PERCEPTIONS OF CONTROL AND SOCIAL COGNITIVE THEORY:

UNDERSTANDING ADHERENCE TO A DIABETES TREATMENT REGIMEN

By

STACY LYNN HUTTON

A Thesis Submitted to the Graduate Faculty of

WAKE FOREST UNIVERSITY

In Partial Fulfillment of the Requirements

for the Degree of

MASTER OF SCIENCE

in the Department of Health and Exercise Science

May 2002

Winston-Salem, North Carolina

Approved by: Shannon L. Mihalko, Ph.D., Advisor ______________________________ Examining Committee: W. Jack Rejeski, Ph.D., Committee Chair ______________________________ Gary D. Miller, Ph.D. ______________________________

DEDICATION

This thesis is dedicated to my Mom and Dad. I could not have been blessed with

two more loving and supporting parents, for I would not have made it this far without you both by my side. You have taught me the value of hard work and dedication and instilled

in me the work ethic that I will carry with me forever. I know that no matter how far I may go, I’ll always have an extraordinary family to come home to.

This thesis is also dedicated to my brother Tom. You have always been there to

help me put things into perspective and shed light on even the darkest situations. No matter how many miles away, you will always be my closest friend.

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ACKNOWLEDGEMENTS I would like to formally thank: Dr. Shannon L. Mihalko, for providing me with the constant support, guidance, and compassion I needed to get me through this year. I could not have been more fortunate then to have had the opportunity to work under your advisement. You have helped me to discover my passion for research and if I had to choose all over again, you would undoubtedly be at the top of my list. Dr. W. Jack Rejeski,, for serving as my committee chair. Thank your for all of your guidance and for challenging me to think beyond the logical. Dr. Gary D. Miller, for serving on my committee. Thank you for all your help and for challenging me to think on the more practical side of research. My fellow graduate students, Theresa, Tammy, Gretchen, Leigh Ann and Jamie, for I could not have been blessed with a finer group of people to share this experience. You have each inspired me in your own way and I wish you the best of luck in all your future endeavors. Beverly Nesbit, for your continuous help, and ever present encouragement and enthusiasm. You have helped to make this experience a truly enjoyable one. The first year graduate students, Aaron, Jamie, Tina, Steve, Racheal Laura and Heather, for all your support and encouragement through those stressful times. The Palmieri’s, Hutton’s, Jursik’s and Mekovetz’s for providing me with the stability of a supporting and loving family. Ellen, for providing me with the comfort of knowing that no matter how far away I go, I always have a good friend to come home to. Jennifer and Blaire for inspiring me to follow my dreams, no matter where they may take me. Lori Denise Shore, for not only providing me with a place to live, but helping to create for me a home. You have demonstrated to me the true meaning of southern hospitality. Fenix , for always tagging along on all the walks, runs and hikes when I needed to get away. Dr. William Herbert, for encouraging me to further my education and explore this opportunity. Jonathan, for being there when I needed you the most. You have provided me with the strength to make it through the most difficult times. Thank you for always knowing how to make me laugh when I needed to, but letting me cry when I wanted to.

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TABLE OF CONTENTS PAGE

LIST OF TABLES.............................................................................................................. v LIST OF FIGURES ........................................................................................................... vi ABSTRACT...................................................................................................................... vii INTRODUCTION .............................................................................................................. 1 REVIEW OF LITERATURE ............................................................................................. 3

Epidemiology and Background....................................................................................... 3 Complications Associated with Diabetes.................................................................... 4

Adherence ....................................................................................................................... 7 Social Cognitive Theory ................................................................................................. 9

Social Cognitive Theory and Diabetes ..................................................................... 11 Self-Efficacy ................................................................................................................. 14

Effects of Increased Self-Efficacy on Diabetes Control ........................................... 16 Perceptions of Control .................................................................................................. 18 Integrating Perceptions of Control with Social Cognitive Theory ............................... 22 Study Purposes.............................................................................................................. 24 Study Hypotheses.......................................................................................................... 26

METHODS ....................................................................................................................... 27 BRIDGE Study Overview............................................................................................. 27 Participants.................................................................................................................... 27 Measures ....................................................................................................................... 28 Procedures..................................................................................................................... 34 Analytic Plan................................................................................................................. 36

RESULTS ......................................................................................................................... 38 Participant Characteristics ............................................................................................ 38 Scale Reliabilities.......................................................................................................... 42 Descriptives for Variables of Interest ........................................................................... 43 Correlational Relationships Among the Primary Variables of Interest ........................ 46 Correlational Relationships Among the Primary and Secondary Variables of Interest 47 Correlational Relationship Between Efficacy for Exercise and PASE......................... 47 Correlational Relationships Among the Primary Variables of Interest and HbA1c..... 48 Correlational Relationships Among Primary Variables of Interest and Time Since ... 49

DISCUSSION................................................................................................................... 53 Limitations and Future Directions .................................................................................... 65 Practical Implications........................................................................................................ 70 APPENDIX A................................................................................................................... 74 APPENDIX B ................................................................................................................... 78 APPENDIX C ................................................................................................................... 83 REFERENCE LIST .......................................................................................................... 98

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LIST OF TABLES

PAGE Table 1. Participant Characteristics .................................................................................. 40

Table 2. Medication Usage ............................................................................................... 41

Table 3. Reliability Information for Self-Report Measures.............................................. 42

Table 4. Descriptive Statistics for Primary Variables of Interest ..................................... 44

Table 5. Descriptive Statistics for Secondary Variables of Interest ................................. 45

Table 6. Spearman Correlations Among Primary Variables of Interest ........................... 46

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LIST OF FIGURES PAGE

Figure 1. Scatterplot of Efficacy for Exercise and Exercise PASE Score ....................... 48

Figure 2. Scatterplot of Time Since DEC and Self-Efficacy ........................................... 50

Figure 3. Scatterplot of Time Since DEC and Outcome Expectatons ............................. 51

Figure 4. Scatterplot of Time Since DEC and Internal Locus of Control........................ 52

Figure 5. Scatterplot of Time since DEC and Personal Perceived Control ..................... 52

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ABSTRACT Stacy L. Hutton

Perceptions of Control and Social Cognitive Theory: Understanding Adherence to a Diabetes Treatment Regimen

Thesis is under the direction of Shannon L. Mihalko, Ph.D., Assistant Professor of Health and Exercise Science Adherence to the diabetes treatment regimen is considered one of the greatest obstacles to

self-management. The current study integrated Social Cognitive Theory (SCT) and

generalized perceptions of control (internal locus of control and perceived control) as its

theoretical basis for understanding adherence to the diabetes regimen. This cross-

sectional study included 16 adults (10 female, 6 male) over the age of 55 who had type 2

diabetes and had recently completed diabetes education. Spearman correlations (rs) were

used to examine the relationships among SCT, internal locus of control and perceived

control with adherence to the diabetes regimen, as measured by HbA1c. Although there

were no statistically significant correlations between these constructs and adherence,

several recommendations have been suggested for future research. Additional analysis

revealed significant (p < .05) inverse correlations between time since diabetes education

(weeks) and self-efficacy (r = -.607), as well as outcome expectations (rs = -.568).

Specifically, both self-efficacy and outcome expectations decreased following diabetes

education, indicating the need for interventions to target these predicators for long term

adherence. Increasing adherence to the diabetes regimen will decrease the risk for

chronic complications, thereby improving quality of life for individuals with diabetes.

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INTRODUCTION

The diabetes treatment regimen is extremely complex (Glasgow, McCaul, &

Schafer, 1986), and it is generally accepted that a patient with a more complex regimen is

less likely to be adherent than a patient with a less demanding regimen (Lutfey &

Wishner, 1999). It is crucial that individuals with diabetes follow a strict treatment

regimen in order to maintain control over their blood sugar. This treatment regimen,

which will be referred to as the diabetes regimen for the remainder of this paper, includes

maintaining a proper diet, engaging in regular physical activity or exercise, blood glucose

monitoring, and taking any prescribed medications. Following this regimen will result in

better self-management of diabetes and lower the risk for experiencing chronic

complications. The diabetes regimen consists of various behavior and lifestyle changes

for incorporating diet, exercise, blood glucose monitoring and medication usage in one’s

daily life (McCaul et al., 1987). In addition, individuals with diabetes must closely

monitor their feet, skin and gums to prevent complications common in these areas

(American Diabetes Association, 2002). Not only must these activities be incorporated

into one’s daily life, but they must all be coordinated in such a way to prevent any

complications. For example, blood sugar should be checked prior to engaging in any

exercise and if the blood sugar is too low, it is important to consume some form of

carbohydrate prior to the exercise bout in order to prevent a hypoglycemic event.

The high incidence of complications in individuals with diabetes indicates that

adherence to the diabetes regimen is an eminent problem. Furthermore, it has been

estimated that about 20% of individuals with type 2 diabetes do not monitor their blood

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glucose (Evans et al., 1999), only about 30% of individuals adhere to their exercise

program (Kamiya et al., 1995), and only 7% of individuals adhere to all aspects of their

regimen (Cerkoney & Hart, 1980).

Findings from both the Diabetes Complications and Control Trial (DCCT

Research Group, 2001) and the United Kingdom Perspective Diabetes Study (UKPDS

Research Group, 2001) demonstrate that successful blood sugar control can decrease an

individual’s risk for various chronic complications. Since most of the management and

care of diabetes is the responsibility of the individual with diabetes (Anderson,

Fitzgerald, & Oh, 1993), education and knowledge are important for effective self-

management (Day, 2000). However, knowledge alone is an insufficient predictor of an

individual’s ability to incorporate the necessary self-care behaviors into their daily lives

(Hurley & Shea, 1992). Indeed, research has determined that the relationship between

knowledge and adherence to the diabetes regimen varies greatly (Hurley & Shea, 1992).

Research indicates that self-efficacy, outcome expectations, internal locus of

control and perceived control play a crucial role in health behavior change and

maintenance, and thus, adherence. An individual’s self-efficacy and behavior can be

affected by the belief that their outcomes are determined by their own actions (Bandura,

1986). In an effort to enhance self-care among individuals with diabetes, it is important

to determine consistent predictors of adherence to the diabetes regimen. Clarifying the

potential relationship between self-efficacy, outcome expectations and perceptions of

control and determining each constructs influence on adherence will be valuable for this

effort. Ultimately, this may help health professionals in developing strategies to improve

self-management of this disease.

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REVIEW OF LITERATURE

Epidemiology and Background

Diabetes mellitus is a detrimental disease that plagues approximately 17 million

people or 6.2% of the United States population (American Diabetes Association, 2002).

Roughly 11.1 million people in this estimate have diagnosed diabetes, whereas the

remaining 5.9 million are undiagnosed. Currently, diabetes is the sixth leading cause of

death in the United States, as well as the sixth leading cause of death by disease. There

were 450,000 deaths due to diabetes in 1999 among individuals 25 years and older. In

addition to afflicting many Americans, diabetes is also a very costly disease. Health care

and other costs directly related to diabetes treatment are approximately $98 billion a year

(American Diabetes Association, 2002).

Diabetes is defined as a group of metabolic diseases in which the body does not

properly produce or use insulin. The inability of the body to produce insulin typifies type

1 diabetes, or insulin dependent diabetes (IDDM). Approximately 5- 10% of all diabetes

cases are type 1. The body’s inability to secrete insulin is often a result of autoimmune

disease in which the islet cells of the pancreas have been damaged. It is the islet cells,

which are responsible for the secretion of insulin. Those at the greatest risk for type 1

diabetes are individuals that have siblings or parents with type 1 diabetes (American

Diabetes Association, 2002).

Type 2 diabetes or non-insulin dependent diabetes (NIDDM) is characterized by

insulin resistance combined with insulin deficiency. Approximately 90 – 95% of all

diabetes cases are type 2. Those at the greatest risk for type 2 diabetes are people who

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are over the age of 45, have a family history of diabetes, are overweight, do not exercise

regularly, have low HDL cholesterol or high triglyceride levels, and those who are of

African American, Latino, or of Native American descent (American Diabetes

Association, 2002).

There are various warning signs or symptoms that may be indicative of either type

1 or type 2 diabetes. Indications of type1 diabetes include frequent urination, unusual

thirst, extreme hunger, unusual weight loss, extreme fatigue, and irritability. Any of

these symptoms may also be apparent in people with type 2 diabetes, and may be

accompanied by frequent infections, blurred vision, cuts or bruises that are slow to heal,

tingling or numbness in the extremities and recurring skin, gum or bladder infections. It

is important to note, however, that people with type 2 diabetes often do not experience

any symptoms (American Diabetes Association, 2002).

Complications Associated with Diabetes

Diabetes affects a vast majority of the population and generates considerable

health costs. Furthermore, it may lead to detrimental chronic complications, which can

worsen health status and increase health costs. The four most prevalent chronic

complications associated with diabetes are atherosclerosis, neuropathy, retinopathy and

nephropathy. Individuals with diabetes are also susceptible to problematic skin

conditions, foot ulcers and gum disease (American Diabetes Association, 1997). Not

only do these complications cause problems for an individual with diabetes, but

oftentimes, they are the gateway to many additional problems.

Athersosclerosis, or hardening of the arteries, accounts for 80% of all diabetes

deaths. Atherosclerosis is responsible for three major complications. The first

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complication, cerebrovascular disease, can occur as a result of hardening of the cerebral

arteries, resulting in a stroke. Individuals with diabetes are 2 to 4 times more likely to

experience a stroke than individuals without diabetes. The second complication is

coronary artery disease, which occurs as a result of hardening of the coronary arteries.

Because of this, individuals with diabetes are at 2 to 4 times the risk of having heart

disease than individuals without diabetes. Approximately 25% of all myocardial

infarctions in the United States occur in individuals with diabetes. Furthermore,

approximately half of the individuals with type 2 diabetes die from heart disease. The

third complication is peripheral vascular disease (PVD), which occurs due to hardening

of the arteries in the peripheries. PVD is 20 times more likely to occur in individuals

with diabetes and can ultimately lead to gangrene and the amputation of limbs (American

Diabetes Association, 1997).

Neuropathies, or nerve damage, can affect any nerve pathway in the body, such as

peripheral, central or autonomic. Neuropathy in any of these pathways can lead to a

multitude of problems, such as sensations of numbness or tingling, gastrointestinal

dysfunction, cardiovascular impairment, bladder dysfunction and sexual dysfunction.

Approximately 60-70% of diabetics suffers from mild to severe neuropathies, and in the

severest form, may lead to amputation (American Diabetes Association, 1997).

Retinopathy results from damage to the small blood vessels within the eye,

causing retinal bleeding, scarring, and eventually, the formation of new, but more fragile

blood vessels. For individuals with type 1diabetes, the incidence of retinopathy, 15 years

following diagnosis, is 97%. In individuals with type 2 diabetes, the incidence is 2-6% at

diagnosis, and, 15 years after diagnosis, 58-80%. Retinopathy, if not cared for, can lead

5

to retinal detachment, progressive visual impairment, glaucoma, cataracts and ultimately

(American Diabetes Association, 1997).

Nephropathy, or a form of kidney disease, results from a break down of the

filtration system due to damage to the blood vessels and “filters” of the kidneys. It is

apparent in 10-20% of all people with diabetes, with diabetes being leading cause of

kidney failure, or end stage renal disease (ESRD) worldwide. It is estimated that 40% of

new cases of ESRD are due to diabetes. People with ESRD require dialysis or a kidney

transplant to live (American Diabetes Association, 1997).

These chronic complications are responsible for many of the deaths associated

with diabetes. However, improved self-care activities and glycemic control may help to

reduce the morbidity and mortality related to diabetes (Johnson, 1996). It is important to

have a reliable measure of blood sugar control in order to assess an individual’s success

in managing their diabetes and maintaining their blood sugar levels. Glycemic control is

often assessed through measurements of the glycosylated hemoglobin or Hemoglobin

A1c level (HbA1c level). This value is expressed as a percentage, and represents an

individual’s average blood glucose for the past 2-3 months. A higher value of HbA1c

would indicate less glycemic control. A normal range in individuals without diabetes is 4

to 6%, and the goal for individuals with diabetes is to keep the HbA1c level below 7%

(American Diabetes Association, 1997). If an individual has been successful at

maintaining normal blood sugar levels for the past 2 to 3 months by following their

regimen, then they will have an HbA1c level that is close to the normal or ideal range.

However, an individual who has not maintained good blood glucose control will have a

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higher HbA1c level. Therefore, HbA1c levels can be considered a good indicator of

adherence to the diabetes regimen (Hanson et al., 1996).

Results from the DCCT (2001) demonstrate the benefits of improved glycemic

control in reducing the risk of long-term complications in individuals with Type I

diabetes. Lowering blood glucose levels resulted in delayed, as well as a slower

progression of retinopathy, neuropathy and nephropathy (DCCT, 2001). In addition, the

UKPDS research (2001) has shown similar benefits of glycemic control in individuals

with type 2 diabetes. A significant reduction in complications was achieved when

HbA1c levels were lowered to 7.0% over 10 years. An analysis of the UKPDS data

showed a relationship between the risk of microvascular complications (nephropathy,

neuropathy, and retinopathy) and glycemic control. Specifically, for every percentage

point decrease in HbA1c (e.g., 9 to 8%), there was a 35% reduction in the risk of

microvascular complications as well as a 25% reduction in diabetes-related deaths, a 7%

reduction in all-cause mortality, and an 18% reduction in combined fatal and nonfatal

myocardial infarction (UKPDS Research Group, 2001). It had been suggested that poor

adherence to this regimen can be considered the greatest obstacle in controlling diabetes

and preventing long term complications (Kelly, 1995; Sullivan & Joseph, 1998).

Adherence

In existing literature, adherence has been defined as the level of participation

achieved in a behavioral regimen once the individual has agreed to undertake it(King,

1994). Adherence has also been defined as the degree to which a patient’s voluntary

behavior corresponds with the clinical recommendations of health care providers (Rand

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& Weeks, 1998). Adherence suggests that patients are self-sufficient individuals who

take an active and voluntary role in defining and achieving goals for their medical

treatment (Lutfey & Wishner, 1999). This is different than compliance, which is defined

as the extent to which a person’s behavior follows a medical or health prescription and

suggests that patients must yield to or obey physician’s instructions (Lutfey et al.,1999).

Kavanagh (1993) suggests that in order to increase adherence to the diabetes

regimen, it is important to determine what predicts an individual’s ability to maintain the

treatment objectives after the initial diabetes education program. Determining reliable

predictors of adherence may allow for a better understanding of how to improve

adherence to this regimen (McCaul et al., 1987).

Various psychosocial variables have been previously examined to determine their

influence on adherence to the diabetes regimen. Predictors such as personality, family

behaviors, health beliefs (Glasgow, Wilson, & McCaul, 1985) demographic

characteristics (Travis, 1997) and beliefs about personal control and social support

(Tillotson & Smith, 1996) have been investigated. In addition, the Transtheoretical

Model (Prochaska & DiClemente, 1982) and the Theory of Reasoned Action (Ajzen &

Fishbein, 1980) have been employed in the past as theoretical frameworks for

investigating health behavior change and adherence to the diabetes regimen (Anderson,

Fitzgerald, & Oh, 1993; Sullivan et al., 1998). Although results from these studies vary,

they do merit the investigation of additional psychosocial constructs as possible

predictors of adherence to the diabetes regimen.

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Social Cognitive Theory

Bandura’s Social Cognitive Theory (SCT) (1986) has been used as a framework

for examining psychosocial factors associated with health behavior change and is a

valuable tool for understanding adherence (Glasgow et al., 1986; Skelly, Marshall,

Haughey, Davis, & Dunford, 1995). Social Cognitive Theory is based on the concept

that an individual’s actions are the result of a reciprocal interaction between the

environment, internal factors, such as cognitive, affective and biological factors, and

behavioral factors. Therefore, this theory proposes that behavior is determined by “social

influences that operate through self-processes which produce actions” (Bandura, 1997,

p.6). Bandura (1997) suggests that SCT is only applicable to specific situations and

behaviors. The outcomes an individual anticipates from performing a specific task or

behavior depend largely on their judgments of how well they will be able to perform that

behavior in a given situation. Therefore, Bandura suggests that behavior change and

maintenance are a function of both efficacy expectations and outcome expectations. A

clear explanation of these two constructs will help to clarify their role in SCT.

Efficacy expectations, also known as self-efficacy perceptions, have been defined

as the belief that one can do a particular behavior in a particular situation (Bandura,

1986). Efficacy expectations vary by level, strength and generality. Level refers to an

individual’s perception of their confidence for a particular task or behavior based on the

difficulty level of that task or behavior. Individuals who have low-level expectations feel

capable of performing simpler tasks, whereas individuals with a high level expectation

will feel confident in performing more difficult tasks. Strength refers to an individual’s

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judgment of how confident they are in performing a task or behavior. Generality refers to

the extent to which efficacy expectations for a particular action or behavior can be

generalized to other situations. For example, if an individual with diabetes is confident in

their ability to make healthful food choices in the comfort of their own home, they may

or may not feel confident in making healthful food choices when they eat out at a

restaurant.

Outcome expectations are defined as an individual’s belief about whether a

particular behavior will result in a particular outcome in a specific situation and can be

classified into three main types (Bandura, 1986). Each type can be a positive or a

negative expectation, with the positive expectation serving as an incentive and the

negative expectation serving as a deterrent. First, there are positive and negative physical

effects, including sensory experiences such as pain or pleasure and physical experiences,

such as comfort or discomfort. The next type is positive and negative social effects.

These include social reactions of others, such as approval and social recognition, or

disapproval and social rejection. The last type is positive and negative self-evaluative

reactions to one’s behavior. This includes a sense of pride and self worth or self-

dissatisfaction.

Bandura (1997) suggests that when an individual has the efficacy to perform well,

the belief that outcomes are dependent on their actions can be empowering to that

individual. Furthermore, individuals who believe that their outcomes are determined by

their behavior tend to engage in those behaviors more so than those who do not believe

that their behavior influences outcomes (Bandura, 1997).

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For example, an individual with diabetes may believe that if they exercise, they

will be able to maintain better blood sugar levels (positive physical outcome expectation).

They may also believe that exercise can lead to weight loss, social acceptance and self-

satisfaction (positive physical, social and self-evaluative outcome expectations).

However, that individual’s decision to begin exercise would greatly depend on their

confidence in their ability to exercise (efficacy expectation). If this individual did not

believe that weight loss had any effect on their physical health, social stigmas or self-

evaluation, and did not have confidence in their ability to exercise, then it would be

unlikely that they would engage in exercise. However, if this individual believes that

exercise will result in many positive outcomes and they are confident in their ability to

exercise, then it is reasonable to suggest that this person would begin an exercise

program.

This example helps to illustrate the interaction between efficacy expectations and

outcome expectations in determining health behaviors. It also demonstrates how the

application of SCT (Bandura, 1986) is intended for specific health behaviors. There has

been supporting research examining the impact of both efficacy expectations and

outcome expectations on health behaviors, especially in individuals with diabetes.

Results from such research have indicated the importance of assessing both efficacy

expectations and outcome expectations for the health behaviors (diet, exercise, and blood

glucose monitoring) specific to individuals with diabetes (McCaul et al., 1987).

Social Cognitive Theory and Diabetes

Glasgow et al. (Glasgow et al., 1989) examined the relationship between diabetes-

specific social learning factors and diabetes self care in 127 individuals with type 2

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diabetes. Measurements included height and weight, behavioral demonstrations related

to diet and social skills and assessments of medications, diet, exercise and glucose

testing. Self-efficacy was assessed using a diabetes self-efficacy scale. This scale

assessed an individual’s confidence in their ability to perform a graded series of diabetes

care behaviors (e.g., test blood sugar levels once a day, twice a day) for diet, exercise,

blood glucose monitoring and medication use. In addition, outcome expectations were

measured using a 20-item outcome expectations scale. This scale measured an

individual’s perception of the consequences of performing the various diabetes self care

behaviors. For example, “If I exercise daily, my diabetes will be better controlled”.

According to Glasgow (1989), although both exercise and diet behaviors were

viewed as being important (high outcome expectations), they were also viewed as

difficult to achieve (low self-efficacy). Conversely, glucose testing was perceived as

relatively easy to perform (high self-efficacy) but as having fewer beneficial outcomes

(low outcome expectations) than the other behaviors. Medications were viewed as having

important consequences (high outcome expectations) as well as having the highest

efficacy rating . The more medically related treatment activities, such as taking

medications and monitoring blood glucose, were seen as behaviors that these individuals

had to do in order to take care of their health. Conversely, lifestyle related behaviors,

such as diet and exercise, although seen as desirable, were viewed as much more difficult

to achieve. Therefore, Glasgow (1989) determined that behavioral research on

individuals with type 2 diabetes should focus primarily on ways to initiate and maintain

lifestyle changes.

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McCaul (1987) employed Bandura’s work (1977) into a study examining 107

individuals with diabetes. The main purpose of this investigation was to determine

predictors of adherence to insulin injections, blood glucose monitoring, exercise and diet.

McCaul examined four categories of predictors, including knowledge, expectancies (self-

efficacy and outcome expectations), skills and environmental support (barriers to

adherence, support of family, satisfaction with medical care). All measures were

collected by interviews and the interview series was repeated 6 months after the initial

assessment. Self-efficacy was measured using a 24-item scale assessing an individual’s

confidence (rated on a 100-point scale) in their ability to perform the regimen behaviors.

A 19-item outcome expectancies scale was administered which contained both positively

and negatively perceived consequences resulting from performing the regimen behaviors.

Adherence for each of the regimen behaviors was assessed through self–reports.

Results showed that self-efficacy, outcome expectancies and environmental

support were the best predictors of adherence, when compared with skills and knowledge

(McCaul et al., 1987). Furthermore, self-efficacy was the only individual variable that

predicted adherence to every regimen behavior. It was concluded that adherence was

better when people believed that they could execute the regimen behavior (self-efficacy)

and believed that the behavior would produce more positive than negative outcomes

(outcome expectations) (McCaul et al., 1987).

From this research, it is apparent that both efficacy expectations and outcome

expectations can be considered good predictors of adherence to a diabetes regimen.

However, self-efficacy has received more attention as a consistent predictor of adherence.

As suggested by Bandura (1986), individuals are much more likely to engage in a

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particular behavior when they have confidence in their ability to perform that behavior.

Therefore, a closer look at self-efficacy may help to clarify its influence on adherence to

a diabetes regimen.

Self-Efficacy

As discussed earlier, efficacy expectations or self-efficacy is defined as a

judgment of one’s capability to perform a particular behavior or task (Bandura, 1986).

Bandura (1997) believes that there are four main sources of self-efficacy information:

performance accomplishments, vicarious experience, verbal persuasion, and

interpretation of physiological or emotional state. A clear understanding of these sources

and how they can be applied to a diabetes population may be useful in determining ways

to influence self-efficacy in these individual’s.

The first source, performance accomplishments, refers to learning through

personal experiences in which one achieves mastery over a difficult or previously feared

task or behavior. Previous experience will serve as the greatest source of efficacy for an

individual. For example, an individual with diabetes may experience difficulty with or

feel uneasy about weight training. However, if this individual works with a personal

trainer to receive instruction on the proper techniques for these exercises, and performs

these exercises themself, then this individual will have a better understanding of how to

properly engage in weight training. Hence, this individual will experience an increase in

self-efficacy for weight training.

The second source of self-efficacy, vicarious experiences, involves learning

through observation of other people. By watching another individual in a similar

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situation perform a particular task or behavior, one may be more inclined to feel he or she

is capable of performing the same task or behavior. For example, if an individual with

diabetes observes a friend with diabetes engaging in an exercise program, that individual

may believe that they are capable of exercising as well.

The third source is verbal persuasion, which consists of encouragement from

others to change behavior. This encouragement can come from health care providers,

family or friends. For example, an individual with diabetes who maintains contact with a

diabetes educator may experience constant praise and encouragement for his or her daily

accomplishments. This will help increase that individual’s self-efficacy diabetes self-

management.

The fourth source of self-efficacy is the interpretation of a physiological or

emotional state. Bandura suggests that high physiological arousal can impair

performance; therefore, an individual is more likely to expect failure when he or she is

tense and agitated (Bandura, 1986). For example, an individual with diabetes who is

experiencing a hypoglycemic event may misinterpret feelings of lightheadedness or

dizziness as something other than hypoglycemia. Therefore, this individual may not feel

capable of remedying this situation and may feel very anxious or nervous as a result.

However, if that individual is aware of the typical signs and symptoms of hypoglycemia

and is educated on how to treat hypoglycemia, then he or she will be less likely to

misinterpret symptoms or feel anxious about the situation.

Bandura (1986) suggests that self-efficacy can influence all aspects of behavior.

This includes the acquisition of new behaviors, inhibition of existing behaviors,

disinhibition of current behaviors, choices of behavioral settings, the amount of effort

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expended in a task, and the length of time one will persist in the face of obstacles. For

example, self-efficacy can have an influence on whether or not an individual with

diabetes begins an exercise program and how much effort that individual puts into each

exercise session. Furthermore, self-efficacy has been shown to contribute to motivation

by influencing aspirations and goals as well as affecting the outcomes expected from

one’s effort (Senecal, Nouwen, & White, 2000).

Effects of Increased Self-Efficacy on Diabetes Control

A review of previous studies that have attempted to manipulate self-efficacy

suggests that self-efficacy can be improved through intervention, and that this

improvement is related to subsequent behavior change (Strecher, DeVellis, Becker, &

Rosenstock, 1986). Research confirms that manipulations of self-efficacy can be

effective in enhancing health behaviors in areas such as cigarette smoking, alcohol abuse,

exercise, and weight control (Strecher et al., 1986). Research has also shown that higher

self-efficacy is associated with higher self-rated adherence (McCaul et al., 1987).

Therefore, self-efficacy should be considered a crucial component in the area of health

behavior change and maintenance.

Research conducted by Kavanagh et al. (1993) in individuals with diabetes has

demonstrated that self-efficacy is a significant predictor of adherence to the diabetes

regimen. The relative contributions from both efficacy expectations and initial adherence

levels were examined for predicting later adherence to a diabetes regimen. The study

was conducted over an eight-week period and involved 72 individuals with either type 1

or type 2 diabetes. Self-efficacy was measured through the administration of a

questionnaire assessing confidence for glucose testing, diet and exercise. Adherence was

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measured through self-reports of glucose tests, diet and exercise. HbA1c levels were

used to determine glycemic control. Results showed that pre-test self-efficacy for

glucose testing, diet and exercise were negatively correlated with post-test HbA1c levels.

In particular, self-efficacy was the best predictor of adherence to the diet and exercise

components of the regimen. Kavanagh et al. (1993) concluded that self-efficacy was a

significant predictor of later adherence to the diabetes regimen, even after past levels of

adherence were taken into account.

In a similar study conducted by Anderson et al. (1995), a six-week patient

empowerment education program was offered to individuals with diabetes (64% had type

2 diabetes) in order to improve self-efficacy and reduce blood glucose levels (as assessed

by HbA1c levels). The education program was arranged as a two-hour group session

offered weekly for six weeks. In each session there was a presentation of key concepts,

completion of individual self-assessments, planning worksheets and group discussions of

worksheet responses and insights. Self-efficacy was assessed by measuring each

participant’s perceived ability to perform various behaviors, such as identifying and

achieving goals, managing stress and attaining social support. The results from this study

demonstrated that the intervention group experienced greater gains in self-efficacy than

the control group. In addition, the intervention group experienced a significantly greater

reduction in HbA1c levels than the control group. From these results, it was determined

that patient empowerment aimed at increasing self-efficacy was consistent with improved

glycemic control (Anderson et al., 1995).

This literature review suggests that increasing self-efficacy influences adherence

to the diabetes regimen, as shown by lower HbA1c levels. These results indicate that

17

self-efficacy has a significant influence on behavior change and maintenance. Therefore,

efforts made to increase self-efficacy for the various diabetes self-care behaviors may

greatly improve self-management of diabetes. However, it has been proposed that self-

efficacy is not the sole determinant of behavior change (Wallston, 1989). Lutfey (1999)

suggests that individuals with diabetes do not follow their prescribed regimens because

they want to have control over their daily eating and living patterns. Individuals with

diabetes resist medical advice as a response to perceived threats on their freedom and in

an attempt to recapture that freedom (Lutfey et al., 1999). Lutfey’s argument suggests

that control over one’s health, specifically management of one’s diabetes, may be a

significant predictor of adherence to the diabetes regimen. Thus, perceptions of control,

specifically health locus of control and a sense of control, may also play a role in

behavior change and adherence (Lutfey et al., 1999; Wallston, 1989).

Perceptions of Control

Health locus of control is defined as a generalized expectation about whether

one’s health is controlled by one’s own behavior or forces external to oneself (Wallston,

Wallston, & DeVellis, 1978). Health locus of control is comprised of two components,

internal locus of control and external locus of control. An individual with an internal

locus of control believes that outcomes are a direct result of his or her own behavior. An

individual with an external locus of control believes that outcomes are a result of either

chance or powerful other people, such as physicians (Wallston & Wallston, 1982).

Rotter’s (1954) Social Learning Theory employs locus of control as a generalized

expectancy. Generalized expectancies are applicable in situations in which an individual

18

relationship between perceptions of control and adherence to the diabetes regimen has

found supporting evidence for the relationship between these two variables.

Reynaert et al. (1995) conducted a study to determine if there was a relationship

between internal locus of control and improved metabolic control (indicated by HbA1c

levels) and whether this relationship was evident in individuals with either type 1 or type

2 diabetes. A total of 61 participants, 36 with type 1 diabetes and 25 with type 2 diabetes,

were included in the study. Every participant attended a scheduled clinic visit in which

they completed questionnaires assessing demographic data, general locus of control,

health locus of control, and diabetes knowledge. Both Rotter’s One Dimensional Locus

of Control Scale (1954) and Wallston’s Multidimensional Health Locus of Control Scale

(1978) were used to assess locus of control. The results indicated that for individuals

with type 1 diabetes, those with an internal locus of control had significantly lower

HbA1c levels than those with an external locus of control. Individuals with type 2

diabetes who had an internal locus of control exhibited lower HbA1c levels than those

with an external locus of control, however, these results were not statistically significant

(Reynaert et al., 1995).

In a study with similar findings, Macrodimitris et al. (2001) examined the

relationship between perceived control and HbA1c levels in 115 individuals with type 2

diabetes. All participants were mailed a questionnaire packet, which they were instructed

to complete and return within one month. The questionnaire packet assessed coping

strategies, perceived control, depression and HbA1c levels through self-report. Perceived

control was measured using the Event Perception measure (Conway & Terry, 1992) and

was adapted for individuals with diabetes. Results indicated that perceived control was

20

negatively related to HbA1c levels. Therefore, high-perceived control has a beneficial

effect on individuals with type 2 diabetes, as demonstrated by lower HbA1c levels. It

was concluded that one’s perception of control over their condition is a good indicator of

whether or not that individual will actually exhibit control over their condition. This

conclusion was substantiated by a quote from a participant in the study, stating, “I can’t

change the fact that I have diabetes, but I can control it” (Macrodimitris & Endler, 2001).

A study conducted by Surgenor et al. (2000) investigated the relationship between

sense of control and metabolic control in 96 females with diabetes. All participants were

mailed questionnaire packets, which assessed demographic data and sense of control.

Control was assessed using a scale that measured three components (Shapiro, 1994): 1)

Sense of control, or a person’s view that they had control, 2) mode of control, or the

means by which the individual attains and maintains a sense of control, and 3) motivation

for control. Metabolic control was assessed using the HbA1c measure. Results were

similar to those from Macrodimitris (2001). Those participants that had optimal HbA1c

levels had significantly higher levels of sense of control in all three domains than those

with poorer HbA1c levels (Surgenor, Horn, Hudson, Lunt, & Tennent, 2000).

From these studies, it appears that perceptions of control, operationalized as locus

of control and perceived control, are consistent predictors of adherence to a diabetes

regimen. Reynaert et al. (1995c) recognized that there are multiple predictors of

metabolic control in addition to locus of control. As was found in studies discussed

previously, self-efficacy is one of those determinants. However, none of the studies, that

assessed perceptions of control, employed either of the constructs from SCT (self-

efficacy and outcome expectations) into their theoretical framework. Therefore, it would

21

be beneficial to integrate perceptions of control, self-efficacy and outcome expectations

into one study to determine each constructs contribution to adherence among individuals

with diabetes.

Integrating Perceptions of Control with Social Cognitive Theory

Bandura (1997) suggests that the combined influence of efficacy beliefs and

expected outcomes would be the best predictor of human behavior and emotional states.

An individual will be less likely to engage in a behavior if they do not believe that a

particular behavior will lead to a particular outcome and if they do not believe that they

are capable of performing a particular behavior (Bandura, 1997). Furthermore, in

reference to Rotter’s theory of personality (1966), Bandura (1997) states “behavior is

influenced by generalized expectations that outcomes are determined either by one’s

actions or by forces beyond one’s control” (Bandura, 1997, p.19). Thus, Bandura

suggests that that health locus of control plays a role in behavior change and

maintenance. Therefore, assessing self-efficacy, outcome expectations and perceptions

of control into one study may allow for the best prediction of adherence to health

behaviors.

Although self-efficacy is a much stronger predictor of behavior than locus of

control (Wallston, 1989), Wallston (1989) suggests that the effect of self-efficacy beliefs

on behavior is modified by one’s locus of control. Furthermore, Wallston (1989, p.100)

believes that “one cannot be self-efficacious without being somewhat internal, but one

can easily be internal without being self-efficacious.” Therefore, it is more likely for an

22

individual with an internal health locus of control to have a higher self-efficacy for health

behaviors when compared to an individual with an external health locus of control.

The diabetes population provides an excellent study group for the integration of

these theoretical constructs. An individual with diabetes may consider weight loss as an

outcome (outcome expectation) of following a proper diet. That individual’s beliefs about

their ability to follow a proper diet (efficacy expectations) may be strengthened by the

belief (perception of control) that they have control (internal locus of control) over their

weight loss. However, if that individual did not believe that they had control over their

weight loss (external locus of control), then it is less likely that they would engage in a

behavior that would cause them to lose weight. This example demonstrates how self-

efficacy, outcome expectations and perceptions of control play a fundamental role in

predicting adherence to the diabetes regimen, as well as supporting the moderating role of

health locus of control in SCT.

Research has demonstrated the independent predictive values of self-efficacy and

perceptions of control in adherence to the diabetes regimen. However, little research

exists that examines the influence of outcome expectations on diabetes self-management.

Although Glasgow (1989) measures both efficacy expectations and outcome expectations

into his research, the assessment of outcome expectations may be considered inaccurate.

Glasgow (1989) suggests that his subjects possessed high outcomes expectations for diet

and exercise by indicating that they viewed these tasks as important. However, Rotter

(1966) would disagree with this interpretation, suggesting that this is an example of a

valued reinforcement. This example demonstrates the inaccuracy in past research of

23

measuring outcome expectations and merits the need for a more precise definition and

accurate assessment of this construct.

Furthermore, there has been little research that has integrated perceptions of

control, outcome expectancies and self-efficacy into one research study to examine their

independent or combined influence on adherence to the diabetes regimen. In addition,

most research that examines adherence to the diabetes regimen relies solely on self-

reported measures of adherence. Since self-reports have been shown to inaccurately

assess adherence, it may be beneficial to employ the HbA1c assay in order to determine

adherence to the diabetes regimen (Hill & Davies, 2001; Sims, Smith, Duffy, & Hilton,

1999; Wallihan, Stump, & Callahan, 1999). Therefore, in an attempt to enhance

adherence to the diabetes regimen, it may be beneficial to examine the independent, as

well as the combined contributions of perceptions of control and Social Cognitive

Theory.

Study Purposes

Considering the conclusions from this review and the limitations from previous

studies, the present investigation was a part of a larger study designed to enhance

adherence to the diabetes regimen. For the interests of this particular investigation, only

the data that was collected at the baseline of the larger study was analyzed.

Social Cognitive Theory (Bandura, 1986) and perceptions of control were

employed as the theoretical framework for this investigation. The first objective was to

determine if there was a correlation between the constructs of Social Cognitive Theory

and perceptions of control. The second general objective was to determine if self-

24

efficacy for the diabetes-specific tasks, outcome expectations for performing these

behaviors, and perceptions of control for overall diabetes management were related to

adherence to the diabetes regimen.

25

Study Hypotheses

Hypothesis 1: There will be significant positive correlations among self-efficacy,

outcome expectations, internal locus of control, and perceived control.

Hypothesis 2: Those individuals with a high self-efficacy for diet, physical

activity and blood glucose monitoring will exhibit better adherence to their diabetes

regimen (HbA1c) than those individuals with a low self-efficacy for these behaviors.

Hypothesis 3: Those individuals with a high self-efficacy for exercise will report

higher levels of physical activity.

Hypothesis 4: Those individuals who have greater expectations that diet, physical

activity and blood glucose monitoring are related to good blood sugar control will exhibit

better adherence to their diabetes regimen (HbA1c) than those individuals who have

lower expectations that these behaviors are related to good blood sugar control.

Hypothesis 5: Those individuals who perceive more control over their diabetes

care will exhibit greater adherence to their diabetes regimen (HbA1c) than those

individuals who perceive less control over their diabetes care.

Hypothesis 6: Those individuals with higher levels of internal locus of control

will exhibit better adherence to their diabetes regimen.

26

METHODS

BRIDGE Study Overview

Glasgow (1985) suggests that health professionals interested in designing

programs to enhance adherence to a diabetes regimen need to emphasize strategies to

improve dietary compliance and physical activity, as these are the areas reported as

having the highest frequency of barriers to adherence. Therefore, the BRIDGE

(BRInging Diabetes General Education to life) intervention was designed to assist

individuals with type 2 diabetes in applying their diabetes education to everyday life.

This will be accomplished over a six-month period. Participants in the study will be

instructed on how to set realistic goals for themselves for their diabetes regimen and

diabetes care and will be educated on how to achieve these goals gradually, one step at a

time. For the purposes of this particular investigation, only baseline data from the

BRIDGE study were analyzed.

Participants

Participants for this study were 16 individuals with type 2 diabetes, recruited from

the Diabetes Care Center at the Wake Forest University Medical Center in Winston-

Salem, North Carolina. Participants were included in the study if they had diabetes

mellitus according to the 1997 American Diabetes Association criteria: (1) fasting plasma

glucose greater than 126 mg/dl or (2) symptoms of hypoglycemia with causal plasma

glucose greater than 200 mg/dl or (3) a two-hour plasma glucose of greater than 200

mg/dl after a 75g oral glucose tolerance test. In addition, participants were required to

27

(1) have an HbA1c level greater than 6.9%, (2) be 55 years or older, (3) have at least one

elevated serum creatinine level, defined as greater than 1.5mg/dl, within the past 12

months, (4) have completed diabetes education within the past 6 months, (5) have

physician approval, and (6) have insurance coverage to cover the cost of the educational

program.

Participants were excluded from the study if they reported (1) a history of

hypoglycemic coma/seizure within the last 12 months, (2) a hypoglycemia event

requiring third party assistance within the last 3 months, (3) type 1 diabetes,

(4) unwillingness to undergo capillary blood testing, (5) inability or unwillingness to

participate in group classes or individual counseling sessions, (6) any ongoing medical

therapy with known adverse interactions with glycemic interventions, (7) any

cardiovascular or other disease which would prohibit participation in physical activity,

(8) renal dialysis, (9) any factors likely to preclude adherence to the study protocol, such

as dementia, alcohol or substance abuse, (10) failure to complete informed consent or

(11) were participating in another clinical trial at the time of recruitment.

Measures The measures used in the present study have been divided into the following

categories: health history and physical activity, adherence, self-efficacy, and outcome

expectations, and locus of control and perceived control (see Appendix C). Although

there were many additional measures employed for the larger BRIDGE study, only those

specific to the current investigation will be described.

28

Health History and Physical Activity

Diabetes Health History. This is a comprehensive questionnaire that is diabetes-

specific and assessed various diabetes-related issues, including history of hypoglycemic

events, kidney, nerve or eye problems, insulin use and blood sugar testing. In addition, it

contains demographic and general health history assessments, including history of

cardiovascular disease, heart attack and stroke (Michigan Diabetes Research and Training

Center (MDRTC), 1998). Also collected were medication lists, including all diabetes

medications, vitamins, supplements and any additional medications, as well as dose and

frequency of each medication. Finally, time since diabetes education class (DEC) was

determined for each participant, based on the number of weeks since the completion of

diabetes education to the day the questionnaires for this study were completed.

Physical Activity Scale for the Elderly (PASE). This questionnaire assessed an

individual’s current level of physical activity and exercise. There are ten items in which

participants were asked to indicate, over the past seven days, how much time they were

involved in each activity. The activities addressed were leisure time activity, household

activity and work-related activity. Answers included “never,” “seldom,” “sometimes,”

and “often.” Participants were then asked to list the specific activities they were involved

in and to indicate how many hours per day that they engaged in these activities. Answers

included “less than 1 hour,” “1 but less than 2 hours,” “2 to 4 hours,” and “more than 4

hours.” The total PASE score was determined by multiplying the amount of time spent in

each activity (hours per day over a 7 day period) by the respective “weight”, or

contribution to overall physical activity of each activity, and summing over all activities.

A higher score on the PASE indicates more physical activity.

29

Washburn et al. (1993) reported a test-retest reliability of 0.75 (95% CI = 0.69-

0.80). Construct validity was determined by correlating PASE scores with health status

and physiologic measures. PASE scores were positively associated with grip strength

(r = 0.37), static balance (r = 0.33), leg strength (r = 0.25) and negatively correlated with

resting heart rate (r = -0.13), age (r = -0.34), and perceived health status (r = -0.34).

Adherence

HbA1c levels. This is a measure of an individual’s average blood glucose over

the past two to three months and is measured by analysis of a blood sample. The normal

range for individuals without diabetes is 4 to 6%, and the goal for individuals with

diabetes is to keep the HbA1c level below 7% (ADA, 1997). Although HbA1c is a

measure of glucose control, for the purposed of this study, HbA1c served as an indication

of adherence to the overall diabetes regimen, including diet, exercise/physical activity

and blood glucose monitoring.

Nathan et al. (1984) reported that the health history and clinical data typically

used by physicians to assess their patients with diabetes does not provide information

about glucose control over time, nor does sporadic fasting or random blood glucose

measures. Furthermore, it was determined that 24% of the physician’s estimates of their

patients’ blood glucose levels were inaccurate by more than 75 mg/dl. Therefore, Nathan

et al. (1984) concluded that the HbA1c provides a measure of glucose control that is

unattainable by any other clinical outcome measure.

Diabetes-Related Self-Efficacy and Outcome Expectations

Diabetes-Related Cognitive and Social Factors (DRCSF). This questionnaire

assessed diabetes-related self-efficacy and outcome expectations in various situations that

30

an individual with diabetes may experience. This measure is Section III of a larger

measure, the Multidimensional Diabetes Questionnaire (MDQ), developed by Talbot et

al. (1997). The self-efficacy scale contained 7 items in which participants was asked to

indicate, on a scale of 0% (not at all confident) to 100% (highly confident), how

confident they were in dealing with various diabetes-specific situations, with higher

scores indicating more confidence. For example, “How confident are you in your ability

to test your blood sugar at the recommended frequency.” The outcome expectations scale

contained 6 items in which participants were asked to indicate on a scale of 0 (not at all

likely) to 100 (very likely) how likely it was that various tasks were related to their

diabetes care. For example, “How likely is it that measuring your blood sugar is related

to controlling your diabetes?” Adding the score of each individual item and dividing by

the total number of items determined the total score for each of these scales, with a higher

score indicating a positive answer. Therefore, the range of possible scores for the self-

efficacy scale was 0 to 7, and the range of possible scores for the outcomes expectations

scale was 0 to 6.

Talbot’s (1997) report of the MDQ’s psychometric properties confirmed this

measure to be a valuable tool for clinical use. The self-efficacy measure proved to be a

valid measure with a Cronbach’s Alpha of α = .89. The outcome expectations measure

also proved to be valid, with a Cronbach’s Alpha of α = .86 (Talbot, 1996).

Efficacy for Exercise

This measure was derived using one item from the self-efficacy subscale of

Talbot’s MDQ (1997) which asked “How confident are you in your ability to exercise

regularly?” The individual was asked to indicate, on a scale from 0 (not at all confident)

31

to 100 (completely confident) how confident they were that they could exercise regularly.

Therefore, a higher score would indicate more confidence in ability to exercise regularly.

This measure was developed specifically for the purposes of this study.

Locus of Control

Multidimensional Health Locus of Control (MHLC). This questionnaire assessed

an individual’s beliefs about their current medical condition. The original form, Form A

contained 11 items, however, over the years, two new forms, Form B and Form C, have

been developed. For our study purposes, we employed Form C, which can be adapted for

specific situations. This 18-item scale is broken down into four subscales: internal locus

of control (6 items), chance locus of control (6 items), doctor locus of control (3 items),

and other people locus of control (3 items). Participants were asked to indicate how

strongly they agree or disagree with each of the 18 items using a 6-point Likert scale,

ranging from strongly disagree (1) to strongly agree (6). Total scores were determined

for each subscale, as all the subscales are independent of one another. This was done by

summing the scores for each item in each of the four subscales. Therefore, the range of

possible scores for the internal and chance subscales was 6 to 36 and the range of

possible scores for the doctor and other people subscales was 3 to 18 (Wallston, Stein, &

Smith, 1994). Wallston et al. (1994) reported Cronbach alpha’s for each of the four

subscales: internal (α = .87), chance (α = .82), doctors (α = .71) and other people (α =

.71). Thus, Form C of the MHLC Scale is a valid measure of locus of control.

Perceived Control

Perceived Control of Diabetes Scale (PCDS). This measure addressed eight

diabetes-specific scenarios. For these study purposes, 4 out of the 8 scenarios were

32

chosen for use in this investigation. Participants were asked to write down the single

most likely cause for each scenario. Participants were then asked to rate on a 7-point

scale, ranging from 6 (absolute contribution) to 0 (no contribution), how five separate

possible causes may have contributed to the likely cause. The possible causes or

subscales were Internality, Externality, Chance, Patient control, and Medical control.

Forseeability and Treatment were two additional subscales used in measure, however,

they were not employed for this study. Scores from each of the subscales were summed

across all four scenarios to determine a total score for internal perceived control, external

perceived control, patient perceived control and doctor perceived control. Therefore, the

range of possible scores for each of the subscales was 0 to 24.

Scores for each of the subscales were summed across all four scenarios to

determine three composite scales, which indicated the extent to which the individual

perceived Personal Control, Medical Control and Situational Control. Only the Personal

Control composite score was calculated for the purposes of the present study. This was

achieved by combining the total scores for the Internality and Patient control subscales

and dividing by 2, producing a range of possible scores from 0 to 24 (Bradley, Lewis,

Jennings, & Ward, 1990). This measure proved to be a good indicator of perceived

control, as the composite scores reported by Bradley et al. (1990) were valid for Personal

Control (α = 0.81), Medical Control (α = 0.75) and Situational Control

(α = 0.79).

33

Procedures

As previously mentioned, the present study utilized data collected at baseline for

the larger BRIDGE study. Individuals who met the inclusion criteria received a letter

explaining the BRIDGE study. The letter informed them that they would be receiving a

phone call by a research assistant to request their participation in the study. Following

receipt of the letter, these individuals completed a telephone screen to further determine

eligibility for the study. If the participant gave verbal consent during the telephone

interview, they were scheduled for a visit to the General Clinical Research Center

(GCRC) for screening. At this point, participants signed an informed consent form (see

Appendix A) and study staff administered the Folstein Mini-Mental State Examination,

Physical Activity Readiness Questionnaire (PAR–Q), and the Geriatric Depression Scale

(GDS) (see Appendix B). These tests were administered to eliminate those participants

with cognitive dysfunction, cardiovascular disease and/or clinical depression. In

addition, sociodemographic and health history data were was collected and blood was

taken to determine serum creatinine and HbA1c levels. Individuals who were eligible for

the BRIDGE study were then randomized to either the control group or the intervention

group.

All eligible participants were then scheduled to return to the GCRC for baseline

measurements. Prescription medications and anthropometric measures were recorded

and blood was taken for a complete metabolic panel analysis. Participants also received

instruction on how to use a pedometer and were given a form for recording daily steps for

the following week. Finally, a questionnaire packet was administered which contained

34

various psychosocial measures to measure self-efficacy, outcome expectations and

perceptions of control (See Appendix C).

35

Analytic Plan

This is a cross-sectional study employing non-parametric statistics for the analysis

and interpretation of the baseline data from the BRIDGE study. The data for this study

must be interpreted with caution, as there was a small sample size and not all data were

normally distributed.

Descriptive Analysis

Descriptive statistics were used to characterize the demographics variables for this

sample of older individuals with type 2 diabetes. These variables include age, sex, race,

marital status, employment, medication use, time since the diabetes education class, and

health and activity history. Means, standard deviations, minimum and maximum values

for all participants were determined for all variables of interest.

Correlational Analysis

Bivariate correlational analyses were performed to determine the relationships

among the psychosocial variables of interest, including self-efficacy, outcome

expectations, internal locus of control and personal perceived control. In addition,

correlations were determined between each of the variables of interest and HbA1c.

Finally, the relationship between physical activity (e.g., total PASE score) and efficacy

for exercise was examined using a correlational analysis. The Speaman correlation

method was employed for the correlational analysis. In addition to this analysis, we

calculated additional correlations among secondary variables of interest that were not part

of our hypotheses.

The Spearman correlation was employed in order to measure the consistency of

the relationship among the variables of interest, as the data were was not normally

36

distributed. Although there were consistent relationships among the data, these

relationships were not always linear. Therefore, converting the raw scores of the data to

ranks (e.g., lowest score is assigned a rank of 1, the next smallest is ranked as 2, and so

on) allowed for the determination of the degree of the relationship for the ranked data,

which will serve as a measure for the degree of consistency for the original data.

37

RESULTS

Participant Characteristics

There were a total of 16 participants in this study, 6 males (37.5%) and 10

females (62.5%). The age ranged from 55 to 87 years (M age = 68.3, SD = 9.5). The

participants were predominantly white (75% White, 25% African American) and most

were married (68.8%), while the remaining participants were either single (12.5%) or

widowed (18.8%). Of the 16 participants, 12 were retired (75%), 3 worked part-time

(18.8%), and 1 worked full time (6.3%). This was a fairly educated sample, with 50.2%

of the participants having at least some college education or higher. As part of the

eligibility for this study, participants were required to have completed a diabetes

education course (DEC). The average amount of time from the completion of the DEC

to the screening for this study was 10 weeks with 69.3% of the participants having

completed the course within the past 10 weeks. All of these values are reported in

Table 1.

In addition to collecting demographic data from the study participants, a thorough

health history questionnaire was administered to assess the many health problems

characteristic of individuals with diabetes. Although eye problems are prevalent among

individuals with diabetes (American Diabetes Association, 1997), less than 50% of the

sample reported any eye problems. Cardiovascular disease is also prevalent among

individuals with diabetes, as approximately 75% of individuals with diabetes die from

heart disease or stroke. This mortality risk can be attributed to the high rates of

hypertension and dyslipidemia common among individuals with diabetes, particularly

38

those with type 2 diabetes (Roman & Harris, 1997). These rates were evident in the

current sample, as 68.8% reported having high cholesterol, 26.7% reported having a

history of angina, and 68.8% reported having high blood pressure.

In addition, medication lists were obtained from each participant, as individuals

with diabetes are subject to many medical problems. A description of the medication

usage of this sample is reported in Table 2. This table includes the diabetes-specific

medications which are used for lowering blood glucose levels.

39

Table 1. Participant Characteristics

Characteristics n (%) Age (yrs.)

55-64 65-75 76-87

7 (43.8) 4 (25.2) 5 (37.7)

Race White African American

12 (75) 4 (25)

Sex Male Female

6 (37.5) 10 (62.5)

Time since DEC (weeks) 1 – 3 6- 10 20-24

4 (30.8) 5 (31.3) 4 (30.8)

40

Table 2. Medication Usage Medication n (%)

Sulfonylureas

Yes 10 (62.5)

No

6 (37.5)

Meglitinides Yes

0 (0)

No

15 (93.8)

Biguanides Yes

7 (43.8)

No 9 (56.3) Thiazolidinedions

Yes 5 (31.3)

No 10 (62.5) Oligosaccharides

Yes 0 (0)

No 15 (93.8)

Insulin Yes No

3 (18.8) 13 (81.3)

41

Scale Reliabilities

Cronbach’s alpha was used to determine internal consistency for each of the self-

report measures used in this study. Results are reported in Table 3. Of the 13 measures

employed for this study, 5 had good internal consistency (> .80), whereas 7 had either

modest ( <.80) or poor ( <.70) internal consistency.

Table 3. Reliability Information for Self-Report Measures Measure Alpha

Diabetes Related Cognitive and Social Factors (Talbot et al, 1997)

Self-Efficacy

.86

Outcome Expectations

.89

Multidimensional Health Locus of Control Scale (Wallston, 1994)

Internal

.85

Doctor

.47

Others

.02

Chance

.42

Perceived Control of Diabetes Scale (Bradley et al., 1990)

Internal

.78

External

.77

Chance

.94

Patient

.92

Doctor

.75

Physical Activity Scale for the Elderly (PASE) (Washburn, 1992)

PASE – total score

0.56

PASE- Exercise 0.45

42

Descriptives for Variables of Interest

The means, standard deviations, minimum and maximum values for each of the

primary variables of interest, including diabetes-related self-efficacy, outcome

expectations, internal locus of control, perceived personal control, PASE total score,

efficacy for exercise, and HbA1c are reported in Table 4. This group as a whole had a

moderate diabetes-related diabetes-related self-efficacy, as the mean score was 72.8 out

of a possible 100, and 87.5% of the sample had a mean value above 50% on this scale.

The efficacy for exercise variable is measured using a single item from the diabetes-

related self-efficacy scale. Although the mean score for exercise-efficacy is not very high

(M = 66.3, SD = 32.8), over 50% of the participants reported being 70% confident or

higher on this single item. The sample had moderately high outcome expectancies, with

a mean score of 70.3 out of a possible 100, and 87.5% reported a mean greater than 50.

Most of the individuals exhibited an internal locus of control, as the mean score was 31.2

out of a possible 36, and all participants scored greater than 20. Over 50% of the sample

reported a score of 20 or greater, out of a possible score of 24, on the personal perceived

control scale. Therefore, most participants attributed control of their diabetes to

themselves.

The Physical Activity Scale for the Elderly was employed as a measure of

adherence to the exercise/physical activity portion of the diabetes regimen. The

participants of this study had an average PASE score of 115.2, which is lower than that

reported by Washburn et al. (144.2) for individuals aged 66-64 years old (1999).

However, activity level is similar to the average (188.9) reported by Washburn et al.

43

(1999) for individuals aged 65 years and older. The mean glycosylated hemoglobin value

(HbA1c) for these participants was 7.2, (SD = 1.51) with a range of 6.1 to 12.4.

According to guidelines set forth by the American Diabetes Association (1997), the goal

for individuals with diabetes is to keep their HbA1c values below 7%, whereas the

normal HbA1c range for individuals without diabetes is 4 to 6%.

Table 4. Descriptive Statistics for Primary Variables of Interest

Efficacy for Exercise 66.3 32.8 10.0 100.0

Outcome Expectations 70.3 16.2 34.3 85.7

Multidimensional Health Locus of Control Internal Health Locus of Control 31.2 4.8 21.0 36.0

Perceived Control of Diabetes Perceived Personal Control 19.6 5.0 7.5 24.0

Adherence HbA1c

7.2 1.5 6.1 12.5

Physical Activity History PASE 115.2 63.2 41.0 281.7

Variable Mean SD Min Max

Diabetes Related Social and Cognitive Factors Diabetes-Related Self-Efficacy 72.8 19.5 37.1 100.00

In addition to the primary variables of interest, a group of secondary variables

were assessed, including additional measures of locus of control and perceived control.

These variables and their descriptive statistics are reported in Table 5. Doctor locus of

control, others locus of control and chance locus of control represent measures of external

44

locus of control. Internal perceived control and patient perceived control are the

components of the perceived personal control score, which is a primary variable of

interest. Chance, external and doctor perceived control represent the external perceived

control measures. PASE exercise represents a modified version of the PASE

questionnaire that includes only those items specific to leisure time/recreational activities

and exercise. Thus, items addressing household or work related activity were removed.

Table 5. Descriptive Statistics for Secondary Variables of Interest

Variable Mean SD Min. Max.

Multidimensional Health Locus of Control (External) Doctor Locus of Control 16.6 2.1 12.0 18.0

Others Locus of Control 7.4 2.6 3.0 11.0

Chance Locus of Control 9.6 3.9 6.0 16.0

Perceived Control of Diabetes Internal Perceived Control 19.7 4.7 9.0 24.0

Patient Perceived Control 19.4 5.7 6.0 24.0

Chance Perceived Control 5.1 6.1 0.0 22.0

External Perceived Control 6.4 5.0 0.0 15.0

Doctor Perceived Control 4.9 5.0 0.0 16.0

Exercise/ Leisure Time Physcial Activity PASE- exercise 19.8 16.7 0.0 65

45

Correlational Relationships Among the Primary Variables of Interest

One of the main objectives of this study was to determine if there were significant

positive correlations among the primary psychosocial variables of interest, including

diabetes-related self-efficacy, outcome expectations, internal locus of control, and

perceived personal control. The Spearman correlation matrix for the Spearman

relationships is presented in Table 6.

Table 6. Spearman Correlations Among Primary Variables of Interest

Variable 1. 2. 3. 4. 5. 6. 1. Diabetes-Related Self-Efficacy

1.0

2. Outcome Expectations

.843** 1.0

3. Internal Locus of Control

.025 .389 1.0

4. Perceived Personal Control

.181 .334 .557* 1.0

5. HbA1c

-.216 -.307 -.256 -.430 1.0

6. Efficacy for Exercise

.873** .821** .238 .167 -.100 1.0

7. PASE .386 .211 .080 .406 -.284 .286 *p < .05 **p < .01

The correlational analysis revealed a significant association between diabetes-

related self-efficacy and outcome expectations (rs = .843, p < .01). Therefore, those

individuals who were confident in their ability to perform the health behaviors needed for

diabetes management (diet, physical activity/exercise and blood glucose monitoring) also

believed that these behaviors were related to controlling diabetes. There was also a

46

significant correlation between internal locus of control and personal perceived control

(rs = .557, p < .05). Therefore, those individuals who believed that they were in control

of their diabetes also attributed the responsibility of managing their diabetes to

themselves.

Correlational Relationships Among the Primary and Secondary Variables of Interest

Although the secondary variables of interest were not part of the hypotheses, they

do merit attention, as there were significant correlations evident among the primary and

secondary variables of interest. There was a significant correlation between internal

locus of control and external perceived control (rs = -.521, p < .05). Therefore, those

individuals who believed that they were in control of their diabetes did not attribute the

responsibility of their diabetes care to other sources. In addition, there was a significant

inverse correlation between age and total PASE scores. (r = -.549, p < .05). Those

individuals who were older reported less physical activity.

Correlational Relationship Between Efficacy for Exercise and PASE

The next objective of this study was to determine if there was a correlation

between efficacy for exercise and total physical activity, as measured by the PASE.

There was no correlation between efficacy for exercise and total PASE score (See Table

7). Therefore, those individuals who were more confident in their ability to exercise

regularly did not report more physical activity as measured by the total PASE score.

47

However, when the items of the PASE specific to household and work activities

were removed and only those items specific to exercise, leisure or recreational activity

remained, there was a significant correlation between efficacy for exercise and the

exercise-specific PASE score. The correlation and scatterplot for this relationship can be

seen in Figure 1. There was also a significant correlation between diabetes-related self-

efficacy and the exercise PASE score (rs = .566, p< .05).

Figure 1. Scatterplot of Efficacy for Exercise and Exercise PASE score

25.0 50.0 75.0 100.0

Efficacy for Exercise (%)

0

20.0

40.0

60.0

Exe

rcis

e PA

SE

rs = .515, p < .05

Correlational Relationships Among the Primary Variables of Interest and HbA1c

The next objective of this study was to see if each of the primary variables of

interest was correlated with lower HbA1c values. Spearman analysis revealed no

significant correlations among any of the variables of interest and HbA1c (See Table 7).

48

Therefore, individuals with greater diabetes-related self-efficacy for diet,

exercise/physical activity and blood glucose monitoring did not have better adherence

levels, indicated by lower HbA1c levels, than individuals with lower diabetes-related

self-efficacy for these behaviors. Furthermore, individuals who more strongly believed

that diet, physical activity/exercise and blood glucose monitoring could control diabetes

did not exhibit lower HbA1c values than those individuals who did not report this

outcome expectation. Individuals who believed that they were in control of their diabetes

care, or who attributed responsibility for their diabetes care to themselves did not have

lower HbA1c values than individuals who did not believe that they were in control of

their care or who attributed outside sources for their diabetes care.

Correlational Relationships Among Primary Variables of Interest and Time Since DEC

Although there was little variability among the variables of interest reported

above, there was variability for time since the diabetes education class (DEC). Although

not a hypothesis for this study, there were correlations between time since DEC and the

psychosocial variables of interest. These relationships deserve attention, as it is

suggested that these variables can decrease over time (Bandura, 1986). The amount of

time since the diabetes education class (DEC) until completing the psychosocial

assessment for this study was calculated in weeks. Diabetes-related self-efficacy was

normally distributed among this sample, therefore, a Pearson correlation was used to

analyze the relationship between diabetes-related self-efficacy and time since DEC.

There was a significant negative correlation between weeks since DEC and diabetes-

related self-efficacy. Therefore, individuals with a longer duration of time since diabetes

49

education reported lower levels of confidence in their ability to perform the tasks needed

for diabetes management (e.g.,diet, exercise/physical activity and blood glucose

monitoring). The correlation and scatterplot for this relationship can be seen in Figure 2.

Outcome expectations were not normally distributed among this sample, therefore

a Spearman correlation was used to analyze the relationship between outcome

expectations and time since DEC. This analysis revealed a significant correlation

between these two variables. Therefore, a longer duration of time since diabetes

education was inversely related to the belief that the behaviors and tasks specific to

diabetes care were related to controlling diabetes. The correlation and scatterplot for this

relationship can be seen in Figure 3.

Figure 2. Scatterplot of Time Since DEC and Diabetes-related self-efficacy

5.0 10.0 15.0 20.0

Time since DES (Weeks)

40.0

60.0

80.0

100.0

Self-

Eff

icac

y (%

)

r = -.607, p < .05

50

Figure 3. Scatterplot of Time Since DEC and Outcome Expectations

5.0 10.0 15.0 20.0

Time since DEC (Weeks)

40.0

50.0

60.0

70.0

80.0

Out

com

e E

xpec

tatio

ns

rs = -.568, p < .05

Neither internal locus of control or personal perceived control were normally

distributed among this sample of participants. According to a Spearman correlation

analysis, there was not a significant correlation evident between internal locus of control

and time since DEC or personal perceived control and time since DEC. Scatterplots and

correlations for these relationships can be seen in Figures 4 and 5.

51

Figure 4. Scatterplot of Time Since DEC and Internal Locus of Control

5.0 10.0 15.0 20.0

Time Since DEC (Weeks)

24.0

28.0

32.0

36.0

Inte

rnal

LO

C

rs = -.431, p > .05

Figure 5. Scatterplot of Time since DEC and Personal Perceived Control

5.0

Tim

8.0

12.0

16.0

20.0

24.0

Perc

eive

d Pe

rson

al C

ontr

ol

rs = -.465, p > .05

10.0 15.0 20.0

e Since DEC (Weeks)

52

DISCUSSION

Adherence to the diabetes regimen of diet, physical activity/exercise and blood

glucose monitoring is considered the greatest barrier in controlling this disease and

preventing its serious chronic complications (Kelly, 1995). Although diabetes education

gives individuals with diabetes the knowledge for how to best manage their diabetes and

maintain proper glucose control, knowledge has not been shown to be a good predictor of

adherence to the diabetes regimen (Hurley & Shea, 1992). Therefore, individuals with

type 2 diabetes may be acquiring the knowledge for how to properly control their

diabetes, yet, they are not successfully incorporating the lifestyle changes needed to carry

out these behaviors over time. This is evident in the poor adherence rates reported for

individuals with diabetes (Evan et al, 1999; Kamiya et al., 1995; Cerkoney & Hart,

1980).

Therefore, the main objectives of the current study were to examine the

relationships among the constructs of Social Cognitive Theory (Bandura, 1986) and

perceptions of control as well as to determine if there was a relationship between each

one of these constructs and adherence to the diabetes regimen (as measured by HbA1c).

53

Social Cognitive Theory and Perceptions of Control

It was hypothesized that there would be significant positive correlations among the

primary variables of interest, including diabetes-related self-efficacy, outcome

expectations, internal locus of control and perceived personal control. The hypothesis

that higher levels of diabetes-related self-efficacy would be significantly related to

outcome expectations was supported. Additionally, there was a significant positive

correlation between internal locus of control and personal perceived control. However,

the remaining relationships among the psychosocial variables were not statistically

significant.

The strong correlation between diabetes-related self-efficacy and outcome

expectations was supported by theory, as these are the underlying constructs of Social

Cognitive Theory (Bandura, 1986). These two constructs function together to predict

behavior, thus, it is suggested that an individual is most likely to perform a behavior if

they are confident in their ability to perform that behavior and believe that performing

that behavior will lead to an expected outcome (Bandura, 1997). Furthermore, if an

individual believes that their action can lead to a particular outcome, they may experience

an increase in efficacy for that action (Bandura, 1997). Therefore, these two constructs

are related to one another.

Although the relationship between diabetes-related self-efficacy and outcome

expectations is theoretically consistent, the strong correlation may have been the result of

the assessment tool employed for this study. The scales used to assess diabetes-related

self-efficacy and outcome expectations were subscales of a larger measure used to assess

social and cognitive factors related to diabetes (Talbot et al., 1997). Although the

54

subscales are based on two separate questionnaires, the items in the diabetes-related self-

efficacy subscale address similar situations (“How confident are you in your ability to

follow your diet?”) as the items in the outcome expectations subscale (“How likely is it

that following your diet is related to controlling your diabetes?”). Therefore, the

participants may have perceived these as similar questions and responded to the questions

based on similar cognitive assessments.

The strong correlation between internal locus of control and perceived personal

control was also theoretically supported, as it has been suggested that those individuals

with an internal locus of control perceive more control than those with an external locus

of control (Wallston, 1989). Therefore, it is reasonable to suggest that individuals who

believe that they are in control of their diabetes also attribute themselves as being

responsible for their diabetes care. Bradley (1994) suggests that these scales measure

similar constructs, yet, locus of control refers to expectations of control over future

events, while perceived control measures attributions for hypothetical or real events in

the past. However, as demonstrated by the results of this study, the subscales to measure

external locus of control may not be similar.

In examining the descriptive analysis of all the subscales used to assess locus of

control and perceived control, it was found that doctor locus of control and doctor

perceived control, both external measures of control, were not positively correlated, as

would be expected. In fact, the average score for this sample on the doctor locus of

control subscale was 16.6 out of 18, while the average score for doctor perceived control

was 4.9 out of a possible 24. Although this is not part of the hypothesis, this finding does

55

deserve attention, as it may support the need for a more clear definition and consistent

assessment for perceptions of control.

Diabetes-Related Self-Efficacy and Adherence

It was hypothesized that those individuals with a high diabetes-related self–

efficacy for diet, physical activity/exercise and blood glucose monitoring would have

better adherence to their diabetes regimen, as determined by lower HbA1c values than

those individuals with a low diabetes-related self-efficacy for these behaviors. This

hypothesis was not supported with our findings, as there was not a significant correlation

between diabetes-related self-efficacy and HbA1c. This was surprising, as diabetes-

related self-efficacy has been found to be a good predictor of adherence to the diabetes

regimen in past research (Kavanagh, Gooley, & Wilson, 1993d; Kavanagh et al.,1993;

McCaul et al.,1987).

The non-significant correlation between diabetes-related self-efficacy and

regimen adherence may be attributed to the low HbA1c levels of the participants in this

study. This may be a result of medication use, as 14 of the 16 participants were

prescribed glucose lowering medications, including insulin. Most (15) of the participants

in this study had HbA1c values between 6.10 and 8.50, with one outlier having an HbA1c

of 12.4. Therefore, these individuals’ glucose levels may have been lowered due to

medication use and not lifestyle behaviors.

Indeed, individuals with type 2 diabetes report higher self-efficacy for the

medically-related treatment activities, such as taking medication (M = 89.3%) and

monitoring blood glucose (M = 80.5%), and report lower self-efficacy for diet

56

(M = 77.7%) and exercise (M = 58.7%) (McCaul, 1987). This was also evident in the

current study, as the participants reported the highest level of self-efficacy for blood

glucose monitoring (M = 83.8%), with lower levels of self-efficacy for diet (M =75.6%),

exercise (M =66.3%) and weight control (M = 59.4%). Furthermore, the average HbA1c

levels for this sample was 7.2%, which is close to the ideal level (less than 7%) for

individuals with diabetes. Although records of the participants’ compliance to their

medication prescriptions were not collected, it is likely that these individuals have HbA1c

values close to the recommended value as a result of taking their medications, as they

reported the highest self-efficacy for these activities when compared to lifestyle-related

activities.

Although diabetes-related self-efficacy was not significantly related to adherence,

interestingly, there was a significant inverse relationship between time (weeks) since

diabetes education and diabetes-related self-efficacy (r = -.607, p < .05). This indicates

that individuals with diabetes may not be maintaining confidence for diet, physical

activity/exercise and blood glucose monitoring after diabetes education. This is an

important finding, as it suggests that although diabetes education programs may be

increasing diabetes-related self-efficacy for these self-care behaviors, this confidence is

not being maintained over time. Indeed, it has been suggested that individuals may reach

a certain level of successful behavior change, but do not maintain that change (Sullivan,

1998). Therefore, it may be crucial for health professionals and diabetes educators to

determine an approach for assisting individuals with diabetes not only with initiating

behavior change, but also maintaining that behavior change.

57

Self-efficacy for Exercise and Physical Activity

It was hypothesized that those individuals with a high efficacy for exercise would

report higher levels of physical activity, as indicated by total PASE scores. This

hypothesis was not supported at the bivariate level. However, in our study sample, 50%

of the participants were 65 years or older, and Washburn et al. (1992) reported that

people aged 65 years and older have lower scores on the PASE when compared to people

aged 55 to 64 years. Indeed, there was a significant inverse relationship between age and

total PASE score (rs = -.549, p < .05) in this study. Therefore, the participants in this

sample may be less physically active due to their older age. The weak correlation

between efficacy for exercise and total PASE score may also be due to the inadequate

assessment of efficacy for exercise, as it was assessed using a single item from the

diabetes-related self-efficacy measure.

However, upon removing the items from the PASE that were specific to work and

household activities, thereby creating a physical activity/exercise-specific measure of

activity, there was a significant correlation between efficacy for exercise and the

exercise-specific PASE scores. This may indicate that assessment tools should be either

exercise or activity specific. Incorporating reports of general activity (i.e., household and

work activities), physical activity and exercise into one scale may inflate the resulting

scores.

It may also be useful to clearly define the differences between physical activity

and exercise, as it may be difficult for individuals completing these measures to

differentiate between the two. A misunderstanding of the difference between physical

58

activity and exercise may cause inaccurate responses to questions assessing either

physical activity or exercise history. In addition, it may have been beneficial to employ a

measure of efficacy for physical activity for this study, as this was an older sample that

may have been more involved in physical activity than exercise. Indeed, a closer look at

the PASE scores revealed that 13 out of the 16 participants in this study reported never

engaging in strength or endurance exercises, while 15 of the participants reported

engaging in walking either seldom (4), sometimes (5), or often (6). Therefore, a stronger

correlation may have been evident between an efficacy for physical activity measure and

a specific measure of physical activity history.

Outcome Expectations and Adherence

It was hypothesized that those individuals who reported a greater belief that diet,

physical activity/exercise and blood glucose monitoring were related to good blood

glucose control would exhibit lower HbA1c levels than those who reported lower levels

of this belief. This hypothesis was not supported, as the correlation between outcome

expectations and adherence was not significant. This is surprising, as outcome

expectations have been found to predict adherence to the diabetes regimen

(McCaul, 1987).

However, the outcome expectations in this sample are comparable to those in

previous research. In past research, individuals reported higher outcome expectations for

diet (M = 76.3%), exercise (M = 78.3%) and medication use (M =75.8%), but lower

outcome expectancies for blood glucose testing (M =63.2%) (Glasgow et al., 1989).

Indeed, the participants in this study reported similar mean values for outcome

59

expectations: diet (M = 80.6%), exercise (M =75.6%), glucose testing (M = 70%) and

medication use (M = 91.9%). Additionally, the individuals in this study reported higher

levels of self-efficacy for the medically related treatment (blood glucose monitoring),

when compared to the lifestyle treatment behaviors (diet and physical activity/exercise),

although we did not have a specific measure of self-efficacy for medication use.

However, considering the high outcome expectations of this sample for medication use,

the similarity between self-efficacy scores for this sample compared to previous research

(Glasgow, 1989), and low HbA1c levels of this sample, these individuals may be

complying to their medication prescriptions, but not performing the lifestyle behaviors

imperative for successful diabetes care. Further, it is suggested that high self-efficacy

and outcome expectations may predict behavior (Bandura, 1986).

There was a significant inverse relationship between time (weeks) since diabetes

education and outcome expectations. This relationship indicates that as time since

diabetes education increased, there was a decrease in the belief that diet,

exercise/physical activity and blood glucose monitoring are related to the control of

diabetes. This is an important finding, as it suggests that diabetes education programs

may be successful at informing individuals about the effectiveness of performing these

diabetes related self-care behaviors, however, this belief diminishes over time.

Therefore, this indicates the importance of developing strategies to help maintain these

beliefs over time, as an individual will be more likely to perform a particular behavior if

they believe that the behavior will lead to a particular outcome (Bandura, 1986).

Furthermore, it would be beneficial to understand the value of well-controlled diabetes to

an individual with diabetes. If maintaining good control over their diabetes is not of

60

value to an individual, then it is less likely that that individual will engage in the self-care

behaviors essential to successful self-management (Rotter, 1982).

Personal Perceived Control and Adherence

It was hypothesized that those individuals who perceived more personal control

over their diabetes would exhibit lower HbA1c levels than those who perceived less

personal control over their diabetes. This hypothesis was not supported, as the

relationship between personal perceived control and HbA1c was not statistically

significant. This is contrary to the findings of Bradley et al. (1990), as stronger

perceptions of personal control were found to be inversely related to HbA1c levels in

individuals with type 2 diabetes who were taking oral hypoglycemic agents.

Furthermore, the mean score for personal perceived control for this study was 19.6 out of

a possible 24, which is slightly lower than the mean (22.3) reported by Bradley (1994).

One potential explanation for the lack of a significant relationship between

personal perceived control and adherence as well as for the lower average value for

personal perceived control in this study as compared to others is that the participants in

this study encountered some difficulty in responding to the Perceived Control of Diabetes

Scale (Bradley, 1994). Participants were confused as to whether they were to answer the

questions according to what they were “supposed” to do or what they actually do. While

filling out these questionnaires, participants explained that they understood that they were

the one’s responsible for their diabetes care, yet they did not always take care of

themselves the way they knew they should. Therefore, this misinterpretations of the

questionnaire items could have led to an inaccurate assessment of personal perceived

61

control. On the other hand, personal perceived control was significantly correlated with

internal locus of control, indicating that the scale used to measure personal perceived

control was a valid measure of perceptions of control.

Internal Locus of Control and Adherence

It was hypothesized that those individuals with higher levels of internal locus of

control would exhibit lower HbA1c values. This hypothesis was not supported, as there

was not a significant correlation between internal locus of control and HbA1c. Similar to

the previously discussed correlations, medication use may be the most likely explanation

for the non-significant relationship between these two variables.

There was a mean score of 31.2 out of a possible 36 on the internal locus of

control scale, which is higher than the average score (range 17.5 – 28.67) reported by

Wallston et al. (1993). Therefore, these results may suggest a skewed distribution of

scores, possibly due to the small sample. Nevertheless, the individuals in this study

reported high levels of internal locus of control. Wallston (1989) would suggest that due

to their high levels of internal locus of control, these individuals would be most likely to

perform those behaviors for which they also possessed a high level of self-efficacy.

Thus, it is likely that the individuals in this sample were successful at taking their

medications, since they have reported high levels of internal locus of control and reported

high levels of self-efficacy for the medically related aspects of their treatment.

The lack of a significant correlation between internal locus of control and lower

HbA1 levels has been found in past research employing the Multidimensional Locus of

Control Scale (Reynaert et al., 1995). Furthermore, Tillotson and Smith (1996) employed

62

a Diabetes Locus of Control scale and were unable to find a correlation between internal

locus of control and adherence to a weight control program among individuals with

diabetes. Contrary to other findings (Gregg, Kriska, Narayan, & Knowler, 1996; Schlenk

& Hard, 1984), the work of Reynaert et al. (1995) and Tillotson and Smith (1996) as well

as the findings in the current study suggest that an internal locus of control may not be

associated with better adherence to the diabetes regimen.

Summary of Findings

It is evident from this discussion that the small sample size, lack of variability in

the study variables, and the reported use of diabetes medications may help to explain the

non-significant relationships found in this study. The small sample size may have

affected the range of scores, as range is directly related to sample size (Gravetter &

Wallnau, 2000). The lack of variability in the primary variables may have also limited

the analysis of study hypothesis. In addition, it is likely that the HbA1c levels of the

participants in this study may be a result of compliance to medication use, not adherence

to the lifestyle behaviors that were of interest for this study, including diet, physical/

activity/exercise and blood glucose monitoring. Therefore, HbA1c may not be the best

measure of adherence in a cross-sectional study.

In addition, the inverse relationship demonstrated between weeks since diabetes

education and both diabetes-related self-efficacy and outcome expectations is an

important finding that should be addressed in future research and diabetes education

programs. It has been suggested that increases in self-efficacy and outcome expectations

will lead to subsequent behavior change (Bandura, 1997). Therefore subsequent

63

decreases in diabetes-related self-efficacy and outcome expectations following diabetes

education may indicate that these individuals are not maintaining the necessary lifestyle

changes needed to successfully manage their diabetes. Although the correlations between

weeks since class and increased HbA1c levels was non-significant, the low self-efficacy

ratings for diet and exercise and high self-efficacy for the medically-related treatments

implies that these individuals are complying to their medication prescriptions, but not

adhering to lifestyle changes.

64

Limitations and Future Directions

This section will address the following areas: 1) sample size and generalizability,

2) measurement tools and operational definitions, and 3) research design issues, in order

to discuss the limitations of this study as well as to make recommendations for future

research in this area.

Sample Size and Generalizability

The small sample size was a major limitation as it had an influence on the

minimum and maximum values for each of the variables assessed. For example, there

was a wide range of HbA1c levels (6.10 to 12.4) evident with this sample. However,

most (15) of the values were between 6.10 and 8.5, with one outlier having an HbA1c of

12.4%. It was later determined that this individual chewed tobacco regularly, which is

known to contain sugar, and is the most likely explanation for the extremely high HbA1c.

Therefore, future research may benefit from a larger sample size, allowing for a better

range of scores for comparison, as well as greater power to predict statistical differences.

Possible explanations for the small sample size may have been due to recruitment

strategies and lack of interest among potential participants. The sample size was

somewhat limited due to the stringent inclusion criteria of the larger BRIDGE study, and

the requirement for participants to have already received diabetes education. Requiring

participants to have received diabetes education may have resulted in a sample of

individuals who were already mindful of the efforts that they need to take to better

manage their diabetes. Thus, this sample of individuals may be a biased sample, as they

may have already been aware that their diabetes management is in their control, have the

65

confidence to perform the tasks needed to better care for themselves, and be aware of the

outcomes to expect from their health care behaviors. Diabetes-related self-efficacy

ratings (M = 72.8%) and outcome expectations (M = 70.3%) only modestly support this

interpretation. However, the elevated levels of internal locus of control (M = 31.2, out of

36) and perceived personal control (M = 19.6, out of 24) may suggest that the participants

were positively influenced by the diabetes education classes.

The study sample size also limits generalizability, as almost all of the participants

(13 of 16) were recruited from the same diabetes care center in Winston-Salem, North

Carolina. Therefore, all of the participants in this study received the same diabetes

education and had access to the same diabetes instructors and health care services.

Measurement Tools and Operational Definitions

This study has demonstrated that HbA1c may be an inadequate assessment of

adherence to the diet, physical activity/exercise and blood glucose monitoring aspects of

the diabetes regimen in a cross-sectional analysis. However, there are no widely

accepted measures of adherence to the diabetes regimen reported in the literature

(Reynaert et al, 1995). Most commonly, studies have employed either self-reports

(Senecal, 2000; Anderson, 1993) or HbA1c levels (Macrodimitris et al., 2001; Reynaert

et al.,1995; Kavanagh et a, 1993; Kamiya et al., 1995; (Coates & Boore, 1998) as

indicators of adherence. In addition, there is no direct relationship between adherence

and glycemic control (Sullivan, 1998), as factors other than self-management may

influence HbA1c (Coates et al. 1998). Therefore, if individuals in a research study are

taking glucose-lowering medication, and adherence is assessed using HbA1c, then it may

appear that these individuals were being adherent to their diabetes regimen. However, it

66

is unlikely that individuals are adhering to the lifestyle-related behaviors of their diabetes

regimen, as demonstrated by the low efficacy ratings for diet and physical

activity/exercise behaviors, as well as the low levels of physical activity/exercise

participation reported in this study. Therefore, it may be beneficial to explore other

avenues of adherence measurement for future studies interested in examining correlates

of adherence to the diabetes regimen.

Although this study focused on adherence to diet, physical activity/exercise and

blood glucose, diet records or blood glucose monitoring records were not obtained to

assess adherence to these behaviors at baseline testing. The only assessment of

adherence to a diabetes-specific lifestyle behavior was the PASE measure of physical

activity history. However, as previously discussed, the PASE may not be an accurate

assessment of activity, as it incorporates assessments of all types of activity (household,

leisure time, recreational and exercise). Therefore, future research assessing adherence to

the diabetes regimen should employ separate measures to assess adherence to each aspect

of the diabetes regimen, including diet, physical activity or exercise, blood glucose

monitoring and medication use.

In addition to using supplementary adherence measures, it may be beneficial to

include an additional measure for assessing the psychosocial characteristics of individuals

with diabetes. Rotter (1982) suggests that the value of the reinforcement or outcome is a

major predictor of behavior, however, this construct was not assessed in this study.

Assessing the value of a particular outcome to an individual will allow for a better

prediction of behavior (Rotter, 1982). Thus, determining the value of maintaining low

67

blood sugars in individuals with diabetes would be useful in future studies attempting to

understand the correlates of adherence to the diabetes regimen.

Furthermore, reevaluating the measures of perceptions of control may lead to

better assessment of this psychosocial construct in individuals with diabetes. Inconsistent

understanding of the definition of control may be the reason for the lack of significant

relationships between perceptions of control and adherence to diabetes management in

the literature (Wallhagen & Lacson, 1999). Control has been defined as “the ability to

cause an influence in the intended direction “ (Rodin 1986). However, control has also

been referred to in the literature as locus of control (Wallston, 1978), sense of control

(Shapiro & Astin, 1998), and perceived control (Wallston, 1989; Bradley, 1994). Having

so many different definitions of control can cause confusion when attempting to assess

this construct.

For example, this study measured control using Wallston’s (1978)

Multidimensional Health Locus of Control Scale (MHLC) and Bradley’s (1994)

Perceived Control of Diabetes Scale. Bradley (1994) suggests that the subscales on the

MHLC scale may be considered equivalent to those on the perceived control of diabetes

scale. Bradley (1994) also suggests that high scores on the internality and powerful

others (doctor) subscales are the most beneficial for an individual with a chronic

condition such as diabetes. However, there was an inconsistency in scores on the

external subscale between the MHLC and the Perceived Control of Diabetes Scale. That

is, participants in this study scored high in doctor perceived control (M = 19.6, out of 24)

and low in doctor locus of control (M = 4.9, out of 18). The alpha reliability for the

doctor locus of control scale was rather low (.47), whereas the reliability for the doctor

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perceived control scale was moderately high (.75). Therefore, this suggests that there may

be inconsistencies in these measures, or perhaps, a misinterpretation of the questions by

the study participants. Since increasing control or belief of one’s control is associated

with better health (Shapiro, 1998), it would be important to determine consistent

measures and definitions of control to better assess this construct in individuals with

diabetes.

Research Design

Unfortunately, much of the research in this area, including the current study, is

cross-sectional in design (Surgenor et al., 2000; Reynaert et al., 1995;. Anderson et al,

1993; Senecal, 2000). Longitudinal studies are necessary to identify the predictors of

adherence to the diabetes regimen over time. Additionally, longitudinal research may

demonstrate the impact of self-management training on psychosocial and clinical

outcomes (Via & Salyer, 1999). This is supported by the research conducted by

Kavanagh et al. (1993), as this study determined that diabetes-related self-efficacy was a

significant predictor of adherence to the diabetes regimen over time.

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Practical Implications

This section will be divided into two sections discussing the clinical as well as the

public health implications. Although the present investigation did not support the

proposed relationships between diabetes-related self-efficacy, outcome expectations,

perceptions of control and HbA1c, explanations for the lack of findings may help to

support the practical implications of this study.

Clinical Implications

Successful diabetes management is heavily dependent on lifestyle habits,

therefore, it is difficult to develop a “prescription” for diet, physical activity/exercise and

blood glucose monitoring. However, vague regimen recommendations for diet and

physical activity or exercise may cause individuals with diabetes to have difficulty with

their self-management (McNabb, 1997). Indeed, individuals with diabetes report having

the most difficulty with following diet and exercise guidelines (Glasgow, 1986), as well

as having lower self-efficacy for these activities (McCaul, 1987).

The American Diabetes Association (2000) recommends that treatment programs

for diabetes should include ongoing support from the clinical care team in order for such

programs to be most effective. Furthermore, maintaining behavior change following

diabetes education is difficult for individuals (Rubin, Peyrot, & Saudek, 1991).

Therefore, supplementing a program to assist individuals with type 2 diabetes in

incorporating the knowledge learned in a diabetes education class into daily life will

provide continued support as well as assist in individuals in personalizing the regimen

behaviors. This may be a valuable supplement to diabetes education programs and can

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be useful to those health professionals, nurses and diabetes educators who are seeking

ways to increase and maintain adherence to the diabetes regimen.

Such a program, similar to the BRIDGE intervention, would be designed to

address the individual difficulties with incorporating diet, physical activity/exercise and

blood glucose monitoring into daily life, as well as serve to increase confidence and

perceptions of control in each of these areas. This may help individuals to develop these

lifestyle behaviors into daily habits, preventing the common decrease in these behaviors

following initial behavior change (Sullivan, 1998).

This type of program may also help individuals with diabetes in developing

specific goals for themselves, goals that address what the individual would like to

achieve, not what their doctor or their diabetes educator would like them to achieve. This

can help individuals to realize that they themselves are in control of their health, and if

that individual values good health, then they would be likely to take better care of their

themselves and their diabetes (Rodin, 1986).

For example, an individual may not be concerned with preventing atherosclerosis,

but they may be concerned about their ability to play with their grandchildren without

becoming tired too quickly. Therefore, acknowledging this expectation, helping to

increase that individuals confidence in being physically active or exercising, and setting a

goal of not getting tired while playing with their grandchildren may help that individual

to be more adherent to the physical activity or exercise portion of their regimen. This

example helps to illustrate the usefulness and practical application of a supplemental

program to diabetes education.

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Public Health Implications

From a public health perspective, there is a need for improved diabetes

management. According to the Center for Disease Control and Prevention (CDC),

diabetes rates increased 6% in 1999 (National Center for Chronic Disease Prevention and

Health Promotion, 2001). Furthermore, from 1990 to 1998, diagnosed diabetes increased

33%. Researchers have linked this increase in diabetes with the increased prevalence of

obesity in America, and incidence rates of diabetes can be expected to increase as a result

of increased obesity. These trends could have a significant impact on health and medical

costs, as it was estimated that $100 billion was spent on health care related to diabetes in

1995 (National Center for Chronic Disease Prevention and Health Promotion, 2001a).

Even more alarming is the fact that there has been a 10-fold increase in type 2 diabetes

among children in the past 20 years (Ludwig & Ebbeling, 2001). In addition, diabetes

has been associated with such psychological disorders as depression, anxiety, eating

disorders, and even substance abuse.

These startling statistics indicate the importance for helping individuals with

diabetes achieve the behavior change needed to develop the lifestyle habits pertinent to

successful diabetes care. This includes an emphasis on increasing levels of diabetes-

related self-efficacy, outcome expectations and perceptions of control for the behaviors of

diet, physical activity/exercise and blood glucose monitoring. This may not only help

prevent the chronic complications associated with diabetes, but may also help to improve

the overall quality of life for individuals with diabetes, as individuals with high levels of

self-efficacy and positive outcome expectations would be less likely to feel threatened or

be depressed about their condition (Bandura, 1986). However, further research in this

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area needs to be conducted in order to clarify the relationship among Social Cognitive

Theory, perceptions of control and adherence to the diabetes regimen.

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APPENDIX A BRIDGE BBrriinnggiinngg DDiiaabbeetteess GGeenneerraall EEdduuccaattiioonn TToo LLiiffee

Consent Form Summary

Diabetes Adherence Intervention in Older Adults: A Pilot (Nickname: BRIDGE)

Investigators

Mary Ann Sevick, ScD, RN & Shannon Mihalko, PhD University of Pittsburgh Wake Forest University

Funding National Institutes of Health National Institute on Aging

Why BRIDGE Is Being Done

The BRIDGE study is looking for better ways to help people follow their diabetes care regimen so their blood sugar will improve. Who Is Being Asked To Participate In BRIDGE

We are asking 50 subjects from the Winston-Salem, North Carolina area who have type 2 diabetes and mild kidney disease to participate in this study. We are recruiting men and women who are 55 years of age and older. We are recruiting individuals who have gone through the Diabetes Care Center’s educational program at CompRehab. What You Do If You Join: 3 Things

If you decide to take part in this research study, you will participate in two procedures that are not part of your usual medical care: FIRST: Screening The Screening Visit determines if you are eligible to take part in the research study. It will take about 60 minutes. You will

• Go to the General Clinical Research (GCRC) Unit at Wake Forest University Baptist Medical Center.

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• Have a small amount of blood drawn from your arm to see if you have some mild kidney

disease, and if your hemoglobin A1c meets our guidelines of 8 or higher. If your blood test indicates early kidney disease, we will notify you and your doctor.

SECOND: Testing If you qualify for the BRIDGE study, you will come for 2 testing visits at the beginning, at the middle and again at the end of the study.

• Testing Visit 1 You will return to GCRC for a 1 1/2 hour visit. You will be asked to fast for 12 hours prior

to this visit and have another blood sample drawn to measure hemoglobin A1c and the balance of a variety of different chemicals in your body that indicate your overall level of health. After that, you will be given a snack. These test results will be shared with you and your physician. You will be administered a series of questionnaires which will tell us about your physical activity, and how you manage your diabetes. We will also ask you to bring all of your prescription and over the counter medications, and vitamin/herb supplements with you to the GCRC, where they will be recorded. We will ask to record your blood glucose readings for one week as well as teach you how to use a pedometer to measure your weekly steps. After you return home from the baseline measurement visit, a dietitian from the General Clinical Research Center will telephone you on 3 different days to ask you to recall foods that you have eaten. Each call will require about 30 minutes of your time.

• Testing Visit 2

The second visit to the GCRC will measure how many calories your body burns when you are at rest. For this test, you will lie in a bed or recliner for 45 minutes, then wear a hood over your head (it looks like a large fish bowl), and breathe normally for 15 minutes. There is no discomfort associated with this test. Your height, weight, waist and hip size, and body fat will be measured too. Body fat is measured at three locations on your body and may feel like a slight pinch. This test will require you to put on a hospital gown.

After the 2 initial baseline visits to the GCRC, the researchers will “randomly assign” you to one of two groups: a classroom education group, or to an education reinforcement group.

THIRD: Placement Into One of Two Experimental Programs: Lifestyle Education Group or Mail Reinforcement Group

If assigned to the classroom education group, you will enter a 6-month educational program. During the first 2 months of the program, you will be asked to attend educational classes on a weekly basis, and to meet regularly (approximately every other week) with some educators who will talk with you about the different aspects of your diabetes regimen. Classes and individual meetings will each require about one hour of your time. Over the 6-month period, the number of classes and appointments will gradually be reduced from weekly, to once every two weeks, to monthly during the final 2 months. Classes and appointments will take place at the CompRehab building at the Wake Forest University Medical Center.

75

If you are in the mail reinforcement group, you will be asked to attend a booster

session at 1, 3, and 5 months during the study. You will also receive regular newsletters that review important points covered in the Diabetes Education Program. You will continue your usual follow-up care through your primary care physician’s office or through your case manager.

Participants in both groups will be asked to conduct routine blood glucose self-monitoring tests using their personal home blood glucose monitors. Participants in the classroom group will be asked to maintain a diary of their daily routine blood glucose tests. Throughout the study, we will keep your primary care physician informed of the results of these tests. We will also periodically assess the number of steps you take each day using a special tool called a “pedometer.” For all participants who are members of QualChoice, we will examine your prescription claims database to determine the number and type of diabetes medication purchases you made during the previous 6 months. This will require no effort on your part.

RISKS and BENEFITS Of BRIDGE

Risks

• Three blood tests are performed over the course of the study. Blood tests are sometimes accompanied by bruising, bleeding, and minor tenderness at the puncture site.

• Adherence to the diabetes regimen may improve as a result of being in the study.

This may cause hypoglycemia at first, but we will review how to prevent and treat this.

• Participants in the classroom education group will be encouraged to increase the number of steps they take each day. This may slightly increase your risk of heart problems. We will monitor you and teach you how to deal with any symptoms. You will be asked to get approval from your doctor if you plan to begin a formal exercise program during this study.

• Finally, people with diabetes are at higher risk of certain injuries as a result of physical

activity. You will be asked to conduct routine foot checks, maintain good hygiene, and avoid excessive jarring or shaking activity, or lifting heavy weights.

Benefits

Participation in this study may result in improved adherence to the diabetes regimen. New Information

You will be promptly notified if any new information develops during the conduct of this research study, which may cause you to change your mind about continuing to participate.

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Program Costs and Payments

All subjects enrolling in the study must either have gone through and educational program through the Diabetes Care Center (DCC), or must be referred by their physician for enrollment in a DCC program prior to enrolling in the study. Costs associated with the DCC are covered by most insurance programs. Participants will be responsible for any out-of-pocket costs associated with attending the DCC program, including co-pays and deductibles. If you have not already attended one of the DCC programs, during the screening process we will help you evaluate your insurance coverage to determine if there will be any costs to you for attending the DCC program.

Neither you, nor your insurance provider will be charged for the costs of any of the procedures performed for the purpose of this research study. However, you will be charged, in the standard manner, for any procedures performed for your routine diabetes care or medications and supplies that you use (e.g., for your medication, glucose monitoring supplies, medical appointments). No Payment For Participation

You will not be paid to participate in BRIDGE. Compensation For Injury

There is no foreseen risk of injury associated with participation in this study. The only risks are those normally associated with being a person with diabetes who closely adheres to their medical regimen. However, should you experience a physical injury or illness as a direct result of your participation in this study, reasonable necessary medical services will be offered at the usual charges. Confidentiality All records related to your involvement in this research study will be stored in a locked file cabinet. Your identity on these records will be indicated by a case number rather than by your name. The information linking these case numbers with your name will be kept separate from the research records. Your research records will be destroyed when such is approved by the sponsor of this study, or per University policy which is 7 years following completion of the study, whichever should occur last. Your Right To Participate Or Withdraw

Your participation in this research study is completely voluntary. You do not have to take part in this research study and, should you change your mind, you can withdraw from the study at any time.

Our Right To Remove You From The Study

It is possible that you may be removed from the research study by the researchers if, for example, we determine that you do not have early kidney disease.

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APPENDIX B Mini-Mental State Examination “Now I would like to ask you some questions to check your memory and concentration. Some of them may be easy

Not and some of them may be hard.”

Error Correct Assessed -------------------------- 0 1 9 1)What is the year?_________________ 0 1 9 2)What is the season of the year?__________________ 0 1 9 3)What is the date?______________________ 0 1 9 4)What is the day of the week?__________________ 0 1 9 5)What is the month?_______________________ 0 1 9 6)Can you tell me where we are?__________________ (For instance, what state are we in?) 0 1 9 7)What country are we in?____________________ 0 1 9 8)What city/town are we in?_____________________ 0 1 9 9)What floor of the building are we on?_______________ 0 1 9 10)What is the name or address of this place?

________________________________________ 0 1 9 11)I am going to name three objects. After I have said them, I want

you to repeat them. Remember what they are because I am going to ask you to name them again in a few minutes.

0 1 9 Apple Please repeat the names for me: 0 1 9 Table (Score first try. Repeat objects for three trials only.) 0 1 9 Penny

12) Now I am going to give you a word and ask you to spell it forwards and backwards. The word is WORLD. First, can you spell it forwards? Now spell it backwards.

(Repeat if necessary, and help subject spell word forward, if necessary)

G Score number of letters given in correct order ___ ___ ___ ___ ___

( 0 to 5; 9=not assessed)

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Not What were the three objects I Error Correct Assessed asked you to remember? -------------------------- 0 1 9 13)Apple ________________ 0 1 9 14)Table_________________ 0 1 9 15)Penny_________________ 0 1 9 16) (Show wrist watch) What is this called?________________ 0 1 9 17) (Show pencil) What is this called?_____________________ 0 1 9 18) I would like you to repeat a phrase after me: (The phrase is) “NO IF’S, AND’S OR BUT’S” Allow only one trial. 0 1 9 19) Read the words on this page, then do what it says.

(The paper reads) “CLOSE YOUR EYES” Code correct if subject closes eyes.

20) I’m going to give you a piece of paper. When I do, take

the paper in your right hand, fold the paper in half with both hands, and put the paper down on your lap 0 1 9 Right hand 0 1 9 Folds 0 1 9 In lap Read full statement, THEN hand over paper. Do not repeat instructions or coach. 0 1 9 21) Write any complete sentence on that piece of paper. 0 1 9 22) Here is a drawing. Please copy the drawing on the same paper. Score correct if the two five-sided figures intersect to form a

four-sided figure and if all angles in the five-sided figure are preserved.

Total Score (the sum of the scores for all 22 questions, Excluding any scores of ‘9.’)

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BRIDGE PAR-Q.doc 01/12/2002

ID:

Date:

PAR-Q Please check Yes or No for each question.

1. Has your doctor ever said that you have a heart conditiondo physical activity recommended by a doctor? 2. Do you feel pain in your chest when you do physical activ

3. In the past month, have you had chest pain when you weactivity? 4. Do you lose your balance because of dizziness or do you 5. Do you have a bone or joint problem that could be made wphysical activity? 6. Is your doctor currently prescribing drugs (for example, wpressure or heart condition? 7. Do you know of any other reason why you should not do p

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Month/Day/Year

and that you should only

ity?

re not doing physical

ever lose consciousness?

orse by a change in your

ater pills) for your blood

hysical activity?

ID: ___ ___ ___ ACROSTIC: ___ ___ ___ ___ ___ Date: ___/ ___/ ___ The next group of items is concerned with your mood. Please read each statement and indicate whether the situation or feeling described CURRENTLY pertains to you by answering YES or NO. Yes No 1. Are you basically satisfied with your life? 1 0 2. Have you dropped many of your activities and interests? 1 0 3. Do you feel your life is empty? 1 0 4. Do you get bored often? 1 0 5. Are you hopeful about the future? 1 0

6. Are you bothered by thoughts you can’t get out of your head? 1 0

7. Are you in good spirits most of the time? 1 0

8. Are you afraid that something bad is going

to happen to you? 1 0

9. Do you feel happy most of the time? 1 0

10. Do you often feel helpless? 1 0

11. Do you often get restless and fidgety? 1 0

12. Do you prefer to stay at home, rather than going out and doing new things? 1 0

13. Do you frequently worry about the future? 1 0

14. Do you feel you have more problems with memory than most? 1 0

15. Do you think it is wonderful to be alive now? 1 0

16. Do you often feel downhearted and blue? 1 0

17. Do you feel pretty worthless the way you are now? 1 0

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Yes No

18. Do you worry a lot about the past? 1 0

19. Do you find life very exciting? 1 0

20. Is it hard for you to get started on new projects? 1 0

21. Do you feel full of energy? 1 0

22. Do you feel that your situation is hopeless? 1 0

23. Do you think that most people are better off than you are? 1 0

24. Do you frequently get upset over little things? 1 0

25. Do you frequently feel like crying? 1 0

26. Do you have trouble concentrating? 1 0

27. Do you enjoy getting up in the morning? 1 0

28. Do you prefer to avoid social gatherings? 1 0

29. Is it easy for you to make decisions? 1 0

30. Is your mind as clear as it used to be? 1 0

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APPENDIX C Diabetes-Related Cognitive and Social Factors Self Efficacy

The following questions ask you about how confident your are in completing various tasks required for diabetes care. Please read each question and circle the number that corresponds with how confident you are in each task. 1. How confident are you in your ability to follow your diet?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT 2. How confident are you in your ability to test your blood sugar at the

recommended frequency?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT

3. How confident are you in your ability to exercise regularly?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT

4. How confident are you in your ability to keep your weight under control?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT

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Please circle the number that corresponds with how confident you are in each task. 5. How confident are you in your ability to keep your blood sugar levels under control?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT 6. How confident are you in your ability to resist food temptations?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT 7. How confident are you in your ability to follow your diabetes treatment? (diet, medications, blood sugar testing, exercise)?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT

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Diabetes-Related Cognitive and Social Factors Outcome Expectations The following questions ask you about how likely it is that various tasks are related to your diabetes care. Please read each question and circle the number that corresponds with how likely it is that each task is related to controlling your diabetes. 1. How likely is it that following your diet is related to controlling your diabetes?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY LIKELY LIKELY LIKELY 2. How likely is it that taking your medications as recommended (pills, insulin) is

related to controlling your diabetes?

0 10 20 30 40 50 60 70 80 90 100 NOT AT ALL MODERATELY HIGHLY

LIKELY LIKELY LIKELY 3. How likely is it that exercise is related to controlling your diabetes?

0 10 20 30 40 50 60 70 80 90 100 NOT AT ALL MODERATELY HIGHLY

LIKELY LIKELY LIKELY 4. How likely is it that measuring your blood sugar is related to controlling your

diabetes?

0 10 20 30 40 50 60 70 80 90 100 NOT AT ALL MODERATELY HIGHLY

LIKELY LIKELY LIKELY

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Please circle the number that corresponds with how likely it is that each task is related to controlling your diabetes 5. How likely is it that following your diabetes treatment (diet, medication, blood

sugar testing, exercise) is related to controlling your diabetes?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY LIKELY LIKELY LIKELY

6. How likely is it that following your diabetes treatment (diet, medications, blood

sugar testing, exercise) is related to delaying and/or preventing long-term diabetes complications (problems related to eyes, kidneys, heart or feet)?

0 10 20 30 40 50 60 70 80 90 100

NOT AT ALL MODERATELY HIGHLY

LIKELY LIKELY LIKELY

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Physical Activity Scale for the Elderly (PASE)

The following questionnaire asSesses your current level of physical activity and exercise. Please check the box or write the response that best answers each question. LEISURE TIME ACTIVITY: 1. Over the past 7 days, how often did you participate in sitting activities

such as reading, watching TV, or doing handcrafts? NEVER (SKIP TO 2) SELDOM (1-2 DAYS) SOMETIMES (3-4 DAYS) OFTEN (5-7 DAYS)

1a. What were these activities?

___________________________________________________ ___________________________________________________

1b. On average, how many hours per day did you engage in

these sitting activities? LESS THAN 1 HOUR 1 BUT LESS THAN 2 HOURS 2-4 HOURS MORE THAN 4 HOURS 2. Over the past 7 days, how often did you take a walk outside your home

or yard for any reason? For example, for fun or exercise, walking to work, walking the dog, etc.?

NEVER (SKIP TO 3) SELDOM (1-2 DAYS) SOMETIMES (3-4 DAYS) OFTEN (5-7 DAYS)

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Please check the box or write the response that best answers each question.

2a. On average, how many hours per day did you spend walking? LESS THAN 1 HOUR 1 BUT LESS THAN 2 HOURS 2-4 HOURS MORE THAN 4 HOURS 3. Over the past 7 days, how often did you engage in light sport or

recreational activities such as bowling, golf with a cart, shuffleboard, fishing from a boat or pier, or other similar activities?

NEVER (SKIP TO 4) SELDOM (1-2 DAYS) SOMETIMES (3-4 DAYS) OFTEN (5-7 DAYS)

3a. What were these activities?

______________________________________________________________________________________________________

3b. On average, how many hours per day did you engage in these light sport or recreational activities? LESS THAN 1 HOUR 1 BUT LESS THAN 2 HOURS 2-4 HOURS MORE THAN 4 HOURS 4. Over the past 7 days, how often did you engage in moderate sport

and recreational activities such as doubles tennis, ballroom dancing, hunting, ice skating, golf without a cart, softball or other similar activities?

NEVER (SKIP TO 5) SELDOM (1-2 DAYS) SOMETIMES (3-4 DAYS) OFTEN (5-7 DAYS)

4a. What were these activities? ___________________________________________________

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Please circle or write the response that best answers each question.

__________________________________________________ 4b. On average, how many hours per day did you engage in these moderate sport and recreational activities?

LESS THAN 1 HOUR 1 BUT LESS THAN 2 HOURS 2-4 HOURS MORE THAN 4 HOURS 5. Over the past 7 days, how often did you engage in strenuous sport

and recreational activities such as jogging, swimming, cycling, singles tennis, aerobic dancing, skiing (downhill or cross country), or similar activities?

NEVER (SKIP TO 6) SELDOM (1-2 DAYS) SOMETIMES (3-4 DAYS) OFTEN (5-7 DAYS)

5a. What were these activities?

______________________________________________________________________________________________________

5b. On average, how many hours per day did you engage in

these strenuous sport and recreational activities? LESS THAN 1 HOUR 1 BUT LESS THAN 2 HOURS 2-4 HOURS MORE THAN 4 HOURS 6. Over the past 7 days, how often did you do any exercises

specifically to increase muscle strength and endurance, such as weights or pushups, etc.?

NEVER (SKIP TO 7) SELDOM (1-2 DAYS) SOMETIMES (3-4 DAYS) OFTEN (5-7 DAYS)

6a. What were these activities?

__________________________________________________

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Please check the box or write the response that best answers each question.

__________________________________________________

6b. On average, how many hours per day did you engage in these exercises that increase muscle strength and endurance?

LESS THAN 1 HOUR 1 BUT LESS THAN 2 HOURS 2-4 HOURS MORE THAN 4 HOURS 7. During the past 7 days, have you done any light housework, such

as dusting or washing dishes?

NO YES 8. During the past 7 days, have you done any heavy housework or

chores, such as vacuuming, scrubbing floors, washing windows, or carrying wood?

NO YES

9. During the past 7 days, did you engage in any of the following activities? Please answer YES or NO for each item.

NO YES

a. Home repairs like painting, wallpapering,

electrical work, etc.

b. Lawn work or yard care, including snow or leaf removal, wood chopping, etc.?

c. Outdoor gardening? d. Caring for another person, such as children,

dependent spouse or another adult?

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Please circle or write the response that best answers each question.

10. During the past 7 days, did you work for pay or as a volunteer?

NO (Skip to end top of next page) YES 10a. How many hours per week did you work for pay or as a volunteer?

_________________________hours. 10b. Which of the following categories best describes the amount of physical

activity required on your job and/or volunteer work? Mainly sitting with slight arm movements.

(Examples: office worker, watchmaker, seated assembly line worker, bus driver, etc.)

Sitting or standing with some walking.

(Examples: cashier, general office worker, light tool and machinery worker, etc.)

Walking, with some handling of materials generally weighing

less than 50 lbs. (Examples: mailman, waiter/waitress, construction worker, heavy tool and machinery worker.)

Walking and heavy manual labor often requiring handling of

materials weighing over 50 lbs. (Examples: lumberjack, stone mason, farm or general laborer.)

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Please read each statement below and use the following scale (1-6) to indicate the extent to which you agree with each statement. Write the corresponding number in the blank next to the statement.

1 = Strongly Disagree 2 = Moderately Disagree 3 = Slightly Disagree 4 = Slightly Agree 5 = Moderately Agree 6 = Strongly Agree

1. If my diabetes worsens, it is my own behavior which determines how

soon I will feel better.

2. As to my diabetes, what will be will be.

3. If I see my doctor regularly, I am less likely to have problems with my

diabetes.

4. Most things that affect my diabetes happen to me by chance.

5. Whenever my diabetes worsens, I should consult a medically trained

professional.

6. I am directly responsible for my diabetes getting better or worse.

7. Other people play a big role in whether my diabetes improves, stays

the same, or gets worse.

8. Whatever goes wrong with my diabetes is my own fault.

9. Luck plays a big part in determining how my diabetes improves.

Multidimensional Health Locus of Control Scale (MHLC)

10. In order for my diabetes to improve, it is up to other people to see that the

right things happen.

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Write the corresponding number in the blank next to the statement.

1 = Strongly Disagree 2 = Moderately Disagree 3 = Slightly Disagree 4 = Slightly Agree 5 = Moderately Agree 6 = Strongly Agree

11. The main thing that affects my diabetes is what I myself do.

12. Whatever improvement occurs with my diabetes is largely a matter of

good fortune

13. I deserve the credit when my diabetes improves and the blame when

gets worse.

14. Following a doctor’s orders to the letter is the best way to keep my

diabetes from getting worse.

15. If my diabetes worsens, it is a matter of fate.

16. If I am lucky, my diabetes will get better.

17. If my diabetes takes a turn for the worse, it is because I have not been

taking proper care of myself.

18. The type of help I receive from other people determines how soon my

diabetes improves.

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Perceived Control of Diabetes

The following questions ask about the causes of various events, which may happen to you. We ask you to imagine that the events described have happened to you recently. Please pick one and only one cause - the major cause of the situation as you see it. Please write this cause in the space provided after each event. Next, please answer the questions about the cause by circling the most appropriate number on a scale from 6 to 0. Please circle only one number. Event #1 Imagine that you have been able to keep your weight at an acceptable level for a period of several weeks and you have felt fit and well. Write down the single most likely cause of this period of weight control and sense of general well being in the space below. ______________________________________________________________________ ______________________________________________________________________ Now rate this cause on the following scales: 1. To what extent was the cause due to something about you? Totally due to me 6 5 4 3 2 1 1

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1. To what extent was the ca to somethduedm(ith owasrthiopsinel circumstances you? )TjETEMC/P <</MC6D 14 >>BDCBT/TT7 1 Tf0 Tc 0 Tw 10.98 0 0 10.98 108.0998 5402Tc97 Tm( )TjETEMC/P <</M27D 15 >>BDCBT/TT7 1 Tf00.0011.6227Dc 0 Tw 10.98 0 0 10.98 108.0926 26321196 Tm(Tota(1. )Tj10.98 0 0 10.98 48290926 26321196 lly (1. )Tj10.98 0 0 10.7. 8119926 26321196 o me )Tj10.98 0 0 10.98 219.0326 26321196 6 .30 3 2/TT01 1. 5 4 4 4 4 4 5 1 . 5 4 4 1 1 . 5

1. To what extent was the cause duechance you?

Tota(1. )Tj10.98 0 0 10.98 4829025 0 10.0397 lly (1. )Tj10.98 0 0 10.98 2156025 0 10.0397 6 .30 3 2/TT01 1. 5 4 4 4 4 5

1. To what extent was the cacontrollabsinby you you? T o t a ( 1 . ) T j 1 0 . 9 8 0 0 1 0 . 9 8 4 9 0 3 . 1 8 7 . 7 4 1 6 0 3 9 7 c o n t r o l l a b s i n ( 1 . ) T j 1 0 . 9 8 0 0 1 0 . 9 5 . 9 1 3 I D 1 8 7 . 7 4 1 6 0 3 9 7 6 . 3 0 ( 4 ) T j 1 0 . 9 8 0 0 1 0 . 9 8 1 9 3 . 0 1 8 7 . 7 4 1 6 0 3 9 7 3 m ( 4 ) T j 1 0 . 9 8 0 0 1 0 3 2 3 . 9 1 3 . 1 8 7 . 7 4 1 6 0 3 9 7 2 / T T 0 1

Event #2 Imagine that you have recently experienced a hypoglycemic event (low blood sugar along with symptoms such as dizziness, lightheadedness, shakes). Write down the single most likely cause of the hypoglycemic even in the space below. ______________________________________________________________________ ______________________________________________________________________ Now rate this cause on the following scales: 1. To what extent was the cause due to something about you? Totally due to me 6 5 4 3 2 1 0 Not at all due to me

2. To what extent was the cause something to do with other people or circumstances? Totally due 6 5 4 3 2 1 0 Not at all due to other people to other people or circumstances or circumstances 3. To what extent was the cause due to chance? circumst8 0 0 10.98 108.0002 ec9.30597425 418.8614 Tm(circumst8 0 0 113.3cg13i305ot )Tj10.98 0 0 10.98 434.1934 113.3cg13i305ot

.942 T0 0 10.98 120.8521 418.86144404C/.38214 Tes .942 T0 0 1Tw 10.98 0 0 10.983e or circumstances?

Event #3 Imagine that your diabetes has been well controlled for a period of several weeks, during which there has been little fluctuation in blood glucose, no reactions, and you have felt fit and well. Write down the single most likely cause of this period of good control in the space below. ______________________________________________________________________ ______________________________________________________________________ Now rate this cause on the following scales: 1. To what extent was the cause due to something about you? Totally due to me 6 5 4 3 2 1 0 Not at all due to me

2. To what extent was the cause something to do with other people or circumstances? Totally due 6 5 4 3 2 1 0 Not at all due to other people to other people or circumstances or circumstances 3. To what extent was the cause due to chance? Totally due 6 5 4 3 2 1 0 Not at all due to chance to chance 4. To what extent was the cause controllable by you? Totally controllable 6 5 4 3 2 1 0 Totally uncontrollable by me by me 5. To what extent was the cause controllable by your doctor? Totally controllable 6 5 4 3 2 1 0 Totally uncontrollable by my doctor by my doctor

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Event #4 Imagine that for several days, you have found high levels of sugar when you have tested your blood or urine. Write down the single most likely cause of the high levels of sugar in your blood or urine in the space below. ______________________________________________________________________ ______________________________________________________________________ Now rate this cause on the following scales: 1. To what extent was the cause due to something about you? Totally due to me 6 5 4 3 2 1 0 Not at all due to me

2. To what extent was the cause something to do with other people or circumstances? Totally due 6 5 4 3 2 1 0 Not at all due to other people to other people or circumstances or circumstances 3. To what extent was the cause due to chance? Totally due 6 5 4 3 2 1 0 Not at all due to chance to chance 4. To what extent was the cause controllable by you? Totally controllable 6 5 4 3 2 1 0 Totally uncontrollable by me by me 5. To what extent was the cause controllable by your doctor? Totally controllable 6 5 4 3 2 1 0 Totally uncontrollable by my doctor by my doctor

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SCHOLASTIC VITA

STACY LYNN HUTTON

Personal Information:

Birthplace: Linden, NJ

Birth Date: January 11, 1978

Undergraduate Study:

1996-2000 Virginia Tech University Blacksburg, Virginia B.S The Science of Human Nutrition, Foods and Exercise Graduate Study: 2002-2002 Wake Forest University Winston-Salem, North Carolina M.S Health and Exercise Science Professional Experience: 2001-2002 Research Assistant Department of Health and Exercise Science Wake Forest University Winston-Salem, North Carolina 2000-2002 Cardiac Rehabilitation Program Leader Wake Forest University Winston-Salem, North Carolina 2000-2002 Course Instructor Department of Health and Exercise Science Wake Forest University Winston-Salem, North Carolina 1999-2000 Fitness Coordinator Blacksburg, Community Center Blacksburg, Virginia 1999 Cardiac Rehabilitation Intern Capital Health Systems at Mercer Trenton, New Jersey

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Lectures: 2001 Cardiovascular Medications 2001 Planning and Developing a Strength Training Program 2000 The Biomechanics of Front Crawl Swimming Certifications: 2000 American College of Sports Medicine Exercise Specialist 2000 American Heart Association Advanced Cardiac Life Support 1999 American Heart Association Basic Life Support 1999 National Strength and Conditioning Association Strength and Conditioning Specialist

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