The

sample consisted of female teachers [17,18], female office

workers [15], female nursery school teachers [14], maleteachers [15,18] and male managers [16].

Except for the nursery teachers, all employees worked

.35 h/week. Working 35 h/week is equivalent to working

full time in Germany. They work an average of 40.3

h/week with only marginal differences between different

groups [19]. The higher proportion of nursery teachers

working ,35 h/week was explained by the fact that a rel-

atively high percentage of this professional group (.50%)

work part time [20].

In our study, discrepancy between CA and BA as de-

pendent variable is defined by taking CA minus BA. BA

was measured with the VMS. This inventory comprises

45 vitality indicators to assess functionality in the cardio-pulmonary system (blood pressure, pulse at rest and after

workload and fitness index after submaximal workload),

the musculoskeletal system (muscular strength, speed

and coordination capabilities) and of the sense organs (vi-

sual/auditory acuity and visual/auditory reaction time).

Furthermore, psychological and mental functions (ver-

bal/cognitive response, concentration, flexibility, strategy

building and memory capacity), physical and psycholog-

ical complaints and social factors (obligations and leisure

time, behaviour and stress and social competence) were

estimated by means of VMS. Values of each indicator

were standardized to values for a defined reference pop-

ulation and summarized in a value for BA [12,13].

Health-and work-related determinants were analysed

by means of the RI questionnaire [21], the WAI

[2224] and the ERI [25,26] questionnaire. Further-

more, body mass index (BMI) and body composition

(body fat and active cell mass) were measured as relevant

health-related factors.

RI was examined using the corresponding subscale

from the standardized RI questionnaire [21]. The RI in-

dex consists of six items referring to inability to relax from

work. The score for RI ranges from 6 to 24 (high RI values

indicate a high RI and low values a high relaxation abil-

ity). The general cut-off for noticeable values is 19 and

for very noticeable values is 21.

The WAI describes how well an employee is capable to

do his job [24]. It is based on the concept of work ability

[2224], which considers that work ability is determined

by the interaction between specific work demands, indi-vidual health conditions and mental resources. The WAI

questionnaire consists of seven subscales referring to dif-

ferent aspects of work ability (WAI 1current work abil-

ity compared with the life time best, WAI 2work ability

in relation to the physical and mental demands at work,

WAI 3current number of diseases or injuries diagnosed

by a doctor, WAI 4subjective estimation of work ability

impairment due to diseases, WAI 5sick leave during the

past 12 months, WAI 6personal prognosis of work abil-

ity in the next 2 years and WAI 7mental attitudes and

resources towards work).

The cumulative index of WAI ranges from 7 to 49

points. It is divided into the following categories: poor(727 points), moderate (2836 points), good (3743

points) and excellent (4449 points) work ability.

The ERI at work was measured by the short version of

ERI questionnaire with 23 items [25,26]. The subscale

effort (range: 630, high values indicate a high effort

at work) refers to the perceived time pressure, work inter-

ruptions and disturbances, responsibilities at work, over-

time, etc. The subscale reward (range: 1155, high

values indicate a high level of occupational reward)

measures satisfaction with financial- and status-related

aspects, esteem rewards and job security. If the ratio

between effort and reward at work is higher than 1 (high

effort and low occupational reward), health risks can beexpected.

Descriptive analyses encompass means and standard

deviations for quantitative measurements and percen-

tages for categorical variables. For ordinal and non-

normally distributed variables, medians are presented.

Normal distribution was assessed by inspecting skewness

and kurtosis of the respective residuals. Skewness and

kurtosis should lie between 21 and 1 to accept normal

distribution. Comparisons between subgroups were

22 OCCUPATIONAL MEDICINE


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conducted using analysis of variance (ANOVA) with sub-

sequent pairwise comparisons (Tukeys B) or chi-square

tests according to the scaling of the target variable. To de-

termine potential work- and health-related predictors and

to weigh positive and negative influences, bivariate corre-

lation and regression analyses were calculated as a first

step and these were then extended to cover multiple re-

gression models. Only such predictors were involved in

the models, which are not in the VMS inventory for

BA measurement; in total, 23 potential predictors were

analysed. The level of significance was 0.05 (two sided)

for all statistical tests. The subgroup comparisons of

Model 1 were corrected for multiplicity using Tukeys

B method. However, no adjustment for multiplicity

was used in Model 2 and Model 3. Thus, P values

,5%/23 0.002 lead to statistical significance after ad-

justment for multiplicity on the 5% level using the

Bonferroni correction.

All analyses were done using commercially available

software (SPSS for Windows, release 15.0).

Results

The sample consisted of 371 subjects belonging to the fol-

lowing five subgroups: female teachers (n5 100) [17,18],

female office workers (n 5 60) [15], female nursery

school teachers (n 5 65) [14], male teachers (n 5 99)

[15,18] and male managers (n 5 47) [16]. Participation

rates differed among the five subgroups (female teachers,

58%; office workers, 57%; nursery school teachers, 86%;

male teachers, 28% and managers, 70%).

The subjects were aged between 20 and 64 years (mean

age: 45.6 years). The majority of subjects were married(80%). Education and vocational training of the groups

differed in the following ways: all female teachers com-

pleted high school, followed by 81% of the group of man-

agers and 78% of male teachers. The majority of office

workers (67%) and nursery school teachers (91%) com-

pleted secondary school. A university education was com-

pleted by 100% of female teachers, 77% of male teachers

and 49% of managers. A vocational training was com-

pleted by 75% of office workers and 100% of nursery

school teachers.

As seen in Figure 1, the highest discrepancy between

CA and BA (9 years) was established for the subgroup of

management personnel, followed by the subgroup of fe-male teachers (5 years), while for the subgroups of male

teachers, nursery school teachers and office workers, no

significant discrepancies between CA and BA were found.

The scatter plots shown in Figure 1 (lower graph) em-

phasize the high degree of interindividual differences with

respect to BA for persons of the same CA. Dots below the

reference line (BA 5 CA) represent subjects who are bi-

ologically younger. Dots above the reference line repre-

sent biologically older subjects. It is obvious that

almost all dots representing the group of managers are lo-

cated below the reference line supporting the high level of

vitality in this subgroup.

Furthermore, they showed the lowest number of dis-

eases and the lowest number of sick leave days (Table 1).

Relaxation ability (measured on basis of RI question-

naire) was also best in the group of managers (mean RI 5

10.2). The female teachers had the worst (mean RI 5

17.1).

Nevertheless, a high proportion of managers showed

health risks as indicated by a high percentage of over-

weight persons (72%), and 9% were obese (BMI $ 30

kg/m2), which is a predictor for cardiovascular diseases.

The proportion of smokers was highest in the group of

nursery school teachers and lowest in the group of female

teachers.

Work ability was also highest in the group of managers

(highest mean WAI score and highest values in the WAI

items; Figure 2) with the greatest percentage of persons

with excellent work ability (94%) compared to teachers

(female: 15% and male: 22%), office workers (30%)and nursery school teachers (26%).

The dimensions effort, reward and the ERI ratio

(Table 2) were most favourable for the group of managers

(lowest values in the effort subscale and highest values in

the reward subscales). The worst values in the dimension

reward were found in the group of male teachers.

The multivariate analysis includes three general linear

models (Table 3).

Model 1 is equivalent to a one-factorial ANOVA with

group as the only factor. The constant in this model

equals the average difference between CA and BA for

the group of manager personnel. The other parameters

represent the difference between the four remaininggroups and this group. Managers had the most favourable

results, followed by female teachers.

Model 2 explores potential characteristics of subjects,

which predict the difference between CA and BA. WAI 7

(mental attitudes and resources towards work), fat mass

and reward are significantly associated with the outcome

variable. From column 2 of Table 3, we can conclude

that an increase of one point of the WAI 7 (mental resour-

ces) increases the difference between CA and BA by 1.6

years with a confidence interval between 0.4 and 2.8

years.

Model 3 demonstrates whether the group differences

of Model 1 may be explained by the predictors identifiedin Model 2. In general, thedifferences between the groups

become lower. There are only slight changes for nursery

teachers and office workers but relevant changes for both

groups of school teachers. Thus, the covariates explain at

least in part why teachers have worse results compared to

management personnel. The covariates explain to a much

lesser degree why office workers and nursery school

teachers also showed worse results compared to the man-

agement personnel.

G. FREUDE ET AL.: DISCREPANCY BETWEEN CALENDAR AND BIOLOGICAL AGE 23


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Discussion

In our study, the highest discrepancy between CA and BA

was found in the group of managers compared to the

other subgroups analysed in this study. BA was consider-

ably below CA with an average gain of9 years, i.e. the

managers examined were biologically substantially youn-

ger and more vital than would be expected from their CA.

The best vitality status is consistent with the result that

this subgroup also exhibited good results in relevant

work- and health-related factors.

Managers rated their work ability as the best compared

to other subgroups. According to the concept of work

ability [2224], we assumed a good consistency between

individual resources and the work demands. This as-

sumption was also supported by the result that managers

Figure 1. Upper graph: differences between CA andBA fordifferent subgroups(means andSEs).Lowergraph: scatter plots forBA versusCA for the

groupsof male teachers andmanagementpersonnel. Theblackline represents a referenceline(BA5CA). Dots under theselines represent individuals

whose BA is lower than CA. It is obvious that almost all managers are biologically younger compared to their CA.



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showed a good balance between effort and reward, which

indicates a relatively low level of work-related stress.

Furthermore, managers reported not only few ill-

nesses, few sick leave days and high relaxation ability

but also fewer health risk factors, such as being over-

weight and smoking.

By means of multiple regression analysis, mental atti-

tudes and resources towards work, occupational reward

and the body fat mass were identified as relevant predic-

tors for the discrepancy between CA and BA.

As already mentioned, the influence of work in combi-

nation with health-related factors on the ageing process

Table 1. Selected health-related factors for different subgroups

Teachers

(female),

n 5 100

Office workers

(female),

n 5 60

Nursery school

teachers

(female),

n 5 65

Teachers

(male),

n 5 99

Management

personnel

(male),

n 5 47

Total,

n 5 371

CA (years) 44.6 6 7.7 42.6 6 8.7 43.9 6 9.2 47.9 6 6.4 49.2 6 7.5 45.6 6 8.1

BA (years) 40.6 6 8.0 44.5 6 5.3 44.9 6 8.4 46.0 6 7.0 40.2 6 8.2 43.4 6 7.8

Illnesses (quantity) 2.2 6 1.8 2.0 6 1.7 2.0 6 1.8 1.6 6 1.5 1.1 6 1.2 1.8 6 1.7

Sick leave days #9 #9 #9 #9 0 #9

RI 17.1 6 3.7 13.5 6 3.8 14.1 6 3.1 14.3 6 3.5 10.2 6 3.4 14.4 6 4.1

BMI (kg/m2) 23.7 6 3.4 24.3 6 3.8 25.0 6 3.8 26.5 6 2.8 26.4 6 2.7 25.1 6 3.5

Body fat mass (%) 20 23 23 21 23 22

Overweight

(BMI $ 25 kg/m2; %)

28 38 43 69 72 49

Smoker (%) 10 15 23 7 17 13

Entriesare means andSDs, percentages, according to thescaling of thevariable. Sick leave days werecodedas an ordinal scale as nosick leave days versusone to ninesick

leave days versus more than nine sick leave days. Entries are medians of this scale, not on a scale coding exact number of days.

Figure 2. WAI for different subgroups with WAI categories (means and SEs).

Table 2. Effort, reward and ERI ratio for different subgroups

Teachers

(female)

Office workers

(female)

Nursery school

teachers (female)

Teachers (male) Management

personnel (male)

Total

Effort 17.16

4.3 14.76

3.6 14.26

3.0 14.96

3.6 13.36

3.2 15.16

3.9Reward 48.6 6 5.1 47.5 6 6.3 51.2 6 3.9 42.7 6 7.6 52.5 6 2.9 47.8 6 6.6

Effortreward ratio 0.7 6 0.2 0.6 6 0.2 0.5 6 0.1 0.7 6 0.3 0.5 6 0.1 0.6 6 0.2



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has hardly been studied to date. The application of highly

standardized instruments in this study to measure BA as

well as RI, WAI and ERI can be regarded as a comprehen-

sive and innovative approach to analyse predictors for the

discrepancy between CA and BA. These instruments

were applied in all subgroups analysed in this study.

Individual employees who participated in the study

benefited from this complex approach because they gotvaluable and comprehensive information about their level

of vitality and related risk factors as well as resources.

However, there are significant limitations on the inter-

pretation of our results in relation to the cross-sectional

design and a non-random sampling of different occupa-

tional groups. From our experiences, individuals with

a high level of vitality are highly motivated to pursue op-

portunities for diagnostic procedures, resulting in a high

participation rate such as in the group of managers. This

contrasts with the lower observed participation rate of the

less vital teachers. Therefore, a correlation between vi-

tality and (self-) selection cannot be excluded. Sampling

different groups with different participation rates mightintroduce significant bias in the analysis of associations

if factors relevant for (self-) selection are correlated both

with determinants and with dependent variables in the

current investigation and hence are classical confounders

in the analysis of potentially causal relationships.

However, there is no systematic correlation between

participation rate and vitality of participants. For exam-

ple, both the occupational group with the highest partic-

ipation rate (nursery school teachers) and that with the

lowest participation rate (male teachers) show no differ-

ences between CA and BA. In contrast, managers with

a lower participation rate than that of nursery school

teachers revealed the highest CABA discrepancy.

Another weakness is that cross-sectional studies do not

control for selection effects over time introduced by a pro-

cess of migration of selected subjects into an occupation

and by a selection of a subgroup of healthy workersremaining in this group. With such cross-sectional data,

it is difficult to determine the direction of the effects be-

tween occupation and the other variables.

We concede that self-selection of subjects may also bias

the strength of predictors in our regression analyses. This

may hold especially in Model 3 where we can explain dis-

crepancies between the groups in part by relevant predic-

tors. On the other hand, we did not find significant

interactions between the study groups and the predictors;

thus, the latter bias may not be crucial.

Because of these possible biases, we have to interpret

the results with caution, and we cannot draw final conclu-

sions about the effects of work- and health-related factorson the CABA discrepancy.

We found interesting associations between different pre-

dictors and discrepancy and between CA and BA discrep-

ancy, which need further clarification on causal

relationships.Onemighthypothesizeonthebasisofourdata

that managers in our sample have good resources, which

compensate for high workload. For example, theyreported

high levels of job satisfaction, esteem rewards and social

support, a high job security (92% of managers believed

Table 3. Predictors of the CABA discrepancy

Predictor Model 1 Model 2 Model 3

Constant 9.0 25.1 0.9

Teachers (female)management

personnel (male)

24.05** (20.3) 21.6n.s. (20.1)

CI: (26.4 to 21.7) CI: (24.0 to 0.8)

Office workers (female)management

personnel (male)

27.8*** (20.4) 26.0*** (20.3)

CI: (20.4 to 25.1) CI: (28.6 to 23.5)

Nursery school teachers (female)management

personnel (male)

27.0*** (20.4) 26.6*** (20.3)

CI: (29.6 to 24.5) CI: (29.0 to 24.1)

Teachers (male)management

personnel (male)

27.2*** (20.5) 23.8** (20.3)

CI: (29.4 to 24.9) CI: (26.4 to 21.2)

Mental resources 1.6** (0.2) 1.7** (0.2)

CI: (0.4 to 2.8) CI: (0.5 to 2.9)

Fat mass 20.2*** (20.2) 20.2*** (20.2)

CI: (20.3 to 20.1) CI: (20.3 to 20.1)

Reward 0.2** (0.2) 0.1* (0.1)

CI: (0.1 to 0.3) CI: (0.01 to 0.2)

R2/R2adj. 0.1/0.1*** 0.2/0.1*** 0.2/0.2***

*P, 0.05, **P, 0.01, ***P, 0.001; n.s., not significant. Entries are unstandardized regression coefficients (within brackets, standardized regression coefficients are

given). Additionally, confidence intervals are presented for the unstandardized coefficients. In the last row of the table, the percentage ofexplained variance is described by

adjusted and unadjusted R

2

values. The Pvaluesin thelast rowrefer to overall tests of theentiremodel.Model1: Pvaluesrefer to thecomparison versus male managementpersonnel; additional significances: teachers (female) are significantly different from office workers, nursery school teachers and teachers (male), P, 0.05 each; office

workers, nursery schoolteachers and teachers (male) arenot different from each other,corrected subgroupcomparisons (Tukeys B).Model 2: Pvalues refer to the test for

beta 5 0 (no influence of the covariate on the discrepancy of BA and CA). Model 3: P values for subgroups refer to the comparison versus male management personnel

adjusted for the covariates below; additional significances: teachers (female) versus office workers (female) and nursery school teachers (female), P, 0.005.



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that their job was not at risk) and high educational qualifi-

cations that may act positively during the ageing process.

Causal relationships between work- and health-related

predictors and CABA discrepancy have to be investi-

gated in longitudinal studies. Our study provides first

valuable information about possible association between

predictors and CABA discrepancy, which can be

regarded as a predictor of the rate of ageing. We assume

that measures focused on promoting health (healthy diet

and physical activities) and improving working conditions

(e.g. job satisfaction and social support and stress preven-

tion) may also affect the ageing process positively.

Funding

Federal Institute for Occupational Safety and Health, Berlin,

Germany.

Conflicts of interest

None declared.

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Key points

This study shows that discrepancy between calen-

dar and biological age is associated with different

work- and health-related determinants.

Protective factors (high work ability, appropriate re-

lation between effort and reward at work and goodrelaxation ability) are associated with a lower bio-

logical age than actual calendar age.

We assume that measures focused on health promo-

tion and improving working conditions (e.g. job sat-

isfaction and social support and stress prevention)

may also affect the ageing process positively.

http://www.iaq.uni-due.de/iaq-report/2009/report2009-01.pdfhttp://www.iaq.uni-due.de/iaq-report/2009/report2009-01.pdfhttp://www.pallas.iab.de/bisds/berufe.htmhttp://www.pallas.iab.de/bisds/berufe.htmhttp://www.pallas.iab.de/bisds/berufe.htmhttp://www.pallas.iab.de/bisds/berufe.htmhttp://www.iaq.uni-due.de/iaq-report/2009/report2009-01.pdfhttp://www.iaq.uni-due.de/iaq-report/2009/report2009-01.pdf


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Documents

Calender and Biological Age