Abstractdspace.lib.hawaii.edu/bitstream/10790/4452/1/EB Final PIP... · Web viewChi-square analysis was used to explore relationships between participant age and sex and the extent

1Running head: EFFECTS OF WEIGHT LOSS ON DIABETES

Effects of Weight Loss on Diabetes: A Practice Inquiry Project

Emily Brewer MSN, APRN, FNP-C

University of Hawai’i at Hilo

Practice Inquiry Project

November 14th, 2018

Committee Chair:

Joan Thompson Pagan, PhD, APRN, RNC

Committee Member:

Jeanette Ayers-Kawakami, DNP, RN

Committee Member:

Michelle Chino, Ph.D

EFFECTS OF WEIGHT LOSS ON DIABETES

Table of ContentsAbstract............................................................................................................................................3Chapter One: The Problem..............................................................................................................4Significance.....................................................................................................................................9Problem Statement.........................................................................................................................10Aims and Objectives……………………………………………………………………………..11Chapter Two: Project Description.................................................................................................11Literature Review..........................................................................................................................11Project Theoretical Framework.....................................................................................................18Project Concept Map.....................................................................................................................22Chapter Three: Project Design and Implementation.....................................................................23Weight Management Program…………………………………………………………………...24Methodology……………………………………………………………………………………..27Data Collection…………………………………………………………………………………..27Chapter Four: Results……………………………………………………………………………28Chapter Five: Discussion, Recommendations, and Conclusions………………………………...35Strengths…………………………………………………………………………………………38Limitations……………………………………………………………………………………….38Impact of Results on Practice……………………………………………….…………………...39Dissemination Plans……………………………………………………………………………...40Future Implications for Practice………………………………………………………………….40Protection of Human Subjects.......................................................................................................41Future Studies……………………………………………………………………………………41Final Summary...............................................................................................................................42References......................................................................................................................................43Appendix A....................................................................................................................................50Appendix B....................................................................................................................................51Appendix C....................................................................................................................................52Appendix D....................................................................................................................................53Appendix E....................................................................................................................................54Appendix F....................................................................................................................................55Appendix G....................................................................................................................................56Appendix H....................................................................................................................................57

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Abstract

Purpose: The purpose of this practice inquiry project was to evaluate the impact of weight loss

on type 2 diabetes mellitus (T2DM). The overall goal of this project was to learn improved ways

of treating diabetes and how to better achieve glycemic control in these individuals. The

expected primary outcome was improved hemoglobin A1c (HbA1c) as weight loss was

achieved. Expected secondary outcomes included decreased serum glucose, weight, BMI, blood

pressure, and waist circumference.

Methods: A secondary analysis of quantitative data from the initiation of a weight management

program and throughout patient participation was conducted. This information was collected and

documented, and a series of statistical tests were conducted to examine the overall impact of the

intervention on key indicators and test relationships between certain variables. The study sample

included 21 patients, studied over a 90-day program. All participants had a diagnosis of T2DM,

obesity, and hypertension. Measurements were taken at baseline, 30 days, 60 days, and 90 days.

Measurements included weight in pounds, body mass index (BMI), blood pressure, body fat

percentage, waist circumference, HbA1c, and glucose levels.

Results: The participants experienced a significant reduction in these measurements at each of

the four measurements (Baseline, 30, 60, 90 days), though not all were considered statistically

significant.

Conclusion: The weight loss program improved patient outcomes by decreasing weight, BMI,

body fat percentage, waist circumference, glucose, and HbA1c levels. This reduction of risks and

complications of diabetes improves the quality of life of patients with diabetes.

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Effects of Weight Loss on Diabetes: A Practice Inquiry Project

Chapter One: The Problem

Type 2 diabetes mellitus (T2DM) is a pervasive health problem resulting in serious

complications to multiple body systems that may lead to disability and early death. It is one of

the costliest diseases burdening the healthcare system and is reaching alarming prevalence rates

in the United States. In 2015, 30.3 million Americans, or 9.4% of the population, had diabetes

and the number diagnosed is anticipated to reach approximately 42 million by 2034 (American

Diabetes Association [ADA], 2017). The total estimated cost of diagnosed diabetes in 2012 was

$245 billion, including $176 billion in direct medical costs and $69 billion in reduced

productivity (ADA, 2013). More than 1.5 million Americans are diagnosed with diabetes every

year. Diabetes remains the 7th leading cause of death in the United States in 2015, with 79,535

death certificates listing it as the underlying cause of death, and a total of 252,806 death

certificates listing diabetes as an underlying or contributing cause of death (ADA, 2017).

In Hawaii, T2DM is an epidemic (ADA, 2016). It is estimated that 154,365 people, or

13.1% of the adult population currently have diabetes in Hawaii. In addition, 41.5% of the adult

population has pre-diabetes with glucose levels higher than normal. Every year an estimated

8,000 people in Hawaii are diagnosed with diabetes (ADA, 2016). People in Hawaii with

diabetes have medical expenses approximately 2.3 times higher than those without diabetes and

total direct medical expenses for diagnosed and undiagnosed diabetes, pre-diabetes, and

gestational diabetes was estimated at $1.1 billion in 2012. In addition, another $419 million was

spent on indirect costs in Hawaii from lost productivity due to diabetes (ADA, 2016).

At the same time as the increase of T2DM, there has been an increasing prevalence in

obesity. Obesity is driving the prevalence of diabetes and the vast majority of patients with

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T2DM are overweight or obese and it is likely that obesity combined with a genetic

predisposition may be necessary for T2DM to develop (Franz, 2007). Excess body weight is

associated with the risk of cardio-metabolic complications, which are the major causes of

morbidity and mortality in T2DM (Wilding, 2014). Approximately 50% of men and 70% of

women are obese at the onset of diabetes (ADA, 2017). Body mass index (BMI) is a simple

index of weight-for-height that is commonly used to classify overweight and obesity in adults. It

is defined as a person's weight in kilograms divided by the square of his height in meters (kg/m2).

Overweight is a BMI greater than or equal to 25 and obesity is a BMI greater than or equal to 30.

More than 90% of patients with diabetes are overweight or obese and the numbers are increasing

rapidly. In prediabetes, weight loss has been shown to delay the onset or decrease the risk of

T2DM, while in established T2DM weight loss has been shown to improve glycemic control,

with severe calorie restriction even reversing the progression of T2DM (Wilding, 2014). Weight

reduction should be a key therapeutic goal in both the prevention and management of T2DM.

Although weight loss is important in the management of type 2 diabetes and the benefits are

irrefutable, most diabetic patients remain overweight or obese.

The importance of tight glycemic control in patients with T2DM for protection against

micro and macro vascular disease has been well documented. Studies have conclusively shown

that the greater the average daily blood glucose, the greater the risk of developing neuropathy,

nephropathy, retinopathy, and cardiovascular disease (ADA, 2016). Consequences of poor

glycemic control contribute to numerous health complications and the overall diabetes disease

burden (ADA, 2016). The past two decades have seen increases in the use of oral diabetes

medications and improvements in glycemic control among persons with diagnosed diabetes. In

combination with evidence for better detection, these results are consistent with improvements in

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diabetes screening and care in the past 20 years (Selvin et al., 2014). Despite these

improvements, large portions of the population are not achieving optimal hemoglobin A1c

(HbA1c) levels, suggesting further efforts are needed (Selvin et al., 2014). Failure to manage

diabetes appropriately is associated with a substantial increase for troublesome complications

that can diminish quality of life and increase risk for premature mortality (ADA, 2013).

The ADA recommends HbA1c levels of <7% in newly diagnosed diabetic patients and

suggests that lowering HbA1c levels to <6% may lower the risk of morbidity and cardiovascular

events (ADA, 2013). According to a 2018 guidance statement by the American College of

Physicians (ACP), clinicians should aim to achieve an HbA1c level between 7-8% in most

diabetic patients (American College of Physicians [ACP], 2018). The ACP said that this higher

target is aimed at helping patients benefit from glycemic control while avoiding the adverse

effects associated with low blood sugar and costs of stricter targets. However, the ADA and the

American Association of Clinical Endocrinologists (AACE) have expressed skepticism about the

higher target, noting that the guidance statement does not take into account the benefits of weight

loss, which itself frequently reduces HbA1c levels (Khardori, 2018).

Achieving optimal glycemic control in diabetes can be problematic as the condition is

highly self-managed and requires the patient to adhere to recommended lifestyle alterations as

well as drug treatment (Hunt, 2011). Several barriers to achieving glycemic control exist and the

medical community must find methods to overcome these barriers in the hope that it will reverse

these ominous trends. Many interconnected obstacles to achieving optimal diabetes care include

patient barriers to adherence to treatment (behavioral, psychosocial, and socioeconomic),

structural and technological hurdles, and provider guideline delivery concerns (Selvin et al.,

2014). People with diabetes receive mixed messages about weight loss from magazines,

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newspapers, friends, family, and healthcare professionals. Few subjects have accumulated as

much misleading and potentially dangerous false information as the subject of obesity.

A study in 2002 randomized thousands of over-weight people with prediabetes into three

groups. One group received individualized lifestyle sessions, one group received the drug

metformin, and the last group received no intervention. Results showed that lifestyle intervention

not only resulted in greater weight loss, but it also delayed or prevented many people from

progressing to diabetes (Knowler et al., 2002). Since this landmark study, some studies have

shown that achievable weight loss has a modest effect on A1C levels, but others show that

weight loss was not associated with improvement in glycemia. A systematic review of published

weight loss trials in overweight and obese patients with T2DM conducted in 2012 showed a

significant correlation between weight loss and HbA1c reduction, with an estimated HbA1c

reduction of 0.1 percentage points for each kilogram of reduced body weight for the overall

population (Gummesson, Nyman, Knutsson, & Karpefors, 2017). Other studies have shown that

modest weight loss of 3-5% of initial body weight improves glucose intolerance and HbA1c,

slows complications of diabetes, and reduces the need for glucose-lowering agents (Bramante,

Lee, & Gudzune, 2017). Some providers believe that early in the disease process, when insulin

resistance is still prominent, weight loss will improve blood glucose levels but as the disease

progresses and insulin deficiency becomes more prominent, that it may be too late for weight

loss to be helpful (Franz, 2007). Another issue that makes weight loss in diabetes even more

confusing is the effect of intentional weight loss on mortality in T2DM. Williamson et al.

reported that people with diabetes who had an intentional weight loss experienced a 25%

reduction in total mortality and a 28% reduction in cardiovascular disease-plus-diabetes

mortality, while another analysis of overweight adults with diabetes showed that intentional

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weight loss had no more effect on mortality than those who were not trying to lose weight

(Williamson et al., 2000).

Evidence-based guidelines support effective lifestyle interventions for weight

management, but most leading guidelines suggest adding one of several anti-hyperglycemic

drugs at the same time lifestyle modifications are introduced, as opposed to leaning heavily on

weight loss and exercise prior to initiating drug therapy (Raz, 2013). Why is this? Are providers

reluctant to take the time to explain the importance of weight loss to their diabetic patients, or are

they unconvinced that it will make a difference in their disease progression? National surveys

reveal a continuing failure to incorporate weight management into clinical practice (Ma et al.,

2013). Implementation of efficacious lifestyle interventions in the real world will require

adaptation to improve generalizability and sustainability while maintaining intervention

effectiveness. Many pharmacological agents used in the treatment of diabetes directly contribute

to weight gain through their glucose-lowering mechanisms (Gaal & Scheen, 2015). The resultant

decrease in blood glucose levels corresponds with a decrease in glycosuria (excess of glucose in

the urine), a major contributing factor to the weight gain observed in patients being treated with

anti-hyperglycemic agents. Lifestyle change to correct the pathophysiology abnormalities of

insulin resistance should precede drug therapy, but guidelines recommend that newly diagnosed

type 2 diabetic patients not at target A1C combine proper lifestyle with Metformin (see

Appendix D for more information) (Raz, 2013).

Beginning in 2012, the federal Affordable Care Act (ACA) allowed states to select an

existing insurance plan to be the state-wide “benchmark plan” which led to 33 states selecting

two types of coverage for diagnoses and treatment of obesity as a medically recognized disease.

As a result, 23 states have a health benefit requirement to cover bariatric surgery and 16 states

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have some coverage and reimbursement for dietary or nutritional screening. Unfortunately, only

5 states have coverage for weight loss programs despite the research that shows that weight loss

programs result in more weight lost by diabetic patients than traditional nutritional counseling.

Under the ACA, Hawaii has coverage for bariatric surgery and nutritional counseling, but no

reimbursement for weight loss programs (National Conference of State Legislators [NCSL],

2016). Medicare has year-long programs that potentially reimburse $425 per patient if they

attend all the classes and lose 5% of their body weight and $450 if they lose 9%, but the money

goes to the providers, not the patients (Clark, 2017). These incentives are long overdue but most

worry it is not enough. The United States has a medication distribution system, but not a lifestyle

distribution system and is in desperate need of one. Successful adaptation of lifestyle

interventions for weight loss will be critical to stem the tide of obesity in diabetes and lessen the

disease burden.

Significance

Nationally, intervention strategies to reduce the burden of diabetes by facilitating the

adoption of proven approaches to prevent and delay the onset of T2DM are being implemented

through the Centers for Disease Control and Prevention (CDC) (Centers for Disease Control and

Prevention [CDC], 2017). The ADA has set up programs nationally and at the state and local

level to help with glycemic control once diagnosed but recent statistics show that less than 6% of

those diagnosed with diabetes have achieved targeted HbA1c levels (ADA, 2017). In Hawaii,

local programs aim to increase knowledge of the disease by educating the public through

community awareness projects as well as increasing efforts for early testing (CDC, 2017). The

goals in caring for these patients are to eliminate symptoms and to prevent or slow the

development of complications. Microvascular risk reduction is accomplished through control of

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glycemia and blood pressure, macrovascular risk reduction through control of lipids and

hypertension, and metabolic and neurologic risk reduction through control of glycemia, all of

which can be improved through weight loss.

Adherence to a weight loss program is difficult, and this may be why it is not suggested

more. Adherence involves a change in behavior and changing established behavior of any kind is

arduous. It is particularly challenging in healthcare because of the complex relationships between

providers, patients, and practice; but that is also what makes it important. Without change, there

can be no growth. Without growth, there can be no acceptance of new knowledge or

implementation of evidence-based practice. To develop a successful strategy for change, it is

necessary to understand the barriers to change faced in healthcare as well as motivation to

change. Change is not just a rational response to the display of new information and is often

found to be disruptive and is resisted by most people (MacGuire, 2006).

Problem Statement

Glycemic control in diabetics is a difficult task for both provider and patient. There are a

variety of methods for achieving glycemic control, and research shows that lifestyle changes can

further reduce HbA1c in over weight adults with T2DM compared to medications (Johansen et

al., 2017). Although the benefits of weight loss in the prevention of diabetes and as a critical

component of managing the disease are well established, weight reduction is difficult, and a high

percentage of diabetic patients continue to be overweight or obese. Despite improved screening

and well-defined guidelines for managing diabetes that includes weight loss through lifestyle

interventions at onset, many patients are given anti-hyperglycemic agents at the time of diagnosis

and then continue to struggle with serum glucose goals. A practice inquiry project was conducted

to contribute to research that is needed to better understand the effectiveness of such

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interventions and to determine if weight loss will improve glycemic control in diabetic patient

groups.

Aims and Objectives

Specific Aim 1: Determine weight loss effects glycemic control in diabetes

Objective 1: Complete secondary analysis of existing data to identify glycemic control initially,

and throughout weight loss

Objective 2: Improve understanding of weight loss and its effect of glycemic control

Specific Aim 2: Determine weight loss effects on other collected data

Objective 1: Complete secondary analysis of existing data to identify additional patient health

indicators initially, and throughout weight loss

Objective 2: Improve understanding of how weight loss affects additional health indicators

Specific Aim 3: Determine if baseline HbA1c is a significant caveat for the relationship between

weight loss and HbA1c

Objective 1: Use secondary data analysis to determine if high HbA1c at baseline is associated

with a greater reduction in HbA1c for the same amount of weight loss

Specific Aim 4: Increase support for weight loss program reimbursement by insurance

companies

Objective 1: Through secondary data analysis that shows improvements in glycemic control

with weight loss, add to research that supports the addition of weight loss program

reimbursement by insurance

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Chapter Two: Project Description

T2DM is not only a difficult disease to treat, but also to understand. The pathophysiology

is complex and barriers to treatment are many. Strategies to improve glycemic control with

treatment plans have been thoroughly researched and are examined in the following chapter. The

theoretical framework of the practice inquiry project is identified and explained thoroughly as

well as the elements of the concept map used in the preliminary brainstorming stages.

Literature Review

An exhaustive review of literature on the topic of diabetes is beyond the scope of this

paper. For this review of literature, the pathophysiology of the disease, treatment including

lifestyle interventions for weight loss, barriers to treatment, and strategies to increase adherence

to treatment plans are reviewed and discussed here.

Pathophysiology of Diabetes Mellitus

T2DM consists of an array of dysfunctions characterized by hyperglycemia and resulting

from the combination of resistance to insulin action, inadequate insulin secretion, and excessive

or inappropriate glucagon secretion. The complications of diabetes can be divided into 2

categories: micro vascular and macro vascular. Micro vascular diseases include diabetic

neuropathy, nephropathy, and retinopathy and can result in end-stage renal disease and

blindness. Macro vascular disease refers to changes in moderate to large blood vessels including

the heart, brain, and periphery and can lead to cardiovascular disease and ultimately death (ADA,

2016). Failure to follow the recommended treatments limits achievement of therapeutic goals

and increases the likelihood of complications. Diabetes requires continuing medical care and

ongoing patient self-management to reduce complications. The management plan must include a

collaborative effort among members of the healthcare team, the patient, and often the patient’s

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family. The ADA suggests that a variety of strategies and techniques be used for patient

education on the different aspects of disease management and implies that goals and treatment

plans should be tailored to the specific patient (ADA, 2016).

Treatment of Diabetes Mellitus

It is essential that individuals with diabetes assume an active role in their care (ADA,

2016). Chronic illnesses require day-to-day management of these health behaviors as well as

increased responsibilities on the part of the patient. Patients are often not ready to assume these

responsibilities, nor do they have an understanding of the dire consequences that can result if

health behaviors are not changed. Failure to reach targeted outcomes, repeat healthcare visits,

and the continued need for outpatient and inpatient care point to the need for improved

understanding and assistance in helping patients help themselves (Ryan, 2009). Successful

management of chronic conditions improves health outcomes, patient well-being, and benefits

the healthcare system and society as a whole by decreasing costs and improving an individual’s

functioning within that society. Personal health behaviors that are required to manage chronic

conditions are many. For example, individuals with T2DM change their behaviors to manage

symptoms such as increased thirst, frequent urination, and fatigue (ADA, 2016). They self-

administer medications and implement behavior changes necessary to control these symptoms.

This includes having and using resources to lose weight, eat differently, obtain medications,

accurately self-administer them, and recognize and report adverse side effects, unattended

outcomes, and failure to reach targeted outcomes (Ryan, 2009). These patients must also

understand how to monitor blood glucose and know what decisions to make if it is outside an

appropriate range. Negative emotions and feelings associate with their diagnosis must also be

successfully managed, which often also requires behavior changes (ADA, 2016).

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Weight loss should be included in these treatment goals and has its own set of difficulty.

In T2DM, insulin levels are high, and insulin is a fat-storage hormone. As the body produces

more insulin to get glucose out of the blood and into the cells, the amount of stored fat increases

and the release of it decreases (ADA, 2013). Instead of losing weight, diabetics continue to gain

weight because of increased insulin levels. In addition, recommended eating patterns of small

meals throughout the days backfire in diabetic patients as these create spikes in blood sugar

followed by drops in blood sugar, which stimulates hunger. Health care providers often find that

people trying to “solve” a weight loss maintenance problem feel guilty about their weight issues

as well. Often, they have been told by family, friends, the public, and even healthcare

professionals that it is their fault, that they simply lack willpower.

Lifestyle interventions such as weight loss programs using dietary, physical activity, or

behavioral interventions have been shown to produce significant improvements in weight among

persons with pre-diabetes, and a significant decrease in diabetes incidence (Gummesson, Nyman,

Knutsson, & Karpefors, 2017). While surgical and pharmacological interventions are effective at

achieving significant weight loss, lifestyle-based interventions focusing on diet remain the

cornerstone of weight loss approaches. The Look AHEAD (Action for Health in Diabetes) trial

studied intentional weight loss and cardiovascular morbidity/mortality in overweight individuals

with T2DM. It showed that a reduction in BMI improved blood pressure, fasting glucose, and

HbA1c (Sunyer, 2014). This supports the growing idea that individuals with diabetes can benefit

from lifestyle change associated weight loss and that should be the foundation of the treatment

plan in overweight and obese diabetic patients.

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Barriers to Treatment Adherence

Researchers have investigated health behavior change across a wide variety of health

conditions and many theories and interventions have been tested. Changing health behavior is a

complex task and often, new health behaviors are not maintained. Results that have been

achieved in research studies have not been reproduced clinically and failure to reach these

outcomes in practice impacts health of the individual and society (Ryan, 2009). Part of the

problem is that healthcare providers overestimate the amount that their patients change. It is

often wrongly assumed that behavior change will occur because the evidence supporting the

change is so obvious and convincing (Center for the Advancement of Health, 2008). Imparting

factual information alone does not result in the maintenance of health behavior change especially

in chronic conditions such as diabetes where long-term effects are not typically noticed in short

term symptoms. There is evidence that course of health behavior change seems to follow similar

patterns no matter the disease process or the individual. The highest rate of failure to adhere to

health behavior change tends to occur very early after the change is implemented (Ryan, 2009).

Strategies to Increase Adherence to Treatment Plans

There is a large body of evidence that supports a wide range of interventions and

treatments to improve health outcomes and disease management in diabetes. Adherence rates to

interventions and treatments are typically higher in acute conditions as compared to those with

chronic conditions (Sharma, Kalra, Dhasmana, Basera, 2014). Diabetes is a challenging disease

to manage successfully due to the requirements of frequent blood glucose monitoring, dietary

modifications, weight loss, exercise, and administration of scheduled medications. Regimen

adherence issues are common, making glycemic control difficult to attain (Sharma, Kalra,

Dhasmana, Basera, 2014). Adherence can be influenced by multiple factors and in turn creates

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many themes when researching interventions. Studies have reported that adherence can be

improved through patient education, reminder systems, behavioral interventions, motivational

interviewing, health coaching, and improved patient-healthcare provider relationships (Fennerty,

West, Davis, Kaplan, & Feldman, 2014; Minet, Moller, Vach, Wagner, & Henrickson, 2010;

Reutens at al., 2011; Hunt, 2011).

Motivational interviewing (MI) is one theme in adherence that has been used to

successfully bring about change with a strategy to assess readiness to change in patients who

need to manage chronic conditions with various forms of treatment (Chen, Creedy, Lin, &

Woolin, 2011). MI refers to a communication method that is a directive client-centered

counseling style for eliciting behavior change by helping patients to explore and resolve their

ambivalence (Hunt, 2011). The overall goal is to increase motivation so that change arises from

within. Several of the studies in the review of literature show that MI has proved effective in

terms of adherence to lifestyle changes as well as medication routines and in turn improved

overall glycemic control in type 2 diabetes management (Minet, Moller, Vach, Wagner, &

Henriksen, 2010). A separate review of literature proved motivational interviewing outperforms

traditional advice in relation to lifestyle change in clinical healthcare settings, specifically in the

management of T2DM in terms of HbA1c (Hunt, 2011). A randomized control trial that studied

participation in a motivational interview for people with T2DM was conducted in 2011. Results

indicate that the motivational interview did improve participant’s outcomes significantly in terms

of self-management, self-efficacy, quality of life, and HbA1c levels (Chen, Creedy, Lin, &

Wollin, 2011).

Patient education is another theme in adherence. A review of the literature showed that

educational interventions with consistent patient contact over several weeks or months was

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effective and offered the most improvement in both clinical outcomes and adherence to treatment

across varied clinical conditions, including diabetes (Marcum, Hanlon, & Murray, 2017;

Viswanathan et al., 2012). In a recent study, empowerment of the patient through education and

follow up through remote telehealth was examined. Educational messages were sent daily to the

intervention group and necessary educational phone calls were conducted 3 times per week for 3

months. Results showed that distance education had a significant, positive impact on the

empowerment of the patient in terms of dissatisfaction and readiness to change, setting and

achieving diabetes goals, and the self-scores of empowerment and disease management

(Zamanzadeh, Zirak, Maslakpak, & Parizad, 2016).

Reminder systems have been found as a promising and effective way to improve health

conditions in chronic conditions (Viswanathan et al., 2012). A meta-analysis of randomized

control trials of reminder interventions to assist patient adherence to prescribed treatment showed

a statistically significant increase in adherence in groups receiving a reminder intervention

compared to controls. Reminder interventions included phone calls, text messages, pagers,

interactive voice responses, video-telephone calls, and programmed electronic audiovisual

reminder devices (Fenerty, West, Davis, Kaplan, & Feldman, 2012). In another study, daily

video telephone or regular phone call reminders increased treatment adherence in older adults

with heart failure as well as heart failure symptoms (Viswanathan et al., 2012). These results can

be translated to other chronic conditions such as hyperlipidemia, diabetes, and hypertension

given the similarities in management requirements (Viswanathan et al., 2012).

Behavioral interventions have been shown to increase adherence as well. A recent study

showed that "adherence therapy"- a brief, cognitive-behavioral approach aimed at facilitating a

process of shared decision making- improved self-reported treatment adherence as well as

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quality of life (Marcum, Hanlon, & Murray, 2017). The use of cognitive behavioral therapy

(CBT) is designed to correct erroneous patient beliefs and faulty cognitions. A review of

literature shows that CBT is less effective than motivational interviewing at improving

adherence, but more effective than motivational interviewing alone when used in conjunction

(Hunt, 2011).

Health coaching (HC) can be a cost-effective way to address adherence in T2DM. HC

interventions target health behavior changes aligned with self-determined goals leading to

improved physical and mental health outcomes (Wayne, Perez, Kaplan, & Ritvo, 2015). HC can

typically be defined as a behavior-change specialist with expertise in chronic disease

management and with an understanding of disease state and ethno cultural backgrounds. With

HC assistance, patients determine health-related goals and monitor progress (Wayne, Perez,

Kaplan, & Ritvo, 2015). HC can be done in person, or in conjunction with mobile phones. A

recent study showed an accelerated reduction in HbA1c with the use of HC through mobile

phone support than versus without in a lower-SES community (Wayne, Perez, Kaplan, & Ritvo,

2015). HC interventions carefully titrated measuring the frequency and duration of patient-health

coach interaction are important elements in HC for eliciting the desired result of improved

adherence to chronic disease management treatment plans.

While the depth and breadth of research on diabetes management, adherence to

management interventions and treatments, and outcomes of poor adherence is considerable, there

continues to be room for new knowledge and ideas. The pattern of diabetes management has

shifted to focus on empowering the patient to manage the disease successfully and in turn

improve quality of life (ADA, 2016). A patient’s inability to adhere to the treatment regimen in

diabetes seems to be rooted in psychological and motivational factors. Previous research studies

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support adherence interventions in diabetes care and suggests that further studies are needed to

establish the most effective adherence intervention.

Project Theoretical Framework

An essential attribute of advanced practice nurses is the ability to use theory in practice.

Theory is useful because it helps to provide an explanation for of various situations and

phenomenon. Three theories were used in the brainstorming and creation of this project, one to

help explain behavior change, one to help explain adherence, and one to explain the power of

secondary data analysis in research.

There are multiple theories that seek to explain the phenomenon of behavior change, and

Polly Ryan wrote one such theory. She argues that a person’s behavior influences their health

and that their health can be improved by managing their condition through engaging in health

promotion behaviors, a process that requires behavior change (Ryan, 2009). The Integrated

Theory of Health Behavior Change (ITHBC) is a midrange descriptive theory that was created to

better understand how practitioners can facilitate health behavior change (see Appendix A for

diagram). It can be used as a foundation for intervention development and suggests that health

behavior change can be enhanced by fostering knowledge and beliefs, increasing self-regulation

skills and abilities, and improving social facilitation (Ryan, 2009). Ryan acknowledges that

nurses and other healthcare professionals play an important role in identifying behaviors

necessary for health, assessing the needs of their patients and recommending certain health

behaviors, preparing and delivering interventions designed to increase participation in health

behaviors, and then evaluating the effectiveness of those interventions in their patient population

as well as the surrounding community (Ryan, 2009). In order to fulfill these responsibilities,

health care professionals’ benefit from an understanding of the science and thought process

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behind behavior change. Engagement in self-management behaviors is seen as the proximal

outcome influencing the long-term outcome of improved health status (Ryan, 2009).

Another theory that can be applied to this project is The Capability, Opportunity, and

Motivation (COM-B) model of behavior. This theory is a psychological model for explaining

human behavior intended to capture the range of mechanisms that may be involved in adherence

(Jackson, Eliasson, Barber, & Weinman, 2014). The COM-B model of behavior is intended to be

comprehensive and applicable to all behaviors, including adherence, and was developed by a

panel of behavioral theorists. It is intended as a starting point in order to choose interventions

that are most likely to be effective, and specific interventions to address each component

suggested (Jackson, Eliasson, Barber, & Weinman, 2014). The model hypothesizes that

interaction between three components, capability, opportunity, and motivation (COM) cause the

performance of behavior (B) and can explain why a recommended behavior is not engaged in. A

depiction of the model as it relates to adherence is shown in Appendix B. This theory can be

applied to a wide range of factors that have been identified to explain non-adherence to

recommended lifestyle change including comprehension of disease and treatment, executive

function, perception of illness, outcome expectancies, beliefs about treatment, complexity, social

support, and provider-patient relationship. With adherence represented as a continuum, this

model is able to reflect the extent a treatment recommendation is adopted and factors that

influence that. COM-B provides a comprehensive explanation of adherence that includes habit,

factors at a systems level, and relationships between the specified components that provide a

description of the relationship between individual determinants and adherence, making it easier

to identify appropriate interventions (Jackson, Eliasson, Barber, & Weinman, 2014).

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The third theory used in this project shows the importance of secondary data analysis, a

form of research in which the data collected and processed are reanalyzed for a different purpose

(Whiteside, Mills, & McCalman, 2012). Advances in technology have led to vast amounts of

data that has been collected, compiled, and archived. This data is easily accessed and can be used

for secondary data analysis and research in every imagined field. While analysis of this data is

flexible, it should still be an empirical exercise and follow a systematic method with procedural

and evaluative steps, just as in collecting and evaluating primary data. Grounded theory, in

which methods provide an inductive process for collecting and analyzing data “grounded” in

participant’s experiences’ with the aim of assisting professional practice and/or guiding future

research, can be a helpful tool to accomplish this (Whiteside, Mills, & McCalman, 2012).

Originally developed by Glaser and Strauss in 1967, grounded theory practice is based on

interconnected features, theoretical sampling, comparison of data, and developing theoretical

constructs that have no preconceived hypothesis to prove or disprove but rather allow issues of

importance to emerge (Whiteside, Mills, & McCalman, 2012).

As the medical director of the weight management program used in this project, the

project manager began to see over and over that as diabetic patients lost weight, they were

decreasing or discontinuing their medications. As part of normal care for this patient population

the clinic also collected data including HbA1c, serum glucose, BMI, weight, and waist

circumference. This data was collected as a way to encourage continued participation in the

program as well as to monitor patient health status but began to show emerging themes and

trends in the diabetic patients that gave way to the idea for this project. The research question

was developed, the dataset was identified, and thorough evaluation was completed using

theoretical knowledge and conceptual skills, as the grounded theory suggests. Cost effectiveness

21


and convenience of secondary data analysis allowed the project manager to accelerate the pace of

the project and because the focus of interest is similar to the guidelines set in place by the weight

management program, the limitations of secondary data analysis are not as present.

The focus of this project was to determine if weight loss improves glycemic control in

type 2 diabetic patients in hopes of contributing to the literature that supports this. The ITHBC,

COM-B, and grounded theories were applied as frameworks for this project. Concepts from

these theories were used to design a research question that was applied to the data analysis of

previously collected information on diabetics in a weight management program. The secondary

data analysis was completed to determine if weight loss in this subset of patients improved

glycemic control as well as other health indicators with the goal of adding to the research that

supports weight loss as a first-line treatment of diabetes. The proximal outcome was measured

by improvements in HbA1c through weight loss. The distal outcome was measured by health and

disease status, specifically through HbA1c levels, weight loss, BMI, and waist circumference.

Behavior change is a dynamic, iterative process. Desire and motivation are prerequisites

to change, and self-reflection facilitates progress. Using these theories for the project fostered

enhanced management of the complex clinical condition of diabetes by providing improved

comprehensive care. This research can help to improve patient health status, well-being,

functioning, and health outcomes by providing a framework and structure to identify factors

essential to behavior change, adherence, and ultimately weight loss (Ryan, 2009; Jackson,

Eliasson, Barber, & Weinman, 2014).

Project Concept Map

For a patient to change the way they manage their health, they must first have knowledge

of a different way, or awareness (National Institute for Health and Clinical Evidence [NIHCE],

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2007). Once this has been discovered, it will lead to many questions in that patient’s mind that

cause them to translate and consider the evidence, to evaluate it (Doolin, Quinn, Bryant, Lyons,

& Kleinpell, 2010). Factors that influence this evaluation include social norms, availability, cost

effectiveness, patient preference, quality of life, provider/patient relationships, resources, and

ease of implementation (NIHCE, 2007). Once the evidence has been considered and found

applicable willingness to change can be determined. This can be affected by perceived barriers

and health beliefs, which also affects a paradigm shift (Leeman, Baernholdt, & Sandelowski,

2006). Willingness to change leads to motivation and buy in by the patient. This affects the

paradigm shift as well and is affected by it. Buy in by the patient leads to implementation of the

behavior change (Leeman, Baernholdt, & Sandelowski, 2006). In thinking about patient health

behavior change for glycemic control, the following supportive concepts are identified and

diagrammed in Appendix C:

Evidence- is there evidence-based practice information that supports the change?

Social Norms- what are common opinions in society regarding diagnosis?

Availability- what is the availability of the proposed intervention to the patient?

Cost Effectiveness- is the proposed intervention available to the patient?

Patient Preference- what are the patient’s preferences in terms of the existing protocols

for glycemic control? What are their perceived barriers to health behavior change?

Quality of Life- will the proposed changes affect quality of life for the patient? Will this

be perceived as positive of negative?

Provider/Patient Relationship- how comfortable are they with one another? What is the

level of trust?

Resources- are there resources available to facilitate the change?

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Ease of Implementation- how much time will it take for the provider? For the patient?

How long will it take to see results?

Provider confidence- do they feel confident in the evidence and their ability to promote

health behavior change in their patients?

Health Beliefs- what are the patient’s current health beliefs regarding their diagnosis?

Paradigm Shift- is there a fundamental change to the patient’s basic fundamental model

or perception of glycemic control and how best to control it?

Willingness- is the patient willing to change? Willingness can be affected by multiple

“buy in” variables including, but not limited to, social norms, provider/patient

relationship, sense of responsibility, peer practice, education, time, and lack of provider

and patient confidence.

In this concept map, each supporting concept affects the primary concept and it aims to

facilitate health behavior change while understanding how it must come about. While all changes

do not lead to improvement, all improvement requires change, as well as adherence to

interventions (see Appendix C for diagram).

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Chapter Three: Project Design and Implementation

This project design seeks to describe the secondary data analysis of collected quantitative

data on diabetic patients enrolled in a weight management program. The overall goal of this

project was to learn improved ways of treating diabetes and how to better achieve glycemic

control in these individuals. The expected primary outcome of the secondary data analysis was

improved HbA1c as weight loss was achieved. Expected secondary outcomes included decreased

serum glucose, weight, BMI, and waist circumference.

Weight Management Program

The weight management program that was used in this project is a medically developed

and scientifically based 4 phase ketogenic weight loss protocol that uses diet as well as one-on-

one coaching to achieve weight loss. It is a supervised, weight loss and lifestyle counseling

program for dieters. The weight loss goal is determined by the health coach and patient taking

into consideration BMI, weight, and personal patient goals such as coming off medications for

blood pressure or diabetes. Phase 1 begins and continues until 100% of the patient weight loss

goal is achieved. This phase includes 3 pre-packaged foods per day, dinner of an 8oz whole

protein, and 4 cups of select vegetables. Phase 2 is to be followed for 14 days after phase 1 is

complete and includes 2 pre-packaged foods, 2 8oz servings of a whole protein, and 4 cups of

select vegetables per day. Phase 3 is to be followed for 14 days after phase 2 is complete and

includes 1 pre-packaged food and 3 servings of 8 oz protein, and 4 cups of select vegetables.

Phase 4 is the maintenance phase and is a 12-month stabilization period with ongoing support to

help maintain weight loss success without pre-packaged foods. Coaches who receive extensive

complimentary training do the counseling and weekly visits that consist of a weigh in, food

journal review, BMI calculation, and waist circumference measurement. For the diabetics

25


enrolled in the program initial serum glucose and HbA1c are ordered by the medical director and

new patient assessments are completed that include history and physical, as well as lab and

medication review. Serum glucose and HbA1c are measured at 4-week intervals. The

intervention is the same for every patient, regardless of weight, until the weight loss goal is

achieved.

As the clinic director of this weight management program the project manager had easy

access to its’ diabetic patients that were willing to allow a secondary analysis of this previously

collected data. The project manager worked with this patient population for more than 2 years as

an advanced practice registered nurse and had access to all past and current routine assessments,

labs, diagnoses, and measurements. Titrating anti-hyperglycemic agents such as Metformin and

insulin through the monitoring of glucose levels as weight loss occurs is a large part of her role

in the clinic. As this happened a theme was introduced: as weight loss increased, glycemic

control improved, and this became the foundation of this project.

Population

As previously mentioned T2DM is an epidemic in Hawaii (ADA, 2016). Hawaiians are

increasingly feeling the effect of diabetes and nearly 155,000 people in Hawaii have the disease.

In addition, 41.5% of the adult population has pre-diabetes with glucose levels higher than

normal (ADA, 2016). The past two decades have seen staggering increases in pre-diabetes and

T2DM and the disease is growing at an epidemic rate in Hawaii (CDC, 2017).

Poor glycemic control in diabetics is prevalent in Hawaii for many reasons. Recent

studies from the ADA show that only 55% of individuals in Hawaii have elected to participate in

diabetes education programs, meaning almost half of the patients in Hawaii newly diagnosed

with diabetes have a decreased knowledge base about their disease. Other interdependent and

26


interrelated issues may include perception of the long-term health impact of the disease, views

on importance of diabetes treatment and adherence, misconceptions, and psychological stress

related to diagnosis (Wayne, Perez, Kaplan, & Ritvo, 2015). Although lifestyle interventions are

shown to be effective in achieving meaningful weight loss and reducing the risk for diabetes,

they are often less effective for ethnic minority groups when compared to non-Hispanic whites.

Research suggests that ethnic minority populations tend to lose less weight and are more likely to

regain their weight than Whites given the same obesity intervention (Kaholokula et al., 2013).

This may be due to socio-demographic, behavioral, and biological variables such as language

barriers, substandard living conditions, and financial barriers that hinder their weight loss efforts.

Though a study specifically on Hawaiians and other Pacific Islander ethnicities would be

beneficial in understanding the disease manifestation and progression in relation to weight loss to

those groups, it would leave out an entire subset of people who live in Hawaii and maintain

similar environments, diets, routines, and thought processes. Due to the fact that this project

wanted to address weight loss and its effect on glycemic control in all T2DM patients, the chosen

population of patients for this project included diabetic patients of any ethnicity that are, or were,

enrolled in the weight management clinic.

Methodology

Using a research question approach, a secondary analysis of collected information from

the initiation of a weight management program and throughout patient participation for an

interval of 3 months was conducted. This information was collected and documented on a

spreadsheet, and a series of statistical tests were conducted to examine the overall impact of the

intervention on key indicators and test relationships between certain variables. A priori

hypothesis was used to look for suitable data sets.

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Data Collection

The population of interest for this secondary analysis project were adults with T2DM in a

private weight management program clinic on Oahu. Participants were volunteers with a

diagnosis of diabetes, ages 33-71, that were patients in the weight management clinic. Initial

conversations were conducted by the co-investigator to discuss participant participation face to

face in the weight management clinic and on the telephone. Informed consent was obtained from

each participant before review of collected data (see Appendix H for more information). The co-

investigator reviewed previously collected labs that included serum HbA1c and glucose levels

that were completed at an outside lab as part of the normal care for this population.

Measurements of weight, BMI, and waist circumference were also reviewed. These are part of

normal care visits as well. Health indicators (HbA1c, weight, BMI, and waist circumference)

were compared from initial and subsequent visits. Data collection was conducted by the co-

investigator only and was done as a secondary data analysis of patient records in the weight

management clinic. Descriptive statistics (ANOVA) and t-tests were used in the data analysis to

determine the percentage of change in the health indicators previously identified. The data

analysis was conducted at the weight management clinic using their electronic database from

October 1st to November 1st, 2018. The data was reviewed in person. Sample size was determined

through convenience sampling and a group of 21 patients participated. The interval time frame

considered in the intervention was 3 months. Data was included for participants who may or may

not have respond to the weight management therapy as long as they continued to participate in

the program.

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Chapter Four: Results

The study sample included 21 patients, studied over a 90-day program. All 21 patients

completed the 90-day program with no attrition. Participants included 9 males and 12 females

ranging in age from 33 to 71, with a mean age of 54 years. All participants had a diagnosis of

T2DM, obesity, and hypertension. Additional diagnoses included hyperlipidemia, non-alcoholic

fatty liver disease (NAFDL), and gastroesophageal reflux disease (GERD). Measurements were

taken at baseline, 30 days, 60 days, and 90 days. Measurements included weight in pounds, body

mass index (BMI), blood pressure, body fat percentage, waist circumference, HbA1c and glucose

levels. The participants experienced a statistically significant reduction in these measurements at

each of the four measurements (Baseline, 30, 60, 90 days). Table 1 provides descriptive

measures for baseline and 90 days and unit change over the 90-day period.

Table 1. Changes in Measurements from Baseline to 90 Days (T4)

Mean Median SD Min Max Decrease P Value

Baseline Weight 234.55 218.00 59.86 163.80 360.00

Weight T4 200.81 187.20 53.35 138.80 315.00 -33.74 <0.001

Baseline BMI 37.84 37.00 7.65 27.30 55.90

BMI T4 32.37 30.60 7.09 23.60 50.70 -5.47 <0.001

Baseline Systolic 138.05 140.00 9.20 120.00 163.00

Systolic T4 128.24 130.00 8.50 100.00 140.00 -9.81 <0.001

Baseline Diastolic 91.76 92.00 6.70 75.00 102.00

Diastolic T4 76.52 78.00 7.23 68.00 88.00 -15.24 <0.001

Baseline BF% 38.71 39.71 7.34 22.30 52.58

BF % 4 30.96 30.40 9.14 15.10 50.30 -7.75 <0.001

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Baseline Waist 46.27 46.00 6.55 35.50 63.00

Waist T4 39.74 40.00 6.17 25.00 51.00 -6.53 <0.001

Baseline HbA1c 7.49 6.90 1.96 5.60 13.10

HbA1c T4 5.68 5.60 0.92 4.60 9.00 -1.81 <0.001

Baseline Glucose 162.24 140.00 73.80 95.00 413.00

Glucose T4 104.57 100.00 23.86 83.00 197.00 -57.67 <0.001

Data Analysis

To better answer the research questions a series of statistical tests were conducted to

examine the overall impact of the intervention on key indicators and test relationships between

certain variables. The data are dependent, being repeated measures from the same individuals.

Baseline measures were the starting values for each measure. Time 2 is 30 days from baseline,

time 3 is 60 days from baseline, and time 4 is 90 days from baseline. All variables were tested

for normality but due to the small sample size, non-parametric tests were used where appropriate.

All results should be interpreted with caution due to the small sample size.

A one-way repeated measures ANOVA was calculated for change in weight, BMI, body

fat percentage, waist circumference, and HbA1c levels. The one-way repeated measures

ANOVA is used to assess if there is a difference in means between 3 or more groups (where the

participants are the same in each group) that have been tested multiple times or under different

conditions. The null hypothesis each of these tests was that there was no significant difference

between groups. The data were assumed to be normally distributed with no significant outliers.

Mauchly’s W test (P<.05) was applied to test sphericity with the use of the Greenhouse-Geisser

for sphericity correction where indicated. Eta squared (2) was used for the ANOVA tests to

estimate effect size.

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Weight Loss

Participant weight was examined from baseline to Time 4. There is a significant

difference between the means of the differences between all four groups F (1.239, 24.787) =

100.8, p<.001, 2= 0.834. Post hoc testing using the Bonferroni correction revealed that weight

decreased significantly as time increased. Figure 1 shows the average weight loss with SD error

bars and mean differences for each measurement time.

Figure 1. Mean Weight Loss over Time Weight Change Comparisons Average Weight Loss for Each Time Period.

BMI Decrease

Participant BMI was examined from baseline to Time 4. There is a significant difference

between the means of the differences between all four groups F (1.387, 27.745) = 126.6, p<.001,

2= 0.864. Post hoc testing using the Bonferroni correction revealed that BMI decreased

significantly as time increased. Figure 2 shows the average decrease in BMI, with SD error bars,

and mean differences for each measurement time.

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Figure 2. Decrease in Mean BMI over TimeWeight Change Comparisons Average BMI Decrease for Each Time Period.

Body Fat Percentage

Body fat percentage was examined from baseline to Time 4. There is a significant


46.53, p<.001, 2= 0.699. Post hoc testing using the Bonferroni correction revealed that the

percentage of participant body fat decreased significantly as time increased. Figure 3 shows the

average reduction in body fat percentage, with SD error bars, and mean differences for each

measurement time.

Figure 3. Reduction in Mean Body Fat Percentage Over Time

Body Fat % Change Comparisons Decrease in Body Fat % for Each Time Period.

Waist Circumference

Waist circumference was examined from baseline to Time 4. There is a significant


32


83.78, p<.001, 2= 0.807. Post hoc testing using the Bonferroni correction revealed that waist

circumference decreased significantly as time increased. Figure 4 shows the average change in

waist circumference, with SD error bars, and mean differences for each measurement time.

Figure 4. Change in Mean Waist Circumference Over TimeTable 4. Waist Circumference Comparisons Figure 4. Average Change in Waist Circumference

for Each Time Period.

HbA1c Levels

HbA1c levels were examined from baseline to Time 4. There is a significant difference


2= 0.623. Post hoc testing using the Bonferroni correction revealed that HbA1c levels decreased

significantly as time increased. Figure 5 shows the average decrease in HbA1c levels, with SD

error bars, and mean differences for each measurement time.

Figure 5. Change in Mean HbA1c Level Over timeHbA1c Change Comparisons Average HbA1c Decrease for Each Time Period.

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Glucose Level

Glucose levels were examined from baseline to Time 4. There is a significant difference


2= 0.375. Post hoc testing using the Bonferroni correction revealed that glucose levels

decreased significantly as time increased. Figure 6 shows the average change in glucose level,

with SD error bars, and mean differences for each measurement time.

Table 6. Change in Mean Glucose Level Over TimeGlucose Change Comparisons Average Glucose Decrease for Each Time

Period.

Weight Related Changes and Blood Glucose Levels

Simple linear regression was used to compare the effect of weight loss indicators on

blood glucose levels. It was hypothesized that weight loss would significantly decrease other

weight related indicators and that weight loss would decrease glucose level indicators. Weight

loss was positively skewed, it is assumed due to participants starting at higher than normal

weights. All other variables could be modelled by a normal distribution. Given the small sample

size and dependent data, results should be interpreted with caution.

Weight loss was a significant predictor of a decrease in BMI (F =(1,19) = 148.0, p<.001);

body fat % (F=(1,19) = 42.60, p<.001); and waist circumference (F=(1,19) = 8.483, p=0.009).

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Given that the data include multiple measures for each individual these weight related indicators

were expected to be strongly correlated.

It was also hypothesized that these weight related indicators would be predictive of

changes in glucose levels. In particular, several recent studies indicate that waist circumference

may be a better indicator of diabetes than indicators such as BMI. However, the regression

analysis failed to show a significant relationship between weight loss, BMI, body fat%, or waist

circumference and HbA1c or glucose. A stepwise multiple linear regression model indicated that

only HbA1c is predictive of changes in glucose (r2 = .724, p<.001). However, given the inter-

dependence of these two variables this is not a relevant finding.

Factors Associated with Higher Weight Loss

Chi-square analysis was used to explore relationships between participant age and sex

and the extent of weight loss in the program. Neither age or sex were associated with changes in

weight over the 90-day program.

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Chapter Five: Discussion, Recommendations, and Conclusions

The aims and objectives of this project were to determine the effects of weight loss on

glycemic control in a diabetic patient population, to improve the understanding of weight loss on

BMI, waist circumference, and body fat percentage, to determine if baseline HbA1c is a

significant caveat for the relationship between weight loss and HbA1c, and to increase support

for weight loss program reimbursement by insurance companies. This weight loss program was

found to be appropriate for patients who were willing and motivated to self-manage their health

condition to improve their outcomes. Although the program had a small sample size, the project

results were consistent with the literature regarding the benefits of a weight loss program in

weight, BMI, body fat percentage, waist circumference, HbA1c, and glucose.

Weight

Weight is the measurement of a body’s relative mass. On average patients in this program

lost 33.74 pounds, which was approximately 15% of their body weight over a period of 90 days.

Modest weight loss of 5-10% of total body weight is recommended for overweight or obese

people with diabetes as it can improve glycemic control, reduce the need for diabetes

medications, and improve cardiovascular risk factors (ADA, 2013).

BMI

BMI is a measurement of body fat based in height and weight that can be applied to men

and women. A high BMI can be an indicator of high body fatness. A BMI of <25 is considered

normal while 25 and above is considered overweight or obese. The mean initial BMI in this

project was 38 and patients saw a reduction of 5.47 on average. Though most remain within the

overweight and obese range, these findings are significant as any decrease in the BMI ratio

reduces risk for morbidity and mortality.

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Body Fat Percentage

Body fat percentage calculates the proportion of fat mass to lean muscle mass. The

distribution of fat can be an indicator of cardiometabolic health and the reduction of body fat

percentage decreases insulin resistance, blood pressure, and heart disease. According to the

American College of Endocrinology, having a body fat of 25% in men and 35% in women is

considered high (American College of Endocrinology, 2016). Higher than normal body fat

percentage leads to increased risks in diabetes and a recent study showed that decreased body fat

mass had an improved effect on glycemic control (Hancu & Radulian, 2016). Though the

average patient’s body fat was 38%, well above the recommended percentage, a reduction of

7.75% on average was reached among participants in this project.

Waist Circumference

In a recent study, participants with a visceral fat mass in the upper 10th percentile had a

higher odds ratio for diabetes than the upper 10th percentile of other adiposity indices, which

contributes to the evidence that the type of excess fat is an important predictor of disease risk

(Hancu & Radulian, 2016). Visceral fat is excess intra-abdominal fat and waist circumference is

a way to measure it clinically. A waist circumference of 40 inches or more in men and 35 inches

or more in women is associated with increased complications in diabetes. On average program

participants had a waist circumference of 46 inches, with a decrease of 6.5 inches over the 90-

day time interval, a reduction of 3%. This can improve clinical outcomes and decrease diabetic

complications as visceral fat is associated with higher levels of insulin resistance.

HbA1c

The data obtained was interpreted as having a positive impact on glycemic control,

though not statistically significant. Patients saw a reduction in their HbA1c levels by 1.8%, on

37


average. These findings are important due to the recommendations by the ADA for HbA1c levels

of <7% in newly diagnosed diabetic patients and the findings that suggest that <6% may lower

the risk of morbidity and cardiovascular events (ADA, 2013). Given the relatively high initial

HbA1c levels of this patient subset, 7.5% on average, a reduction of 1.8% puts them well below

these levels. While a 1% reduction in HbA1c has been associated with significant reductions in

microvascular and macrovascular complications and diabetes-related mortality, smaller

reductions of 0.5% have also been associated with clinically meaningful improvements in risk

factors (Wing et al, 2011).

Glucose

The blood glucose level is the amount of sugar in the blood that comes from the foods we

eat. On average, patients began the protocol with a daily glucose of 162.24 and decreased it by

57.6, a reduction of 9.2%. The ADA recommends that diabetics keep their daily blood glucose

below 170 to decrease the risk of disease complications (ADA, 2013). This can be referred to as

glucose control and the tighter the control, the lower the risk of complications associated with

hyperglycemia. At the end of the time interval, patients had a daily average glucose of 104,

which is considered normal.

Blood Pressure

Blood pressure is the pressure of the blood in the circulatory system, often measured for

diagnosis since it is closely related to the force and rate of the heartbeat and the diameter and

elasticity of the arterial walls. The ADA supports targeting blood pressure at <130/80 mm Hg as

it reduces cardiovascular events as well as some microvascular complications in diabetic patients

(ADA, 2013). These results are also important given that each patient has a diagnosis of

hypertension, which puts them at increased risk for cardiovascular events and disease. There was

38


a statistical reduction in blood pressure measurements at each of the time intervals and therefore,

overall. Mean systolic pressure at onset of protocol was 138 and mean diastolic pressure was 91.

After a reduction of 9.8 in systolic and 15.24 in diastolic, the mean blood pressure for patients at

the end of the project was 128/76.

Strengths

Strengths of this project include the use of correlational research to help us better

understand the complex relationships between different variables. This allows us to make

predictions and can tell us if two variables are, or are not, related. The analysis of existing data is

a cost-efficient way to make full use of data that are already collected to address potentially

important research questions.

Limitations

Limitations of the project include those associated with the use of secondary data analysis

as well as the use of a unique diet protocol that may form the basis of weight loss and improved

glycemic control, as opposed to weight loss alone. Concerns in the use of secondary data stem

from ethical questions, the quality of the data, data “fit”, and the nature of the relationship

between the researcher and the data. The closeness of the investigator may present a potential

risk for premature certainty about the phenomenon being studied in the data set but did not

influence the outcome of the statistical analysis. The unique weight loss program being used in

this analysis may produce enhanced weight loss and/or glycemic control specific to the type of

diet used in the intervention, which may decrease generalizability. The sampling size may also

limit the generalizability of the results.

39


Impact of Results on Practice

The impact of obesity and diabetes on morbidity, mortality, and health care costs is

profound. Obesity and weight related complications in diabetes exert a huge burden on patient

suffering and social costs. Adipose tissue itself is an endocrine organ which can become

dysfunctional in diabetes and contribute to systemic metabolic disease. Weight loss can be used

to prevent and treat metabolic disease concomitant with improvements in adipose tissue

functionality. New therapeutic tools and scientific advances necessitate development of

improved medical care models and robust evidenced-based therapeutic approaches, with the

intended goal of improving diabetic patient well-being. Assisting patients with diabetes to

appreciate and learn the value of weight loss is an important step in treatment. Patient

empowerment and a collaborative approach among the multidisciplinary team at the weight loss

center were imperative to their success in the program. The weight loss program improved

patient outcomes by decreasing weight, BMI, body fat percentage, waist circumference, glucose,

and HbA1c levels. Though not all improvements were statistically significant, this reduction of

risks and complications of diabetes improves the quality of life of patients with diabetes.

Diabetes self-management and weight loss is the foundation of diabetes care for those who are

obese and is essential for improving outcomes. Preventing complications of diabetes and

maintaining glycemic control requires a multi-disciplinary approach utilizing many interventions

and behavior changes.

A wide array of national stakeholders with a vested interest in obesity and diabetes exist.

The participation of these stakeholders is recognized as necessary to support an effective overall

action plan and they include health professionals and their organizations, government regulatory

agencies, employers, health care insurers, pharmaceutical industry representatives, research

40


organizations, disease advocacy organizations, and health profession educators. A need to

address the totality and complexity of these issues is required to provide effective,

comprehensive obesity and diabetes management applicable to real-world patient care.

Moreover, the nuances of obesity care in the obesogenic-built environment that is America will

have an overwhelming socioeconomic influence and will require diligent analysis of the full

weight of evidence-based practice.

Dissemination Plans

Plans to disseminate the results of this project past the committee at UH Hilo are not in

place at this time. The results peeked the interest of the weight management owner and he has

mentioned conducting similar research for marketing efforts and to improve patient care and

adherence.

Future Implications for Practice

As the incidence and prevalence of diabetes increase, health care providers can

recommend or implement weight management programs as part of first-line treatment. This can

benefit society indirectly through improved knowledge and awareness of diabetic treatment

strategies, and through decreased healthcare costs by improving chronic disease status. Results of

this secondary data analysis support previous evidence-based guidelines that state that weight

loss should be the first-line treatment for T2DM as a means of improved disease control. Data

that supports this may also improve reimbursement of weight loss programs by insurance

companies and is a long-term goal as well.

Protection of Human Subjects

Protection of human subjects was implemented through careful review and submission of

this proposal to the Institutional Review Board (IRB). This project was not conducted until IRB

41


approval was granted. Consents for participation in the project were reviewed with each

participant and only upon signed consent was the patient’s data reviewed (see Appendix H for

more information). This project manager has gone through the Collaborative Institutional

Training Initiative (CITI) Program for Human Subjects Research (HSR) to gain a better

understanding of responsibilities associated with this type of data collection and gained a

completion certificate (see appendix D, E, F, and G for more information). Ethical assurance was

always strived for and all patient identifiers were removed from data. Corresponding numbers

were placed, and the key was held by the project manager, and the project manager alone.

Future Studies

Future studies should be conducted to detect clinical differences in outcomes in order to

identify different arrangements of behavior change techniques among effective weight loss

interventions. They should take into consideration other factors than weight loss that could

impact health outcomes in order to draw valid conclusions that can be applied clinically.

Allowing the patient opportunities to be more involved in the management of their care,

understand the patient’s underlying knowledge of diabetes, and screening for readiness to change

are all areas in which future studies can examine the impact of these strategies on treatment and

intervention adherence and related health outcomes in diabetic patients. Interesting to note there

was no attrition in the patient subset. Adherence to lifestyle management programs has

traditionally been difficult but for this specific program it was not an issue. This should be

considered in practice and possibly studied further to improve adherence to other lifestyle

management programs and behavior changes. Emphasizing separate research studies for

different age groups and populations can also help direct appropriate intervention strategies.

42


Future patient-centered interventions should be developed and tested using evidence-based

principles to improve weight loss and health outcomes in diabetic patients.

Final Summary

Healthy behavior is essential for optimal health. In terms of the management of chronic

disease, healthy behavior is essential for improvement of conditions and disease status. Knowing

how to support the initiation and maintenance of health behavior change is an important role

advanced practice nurses must fulfill. By combining concepts in the ITHBC, the COM-B, and

grounded theory to create a secondary analysis project that seeks to identify effects of weight

loss on glycemic control, this project hopes to distally affect their disease status in a positive way

and promote improved health outcomes. There remains an ongoing need to advance our efforts

to effectively achieve and maintain weight loss and glycemic control in adults with type 2

diabetes and cost-effective, non-surgical treatment options are warranted. A shift in the approach

to treatment in people with type 2 diabetes is needed as healthcare practitioners must consider

the weight effects of pharmacotherapy in the management of their diabetic patients and promote

lifestyle interventions and weight loss as the primary intervention.

43


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Appendix A

The Integrated Theory of Health Behavior Change (Ryan, 2009)

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Appendix B

The COM-B Model of Behavior (Jackson, Eliasson, Barber, & Weinman, 2014)

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Appendix C

Concept Map

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Appendix D

(Raz, 2013)

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Appendix E

CITI Certificate 1

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Appendix F

CITI Certificate 2

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Appendix G

CITI Certificate 3

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Appendix H

Participant Consent

Aloha! You are being asked to participate in a research study conducted by Emily Brewer, MSN, APRN, FNP-C from the Department of Nursing at the University of Hawaii. As a doctoral student, results of this research will be part of her final project.

What am I being asked to do? If you participate in this project, you will be asked to allow the investigators to access information previously collected at the weight management clinic for the purpose of review and secondary analysis. Lab data that was collected as part of your normal care visit will be reviewed as well as other routine measurements taken such as BMI, weight, and waist circumference. The co-investigator of this study is also the medical director of the clinic where the weight management program is conducted and recruitment for this study is taking place.

Taking part in this study is your choice. You can choose to take part or you can choose not to take part in this study. You also can change your mind at any time. If you stop being in the study, there will be no penalty or loss to you. Participation, or declining participation will have no effect on your relationship with the medical director of the program or your care at the clinic.

Why is this study being done?The purpose of this project is to see if weight loss improves hemoglobin A1c- a measurement that is an average of daily blood glucose over a time period of 2-3 months- in patients with type 2 diabetes. Overall goals include contributing to research that aims to improve disease management. I am asking you to participate because you are a diabetic, over the age of 18, and are currently or were previously participating in the weight management program.

What will happen if I decide to take part in this study?If you decide to participate in this study, you will be asked to do the following: Allow the investigators to have access to your weight management patient chart as well as normal care visit labs and measurements. You will be one of approximately 20 people in this data review.

What are the risks and benefits of taking part in this study?Loss of privacy is a potential risk in this study. There may be no direct benefit to participants, but possible indirect benefits to society may include improved diabetic treatment protocols, improved understanding of diabetes, improved glycemic control in diabetics, as well as increased intentional weight loss in patients with diabetes. The results of this project may help to improve the overall treatment of diabetes and in turn improve overall health in these patients.

Results of Research:Upon request, results of research will be disclosed to each participant.

Privacy and Confidentiality:

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Any information that is obtained in connection with this study and that can be identified with you will remain confidential and will be disclosed only with your permission or as required by law. Confidentiality will be maintained by means of removing personal identifying information from all data used in the write-up of the project. The data being reviewed will be accessed through the weight management database and computer as well as on my personal laptop. Both of these computers are password protected and information will be stored in encrypted files.

Other agencies that have legal permission have the right to review research records. The University of Hawaii Human Studies Program has the right to review research records for this study. When I report the results of my research project, I will not use your name. I will not use any other personal identifying information that can identify you. I will report my findings in a way that protects your privacy and confidentiality to the extent allowed by law.

Future Research Studies: Identifiers will be removed from your identifiable private information. Results of this study may be used for future research by me or distributed to another investigator for future research studies.

Compensation:There will be no compensation for this research.

Questions: If you have any questions about this study, please call or email me at [email protected] or (808) 488-5555. You may also contact my advisor, Dr. Joan Pagan, at [email protected]. You may contact the UH Human Studies Program at 808.956.5007 or [email protected]. to discuss problems, concerns and questions; obtain information; or offer input with an informed individual who is unaffiliated with the specific research protocol. Please visit http://go.hawaii.edu/jRd for more information on your rights as a research participant.

If you agree to participate in this project, please sign and date the following signature page:

Keep a copy of the informed consent for your records and reference.

Signature(s) for Consent:

I give permission to join the research project entitled, (insert title here)

Name of Participant (Print): ___________________________________________________

Participant’s Signature: _____________________________________________

Signature of the Person Obtaining Consent: ___________________________________

Date: ____________________________

Mahalo!

59

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Abstractdspace.lib.hawaii.edu/bitstream/10790/4452/1/EB Final PIP... · Web viewChi-square analysis was used to explore relationships between participant age and sex and the extent