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Maternal efforts to prevent type 1 diabetes in genetically screened infants:

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Maternal efforts to prevent type 1 diabetes in genetically screened infants:
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Baughcum, Amy E
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Anxiety ( jstor )
Child psychology ( jstor )
Disease risks ( jstor )
Diseases ( jstor )
Genetic screening ( jstor )
Mothers ( jstor )
Neonatal screening ( jstor )
Neonates ( jstor )
Type 1 diabetes mellitus ( jstor )
Type 2 diabetes mellitus ( jstor )
Clinical and Health Psychology thesis, Ph. D ( lcsh )
Dissertations, Academic -- Clinical and Health Psychology -- UF ( lcsh )
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theses ( marcgt )
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Thesis (Ph. D.)--University of Florida, 2004.
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Includes bibliographical references.
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Printout.
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Vita.
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by Amy E. Baughcum.

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MATERNAL EFFORTS TO PREVENT TYPE 1 DIABETES
IN GENETICALLY SCREENED INFANTS














By

AMY E. BAUGHCUM


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2004
































Copyright 2003

by

Amy Baughcum
































This dissertation is dedicated to my parents and grandparents who instilled in me the
value of life-long learning.














ACKNOWLEDGMENTS

First, I want to thank the families that willingly gave their time to participate in this

study. Second, I would like to thank my collaborators at the University of Florida and

Medical College of Georgia as well as the Children's Miracle Network and the American

Diabetes Association for funding this work.

On a more personal note, I am grateful to my family for their constant love,

encouragement, and support. I appreciate their sacrifices that allowed me to pursue my

education. My friends and labmates were also huge helps to me in this process by

providing feedback, caring, and welcomed distraction. I am grateful to my research

assistants (Adam Lewin, MS; and Jennifer Walsh, BS) who assisted me with this study.

Without them, this project could not have happened.

I am forever indebted to all of my mentors who helped me along the way, including

Scott Powers, PhD; and Robert Whitaker, MD, MPH, with whom I worked before

entering graduate school. They both gave me a strong foundation of skills and

knowledge that continue to serve me well as I pursue my career.

Most importantly, I appreciate the incredible opportunity I had at the University of

Florida to be mentored by two brilliant, inspiring women (Suzanne Bennett Johnson,

PhD; and Dr. Alexandra Quittner, PhD) both of whom I consider to be exemplary

academicians. Several other faculty members (including Desmond Schatz, MD; Samuel

Sears, PhD; and Fonda Eyler, PhD) have also provided me with support, mentorship, and

sound guidance, which have proven influential in my graduate education.














TABLE OF CONTENTS
Page

ACKNOWLEDGMENTS......................................................................................... iv

A B STR A C T ..................................... .............................................. vii

CHAPTER

1 IN TR O D U CTIO N ................................................................................................. 1

2 REVIEW OF THE LITERATURE.............................................................................. 5

Psychological Impact of Genetic Testing .............................................. .............. 5
Behavioral Impact of Genetic Testing ................................................. ................ 9
Theoretical Models of Genetic Screening and Behavioral Change ....................... 12
Newborn Genetic Screening .............................................................................. 17
Type 1 Diabetes: Etiology and Prevention .......................................... ............ .. 21
Prediction and Pre-Symptomatic Screening for Type 1 Diabetes............................ 25

3 RATIONALE AND PURPOSES ................................................................... 37

Objective 1: To Investigate the Extent of Reported Maternal Behavior Change as a
Result of Genetic Screening for Type 1 Diabetes............................................ 38
Objective 2: To Assess Predictors of Maternal Behavior Change as a Result of
Genetic Screening for Type 1 Diabetes....................................................... 39
Objective 3: To Assess Psychological Effects (i.e., Anxiety) of Maternal Behavior
Change O ver Tim e .......................................................... .............................. 42
Objective 4: To Compare Reported Behavior Change between Mothers of Children
Genetically at Risk for Developing Type 1 Diabetes with Mothers of Children in
the Diabetes Prevention Trial Who Were ICA+, and Therefore, at Even Greater
Risk for Diabetes Onset................................................... .............................. 43

4 METHODS AND MATERIALS ............................................................................... 45

Prospective Assessment of Newborn Diabetes Autoimmunity (PANDA) Study
Procedures ....................................................................................................... 45
Participants........................................................................................................... 48
Procedures............................................................................................................ 49
M measures .............................................................................................................. 51
Statistical A nalyses ............................................................................................... 61








5 RESU LTS .................................................................................................................. 75

Sam ple Characteristics ......................................................................................... 75
O objective 1 ........................................................................................................... 76
O objective 2 ........................................................................................................... 81
O objective 3 ................................................................................................................. 88
O objective 4 ........................................................................................................... 89

6 D ISCU SSIO N .......................................................................................................... 117

H ypotheses ......................................................................................................... 118
Strengths and Lim stations ........................................................................................ 130
Im plications and D directions for Future Research .................................................. 134

APPENDIX STRUCTURED TELEPHONE INTERVIEW...................................... 137

LIST O F REFEREN CES ................................................................................................ 154

BIO G RA PH ICA L SKETCH .................................................................................... 170

































vi















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

MATERNAL EFFORTS TO PREVENT TYPE 1 DIABETES IN GENETICALLY
SCREENED INFANTS

By

Amy E. Baughcum

August 2004

Chair: Suzanne Bennett Johnson
Cochair: Alexandra Quittner
Major Department: Clinical and Health Psychology

Currently, research programs exist to screen newborns in the general population for

genetic risk of developing Type 1 diabetes, including the Florida Prospective Assessment

of Newborn Diabetes Autoimmunity (PANDA) study. These screening programs are part

of longitudinal studies addressing the etiology of type 1 diabetes, with the ultimate goal

of developing preventative interventions. However, little is currently known about the

impact of newborn genetic screening on maternal behaviors of newborns found to be at

increased risk for the disease. Additionally, since we do not presently know how to

effectively prevent type 1 diabetes, health care professionals are not able to offer

definitive recommendations to mothers regarding specific behaviors to prevent diabetes

in their at risk children. In the absence of this information, mothers may take their own

actions in an effort to prevent the disease in their children. The purpose of this

exploratory study was to examine maternal reported behavior changes associated with

identifying at risk infants via genetic screening.








Structured telephone interviews were conducted with 192 mothers of children

between the ages of 2 and 7 years who were previously identified as at increased risk

through genetic screening. Interview questions elicited qualitative and quantitative

information regarding maternal behavior changes affecting the child's diet, physical

activity, stress level, environment, and health surveillance. Additional questions assessed

mothers' anxiety, perceived control, perceived risk, information-seeking behaviors, and

sources of information regarding their children's risk for diabetes.

Results indicated that most mothers reported engaging in behavior change (67%)

and typically these behaviors involved increased health surveillance and healthy lifestyle

changes. Significant predictors of behavior change included family history of diabetes,

anxiety, coping, perceived risk, and information seeking. Overall, these findings suggest

that genetic screening for type 1 diabetes has minimal negative impact on maternal

behavior. Despite the positive nature of subsequent behavior modifications, such

behavior changes that may occur in individuals' everyday lives in response to a health

risk could threaten the internal validity of natural history studies and prevention trials if

not carefully monitored.













CHAPTER 1
INTRODUCTION

Advances in the field of human genetics are rapidly changing the practice of

medicine. The Human Genome Project (HGP), completed in 2000, has played a key role

in this revolution by identifying and sequencing the genes that constitute the entire

human genome. Genetic mutations account for an estimated 5,000 diseases and influence

the development of thousands of others. Estimates suggest that 20 diseases account for

80% of the deaths in the Western world; and these diseases are due to the influence of

100 to 200 individual genes which will be identified in the next few years (Roberts, 2000;

Patenaude, Guttmacher, & Collins, 2002). Tests are currently available to identify

specific genetic markers that may lead to a disease in those who are at risk for developing

the disease at some point during their lives. There are two types of tests. One type is

known as "genetic testing," which involves using "specific assays to determine the

genetic status of an individual already suspected to be at high risk for a particular

inherited condition because of family history or clinical symptoms." The other, genetic

screening, involves the use of "various genetic tests to evaluate populations or groups of

individuals independent of a family history of a disorder" (Committee on Assessing

Genetic Risks, Institutes of Medicine, 1994, p. 4). However, the terms "genetic testing"

and "genetic screening" are often used interchangeably; and thus, these words are used

interchangeably in this paper as well.

As we come to better understand human genetics, we have the opportunity to learn

more about the role that specific genes play in the etiology of disease. It is estimated that








each person's genetic make-up contains 5 to 30 alterations in DNA that could predispose

the development or transmission of a genetically based disease. It is apparent that

advances in genetics will continue to expand, and screening for genetic susceptibility for

diseases will become more commonplace in the coming decades (Juengst, 1995).

Longitudinal research studies of genetically at risk individuals are necessary to learn

about the natural progression of disease in order to develop effective prevention

strategies. These studies will provide a better understanding of the interactions between

multiple genes in disease development as well as interactions between genes and the

environment. While there are diseases that are determined by a single gene mutation,

such as Huntington's disease, many conditions are genetically more complex, involving

multiple genes and environmental factors. Thus, as we learn more about genetic

predispositions, it will be increasingly important to examine environmental factors

(including individuals' health behaviors) to complete our scientific understanding of

disease etiology. The exciting new opportunities in genetics are accompanied by many

unanswered questions about how the public at large will accept and understand these new

techniques and the risk information they provide.

Unfortunately, new advances in medical technology have outpaced the rate at

which psychological research has proceeded. Genetic medicine can have a huge

influence in life and death issues, raising ethical, social and legal concerns. It is ethically

imperative to consider how the new genetic revolution impacts, both positively and

negatively, the quality of life for individuals and their families. By identifying an

individual's genetic risk for disease, there is potential for early treatment or disease

prevention, or in the case of an incurable disease, the ability to initiate health surveillance








and/or plan for the future. Current literature suggests that certain health behaviors (i.e.,

diet and exercise) can moderate risk for several diseases, such as heart disease, cancer or

type 2 diabetes. Therefore, awareness of one's genetic risk may directly affect behavior

change, and consequently disease progression. Genetic counseling is now shifting to

providing information regarding personal risk reduction; and allowing individuals to

make better-informed medical decisions (Lerman et al., 2002). However, there are also

many diseases for which there is no known method of prevention or cure. In these

instances, it may not be possible to make behavioral recommendations regarding health

behavior change, other than increased medical surveillance. Despite this, individuals on

their own may engage in behaviors they perceive to be beneficial or preventative.

Recently, there has been a large push for clinical psychologists to become more

involved in genetics; and to lend their expertise as clinicians, researchers, and educators

to advance our understanding of the psychosocial costs and benefits of genetic screening

(Fisher et al., 2002, Gallier, 2002; Lerman et al., 2002; Patenaude et al., 2002; Patenaude

et al., 2003). A number of agencies have made the psychosocial implications of genetic

advances a funding priority, including the Human Genome Project which designated 5%

of the total budget to ethical, legal, and social issues (Jeffords & Daschle, 2001; cited in

Patenaude et al., 2002).

Clinical psychologists can play an important role in answering how genetic risk

information impacts individuals in cognitive, affective, and behavioral realms.

Psychologists can assess the role that different personal, social, and cultural factors

contribute to the development or prevention of disease. As clinicians, psychologists can

help individuals and families understand risks, make informed behavioral and








reproductive choices, provide psychosocial support, and evaluate outcomes. A recent

article by Patenaude et al. (2003) highlights the many important roles pediatric

psychologists can play in the research and public policy arenas to inform ethical debates

on the merits of genetic testing; and to provide competent clinical care to affected

families (Patenaude et al., 2003).

While there has been some research addressing attitudes toward genetic testing,

comprehension of genetic information, and the psychological impact of genetic testing,

there is still much to be learned about the impact of genetic screening on individuals'

behavior. This exploratory study examined the behavioral impact of newborn genetic

screening for mothers whose children were found to be at risk for type 1 diabetes.

Currently, little is understood about the specific behavior changes that may result from

knowing one's child is genetically predisposed to a condition for which there is currently

no known prevention method or cure. Additionally, our present understanding of the

etiology of type 1 diabetes suggests that it develops from a combination of both genetic

and environmental influences, which are not well-defined. In the absence of definitive

recommendations from the health care community, mothers of newborns identified as "at

risk" may take actions they believe are effective in preventing type 1 diabetes in their

children. This study assessed the extent of mothers' self-reported behavior changes; and

assessed associations between reported behavior change and maternal psychological

(i.e., anxiety, perceived control, coping), and sociodemographic variables. The upcoming

sections discuss existing literature on the psychological and behavioral impact of genetic

testing, including newborn genetic screening; and our current knowledge of the etiology

and prevention of type 1 diabetes.













CHAPTER 2
REVIEW OF THE LITERATURE

Psychological Impact of Genetic Testing

Literature assessing the psychological impact of genetic testing has largely focused

on predictive testing for Huntington's disease and breast and ovarian cancer susceptibility

(BRCA1/BRCA2 genes). Many studies have examined other uses of risk screening,

including prenatal screening and carrier screening. This review focuses on predictive

genetic testing. Generally, studies of predictive testing have focused on both the short-

and longer-term effects of genetic testing on affective outcomes for screening participants

and their family members. Overall, voiced criticisms that genetic testing leads to poor

psychological adjustment appear unfounded based on published literature; and such

claims may create unnecessary panic (Horowitz et al., 2001; Palmer et al., 2002).

Longitudinal prospective studies examined levels of anxiety and depression before

genetic testing; and provided new information regarding the potential for poor

psychological adjustment. Evidence suggests that contrary to earlier concerns, making

the choice to be tested signals psychological preparedness for the outcome and ability to

handle the news well. Most people who choose to participate in population-based

screening programs do not have a family history and therefore, will most likely expect

and receive a negative result. In screening members from high-risk families (those with a

family history of the disease), patients tend to overestimate (not underestimate) their risk;

and expect to receive positive results (Croyle & Lerman, 1999; Lynch et al., 1999; Lynch

et al., 1993). While one might expect that receiving positive results would result in








clinically significant distress and increased mood symptomatology, studies have found

that a positive test result is usually not associated with clinical levels of anxiety or

depression (Broadstock et al., 2000; Lerman et al., 2002; Schwartz et al., 2002). This

may be partly because reducing one's uncertainty regarding risk may actually decrease

stress by providing relief from what was previously unknown (Marteau & Michie, 1995;

Baum et al., 1997). While some studies suggest elevated scores on measures of distress,

such as depression or anxiety (Shaw, Abrams & Marteau, 1999), scores generally return

to baseline levels after 3 to 12 months (e.g., Croyle et al., 1997; Lerman et al., 1996;

Wiggins et al., 1992). While there may be some immediate distress upon risk

notification, it appears to be neither inevitable nor long-lasting.

A literature review conducted by Broadstock et al. (2000) examined existing

prospective studies of the impact of genetic testing for Huntington's disease, ovarian and

breast cancer, and familial adenomatous polypsosis (FAP). To be included in the review,

studies had to contain both pre and post measures of psychological distress. Examples of

measures of distress used in these types of studies include the Impact of Events Scale

(IES; Horowitz, 1979), State-Trait Anxiety Inventory (STAI; Speilberger et al., 1970),

and the Beck Depression Inventory (BDI; Beck, 1961). The authors' extensive search

uncovered 11 studies, none of which found an increase in distress (defined as general or

test-specific anxiety or depression) at any point in the 12 months after testing. After

notification, distress decreased in individuals who received either a positive or negative

test result. However, this decline was greater and more rapid in those who received

negative test results. Furthermore, in regression models, the actual test result rarely

predicted psychological outcomes beyond the first month post-risk notification.








Congruent with the Stress Disease Coping Model proposed by Baum et al. (1997), the

individual's pretest emotional state, social support, and expectations were the most

predictive of subsequent distress (Marteau & Croyle, 1998), suggesting that personal

variables may play a role in how one handles genetic risk information.

Taken together, these studies generate little empirical support for the notion that

genetic testing is associated with adverse psychological outcomes (Lerman et al., 2002).

However, most of these studies involved participants in research registries and these

results may not generalize to the broader population. It should be noted that those in

clinical settings may be self-referred and more naive, and thus, less equipped to cope with

knowledge of their risk status (Broadstock et al., 2000).

Previous research suggests that psychological distress may be associated with

specific personal (e.g., optimism) or demographic characteristics (e.g., race, education).

Audrain et al. (1998) studied women with a first degree relative with breast or ovarian

cancer before testing and found that pre-test distress was predicted by age, ethnicity,

marital status, optimism, perceived control, and overestimated risk perception. Those

who perceived less control, were younger, not Caucasian, married, and less optimistic

were more likely to experience greater distress before risk screening (Audrain et al.,

1998). Hughes et al. (1997) studied ethnic differences in knowledge and attitudes

regarding testing for BRCA1 gene in at risk women; and found that African American

women had lower levels of knowledge, but more positive attitudes toward genetic testing

than Caucasian women. Risk perception appears to vary by ethnic status, with African

American women who have a family history of breast cancer having greater concerns








about their own personal risk of breast cancer and appearing more likely to avoid breast

cancer-related thoughts and feelings (Hughes et al., 1996).

Studies have examined coping strategies associated with receiving genetic risk

information to determine if coping mediates distress in genetically at risk individuals.

For these types of studies, coping has been conceptualized as the degree to which one

either seeks more information (monitoring) or avoids or distracts oneself from the

situation (blunting/avoidance) (Miller, 1987). Studies have used the Miller Behavioral

Style Scale (MBSS; Miller, 1987), to determine the style in which individuals deal with

risk information given. It has been hypothesized that individuals cope with health threats

in one of two ways. In general, an interaction has been reported between the amount of

information provided and whether monitoring or blunting characterizes the individual's

coping style.

Studies have found that matching the amount of information received to the amount

the individual desires, lowers distress (Ludwick-Rosenthal & Neufeld, 1993; Miller,

1980; Miller & Managan, 1983). There does not appear to be consensus regarding

whether coping mediates distress in those notified of increased genetic risk status. For

example, in a study of patients from high-risk families screened for BRCA1/BRCA2

genes, coping efforts (both active and avoidant) were associated with higher levels of

distress prior to notification; whereas post-notification distress was associated with the

test result, not coping (Tercyak et al., 2001a). Lerman et al. (1993) found that a high

level of monitoring in women at risk for breast cancer predicted an increase in distress

over a 3-month follow-up period; whereas Vernon and colleagues' (1997) study of FAP

screening found the opposite to be true. Anxiety appears to be influenced by whether or








not the event is controllable and by the amount of information given to an individual

(Miller et al., 1989). Sex differences may also play a role in risk appraisal and coping.

Marteau et al. (1997) found that women have a greater fear of threat, worry more about

negative outcomes, and perceive greater risks from technology than men; whereas men

show higher threat minimization after positive carrier testing for cystic fibrosis (CF).

Behavioral Impact of Genetic Testing

A major question of interest to researchers is whether results of predictive genetic

testing lead to increases or decreases in health behaviors and medical surveillance. Does

informing people of genetic susceptibility to disease motivate them to take action to

reduce their risk? Or does knowing that one is genetically predisposed suggest a sense of

pre-determined destiny and perceived immutability (Senior, Marteau, & Peters, 1999;

Senior, Marteau, & Weinman, 1999)? Marteau & Lerman (2001) reviewed literature

related to cancer, smoking, and heart disease and espoused that providing genetic

information may not increase motivation to change behaviors and may even result in

reducing motivation. However, these authors also suggested that genetic information

might better facilitate change if individuals are offered effective risk-reducing

interventions tailored to their genetic risk. Most existing research studies in this area

focused on cancer, particularly, breast cancer screening; and physician recommended

behaviors, such as mammography and breast self-examinations.

Studies have examined the impact of distress caused by risk notification to

determine if distress predicts health-protective or preventative behaviors. Croyle &

Lerman (1999) reviewed studies on how coping and distress influenced the processing of

genetic risk information and subsequent decision-making. Studies have found that risk

information can be too anxiety provoking for some; and therefore, anxiety acts as a








barrier to following through with screening recommendations (Kash et al., 1992; Lerman

et al., 1994; Lerman et al., 1993). Other studies have suggested that increased distress or

worry actually increases health behaviors (Burnett et al., 1999; Diefenbach et al., 1999)

and even leads to excessive health practices (i.e., breast self-examinations) (Epstein et al.,

1997; Epstein & Lerman, 1997; Lerman et al., 1994; Lerman & Schwartz, 1993). Epstein

et al. (1997) found that those who were excessive in their protective behaviors were more

likely to be African American, older, and less educated. These findings may be

explained by results from Audrain et al. (1998) suggesting that African American women

undergoing genetic screening experience greater distress and have lowers levels of

cancer-related knowledge. Others have found no significant relationship between distress

and adherence to recommended medical surveillance (Lerman et al., 2000). Taken

together, these studies suggest that an inverted U-shape curve may explain the

relationship between distress and screening behavior, with highest rates of adherence

predicted by a moderate level of anxiety (Hailey, 1991; Lerman et al., 1991; Lerman &

Rimer, 1993).

There have been conflicting reports of how perceived versus actual risk impacts

screening behaviors (Hailey, 1991). Overall, some cancer studies found an increase in

screening rates in those informed they are genetically at higher risk (Meiser et al., 2000;

Ritvo et al., 2002; Schwartz, Taylor, et al., 1999), while other studies have found

adherence rates similar to those of the general population (Bratt et al., 2000; cited in

Marteau & Lerman, 2001; Clavel-Chapelon et al., 1999). In a study of colon cancer

screening and behavior intentions, half of respondents indicated that they would decrease

their use of screening tests and make fewer attempts to reduce their dietary fat intake if








their test results indicated that they were at low risk (Lerman et al., 1996). Women with a

family history of breast cancer were more likely to perceive higher genetic risk and

engage in appropriate screening behaviors (Hailey et al., 2000; McCaul et al., 1996).

Perceived risk has been found to predict screening compliance above and beyond the

actual risk associated with family history of a disease (Aiken et al., 1994). Women who

perceived themselves to be at greater risk were more likely to engage in initial as well as

repeated screenings (Lerman et al., 1990; McCaul et al., 1996). However, the perception

of risk does not appear to be necessarily related to the accuracy of risk. It has been

suggested that accurate recall of risk information does not necessarily lead to risk-

reducing behavior. Therefore, many have begun to examine the links between risk

perception and risk-reducing behavior, particularly the potentially mediating variable of

disease-specific worry or anxiety.

Many studies have been conducted examining the role of information-seeking and

health behavior change, particularly as it relates to public health issues (i.e,. HIV/AIDS

prevention). Rakowski (1990) conducted a randomized survey among adults in the

general population and found a positive association between more frequent information

seeking and personal health-related practices. However, hardly any studies have

examined this issue in the context of genetic screening. One such study examined

women at genetic risk of ovarian cancer; and found that monitors (information-seekers)

demonstrated greater adherence to behavioral recommendations, such as attending cancer

screenings (Wardle, 1995).

Demographics factors are also important in predicting health behaviors. Schwartz

et al. (1999b) found that women with less education who were at risk for breast cancer








and screened, reduced their use of mammography after breast cancer risk counseling.

Additionally, studies have found that women from ethnic minority groups and women

with lower levels of education reported greater disease-specific worry (Aiken et al., 1994;

Audrian et al., 1998) and retained less information about screening programs in general

(Browner et al., 1996, Donovan & Tucker, 2000; Hughes et al., 1997)

Lerman et al. (1993) found that reproductive behaviors were also impacted by

cancer screening. In a study of women under age 49, 22% reported that they would be

less likely to have children if they tested positive; and 17% reported being uncertain

whether they would continue a pregnancy. Other studies that assessed the reproductive

impact of genetic testing, found that 46-83% of subjects within reproductive age in the

general population would not have children or would limit further reproduction if they

tested positive for a disease gene (Kessler et al., 1989; Schoenfeld et al., 1984; Zerres et

al., 1986). Unfortunately, little is known about cancer screening and other lifestyle

changes involving smoking, physical activity, or diet (Marteau & Lerman, 2001).

In the current study, both lifestyle and health surveillance behaviors were included

as outcomes. Similar to other studies, relationships between reported behavioral

outcomes and psychological variables (such as anxiety, perceived control, coping, and

risk perception) were assessed.

Theoretical Models of Genetic Screening and Behavioral Change

Most studies in the area of genetic testing fail to use a theoretical model to conduct

or interpret the findings. However, theoretical models are important as they can serve as

a contextual framework for interpreting complex results. Generally, most models of

health behaviors assume that the motivation for health-protective behavior comes from








both the anticipation of a negative health outcome and the hope of avoiding it (Weinstein,

1993).

Tercyak (2000) advocated for conceptualizing the impact of genetic testing as a

family systems issue (Tercyak, 2000). The rationale for this was that genetic risk

information impacts multiple family members and the family's dynamics as a whole.

Additionally, Tercyak (2000) reasoned that families with no history or experience with a

particular illness would fare differently than those with a family member who is already

ill. Pre-existing illness in one member of the family provides personal experience and an

increased knowledge base about that condition for other family members that families

without a history of that particular disease would not have. Therefore, the meaning and

implications of genetic test results would be different depending on family history. By

translation, subsequent behavior change may also differ.

Rolland (1999) advocated the use of a specific model, the Family Systems-Illness

Model, when examining the psychological impact of predictive testing. Rolland (1999)

stated it to be a "useful guide," as it emphasizes the psychosocial demands of different

disorders over time and emphasizes the key components of family functioning (i.e.,

multigenerational patterns), illness life cycles, and belief systems. Rolland recognized

that psychosocial challenges varied according to biological variables, including the

degree to which a disease is influenced by both the environmental and genetic factors and

the degree to which prevention is possible (Rolland, 1999). Rolland advocated for

longitudinal follow-up of families after genetic testing, as psychosocial strains related to

knowledge of future risk do not just present themselves upon result notification, but will

tend to surface at major life-cycle transitions (Rolland, 1999). These challenges








influence family decision making and health behaviors. In his descriptions, Rolland did

not address specific mechanisms of his model or how they apply to screening results

(Rolland, 1999). It is apparent that Rolland was using his model as a conceptual guide

rather than as a testable model. To date, although genetic testing is recognized as

impacting the daily lives of entire family units, there has been no formal testing of a

family systems model pertaining to the psychosocial impact of genetic testing.

The Health Belief Model (HBM) (Rosenstock, 1974) has been used most

frequently in previous genetic testing research. Investigators have used this model to

explain preventative health care behavior in the context of one's perceived susceptibility

to an illness, the perceived severity of that illness, and the potential benefits and costs of

performing a specific behavior to reduce the risk (i.e., Aiken et al., 1994; Becker &

Maiman, 1975; Frets et al., 1990; Rowley et al., 1991; Sagi et al., 1992; Shiloh & Saxe,

1989; Sorenson et al., 1987). These factors are hypothesized to be predictive of the

decision to engage in health behavior change or to increase surveillance. Preventative

action is most likely taken when individuals perceive themselves to be at risk for a

serious disease and when the benefits to action outweigh the costs of not engaging in the

specific health behavior. For the purposes of genetic screening studies, most have

focused more on the perceived susceptibility component rather than the perceived

severity.

The HBM has several significant weaknesses. The perceived severity component

has not gained strong empirical support as a major predictor for preventative behavior

(Leventhal et al., 1983). Additionally, the HBM has proven influential for health

attitudes, but not consistently for health behaviors. HBM also assumes that health








behaviors arise from a single rational decision based on cost-benefit analysis, which may

be an oversimplification (Home & Weinman, 1998). Finally, this model does not specify

underlying beliefs, how to change beliefs (Home & Weinman, 1998) or what beliefs need

to be changed in order to change behavior. Other social variables and personal factors, as

reviewed in later sections, may have more importance in influencing health behavior than

this model would suggest.

While many studies have used the HBM, Lerman et al. (1997) endorsed a different

model, the Self-Regulation Model of Health Behavior (SRM; Leventhal, 1965), to more

specifically address why women at risk for breast cancer experiencing too great or too

little worry were less likely to practice risk-reduction behavior. AccordiAg to Leventhal

(1970), a health threat results in both cognitive and affective responses, which occur in

parallel. The SRM suggests that moderate levels of perceived health threat (e.g.,

diagnosis of cancer in a relative) engender a moderate level of concern/worry, which in

turn, leads individuals to take actions that will reduce the anxiety caused by a health

threat. Fear arousal coupled with an action plan leads to a "cognitive representation" of

the threat (Home & Weinman, 1998). Excessive cancer-related anxiety might produce

avoidance of screening, and at least a minimal level of anxiety is necessary to motivate

these behaviors. Similar to this model is the Fear Arousing Communications Theory

(Janis and Feshback, 1953), which states some degree of fear arousal is needed to predict

adoption of health care behaviors. If individuals are not concerned, they may deny the

threat; and if they are overconcerned, they may come to avoid preventative health

practices (Kash et al., 1992).








However, contrary to the aforementioned models, communication of a threat alone

may be insufficient to change one's behavior (Home & Weinman, 1998). Relatively few

studies have used the SRM, perhaps because of its complexity, which makes it difficult to

operationalize. However, there does appear to be some empirical support for this model

in studies of medication adherence in hypertension (Meyer et al., 1985) and regimen

adherence in diabetes (Gonder-Frederick & Cox, 1991; cited in Home & Weinman,

1998).

The model that best informs the current study is a transactional model of stress and

coping, known as the Stress-Disease Risk-Coping Model, which is a comprehensive

model specifically designed for studies of genetic testing (Baum et al., 1997). Baum and

colleagues' (1997) model is based on the concept of risk appraisal espoused by Lazarus

& Folkman (1984), in which primary appraisal involves the judgment of the threat of a

stressor; and secondary appraisal consists of a judgment regarding available resources to

deal with the threat. This model is particularly concerned with the relationship between

uncertainty and risk perception, which influence one's stress response, and consequently

affect one's behavior. Proponents believe this model is useful in predicting

psychological and behavioral responses to genetic testing results (Lerman, 1997). This

model hypothesizes that distress and behavior changes will be affected by the interaction

between personal factors (perceived risk influenced by family history, optimism), actual

test results, characteristics of disease, and degree of uncertainty remaining after testing

(Figure 1). The central component of the model involves the appraisal process regarding

the test results. In this step, appraisal of increased certainty regarding future outcomes is

coupled with perceived available options for action. This appraisal process is influenced








by the degree to which one perceives him/herself to be at risk; and is influenced by

surveillance and prevention options, along with other variables (such as social support,

optimism, perceived control, etc.). This appraisal process is associated with the stress

response to the information. The more resources available, the better one may be able to

cope with the stressor (Wallston, 2000). This stress response and these coping

mechanisms in turn, relate to behavioral consequences. This model suggests that the

adoption of health behaviors is influenced by personal factors, perceived risk, perceived

control, distress, and coping resources. In describing the model, Baum et al. (1997)

review the studies that influenced the design of the model, indicating that this model was

originally informed by both theoretical and empirical evidence.

Baum and colleagues' (1997) model is fairly new; and presently, no known

published studies have tested this model. Despite a lack of available empirical support,

this appears to be the most comprehensive and relevant model to use when examining the

behavioral impact of genetic screening. The model incorporates many variables that have

been examined in the context of genetic screening studies (i.e., perceived risk distress). It

should be noted that this model applies to individuals and unfortunately, does not directly

incorporate the family unit, which is undoubtedly affected by results of genetic testing.

Despite this limitation, for the purposes of the current study, this model was applied to

maternal behavior change in response to risk identification in their children.

Newborn Genetic Screening

The current study examined the impact of genetic screening of infants, which is

ethically more complicated than testing within adult populations. The Institutes of

Medicine (IOM) reports that 3% of children have an illness or disorder of probable

genetic origin (IOM, 1994). Understandably, while one would want to extend the








benefits of biomedical advances to children, additional considerations are involved.

Consequently, organizations have generated ethical guidelines for performing genetic

tests in pediatric populations, including the American Academy of Pediatrics (AAP),

American Society of Human Genetics and American College of Medical Genetics,

Clinical Genetics Society, and Institutes of Medicine (American Society of Human

Genetics and American College of Medical Genetics, Boards of Directors, 1995; Clarke

et al., 1994; Wertz et al., 1994). These guidelines are especially important for testing for

diseases for which there are no known cures or modes of prevention. In the absence of

clearly beneficial treatments or effective methods of prevention, it is difficult to justify

the genetic testing of children and adolescents, including newborn screening. Because

young children are unable to understand the value of genetic information for their own

lives, particular care must be exercised by parents and pediatricians when making

decisions about genetic testing for children (AAP Committee on Bioethics, 2001). Other

important factors to consider include the psychological and economic impact on the

family, time of disease onset, degree of risk, and possible medical benefits.

For these reasons, newborn genetic screening is controversial, especially for those

diseases with no known cure. The Institute of Medicine (IOM) report recommended

three principles (IOM 1994) to govern the maintenance of existing screening tests and the

introduction of new newborn tests:

identification of the genetic condition must provide a clear benefit to the child
a system must be in place to confirm the diagnosis
treatment and follow-up must be available for affected newborns

In other words, newborn genetic screening is supported only if the infants would benefit

from early identification and prevention/treatment. Other guidelines exist that allow









regulated research protocols to test children when no immediate medical benefit exists

but the contribution to scientific knowledge is great (American Society of Human

Genetics/American College of Medical Genetics Board of Directors, 1995; Clark, 1994).

Newborn screening is the most widely used type of genetic screening, with nearly

all states in the U.S. mandating newborn screening for phenylketonuria (PKU) and

congenital hypothyroidism, in which early diagnosis leads to treatment and better medical

outcomes (IOM, 1994). Recently, in some states, newborn screening has expanded to

include testing for congenital adrenal hyperplasia and cystic fibrosis (CF) (in WI, CO,

and WY). As a point of comparison for CF screening, only 6% of newborn U.S. children

are screened versus 92% of newborns in Australia (Wilcken & Travert, 1999). In the past

decade, newborn screening has been implemented in research settings to test for risk of

type 1 diabetes (discussed in a later section).

Currently, there has been relatively little research on the psychological implications

of screening newborns. Much of the research has occurred in other countries. Studies

from Wales on newborn screening for Duchenne muscular-dystrophy, an incurable X-

linked condition eventually leading to death during early adulthood (Fenton-May et al.,

1994), suggest that the screening has been well-received, with few adverse psychological

outcomes reported and a participation rate of 90% for eligible families (Bradley et al.,

1993; Parsons et al., 2002). Such a favorable outcome is not always the case. A

screening program for alpha-1-antitrypsin (lung disease) in newborns in Sweden had to

be terminated prematurely because of adverse effects. These included negative changes

in family dynamics and parental nonadherence to medical recommendations, including

increased smoking behavior (McNeil et al., 1989).








Most of the newborn screening literature has been dedicated to screening for cystic

fibrosis (Kerem et al., 1989). CF screening remains controversial (especially for those

without a family history) as some view the psychological costs as outweighing the

medical benefits of early diagnosis (Wald & Morris, 1998). Adverse psychological

outcomes have included greater parenting stress (Baroni et al., 1997) and a small

percentage of mothers experiencing short-lived feelings of rejection toward their child

(Al-Jader et al., 1990). It should be noted that these effects might also be present when

diagnosis is made through conventional means when children are a little older (Al-Jader

et al., 1990; Boland & Thompson, 1990; Wilcken et al., 1983). Boland & Thompson

(1990) found newborn screening versus traditional screening did not produce greater

overprotectiveness in mothers. The delay in diagnosis that occurred when screening was

not conducted resulted in greater maternal distress and anger. Therefore, these

psychological risks do not appear significant when the potential benefits of newborn

screening include better health outcomes due to earlier initiation of treatment (Waters et

al., 1999).

Further exploration of the psychological effects of newborn screening is an

important area of research as new genetic tests become available; and decisions will need

to be made regarding the appropriateness of their use. Whether testing is conducted in

the general population or in research settings only; and whether it is conducted with all

families or just those with a family history of the disease, are important questions to be

answered. How risk information is understood and used by families; and whether it then

translates into emotional and/or behavioral changes are key areas for future research.








Type 1 Diabetes: Etiology and Prevention

In the U.S., the prevalence of insulin dependent diabetes mellitus (IDDM; type 1

diabetes) is approximately 2-3/1,000 children, which makes it one of the most prevalent

childhood chronic illnesses (Arslanian et al., 1997; LaPorte et al., 1995). Annual

incidence is estimated to be over 12,000 children each year, with peak incidence of

diagnosis occurring between five and six years of age and again between the ages of

eleven and thirteen. The prevalence of type 1 diabetes is higher among Caucasians

(National Diabetes Data Group, 1995). In type 1 diabetes, the body produces little or no

insulin due to the autoimmune destruction of islet cells in the pancreas. This leads to

high blood glucose levels. Type 1 diabetes is thought to be the endpoint of an

immunologically mediated attack on pancreatic beta cells. It is an autoimmune disorder

where islet cells are destroyed by an immune response, or more simply, destroyed by

cells within one's own body that normally protect a person from germs. Complications

of type 1 diabetes can include retinopathy, blindness, renal disease, neuropathy, lower

extremity ulcers, digestive disorders, heart disease, and vascular disease (National

Diabetes Data Group, 1995). The average life span for those with diabetes is generally

shortened due to vascular complications. With no cure available, type 1 diabetes is

currently medically managed by administering insulin on a daily basis and adhering to a

specialized diet and exercise program. These daily treatment demands can greatly affect

an individual and their family's lifestyle.

In addition to the impact on the family, type 1 diabetes is a substantial societal and

economic burden. Therefore, an obvious need exists for diabetes prevention. Currently,

diabetes (including treatment, prevention, and research) consumes one in every seven

dollars spent on health care in the U.S. (Schatz et al., 2002). Often diabetes is not








diagnosed until a patient is having a crisis episode (ketoacidosis), which can lead to

increased medical complications and longer hospitalizations (Beisswinger, 2000; cited in

Schatz, 2002).

Unfortunately, we do not fully understand the etiology of type 1 diabetes. Nearly

90% of type 1 diabetes occurs in families with no history of the disease (Dalquist et al.,

1985) and there is only a 30-50% concordance rate among monozygotic twins (National

Diabetes Data Group, 1995; Kyvik et al., 1995; LaPorte et al., 1995). However,

approximately 3-6% of first-degree relatives with type 1 will develop the disease as well

(Tillil & Kobberling, 1987). The chance of developing diabetes for the general

population is about 1 in 300 while, for those with first-degree relatives with diabetes, the

chances increase to 1 in 20 (National Diabetes Data Group, 1995). These data suggest

IDDM is caused by a combination of genetic and environmental factors.

It is generally thought that environmental triggers initiate an autoimmune process

that leads to the destruction of pancreatic beta-cells and consequently, type 1 diabetes. It

is still unclear the degree to which these environmental factors play a role. In order to

determine the interactions between genetics and the environment, longitudinal studies are

needed to follow at risk individuals over time. To date, research studies have suggested

viral illness enteroviruss and rotovirus) may be one class of environmental triggers

(Akerblom & Knip, 1998, Couper, 2001, Dorman et al., 1995). Additionally, Classen &

Classen (2001) argue that timing of vaccines increases the risk of type 1 diabetes. The

risk of type 1 diabetes decreases when children receive vaccinations after at least two

months of age, arguing for the benefits of delayed immunization schedules. A recent

study found increased social mixing in young children (i.e., attendance of daycare) in








early infancy was protective against the development of type 1 diabetes because it

increased exposure to infections and strengthened immunity (McKinney et al., 2000).

However, there has been no other direct evidence in favor of such an association

(Akerblom & Knip, 1998). Finally, early emotional stress may also be a contributing

factor (Thernlund et al., 1995).

Dietary factors have been implicated as important environmental contributors to the

development of type 1 diabetes. Such dietary factors included not breastfeeding

(Akerblom & Knip, 1998), early introduction of cow's milk (Akerblom, et al, 1993;

Gerstein, 1994; Virtanen et al., 2000), high intake of nitrites/nitrates (Virtanen & Aro,

1994), accelerated prenatal growth (Dahlquist et al., 1996), high intake of proteins

(Akerblom & Knip, 1998), high intake of carbohydrates (Akerblom & Knip, 1998) and

increased weight gain in infancy (Hypponen et al., 1999). Although, based on both

animal and human studies, the most likely putative dietary factors are hypothesized to be

cow's milk, proteins, and nitrates/nitrites (Akerblom & Knip, 1998

The greatest amount of research regarding environmental factors related to type 1

diabetes has examined whether breastfeeding is protective and how this interacts with

exposure to cow's milk in infancy. Cow's milk is implicated because it has a higher

protein content, specifically the protein casein, than that found in human breastmilk.

Many studies have been conducted to address this issue with no firm consensus reached

(Akerblom & Knip, 1998; Couper, 2001). To examine the role of cow's milk, the multi-

national Trial to Reduce IDDM in the Genetically at Risk (TRIGR) is ongoing to

determine if delayed exposure to cow's milk until after 6 months of age will have an

effect on the subsequent development of diabetes (Karges et al., 1997; Schatz, 2002;








Virtanen et al., 1997). Schatz & Maclaren (1996) warn it is premature to recommend

eliminating cow's milk from an at risk child's diet as there is no convincing evidence to

suggest the nutritional benefits of milk for young children outweigh the potential dangers.

To answer questions regarding the prevention of type 1 diabetes, The Diabetes

Prevention Trial (DPT-1) was initiated in 1994 to determine whether subcutaneous or

oral insulin could prevent or delay the onset of diabetes in at risk relatives (DPT-1 Study

Group, 1995, 2002). Within this large-scale randomized, nonblind study, there were two

separate trials for the two types of insulin administration. Three hundred and thirty nine

participants, who were between 3 and 45 years of age and had a first degree relative with

type 1 diabetes were randomized in the subcutaneous insulin trial (out of 84,228 screened

first degree relatives). To be eligible, participants had to be determined as "high risk,"

defined as a 50% chance of developing type 1 diabetes over the next five years. This was

determined by the absence of protective genetic markers, positive antibody testing, and a

low first-phase insulin response in glucose tolerance testing. Participants were

randomized to either the intervention group, which received low dose subcutaneous

insulin, or the close observation group, and all of whom were followed for an average of

3.7 years. Results from the subcutaneous insulin trial were recently published. Results

suggested that injected insulin does not delay or prevent type 1 diabetes (DPT-1 Study

Group, 2002). The oral insulin trial is ongoing and results are not currently available.

In contrast to type 1 diabetes, type 2 diabetes (non-insulin dependent diabetes) is a

different form of diabetes that is considered a metabolic disorder, rather than an

autoimmune disease. It is usually diagnosed in adulthood, although it can develop in

childhood. In type 2 diabetes, the body is unable to make enough or properly use insulin;








however, beta cells are preserved. This is in contrast to type 1 diabetes, in which beta

cells are destroyed, leading to insulin deficiency. Type 2 diabetes accounts for 90-95%

of diabetes and researchers have found obesity and a sedentary lifestyle to be contributing

factors, as well as genetic predispotion (Fletcher et al., 2002). Because the prevalence of

type 2 diabetes is rapidly increasing to epidemic proportions, the health care community

and the media have recently focused significant attention on type 2 diabetes, advocating

for healthy lifestyle changes. Recent research has indicated moderate diet and exercise

reduces risk for type 2 diabetes more effectively than even oral insulin (Tuomilehto, et

al., 2001). A healthy diet is effective because it reduces the insulin load and exercise is

effective because physical inactivity reduces tissue glucose tolerance and is associated

with insulin resistance. Scientific evidence is not clear as to whether these same

behaviors have an impact on the development of type 1 diabetes; however, at the present

time, it seems unlikely (Schatz, personal communication). People who do not understand

the distinction between type 1 and 2 diabetes may apply recommendations for type 2 to

their children at risk for type 1. The current study explored whether this hypothesis was

true for our sample population of mothers of at risk young children.

Prediction and Pre-Symptomatic Screening for Type 1 Diabetes

While we do not fully comprehend the natural history of the development of

diabetes, we do know that the destruction of pancreatic cells is a precursor to type 1

diabetes and begins long before overt symptoms. It is currently possible to detect

pancreatic cell destruction and identify those at risk for developing Type 1 diabetes. Riley

et al. (1990) found the determination of islet-cell antibodies in relatives of probands with

Type 1 diabetes increased an individuals' risk for developing the disease in the future.








Currently there are two types of screening for diabetes, autoantibody screening and

genetic screening. The most recently developed test, genetic screening, is typically done

in newborns to determine the present of high-risk genetic markers (DR 3/4, DR 4/4, DR

3/3) in the Human Leukocyte Antigen (HLA) region (the Major Histocompatability

Complex (MHC)) on chromosome 6. This is an area that helps control immune response,

and such markers are known to confer 50% of the genetic risk for Type 1 diabetes (Yu et

al., 1999) (Table 1). The second type of testing, antibody screening, is a process that has

been in existence for longer and detects islet-related autoantibodies, including

autoantibodies to insulin (Christie et al., 1994; Landin-Olsson et al., 1992), GAD or islet

antigen-2 (IA-2), as well as islet cell antibodies (ICA) (Riley et al., 1990; Schatz et al.,

1994) present in blood serum. It has been shown the presence and number of these

antibodies is directly related to risk for type 1 diabetes (Knip, 1998). An ICA positive

result signifies that the process of beta cell destruction has begun and therefore, those

who are ICA positive are farther along in the process of developing type 1 diabetes. For

example, individuals who test positive for ICA have approximately a 45% chance of

developing diabetes in the next ten years. Antibody screening has been conducted with

children and adults and used as a primary screening method and as follow-up to newborn

genetic screening.

While critics oppose screening for risk of developing type 1 diabetes before

symptoms appear, Schatz, et al. (2002) argue it is very important to the future of diabetes

prevention research. The authors assert screening helps us in a number of ways: it allows

us to better understand the prediabetic period and diabetes pathogenesis, assists in

identification of individuals for prevention trials, facilitates earlier diagnoses which








reduces the mortality and morbidity associated with type 1 diabetes (Schatz et al., 2002).

Currently, genetic screening is only conducted within research settings since widespread

screening of the general population, when there is no available effective intervention, is

considered unethical. Many longitudinal studies are now ongoing to follow newborns

found to be genetically at risk for Type 1 to better study the development of diabetes.

These trials, taking place in Germany (BABYDIAB), Finland (DIPP), Denver, CO

(DAISY), and Gainesville, FL (PANDA), include studies of the participants from the

general public and at risk families (e.g., Nejentsev et al., 1999; Rewers et al., 1996;

Schatz et al., 2000; Schenker et al., 1999; Ziegler et al., 1999).

Opponents of screening argue that without a prevention strategy, studies should

avoid disclosing results to participating families and that if disclosure is necessary then

research should only be conducted with infants who have a first degree relative with type

1 diabetes (Friedman Ross, 2003). Critics argue that screening under any other

circumstances may result in harm to children and their parents. Friedman Ross (2003)

stated that genetic screening can only convey at most a susceptibility that is a 20%

probability. She claims that as a result of these tests, parents may prepare unnecessarily

and treat their child as ill (Friedman Ross, 2003). As the debate continues regarding the

merits of genetic screening of the general population and as interest in diabetes

prevention continues to rise, research on the psychological and behavioral impact of

genetic screening becomes timely and highly relevant.

Psychological Impact of Diabetes Screening

Relatively little research has been conducted examining the parents' psychological

reactions to participation in a newborn screening program for type 1 diabetes. However,

parents have indicated favorable attitudes towards risk screening and prevention trials for








type I diabetes (Lucidarme et al., 1998; Ludvigsson et al., 2002). To explore the

psychological impact of risk screening, Dr. Johnson and her research group have

conducted several studies of adults and children identified as at risk via autoantibody and

newborn genetic screening (Carmichael et al., 2003; Johnson, 2001; Johnson &

Carmichael, 2000; Johnson & Tercyack, 1995; Johnson et al., 1990). As explained

above, a determination that an individual is at risk as identified through presymptomatic

screening does not mean an individual will definitely develop diabetes. How this

information and level of uncertainty impacts individuals, particularly newborns and their

families, is an important factor to consider when evaluating the ethical nature of genetic

risk screening.

In one of the first studies in this area, Johnson et al. (1990) reported individuals

found to be at high risk (as identified through ICA screening) and their family members

exhibited clinically significant levels of anxiety subsequent to at risk notification. Those

testing ICA+ were told their chances of developing diabetes were 50%. Johnson and

Tercyak (1995) subsequently found notification of islet cell antibody positive (ICA+)

status had an emotional impact on the at risk individual (adults and children) and their

family members (i.e., spouses, parents). Initial notification was associated with

considerable situationally-specific anxiety (as measured by the state portion of the State-

Trait Anxiety Inventory (STAI; Speilberger, 1970) and the State-Trait Anxiety Inventory

for Children (STAI-C; Speilberger, 1973) in both individuals with the risk and their

family members. This was especially true in parents of ICA+ children. In addition,

parent and child anxiety was highly correlated. However, initial anxiety seemed to

decrease to normal levels over time, as measured in a 4-month follow-up interview.








In a similar study with fewer participants, Galatzer et al. (2001) examined antibody

positive children (n=10) and their parents using the Impact of Events Scale (IES;

Horowitz, 1979) and found that high levels of distress reported by parents upon results

notification decreased by the 3-month interview. Galatzer et al. compared their results

with a study of parents of children newly diagnosed with type 1 diabetes (Kovacs, 1985)

and found similarly strong emotional reactions, but more so in the group of parents of

children with diabetes. Another small-scale study conducted by Yu et al. (1999) (n= 88)

found notification of high-risk genetic status in newborns was not associated with

increased parenting stress as measured by total stress score (TSS) of the Parenting Stress

Index (PSI; Abidin, 1990) more than three months after notification.

A follow-up study to Johnson & Tercyak (1995) examined how individuals found

to be at risk (ICA+) coped with their own or a loved one's at risk status, by administering

the Ways of Coping Checklist-Revised (WCC-R; Folkman & Lazarus, 1980) (Johnson &

Carmichael, 2000). Using this multi-dimensional measure allows for closer examination

of coping styles (i.e., problem-focused, seeking social support, wishful thinking,

avoidance, self blame) beyond the concept of monitoring vs. blunting found in previous

cancer genetic screening studies. Johnson & Carmichael (2000) found at risk children

used more avoidance coping (e.g., tried to forget the whole thing, kept your feelings to

yourself; slept more than usual) than at risk adults, mothers of at risk children, or spouses

of at risk adults. At risk children also used more wishful thinking (e.g., hoped a miracle

would happen; wished the situation would go away) than at risk adults. Initial state

anxiety in response to risk notification was related with subsequent coping as mothers

who were more anxious tended to use more wishful thinking, avoidance, and they tended








to blame themselves for their child's at risk status. Coping strategies appeared to

influence the maintenance of anxiety over time as mothers who blamed themselves

tended to remain anxious.

In the late 1990s, testing moved from biological to genetic markers, and from

family cohorts to the general population. Carmichael et al. (2003), Johnson &

Carmichael (2000) and Johnson et al. (submitted) interviewed mothers of infants at risk

for developing Type 1 diabetes as identified through participation in the longitudinal

Prospective Assessment of Newborns for Diabetes Autoimmunity (PANDA) study

(Schatz, 2000). As described earlier, PANDA involves HLA genotyping and serial

antibody screenings over time. Interviews assessing the psychological impact of

participation in PANDA were conducted approximately 4 weeks post-notification, and

again 4 and 12 months after notification. Similar to the ICA+ studies, they found

maternal anxiety levels were clinically elevated after initial notification of risk status, but

appeared to dissipate over time to normal levels (Johnson et al., submitted).

Risk understanding was examined in mothers who participated in the initial and 4-

month follow-up PANDA interviews (Carmichael et al., 2003). Almost 75% of mothers

gave a correct estimate of their child's genetic risk at the initial interview; however, over

time, mothers were less likely to be accurate and more mothers underestimated their

child's risk. Overall, very few mothers overestimated their child's risk. Mothers who

were Caucasian and who had higher levels of education were more likely to be accurate.

Mothers whose children were in the highest risk group were least accurate. Mothers of

children with a family history of a first degree relative with diabetes were more likely to

underestimate their child's risk at the initial interview. Maternal anxiety was a predictor








of risk underestimation at the 4-month interview, but was not significant in predicting to

earlier underestimation or accuracy at either time point. As one might expect, mothers

who were more anxious were less likely to underestimate their child's risk.

In studies of maternal anxiety in this population, initial anxiety levels were found to

be higher in mothers who were Hispanic, with less education, in those whose infants were

at greater risk, and in mothers who overestimated their child's actual risk (Johnson et al.,

submitted). Coping strategies also appeared to be related to anxiety as wishful thinking

and blaming one's self predicted anxiety at the 4 and 12-month follow-up interviews

(unpublished data). As explained in later chapters, participants for this study were

recruited from this larger sample.

These studies, taken together, suggest newborn screening does not have long-term

detrimental effects on parental adjustment, as measured by either anxiety or stress.

Additionally, it appears that a majority of mothers correctly recall their infant's risk with

few mothers overestimating their child's risk and consequently becoming more anxious.

These findings are congruent with other studies of genetic testing previously discussed.

It is likely parents' reactions to the news and subsequent coping style may influence an

individual's or family's decision to participate in longitudinal trials or natural history

studies, such as PANDA, that will provide the scientific bases of a prevention or cure for

type 1 diabetes. These studies can play an important role in informing debates about the

ethics of newborn screening.

Behavioral Impact of Diabetes Screening

Johnson & Tercyak's (1995) study of ICA+ children and adults, assessed after

notification of screening results, found 52% of ICA+ children and 24% of ICA+ adults

reported making a change in their behaviors and/or lifestyle in an attempt to delay or








prevent the onset of Type 1 diabetes. While details were not reported, the authors made a

general statement that these reported changes most often reportedly occurred in the areas

of diet and increased exercise. The authors also found that a higher level of anxiety was

associated with greater lifestyle/behavior modifications. Similarly, in a later study of

genetically at risk infants, mothers who continued their child's participation in the

longitudinal PANDA study tended to be more anxious with infants at higher risk.

Mothers who believed their at risk children would never get diabetes were less likely to

continue study participation (Carmichael et al., 1999b).

In a recent study of intentions for behavior change, Hendrieckx et al. (2002)

surveyed a sample of 403 adults with first-degree relatives with type 1 diabetes who were

undergoing antibody screening for type 1 diabetes and were assessed prior to results

notification. This novel study sought to better understand the relationships between

perceived control, distress, and behavioral intentions. Results indicated 73% of

participants stated they intended to make a lifestyle change if found to be at high risk,

with diet (87%) and exercise (30%) most frequently endorsed (Hendrieckx et al., 2002).

These results suggested individuals' beliefs regarding the prevention of type 1 diabetes

did not correspond well with current scientific knowledge; however, beliefs appeared

more congruent with an understanding of type 2 diabetes (Hendrieckx et al., 2002).

Hendrieckx et al. (2002) found general anxiety did not appear to be a significant predictor

of behavior change, nor were behavioral intentions predicted by education level.

However, similar to Johnson & Tercyak (1995), diabetes-specific worry was related to

intentions towards behavioral change, along with perceived internal control. Hendrieckx

et al. (2002) also found those who were female, married, and older were more likely to








report anticipating making lifestyle changes. Additionally, perceived internal control was

related to beliefs regarding the causes of diabetes. More specifically, those who believed

their relative developed diabetes largely due to heredity or chance, were more likely to

believe they were unable to do something to reduce their risk of developing diabetes.

While this study provided important exploratory data, the results were limited because

the data were collected prior to the screening results, and intentions --rather than actual

behaviors-- were assessed. Additionally, it used several new measures, which have not

been psychometrically validated.

Additional data on diabetes screening and behavioral change come from the

Participant Experience Survey, designed by Johnson, for participants who completed the

DPT-1 study (Johnson, 2002). The survey was administered anonymously across study

sites to examine subjective experiences of participants (who were at least 10 years old) in

the trial, as well their parents (of participants under the age of 18). Questions assessed a

broad range of issues, including study adherence, satisfaction, reasons for participation,

perceived need for psychological support, and efforts to prevent or delay type 1 diabetes

from developing. Items that assessed efforts to prevent/delay type 1 diabetes were

designed to reflect intentional changes in weight diet, exercise, lifestyle, stress level,

monitoring, and alternative medication use. Items were scored as either "yes" or "no" to

reflect whether or not an individual reported engaging in a specific behavior. Only data

from those who were unaware of the study's results were analyzed. Sixty-five percent of

DPT-1 participants, who were all over the age of 10, responded to the survey, with 82

from the intervention (IN) group (which received preventative insulin) and 81 from the

close observation (CO) group. Over half (54%) of all participants reported modifying at








least one behavior in an effort to delay or prevent type 1 diabetes onset, with no

significant differences between groups, with the exception of alternative medication use

(significantly greater use in IN group). Results indicated dietary changes were the most

common behaviors reported, with approximately one-third of participants stating they

reduced candy or sweets intake, reduced intake of regular soda, or increased intake of diet

soda. Twenty-eight percent of participants indicated they would increase their physical

activity. Seventeen percent stated they took alternative medications. Ten percent of

participants reported attempting weight loss. Seven participants (4 in the experimental

group and 3 in control) stated they took extra insulin in an effort to delay or prevent

diabetes onset. No significant predictors of behavior change were found in this study.

These results were congruent with Tercyak & Carmichael (1995) and Hendrieckx et al.

(2002) and indicated a substantial proportion of individuals' who are found at risk for

type 1 diabetes engage in behaviors that correspond to those found to be effective in the

treatment and prevention recommendations for type 2 diabetes (ADA, 2002b;

Tuomilehto, et al., 2001). Data are currently available from parents whose children

participated in the DPT-1 study. These data were used as a comparison group in the

current study.

These studies described herein suggest there are unanswered questions to be

explored regarding the behavioral outcomes associated with risk screening for type 1

diabetes. Existing data suggest individuals report engaging in behavior changes in

response to risk information, although it is unclear what predicts these behavioral efforts.

Based on existing literature, risk perception, perceived control, and psychological distress

appear important factors to consider. Behavior changes that result from risk notification






35


may or may not be related to scientifically validated methods of risk reduction. However,

in the case of type 1 diabetes, whether a behavior is scientifically valid is not necessarily

important, since we do not currently know what delays or prevents the onset of the

disease.


















APPRAISAL (risk perception)

*Exposure variables (uncertainty reduction,
prevention and surveillance options)

*Personal factors (social support, perceived
control, information seeking)



No stress Stress and
stress response
I


Figure 2-1. Partial representation of the Stress-Disease Risk-Coping Model adapted from
Baum et al. (1997)


STRESSOR
CHARACTERISTICS
-test results
-uncertainty
-disease characteristics













CHAPTER 3
RATIONALE AND PURPOSES

The purposes of this study were to better understand predictors of self-reported

behavior change in mothers of newborns who were identified as at-risk for type 1

diabetes through genetic screening. Currently, little is understood about the specific

behavior changes that result from knowing one's child is genetically predisposed to a

condition for which there is currently no known prevention method or cure.

Additionally, our present understanding of the etiology of type 1 diabetes suggests it

develops from a combination of both genetic and environmental influences, which are not

well-defined. In the absence of definitive recommendations from the health care

community, mothers of newborns identified as "at-risk" may take actions they believe are

effective in preventing type 1 diabetes in their children.

Based on previous studies, possible behavioral changes may include altering their

children's environment, feeding schedules, activity patterns, and/or medical surveillance

behavior (Hendrieckx, 2002; Johnson, 2001; Johnson, 2002). These efforts may represent

mothers' attempts to reduce their anxiety and better cope with the situation. However,

we have yet to document the nature and extent of such behavior changes, including the

incidence of excessive prevention efforts that may become burdensome and impact daily

functioning. Further, since the onset of diabetes is thought to be an interaction between

genetics and the environment, it is unclear to what extent certain types of behaviors could

advance or delay disease onset. Although current science does not permit us to








recommend certain behaviors as preventative, it is important for us to monitor the role of

relevant behaviors if we are to understand the natural history of this disease.

Monitoring possible behavior change associated with high-risk notification is

equally important to current and future diabetes prevention trials. In the DPT-1, for

example, behavior change efforts taken by the control group (e.g., taking insulin or

nicotinimide) could undermine the trial's internal validity. Unless these behavior

changes are monitored, interpretation of study results can become exceedingly difficult.

This is not unique to diabetes-or genetic screening-specific trials as certain behavior

changes could potentially impact other types of clinical trials as well.

This study involves both qualitative and quantitative data to examine reported

behavioral outcomes associated with participation in the Perspective Assessment of

Newborn Diabetes Autoimmunity (PANDA) study. Findings will be examined in the

context of Baum and colleagues' (1997) model of genetic testing, in which behavior

change in response to genetic test results is influenced by one's risk appraisal, affective

response to the information, and available coping resources. Objectives of the study are

listed below.

Objective 1: To Investigate the Extent of Reported Maternal Behavior Change as a
Result of Genetic Screening for Type 1 Diabetes

Hypothesis 1.1: Reported behavior changes will most likely correspond to

recommendations for the treatment of diabetes (American Diabetes Association (ADA),

2002a, 2002b) and the prevention of type 2 diabetes (Pierce et al., 1995), including

changes in diet and physical activity patterns (Forsyth et al., 1997; Pierce et al., 1995).

Rationale: There is scientific uncertainty regarding the environmental factors

associated with the development of type 1 diabetes. The health care community and








media have recently focused significant attention on type 2 diabetes, advocating for

healthy lifestyle changes. In a study of parents with type 2 diabetes, nearly half thought

they could reduce their children's risk of developing diabetes by altering their children's

diet and exercise patterns (Pierce et al., 1999). Mothers, who may not understand the

distinctions between type 1 and 2 diabetes may apply such recommendations to their

children at-risk for type 1. This hypothesis is congruent with findings from Johnson &

Tercyak (1995), Hendrieckx (2002), and unpublished data from the DPT-1 survey

(Johnson, 2002).

Objective 2: To Assess Predictors of Maternal Behavior Change as a Result of
Genetic Screening for Type 1 Diabetes

Hypothesis 2.1: Mothers who perceive they have control over their child

developing diabetes will be more likely to report engaging in behavior changes.

Rationale: Mothers may be more likely to report taking action to help prevent such

an outcome if they believe they have some control over the situation. Behavioral change

to reduce a health threat is more likely if there is a belief that change can be affected

(Diefenbach et al., 1999; Hendrieckx et al., 2002). Perceived control is related to both

uncertainty reduction and available prevention/surveillance options, which are integral to

risk perception, a key component of health behavior change (Baum et al., 1997).

Therefore, perceived control will be examined in the context of other predictors of

behavior to determine possible interaction effects.

Hypothesis 2.2:_Mothers who perceive their children to be at greater risk will be

more likely to report engaging in behavior change.

Rationale: Perceived risk, more so than infant's actual risk, will be a better

predictor of behavior. Mothers of children who perceive their children to be at higher








risk than has been identified through testing (overestimate their risk) will be more likely

to engage in behavior change. Mother of children who underestimate their child's actual

risk will be less likely to engage in behavior change. Children who are perceived to be at

high or extremely high risk may have mothers who will be more likely to try to intervene.

Studies of genetic screening for breast cancer have found that increased perceived risk

predicts likelihood of engaging in health behavior change and health surveillance

behaviors (e.g., Aiken et al., 1994; Meiser et al., 2000; Ritvo et al., 2002; Schwartz et al.,

1999).

Perceived control may also interact with risk perception as mothers who perceive

their child to be at greater risk may be more likely to engage in behavior change if they

also believe they are able to control whether their child develops diabetes.

Hypothesis 2.3: Mothers who are more anxious will be more likely to report

engaging in behavior change.

Rationale: Mothers who are more concerned and worried about their child

developing diabetes will be more likely to report taking preventative actions. Studies

indicate disease-specific worry (Hendrieckx, 2002; Johnson and Tercyak, 1995) and

beliefs regarding the effectiveness of preventative actions (Diefenbach et al., 1999)

predicted either intentions for behavior change or increased adherence to health-

protective behaviors. Maternal anxiety may also be related to the degree of maternal

perceived control, which may in turn influence behavior. Mothers who are more

anxious/worried may report more behavior change if they also believe they have some

control over the situation.








Hypothesis 2.4: Mothers who use more coping strategies, particularly active

coping (i.e., problem-focused, seeking social support), will be more likely to report

behavioral changes.

Rationale: Engaging in risk reducing behavior can be seen as a means of coping

with a health threat. Behavior change is an active coping approach and likely to be

associated with other ways of coping, particularly those that are also more active, namely

problem-focused coping and seeking social support. Mothers who use avoidant

strategies, and try not to think about the problem, may be less likely to engage in risk-

reducing behaviors.

Additionally, those who perceive greater control over the situation may be more

likely to engage in more proactive coping methods, whereas, mothers who perceive less

control may engage in more avoidant coping and be less likely to report behavior change.

Hypothesis 2.5: Mothers who report information seeking and/or report receiving

recommendations from medical professionals or other family members related to

behavior change will be more likely to report engaging in behavior change.

Rationale: Mothers who are given advice to change their behavior by those they

feel are authoritative will be more likely to follow through with recommendations.

Research findings suggest "monitors" (or information-seekers) are thought to cope more

effectively with stressful situations using more problem-focused, information-obtaining

coping strategies rather than avoiding the situation and not seeking out information

(Scheier et al., 1986; Carver et al., 1989). Participation in research studies may be

viewed as an additional form of information seeking about health status. Information

seeking may also influence one's sense of perceived control, and consequently, may








influence behavior both directly and indirectly. Those who seek and utilize information

from various sources may perceive greater control over the situation, and consequently,

be more likely to report behavior changes. Therefore, information seeking will be

measured in this study and used as an independent predictor, as well as in conjunction

with perceived control.

Hypothesis 2.6: Mothers who continue participation in the PANDA Part II study

(repeated blood testing for antibodies), will be more likely to report other behavior

changes.

Rationale: Health surveillance behaviors, such as participation in additional blood

draws, may be a likely outcome following risk notification. Increased health surveillance

may also signify increased contact with health care professionals. For those who

continue in the PANDA study, the risk of developing diabetes may be more salient and

seen as something they should address. Mothers who continue in the study have contact

with investigators and study staff over time and therefore, this contact may influence their

behavior. Mothers who are sufficiently concerned about their child's risk enough to

monitor their child's risk more closely, may be more likely to report other behavior

changes. Data on participation in PANDA Part II blood draws are available and these

data can be compared with maternal report.

Objective 3: To Assess Psychological Effects (i.e., Anxiety) of Maternal Behavior
Change Over Time

Hypothesis 3.1: Mothers who report modifying behaviors will show a greater

reduction in anxiety over time than mothers who do not report behavior change.

Rationale: Behavior changes, including health surveillance behaviors, may

represent means of coping with a health threat. Engaging in behaviors perceived as risk








reducing may help lower maternal anxiety regarding the situation. However, the

influence of reported behavior change on maternal anxiety may be influenced by

maternal perceived control (i.e., mothers who perceive control over diabetes onset and

engage in behavior change may show greater reduction in anxiety).

Objective 4: To Compare Reported Behavior Change between Mothers of Children
Genetically at Risk for Developing Type 1 Diabetes with Mothers of Children in the
Diabetes Prevention Trial Who Were ICA+, and Therefore, at Even Greater Risk
for Diabetes Onset

Hypothesis 4.1: Mothers of genetically at-risk children will be less likely to

report behavior change than mothers of ICA+ children enrolled in Diabetes Prevention

Trial-1 (DPT-1).

Rationale: Participants enrolled in the DPT-I trial were at increased risk for

diabetes as identified through positive family histories and positive ICA screening. Their

risk level was collectively higher than 98% of our original total sample population for the

PANDA Part III study (of whom 7 of 435 were ICA positive). For this reason, we

believe that mothers in the current study will report fewer behavior changes than mothers

of children in the DPT-I since children of mothers in the proposed study are at relatively

less risk than the DPT-lstudy children.

Examining this hypothesis will allow us the unique opportunity to explore

differences between maternal reports of behavior change in two at-risk groups: children

identified at birth as genetically at-risk and higher-risk children who have entered a

prevention trial.

Pierce et al's (1999) study of parents with type 2 diabetes found that those who

believed they could prevent their children from developing diabetes and who perceived





44


their child's risk to be higher, were more likely to experience greater anxiety (Pierce et

al., 1999)

For the purposes of examining this hypothesis, maternal data from the DPT-1

survey (n = 134) will be compared with mothers' reported from the PANDA study.

Based on DPT-1 participant data, over half of the sample reported at least one behavior

change, with dietary changes most often reported, followed by increased exercise, weight

loss attempts, and alternative medicine use (i.e., vitamins) use (Johnson, 2002).













CHAPTER 4
METHODS AND MATERIALS

Prospective Assessment of Newborn Diabetes Autoimmunity (PANDA) Study
Procedures

Part I

Participants were mothers whose infants were screened at birth to determine their

genetic risk for the development of Type 1 diabetes (1997-1999) through Part I of the

Prospective Assessment of Newborn Diabetes Autoimmunity (PANDA) study. This

study is a National Institutes of Health and Juvenile Diabetes Research Foundation

Internal-supported registry that uses genetic testing to identify newborns at risk for type 1

diabetes (Schatz et al., 2000). In this study, mothers were contacted at the time of their

child's birth and asked permission to screen the newborn for the presence of the high-risk

HLA-DQB1 alleles using blood spots on filter paper (obtained by heel stick at the time of

state-mandated phenylketonuria testing). Informed consent was obtained and consenting

participants were told they would only be re-contacted if their child was at increased risk

for type 1 diabetes. The majority of these women gave birth at participating locations in

Gainesville, Florida, or Pensacola, Florida, were all English speaking, and were over the

age of 18.

PANDA genetic testing results placed infants into one of six risk categories: very

low risk (1/6000), low risk (1/300), slightly increased risk (1/125), moderate risk (2/100)

high risk (5-10/100) and extremely high risk (20-25/100) (Table 4-1). Only children who








were at moderate, high or extremely risk children are followed longitudinally in the

PANDA study.

If a child was determined to be "at risk," in other words, classified as either at

"moderate," "high" or "extremely high risk," mothers were sent letters notifying them

that their children's genetic test results were available. Results were usually available

after approximately 12-20 weeks following birth. In the letters, participants were

requested to call the PANDA study coordinator to discuss the results and possible

continued study participation in PANDA Part II according to PANDA protocol. If no

response was received approximately 30 days after sending the notification letter, the

PANDA study staff attempted to contact the mother by phone to notify her of her infant's

risk.

For mothers who called for results or were contacted by phone, the study

coordinator followed a scripted presentation of the risk information, including both

categorical and numerical risk figures. Additionally, she presented available options,

including participation in the PANDA Part II study, and an opportunity to ask questions

about the study or the meaning of the results. Parents had the option at that time to

decline further participation, continue with the study, or delay their decision. Regardless

of participation status in the PANDA Part II study, all mothers were asked for their

permission to be contacted by a second individual from the Pediatric Psychology

Research lab who would ask them questions about their understanding of the study and

its psychological impact. See Figure 4.1 for procedural outline of entire PANDA study.

Part II

Part II of the PANDA study involves longitudinal follow-up of children screened at

birth. These children are periodically screened (via blood draws) starting when the child








is at least six months of age for the presence of autoantibodies, which are additional

markers of diabetes disease progression. A positive screening for autoantibodies would

suggest that the child is at even greater risk of developing type 1 diabetes. Blood draws

could be conducted either by (1) mailing out supplies to parents and to have their

pediatricians draw the blood and mail back to the PANDA staff or (2) scheduling directly

at study sites in Gainesville, Orlando, or Pensacola, Florida. Blood draws were expected

to occur every at 3, 6 ,or 12 months, depending on risk level.

Part III

Part II of the PANDA study examined the psychological impact of participation in

PANDA, including maternal affective (i.e., anxiety) and cognitive responses (i.e., risk

understanding) as well as coping response. Mothers who agreed to be contacted at the

time of notification were interviewed by telephone approximately 4 weeks following

notification (M = 3.50, SD = 1.96) and again at 4 (M = 3.93, SD = 1.96) and 12 months

(M = 12.83, SD = 2.45) post-notification (see Figure 4-1).

For the initial interview, the Part III participation rate was high. Approximately

90% (n = 435) of the mothers we were able to contact (of over 700 eligible) agreed to

complete the initial interview, 79% participated in a second interview (n = 344), and 62%

participated in the third interview (n = 269). Sixty percent (n = 262) completed all three

interviews. Of those who did not complete all three interviews, 67 declined to be

contacted beyond the first interview (no attempts were made to contact these mothers to

participate in the current interview), and 106 were unable to be contacted by phone due to

either disconnected numbers or the time that had elapsed between or study personnel

could not reach them.








Participants

To be eligible for the current study, mothers must have completed at least the initial

interview of the PANDA Part III study and at no point declined participation in either of

the subsequent two interviews (n = 368). Out of 368 eligible mothers for the current

study (i.e., those with > 1 previous interview and who did not previously decline

participation), 204 were successfully contacted (55%). Of these mothers, 192 (94%)

completed the interview, ten declined participation (5%), and two mothers were no longer

eligible because their "at risk" children had recently developed type 1 diabetes (1%). Of

the 163 mothers who could not be contacted (44%), 145 had disconnected numbers

and/or had no forwarding contact information, and 18 mothers with presumably correct

up-to-date contact information were "unable to be contacted" after multiple attempts.

Families were deemed "unable to be contacted" when there was no response after at least

fifteen attempts were made over a two-month period, with at least three messages left if a

family member or answering machine was available.

Maternal Characteristics

Mothers who completed the current interview ranged in age from 20 to 46 (M =

33.67, SD = 5.38). Eighty five percent of mothers were married and 44% had a 4-year

college degree at the time of interview (Table 4-2). Eighty-five percent of mothers where

Caucasian and therefore, minority members were under-represented compared to the

population in Alachua County, Florida, and Florida in its entirety, where among women

of childbirth age (18-44 years) approximately 25% and 23% are minorities, respectively

(Florida Office of Economic and Demographic Research, 2001). On average, mothers in

this sample had two children at the time of interview (M = 2.09, SD = 1.11). Eighty three








percent of participating mothers completed all three interviews and 62% attended at least

one blood draw.

Child Characteristics

Target at risk children of participating mothers were between the ages of 2 and 7

years (M = 4.25, SD = 0.89) and evenly split between males and females (Table 4-3).

Within this at risk sample, the majority of infants were at "moderate" risk (56%), 37%

were at "high" risk, and 7% were at "extremely high" risk. Five out of the six eligible

mothers of children who were antibody positive participated in the current interview.

Most children of participating mothers were reported as having a family history of

diabetes (72%) (type 1 or 2). Sixty-five percent of children have at least one distant (>

second degree) relative with diabetes. Thirty-seven children (19%) have at least one

first-degree family member with diabetes. Of these, 30 children have immediate family

members with type 1 diabetes (81%), including 15 participating mothers themselves,

along with seven fathers and 14 siblings. In five of these families, two immediate family

members have type 1 diabetes.

Procedures

The current interview was conducted at least one-year post PANDA Part III study

completion and therefore, two to four years post-notification (M = 3.60, SD = 0.78).

Attempts were made by the Principal Investigator or research staff to contact all eligible

mothers (n = 368) for an additional follow-up interview to measure reported maternal

behavior changes resulting from knowledge of their children's risk for type 1 diabetes.

Contact information for these mothers was kept within a computerized database with

restricted access, so telephone numbers were available only to study staff.








When participants were contacted, they were reminded of their earlier

participation in PANDA Part III interviews and asked if they would agree to participate

in an additional interview. Participants were reminded of the voluntary and confidential

nature of the study and those who agreed to participate, were given a $5 gift certificate to

Publix or Target (their choice) as a token of appreciation.

Asking mothers about their behavior might have had the potential to raise

mothers' anxiety and curiosity levels regarding what they should or should not be doing

to help their children. Therefore, at the beginning and end of the interview, there was a

disclaimer read to remind mothers that we do not currently know what causes type 1

diabetes, and that we did not have specific recommendations to offer other than

encouraging a health lifestyle, including a healthy diet, physical activity, and rest. For

mothers who asked more specific questions beyond this, we had prepared documents

from the American Medical Association (AMA) on developmentally appropriate

guidelines regarding eating, exercise, and sleep (found at www.ama.org; last accessed

6/1/02), which could be mailed to mothers upon request. Eleven mothers requested

additional information. The most frequently requested materials pertained to information

regarding signs and symptoms of type 1 diabetes.

For quality assurance purposes, data were entered twice into a computerized

database, systematically compared and cleaned, before analyses were conducted. Data

from this interview were linked to previously collected data (PANDA Part III) on these

study participants through their unique identification numbers assigned by the PANDA

study staff. This allowed for longitudinal analyses of the data. This study was approved

by the UF Health Science Center Institutional Review Board (9/1/02) and documentation








of written consent was waived. Funding for this study was obtained from the North

Central Florida's Children's Miracle Network.

Measures

Descriptive Variables

Descriptive data were collected to examine the maternal and child demographic

characteristics, overall participation rate as well as demographic differences between

mothers who agreed to participate versus those who declined or were unable to be

contacted (for further detail see section on "Predictor Variables"). These two groups of

mothers were compared across outcome and predictor variables based on data from the

initial interview.

Outcome Variable: Reported Behavior Change

A component of the structured interview was developed to assess behavioral

changes across six domains: (1) diet/eating patterns, (2) physical activity, (3) emotional

stress, (4) medical interventions, (5) medical surveillance, and (6) illness prevention

behaviors. These questions and constructs were adapted from the Participant Experiences

Survey used in the Diabetes Prevention Trial-1 (DPT-1) study (Johnson, 2002) and

constructs were classified based on the DPT-1 survey and Hendrieckx et al. (2002).

Additional questions were added to address other potential environmental triggers or

influences hypothesized in the research literature to be related to diabetes development

(Akerblom, et al, 1998) (see Appendix A).

Diet and eating patterns (a = .58). Sixteen questions addressed changes in the

frequency, amount, and types of food/drink (i.e., sweets, soda, juice, cow's milk) given to

the child as well as attempts to modify the child's weight. Also, included were questions

assessing changes in early feeding history, including timing of the introduction of solid








foods and breastfeeding. The 16 questions represented ten different types of behavior

changes, as some questions are paired to assess in which the direction changes occurred

(i.e., decrease vs. increase). Additionally, two questions referred to behaviors for which

the concepts of frequency and duration do not apply, and therefore, these detailed follow-

up questions were not asked.

Physical activity/Physical stress (a = 0.54). Four questions assessed whether

mothers increased or decreased their children's physical activity or physical exertion in

response to their child's risk for type 1 diabetes.

Emotional stress (q = 0.47). Four items were designed to assess lifestyle changes

that foster the reduction of the child's level of emotional stress.

Medications (a = 0.54). Five items addressed whether mothers provided their

children with medications, such as dietary supplements, vitamins, or insulin.

Illness Prevention (a = 0.72). Eight questions representing seven unique concepts,

assessed the degree to which mothers altered their children's environment to minimize

risk of illness or infection.

Medical surveillance (a = 0.37). Five questions were designed to assess whether

participants engaged in health- monitoring behaviors for their children, such as more

frequent doctor's visits, glucose monitoring, and reported participation in PANDA Part II

study (autoantibody screening). However, reported PANDA Part II participation was not

included in calculating the domain score, as it was used for reliability purposes and also

used as a predictor variable.

This portion of the interview began with a simple "yes/no" question assessing if,

in general, participants felt they engaged in any behavior change to prevent diabetes in








their child. This question was followed by more detailed questions regarding different

types of behaviors relevant to the six domains described above. For each section,

participants were first given an open-ended question to solicit spontaneous answers (e.g.,

Have you done anything different with your son's physical activity patterns to prevent

him from developing diabetes?) followed by more detailed forced-choice questions.

When a response was given to an open-ended question that would be later addressed by a

forced choice item, the corresponding forced choice item was also endorsed.

Within each domain, forced choice items were designed to assess a wide variety

of behavior changes. For each question, participants were reminded that these questions

apply only to behaviors initiated specifically to prevent diabetes in their children. For

forced choice questions in which the response was "yes," follow-up questions were asked

to assess duration/consistency and frequency of given behavior. Forced choice items

were scored as either "yes" or "no." Duration or consistency of the behavior was scored

as "never" (0), if the behavior never occurred, "inconsistent" (1), if the behavior was

initiated early on but stopped, or began only recently; or "consistent" (2), if the behavior

has been ongoing since results notification. Frequency of a behavior was scored as

"never" (0), "occasionally" (1) or "always/nearly everyday" (2).

Each question was scored as dichotomous "yes/no" (0 or 1) as well as given a

continuous composite score value for duration, frequency, and duration x frequency.

However, duration was relatively static as 86% of those endorsing a certain behavior

reported consistent engagement since time of notification. As for frequency of behaviors,

60% reported engaging in the behavior "always/nearly everyday" (Table 5-8).








Due to relatively low frequencies for most items and low variability in duration

scores, only the dichotomized "yes/no" scores were used for analyses. Domain scores

were calculated in two ways: (1) calculating sum of the number of behaviors endorsed

and (2) whether at least one behavior change occurred with each domain. A total score

for behavior change was similarly obtained by collapsing domains. A factor analysis of

this measure was not conducted due to low variability on the items and inadequate

sample size for the number of items in the measure. To determine the statistical strength

of the scores for the six domains and total score, coefficient alphas were calculated to

determine the reliability of each construct (Table 4-4).

Reliability was relatively strong for the total behavior score (a = 0.77) and illness

prevention domain (a = 0.72), but weaker for the other five domains, with alphas ranging

from 0.37 to 0.58. Correlations between domain scores ranged from 0.10 to 0.44 (Table

4-4). As expected given the data, the total behavior score was best correlated with diet

and health surveillance behavior scores. Due to the low frequency of behavior changes

within several domains, as well as the non-normal distribution and relatively poor

reliability of domain scores (a < 0.60), no further analyses of domain-specific behaviors

were conducted (Table 4-5). Additionally, due to the relative low frequency of

endorsement of items overall and the non-normal distribution of the total behavior score,

the total behavior score used in subsequent analyses was the dichotomous variable of

whether at least one behavior change was reported (1 = 'yes') versus no behavior change

reported (0 = 'no') (see Figure 4-2).

Reliability of self-reported behavior change. Self-reported participation in Part II

of the PANDA study was collected in the structured interview and compared with data on








actual Part II participation. These data were available through the PANDA computerized

database. Continuation in the PANDA Part II study was defined as those mothers who

brought their child in for at least one blood draw for autoantibody screening, coded as

"participated" (1) and "did not participate" (0). Actual participation in PANDA II blood

draws was the only observed behavioral data available to us. It permitted us to examine

the validity of maternal self-report data concerning this particular component of medical

surveillance. PANDA Part II participation data indicated that 61% of mothers

participated in at least one subsequent blood draw and 26% participated in two or more.

When asked in the interview, 174 mothers reported accurately whether they participated

in Part II of the study (91%), with 72 accurately reporting they had not continued

participation and 102 correctly reported they had. Three mothers reported participating

when they actually had not (1%), and 15 reported they had not participated when they

actually had (8%). These findings suggest mothers may have been open and honest when

completing the interview and that social desirability effects were not strong. If anything

it is possible mothers may have underreported efforts to prevent diabetes.

Predictor Variables

Sociodemographics

The following variables were assessed during the first PANDA Part II telephone

interview: date of interview (to calculate length of time since notification), maternal date

of birth, child date of birth, maternal and paternal education level, family income bracket,

maternal and child ethnicity, marital status, number of children and whether or not this is

her first child. The number of first-degree relatives, second-degree relatives, or greater

relatives of the child with type 1 or type 2 diabetes was also assessed, if known. In the

current interview, several non-static demographic variables were updated in this current








interview to ensure that information was current, including marital status, number of

children, family income bracket, maternal and paternal education level, and family

diabetes history. Mothers were also asked for a current address in order for gift

certificates to be sent.

Perceived control

This construct was assessed by a series of questions adapted from a questionnaire

developed by Bradley et al. (1999) and used in Hendrieckx et al. (2002). These questions

assessed whether participants believed there was anything a parent or a medical

professional could do to prevent diabetes in the children, as well as a question about

diabetes onset being determined by chance or fate. Responses were scored on a 5-point

Likert scale, anchored by "strongly disagree" (scored as 1) and "strongly agree (scored as

5)." Internal consistency of this 3-item scale was a = 0.55. This was unsatisfactory

based on the study's criteria of using an alpha score of 0.60 as the cut-off for acceptable

reliability. However, when chance was not retained as a part of this composite, internal

consistency increased ( = 0.66), therefore, only the two-item measure of perceived

control was retained as a composite measure (Table 4-7). The composite score of

perceived control was calculated by averaging the scores of the two items (Table 4-6).

Risk perception

(1) Perceived absolute risk. An absolute measure of perceived risk and its

accuracy was assessed in the previous interviews and was assessed in a similar way in the

current interview. Mothers were presented with a list of the possible risk categories (with

numerical estimates) and asked whether or not any of these were the risk group they were

told their child was in. "I don't remember" was recorded if they were unable to recall or

recognize their child's risk category or number. (2) Perceived estimated risk. Perceived








risk was considered accurate if the participant was able to recognize the infant's correct

risk status from the list. Responses were classified as "accurate" (scored 2),

"overestimates" (scored 3), "underestimates" (scored 0), or "unknown" (scored 1) based

on the relationship of the response to the child's actual risk status This component

reflected perceived absolute risk while controlling for actual risk and was included in the

composite score of risk perception, whereas, absolute risk was not. (3) Perceived

comparative risk. A question adapted from Hendrieckx et al. (2002) assessing perceived

comparative risk was included. The question was stated as follows: "How do you think

your child's risk for developing diabetes compares to other children?" The response was

rated on a 5-point Likert scale ranging from 1 to 5, anchored by "much lower" and "much

higher." (4) Expectations. A question used in all previous interviews assessed whether

participants believed their child will develop diabetes. This question was coded as "yes,

my child will develop diabetes in the near future" (scored 3), "my child will eventually

develop diabetes but not for a long from now," (scored 2) "my child will not ever develop

diabetes," (scored 0) or "I am unsure." (scored 1) This variable was previously used in

Carmichael et al. (1999).

Intercorrelations between the three risk perception variables (estimate risk,

relative risk, and expectations) were examined and a composite score was calculated. To

accomplish this, scores were transformed into z-scores and mean of the three variables

was derived as the composite. Additionally, reliability of the composite score was

assessed (.= 0.61; Table 4-7). For those whose response to an item was "unknown,"

when calculating reliability for the composite score, their score was replaced by the item








mean. When computing the individual's composite score in cases with an "unknown"

response, the individual's score was the average of the other two risk items.

Anxiety

Anxiety was measured by a 10-item short form of the state-component of the

State Trait Anxiety Inventory STAI (STAI; Spielberger, 1970). Respondents were asked

to rate the questions according to how anxious they presently felt about their child's risk

for developing type 1 diabetes on a four-point scale (i.e., Not at all, Somewhat,

Moderately, or Very much). The 10-item STAI was also administered at all previous

interviews and results were reported on in published studies (Johnson et al., submitted;

Carmichael et al., 2003).

The 10-item short form was derived from a sample of 231 mothers who

completed the full 20-item scale at the initial interview. Ten items were selected by

examining the items that most highly correlated with the full 20-item scale scores for

these participants. This form was found to be highly reliable at the initial (a = 0.93), four

month follow-up (a = 0.92) and 12 month (a = 0.90) follow-up interviews. The 10-item

short and 20-item full forms of the STAI were highly correlated (r = 0.97). The practice

of creating a short form of this measure is not unusual. The STAI-SF, a six-item Short

Form, was developed and used in a prior study related to genetic screening (Marteau &

Bekker, 1992).

A regression equation was developed which converts the short form scores into

scores compatible with STAI norms to allow for comparisons with normative data

provided in the STAI Manual. Data compiled by Carmichael et al. (2000) provides

additional comparisons to similar samples including mothers learning of their child's








increased risk status as a result of ICA testing and pregnant women undergoing

amniocentesis.

An additional question, adapted from Hendrieckx (2001) was asked to assess how

often mothers worry about their children's risk. This question was stated as "how often

do you worry about your child's risk for developing diabetes?" and rated on a 5-point

Likert scale ranging from 0 to 4 anchored by "never" and "very often".

A composite score was derived by converting both scores into z-scores and

calculating the mean z-score of the two items (a = 0.80) (Table 4-8).

Coping

The Ways of Coping Checklist-Revised (WCC-R) (Folkman & Lazarus, 1980) is a

69-item dichotic (yes/no) questionnaire used to assess the use of coping strategies and

preferred coping style. In the PANDA Part III study, the WCC-R was administered at the

4 follow-up interviews to assess maternal coping regarding their infant's genetic risk of

developing Type 1 diabetes (n = 178). This measure has also been used in similar risk

screening studies (Johnson & Tercyak, 1995; Johnson & Carmichael, 2000). Factor

scores were calculated using Vitaliano et al. (1985) factor structure, which uses 42 items.

The five factors included the following coping styles/strategies: Problem-focused Coping,

Seeking Social Support, Wishful Thinking, Self-Blame, and Avoidance. The WCC-R

was not administrated in the current interview. However, subscale scores obtained at the

second (4 month) interview were used as predictor variables. To be able to compare

across factors having a varying number of items, mean scores were calculated for each

subscale as well as for the total measure.








For this sample reliabilities for the subscales Wishful Thinking (g = 0.70), Seeks

Social Support (g = 0.73) and Problem-Focused coping (g = 0.81) were satisfactory.

However, reliabilities for the Avoidance (g = 0.36) and Self-Blame (a_= 0.53) subscales

were poorer and did not meet criteria for further analyses (a_< 0.60) (Table 4-9). The

reliability scores of the factors were consistent with previously published studies of

similar populations (Johnson & Carmichael, 2000). Correlations between variables were

significant, particularly between the total coping score and Problem Focused coping,

Seeks Social Support, and Wishful Thinking (Table 4-9). Mean factor scores were

similar to previous studies. Seeks social support was the most favored used coping style,

followed by Problem-Focused coping. Self-blame was the least used coping style (Table

4-10).

Information seeking

A self-report measure of information-seeking was given to assess participants'

sources of information regarding diabetes risk and/or behavior change. Questions were

designed to assess if participants consulted with their physicians, family members of

friends, including those who may have diabetes themselves. Follow-up questions were

asked to determine if participants were given specific advice from these sources and if

they followed the advice. Additional questions assessed behaviors such as searching the

internet, consulting written materials about diabetes, or watching diabetes-related

television news stories. This measure was scored as a continuous variable by calculating

the number of information sources reported and as a dichotomous variable denoting

whether any information seeking occurred (1 = 'Yes' and 0 = 'No') (Table 4-11).








Questions regarding the nature of the relationship and the content of the advice were used

as descriptive data.

Participation in PANDA Part II

Data on Part II participation was available through the PANDA computerized

database. Continuation in the PANDA Part II study was coded as two different variables,

(1) number of blood draws and (2) at least one blood draw (1 = 'participated') versus no

blood draws (0 = 'did not participate'). For more details, please refer to earlier section

entitled "Reliability of self-reported behavioral change".

Statistical Analyses

Data analyses were conducted using SPSS 11.0. Internal reliability of predictor

and outcome scores were calculated using Cronbach alpha and only constructs with

alphas greater than 0.60 were retained for regression analyses. Additionally, components

of risk perception and anxiety composite scores were transformed into z-scores because

they were measured on different numerical scales. Consequently these composite scores

reflect a z-transformation as well. Descriptive statistics were conducted, including

ANOVA, t-tests and chi-square analyses, to compare demographic variables between

participants and non-participants. When expected cell size was < 5, Fisher's Exact test

statistic was used instead of chi-square. Hierarchical logistic regressions were used to

predict to behavior change, as well as linear regression to predict to the continuous

outcome measure of anxiety. For further details, refer to Chapter 5 (Results).











Initial genetic screening of newborn
for diabetes risk @ birth

Scripted notification of results
to mother by telephone"
-12 weeks after birth

Initial structured telephone interview
with mother of at-risk infant to assess maternal distress
~4 weeks after notification

Second structured telephone interview
with mother of at-risk infant to assess maternal distress
-4 months after notification

Opportunity to participate in PANDA Part II Blood Draw
when infant is at least 6 months old


Third structured telephone interview
with mother of at-risk infant
to assess maternal distress-12 months after notification


CURRENT STUDY
Fourth structured telephone interview
to assess maternal behavior change
4 years post-notification


S PART I:
PANDAa
STAFF




PART III:
PPRb STAFF





PART H:
PANDA STAFF


PART III:
PPR STAFF



CURRENT:
PPR STAFF


Figure 4-1. Procedural outline of PANDA study. PANDA = Prospective Assessment of
Newborn Diabetes Autoimmunity. PPR = Pediatric Psychology Research
Lab.





'f


.00 2.00
1.00


4.00


5.00


6.00


7.00


C.


10.00


15.00


total behavior score
Figure 4-2. Frequency distribution of total behavior score (n = 192)


8.00 13.00








Table 4-1. Diabetes genetic risk factors
With first


DR/DQ
Alleles/genotypes
DR 3/4, DR 4/4
DQ 0201/0300
DQ 0300/0300


DR 3/4, DR 4/Xa
DQ 0201/0201
DQ 0300/Xb


DR 3/4 or X/X
DQ X/X


DR 3/4 or X/X
DQ 0602


degree
relative
20-25/100
Extremely
high risk


10/100
High risk


1/125
Intermediate
risk


1/15,000
Protective


Without first


degree
relative
5/100
High risk


2/100
Moderate
risk


1/600
Very low
risk


1/15,000
Protective


X is a non-defined allele, 75% of the time X= DR 4 or DQ 0301. X allele is not 0602.


% General
population
5%


10%


85%


% Type 1
patients
>50%


30-40%


10%








Table 4-2. Maternal characteristics of current sample (n = 192)
Variable


Maternal age at notification
Current maternal age


Race
Caucasian
African American
Hispanic
Asian/other


Mothers level of education
High school or less
Some college/trade school
College degree or beyond

Marital status (married)

Annual income (in $10,000 intervals)


30.49 + 5.36
33.67 + 5.38


162 (85%)
6 (3%)
13 (6%)
10 (5%)


45 (23%)
62 (32%)
85 (44%)

164 (85%)

4.97 + 2.50

2.09+ 1.11

2.79 + 0.51
9 (5%)
23 (12%)
160 (83%)

1.19+1.17
74 (39%)
47 (25%)
29 (15%)
41(21%)


Number of children


Number of previous interviews
1
2
3

Number of blood draws (Part II)
0
1
2
3


Note: Data are n (%) and means + SD.









Table 4-3. Child characteristics of current sample (n = 192)
Variable

Infant risk classification
Moderate (2/100) 108 (56%)
High (1/10) 71(37%)
Very high (1/5) 13 (7%)

Child age at notification (mo.) 7.85 + 6.24

Current child age (years) 4.25 + 0.89

Child sex (Male) 97 (51%)

Only child (Yes) 62 (33%)

Family history
No family history 50 (26%)
Third degree relative 98 (51%)
Second degree relative 82 (43%)
First degree relative 37 (19%)

Note: Data are n (%) and means + SD.





67


Table 4-4. Intercorrelations and coefficient alphas for domain scores of reported
behaviors
Domain 1 2 3 4 5 6 7
1. Health surveillance 0.37
2. Diet 0.44* 0.58
3. Physical activity 0.27** 0.32** 0.54
4. Illness prevention 0.35** 0.24** 0.36** 0.72
5. Medications 0.28** 0.28** 0.12 0.36** 0.54
5. Stress 0.16* 0.18* 0.10 0.28** 0.22** 0.47
6. Total 0.74** 0.79** 0.58** 0.65** 0.47** 0.35** 0.77

Note: Coefficient alphas are presented in boldface along the diagonal. p < 0.05 p <
0.01.





68


Table 4-5. Mean domain scores of reported behavior changes for total sample
Domain # Items Range M SD
Health surveillance 4 0-3 0.85 0.86
Diet 16 0-6 0.69 1.15
Physical activity 4 0-3 0.21 0.57
Illness prevention 8 0-5 0.18 0.69
Medications 5 0-3 0.04 0.27
Stress 4 0-2 0.04 0.24
Total 41 0-15 2.00 2.53
# of domains 6 1-6 1.22 1.20








Table 4-6. Mean values or frequencies for perceived control scales
Item


I can do something
Strongly disagree
Somewhat disagree
Neutral
Somewhat agree
Strongly agree

Doctors can do somethinga
Strongly disagree
Somewhat disagree
Neutral
Somewhat agree
Strongly agree

It is up to chance, b
Strongly disagree
Somewhat disagree
Neutral
Somewhat agree
Strongly agree

Composite score (z-score)


2.95 + 1.18
28 (15%)
39 (20%)
53 (28%)
58 (30%)
14 (7%)

2.63 + 1.11
28 (15%)
74 (39%)
38 (20%)
47 (23%)
8 (4%)

3.26+ 1.11
15 (8%)
37 (19%)
39 (20%)
83 (24%)
17 (9%)

2.79 + 0.99


Note: Data are n (%) and means + SD. a Scored as follows: Strongly Disagree = '1',
Disagree = '2', Neutral = '3', Agree = '4', Strongly Agree = '5'. b Variable not used
in composite score.








Table 4-7. Mean values or frequencies for perceived risk scales
Item
Relative risk 3.46 + 1.05
Much less 13 (7%)
Somewhat less 15 (8%)
About the same 61 (32%)
Somewhat higher 74 (39%)
Much higher 27 (14%)

Belief about when child may develop diabetes 1.88 + 0.68
Never 53 (28%)
Unsure 113 (59%)
Yes, but not for a long time from now 22 (12%)
Yes, in the near future 4 (2%)

Risk estimation
Overestimate 12 (6%)
Accurate 76 (40%)
Underestimate 80 (42%)
Don't know/don't remember 24 (13%)

Risk composite (z-score) -.004 + 0.74

Note: Data are n (%) and means + SD. a Scored as follows: Much less = '1', Somewhat
less = '2', About the same= '3', Somewhat higher = '4', Much higher = '5'.
Scored as follows: Never = '0', Unsure = '2', Yes, but not for a long time from now
= '3', Yes, in the near future = '4'. c Overestimate = '3', Accurate = '2',
Underestimate = '1', Don't know/don't remember- '0' but value not included in
analyses.









Table 4-8. Mean values or frequencies for anxiety/worry scale


Item
Worry
Never
Rarely
Sometimes
Often
Always


1.00 + 1.02
73 (38%)
66 (34%)
39 (20%)
8 (4%)
6 (3%)


Anxiety (10-item STAI)b

Anxiety composite (z-score)


30.79 + 9.66


0 + 0.91


Note: Data are n (%) and means + SD.a Scored as follows: Never = '0', Rarely = '1',
Sometimes = '2', Often = '3', Always = '4'. b Predicted full scale score based on 10
item measure.









Table 4-9. Intercorrelations and coefficient alphas for coping variables
Domain 1 2 3 4 5 6 7


1. Problem focused

2. Seeks social support

3. Avoidance

4. Wishful thinking
5. Self-blame
6. Total score


0.81
0.64*

0.15

0.53*
0.22*

0.84*


0.73
-0.02

0.31'"
0.12

0.78**


0.36
0.37*

0.33**
0.37


0.70
0.28** 0.53
0.74*** 0.44*** 0.86


Note: Coefficient alphas are presented in boldface along the diagonal. p< .01. p<
0.001.






73


Table 4-10. Mean scores for Ways of Coping-Revised (WCC-R) scales
Constructa # Items

Problem focused 15 0.48 + 0.24
Seeks social support 6 0.54 + 0.31
Avoidance 6 0.13 + 0.11
Wishful thinking 10 0.25 + 0.23
Self-blame 3 0.03 + 0.13

Total score 42 0.29 + 0.14

Note: Data are means + SD. a Scored as the mean of the items in each subscale. Each
item in subscale is scored as '0' = No, '1' = Yes.





74


Table 4-11. Mean values or frequencies for information seeking scale
Item
Any information source 115 (60%)

Literature 63 (33)
Doctor 46 (24)
Family/friend 33 (17)
Television 27 (14)
Internet 22 (12)

# of information sources 0.99 + 1.04

Note: Data are n (%) and means + SD. aScored as Yes= '1', No = '0'.













CHAPTER 5
RESULTS

Sample Characteristics

Compared to mothers who were eligible but did not complete the current interview

(n = 176), participants in this current study (n = 192) were significantly more likely to be

married (p < 0.001) and older at time of notification (p < 0.01) and the current interview

(p < 0.001) (for those who were not contacted, age was estimated based on end date of

data collection 4/1/03) (Table 5-1). Additionally, they had higher levels of education (p <

0.01) and annual family income (p < 0.001). There were no differences between the two

groups in terms of ethnicity. Overall, these results suggest the current sample was a

highly select sample of mothers who were more economically stable and possessed more

personal resources than mothers in the original larger sample. It is important to consider

the sample bias in interpreting results of this study, as the behaviors of these mothers may

not be reflective of the general population.

For mothers who participated in the current interview, 85% completed all three

previous PANDA Part II interviews versus 57% of eligible non-participating mothers (x

(1, N = 368) = 35.12, < 0.001). Participating mothers had a higher number of

completed interviews (p < 0.001). Participation rates differed significantly for the

longitudinal component of the PANDA study (Part II), which involves periodic

autoantibody screening. Sixty one percent of current study participants and 49% of non-

participants completed at least one autoantibody screening (X2 (1, N = 368) = 5.02 p <

0.05). Participating mothers had a higher number of blood draws (p < 0.001). These








participation rates are less than rates reported in Finland, where approximately 80% of

infants who were genetically screened joined their antibody surveillance study (Kupila et

al., 2001).

As assessed in the initial interview, there was no significant difference in anxiety

scores, as measured by the state STAI, between participating and non-participating

mothers (Table 5-1). There was no significant difference between the two groups of

mothers in their perceived likelihood that their child would develop diabetes in the future.

However, at the time of the initial interview, mothers who participated in the current

interview reported greater accuracy in estimating their child's risk status than mothers

who did not participate (p < 0.01) and fewer mothers underestimated their child's risk (p

< 0.05) (Table 5-1).

There were no differences between children of participants versus non-participants

in regards to age, genetic risk status, sex, only child status, or family history (Table 5-2).

Objective 1

Hypothesis 1.1

At the outset of the study, it was hypothesized that reported behavior changes

endorsed would most likely correspond to recommendations for the treatment of diabetes

(ADA, 2001) and the prevention of type 2 diabetes (ADA, 2002a, 2002b; Pierce et al.,

1995), including changes in diet and physical activity patterns.

The questionnaire's design permitted the use of both open and closed ended

questions within each behavioral domain. Descriptive analyses of each behavioral

construct, both as dichotomous and continuous variables, were conducted, including

frequencies, means, standard deviations, and correlations. Qualitative data from other








open-ended questions addressing advice received and perceived control were also coded

as descriptive data.

Open-Ended Questions

The initial open-ended item assessing behavioral change simply asked whether

mothers did anything special to reduce their child's risk of developing type 1 diabetes

(yes or no). Fifty-five mothers (29%) responded that they had done something

preventative. Open-ended questions were also asked at the beginning of each of the six

domains and again, at the end of the interview to assess maternal recall of behavior

changes. At least one spontaneous behavior was reported in response to domain specific

open ended questions by sixty nine mothers (39%), somewhat more than were identified

through the initial broad question, yielding a total of 118 spontaneous responses (Table 5-

3). Of these, 51 mothers indicated making a change in their child's diet and/or exercise

(74%), corresponding with recommendations to prevent and/or treat type 2 diabetes

(Table 5-4). One domain, medications, yielded no spontaneous responses and stress only

yielded one response. Sixty three percent of the responses to the open-ended questions

were later addressed in forced choice items asked subsequently in each domain.

To further examine the hypothesis that mothers were following recommendations

for the prevention of type 2 diabetes, responses to open-ended questions regarding advice

received and maternal beliefs were analyzed to determine if mothers' reported actions

were based on a premise that a healthy lifestyle is an effective prevention method for type

1 diabetes. An open-ended question assessing maternal beliefs about what they could do

to prevent their child from developing type 1 diabetes was asked of mothers who agreed

or strongly agreed that they could do something to prevent their child from developing

type 1 diabetes (Table 5-5). Seventy two mothers (38%) reported believing they could do








something preventative, with 108 responses generated. Of these, 61 mothers reported

dietary and/or exercise changes (92%).

Additionally, 46 (24%) mothers reported receiving advice from a medical

professional, generating 63 pieces of advice, and 33 (20%) mothers reported receiving

advice from family or friends, generating 39 pieces of advice (Table 5-6). Ninety percent

of mothers reported following advice from a medical professional and 95% reported

following advice from family members/friends. Of the advice received from medical

professionals, 43% suggested making healthier dietary and physical activity changes. Of

the advice received from family members or friends, 28% of advice from family

suggested healthy lifestyle changes in diet and exercise.

Forced Choice Questions

Forced choice items were asked with yes or no responses to assess maternal

recognition of reported behavior changes. These items were used to assess specific

behaviors and were expected to yield more positive responses than the use of open-ended

questions.

Results based on the forced choice items within each domain, indicated that out of

192 mothers, 129 (67%) reported changing at least one behavior in an attempt to prevent

diabetes from developing in their at risk child (M =2.00, SD = 2.53). Domain scores

were calculated for each of the six possible categories of behavior determined a priori.

Of those who reported at least one behavior change, 30% reported two to three changes,

24% reported four to six changes, and 8% reported changing more than six behaviors (M

= 2.98, SD = 2.57) (Table 5-7). Changes in health surveillance behaviors were most

frequently endorsed (59%), including blood glucose monitoring and watching for signs of

diabetes development. Changes in child's diet (34%) were the next most commonly








reported, followed by changes in physical activity (14%), illness prevention (9%),

medications (3%), and stress (3%) (Table 5-8). The item most frequently endorsed (>

10%) was checking for specific signs of type 1 diabetes (50.5%). An open-ended follow-

up question asked mothers to specify the nature of the symptoms they look for in their

children. Ninety seven mothers reported they look for signs of diabetes in their at risk

child, each responding with approximately two signs each (M = 2.16). Seventy nine

percent of mothers reported at least one correct diagnostic criterion type 1 diabetes (i.e.,

polyuria, polydipsia, weight loss, and increased appetite), 32% identified behaviors that

were not indicative of diagnosis, but were related to diabetes symptomatology (i.e., signs

of hyperglycemia or diabetic ketoacidosis), and 45% identified signs that were not related

to diabetes (Table 5-9). Only 5% of mothers did not identify one correct or related

symptom of type 1 diabetes. Of those who reported an accurate symptom, 38% also

listed inaccurate symptoms. Additionally, testing the child's blood glucose level either at

home or at a physician's office, feeding the child less soda, juice and other sweet foods,

and encouraging the child to exercise more often were the next most commonly endorsed

behavior changes. Items that might indicate maternal overprotectiveness or items

suggesting unwarranted use of medications were rarely endorsed. Reported behavior

changes ranged across domains for those endorsing more than one behavior change with

only 19% reporting changes within only one domain (M = 2.33, SD = 1.02). This

suggests that mothers engaged in a wide variety of behavior changes.

In comparing forced choice item responses with responses to open-ended questions,

results indicated that significantly more mothers endorsed forced choice items rather than

made spontaneous responses. This suggests that mothers may either have had difficulty








recalling behaviors that were not as salient, with forced choice items serving as a

recognition task to help refresh their memory. Or, perhaps there may have been a

demand characteristics associated with presenting individual specific behaviors in yes/no

format. According to forced choice items, 67% of mothers reported making at least one

behavior change versus 36% of mothers responding to open ended questions (Table 5-

10). All mothers who spontaneously reported behavior changes also responded similarly

to forced choice items, so there were no mothers who spontaneously reported behavior

change who did not also report changes according to forced choice items. In comparing

forced choice versus open-ended questions, the primary difference was that mothers were

less likely to spontaneously report changes in health surveillance that were later identified

through forced choice items. Mothers may not consider increased health surveillance as a

way of actively preventing diabetes.

Reported behaviors specific to healthy lifestyle changes, within diet and exercise

domains, consistent with recommendations for prevention and treatment of type 2 were

coded and compared to address the hypothesis that behavior change would likely

correspond with recommendations for prevention of type 2 diabetes. Overall, based on

responses to open-ended questions, 51 mothers (27%) reported making at least one such

behavior change (Table 5-4) and 59 mothers (31%) indicated a similar behavior change

via responses to forced choice items (Table 5-8). Behaviors related to recommendations

for prevention of type 2 diabetes were more prevalent among open-ended responses than

among forced choice responses, in which health surveillance changes and overall dietary

changes in general were more frequently reported.








Objective 2

Exploratory model testing was conducted through the use of logistic regression

analyses predicting whether mothers reported behavior change. As stated previously, due

to the non-normal distribution of reported behavior scores, the outcome measure of

behavior was examined dichotomously, comparing mothers who reported at least one

behavior change (1 = > 1 behavior change) versus mothers who reported none (0 = no

behavior change). Regressions were also conducted to predict whether a behavior change

was spontaneously reported in response to open ended questions; however, results were

nearly identical to using the forced choice items and therefore, quantitative analyses

based on responses to open ended questions were reported.

In each regression model, predictor variables were entered in blocks according to

hypothesized relationships from prior literature. Each block of variables was added

successively. When each block was added to the model, only variables that were

significant at p< 0.10 were retained. For these analyses, several variables were recorded

for ease of interpretation. Due to the sample's unbalanced distribution by maternal race,

minority ethnic groups were collapsed into one group and maternal ethnicity was

categorized as "Caucasian" (1) and "not Caucasian" (0). Maternal marital status was

coded as "1" for married and "0" for single, separated, widowed or divorced. Child's sex

was coded as 1 "male" and 2 "female". Only child status coded as 1 "yes" and 0 "no".

The first block of variables entered into the regression model contained one

variable, time elapsed between notification and current interview, to control for effects of

time. The second block of variables contained maternal demographic variables,

including maternal education level, ethnicity, marital status, number of children, and age

at the time of the interview. The third block entered contained child demographic








variables, including child's sex, whether an only child, and age at the time of interview.

Family history of diabetes was also included in this block, using two dichotomous

variables: (1) the presence of a first-degree relative with diabetes (yes/no) or (2) the

presence of a second or higher degree relative (yes/no).

The fourth block of variables contained the hypothesized predictor variable.

Predictor variables consisted of standardized composite scores on measures of perceived

control, risk perception, and anxiety, as well as total scores on measures of coping and

information seeking. Participation in PANDA Part II study was also used as

dichotomous predictor variable (yes/no). Reliability analyses were conducted on

composite scores suggesting that internal consistency was fair for these variables (Table

4-6). Each of the following hypotheses was examined separately. Within each model,

main effects were examined as well as interactions between perceived control and other

predictors, where noted. Only significant predictors were retained. Ultimately, a final

model was produced from these separate models to account for the highest classification

rate in the behavioral outcome variable. To account for type 1 error, a more conservative

level of significance was chosen at p < 0.01 and this is noted where appropriate.

Hypothesis 2.1

It was hypothesized that mothers who perceived they have control over their child

developing diabetes would be more likely to report engaging in behavior changes. Based

on statements regarding perceived control, which required an agree/disagree response,

38% percent of mothers reported believing they could do something and 27% believed

doctors could do something to prevent their child from developing type 1 diabetes.

Meanwhile, 52% reported believing it was up to chance or fate whether their child

develops type 1 diabetes (Table 4-6).








Hierarchical logistic regression analyses were conducted, using the composite score

for perceived control (belief that mother could do something or medical professional

could do something) to predict whether any behavior change was reported (yes/no) when

controlling for demographic factors (Table 5-11). Results indicated that mothers whose

children had a first degree relative with diabetes were significantly more likely to engage

in behavior change (odds ratio = 24.22, p < 0.001) and maternal perceived control was

not a significant predictor of behavior change, resulting in an overall model that

accounted for 67.5% overall correct classification.

Hypothesis 2.2

It was hypothesized that mothers who perceived their children to be increased risk

for type 1 diabetes would be more likely to report engaging in behavior change.

Hierarchical logistic regression was conducted as described previously; however, both

actual risk and the risk composite score were entered as the final block in the logistic

regression model. Results indicated that again, the presence of a first degree relative was

a significant predictor of behavior change (odds ratio = 18.98, p < 0.01). The child's

actual risk was found not to be significant. When controlling for actual risk, perceived

risk was a significant predictor (2.32, p< 0.01) (Table 5-12). Mothers who perceived

their children to be at greater risk were more likely to engage in behavior change. This

model resulted in an overall classification rate of 68.4%. An interaction between

perceived control and perceived risk was also tested and was not significant.

Hypothesis 2.3

It was hypothesized that mothers who were more anxious would be more likely to

report engaging in behavior change. This hypothesis was tested by entering the anxiety

composite score as the final block in the logistic regression model. Results indicated that








anxiety, as measured at the initial interview following notification, was not a significant

predictor of subsequent behavior change and was not retained in the final model.

However, mothers who were more anxious at the time of the current interview were more

likely to report behavior change (odds ratio = 2.98, p< 0.001). This model resulted in a

correct classification rate of 72.9% (Table 5-13). Follow-up analyses were conducted to

determine if there was an interaction between anxiety and perceived control; however,

none was found. Results demonstrated that while anxiety remained a significant

predictor, there was no main effect of perceived control, nor was the interaction term

significant, suggesting that mothers who were more anxious were more likely to report

behavior change to prevent diabetes in their child regardless of their level of perceived

control over the situation.

Hypothesis 2.4

It was hypothesized that mothers who used more coping strategies, particularly

active coping (i.e., problem-focused, seeking social support), would be more likely to

report engaging in behavioral change. Data were available for 176 mothers who

completed the Ways of Coping Checklist- Revised (WCC-R) at the 4-month interview.

In separate logistic regression models, each coping scale score was entered as the final

block of variables. Results indicated that after controlling for the significant effect of the

presence of a first degree relative, problem-focused coping (odds ratio = 10.72, P< 0.01),

seeking social support (odds ratio = 4.99, p< 0.01), and wishful thinking (odds ratio =

14.48, p< 0.01) were significant predictors of behavior change (Tables 5-14, 5-15, and 5-

16). While the two active coping factors were significantly related to behavior change, a

more passive coping style, wishful thinking, was also significant and to a relatively








higher degree. Item analysis of the wishful thinking scale indicated that this scale

included items related to optimistic thinking but also a desire for the problem to "go

away" or "be over with." It may be that wishful thinking reflects a sense of optimism and

urgency that might be associated with engaging in preventative actions believed by

mothers to be efficacious. Additionally, total coping as measured by the mean of all

reported coping behaviors was a significant predictor of reported behavior change (odds

ratio = 160.06, p< 0.001) (Table 5-17).

Hypothesis 2.5

It was hypothesized that mothers who engaged in information seeking and/or were

given recommendations by medical professionals or other family members related to

behavior change, would be more likely to report engaging in behavior change. Overall,

60% reported receiving information from at least one source, and the mean number of

sources was 0.99 (SD = 1.04). Overall, 33% reported receiving diabetes-specific

information from a book or other literature, 14% reported watching diabetes-related

television programming, and 12% reported seeking information using the internet (Table

4-11). As stated previously,

Fifty nine percent of mothers reported talking to their physician about their child's

genetic risk screening results. Of those, 41% reported receiving advice from their

physician, with over 89% reportedly taking their physician's advice. When specifically

asked in an open-ended question about the nature of the guidance given, mothers

specified a wide range of advice (Table 5-6). The most frequent advice given was to

monitor their child and promote a healthy lifestyle. Additionally, six mothers were told

to continue with PANDA study and five mothers were told by their physicians not to

worry about their child's risk.








Advice from family friends was similarly assessed. Eighty-six percent reported

talking to a family member or friend. Seventy percent reported talking with their spouse

about their child's genetic screening results, 63% reported talking with the child's

grandparent, 13% reported taking to a family member of friend who has diabetes and

32% reported talking with a family member or friend who does not have diabetes.

Seventeen percent of mothers reported receiving advice from at least one family

member/friend. Typically, advice was given by a child's grandparent (62%), followed by

spouse (15%) and friend or family member who does not have diabetes (15%), then

friend or family member who does have diabetes (5%). When specifically asked in open-

ended questions about the advice that was given, most frequent advice was to help child

maintain a healthy diet and five mothers were told not to worry. (Table 5-7)

Logistic regression was used to determine if the number of information sources

predicted the likelihood of engaging in behavior change. The number of information

sources was entered as the last block of predictor variables in a logistic regression model

(Table 5-18). When controlling for the presence of a first degree relative (odds ratio =

26.31, p < 0.01), those with more sources of diabetes-specific information were

significantly more likely to report engaging in behavior change (odds ratio = 2.27, p<

0.001). The presence of a first degree relative combined with the degree of information

sources together resulted in an overall classification rate of 74.3%.

Follow-up logistic regression analyses were conducted to determine if an

interaction was present between perceived control and the number of information

sources; none was found.








Hypothesis 2.6

It was hypothesized that mothers who continued their participation in the

Prospective Assessment of Newborn Diabetes Autoimmunity (PANDA) study by

participating in periodic blood testing for antibodies, would be more likely to report

behavior changes. Overall, 61% of mothers participated in at least one subsequent blood

draw. Twenty-six percent participated in two or more. However, when asked in the

interview, 174 mothers reported accurately whether they participated in part II of the

study (91%), and 3 reported participating when they actually have not (1%), and 15

reporting they had not participated when they actually had (8%).

Surprisingly, in the logistic regression model, registry participation using either

number of blood draws or continued participation (yes/no) did not predict to reported

behavior change (Table 5-19a, b). Mothers who continued with the PANDA study were

no more likely to report engaging in preventative efforts, despite their already active

participation in health surveillance.

Summary Model

Logistic regression was conducted to determine which of the previously listed

variables were most predictive of behavior change. As in previous analyses, family

history, characterized by presence of a first-degree relative, was entered as the first block

of variables as it had been found to be consistently significant in all previous models.

Actual risk was entered next in the model followed by all six variables also found to be

significant at the 0.01 level in previous models (i.e., perceived risk, anxiety, number of

information sources, problem focused coping, seeking social support, and wishful

thinking) were subsequently entered simultaneously. Problem focused coping and








seeking social support were dropped from the resulting model, as they were not

significant.

The final logistic regression model showed the presence of a first degree relative

was once again a significant predictor of behavior change (odds ratio = 19.34, P = 0.01).

Number of information sources, anxiety, perceived risk, and wishful thinking were also

significant predictors (Table 5-20). Overall, the model's classification rate was 77.7%.

Objective 3

It was hypothesized that mothers who reported modifying behaviors would show a

greater reduction in anxiety over time than mothers who did not report engaging in

behavior change. To examine this hypothesis, hierarchical linear regression was

conducted similarly to logistic regression procedures described for Objective 2, except

the dependent variable was the anxiety composite score, a continuous variable. Anxiety

at the initial interview was entered as the first block of variables followed by same

ordering of blocks of variables of demographic variables described previously. Reported

behavior change and the composite score for perceived control were entered as the final

(fourth) block of predictor variables to determine if behavior contributed significantly to

anxiety at the final follow-up interview, above and beyond the effect of initial anxiety

and demographic predictors. In a follow-up model, the interaction term between

behavior change and perceived control was added as the fifth block of predictors.

Results indicated that initial anxiety was a significant predictor of anxiety at the

current interview (J = 0.42, p< 0.001) accounting for 22% of the variance (Table 5-21).

Current age of the child (B = -0.18, p< 0.01) along with the presence of a first degree

relative (j = 0.29, p< 0.001) and the presence of a second or higher degree relative (3 =

0.18, p< 0.01) together accounted for an additional 13% of the variance (p < 0.001.








Reported behavior change (j = 0.24, p< 0.001) and perceived control (j = 0.20, p< 0.01)

were entered as the final block and both were found to be significant predictors,

accounting for an additional 9% of variance (p < 0.001). Overall, the model accounted

for 43% of the total variance. In the follow-up model, the interaction term was added and

not found to be significant. Results indicated that initial anxiety was the primary

predictor of anxiety at the time of the current interview. However, above and beyond

initial anxiety, mothers whose children were younger and had a relative with diabetes

were more anxious at the time of the current interview. Mothers who reported at least

one behavior change were significantly more anxious at both post-notification and

current interviews (as measured by the STAI only) than mothers who reported no

behavior changes (initial interview: M = 42.75, SD = 14.54 versus M = 36.80, SD =

12.54, t(1, 189) = 2.76, p < 0.01) (current interview M = 32.57, SD = 10.28 versus M =

27.14, SD = 7.02, t(1, 190) = 4.29, p < 0.001). Contrary to the original hypothesis, when

controlling for demographics and initial anxiety, mothers who reported at least one

behavior change and who perceived greater control over the onset of diabetes in their

children were more anxious at the time of current interview than mothers who did not.

This suggests behavior change maintains, rather than reduces anxiety over time for these

mothers.

Objective 4

Questions regarding behavior change used in the current interview were

developed from the DPT-1 survey (Johnson, 2002) and therefore, dichotomous scoring

for some of the questions in the current interview were comparable. The database from

the current study was merged with the maternal report data from the DPT-I study. Only

data collected from mothers who were not aware of the study results were included (n =








116). Of these mothers, 63 (53%) had children who participated in the control group of

the study and 53 (47%) had children enrolled in the experimental arm. Children whose

mothers completed the DPT-1 survey were significantly older than children in the

PANDA sample, ranging in age from 5 to 19 years old (M = 12.18, SD = 3.24) (t(2, 306)

= 32.02, p < 0.001). Reported maternal behavioral data from these two populations were

compared on 17 overlapping variables. Analyses were conducted across the

corresponding individual questions and similar domain scores.

We hypothesized mothers of genetically at risk children would be less likely to

report behavior change than mothers of ICA+ children enrolled in Diabetes Prevention

Trial-1 (DPT-1) ( = 116). On questionnaire items that were shared by both studies,

43.2% of mothers whose children were in the DPT-1 study and 33.3% of mothers in the

current sample reported at least one behavior change (Table 5-22). However, this

difference was not significant (p = 0.08). Mothers in the two samples reported similar

proportions of behavior change in the domains of diet and exercise; however,

medications differed by groups with mother from the DPT-1 sample reporting greater use

of medications/supplements.

There were few significant differences between the two samples of mothers on

specific items. Mothers in the DPT-1 sample were nearly four times more likely to report

feeding their children more diet and sugar free drinks (p< 0.001), and more often reported

feeding their children less regular soda (p< 0.05), whereas, mothers in the current sample

more often reported feeding their children less juice (p< 0.05). Administering vitamins

(p< 0.05) and administering insulin at home (p< 0.05) were practices that were

significantly more common in the DPT-1 sample. This is not surprising given that 53 of





91

the mothers had children who were in the experimental arm of the study involving home

insulin injections (46%) and the question involved giving "extra" insulin above and

beyond study protocol. Out of the 5 mothers who reported giving their child insulin, 4

(80%) were mothers of children enrolled in the experimental group.








Table 5-1. Comparisons of maternal demographic variables between participants in current
sample versus those eligible who were unable to be contacted or declined
participation (N= 368)
Unable to
Completers contact/declined Total
(n = 192) (n = 176) (n = 368) F (1, 434)
or_


Maternal age at notification
Current maternal agea


Race
Caucasian
African American
Hispanic
Asian/Other


Mothers level of education
High school or less
Some college/trade school
College degree or beyond

Marital Status (married)


Annual income
(in $10,000 intervals)

Number of Children


33.49 + 5.36
33.67 + 5.38


162 (84.9)
6(3.1)
13 (6.8)
10 (5.2)


45 (23.7)
62(32.1)
85 (44.2)

164 (85.4)

4.97+2.50


2.09+ 1.11


27.71+5.35
31.21+ 5.33


135 (76.7)
17 (9.7)
17(9.7)
7 (4.0)


50 (28.2)
74(41.8)
53 (29.9)

112(64.4)

3.82 + 5.30


2.03 + 1.26


29.28+5.63
32.41+5.44


297 (80.9)
23 (6.3)
30 (8.2)
17 (4.6)


95 (25.9)
137 (36.8)
138 (37.3)

276 (75.5)

4.43 + 2.40


2.06 + 1.18


Number of previous 39.83*
interviews


Number of blood draws (II)
0
1
2
3


Anxietyb


Belief about when child may
develop diabetes
Never


9 (4.7)
23 (12.0)
160 (83.3)


74(39.1)
47 (24.5)
29(15.1)
41(21.4)

40.74+14.13


36 (20.3)
41(23.2)
100 (56.5)


90 (50.8)
59 (33.3)
20(11.3)
8 (4.5)


39.83 + 14.29


45 (12.0)
64 (17.2)
260 (70.8)


164 (44.7)
106 (28.6)
49 (13.4)
49 (13.4)


40.30 + 14.19


43 (22.4) 34 (19.3) 77 (20.8)


14.42***
12.18**


7.32


11.72**


18.71**

20.22***


44.03**


0.70


1.90


43 (22.4) 34 (19.3)


77 (20.8)




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MATERNAL EFFORTS TO PREVENT TYPE 1 DIABETES IN GENETICALLY SCREENED INF ANTS By AMYE. BAUGHCUM A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2004

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Copyright 2003 by Amy Baughcum

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This disse1tation is dedicated to my parents and grandparents who instilled in me the value of life-long learning.

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ACKNOWLEDGMENTS First I want to thank the families that willingly gave their time to participate in this study. Second, I would like to thank my collaborators at the University of Florida and Medical College of Georgia as well as the Children s Miracle Network and the American Diabetes Association for funding this work. On a more personal note, I am grateful to my family for their constant love, encouragement and support. I appreciate their sacrifices that allowed me to pursue my education. My friends and labmates were also huge helps to me in this process by providing feedback, caring and welcomed distraction. I am grateful to my research assistants (Adam Lewin MS; and Jennifer Walsh BS) who assisted me with this study. Without them this project could not have happened I am forever indebted to all of my mentors who helped me along the way, including Scott Powers, PhD; and Robert Whitaker MD, MPH, with whom I worked before entering graduate school. They both gave me a strong foundation of skills and knowledge that continue to serve me well as I pursue my career. Most importantly I appreciate the incredible opportunity I had at the University of Florida to be mentored by two brilliant inspiring women (Suzanne Bennett Johnson, PhD ; and Dr. Alexandra Quittner PhD) both of whom I consider to be exemplary academicians. Several other faculty members (including Desmond Schatz, MD; Samuel Sears PhD; and Fonda Eyler PhD) have also provided m e with support mentorship and sound g uidance which have proven influential in my graduate education. IV

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TABLE OF CONTENTS ACKNOWLEDGMENTS ..... . ...... . ... . ............ .... ....... ........ ............ .... .......... .... . ... . ....... iv ABSTRACT ....................................... . ................. .. .. ......................... vii CHAPTER 1 INTRODUCTIO N .. ............. .... . ....... ....... .......... .... ...... ....... .... . ....... . .... ... ..... ...... . 1 2 REVIEW O F TI-IE LITERATURE .......... .... ..... ....... . . ..... .............. ... .... .... . . ... . . ..... 5 Psychological Impact of Genetic Testi n g .................................................... ................ 5 Behavioral Impact of G enetic Testing . . ........... ..... ...... ..... ........ ........... ................... 9 Theoretical Models of Genetic Screening and Behavioral Change ......... .... ....... . . ... 12 Newborn Genetic Screening ...... ............ . .... ....... ..... ... .. ...... .... .. . ...... ... . ........... . 17 Type 1 Diabetes : Etiology and Prevention ........ . .... ... ....... .... .. .... . .... . .... ... ......... 21 Prediction and Pre-Symptomatic Screening for Type 1 Diabetes ...... .... ........ ............ 25 3 RATIONALE AND PURPOSES . .... ............. ........... ... ........ . . . .. ... ... .... . . ............ 37 Obj ective 1: To Investigate the Extent of Reported Maternal Behavior Change as a Result of Genetic Screening for Type 1 Diabetes . .... ....... ..... .... ....... .......... . . .... 38 Objective 2: To Assess Predictors of Maternal Behavior Change as a Result of Genetic Screening for Type 1 Di abetes .................................... ... ... .. ........ . ... . . 39 Objective 3: To Assess Psychological Effects (i.e ., Anxiety) of Maternal Behavior Change Over Time .... .......... ...... ....... ... ......... ...... ... ......... ... ... .... ........ ..... ............. 42 Obj ective 4 : To Compare Reported Behavior Change between Mothers o f Children Genetically at Risk for Developing Type 1 Diabetes with Mothers of Children in the Di abetes Prevention Tria l Who Were ICA + and Therefore at Even Greater Risk for Diabetes On set. .... . . . .......... .............. ...... .... .. .. ........ . ....... .... ..... .... ..... 43 4 METHODS AND MATERIALS ... ......... ..... .... .... ... . ........ ... ... ... ... . . ..... ................. 45 Prospective Assessment of Newborn Di abetes Autoimmunity (PANDA) Study Procedures ... ........................ . ...... .............. ...... .... ........ ...... ......... .... .... . ... .... ...... 45 Participants ........ ...... ............ ........ ... . .... ..... .. ................... .... .. .... ............. .... ....... .... . 48 Procedures ................. .... . ..... ........ ....... ..... ... ......... ... .... ......................... . ..... .... . . 49 Measures ... ...... ... ... . ...... . ... ........ . .... . . ............. . .. .... ..... ......... .... ....... .... ... ... ... .... 51 Statistical Analyses .............. .... ...... . ... .......................... ................. .... ......... . ....... ... 6 1 V

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5 RESULTS ... ..... ............... . . ..... ... .... . .... ................... ........ .... .... .... ... ... ..... ..... .... ... 75 Sample Characteristics ... . .... . ...... . ... . . .......... ............ . . . .... . . .... .... . ... ......... ... . 75 Objective 1 .... . ................ ....... ..................... ... .. .......... . ..... ..... .......... ..... ... ... ..... . ...... 76 Objective 2 . .... ......... .... ............. ............ . ... ...... ............. ............. ....... ....... ... ...... 81 Object ive 3 .... . ......................... . .... ... .... ................ ....... ... ... ...... ...... .... .... ....... .... . ... 88 Obje ctive 4 .... ................. .... ..... . . ......... ................................................................... 89 6 DISCUSSION ....... ................................................. .... . ........................ . ..... ....... 117 Hypotheses ...................................................................... .... ............. ... . . ... .... ...... 118 Strength s a nd Limitations ......... ......... ..... ........ ...................................................... 130 Implications and Direction s for Future Rese arch .................................................... 134 APPENDIX STRUCTURED TELEPHONE INTERVIEW ... ............. ...... . .......... . 137 LIST OF REFERENCES . . ........... ....... ........................... .... ... . ... ..... . ........... ... . .... ...... 154 BIOGRAPHICAL SKETCH .......... ...... .... ..... .... . . . . ... ........ ... .................................... 170 Vt

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Abstract of Dissertation Presented to the Graduate School of the Unjversity of Florida in Partial Fulfillment of the Requjrements for the Degree of Doctor of Phllosophy MATERNAL EFFORTS TO PREVENT TYPE 1 DIABETES IN GENETICALLY SCREENED INFANTS By Arny E. Baughcum August 2004 Chair: Suzanne Bennett Johnson Cochair: Alexandra Quittner Major Department: Clinical and Health Psychology Currently research programs exist to screen newborns in the general population for genetic risk of developing Type 1 diabetes including the Florida Prospective Assessment of Newborn Diabetes Autoimmunity (PANDA) study. These screening programs are part of longitudinal studies addressing the etiology of type 1 diabetes, with the ultimate goal of developing preventative interventions. However little is currently known about the impact of newborn genetic screening on maternal behaviors of newborns found to be at increased risk for the disease. Additionally since we do not presently know how to effectively prevent type 1 diabetes health care professionals are not able to offer definitive recommendations to mothers regarding specific behaviors to prevent diabetes in their at risk children. In the absence of this information, mothers may take their own actions in an effort to prevent the disease in their children. The purpose of thjs exploratory study was to examine maternal reported behavior changes associated with identifying at risk infants via genetic screening Vll

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Structured telephone interviews were conducted with 192 mothers of children between the ages of 2 and 7 years who were previously identified as at increased risk through genetic screening Interview questions elicited qualitative and quantitative information regarding maternal behavior changes affecting the chjld's diet, physical activity stress level environment and health surveillance. Additional questions assessed mothers' anxiety perceived control perceived risk information-seeking behaviors and sources of information regarding their children's risk for diabetes. Results indicated that most mothers reported engaging in behavior change (67 % ) and typically these behaviors involved increased health surveillance and healthy lifestyle changes. Significant predictors of behavior change included family history of diabetes anxiety coping, perceived risk, and information seeking. Overall, these findings suggest that genetic screening for type 1 diabetes has minimal negative impact on maternal behavior. Despite the positive nature of subsequent behavior modifications such behavior changes that may occur in individuals' everyday lives in response to a health risk could threaten the internal validity of natural history studies and prevention trials if not carefully monitored. Vlll

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CHAPTER 1 INTRODUCTION Advances in the field of human genetics are rapidly changing the practice of medicine The Human Genome Project (HOP), completed in 2000, has played a key role in this revolution by identifying and sequencing the genes that constitute the entire human genome. Genetic mutations account for an estimated 5 ,000 diseases and influence the development of thousands of others. Estimates suggest that 20 diseases account for 80 % of the deaths in the Western world; and these diseases are due to the influence of 100 to 200 individual genes which will be identified in the next few years ( Roberts 2000; Patenaude, Guttmacher & Collins, 2002). Tests are currently available to identify specific genetic markers that may lead to a disease in those who are at risk for developing the disease at some point during their lives. There are two types of tests. One type is known as "genetic testing ," which involves using "s pecific assays to determine the genetic status of an individual already suspected to be at high risk for a particular inherited condition because of family history or clinical symptoms." The other genetic screening, involves the use of "various genetic tests to evaluate populations or groups of individuals independent of a family history of a disorder (Committee on Assessing Genetic Risks Institutes of Medicine, 1994 p. 4). However, the terms "genetic testing and "genetic screening" are often used interchangeably ; a nd thus, these words are used interchangeably in this paper as well. As we come to better understand human ge n etics, we have the opportunity to learn more about the role that specific genes play in the etiology of disease. It is estimated that 1

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2 each person s genetic make-up contains 5 to 30 alterations in DNA that could predispose the development or transmission of a genetically based disease It is apparent that advances in genetics will continue to expa nd and screening for genetic susceptibility for diseases will become more commonplace in the coming decades (Juengst 1995) Longitudinal research studies of genetically at risk individuals are necessary to learn about the natural progression of disease in order to develop effective prevention strategies. These studies will provide a better understanding of the interactions between multiple genes in disease development as well as interactions between genes and the environment. While there are diseases that are determined by a single gene mutation such as Huntington s disease many conditions are genetically more complex, involving multiple genes and environmental factors Thus as we learn more about genetic predispositions, it will be increasingly important to examine environmental factors (including individuals health behaviors) to complete our scientific understanding of disease etiology The exciting new opportunities in genetics are accompanied by many unanswered questions about how the public at large will accept and understand these new techniques and the risk information they provide. Unfortunately new advances in medical technology have outpaced the rate at which psychological research has proceeded. Genetic medicine can have a huge influence in life and death issues, raising ethica l social and legal concerns. It is ethically imperative to consider how the new genetic revolution impacts both positively and negatively the quality of life for individua l s and their families. By identifying an individual's g enetic risk for disease there is potential for early treatment or disease prevention or in the case of an incurable disease the ability to initiate health surveillance

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3 and/or plan for the future. Current literature suggests that certain health behaviors ( i.e. diet and exercise) can moderate risk for several diseases such as heart disease cancer or type 2 diabetes. Therefore, awareness of one's genetic risk may directly affect behavior change and consequently disease progression. Genetic counseling is now shifting to providing information regarding personal risk reduction; and allowing individuals to make better-informed medical decisions (Lerman et al., 2002) However, there are also many diseases for which there is no known method of prevention or cure. In these instances it may not be possible to make behavioral recommendations regarding health behavior change other than increased medical surveillance. Despite this individuals on their own may engage in behaviors they perceive to be beneficial or preventative. Recently, there has been a large push for clinical psychologists to become more involved in genetics ; and to lend their expertise as clinicians researchers and educators to advance our understanding of the psychosocial costs and benefits of genetic screening (Fisher et al. 2002 Gallier, 2002; Lerman et al. 2002; Patenaude et al. 2002; Patenaude et al. 2003). A number of agencies have made the psychosocial implications of genetic advances a funding priority including the Human Genome Project which designated 5 % of the total budget to ethical legal, and social issues (Jeffords & Daschle 2001 ; cited in Patenaude et al. 200 2). Clinical psychologists can play an important role in answering how genetic risk information impacts individu a ls in cognitive affective and behavioral realms. Psychologists can assess the role that different personal social, and cultural factors contribute to the development or prevention of disease. As clinicians psychologists can help individuals and families understand risks make informed behavioral and

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4 reproductive choices, provide psychosocial support, and evaluate outcomes. A recent article by Patenaude et al. (2 003) highlights the many important roles pediatric psychologists can play in the research and public policy arenas to inform ethical debates on the merits of genetic testing; and to provide competent clinical care to affected families (Patenaude et al., 2003). While there has been some research addressing attitudes toward genetic testing, comprehension of genetic information, and the psychological impact of genetic test i ng ther e is still much to be learned about the impact of genetic screening on individuals' behavior This exploratory study examined the behavioral impact of newborn genetic screening for mothers whose children were found to be at risk for type 1 diabetes Currently, little is understood about the specific behavior changes that may result from knowing one's child is genetically predisposed to a condition for which there is currently no known prevention method or cure. Additionally, our present understanding of the etiology of type 1 diabetes suggests that it develops from a combination of both genetic and e nvironm e ntal influences, which are not well-defined. In the absence of definitive recommendations from the health care community, mothers of newborns identified as "at risk" may take actions they believe are effective in preventing type 1 diabetes in their children. This study assessed the extent of mothers' self-reported behav ior changes; and assessed associations between reported behavior change and maternal psychological (i. e ., anxiety, perceived control, coping) and sociodemographic variables. The upcoming sections discuss existing literature on the psychological and behavioral impact of genetic testing includin g newborn genetic screening; and our current knowledge of the etiology and prevention of type 1 diabetes.

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CHAPTER2 REVIEW OF THE LITERATURE Psychological Impact of Genetic Testing Literature asses s ing the psychological impact of genetic testing has largely focused on predictive testing for Huntington s disease and breast and ovarian cancer susceptibility (BRCA1/BRCA2 genes). Many studies have examined other uses of risk screening including prenatal screening and carrier screening. This review focuses on predictive genetic testing. Generally studies of predictive testing have focused on both the short and longer-term effects of genetic testing on affective outcomes for screening participants and their family members. Overa11, voiced criticisms that genetic testing leads to poor psychological adjustment appear unfounded based on published literature; and such claims may create unnecessary panic (Horowitz et al., 2001 ; Palmer et al., 2002). Longitudinal prospective studies examined levels of anxiety and depression before genetic testing ; and provided new information regarding the potential for poor psychological adjustment. Evidence suggests that contrary to earlier concerns making the choice to be tested signals psychological preparedness for the outcome and ability to handle the news well. Most people who choose to participate in population-based screening programs do not have a family history and therefore will most likely expect and receive a negative result. In screening members from high-risk families (those with a family history of the disease) patients tend to overestimate (not underestimate) their risk; and expect to rec e ive pos itive results (Croyle & Lerman 1999; Lynch et al. 1999 ; Lynch et al. 1993) While one might expect that receiving positive results would result in 5

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6 clinically significant distress and increased mood symptomatology studies have found that a positive test result is usually not associated with clinical levels of anxiety or depression (Broadstock et al., 2000; Lerman et al., 2002; Schwartz et al., 2002). This may be partly because reducing one's uncertainty regarding risk may actually decrease stress by providing relief from what was previously unknown (Marteau & Michie, 1995; Baum et al. 1997) While some studies suggest elevated scores on measures of distress such as depression or anxiety (Shaw, Abrams & Marteau 1999), scores generally return to baseline levels after 3 to 12 months (e.g., Croyle et al., 1997; Lerman et al., 1996; Wiggins et al., 1992). While there may be some immediate distress upon risk notification, it appears to be neither inevitable nor long-lasting A literature review conducted by Broadstock et al. (2000) examined existing prospective studies of the impact of genetic testing for Huntington's disease, ovarian and breast cancer, and familial adenomatous polypsosis (FAP). To be included in the review, studies had to contain both pre and post measures of psychological distress Examples of measures of distress used in these types of studies include the Impact of Events Scale (JES; Horowitz, 1979), State-Trait Anxiety Inventory (STAI; Speilberger et al., 1970), and the Beck Depression Inventory (BDI; Beck, 1961). The authors extensive search uncovered 11 studies, none of which found an increase in distress (defined as general or test-specific anxiety or depression) at any point in the 12 months after testing After notification, distress decreased in individuals who received either a positive or negative test result. However, this decline was greater and more rapid in those who received negative test results. Furthermore, in regression models the actual test result rarely predicted psychological outcomes beyond the first month post-risk notification.

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7 Congruent with the Stress Disease Coping Model proposed by Baum et al. (1997), the individual's pretest emotional state social support, and expectations were the most predictive of subsequent distress (Marteau & Croyle, 1998) suggesting that personal variables may play a role in how one handle s genetic risk information. Taken together, these studies generate little empirical support for the notion that genetic testing is associated with adverse psychological outcomes (Lerman et al., 2002). However, most of these studies involved participants in research registries and these results may not generalize to the broader population It should be noted that those in clinjcal settings may be self-referred and more nai've, and thus less equipped to cope with knowledge of their risk status (Broadstock et al., 2000). Previous research suggests that psychological distress may be associated with specific personal (e.g. optirrusm) or demographlc characteristics (e.g., race, education) Audrain et al. ( 1998 ) studied women with a first degree relative with breast or ovarian cancer before testing and found that pre-test distress was predicted by age, ethnicity, marital status optirrusm perceived control and overestimated risk perception. Those who perceived less control, were younger, not Caucasian, married and less optirrustic were more likely to experience greater distress before risk screening (Audrain et al., 1998) Hughes et al. (1997) studied ethnic differences in knowledge and attitudes regarding testing for BRCAl gene in at risk women; and found that African American women had lower levels of knowledge, but more positive attitudes toward genetic testing than Caucasian women. rusk perception appears to vary by ethnic status with African American women who ha ve a farruly history of breast cancer having greater concerns

PAGE 16

8 about their own persona] risk of breast cancer and appearing more likely to avoid breast cancer-related thoughts and feelings (Hughes et al. 1996). Studies have examined coping strategies associated with receiving genetic risk information to determine if coping mediates distress in genetically at risk individuals. For these types of stud i es, coping has been conceptuaJized as the degree to whjch one either seeks more information (monitoring) or avoids or distracts oneself from the situation (blunting/avoidance) (Miller, 1987) Studies have used the Miller Behavioral Style Scale (MBSS ; Miller 1987), to determine the style in which individuals deal with risk information given It has been hypothesized that inruviduals cope with health threats in one of two ways. In general, an interaction has been reported between the amount of information provided and whether monitoring or blunting characterizes the inruvidual's coping style. Studies have found that matching the amount of information received to the amount the individual desires lowers distress (Ludwick-Rosenthal & Neufeld 1993; Miller 1980; Miller & Managan 1983) There does not appear to be consensus regarrung whether coping mediates distress in those notified of increased genetic risk status. For example, in a study of patients from high-risk families screened for BRCA1/BRCA2 genes coping efforts (both active and avoidant) were associated with rugher levels of distress prior to notification ; whereas post-notification distress was associated with the test result not coping (Tercyak et al. 2001a). Lerman et al. (1993) found that a rugh level of monitoring in women at risk for breast cancer predicted an increase in rustress over a 3-month follow up period; whereas Vernon and colleagues (1997) study of FAP screening found the opposite to be true. Anxiety appears to be influenced by whether or

PAGE 17

9 not the event is controllable and by the amount of information given to an individual (Miller et al. 1989 ) Sex differences may also play a role in risk appraisal and coping. Marteau et al. (1997) found that women have a greater fear of threat worry more about negative outcomes and perceive greater risks from technology than men; whereas men show higher threat minimization after positive carrier testing for cystic fibrosis ( CF). Behavioral Impact of Genetic Testing A major question of interest to researchers is whether results of predictive genetic testing lead to increases or decreases in health behaviors and medica l surveillance. Does informing people of genetic susceptibility to disease motivate them to take action to reduce their risk? Or does knowing that one is geneticaJly predisposed suggest a sense of pre-determined destiny and perceived immutability (Senior Marteau, & Peters 1999 ; Senior Marteau & Weinman 1999)? Marteau & Lerman (2001) reviewed literature related to cancer, smoking and heart disease and espoused that providing genetic information may not increase motivation to change behaviors and may even result in reducing motivation However these authors also suggested that genetic information might better facilitate change if individuals are offered effective risk-reducing interventions tailored to their genetic risk Most existing research studies in this area focused on cancer particularly, breast cancer screening; and physician recommended behaviors, such as mammography and breast self-examinations. Studies have examined the impact of distress caused by risk notification to determine if distress predicts health-protective or preventative behaviors. Croyle & Lerman (1999) reviewed studies on how coping and distress i nfluenced the processing of genetic risk information and subsequent decision-making. Studies have found that risk information can be too anxiety provoking for some; and therefore, anxiety acts as a

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10 barrier to following through with screening recommendations (Kash et al., 1992; Lerman et al., 1994 ; Lerman et al., 1993). Other studies have suggested that increased distress or worry actually increases health behaviors (Burnett et al., 1999; Diefenbach et al., 1999 ) and even leads to excessive health practices (i.e., breast self-examinations) (Epstein et al., 1997 ; Epstein & Lerman 1997 ; Lerman et al., 1994 ; Lerman & Schwartz 1993). Epstein et al. (1997) found that those who were excessive in their protective behaviors were more likely to be African American, older and less educated. These findings may be explained by results from Audrain et al. (1998) suggesting that African American women undergoing genetic screening experience greater distress and have lowers levels of cancer-related knowledge. Others have found no significant relationship between distress and adherence to recommended medical surveillance (Lerman et al., 2000). Taken together, these studies suggest that an inverted U-shape curve may explain the relationship between distress and screening behavior, with highest rates of adherence predicted by a moderate level of anxiety (Hailey 1991 ; Lerman et al., 1991; Lerman & Rimer, 1993). There have been conflicting reports of how perceived versus actual risk impacts screening behaviors (Hailey, 1991). Overall some cancer studies found an increase in screening rates in those informed they are genetically at higher risk (Meiser et al., 2000; Ritvo et al., 2002; Schwartz Taylor, et al., 1999), while other studies have found adherence rates similar to those of the general population (Bratt et al., 2000; cited in Marteau & Lerman, 2001; Clavel-Chapelon et al., 1999). In a study of colon cancer screening and behavior intentions, half of respondents indicated that they would decrease their use of screening tests and make fewer attempts to reduce their dietary fat intake if

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11 their test results indicated that they were at low risk (Lerman et al. 1996). Women with a family history of breast cancer were more likely to perceive higher genetic risk and engage in appropriate screening behaviors (Hailey et al., 2000; McCaul et al., 1996). Perceived risk has been found to predict screening compliance above and beyond the actual risk associated with family history of a disease (Aiken et al., 1994). Women who perceived themselves to be at greater risk were more likely to engage in initial as well as repeated screenings (Lerman et al., 1990 ; McCaul et al., 1996) However the perception of risk does not appear to be necessarily related to the accuracy of risk. It has been suggested that accurate recall of risk information does not necessarily lead to risk reducing behavior. Therefore many have begun to examine the links between risk perception and risk-reducing behavior particularly the potentially mediating variable of disease-specific worry or anxiety Many studies have been conducted examining the role of information seeking and health behavior change particularly as it relates to public health issues ( i.e,. HIV/AIDS prevention). Rakowski (1990) conducted a randomized survey among adults in the general population and found a pos itive association between more frequent information seeking and personal health-related practices. However hardly any studies have examined this issue in the context of genetic screening. One such study examined women at genetic risk of ovarian cancer; and found that monitors (information-seekers) demonstrated g reater adherence to behavioral recommendations such as attending cancer screenings (Wardle 1995). Demographics factors are also important in predicting health behaviors Schwartz et al. (1999b) found that women with less education who were at risk for breast cancer

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12 and screened, reduced their use of mammography after breast cancer risk counseling. Additionally, studies have found that women from ethnic minority groups and women with lower levels of education reported greater disease-specific worry (Aiken et al., 1994 ; Audrian et al., 1998) and retained less information about screening programs in general (Browner et al., 1996 Donovan & Tucker 2000; Hughes et al., 1997) Lerman et al. (1993) found that reproductive behaviors were also impacted by cancer screening. In a study of women under age 49, 22% reported that they would be less likely to have children if they tested positive; and 17 % reported being uncertain whether they would continue a pregnancy. Other studies that assessed the reproductive impact of genetic testing, found that 46-83 % of subjects within reproductive age in the general population would not have children or would limit further reproduction if they tested positive for a disease gene (Kessler et al., 1989 ; Schoenfeld et al., 1984; Zerres et al., 1986). Unfortunately, little is known about cancer screening and other lifestyle changes involving smoking, physical activity, or diet (Marteau & Lerman, 2001). In the current study, both lifestyle and health surveillance behaviors were included as outcomes. Similar to other studies, relationships between reported behavioral outcomes and psychological variables (such as anxiety, perceived control, coping, and risk perception) were assessed. Theoretical Models of Genetic Screening and Behavioral Change Most studies in the area of genetic testing fail to use a theoretical model to conduct or interpret th e findings. However theoretical models are important as they can serve as a contextual framework for interpreting complex results. Generally most models of health behaviors assume that the motivation for health-protective behavior comes from

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13 both the anticipation of a negative health outcome and the hope of avoiding it (Weinstein 1993). Tercyak (2000) advocated for conceptualizing the impact of genetic testing as a family systems issue (Tercyak, 2000). The rationale for this was that genetic risk information impacts multiple family members and the family's dynamics as a whole Additionally Tercyak (2000) reasoned that families with no history or experience with a particular illness would fare differently than those with a family member who is already ill. Pre-existing illness in one member of the family provides personal experience and an increased knowledge base about that condition for other family members that families without a history of that particular disease would not have. Therefore, the meaning and implications of genetic test results would be different depending on family history By translation, subsequent behavior change may also differ. Rolland (1999) advocated the use of a specific model, the Family Systems-Illness Model when examining the psychological impact of predictive testing. Rolland (1999 ) stated it to be a "useful guide," as it emphasizes the psychosocial demands of different disorders over time and emphasizes the key components of family functioning (i. e ., multigenerational patterns), illness life cycles, and belief systems. Rolland recognized that psychosocial challenges varied according to biological variables, including the degree to which a disease is influenced by both the environmental and genetic factors and the degree to which prevention is possible (Rolland, 1999) Rolland advocated for longitudinal follow-up of families after genetic testing, as psychosocial strains related to knowledge of future risk do not just present themselves upon result notification but will tend to surface at major life-cycle transitions (Rolland, 1999 ) These challenges

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14 influence family decision making and health behaviors. In his descriptions Rolland did not address specific mechanisms of his model or how they apply to screening results (Rolland, 1999) It is apparent that Rolland was using his model as a conceptual gu i de rather than as a testable model. To date, although genetic testing is recognized as impacting the daily lives of entire family units, there has been no formal testing of a family systems model pertaining to the psychosocial impact of genetic testing. The Health Belief Model (HBM) (Rosenstock 1974) has been used most frequently in previous genetic testing research Investigators have used this model to explain preventative health care behavior in the context of one's percei ved susceptibility to an illness, the perceived severity of that illness, and the potential benefits and costs of performing a specific behavior to reduce the risk (i.e ., Aiken et al. 1994; Becker & Maiman 1975; Frets et al. 1990 ; Rowley et al. 1991; Sagi et al., 1992 ; Shi l oh & Saxe 1989; Sorenson et al., 1987). These factors are hypothesized to be predictive of the decision to engage in health behavior change or to increase surveillance. Preventative action is most likely taken when individuals perceive themselves to be at risk for a serious disease and when the benefits to action outweigh the costs of not engaging in the specific health behavior. For the purposes of genetic screening studies most have focused more on the perceived susceptibility component rather than the perceived severity. The HBM has several significant weaknesses. The perceived severity component has not gained strong empirical support as a major predictor for preventative behavior (Leventhal et al. 1983). Additionally, the HBM has proven influential for health attitudes but not consistently for health behaviors HBM also assumes that hea l th

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15 behaviors arise from a single rational decision based on cost-benefit analysis which may be an oversimplification (Home & Weinman, 1998) Finally, this model does not specify underlying beliefs how to change beliefs (Home & Weinman 1998) or what beliefs need to be changed in order to change behavior. Other sociaJ variables and personal factors, as reviewed in later sections, may have more importance in influencing heaJth behavior than this model would suggest. While many studies have used the HBM, Lerman et al. (1997) endorsed a different model, the Self-Regulation Model of HeaJth Behavior (SRM; LeventhaJ, 1965) to more specifically address why women at risk for breast cancer experiencing too great or too little worry were less likely to practice risk-reduction behavior Accardi g to Leventhal (1970) a health threat results in both cognitive and affective responses, which occur in paraJlel. The SRM suggests that moderate levels of perceived heaJth threat (e.g., diagnosis of cancer in a relative) engender a moderate level of concern/worry, which in tum leads individuals to take actions that wiJI reduce the anxiety caused by a health threat. Fear arousal coupled with an action plan leads to a "cognitive representation" of the threat (Home & Weinman 1998). Excessive cancer-related anxiety might produce avoidance of screening, and at least a minimal level of anxiety is necessary to motivate these behaviors. Similar to this model is the Fear Arousing Communications Theory (Janis and Feshback, 1953), which states some degree of fear arousaJ is needed to predict adoption of health care behaviors If individuals are not concerned, they may deny the threat; and if they are overconcemed, they may come to avoid preventative health practices (Kash et al., 1992).

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16 However, contrary to the aforementioned models, communication of a threat alone may be insufficient to change one's behavior (Home & Weinman, 1998). Relatively few studies have used the SRM, perhaps because of its complexity, which makes it difficult to operationalize. However, there does appear to be some empirical support for this model in studies of medication adherence in hypertension (Meyer et al., 1985) and regimen adherence in diabetes (Gonder-Frederick & Cox, 1991; cited in Home & Weinman, 1998). The model that best informs the current study is a transactional model of stress and coping known as the Stress-Disease Risk-Coping Model, which is a comprehensive model specifically designed for studies of genetic testing (Baum et al., 1997). Baum and colleagues' (1997) model is based on the concept of risk appraisal espoused by Lazarus & Folkman (1984) in which primary appraisal involves the judgment of the threat of a stressor; and secondary appraisal consists of a judgment regarding available resources to deal with the threat. This model is particularly concerned with the relationship between uncertainty and risk perception, which influence one s stress response, and consequently affect one's behavior. Proponents believe this model is useful in predicting psychological and behavioral responses to genetic testing results (Lerman, 1997). This model hypothesizes that distress and behavior changes will be affected by the interaction between personal factors (perceived risk influenced by family history optimism) actual test results characteristics of disease, and degree of uncertainty remaining after testing (Figure 1) The central component of the model involves the appraisal process regarding the test results In this step appraisal of increased certainty regarding future outcomes is coupled with perceived available options for action. This appraisal process is influenced

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17 by the degree to which one perceives him/herself to be at risk; and is influenced by surveillance and prevention options, along with other variables ( such as social support, optimism perceived control etc.). This appraisal process is associated with the stress response to the information. The more resources available, the better one may be able to cope with the stressor (Wallston, 2000). This stress response and these coping mechanisms in tum relate to behavioral consequences. This model suggests that the adoption of health behaviors is influenced by personal factors, perceived risk perceived control, distress and coping resources. In describing the model Baum et al. (1997) review the studies that influenced the design of the model indicating that this model was originally informed by both theoretical and empirical evidence. Baum and colleagues' (1997) model is fairly new; and presently no known published studies have tested this model. Despite a lack of available empirical support, this appears to be the most comprehensive and relevant model to use when examining the behavioral impact of genetic screening. The model incorporates many variables that have been examined in the context of genetic screening studies (i.e., perceived risk distress) It should be noted that this model applies to individuals and unfortunately does not directly incorporate the family unit which is undoubtedly affected by results of genetic testing. Despite this limitation for the purposes of the current study, this model was applied to maternal behavior change in response to risk identification in their children Newborn Genetic Screening The current study examined the impact of genetic screening of infants which is ethically more complicated than testing within adult populations The Institutes of Medicine (IOM) reports that 3 % of children have an illness or disorder of probable ge netic origin (IOM, 1994). Understandably, while one would want to extend the

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18 benefits of biomedical advances to children additional considerations are involved. Consequently organizations have generated ethical guidelines for performing genetic tests in pediatric populations including the American Academy of Pediatrics (AAP), American Society of Human Genetics and American College of Medical Genetics Clinical Genetics Society and Institutes of Medicine ( American Society of Human Genetics and American College of Medical Genetics Boards of Directors 1995; Clarke et al., 1994 ; Wert z et al., 1994). These guidelines are especially important for testing for diseases for which there are no known cures or modes of prevention. In the absence o f clearly beneficial treatments or effective methods of prevention it is difficult to justify the genetic testing of children and adolescents, including newborn screening. Because young children are unabl e to understand the value of genetic information for their own lives particular care must be exercised by parents and pediatricians when making decisions about genetic testing for children (AAP Committee on Bioethics 2001). Other important factors to consider include the psychological and economic impact on the family time of disease onset, degree of risk, and possible medical benefits For these reasons, newborn genetic screening is controversial especially for those diseases with no known cure The Institute of Medicine (IOM) report recommended three principles (IOM 1994) to govern the maintenance of existing screening tests and the introduction of new newborn tests: identification of the ge netic condition must provide a clear benefit to the child a system must be in place to confirm the diagnosis treatment and follow-up must be available for affected newborns In other words newborn genetic screening i s supported only if the infants would benefit from early identification and prevention/treatment. Other guidelines exist that allow

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19 regulated research protocols to test children when no immediate medical benefit exists but the contribution to scientific knowledge is great (American Society of Human Genetics/ American College of Medical Genetics Board of Directors, 1995; Clark 1994 ). Newborn screening is the most widely used type of genetic screening with nearly all states in the U.S mandating newborn screening for phenylketonuria (PKU) and congenital hypothyroidism in which early diagnosis leads to treatment and better medical outcomes (IOM 1994). Recently, in some states newborn screening has expanded to include testing for congenital adrenal hyperplasia and cystic fibrosis (CF) (in WI, CO, and WY). As a point of comparison for CF screening only 6 % of newborn U.S. children are screened versus 92% of newborns in Australia (Wilcken & Travert, 1999) In the past decade, newborn screening has been implemented in research settings to test for risk of type 1 diabetes (discussed in a later section). Currently there has been relatively little research on the psychological implications of screening newborns Much of the research has occurred in other countries. Studies from Wales on newborn screening for Duchenne muscular-dystrophy, an incurable X linked condition eventually leading to death during early adulthood (Fenton-May et al., 1994) suggest that the screening has been well-received with few adverse psychological outcomes reported and a participation rate of 90% for eligible families (Bradley et al., 1993; Parsons et al. 2002). Such a favorable outcome is not always the case. A screening program for alpha-1-antitrypsin (lung disease) in newborns in Sweden had to be terminated prematurely because of adverse effects. These included negative changes in family dynamics and parental nonadherence to medical recommendations, including increased smoking behavior (McNeil et al., 1989)

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20 Most of the newborn screening literature has been dedicated to screening for cystk fibrosis (Kerem et al., 1989). CF screening remruns controversial ( especially for those without a family history) as some view the psychological costs as outweighing the medical benefits of early diagnosis (Wald & Morris, 1998). Adverse psychological outcomes have included greater parenting stress (Baroni et al., 1997) and a small percentage of mothers experiencing short-lived feelings of rejection toward their child (Al-Jader et al., 1990) It should be noted that these effects might also be present when diagnosis is made through conventional means when children are a little older (Al-Jader et al. 1990; Boland & Thompson, 1990; Wilcken et al., 1983). Boland & Thompson (1990) found newborn screening versus traditional screening did not produce greater overprotectiveness in mothers. The delay in diagnosis that occurred when screening was not conducted resulted in greater maternal distress and anger. Therefore, these psychological risks do not appear significant when the potential benefits of newborn screening include better health outcomes due to earlier initiation of treatment ( Waters et al., 1999). Further exploration of the psychological effects of newborn screening is an important area of research as new genetic tests become avrulable; and decisions will need to be made regarding the appropriateness of their use. Whether testing is conducted in the general population or in research settings only; and whether it is conducted with all families or just those with a family history of the disease are important questions to be answered. How risk information is understood and used by families; and whether it then translates into emotional and/or behavioral changes are key areas for future research.

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21 Type 1 Diabetes: Etiology and Prevention In the U.S. the prevalence of insulin dependent diabetes mellitus (IDDM; type 1 diabetes) is approximately 2-3/1,000 children, which makes it one of the most prevalent childhood chronic illnesses (Arslanian et al., 1997; LaPorte et al., 1995). Annual incidence is estimated to be over 12,000 children each year, with peak incidence of diagnosis occurring between five and six years of age and again between the ages of eleven and thirteen. The prevalence of type 1 diabetes is higher among Caucasians (National Diabetes Data Group 1995). In type 1 diabetes the body produces little or no insulin due to the autoimmune destruction of islet cells in the pancreas. This leads to high blood glucose levels. Type 1 diabetes is thought to be the endpoint of an immunologically mediated attack on pancreatic beta cells. It is an autoimmune disorder where islet cells are destroyed by an immune response, or more simply, destroyed by cells within one's own body that normally protect a person from germs. Complications of type 1 diabetes can include retinopathy, blindness renal disease, neuropathy lower extremity ulcers digestive disorders heart disease and vascular disease (National Diabetes Data Group 1995). The average life span for those with diabetes is generally shortened due to vascular complications. With no cure available type 1 diabetes is currently medically managed by administering insulin on a daily basis and adhering to a specialized diet and exercise program. These daily treatment demands can greatly affect an individual and their family's lifestyle. In addition to the impact on the family, type 1 diabetes is a substantial societal and economic burden Therefore, an obvious need exists for diabetes prevention. Currently, diabetes (including treatment prevention, and research) consumes one in every seven dollars spent on health care in the U.S. (Schatz et al., 2002). Often diabetes is not

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22 diagnosed until a patient is having a crisis episode (ketoacidosis), which can lead to increased medical complications and longer hospitalizations (Beisswinger, 2000; cited in Schatz 2002) Unfortunately we do not fully understand the etiology of type 1 diabetes. Nearly 90 % of type 1 diabetes occurs in families with no history of the disease (Dalquist et al., 1985) and there is only a 30-50 % concordance rate among monozygotic twins (National Diabetes Data Group 1995; Kyvik et al. 1995; LaPorte et al. 1995) However approximately 3-6 % of first-degree relatives with type 1 will develop the disease as well (Tillil & Kobberling, 1987). The chance of developing diabetes for the general population is about 1 in 300 while for those with first-degree relatives with diabetes the chances increase to 1 in 20 (National Diabetes Data Group 1995). These data suggest IDDM is caused by a combination of genetic and environmental factors. It is generally thought that environmental triggers initiate an autoimmune process that leads to the destruction of pancreatic beta-cells and consequently type 1 diabetes. It is still unclear the degree to which these environmental factors play a role. In order to determine the interactions between genetics and the environment longitudinal studies are needed to follow at risk individuals over time. To date, research studies have suggested viral illness (enterovirus and rotovirus ) m a y be one class of environmental triggers (Akerblom & Knip 1998 Couper 2001 Dorman et al. 1995). Additionally Classen & Classen (2001) ar g ue that timing of vaccines increases the risk of type 1 diabetes. The risk of type 1 diabetes decreases when children receive vaccinations after at least two months of ag e, ar g uing for the benefits of delayed immunization schedules. A recent study found increas e d social mixing in young children (i. e ., attendance of daycare) in

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23 early infancy was protective against the development of t ype 1 diabetes bec a use it increased exposure to infections and strengthened immunity (McKinney et aJ., 2000 ) However there has been no other direct evidence in favor o f such an assoc ia tion (Akerblorn & Knip 1998) Finally early emotional stress may aJso be a contribut i ng factor (Themlund et al. 1995). Dietary factors have been implicated as important environmental contributor s to the development of type 1 diabetes. Such dietary factors included not breastfeeding (Akerblom & Knip 1998), early introduction of cow's milk ( Akerblorn et al 1993 ; Gerstein 1994; Virtanen et al. 2 000) high intake of nit ri tes/nitrates (Virtanen & Aro 1994) accelerated prenatal growth (Dahlquist et aJ., 1996) high intake of prote i ns (Akerblom & Knip 1998) high intake o f carbohydrates ( Akerblorn & Knip 1998 ) and increased weight gain in infancy ( Hypponen et al. 1999 ). Although based on both animal and human studies the most likely putative dietary factors are hypothesized to be cow s milk proteins and nitrates/nitrites (Akerb lom & Knip 1998 The greatest amount of research regarding environmental factors related to type 1 diabetes has examined whether breastfeeding is protective and how this interacts wi t h exposure to cow s milk in infancy Cow s milk is implicated becau s e it has a h i ghe r protein content specifically the protein casein than that found in human breastrnilk Many studies have been conducted to address this issue with no firm consensus reached (Akerblom & Knip 1998 ; Couper 2001 ) To examine the role of cow's milk the mult i national Trial to Reduce IDDM in the GeneticaJly at Risk (TRIGR ) is ongoing to determine if delayed exposure to cow s milk until a f ter 6 months of a ge will ha v e a n effect on the subsequent development o f diabetes (Karges et al. 199 7; S chatz, 2 00 2;

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24 Virtanen et al., 1997). Schatz & Maclaren (1996) warn it is premature to recommend eliminating cow's milk from an at risk child's diet as there is no convincing evidence to suggest the nutritional benefits of milk for young children outweigh the potential dangers. To answer questions regarding the prevention of type 1 diabetes, The Diabetes Prevention Trial (DPT-1) was initiated in 1994 to determfoe whether subcutaneous or oral insulin could prevent or delay the onset of diabetes in at risk relatives (DPT-1 Study Group, 1995, 2002). Within this large-scale randomized, nonblind study, there were two separate trials for the two types of insulin administration Three hundred and thjrty nine participants, who were between 3 and 45 years of age and had a first degree relative with type 1 diabetes were randomized in the subcutaneous insulin trial (out of 84 228 screened first degree relatives). To be eligible, participants had to be determined as high risk ," defined as a 50 % chance of developing type 1 diabetes over the next five years. This was determined by the absence of protective genetic markers, positive antibody testing, and a low first-phase insulin response in glucose tolerance testing. Participants were randomized to either the intervention group, whjch received low dose subcutaneous insulin, or the close observation group, and all of whom were followed for an average of 3.7 years. Results from the subcutaneous insulin trial were recently published. Results suggested that injected insulin does not delay or prevent type 1 diabetes (DPT-1 Study Group 2002) The oral insulin trial is ongoing and results are not currently available. In contrast to type 1 diabetes type 2 diabetes (non insubn dependent diabetes ) is a different form of diabetes that is considered a metabolic disorder rather than an autoimmune disease It is usually diagnosed in adulthood although it can develop in childhood. In type 2 diabetes the body is unable to make enough or properly use insulin;

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25 however, beta cells are preserved. This is in contrast to type 1 diabetes in which beta cells are destroyed, leading to insulin deficiency. Type 2 diabetes accounts for 90-95 % of diabetes and researchers have found obesity and a sedentary lifestyle to be contributing factors, as well as genetic predispotion (Fletcher et al., 2002) Because the prevalence of type 2 diabetes is rapidly increasing to epidemic proportions the health care community and the media have recently focused significant attention on type 2 diabetes advocating for healthy lifestyle changes. Recent research has indicated moderate diet and exercise reduces risk for type 2 diabetes more effectively than even oral insulin ( Tuomilehto et al., 2001). A healthy diet is effective because it reduces the i nsulin load and exerc i se is effective because physical inactivity reduces tissue glucose tolerance and is associated with insulin resistance. Scientific evidence is not clear as to whether these same behaviors have an impact on the development of type 1 diabetes; however at the present time it seems unlikely (Schatz personal communication). People who do not understand the distinction between type 1 and 2 diabetes may apply recommendations for t y pe 2 to their children at risk for type 1 The current study explored whether this hypothesis was true for our sample population of mothers of at risk young children. Prediction and Pre-Symptomatic Screening for Type 1 Diabetes While we do not fully comprehend the natural history of the development of diabetes, we do know that the destruction of pancreatic cells is a precursor to type 1 diabetes and begins lon g before overt symptoms It is currently possible to detect pancreatic cell destruction and identify those at risk for developing Type 1 diabetes Riley et al. (1990) found the determination of islet cell antibodies in relatives of probands with Type 1 diabetes increased an individuals risk for developing the disease in the future.

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26 Currently there are two types of screening for diabetes, autoantibody screening and genetic screening. The most recently developed test, genetic screening is typically done in newborns to determine the present of hjgh-risk genetic markers (DR 3/4 DR 4/4, DR 3/3) in the Human Leukocyte Antigen (HLA) region (the Major ffistocompatability Complex (MHC)) on chromosome 6. This is an area that helps control immune response, and such markers are known to confer 50 % of the genetic risk for Type 1 diabetes ( Yu et al. 1999) (Table 1) The second type of testing, antibody screening is a process that has been in existence for longer and detects islet-related autoantibodies including autoantibodies to insulin (Christie et al. 1994 ; Landin-Olsson et al. 1992), GAD or islet antigen-2 (IA-2) as well as islet cell antibodies (ICA) (Riley et al. 1990; Schatz et al. 1994) present in blood serum. It has been shown the presence and number of these antibodies is directly related to risk for type 1 diabetes (Knip, 1998 ). An ICA positive result signifies that the process of beta cell destruction has begun and therefore, those who are ICA positive are farther along in the process of developing type 1 diabetes For example individuals who test positive for ICA have approximately a 45 % chance of developing diabetes in the next ten years. Antibody screening has been conducted with children and adults and used as a primary screening method and as follow-up to newborn genetic screening. While critics oppose screening for risk of developing type 1 diabetes before symptoms appear Schatz, et al. (2002) argue it is very important to the future o f di a betes prevention research. The authors assert screenjng helps us in a number of ways: it allows us to better understand the predjabetic period and diabetes pathogenesis assists in identification of individuals for prevention trials facilitates earlier diagnoses which

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27 reduces the mortality and morbidity associated with type 1 diabetes (Schatz et al. 2002). Currently, genetic screening is only conducted within research settings since widespread screening of the general population, when there is no available effective intervention, is considered unethical. Many longitudinal studies are now ongoing to follow newborns found to be genetically at risk for Type 1 to better study the development of diabetes. These trials, taking place in Germany (BABYDIAB), Finland (DIPP), Denver, CO (DAISY), and Gainesville, FL (PANDA) include studies of the participants from the general public and at risk families (e g Nejentsev et al. 1999; Rewers et al. 1996; Schatz et al. 2000; Schenker et al., 1999; Ziegler et al. 1999 ) Opponents of screening argue that without a prevention strategy studies should avoid disclosing results to participating families and that if disclosure is necessary t hen research should only be conducted with infants who have a first degree relative with type 1 diabetes (Friedman Ross, 2003). Critics argue that screening under any other circumstances may result in harm to children and their parents. Friedman Ross (2003) stated that genetic screening can only convey at most a susceptibility that is a 20 % probability. She claims that as a result of these tests, parents may prepare unnecessarily and treat their child as ill (Friedman Ross, 2003) As the debate continues regarding the merits of genetic screening of the general population and as interest in diabetes prevention continues to rise, research on the psychological and behavioral impact of genetic screening becomes timely and highly relevant. Psychological Impact of Diabetes Screening Relatively little research has been conducted examining the parents psychological reactions to participation in a newborn screening program for type 1 diabetes. However parents have indicated favorable attitudes towards risk screening and prevention tri a ls for

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28 type I diabetes (Lucidanne et aJ., 1998 ; Ludvigsson et aJ., 2002). To explore the psychological impact of risk screening Dr. Johnson and her research group have conducted several studies of adults and children identified as at risk via autoantibody and newborn genetic screening (Carmichael et al. 2003; Johnson 2001 ; Johnson & Carmichael, 2000; Johnson & Tercyack, 1995; Johnson et aJ., 1990). As explained above, a determination that an individual is at risk as identified through presyrnptomatic screening does not mean an individual will definitely develop diabetes. How this information and level of uncertainty impacts individuals particularly newborns and their families, is an important factor to consider when evaluating the ethical nature of genetic risk screening. In one of the first studies in this area Johnson et al. (1990) reported individua l s found to be at high risk (as identified through ICA screening) and their family members exhibited clinically significant levels of anxiety subsequent to at risk notification. Those testing ICA+ were told their chances of developing diabetes were 50% Johnson and Tercyak (1995) subsequently found notification of islet cell antibody positive (ICA+) status had an emotional impact on the at risk individual (adults and children) and their family members (i.e., spouses parents). Initial notification was associated with considerable situationally-specific anxiety (as measured by the state portion of the State Trait Anxiety Inventory (STAI ; Speilberger, 1970) and the State-Trait Anxiety Inventory for Children (STAI C ; Speilberger 1973) in both individuals with the risk and their family members. This was especially true in parents o f ICA+ children. In add i tion parent and child anxiety was hig hly correlated. However initial anxiety seemed to decrease to normal levels over time as measured in a 4-month follow-up interview

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29 In a similar study with fewer participants Galatzer et al. (2001) examined antibody positive children (n=lO) and their parents using the Impact of Events Scale (IES; Horowitz 1979) and found that high levels of distress reported by parents upon results notification decreased by the 3-month interview Galatzer et al. compared their results with a study of parents of children newly diagnosed with type 1 diabetes (Kovacs 1985) and found similarly strong emotional reactions but more so in the group of parents of children with diabetes Another small-scale study conducted by Yu et al. (1999) (n= 88) found notification of high-risk genetic status in newborns was not associated with incre a sed parenting stress as measured by total stress score (TSS) of the Parenting Stress Index (PSI; Abidin 1990) more than three months after notification. A follow up study to Johnson & Tercyak (1995) examined how individuals found to be at risk (ICA+) coped with their own or a loved one s at risk status by administering the Ways of Coping Checklist Revised (WCC R; Folkman & Lazarus 1980) (Johnson & Carmichael 2000 ) Using this multi-dimensional measure allows for closer examination of coping styles (i. e. problem-focused seeking social support wishful thinking avoidance self blame) beyond the concept of monitoring vs. blunting found in previous cancer geneti c screenfog studies. Johnson & Carmichael (2000) found at risk children used more avoid a n c e coping ( e .g tried to for g et the whole thin g, kept your feelings to yourself; slept more than usual) than at risk adults mothers of at risk children or spouses of at risk adult s At risk c hildren al so u se d mor e wishful thinkin g ( e .g. hoped a miracle would happen ; wis hed the situation would go away) than at risk adults. Initial state a n x iety in res pon s e to risk notification was related with s ubsequent copin g as mothers who were more anxious tended to use more wishful thinking avoidance and they tended

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30 to blame themselves for their child's at risk status. Coping strategies appeared to influence the maintenance of anxiety over time as mothers who blamed themselves tended to remain anxious. In the late 1990s, testing moved from biological to genetic markers, and from family cohorts to the general population. Carmichael et al. (2 003) Johnson & Carmichael (2000) and Johnson et al. (submitted) interviewed mothers of infants at risk for developing Type 1 diabetes as identified through participation in the longitudin a l Prospective Assessment of Newborns for Diabetes Autoimmunity (PANDA) study (Schatz, 2000). As described earlier, PANDA involves HLA genotyping and serial antibody screenings over time. Interviews assessing the psychological impact of participation in PANDA were conducted approximately 4 weeks post notification, and again 4 and 12 months after notification. Similar to the ICA+ studies, they found maternal anxiety levels were clinicaJly elevated after initial notification of risk status, but appeared to dissipate over time to normal levels ( Johnson et al., submitted). Risk understanding was examined in mothers who participated in the initial and 4month follow-up PANDA interviews (Carmichael et al., 2003). Almost 75 % of mothers gave a correct estimate of their child's genetic risk at the initial interview; however, over time, mothers were less likely to be accurate and more mothers underestimated their child's risk. Overall very few mothers overestimated their child's risk. Mothers who were Caucasian and who had higher levels of education were more likely to be accurate. Mothers whose children were in the highest risk group were least accurate. Mothers of children with a family h istory of a first degree relative with diabetes were more likely to underestimate their child's risk at the initial interview. Maternal anxiety was a predictor

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31 of risk underestimation at the 4-month interview, but was not significant in predicting to earlier underestimation or accuracy at either time point. As one might expect, mothers who were more anxious were less likely to underestimate their child's risk. In studies of maternal anxiety in this population initial anxiety levels were found to be higher in mothers who were Hispanic, with less education, in those whose infants were at greater risk and in mothers who overestimated their child's actual risk (Johnson et al., submitted). Coping strategies also appeared to be related to anxiety as wishful thinking and blaming one's self predicted anxiety at the 4 and 12-month follow-up intervie ws (unpublished data). As explained in later chapters participants for this study were recruited from this larger sample. These studies, taken together, suggest newborn screening does not ha ve long-term detrimental effects on parental adjustment, as measured by either anxiety or stress. Additionally, it appears that a majority of mothers correctly recall their infant's risk with few mothers overestimating their child's risk and consequently becoming more anxious These findings are congruent with other studies of genetic testing previously discussed It is likely parents reactions to the news and subsequent coping style may influence an individual's or family's decision to parti ci pate in longitudinal trials or natural history studies, such as PANDA, that will provide the scientific bases of a prevention or cure for type 1 diabetes. These studies can play an important role in informing debates about the ethics of newborn scree ning Behavioral Impact of Diabetes Screening Johnson & Tercyak's ( 1995 ) study of ICA+ children and adults assessed after notification of screening results found 52% of ICA+ children and 24% of ICA+ adults reported making a change in their behaviors and/or life sty le in an attempt to delay or

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32 prevent the onset of Type 1 diabetes. While details were not reported the authors made a general statement that these reported changes most often reportedly occurred in the areas of diet and increased exercise The authors also found that a higher level o f anxiety was associated with greater lifestyle/behavior modification s. Similarly in a later study of genetically at risk infants mothers who continued their child's participation in the longitudinal PANDA study tended to be more anxious with infants at higher risk. Mothers who believed their at risk children would never get diabetes were less likely to continue study participation (Carmic hael et a l. 1999b) In a recent study of intentions for behavior change, Hendrieckx et al. (2002) surveyed a sample of 403 adults with first-degree relatives with type 1 diabetes who were undergoing antibody screening for type 1 diabetes and were assessed prior to results notification. This novel study sought to better understand the relationships between perceived control, distress, and behavioral intentions. Results indicated 73 % of participants stated they intended to make a lifestyle change if found to be at high risk, with diet (87 % ) and exercise (30%) most frequently endorsed (Hendrieckx et al., 2002). These results suggested individuals' beliefs regarding the prevention of type 1 diabetes did not correspond well with current scientific knowledge; however, beliefs appeared more congruent with an understanding of type 2 diabetes (Hendrieckx et al., 2002). Hendrieckx et al. (2002) found general anxiety did not appear to be a significant predictor of behavior change, nor were behavioral intentions predicted by educatio n level. However, similar to Johnson & Tercyak ( 1995) diabetes-specific worry was related to intentions towards behavioral cha nge, along with perceived internal control. Hendrieckx et al. (2002) also found those who were female, married, and older were more likely to

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33 report anticipating making lifestyle changes. Additionally perceived in t ernal control was related to beliefs regarding the causes of diabetes More specifically, those who believed their relative developed diabetes largely due to heredity or chance, were more likely to believe they were unable to do something to reduce their risk of developing diabetes. While this study provided important exploratory data, the results were limited because the data were collected prior to the screening results, and intentions --rather than actual behaviors-were assessed Additionally, it used several new measures, which have not been psychometrically validated. Additional data on diabetes screening and behavioral change come from the Participant Experience Survey designed by Johnson for participants who completed the DPT 1 study (Johnson 2002). The survey was administered anonymously across study sites to examine subjective experiences of participants (who were at least 10 years old) in the trial as well their parents (of participants under the age of 18) Questions assessed a broad range of issues, including study adherence, satisfaction reasons for participation perceived need for psychological support, and efforts to prevent or delay type 1 diabetes from developing. Items that assessed efforts to prevent/delay type 1 diabetes were designed to reflect intentional changes in weight diet exercise, lifestyle stress level, monitoring, and alternative medication use. Items were scored as either yes or no" to reflect whether or not an individual reported engaging in a specific behavior. Only data from those who were unaware of the study s results were analyzed. Sixty-five percent o f DPT-1 participants, who were all over the age of 10 responded to the survey with 82 from the intervention (IN) group (which received preventative insulin) and 81 from the close observation (CO) group. Over half (54 % ) of all participants reported modifying at

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34 least one behavior in an effort to delay or prevent type 1 diabetes onset, with no significant differences between groups, with the exception of alternative med ication use (significantly greater use in IN group). Results indicated dietary changes were the most common behaviors reported, with approximately one-third of participants stating they reduced candy or sweets intake, reduced intake of regular soda, or increased intake of diet soda. Twenty-eight percent of participants indicated they would increase their ph ysical activity. Seventeen percent stated they took alternative medications. Ten percent of participants reported attempting weight loss Seven participants (4 in the experimental group and 3 in control) stated they took extra insulin in an effort to delay or prevent diabetes onset. No significant predictors of behavior change were found in this study. These results were congruent with Tercyak & Carmichael (1995) and Hendrieckx et al. (2002) and indicated a substantial proportion of individuals' who are found at risk for type 1 diabetes engage in behaviors that correspond to those found to be effective in the treatment and prevention recommendations for type 2 diabetes (ADA, 2002b; Tuomilehto, et al. 2001). Data are currently avai lable from parents whose children participated in the DPT-1 study. These data were used as a comparison group in the current study. These studies described herein suggest there are unanswered questions to be explored regarding the behavioral outcomes associated with risk screening for type 1 diabetes. Existing data suggest individuals report engaging in behavior changes in response to risk information, although it is unclear what predicts these behavioral efforts. Base d on existing literature, risk perception perceived control and psychological distress appear important factors to consider. Behavior changes that result from risk notification

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35 may or may not be related to scientifically validated methods of risk reduction However, in the case of type 1 ctiabetes whether a behavior is scientifically valid is not necessarily important since we do not currently know what delays or prevents the onset of the disease.

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36 STRESSOR CHARACTERISTICS -test results -uncertainty -disease characteristics APPRAISAL (risk perception) Exposure variables ( uncertainty reduction prevention and surveillance options) Personal factors (social support, perceived control information seeking) I No stress I Stress and stress response I Coping, intrusive thoughts I Health Behavior Changes (e.g., dietary changes, study participation) ,___ Psychological consequences (e.g., anxiety depression) Figure 2 -1. Partial representation of the Stress Disease Risk -Copi ng Model adapted from B a um et al. (1997)

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CHAPTER3 RATIONALE AND PURPOSES The purposes of this study were to better understand predictors of self-reported behavior change in mothers of newborns who were identified as at-risk for type 1 diabetes through genetic screenjng. Currently, little is understood about the specific behavior changes that result from knowing one's chlld is genetically predisposed to a condition for which there is currently no known prevention method or cure. Additionally our present understanding of the etiology of type 1 diabetes suggests it develops from a combination of both genetic and environmental influences w hjch are not well-defined. In the absence of definitive recommendations from the health care community, mothers of newborns identified as "at-risk may take actions they bel ieve are effective in preventing type 1 diabetes in their children. Based on previous studies, possible behavioral changes may include altering their children's environment, feeding schedules, activity patterns, and/or med i cal surveillance behavior (Hendrieckx, 2002 ; Johnson 2001; Johnson 2002) These efforts may represent mothers attempts to reduce their anxiety and better cope with the situation. However, we have yet to document the nature and extent of such behavior changes, includin g the incidence of excessive prevention efforts that may become burdensome and impact daily functioning. Further, since the onset of diabetes is thought to be an interaction between genetics and the environment, it is unclear to what extent certain types of behaviors could advance or delay disease onset. Although current science does not perrrut us to 37

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38 recommend certain behaviors as preventative, it is important for us to monitor the role o f relevant behaviors if we are to understand the natural history of this disease Monitoring possible behavior change associated with high-risk notification is equaJly important to current and future diabetes prevention trials. In the DPT-I, for example, behavior change efforts taken by the control group (e.g taking insulin or nicotinimide) could undermine the trial's internal validity. Unless these behavior changes are monitored, interpretation of study results can become exceedingly difficult. This is not unique to diabetes-or genetic screening-specific trials as certain behavior changes could potentiaJly impact other types of clinical trials as well. This study involves both qualitative and quantitative data to examine reported behavioral outcomes associated with participation in the Perspective Assessment o f Newborn Diabetes Autoimmunity (PANDA) study. Findings will be examined in the context of Baum and colleagues (1997) model of genetic testing in which behavior change in response to genetic test results is influenced by one's risk appraisal affective response to the information, and available coping resources. Objectives of the study are listed below Objective 1: To Investigate the Extent of Reported Maternal Behavior Change as a Result of Genetic Screening for Type 1 Diabetes Hypothesis 1.1: Reported behavior changes will most likely correspond to recommendations for the treatment of diabetes (American Diabetes Association (ADA ), 2002a, 2002b) and the prevention of type 2 diabetes (Pierce et aJ., 1995), including changes in diet and physical activity patterns ( Forsyth et aJ., 1997 ; Pierce et aJ., 1995). Rationale: There is scientific uncertainty regarding the environmental factors associated with the development of type 1 diabetes. The health care commun i ty and

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39 media have recently focused signjficant attention on type 2 diabetes, advocating for healthy lifestyle changes. In a study of parents with type 2 diabetes, nearly half thought they could reduce their children's risk of developing diabetes by altering their children's diet and exercise patterns (Pierce et al., 1999). Mothers, who may not understand the distinctions between type 1 and 2 diabetes may apply such recommendations to their children at-risk for type 1. This hypothesis is congruent with findings from Johnson & Tercyak (1995), Hendrieckx (2002), and unpublished data from the DPT-1 survey (Johnson, 2002) Objective 2: To Assess Predictors of Maternal Behavior Change as a Result of Genetic Screening for Type 1 Diabetes Hypothesis 2.1: Mothers who perceive they have control over their child developing diabetes will be more likely to report engaging in behavior changes. Rationale: Mothers may be more likely to report taking action to help prevent such an outcome if they believe they have some control over the situation. Behavioral change to reduce a health threat is more likely if there is a belief that change can be affected (Diefenbach et al. 1999; Hendrieckx et al., 2002). Perceived control is related to both uncertainty reduction and available prevention/surveillance options whjch are integral to risk perception, a key component of health behavior change (Baum et al. 1997). Therefore, perceived control will be examined in the context of other predictors of behavior to determine possible interaction effects. Hypothesis 2.2: Mothers who perceive their children to be at greater risk will be more likely to report engaging in behavior change. Rationale: Perceived risk more so than infant s actual risk, will be a better predictor of behavior. Mothers of children who perceive their chjldren to be at higher

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40 risk than has been identified through testing (overestimate their risk) will be more likely to engage in behavior change. Mother of children who underestimate their child's actual risk will be less likely to engage in behavior change. Children who are perceived to be at high or extremely high risk may have mothers who will be more likely to try to intervene. Studies of genetic screening for breast cancer have found that increased perce i ved risk predicts likelihood of engaging in health behavior change and health surveillance behaviors (e.g., Aiken et al., 1994; Meiser et al., 2000; Ritvo et al. 2002; Schwartz et al. 1999) Perceived control may also interact with risk perception as mothers who perceive their child to be at greater risk may be more likely to engage in behavior change if they also believe they are able to control whether their child develops diabetes. Hypothesis 2.3: Mothers who are more anxious will be more likely to report engaging in behavior change. Rationale: Mothers who are more concerned and worried about their child developing diabetes will be more likely to report taking preventative actions. Studies indicate disease-specific worry (Hendrieckx, 2002; Johnson and Tercyak, 1995) and beliefs regarding the effectiveness of preventative actions (Diefenbach et al. 1999 ) predicted either intentions for behavior change or increased adherence to health protective behaviors. Maternal anxiety may also be related to the degree of maternal perceived control which may in tum influence behavior. Mothers who are more anxious/worried may report more behavior change if they also believe they have some control over the situation.

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41 Hypothesis 2.4: Mothers who use more coping strategies, particularly active coping (i.e., problem-focused seeking social support) will be more likely to report behavioral changes. Rationale: Engaging in risk reducing behavior can be seen as a means of coping with a health threat. Behavior change is an active coping approach and likely to be associated with other ways of coping, particularly those that are also more active namely problem-focused coping and seeking social support. Mothers who use avoidant strategies, and try not to think about the problem may be less likely to engage in risk reducing behaviors. Additionally those who perceive greater control over the situation may be more likely to engage in more proactive coping methods whereas mothers who perceive less control may engage in more avoidant coping and be l ess likely to report behavior change. Hypothesis 2.5: Mothers who report information seeking and/or report receiving recommendations from medical professionals or other family members related to behavior change will be more likely to report engaging in behavior change. Rationale: Mothers who are given advice to change their behavior by those t hey feel are authoritative will be more likely to follow through with recommendations. Research findings sug g est monitors (or information seekers) are thought to cope more effectively with stressful situations using more problem focused information obtaining coping strategies rather than avoiding the situation and not seeking out information (Scheier et al. 1986 ; Carver et al. 1989). Participation in research studies may be viewed as an additional form of information seeking about health status. Information se e king may also influence one s sense of perceived control a nd consequently, may

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42 influence behavior both directly and indirectly. Those w ho seek and ut il ize information from various sources may perceive greater control over t he situation, and consequently, be more likely to report behavior changes. Therefore information seeking will be measured in this study and used as an independent predictor as well as in conjunction with perceived control. Hypothesis 2.6: Mothers who continue participation in the PANDA Part II study (repeated blood testing for antibodies) will be more likely to report other behavior changes Rationale: Health surveillance behaviors such as participation in additional blood draws may be a likely outcome following risk notification. Increased health surveillance may also signify increased contact with health care professionals. For those who continue in the PANDA study, the risk of developing diabetes may be more salient and seen as something they should address. Mothers who continue in the study have contact with investigators and study staff over time and therefore this contact may influence their behavior. Mothers who are sufficiently concerned about their child's risk enough to monitor their child's risk more closely may be more likely to report other behavior changes Data on participation in PANDA Part II blood draws are available and these data can be compared with maternal report Objective 3: To Assess Psychological Effects (i.e., Anxiety) of Maternal Behavior Change Over Time Hypothesis 3.1: Mothers who report modifying behaviors will show a greater reduction in anxiety over time than mothers who do not report beh avior change. Rationale: Behavior changes, includin g health surveillance behaviors may represent means of coping with a health threat. Engaging in behaviors perceived as risk

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43 reducing may help lower maternal anxiety regarding the situation. However the influence of reported behavior change on maternal anxiety may be influenced by maternal perceived control (i.e., mothers who perceive control over diabetes onset and engage in behavior change may show greater reduction in anxiety). Objective 4: To Compare Reported Behavior Change between Mothers of Children Genetically at Risk for Developing Type 1 Diabetes with Mothers of Children in the Diabetes Prevention Trial Who Were ICA+, and Therefore, at Even Greater Risk for Diabetes Onset Hypothesis 4.1: Mothers of genetically at-risk children will be less likely to report behavior change than mothers of ICA+ children enrolled in Diabetes Prevention Trial-1 (DPT-1) Rationale: Participants enrolled in the DPT-I trial were at increased risk for diabetes as identified through positive family histories and positive ICA screening. Their risk level was collectively higher than 98 % of our original total sample population for the PANDA Part III study (of whom 7 of 435 were ICA positive ) For this reason, we believe that mothers in the current study will report fewer behavior changes than mothers of children in the DPT-I since children of mothers in the proposed study are at relatively less risk than the DPT-lstudy children. Examining this hypothesis will allow us the unique opportunity to explore differences between maternal reports of behavior change in two at-risk groups: children identified at birth as genetically at-risk and higher-risk children who have entered a prevention trial. Pierce et al' s (1999) study of parents with type 2 d i abetes found that those who believ e d they could prevent their children from d e veloping di a betes and who perceived

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44 their child s risk to be higher were more likely to experience greater anxiety ( Pierce et al., 1999) For the purposes of examining this hypothesis, maternal data from the DPT1 survey (n = 134) will be compared with mothers reported from the PANDA study. Based on DPT-1 participant data over half of the sample reported at least one behavior change, with dietary changes most often reported, followed by increased exercise weight loss attempts and alternative medicine use (i. e., vitamins) use (Johnson 2002 )

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CHAPTER4 METHODS AND MATERIALS Prospective Assessment of Newborn Diabetes Autoimmunity (PANDA) Study Procedures Part I Participants were mothers whose infants were screened at birth to determine their genetic risk for the development of Type 1 diabetes (1997-1999) through Part I of the Prospective Assessment of Newborn Diabetes Autoimmunity (PANDA) study. This study is a National Institutes of Health and Juvenile Diabetes Research Foundation Internal-supported registry that uses genetic testing to identify newborns at risk for type 1 diabetes (Schatz et al., 2000). In this study, mothers were contacted at the time of their child's birth and asked permission to screen the newborn for the presence of the high-risk HLA -DQBl alleles using blood spots on filter paper (obtained by heel stick at the time of state-mandated phenylketonuria testing) Informed consent was obtained and consenting participants were told they would only be re -c ontacted if their child was at increased risk for type 1 diabete s The majority of these women gave birth at participating locations in Gainesville, Florida, or Pensacola Florida, were all English speaking, and were over the age of 18 PANDA genetic testing results placed infants into one of six risk categories: very low risk (1/6000) low risk (1/300), slightly increased risk (1/125), moderate risk (2/100) high risk (5-10/100) and extremely high risk (20-25/100) (Table 4-1). Only children who 45

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46 were at moderate, high or extremely risk children are followed longitudinally in the PANDA study. If a child was determined to be "at risk in other words classified as either at moderate ," high or "extremely high risk," mothers were sent letters noti f ying them that their children s genetic test results were available. Results were usually available after approximately 12-20 weeks following birth. In the letters participants were requested to call the PANDA study coordinator to discuss the results and poss i ble continued study participation in PANDA Part II according to PANDA protocol. If no response was received approximately 30 days after sending the notificat i on letter the PANDA study staff attempted to contact the mother by phone to notify her of her infant s risk For mothers who called for results or were contacted by phone the stud y coordinator followed a scripted presentation of the risk information, including both categorical and numerical risk figures. Additionally she presented available options including participation in the PANDA Part II study, and an opportunity to ask quest i ons about the study or the meaning of the results. Parents had the option at that time to decline further participation, continue with the study or delay their decision. Regardless of participation status in the PANDA Part II study all mothers were asked for their permission to be contacted by a second individual from the Pediatric Psychology Research lab who would ask them questions about their understa nding of the study a nd its psychological impact. See Figure 4.1 for procedural outline of entire PANDA study. Part II Part II of the PANDA study involves longitudinal follow-up o f ch i ldren screened at birth. These children are periodically screened (via blood draws) starting when the child

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47 is at least six months of age for the presence of autoantibodies, which are additional markers of diabetes disease progression. A positive screening for autoantibodies would suggest that the child is at even greater risk of developing type 1 diabetes. Blood draws could be conducted either by (1) mailing out supplies to parents and to have their pediatricians draw the blood and mail back to the PANDA staff or (2) scheduling directly at study sites in Gainesville, Orlando, or Pensacola, Florida. Blood draws were expected to occur every at 3, 6 ,or 12 months, depending on risk level. Part III Part III of the PANDA study examined the psychological impact of particip at ion in PANDA, including maternal affective (i.e., anxiety) and cognitive responses ( i.e risk understanding) as well as coping response. Mothers who agreed to be contacted at the time of notification were interviewed by telephone approximately 4 weeks following notification (M = 3.50, SD = 1.96) and again at 4 (M = 3.93, SD = 1.96) and 12 months (M._ = 12.83 SD= 2.45) post-notification (see Figure 4-1). For the initial interview, the Part III participation rate was high Approximately 90% (n = 435) of the mothers we were able to contact (of over 700 eligible) agreed to complete the initial interview, 79 % participated in a second interview (n = 344), and 62 % participated in the third interview (n = 269). Sixty percent (n = 262) completed all three interviews. Of those who did not complete all three interviews, 67 declined to be contacted beyond the first interview (no attempts were made to contact these mothers to participate in the current interview) and 106 were unable to be contacted by phone due to either disconnected numbers or the time that had elapsed between or study personnel could not reach them.

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48 Participants To be eligible for the current study, mothers must have completed at least the initial interview of the PANDA Part ill study and at no point declined participation in either of the subsequent two interviews (n = 368) Out of 368 eligible mothers for the current study (i.e those with> 1 previous interview and who did not previously decline participation) 204 were successfully contacted (55 % ) Of these mothers, 192 ( 94 % ) completed the interview, ten declined participation (5%), and two mothers were no longer eligible because their "at risk" children had recently developed type 1 diabetes ( 1 %) Of the 163 mothers who could not be contacted (44 %), 145 had disconnected numbers and/or had no forwarding contact information, and 18 mothers with presumably correct up-to-date contact information were unable to be contacted" after multiple attempts. Families were deemed "unable to be contacted" when there was no response after at least fifteen attempts were made over a two-month period, with at least three messages left if a family member or answering machine was available. Maternal Characteristics Mothers who completed the current interview ranged in age from 20 to 46 (M = 33.67, SD= 5.38). Eighty five percent of mothers were married and 44% had a 4-year college degree at the time of interview (Table 4-2). Eighty-five percent of mothers where Caucasian and therefore, minority members were under-represented compared to the population in Alachua County Florida and Florida in its entirety, where among women of childbirth age (18 -44 years) approximately 25% and 23 % are minorities respectively (Florida Office of Economic and Demographic Research, 2001) On average mothers in this sample had two children at the time of interview (M = 2.09, SD= 1.11). Eighty three

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49 percent of participating mothers completed all three interviews and 62% attended at least one blood draw. Child Characteristics Target at risk children of participating mothers were between the ages of 2 and 7 years (M = 4.25, SD= 0.89) and evenly split between males and females (Table 4-3) Within this at risk sample, the majority of infants were at "moderate" risk (56 % ) 37% were at "high risk, and 7 % were at "extremely high" risk. Five out of the six eligible mothers of children who were antibody positive participated in the current interview Most children of participating mothers were reported as having a family history of diabetes (72 % ) (type 1 or 2). Sixty-five percent of children have at least one distant (2:: second degree) relative with diabetes. Thirty-seven children (19 % ) have at least one first-degree family member with diabetes. Of these, 30 children have immediate family members with type 1 diabetes (81 % ), including 15 participating mothers themselves, along with seven fathers and 14 siblings. In five of these families two immediate family members have type 1 diabetes Procedures The current interview was conducted at least one year post PANDA Part ID study completion and therefore, two to four years post-notification (M = 3 60, SD= 0.78). Attempts were made by the Principal Investigator or research staff to contact all eligible mothers (n = 368) for an additional follow up interview to measure reported maternal behavior changes resulting from knowledge of their children s risk for type 1 diabetes. Contact information for these mothers was kept within a computerized database with restricted access so telephone numbers were available only to study staff.

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50 When participants were contacted, they were reminded of their earlier participation in PANDA Part III interviews and asked if they would agree to participate in an additional interview. Participants were reminded of the voluntary and confidential nature of the study and those who agreed to participate, were given a $5 gift certificate to Publix or Target (their choice) as a token of appreciation. Asking mothers about their behavior might have had the potential to raise mothers' anxiety and curiosity levels regarding what they should or should not be doing to help their children. Therefore, at the beginning and end of the interview, there was a disclaimer read to remind mothers that we do not currently know what causes type 1 diabetes and that we did not have specific recommendations to offer other than encouraging a health lifestyle, including a healthy diet physical activity and rest. For mothers who asked more specific questions beyond this, we had prepared documents from the American Medical Association (AMA) on developmentally appropriate guidelines regarding eating exercise, and sleep (found at www.ama.org; last accessed 6/1/02) which could be mailed to mothers upon request. Eleven mothers requested additional information. The most frequently requested materials pertained to information regarding signs and symptoms of type 1 diabetes. For quality assurance purposes, data were entered twice into a computerized database systematically compared and cleaned before analyses were conducted. Data from this interview were linked to previously collected data (PANDA Part III) on these study participants through their unique identification numbers assigned by the PANDA study staff This allowed for longitudinal analyses of the data. This study was approved by the UF Health Science Center Institutional Review Board (9/1/02) and documentation

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51 of written consent was waived. Funding for this study was obtained from the North Central Florida's Children's Miracle Network. Measures Descriptive Variables Descriptive data were collected to examine the maternal and child demographic characteristics, overall participation rate as well as demographic differences between mothers who agreed to participate versus those who declined or were unable to be contacted (for further detail see section on "Predictor Variables") These two groups of mothers were compared across outcome and predictor variables based on data from the initial interview. Outcome Variable: Reported Behavior Change A component of the structured interview was developed to assess behavioral changes across six domains: (1) diet/eating patterns, (2) physical activity, (3) emotional stress (4) medical interventions, (5) medical surveillance, and (6) illness prevention behaviors. These questions and constructs were adapted from the Participant Experiences Survey used in the Diabetes Prevention Trial-1 (DPT-1) study (Johnson, 2002) and constructs were classified based on the DPT-1 survey and Hendrieckx et al. (2002) Additional questions were added to address other potential environmental triggers or influences hypothesized in the research literature to be related to diabetes development (Akerblom et al, 1998) (see Appendix A). Di e t and e ating patt e rns (g = .58) Sixteen questions addressed changes in the frequency, amount, and types of food/drink (i.e., sweets, soda, juice cow s milk) given to the chjld as well as attempts to modify the child s weight. Also included were questions assessing changes in early feeding history including timing of the introduction of solid

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52 foods and breastfeeding. The 16 questions represented ten different types of behavior changes, as some questions are paired to assess in which the direction changes occurred (i .e., decrease vs increase) Additionally, two questions referred to behaviors for which the concepts of frequency and duration do not apply, and therefore, these detailed follow up questions were not asked. Phy sical a c tivity/Ph y sical stress (_g = 0.54). Four questions assessed whether mothers increased or decreased their children s physical activity or physical exertion in response to their child s risk for type 1 diabetes. Emotional stre ss (_g = 0 47). Four items were designed to assess lifestyle changes that foster the reduction of the child's level of emotional stress. M edi c ation s (_g = 0.54) Five items addressed whether mothers provided their children with medications such as dietary supplements vitamins or insulin Illn e ss Preve ntion (_g = 0.72) Eight questions representing seven unique concepts assessed the degree to which mothers altered their children's environment to minimize risk of illness or infection. M e di c al surv e illanc e (_g = 0.37) Five questions were designed to assess whether participants engaged in healthmonitoring behaviors for their children such as more frequent doctor s visits glucose monitoring and reported participation in PANDA Part II study (autoantibody screening) However reported PANDA Part II participation was not included in calculating the domain score as it was used for reliability purposes and also used as a predi c tor variable This portion of the interview began with a simple yes/no question assessing if, in general participants felt they engaged in any behavior change to prevent diabetes in

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53 their child. This question was followed by more detailed questions regarding different types of behaviors relevant to the six domains described above. For each section participants were first given an open-ended question to solicit spontaneous answers (e.g Have you done anything different with your son's physical activity patterns to prevent him from developing diabetes?) followed by more detailed forced-choice questions. When a response was given to an open-ended question that would be later addressed by a forced choice item, the corresponding forced choice item was also endorsed Within each domain forced choice items were designed to assess a wide variety of behavior changes For each question participants were reminded that these questions apply only to behaviors initiated specifically to prevent diabetes in their children For forced choice questions in which the response was "yes," follow-up questions were asked to assess duration/consistency and frequency of given behavior. Forced choice items were scored as either "yes" or "no." Duration or consistency of the behavior was scored as "never" (0) if the behavior never occurred, "inconsistent" (1), if the behavior was initiated early on but stopped, or began only recently; or consistent (2), if the behavior has been ongoing since results notification. Frequency of a behavior was scored as never (0), "occasio nally (1) or "always/nearly everyday" (2). Each question was scored as dichotomous "yes /no (0 or 1) as well as given a continuous composite score value for duration frequency, and duration x frequency However duration was relatively static as 86 % of those endorsing a certain behavior reported consistent engagement since time of notification. As for frequency of behaviors 60% reported engaging in the behavior "always/nearly everyday" (Table 5-8).

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54 Due to relatively low frequencies for most items and low variabiljty in dura t ion scores only the dichotomized "yes/no" scores were used for analyses Domain scores were calculated in two ways: ( 1) calculating sum of the number of behaviors endorsed and (2) whether at least one behavior change occurred with each domain. A total score for behavior change was similarly obtained by collapsing domains A factor analysis of this measure was not conducted due to low variability on the items and inadequate sample size for the number of items in the measure To determine the statistical strength of the scores for the six domains and total score, coefficient alphas were calculated to determine the reliability of each construct (Table 4-4). Reliability was relatively strong for the total behavior score (g = 0.77 ) and i llness prevention domain (g = 0.72) but weaker for the other five domains, with alphas ranging from 0.37 to 0.58 Correlations between domain scores ranged from 0.10 to 0.44 (Table 4-4) As expected given the data, the total behavior score was best correlated with diet and health surveillance behavior scores. Due to the low frequency of behavior changes within several domains, as well as the non-normal distribution and relatively poor reliability of domain scores (g < 0.60) no further analyses of domain-specific beha v iors were conducted (Table 4-5) Additionally due to the relative low frequency of endorsement of items overall and the non-normal distribution of the total behavior score, the total behavior score used in subsequent analyses was the dichotomous variable of whether at least one behavior change was reported (1 = 'yes') versus no behavior change reported (0 = no') (see Figure 4-2). Reliability of self-reported behavior change. Sel f -reported participation in Part II of the PANDA study was collected in the structured interview and compared with data on

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55 actual Part II participation. These data were available through the PANDA computerized database. Continuation in the PANDA Part II study was defined as those mothers who brought their child in for at least one blood draw for autoantibody screening coded as "participated (1) and "did not participate (0). Actual participation in PANDA II blood draws was the only observed behavioral data available to us. It permitted us to examine the validity of maternal self-report data concerning this particular component of medical surveillance. PANDA Part II participation data indicated that 61 % of mothers participated in at least one subsequent blood draw and 26% participated in two or more. When asked in the interview 174 mothers reported accurately whether they participated in Part II of the study (91 % ) with 72 accurately reporting they had not continued participation and 102 correctly reported they had Three mothers reported participating when they actually had not (1 % ), and 15 reported they had not participated when they actually had (8 %) These findings suggest mothers may have been open and honest when completing the interview and that social desirability effects were not strong. If anything it is possible mothers may have underreported efforts to prevent diabetes. Predictor Variables Sociodemographics The following variables were assessed during the first PANDA Part ill telephone interview : date of interview (to calculate length of time s ince notification), maternal date of birth child dat e of birth maternal and paternal education level family income bracket maternal and child ethnicity marital status number of children and whether or not this is her first child The number of first-degree relatives second-degree relatives or greater relatives of the child with type 1 or type 2 diabetes was also assessed if known. In the current interview s everal non-static demographic variables were updated in this current

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56 interview to ensure that information was current, including marital status number of children family income bracket maternal and paternal education level, and family diabetes history. Mothers were also asked for a current address in order for gift certificates to be sent. Perceived control This construct was assessed by a series of questions adapted from a questionnaire developed by Bradley et al. (1999) and used in Hendrieckx et al. (2002) These questions assessed whether participants believed there was anything a parent or a medical professional could do to prevent diabetes in the children as well as a question about diabetes onset being determined by chance or fate. Responses were scored on a 5-point Likert scale anchored by strongly disagree (scored as 1) and strongly agree (scored as 5)." Internal consistency of this 3 item scale was g, = 0.55 This was unsatisfactory based on the study s criteria of using an alpha score of 0 60 as the cut-off for acceptable reliability. However when chance was not retained as a part of this composite internal consistency increased(!!= 0 66) therefore only the two-item measure of perceived control was retained as a composite measure (Table 4-7). The composite score of perceived control was calculated by avera g ing the scores of the two items (Table 4 6) Risk perception (1) P e r ce ive d ab so lut e ri sk. An absolute measure of perceived risk and its accuracy was asses sed in the previous interviews and was assessed in a similar way in the current interview. Mothers were presented with a list of the possible risk categories (with numerical estimates) and asked whether or not any of these were the risk group they were told their child was in "I don't remember" was recorded if they were unable to recall or recognize their child' s risk categ ory or number. (2) P e r ce i ve d es timat e d risk. Perceived

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57 risk was considered accurate if the participant was able to recognize the infant's correct risk st a tus from the list. Responses were classified as "accurate" (scored 2) "overestimates" (scored 3) "underestimates" (scored 0) or "unknown" (scored 1) based on the relationship of the response to the child's actual risk status This component reflected perceived absolute risk while controlling for actual risk and was included in the composite score of risk perception whereas absolute risk was not. (3) Perceived comparativ e risk. A question adapted from Hendrieckx et al (2002) assessing perceived comparative risk was included The question was stated as follows : "How do you think your child s risk for developing diabetes compares to other children ?" The response was rated on a 5 point Likert scale ranging from 1 to 5, anchored by "much lower and much higher. (4) E x p ec tations. A question used in all previou s interviews assessed whether participants believed their child will develop diabetes. This question was coded as yes, my child will develop diabetes in the near future (scored 3) "my child will eventually develop diabetes but not for a long from now ," (scored 2) "my child will not ever develop diabetes ," (scored 0) or "I am unsure. ( scored 1) This variable was previously used in Carmichael et al. (1999) Intercorrelations between the three risk perc e ption vari a bles (estim a te risk relative ris k and expectations) were examined and a composite score was calculated. To accomplish this scores were transformed into z scores and mean of the three variables was d e riv e d a s th e composite Additionally reliability of the composite score was assessed (g.= 0.61; Table 4-7) For those whose response to an item was unknown ," when calculating reliability for the composite score their score was replaced by the item

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58 mean. When computing the individual s composite score in cases with an "unknown" response, the individual's score was the average of the other two risk items. Anxiety Anxiety was measured by a 10-item short form of the state-component of the State Trait Anxiety Inventory STAI (STAI; Spielberger, 1970). Respondents were asked to rate the questions according to how anxious they presently felt about their child s risk for developing type 1 diabetes on a four-point scale (i.e Not at all, Somewhat, Moderately or Very much). The 10-item STAI was also adminjstered at all previous interviews and results were reported on in published studies (Johnson et al., subrrutted ; Carmichael et al. 2003). The 10-item short form was derived from a sample of 231 mothers who completed the full 20-item scale at the initial interview Ten items were selected by exarruning the items that most hjghly correlated with the full 20-item scale scores for these participants Thjs form was found to be highly reliable at the initial (g = 0.93), four month follow-up (g = 0.92) and 12 month (g = 0.90) follow-up interviews. The 10-item short and 20-item full forms of the STAI were highly correlated (r = 0.97). The practice of creating a short form of this measure is not unusual. The ST AI-SF a six-item Short Form was developed and used in a prior study related to genetic screening (Marteau & Bekker 1992). A regression equation was developed whjch converts the short form scores into scores compatible with STAI norms to allow for comparisons with normative data provided in the STAI Manual. Data compiled by Carmichael et al. (2000) provides additional comparisons to sirrular samples including mothers learning of their child's

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59 increased risk status as a result of ICA testing and pregnant women undergoing amniocentesis. An additional question, adapted from Hendrieckx ( 2001) was asked to assess how often mothers worry about their children's risk. This question was stated as how often do you worry about your child's risk for developing diabetes?" and rated on a 5-point Likert scale ranging from Oto 4 anchored by "never" and "very often". A composite score was derived by converting both scores into z-scores and calculating the mean z-score of the two items (g = 0.80 ) (Table 4-8 ) Coping The Ways of Coping Checklist-Revised (WCC-R) (Folkman & Lazarus, 1980) is a 69-item dichotic (yes/no) questionnaire used to assess the use of coping strategies and preferred coping style. In the PANDA Part III study, the WCC-R was administered at the 4 follow-up interviews to assess maternal coping regarding their infant's genetic risk of developing Type 1 diabetes (n = 178). This measure has also been used in similar risk screening studies (Johnson & Tercyak 1995 ; Johnson & Carmichael, 2000). Factor scores were calculated using Vitaliano et al. (1985) factor structure which uses 42 items. The five factors included the following coping styles/strategies: Problem-focused Coping, Seeking Social Support Wishful Thinking Self Blame, and Avoidance The WCC-R was not administrated in the current interview. However subscale scores obtained at the second ( 4 month) interview were used as predictor variables To be able to compare across factors having a varying number of items, mean scores were calculated for each subscale as well as for the total measure.

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60 For this sample reliabilities for the subscales Wishful Thinking (g = 0 .70), Seeks Social Support (g = 0.73) and Problem-Focused coping (g = 0.81 ) were satisfactory. However reliabilities for the Avoidance (g = 0.36 ) and Self-Blame (g_ = 0.53 ) subscales were poorer and did not meet criteria for further analyses (g_ < 0.60) (Table 4-9). The reliability scores of the factors were consistent with previously published stud i es of similar populations (Johnson & Carmichael, 2000). Correlations between variables were significant, particularly between the total coping score and Problem Focused coping, Seeks Social Support and Wishful Thinking ( Table 4-9). Mean factor scores were similar to previous studies. Seeks social support was the most favored used coping style, followed by Problem-Focused coping. Self-blame was the least used coping style (Table 4-10). Information seeking A self-report measure of information-seeking was given to assess part ic ipants sources of information regarding diabetes risk and/or behavior change. Questions w ere designed to assess if participants consulted with their physicians family members of friends, including those who may have diabetes themsel ves. Follow-up questions were asked to determine if participants were given specific advice from these sources and if they followed the advice. Additional questions assessed behaviors such as searching the internet, consulting written materials about diabetes, or watching diabetes-related television news stories This measure was scored as a continuous variable by calculating the number of information sources reported and as a dichotomous variable denotin g whether any information seeking occurred (1 = 'Yes' and 0 = 'No') (Table 4-11).

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61 Questions regarding the nature of the relationship and the content of the advice were used as descriptive data. Participation in PANDA Part II Data on Part II participation was available through the PANDA computerized database. Continuation in the PANDA Part II study was coded as two different variables, (1) number of blood draws and (2) at least one blood draw (1 = participated' ) versus no blood draws (0 = did not participate '). For more details please refer to earlier sect i on entitled Reliability of self-reported behavioral change ". Statistical Analyses Data analyses were conducted using SPSS 11.0. Internal reliability of pred ictor and outcome scores were calculated using Cronbach alpha and onl y constructs with alphas greater than 0.60 were retained for regression analyses. Additionally components of risk perception and anxiety composite scores were transformed i nto z-scores because they were measured on different numerical scales. Consequently these composite scores reflect a z-transformation as well. Descriptive statistics were conducted, including ANOV A t-tests and chi-square analyses, to compare demographic variables between participants and non-participants. When expected cell size was< 5, Fisher's Exact test statistic was used instead of chi-square. Hierarchical lo gis tic regressions were used to predict to behavior change, as well as linear regression to predict to the c ontinuou s outcome measure of anxiety. For further details refer to Chapter 5 (Results).

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62 Initial genetic screening of newborn for diabetes risk @ birth I Scripted notification of results to mother by telephone** -12 weeks after birth I Initial structured telephone interview with mother of at-risk infant to assess maternal distress -4 weeks after notification I Second structured telephone interview with mother of at-risk infant to assess maternal distress -4 months after notification I Opportunity to participate in PANDA Part II Blood Draw when infant is at least 6 months old I Third structured telephone interview with mother of at-risk infant to assess maternal distress-12 months after notification I CURRENT STUDY Fourth structured telephone interview to assess maternal behavior change -4 years post-notification PART I: PANDA3 STAFF PART III: PPRbSTAFF PART II: PANDA STAFF PART III: PPR STAFF CURRENT: PPR STAFF Figure 4-1. Procedural outline of PANDA study. PANDA= Prospective Assessment o f Newborn Diabetes Autoimmunity. PPR= Pediatric Psychology Research Lab.

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60 50 40 30 20 ..... 10 C ::::, 0 (.) 0 63 .00 2 .00 4 .00 6 .00 8.00 13.00 1.00 3.00 5.00 7 .00 10.00 15.00 total behavior score Figure 4-2. Frequency distribution of total behavior score (n = 192)

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64 Table 4-1. Diabetes genetic risk factors With first Without first DR/DQ degree degree % General % Type 1 Alleles/ genotypes relative relative population patients DR 3/4, DR 4/4 20-25/100 5/100 5% >50% DQ 0201/0300 Extremely High risk DQ 0300/0300 high risk DR 3/4 DR 4/X a 10/100 2/100 10 % 30-40 % DQ 0201/0201 High risk Moderate DQ 0300/Xb risk DR 3/4 or XIX 1/125 1/600 85 % 10 % DQXIX Intermediate Very low risk risk DR 3/4 or XIX 1/15 000 1/15,000 DQ 0602 Protective Protective Xis a non-defined allele 75 % of the time X= DR 4 or DQ 0301. X allele is not 0602

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65 Table 4-2. Maternal characteristics of current sample (n = 192) Variable Maternal age at notification Current maternal age Race Caucasian African American Hispanic Asian/other Mothers level of education High school or less Some college/trade school College degree or beyond Marital status (married) Annual income (in $10 ,000 intervals) Number of children Number of previous interviews 1 2 3 Number of blood draws (Part II) 0 1 2 3 Note: Data are n ( % ) and means SD. 30.49 5.36 33.67 5 38 162 (85 % ) 6 (3%) 13 (6 % ) 10 (5 % ) 45 (23 % ) 62 (32 % ) 85 (44 % ) 164 (85 % ) 4 .97 2 .50 2.09 1.11 2.79 0.51 9 (5 % ) 23 (12 % ) 160 (83%) 1.19 1.17 74 (39 % ) 47 (25 % ) 29 (15 % ) 41 (21 % )

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66 Table 4-3. Child characteristics of current sample (n = 192) Variable Infant risk c l assification Moderate (2/100) High (1/10) Very high ( 1/5) Child age at notification (mo.) Current child age (years) Child sex (Male) O nly child (Yes) Family history No family history Third degree relative Second degree relative First degree relative Note : Data are n ( % ) and means SD. 108 (56 % ) 71 ( 37 %) 13 ( 7 %) 7.85 6.24 4.25 0.89 97 (51 %) 62 (33%) 50 ( 26 %) 98 (51 %) 82 ( 43 %) 37 ( 19 %)

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67 Table 4-4. Intercorrelations and coefficient alphas for domain scores of reported behaviors Domain 1 2 3 4 5 6 7 1 Health surveillance 0. 3 7 2 Diet 0.44** 0. 58 3. Physical activity 0 27** 0.32** 0 5 4 4. Illness prevention 0.35** 0.24** 0 36** 0.7 2 5 Medications o.2s* o.2s* 0.12 0.36** 0 5 4 5. Stress 0.16 0.ls* 0.10 o.2s* 0.22** 0 .47 6 Total 0 74** 0 7 9** o.5s** 0.65** 0.47** 0.35** 0. 7 7 Note: Coefficient alphas are presented in boldface along the diagonal. p < 0 05 p < 0.01.

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68 Table 4-5 Mean domain scores of reported behavior changes for total sample Domain # Items Range M SD Health surveillance 4 0 3 0.85 0.86 Diet 16 0-6 0.69 1.15 Physical activity 4 0-3 0.21 0.57 Illness prevention 8 0-5 0 .18 0.69 Medications 5 0-3 0.04 0.27 Stress 4 0-2 0.04 0.24 Total 41 0-15 2.00 2.53 # of domains 6 1-6 1.22 1.20

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69 Table 4-6 Mean values or frequencies for perceived control scales Item I can do something3 2.95 1.18 Strongly disagree 28 (15 %) Somewhat disagree 39 (20%) Neutral 53 (28%) Somewhat agree 58 (30%) Strong! y agree 14 (7%) Doctors can do something a 2.63 1.11 Strongly disagree 28 (15%) Somewhat disagree 74 (39%) Neutral 38 (20 %) Somewhat agree 47 (23%) Strongly agree 8 (4 % ) It is up to chancea b 3 26 1.11 Strongly disagree 15 (8%) Somewhat disagree 37 (19 %) Neutral 39 (20%) Somewhat agree 83 (24%) Strongly agree 17 (9%) Composite score (z-score) 2.79 0.99 Note: Data are n (%) and means SD a Scored as follows: Strongl y Disagree= '1 ', Disagree= '2', Neutral= '3', Agree= '4', Strongl y Agree= '5'. b Variable not used in composite score

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70 Table 4-7. Mean v alue s or fr equencies for perceived risk s cales Item Relative risk a Much less Somewhat less About the same Somewhat higher Much higher Belief about when child may develop diabetes b Never Unsure Yes but not for a long time from now Yes in the near future Risk estimation c Overestimate Accurate Underestimate Don t know/don t remember Risk composite (z-score) 3.46 + 1.05 13 (7% ) 15 (8 % ) 61 (32 % ) 74 (39 % ) 27 (14 % ) 1.88 + 0.68 53 (28 %) 113 (59 % ) 22 (12 % ) 4 (2 % ) 12 (6 % ) 76 (40 % ) 80 (42 % ) 24 (13 % ) -.004 0.74 Note: D a ta are n (% ) and mean s SD. a Scored a s fo1lows: Much less = 1', Somewhat le ss= '2', About the s a me= 3', Somewhat higher= '4', Much higher= '5 b Scored as follows: N e v e r = O Unsure= 2', Yes but not for a long time from now = '3', Yes in the near future= 4 c Overestimate= 3', Accurate= '2', Underestimate= 1', Don t know/don t remember= 'O' but value not included in analyses

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71 Table 4 -8. Mean v alues or frequencies for anxiety/worry scale Item Worr/ Never Rarely Sometimes Often Always Anxiety (10-item STAI/ Anxiety composite ( z-score) 1.00 1.02 73 (38 % ) 66 (34 % ) 39 (20 % ) 8 (4 % ) 6 (3 % ) 30.79 9.66 0 0 .91 Note : Data are n ( % ) and me a ns + SD. a Scored as follows: Never= O', Rarely= '1', Sometimes= 2', Often= 3', Always= '4' b Predicted full scale score based on 10 item measure.

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72 Table 4-9 Intercorrelations and coefficient alphas for coping variables Domain 1 2 3 4 5 6 1. Problem focused 0.81 2. Seeks social support 0.64*** 0.73 3. Avoidance 0 .15 -0.02 0.36 4. Wishful thinking 0_53*** 0.31 *** 0.37*** 0.70 5. Self-blame 0 .22** 0.12 0 .33*** o.2s*** 0.53 6 Total score 0.84** 0.7s*** 0.37 0.74*** 0.44*** Note: Coefficient alphas are presented in boldface along the diagonal. 0.001. 0.86 7 p<

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73 Table 4-10. Mean scores for Ways of Coping-Revised (WCC-R) scales Construct3 # Items Problem focused 15 0.48 0.24 Seeks social support 6 0.54 0.31 Avoidance 6 0.13 0.11 Wishful thinking 10 0.25 0 23 Self-blame 3 0 03 0.13 Total score 42 0.29 0.14 Note: Data are means SD a Scored as the mean of the items in each subscale Each item i n subscale is scored as '0' = No, 'l' = Yes.

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74 Table 4-11. Mean values or frequencies for information seeking scale Item Any information sourcea Literature Doctor Family/friend Television Internet # of information sources 115 (60%) 63 (33) 46 (24) 33 (17) 27 (14) 22 (12) 0 99 1 .04 Note: Data are n (%) and means SD. 3Scored as Yes= '1 ',No= 'O'

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CHAPTER 5 RESULTS Sample Characteristics Compared to mothers who were eligible but did not complete the current interview (n = 176), participants in this current study (n = 192) were significantly more likely to be married (Q < 0.001) and older at time of notification (Q < 0.01) and the current interview (Q < 0.001) (for those who were not contacted age was estimated based on end date of data collection 4/1/03) (Table 5-1). Additionally they had higher levels of education (Q < 0 01 ) and annual family income (Q < 0.001). There were no differences between the two groups in terms of ethnicity. Overall, these results suggest the current sample was a highly select sample of mothers who were more economically stable and possessed more personal resources than mothers in the original larger sample It is important to consider the sample bias in interpreting results of this study, as the behaviors of these mothers may not be reflective of the general population. For moth ers who paiticipated in the current interview 85% completed all three previous PANDA Part ill interviews versus 57% of eligible non-participating mothers (i (1, N = 368) = 35.12 Q < 0.001). Participating mothers had a higher number of completed interviews (Q < 0.001). Participation rates differed significantly for the lon g itudinal component of the PANDA study (Part II) which involves periodic autoantibody screening. Sixty one percent of current study participants and 49% of non participants completed at least one autoantibody screening (i (1, N = 368) = 5.02 Q < 0 05). Participating mothers had a higher number of blood draws (Q < 0.001). These 75

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76 participation rates are less than rates reported in Finland where approximately 80 % of infants who were genetically screened joined their antibody surveillance study (Kupila et al. 2001). As assessed in the initial interview there was no significant difference in anxiety scores as measured by the state ST AI between participating and non-participating mothers (Table 5-1 ) There was no significant difference between the two groups of mothers in their perceived likelihood that their child would develop diabetes in the future However at the time of the initi a l interview, mothers who participated in the current interview reported greater accuracy in estimating their child's risk status than mothers who did not participate (n < 0 01) and fewer mothers underestimated their child s risk (n < 0.05 ) (Table 5-1). There were no differences between children of participants versus non-participants in regards to age genetic risk status sex only child status, or family history (Table 5-2). Objective 1 Hypothesis 1.1 At the outset of the study it was hypothesized that reported behavior changes endorsed would most likely correspond to recommendations for the treatment of diabetes (ADA 2001) and the prevention of type 2 diabetes (ADA 2002a 2002b; Pierce et al. 1995), including chang e s in diet and physical activity patterns The que s tionnaire's de s ign permitted the use of both open and closed ended questions within each b e havioral domain. Descriptive analyses of each behavioral construct both as d i chotomous and continuous variables were conducted including frequencies mean s, st a ndard d e viations and correlations. Qualitative data from other

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77 open-ended questions addressing advice received and perceived control were also coded as descriptive data. Open-Ended Questions The initial open-ended item assessing behavioral change simply asked whether mothers did anything special to reduce their child's risk of developing type 1 diabetes (yes or no). Fifty-five mothers (29 % ) responded that they had done something preventative Open-ended questions were also asked at the beginning of each o f the six domains and again at the end of the interview to assess maternal recall of behav i or changes. At least one spontaneous behavior was reported in response to doma i n spe c ific open ended questions by sixty nine mothers (39 % ), somewhat more than were identified through the initial broad question yielding a total of 118 spontaneous responses ( Table 53). Of these 51 mothers i ndicated making a change in their child s diet and/or exercise (74 % ), corresponding with recommendat i ons to prevent and/or treat type 2 diabetes (Table 5-4). One domain medications, yielded no spontaneous responses and stress only yielded one response. Sixty three percent of the responses to the open-ended questions were later addressed in forced choice items asked subsequently in each domain. To further examine the hypothesis that mothers were following recommendations for the prevention of type 2 diabetes responses to open-ended questions regarding advice received and maternal beliefs were analyzed to determine if mothers reported actions were based on a premise that a healthy lifestyle is an effective prevention method fo r type 1 diabetes. An open ended question assessing maternal beliefs about what they could do to prevent their child from developing type 1 diabetes was asked of mothers who agreed or strongly agreed that they could do something to prevent their child from developing type 1 diabetes (Table 5-5). Seventy two mothers (38 % ) reported believing they could do

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78 something preventative, with 108 responses generated. Of these 61 mothers reported dietary and/or exercise changes (92 % ) Additionally 46 (24 % ) mothers reported receiving advice from a medical professional generating 63 pieces of advice, and 33 (20 % ) mothers reported receiving advice from family or friends generating 39 pieces of advice (Table 5-6). Ninety percent of mothers reported following advice from a medical professional and 95 % reported following advice from family members/friends. Of the advice received from medical professionals 43 % suggested making healthier dietary and physical activity changes. Of the advice received from family members or friends, 28 % of advice from family suggested healthy lifestyle changes in diet and exercise. Forced Choice Questions Forced choice items were asked with yes or no responses to assess maternal recognition of reported behavior changes These items were used to assess specific behaviors and were expected to yield more positive responses than the use of open-ended questions. Results based on the forced choice items within each domain, indicated that out of 192 mothers 129 (67 % ) reported changing at least one behavior in an attempt to prevent diabetes from developing in their at risk child (M =2 .00, SD = 2.53). Domain scores were calculated for each of the six possible categories of behavior determined a priori. Of those who reported at least one behavior change 30% reported two to three changes 24% reported four to six changes and 8 % reported changing more than six behaviors (M = 2.98 SD = 2 57) (Table 5 7) Changes in health surveillance behaviors were most frequently endorsed (59 % ) including blood glucose monitoring and watching for signs of diab e tes developm e nt. Chang es in child s diet (34 % ) were the next most commonly

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79 reported followed by changes in physical activity (14 % ) illness prevention (9 % ) medications (3% ) and stress (3 % ) (Table 5-8). The item most frequently endorsed~ 10 % ) was checking for specific signs of type 1 diabetes (50.5%). An open-ended follow up question asked mothers to specify the nature of the symptoms they look for in their children. Ninety seven mothers reported they look for signs of diabetes in their at risk child each responding with approximately two signs each (M = 2.16). Seventy rune percent of mothers reported at least one COlTect diagnostic criterion type 1 diabetes (i.e., polyuria polydipsia weight loss and increased appetite) 32% identified behaviors that were not indicative of diagnosis, but were related to diabetes symptomatology (i.e. signs of hyperglycemia or diabetic ketoacidosis) and 45 % identified signs that were not related to diabetes (Table 5 9) Only 5 % of mothers did not identify one correct or related symptom of type 1 diabetes. Of those who reported an accurate symptom 38% also listed inaccurate symptoms. Additionally testing the child s blood glucose level either at home or at a physician's office feeding the child less soda juice and other sweet foods, and encouraging the child to exercise more often were the next most commonly endorsed behavior changes. Items that might indicate maternal overprotectiveness or items suggesting unwarranted use of medications were rarely endorsed. Reported behavior changes ranged across domains for those endorsing more than one behavior change with only 19% reporting changes within only one domain (M = 2.33 SD= 1.02) This suggests that mothers engaged in a wide variety of behavior changes In comparing forced choice item responses with responses to open-ended questions, results indicated that s ignificantly more mothers endorsed forced choice items rather than made spontaneous responses This suggests that mothers may either have had difficulty

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80 recalling behaviors that were not as salient with forced choice items serving as a recognition task to help refresh their memory. Or, perhaps there may have been a demand characteristics associated with presenting individual specific behaviors in yes/no format. According to forced choice items 67 % of mothers reported making at least one behavior change versus 36 % of mothers responding to open ended questions (Table 510) All mothers who spontaneously reported behavior changes also responded similarly to forced choice items, so there were no mothers who spontaneously reported behavior change who did not also report changes according to forced choice items. In comparing forced choice versus open-ended questions, the primary difference was that mothers were less likely to spontaneously report changes in health surveillance that were later identified through forced choice items. Mothers may not consider increased health surveillance as a way of actively preventing diabetes Reported behaviors specific to healthy lifestyle changes within diet and exercise domains consistent with recommendations for prevention and treatment of type 2 were coded and compared to address the hypothesis that behavior change would likely correspond with recommendations for prevention of type 2 diabetes. Overall, based on responses to open-ended questions 51 mothers (27 % ) reported making at least one such behavior change (Table 5-4) and 59 mothers (31 % ) indicated a similar behavior change via responses to forced choice items (Table 5-8). Behaviors related to recommendations for prevention of type 2 diabetes were more prevalent among open ended responses than among forced choice responses in which health surveillance changes and overall dietary changes in general were more frequently reported.

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81 Objective 2 Exploratory model testing was conducted through the use of logistic regression analyses predicting whether mothers reported behavior change As stated previously, due to the non-normal distribution of reported behavior scores the outcome measure of behavior was examined dichotomously, comparing mothers who reported at least one behavior change (1 = 1 behavior change) versus mothers who reported none (0 = no behavior change) Regressions were also conducted to predict whether a behavior change was spontaneously reported in response to open ended questions ; however, results were nearly identical to using the forced choice items and therefore, quantitative analyses based on responses to open ended questions were reported. In each regression model predictor variables were entered in blocks according to hypothesized relationships from prior literature. Each block of variables was added successively. When each block was added to the model only variables that were significant at 12< 0 10 were retained. For these analyses several variables were recoded for ease of interpretation. Due to the sample s unbalanced distribution by maternal race, minority ethnic groups were collapsed into one group and maternal ethnicity was categorized as Caucasian (1) and "not Caucasian (0). Maternal marital status was coded as 1 for married and "O" for single separated widowed or divorced. Child s sex was coded as 1 male and 2 female Only child status coded as 1 yes" and O "no". The first block of variables entered into the regression model contained one variable time elapsed between notification and current interview to control for effects of time. The second block of variables contained maternal demographic variables including maternal education level ethnicity marital status number of children, and age at the time of the interview. The third block entered contained child demographic

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82 variables including child's sex, whether an only child, and age at the time of interview. Family history of diabetes was also included in this block, using two dichotomous variables: (1) the presence of a first-degree relative with diabetes (yes/no) or (2) the presence of a second or higher degreed relative (yes/no). The fourth block of variables contained the hypothesized predictor variable. Predictor variables consisted of standardized composite scores on measures of perceived control, risk perception and anxiety, as well as total scores on measures of coping and information seeking. Participation in PANDA Part II study was also used as dichotomous predictor variable (yes/no). Reliability analyses were conducted on composite scores suggesting that internal consistency was fair for these variables (Table 4 6). Each of the following hypotheses was examined separately. Within each model, main effects were examined as well as interactions between perceived control and other predictors, where noted Only significant predictors were retained. Ultimately, a final model was produced from these separate models to account for the highest classification rate in the behavioral outcome variable. To account for type 1 error a more conservative level of significance was chosen at Q < 0.01 and this is noted where appropriate. Hypothesis 2.1 It was hypothesized that mothers who perceived they have control over their child developing diabetes would be more likely to report engaging in behavior changes. Based on statements regarding perceived control, which required an agree/disagree response, 38 % percent of mothers reported believing they could do something and 27 % believed doctors could do something to prevent their child from developing type 1 diabetes. Meanwhile 52 % reported believing it was up to chance or fate whether their child develops type 1 diabetes (Table 4-6)

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83 Hierarchical logistic regression analyses were conducted using the composite score for perceived control (belief that mother could do something or medical professional could do something) to predict whether any behavior change was reported (yes/no) when controlling for demographic factors (Table 5-11). Results indicated that mothers whose children had a first degree relative with diabetes were significantly more likely to engage in behavior change ( odds ratio= 24.22 12 < 0.001) and maternal perceived control was not a significant predictor of behavior change, resulting in an overall model that accounted for 67 5 % overall correct classification. Hypothesis 2.2 It was hypothesized that mothers who perceived their children to be increased risk for type 1 diabetes would be more likely to report engaging in behavior change. Hierarchical logistic regression was conducted as described previously ; however both actual risk and the risk composite score were entered as the final block in the logistic regression model. Results indicated that again, the presence of a first degree relative was a significant predictor of behavior change (odds ratio= 18.98, 12 < 0 01). The child s actual risk was found not to be significant. When controlling for actual risk perceived risk was a significant predictor (2.32, 12< 0.01) (Table 5-12) Mothers who perceived their children to be at greater risk were more likely to en g a g e in behavior change This mode l resulted in an overall classification rate of 68.4 % An interaction between perceived control and perceived risk was also tested and was not significant. Hypothesis 2.3 It was hypothesized that mothers who were more anxious would be more likely to report engaging in behavior change. This hypothesis was tested by entering the anxiety composite score as the final block in the lo g istic regression model. Results indicated that

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84 anxiety, as measured at the initial interview following notification, was not a significant predictor of subsequent behavior change and was not retained in the final model. However, mothers who were more anxious at the time of the current interview were more likely to report behavior change (odds ratio= 2.98, 12< 0 001). This model resulted in a correct classification rate of 72.9 % (Table 5-13). Follow-up analyses were conducted to determine if there was an interaction between anxiety and perceived control; however, none was found. Results demonstrated that while anxiety remained a significant predictor there was no main effect of perceived control nor was the interaction term significant, suggesting that mothers who were more anxious were more likely to report behavior change to prevent diabetes in their child regardless of their level of perceived control over the situation Hypothesis 2.4 It was hypothesized that mothers who used more coping strategies particularly active coping (i.e. problem-focused, seeking social support), would be more likely to report engagin g in behavioral change. Data were available for 176 mothers who completed the Ways of Coping Checklist Revised (WCC R) at the 4-month interview In separate logistic regression models each coping scale score was entered as the final block of variables. Results indicated that after controlling for the significant effect of the presence of a fir s t degr e e relative problem-focused coping (odds ratio= 10 72, 12< 0.01) seeking soc i al support (odds ratio= 4 99 12< 0.01) and wishful thinking (odds ratio= 14.48 12< 0 01) were significant predictors of behavior change (Tables 5-14 5-15 and 516). While the two active coping factors were significantly related to behavior change, a more passive copin g s tyle wishful thinking, was also significant and to a relatively

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85 higher degree. Item analysis of the wishful thinking scale indicated that this scale included items related to optimistic thinking but also a desire for the problem to "go away" or "be over with." It may be that wishful thinking reflects a sense of optimism and urgency that might be associated with engaging in preventative actions believed by mothers to be efficacious. Additionally, total coping as measured by the mean of all reported coping behaviors was a significant predictor of reported behavior change (odds ratio= 160.06, Q< 0.001) (Table 5-17). Hypothesis 2.5 It was hypothesized that mothers who engaged in information seeking and/or were given recommendations by medical professionals or other family members related to behavior change, would be more likely to report engaging in behavior change. Overall, 60 % reported receiving information from at least one source, and the mean number of sources was 0.99 (SD= 1.04) Overall 33 % reported receiving diabetes -s pecific information from a book or other literature, 14% reported watching diabetes-related television programming, and 1 2% reported seeking information using the internet (Table 4-11). As stated previously Fifty nine percent of mothers reported talking to their physician about their child's genetic risk screening results Of those, 41 % reported receiving advice from their physician with over 89 % reportedly taking their physician's advice. When specifically asked in an open-ended question about the nature of the guidance given, mothers specified a wide range of advice (Table 5 6). The most frequent advice given was to monitor their child and promote a healthy lifestyle. Additionally six mothers were told to continue with PANDA study and five mothers were told by their physicians not to worry about their child's risk.

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86 Advice from family friends was similarly assessed Eighty-six percent reported talking to a family member or friend. Seventy percent reported talking with their spouse about their child's genetic screening results 63 % reported talking with the child's grandparent 13% reported taking to a family member of friend who has diabetes and 32 % reported talking with a family member or friend who does not have diabetes. Seventeen percent of mothers reported receiving advice from at least one family member/friend Typically advice was given by a child s grandparent (62 % ), followed by spouse (15 % ) and friend or family member who does not have diabetes (15 % ) then friend or family member who does have diabetes (5%). When specifically asked in open ended questions about the advice that was given, most frequent advice was to help child maintain a healthy diet and five mothers were told not to worry (Table 57) Logistic regression was used to determine if the number of information sources predicted the likelihood of engaging in behavior change. The number of information sources was entered as the last block of predictor variables in a logistic regression model (Table 5-18) When controlling for the presence of a first degree relative (odds ratio= 26.31 Q < 0.01) those with more sources of diabetes-specific information were significantly more likely to report engaging in behavior change (odds ratio= 2.27 Q< 0.001) The presence of a first degree relative combined with the degree of information sources together resulted in an overall classification rate of 74 3 % Follow-up logistic regres s ion analyses were conducted to determine if an interaction was present between perceived control and the number of information sources ; none wa s found.

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87 Hypothesis 2.6 It was hypothesized that mothers who continued their participation in the Prospective Assessment of Newborn Diabetes Autoimmunity (PANDA) study by participating in periodic blood testing for antibodies would be more likely to report behavior changes Overall 61 % of mothers participated in at least one subsequent blood draw. Twenty-six percent participated in two or more. However, when asked in the interview, 174 mothers reported accurately whether they participated in part II of the study (91 % ), and 3 reported participating when they actually have not (1 % ) and 15 reporting they had not participated when they actually had (8 % ). Surprisingly in the logistic regression model, registry participation using either number of blood draws or continued participation (yes/no) did not predict to reported behavior change (Table 5-19a, b) Mothers who continued with the PANDA study were no more likely to report engaging in preventative efforts despite their already active participation in health surveillance. Summary Model Logistic regression was conducted to determine which of the previously listed variables were most predictive of behavior change. As in previous analyses, family history characterized by presence of a first degree relative was entered as the first block of variables as it had been found to be consistently sigruficant in all previous models. Actual risk was entered next in the model followed by all six variables also found to be significant at the 0.01 level in previous models (i.e., perceived risk, anxiety number of information sources, problem focused copin g, seeking social support, and wishful thinking) were subs equently entered simultaneously Problem focused coping and

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88 seeking social support were dropped from the resulting model, as they were not significant. The final logistic regression model showed the presence of a first degree relative was once again a significant predictor of behavior change (odds ratio= 19.34 n = 0.01). Number of information sources anxiety perceived risk and wishful thinking were also significant predictors (Table 5-20). Overall, the model's classification rate was 77.7 % Objective 3 It was hypothesized that mothers who reported modifying behaviors would show a greater reduction in anxiety over time than mothers who did not report engaging in behavior change. To examine this hypothesis, hierarchical linear regression was conducted similarly to logistic regression procedures described for Objective 2 except the dependent variable was the anxiety composite score, a continuous variable. Anxiety at the initial interview was entered as the first block of variables followed by same ordering of blocks of variables of demographic variables described previously. Reported behavior change and the composite score for perceived control were entered as the final (fourth) block of predictor variables to determine if behavior contributed significantly to anxiety at the final follow up interview above and beyond the effect of initial anxiety and demographic predictors. In a follow-up model the interaction term between behavior change and perceived control was added as the fifth block of predictors. Results indicated that initial anxiety was a significant predictor of anxiety at the current interview(]= 0.42, n< 0.001) accounting for 22 % of the variance (Table 5 21). Current age of the child(]= 0.18 n< 0.01) along with the presence of a first degree relative(]= 0 29 n< 0.001) and the presence of a second or higher degree relative(]= 0.18 n< 0.01) together accounted for an additional 13% of the variance (n < 0.001.

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89 Reported behavior change ill= 0.24, n< 0.001) and perceived control ill= 0.20, n< 0.01) were entered as the final block and both were found to be significant predictors, accounting for an additional 9% of variance (Q < 0.001). Overall, the model accounted for 43% of the total variance. In the follow-up model, the interaction term was added and not found to be significant. Results indicated that initial anxiety was the primary predictor of anxiety at the time of the current interview However, above and beyond initial anxiety, mothers whose children were younger and had a relative with diabetes were more anxious at the time of the current interview. Mothers who reported at least one behavior change were significantly more anxious at both post-notification and current interviews (as measured by the STAI only) than mothers who reported no behavior changes (initial interview: M = 42.75, SD= 14.54 versus M = 36.80, SD= 12.54, t(l, 189) = 2.76, n < 0.01) (current interview M = 32.57, SD= 10.28 versus M = 27 14, SD= 7.02, t(l, 190) = 4.29 n < 0.001). Contrary to the original hypothesis, when controlling for demographics and initial anxiety mothers who reported at least one behavior change and who perceived greater control over the onset of diabetes in their children were more anxious at the time of current interview than mothers who did not. This suggests behavior change maintains, rather than reduces anxiety over time for these mothers. Objective 4 Questions regarding behavior change used in the current interview were developed from the DPT-1 survey (Johnson 2002) and therefore dichotomous scoring for some of the questions in the current interview were comparable. The database from the current study was merged with the maternal report data from the DPT-I study. Only data collected from mothers who were not aware of the study results were included (!l =

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90 116). Of these mothers, 63 (53 % ) had children who participated in the control group of the study and 53 (47 % ) had children enrolled in the experimental arm. Children whose mothers completed the DPT-1 survey were significantly older than children in the PANDA sample ranging in age from 5 to 19 years old (M = 12.18, SD= 3.24) (t(2, 306) = 32.02, R < 0.001). Reported maternal behavioral data from these two populations were compared on 17 overlapping variables. Analyses were conducted across the con-esponding individual questions and similar domain scores. We hypothesized mothers of genetically at risk children would be less likely to report behavior change than mothers of ICA+ children enrolled in Diabetes Prevention Trial-1 (DPT-1) (n = 116). On questionnaire items that were shared by both studies, 43.2 % of mothers whose children were in the DPT-1 study and 33.3 % of mothers in the cun-ent sample reported at least one behavior change (Table 5-22). However, this difference was not significant CR = 0.08). Mothers in the two samples reported similar proportions of behavior change in the domains of diet and exercise; however, medications differed by groups with mother from the DPT-1 sample reporting greater use of medications/supplements There were few significant differences between the two samples of mothers on specific items Mothers in the DPT-1 sample were nearly four times more likely to report feeding their children more diet and sugar free drinks CR< 0.001), and more often reported feeding their children less regular soda CR< 0.05) whereas mothers in the cun-ent sample more often reported feeding their children less juice CR< 0.05) Administering vitamins CR< 0 05) and administering insulin at home CR< 0.05) were practices that were significantly more common in the DPT-1 sample. This is not surprising given that 53 of

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91 the mothers had children who were in the experimental arm of the study involving home insulin injections ( 46 % ) and the question involved giving "extra" insulin above and beyond study protocol. Out of the 5 mothers who reported giving their child insulin, 4 (80 % ) were mothers of children enrolled in the experimental group.

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92 Table 5-1. Comparisons of maternal demographic variables between participants in current sample versus those eligible who were unable to be contacted or declined earticieation (N= 368) Unable to Completers contact/declined Total (n = 192) (n = 176) (n = 368) F (1,434} or 2 Maternal age at notification 33.49 + 5.36 27.71+5.35 29 28+5.63 14.42*** Current maternal agea 33 67 + 5.38 31.21 + 5.33 32.41+5.44 12.18** Race 7.32 Caucasian 162 (84.9) 135 (76 7) 297 (80.9) African American 6 (3.1) 17 (9.7) 23 (6.3) Hispanic 13 (6.8) 17 (9 7) 30 (8. 2) Asian/Other 10 (5.2) 7 (4.0) 17 (4.6) Mothers level of education 11.72'* High school or less 45 (23 7) 50 (28.2) 95 (25 9) Some college/trade school 62 (32 .1) 74 (41.8) 137 (36.8) College degree or beyond 85 (44.2) 53 (29 9) 138 (37.3) Marital Status (married) 164 (85.4) 112 (64.4) 276 (75.5) 18.71 *** Annual income 4.97+2.50 3.82 + 5.30 4.43 + 2.40 20.22*** (in $10 000 intervals) Number of Children 2 09 + 1.11 2.03 + 1.26 2.06+1.18 Number of previous 39 83*** interviews 1 9 (4 7) 36 (20 3) 45 (12 0) 2 23 (12.0) 41 (23.2) 64 (17.2) 3 160 (83.3) 100 (56 5) 260 (70.8) Number of blood draws (II) 44.03*** 0 74 (39.1) 90 (50.8) 164 (44.7) 1 47 (24 5) 59 (33 3) 106 (28.6) 2 29 (15.1) 20 (11.3) 49 (13.4) 3 41 (21.4) 8 (4.5) 49 (13.4) Anxiet/ 40.74+14 .13 39.83 + 14.29 40.30 + 14 19 0 70 Belief about when child may develop diabetesb 1.90 Never 4 3 ( 2 2.4) 34 (19 3) 77 (20 8)

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93 Table 5-1 Continued Unable to Completers contact/declined Total (n = 192) ( n = 176) (n = 368) F (1,434} or Uns u re 135 (70.3) 126 (71.6) 261 (71.0) In distant future 13 (6.8) 12 (6 8) 25 (6.8) Soon 1 (0 5) 4 (2 3) 5 (1.4) Risk estimationb O verest i mate 7 (3. 6) 7 (4 0) 14 (3.8) Accurate 155 (81.3) 117 (65.5) 272 (73.6) 11.12** Underestimate 20 (10 4) 28 (15 8) 48 (13 1) 3.64 Don't know/don t remember 9 (4.7) 26 (14.7) 35 (9 5) Note: Data are n (% ) and means+ S D. Compariso n s tested using chi-square or t-tests between completers and non completers Non-significant p val u es not reported. aEstimated from 4/1/03 (end date of data collection) b Scores from initial interview (3-5 weeks post-risk notification). n < 0.05. ** n< 0.01. ***n < 0 001.

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94 Table 52 Comparisons of child demographic variables between participants in current sample versus those eligible who were unable to be contacted or declined participation (N= 368) Infant risk classification Moderate (2/100) High (1/10) Very high (1/5) Child age at notification (mo. ) Current child age (yearsl Child sex (Male) Only child (Yes) Family history No family history Third degree relative Second-degree relative First-degree relati v e Completers ( n = 192) 108 (56.3) 71 (37.0) 13 (6.8) 7.85 + 6 24 4.25 + 0 89 97 (50.5) 62 (32.6) 50 (26.0) 98 (51.0) 82 (42.7) 37 (19.3) Unable to contact/ declined ( n = 176) 113 (63 8) 56 (31.6) 8 (4 5) 7.29 + 4 34 4.45 + 0 72 a 88 (50.9) 72 (41.4) 37 (20 9) 104 (58 8) 74 (41.8) 23 (13.0) Total F (1, 434) or.i._ (n = 368) 2.75 221 (60.2) 125 (34 1 ) 21 (5. 7) 7 59 + 5.43 3.35 4 .34 + 0 82 0.17 185 (50.4) 0.25 134 (36.5) 3 05 5.71 87 (23 7) 202 (55 0) 156 (42.5) 60 (16.3) Note: Data are n ( % ) and means+ SD Comparisons tested using chi square or t-tests between completers and non-completers. Non significant p values not reported a Estimated from 4/1/03 (end date of data collection). n < 0.05. ** n< .01. ***n < 0 001.

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95 Table 5-3. Mothers' responses to open-ended questions in each behavioral domain regarding behavioral change Responses Diet/eating patterns Healthier diet total Less sugar Ate healthy foods Decreased carbohydrates Monitored eating Ate more vegetables Limited juice Limited fast food/junk food Ate more protein Drank more water Ate smaller portions Taught good nutrition Ate a balanced diet Ate a low-fat diet Delayed milk Varied diet Physical activity Increased exercise Encouraged activity Kept active Played outside in yard Daily exercise Dance class Exercised as family Joined gymnastics team More walkin g More exercise Health surveillance Checked blood glucose Blood draw with PANDA Looked for symptoms Watched w e i g ht Blood draws Tested for k e tone s Took to specialist # of responses Addressed in questionnaire 53 20 12 5 3 3 3 2 l l 1 1 1 1 1 l 14 3 3 2 l 1 1 1 1 1 15 8 7 2 2 1 1

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Table 5-3 Continued Responses Checked at doctor s office Annual exams Illness prevention Protect child from the cold A voiding things allergic to Keep away if someone is sick Monitor allergies Prevent ear infection No daycare Stress Decreased child's stress Extra (not included in domains) 96 # of responses Addressed in questionnaire 1 1 2 1 1 1 1 1 1 Prayer 2 Note: No responses for the medications domain of behavior change

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97 Table 5-4 Mothers' responses to open-ended questions in each behavioral domain Responses N Type 2 diabetes recommendations 51 Diet/eating patterns 49 Health surveillance 30 Physical activity 13 Illness Prevention 8 Prayer (extra) 2 Stress 1 % of open ended responses (n= 69) 74 % 71% 43 % 19 % 12 % 3 % 1 % Note: No responses for the medications domain of behavior change % of total (n = 192) 27 % 26 % 16 % 7 % 4 % 1 % 0.5 %

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98 Table 5-5. Mothers' statements on what they believe they or health care professionals can do to prevent type 1 diabetes in their children Question What I believe I can do (n = 72) Balanced diet/Healthy eating Exercise/activity Limit sweets/sugar/carbohydrates Look for symptoms Don't know Teach healthy lifestyle Keep healthy Prayer Positive attitude/mental empowerment Breastfed PANDA study/Research Set example/model behavior Learn more Insulin early Give child omega 3 fatty acid Lower stress What I believe doctors can do (n = 51) Research Don't know Educate parents Monitor child's health Give medications PANDA study New technology Early testing Prevention Develop new medications Provide support Help in treatment Public awareness "Hoping they can " Nothing now, maybe later on" n 42 25 10 6 6 4 2 2 3 1 2 1 1 1 1 1 13 11 10 4 5 5 2 2 2 1 1 1 1 1 1

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99 Table 5-6 Advice received by mothers regarding their child's risk screening results n Medical professional (n = 46) Monitor 17 Healthy diet/eating habits 11 Exercise/activity 7 Continue with PANDA study 6 Don't worry 5 Limit sugar 5 Check blood glucose 4 Avoid milk 1 Changed vaccine schedule 1 Lower risk of illness 1 Flu short 1 Be careful for insurance purposes 1 Recheck it later 1 Nothing you can do 1 Watch weight 1 Family member or friend (n = 33) Healthy diet/eating habits 10 Don't worry 8 Monitor symptoms 5 Pray 4 Continue with PANDA study 3 Continue to check 2 Talk to doctor 2 Get more information 2 Watch child s weight 2 Exercise 1

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100 Table 5-7. Mean domain scores of reported behavior changes for those who m ade at least one behavior change Domain Health surveillance Diet Physical activity Illness pre v ention Medications Stress Total # of domains # Items 4 16 4 8 5 4 41 6 Range 0-3 0-6 0-3 0-5 0-3 0-2 1-15 1-6 M 1.26 1.02 0.31 0 27 0 .06 0 .05 2.98 2.33 SD 0 .77 1.28 067 0 83 0.32 0.29 2 57 1.02

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101 Table 5-8. Prevalence of reported behavior changes according to forced-choice items Yes Always/nearly Item n (%) Consistent8 everydal Healt h s u rveillance Watched for signs Tested child's blood glucose l eve l at doctor's office Tested child's blood glucose l evel at home Attended more frequent pediatrician visits Die t/ eating behaviors Fed child less sweet foods Fed child less soda Fed child less juice Fed more diet and sugar free drinks Increased duration of breast feeding Fed child more often Delayed introduction of cow's milk Tried to get child to lose weight Fed child less to eat Fed child more juice Fed more to eat Fed child less often Tried to get child to gain weight Changed timing of introduction to solid foods Avoided cow's milk altogether Physical activity Child exercise more often Encouraged child to be active everyday Encouraged child to rest more during exercise Encouraged child to exercise less often Illness prevention Worked harder to protect chi l d from germs Limited child's exposure to other kids Kept child out of daycare Had child wash hands more often Avoided child exposure to chemicals (i.e., polJution food additives) Delayed immunizations for child Avoided child's exposure to smoke Increased child's exposure to other children to boost immunity Medicati o ns Administered vitamins to child Administered diabetes medications to child 114 ( 59 ) 97 (51) 36 (19) 27 (14) 3 (2) 65 (34) 38 (20) 30 (16) 23 (12) 12 (6) 8 (4) 5 (3) 5 (3) 4 (2) 3 (2) 2 (1) 1 (1) 1 (1) 0 (0) 0 (0) 0 (0) 26 (14) 19 (10) 17 (9) 3 (2) 1 (1) 18 (9) 15 (8) 8 (4) 5 (3) 2 (1) 2 (1) 2 (1) 1 (1) 0 (0) 6 (3) 6 (3) 1 (1) 87 (90) 46 (48) 19 (53) n/a c 21 (78) 2 (7) 2 (67) n/a 34 (90) 28 (73) 29 (95) 23 (77) 22 (96) 20 (87) 9 (75) 8 (67) n/a n/a 5 (100) 5 (100) n/a n/a 3 (75) 2 (50) 2 (67) 3 (100) 2 (100) 2(100) 1 (100) 1 (100) 1 (100) 1 (100) C 19 (100) 15 (79) 15 (88) 13 (81) 2 (67) 2 (67) 0 (0) 1 (100) 14 (93) 12 (80) 7 (88) 4 (50) 4 (80) 5 (100) 2 (100) 2(100) 2 (100) 2(100) n/a n/a 1 (100) 1 (100) 5 (83) 5 (83) 0 (0) 0 (0)

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Table 5-8 Continued Item Administered insulin to child at home Administered nicotinamide Used herbal supplements Stress Had child rest more often Actively lowered child's stress level 102 Actively distracted child s focus during stressful situations A voided distressing situations for child Yes n ( % ) 1 (1) 0 (0) 0 (0) 5 (3) 3 (2) 2 (1) 1 (1) 1 (1) Always/nearly Consistent everyday 0 (0) 0(0 ) 2 (67) 2 (100 ) 1 (100) 1 (100) 2 (67) 2(100) 0 ( 0) 0(0) Note: Data reported in n ( % ). a Frequency of those who reported engaging in specific behavior since risk notification. b Freequency of those who reported engaging in specific behavior for "always/nearly everyday c Not applicable d Not calculated (n = 0).

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103 Table 5-9. Signs used by mothers to monitor diabetes symptoms in their children (n = 97) Response # of Accurate symptom Related symptom Thirst/Drinking more Urination Tired/fatigue Weight loss Weight gain Changes in child s behavior (general) Irritability Eating more H yperacti vi ty Craving sweets Headaches Bed wetting Sweating Shakiness Dizziness Blurred vision Seizures "List from doctor/don t remember Blood pressure Dry mouth Not eating Appetite change Vomiting Wounds not healing Skin rash Fever Cold symptom s Shortness of breath General sickness Poor circulation responses of type 1 diabetes of type 1 diabetes 59 47 21 12 6 6 6 6 6 5 4 4 3 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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104 Table 5-10. Prevalence of reported behavior changes as reported in responses to forced choice versus open-ended questions for those who reported at least one behavior change Responses Health surveillance Diet/eating patterns Type 2 recommendations Physical activity Illness Prevention Medications Stress Forced Choice (n = 129) Open ended (n = 69) 114 (88) 30 (43) 65 (50) 49 (71) 59 (46) 51 (74) 26 (20) 13 (19) 18 (14) 8 (12) n/a 2 (3) 6 (5) 0 (0) 5 (4) 1 (1) Note: Data reported as n ( % ) a Spontaneous response item is not included in domains of forced choice items

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105 Table 5-11. Logistic regression demonstrating relationship between perceived control and reported behavior change a Predictor variable 13, SE O dds ratio Wald statistic First degree relative Control composite 3 19 0.08 1.03 0.17 24.22 1.09 9 .6o** 0.24 Note : a Reported behavior change coded as At least one reported behavior change = l ', No reported behavior change = 0 . bCoded as 0 = No and 1 = Yes. ** Q< 0 01.

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106 Table 5-12. Logistic regression demonstrating relationship between perceived risk and reported behavior change a Predictor variable B SE Odds ratio Wald statistic First degree relativeb Actual risk c Risk composite 2.94 -0.35 0.84 1.07 0.35 0.27 18.98 0.70 2 32 7_53** 1.06 9_59** Note: 3Reported behavior change coded as At least one reported behavior change= 1 ', No reported behavior change= 'O' .. bCoded as O = No and 1 = Yes. c Coded as 4= moderate 5 = high, 6 = extremely high. ** p_< 0.01.

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107 Table 5-13. Logistic regression demonstrating relationship between anxiety and reported behavior changea Predictor variable ll SE Odds ratio Wald statistic First degree relativeb 2 70 Current anxiety composite 1.09 1.04 0 .28 14.92 2 98 6.72 15.54 Note: aReported behavior change coded as At least one reported behavior change= '1 ', b ** *** No reported behavior change= '0' .. Coded as Yes= '1' and No = '0'. Q< 0 01. Q < 0.001.

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108 Table 5-14. Logistic regression demonstrating relationship between problem focused coping and reported behavior change a Predictor variable ll SE Odds ratio Wald statistic First degree relativeb Problem focused 3.19 2.34 1.03 0.76 21.91 10.41 8.92** 9 .ss* Note: 3Reported behavior change coded as At least one reported behavior change = 'l No reported behavior ch ange= 0 .. bCoded as Yes= 'l' and No = 0 ** Q< 0.01.

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109 Table 5 15. Logistic regression demonstrating relationship between seeking social support and reported behavior change a Predictor variable ll SE Odds ratio Wald statistic First degree relativeb Seeks social support 3.01 1.61 1.03 0.56 20 26 4.99 8.5o s.2s** Note : 3Reported behavior change coded as At least one reported behavior change = 1 ', No reported behavior change= 0 . bCoded as Yes= 1 and No = 0 ** p< 0.01.

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110 Table 5-16. Logistic regression demonstrating relationship between wishful thinking and reported behavior changea Predictor variabl e fl SE Odds ratio Wald statistic First degree relativeb Wishful thinking 2.96 2.67 1.03 0 86 19 37 14.48 8.23** Note: a Reported behavior change coded as At least one reported behavior change= 1 ', No reported behavior change= '0' .. bCoded as Yes= 1 and No = '0'. ** p< 0.01.

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111 Table 5-17 Logistic regression demonstrating relationship between total coping score and reported behavior change a Predictor variable fl SE Odds ratio Wald statistic First degree relativeb Total coping score 3.00 5 08 1.03 1.38 20 04 160.06 8.40** 13.56*** Note : a Reported behavior change coded as At least one reported behavior change= 1 b ** *** No reported behavior change= 0 .. Coded as Yes=' 1' and No = '0'. p< 0 01. p< 0 001.

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112 Table 5-18 Logistic regression demonstrating relationship between number of information sources and reported behavior change a Predictor variable ll SE Odds ratio Wald statistic First degree relativeb 3 27 # of information sources 0 82 1.04 0.21 26 .21 2.27 9 .96** 16.00*** Note: aReported behavior change coded as At least one reported behavior change= 1 ', b ** *** No reported behavior change = '0'.. Coded as Yes = 1 and No = '0'. p< 0 01. p< 0.001.

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113 Table 5-19a. Logistic regression demonstrating relationship between continued participation in PANDA study (yes/no) and reported behavior change a Predictor variable A SE O dds ratio Wald statistic First degree relativeb 3.22 1.03 Attended at least one blood draw -0 12 0.33 24 95 1.89 9 .71 0.14 Note: aReported behavior change coded as At least one reported behavior change = l ', No reported behavior change= '0' . bCoded as Yes= '1 and No = '0'. n< 0 05. Table 5-19b. Logistic regression demonstrating relationship between continued participation in PANDA study(# of blood draws) and reported behavior chan e a Predictor variable A SE Odds ratio Wald statistic First degree relativeb # of blood draws 3.09 0.07 1.04 0 16 22.01 1.07 9 79* 0.21 Note: aReported behavior change coded as At least one reported behavior change = 1 ', No reported behavior change= '0' .. bCoded as Yes= 1' and No = '0'. n< 0 05.

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114 Table 5-20 Summary logistic regression model predicting to reported behavior changea Predictor variable J1 SE Odds ratio Wald statistic Stepl First degree relativeb Step 2 Actual risk Step 3 Risk composite Anxiety composite # of information sources Wishful thinking 2.96 -0 65 0.73 0.73 0.69 2.47 1.15 0.44 0.34 0.32 0.24 1.01 19.34 0.52 2.08 2.07 1.99 11.79 6.66** 2.16 4.67 5.14 8 02** 5.91 Note: a Reported behavior change coded as At least one reported behavior change= 1 No reported behavior change= '0' . bCoded as Yes= '1' and No = '0'. Q< 0 05 ** Q < 0.01.

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115 Table 5-21. Hierarchical linear regression model predicting to anxiety composite score Predictor variable B SE J2 R2 ~R2 Stepl Anxiety at initial interview 2.70e-02 Step2 Child age (c urrent) First degree relativea Second or higher degree relativea Step 3 Behavior changeb Control composite -0.19 0.68 0.34 0.46 0.18 4.00e-03 0.42*** 0 06 0.14 0 .11 0.12 0.05 -0.18** 0.29*** 0 18** 0.24*** 0 .20*** 0 .22''** 0_34*** 0.13*** 0.43*** 0.09*** Note: a Coded as 0= no_ and 1 = yes 6 Co~ed as At l ~ast one rep2.rted behavior change= 'l ', No reported behavior change= '0' .. Q< 0.05. Q< 0.01. Q< 0.001.

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116 Table 5-22. Prevalence of reported behavior changes among current study and Diabetes Prevention Trial -1 (DPT-1) studl'. earticieants Current DPT-1 Item (rr = 192) (rr = 116) x 2 (346) Any behavior change3 64(33 % ) 50 (43 % ) 2 96 Diet/eating patterns 59 (31 % ) 42 (36 % ) 0.99 Fed more diet and sugar free drinks 12 (6% ) 28 (24 % ) 20.48*** Fed child less soda 30 (16 % ) 30 (26 % ) 4.83* Fed child less juice 23 (12 % ) 5 (4 % ) 5.15 Fed chjld less sweet foods 38 (20 % ) 25 (22 % ) 0.14 Tried to get child to lose weight 4 (2 % ) 1 (1 % ) 0.68 Fed chlld less to eat 3 (2 % ) 0 (0 % ) 1.83 Fed chjld more juice 2 (1% ) 1 (1 % ) 0.05 Fed more to eat 1 (1 % ) 0 0.61 Tried to get child to gain weight 0 0 n/a Physical activity 20 (10 % ) 11 (10 % ) 0 07 Child exercised more often 19 (10 % ) 11 (10 % ) 0.01 Encouraged child to exercise less often 1 (0.5) 0 0 .61 Medications 6 (3% ) 15 (13 % ) 10.94** Admjnistered vitamjns to child 6 (3% ) 15 (10 % ) 6.4s* Admjnistered insulin to child at home 1 (0.5 % ) 5 (4 % ) 5.44 Used herbal supplements 0 2 (1 % ) 3.33 Admjnjstered nicotinamjde 0 1 (1 % ) 1.66 Stress Had child rest more often 3 (1.6 % ) 3 (3% ) 0.40 Note: Data reported as n ( %). 3Reported ?ehavior change C_?ded as A}}east one ~~ported behavior change= '1 No reported behav10r change= '0' .. Q< 0.05. Q< 0.01. Q< 0.001.

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CHAPTER6 DISCUSSION Understanding the behavioral impact of genetic risk knowledge helps to inform us about the ethical and social implications of genetic screening and has the potential to influence future legislative policies as screening becomes more widespread. Studies are needed in this area as there is a lack of empirical data examining the effects of genetic screening on families everyday lives. This study was exploratory in nature and represents a first look into the behavioral impact of newborn screening for type 1 diabetes. As such, results should be interpreted with some caution, especially due to psychometric weakness in some of the measures used and an unavoidably select sample of mothers Results of this study demonstrated risk identification of type 1 diabetes through newborn genetic screening did not lead to widespread maladaptive behaviors that might endanger the child or family or disrupt daily functioning. Contrary to this, results indicated healthy lifestyle changes and increased health surveillance. At a bare minimum these contribute positively to a child's health and well-being overall with the added benefit of the increased potential for earlier detection and diagnosis should the child develop symptoms of type 1 diabetes. In addition, it is possible that in the long run researchers may find that some of these specific behaviors may have protective effects for type 1 diabetes. 117

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118 Hypotheses Objective 1: To Investigate the Extent of Reported Maternal Behavior Change as a Result of Genetic Screening for Type 1 Diabetes Over 60 % of mothers reported engaging in at least one behavior change These results are consistent with results from the Hendrieckx et al. (2002) study on intentions for behavioral change in adults with a first degree relative with type 1 diabetes who were undergoing antibody screening and assessed prior to results notification Hendrieckx et al. (2002) found that 73 % reported an intention to modify at least one behavior if testing results placed them at a high risk for diabetes. Our results are slightly higher than the 52 % of children and 24 % of adults who reported making a behavioral change as in the Johnson & Tercyak (1995) study on participants who were identified as antibody positive. Taken together, these results indicated that participants in screening studies may not only intend to make behavior changes prior to notification but will actually take action if identified as "at risk" for developing type 1 diabetes. In making comparisons between studies, it should be noted these are not direct comparisons as the studies used similar but not identical self-report measures of behavior change. Hendrieckx et al. (2002) used a prospective design with an open-ended question to assess intention s for behavioral change. Respondents were asked to report what they would do differently in their daily lives if they were found to be at risk and responses were coded and cate g ori z ed. Johnson & Tercyak (1995) used a retrospective design but asked a very similar open-ended question in assessing actual behavior change following antibody screenin g However they did not present results on specific types of behavior changes For this current study a more detailed assessment was conducted with both forced choice and open-ended que s tions across different domains of b e haviors

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119 Reported changes were most frequently reported in response to forced choice items (yes or no) rather than open-ended questions, suggesting that it was more difficult for mothers to spontaneously recall specific behaviors than it was for them to recognize behaviors that were presented to them. This may be particularly true of health surveillance behaviors which mothers may not immediately recall as they may not consider monitoring to be preventative in nature. According to forced choice items increased health surveillance behaviors were the most commonly reported behavior changes It is also possible that forced choice items may have indirectly created a demand characteristic for mothers to endorse behaviors. In open-ended questions spontaneous responses most frequently corresponded with healthier diet and exercise changes. In forced choice items alterations in diet and exercise patterns were less common than health surveillance behaviors, but more prevalent than illness prevention, medications, or stress reduction. This is consistent with Henrieckx et al. (2002) who found that modifications of diet and exercise patterns were often cited as behaviors participants reportedly intended to change if found to be at risk for type 1 diabetes. While prevalence rates were not specified for different types of behavior changes according to Johnson & Tercyak (1995) the majority of behavior changes reported in their s tudy were in the areas of diet and increased physical activity. Nearly all changes in diet and exercise reported in this study would be considered healthy lifestyle changes consistent with current recommendations for the prevention of type 2 diabetes. Almost one half of participants who reported engaging in at least one behavioral prevention effort reported engaging in specific behaviors congruent with current recommendations for type 2 diabetes prevention. While not as prevalent as

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120 increased health surveillance these results suggest that mothers may have applied recommendations for type 2 to their children at risk for type 1 diabetes. Our descriptive data indicated that 92 % of mothers who believed they could do something reported changing their child s diet and exercise changes as possible methods of prevention These findings are consistent with Pierce et al (1999) who found that nearly half of parents with type 2 diabetes reported believing they could reduce their child s risk of developing diabetes Nearly all efforts noted by these parents to be perceived as effective were diet changes and increased exercise (Pierce et al. 1999). The key difference between these studies as that diet and exercise are effective in preventing type 2 diabetes (Ryan et al. 2003; Tuomilehto, et al. 2001). This reflects a lack of understanding about the etiology of type 1 diabetes and highlights the fact that mothers may believe that changes in diet and exercise may be as important to the prevention of type 1 diabetes as they are to the prevention of type 2 diabetes. In fact there are no currently available data that lifestyle behaviors are linked to the etiology of type 1 diabetes. Therefore mothers appear to rely on available information to fill in where current scientific evidence is absent. Additional evidence of this lack of understanding comes from data indicating that while nearly 80 % of mothers who reported ch e cking for signs of diabetes in their children correctly identified at least one symptom 45 % of mothers also reported at least one incorrect symptom Furthermore approximately one fourth of mothers received advice from health professionals and family and friends; nearly all mothers reported following the advice they were given Results indicated that of these almost half received recommendations

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121 for healthy lifestyle changes from health care professionals and one fourth received similar advice from family members or friends. Current research suggests that there are environmental triggers of the immunological process leading to the development of type 1 diabetes. At the present time, these triggers are unknown. Thus far, lifestyle behaviors, specifica11y diet and exercise have not been implicated as such triggers. There is no scientific evidence that diet and exercise changes can prevent type !diabetes. Results of this study suggest that the public believes that healthy lifestyle changes advocated to prevent type 2 diabetes might also be effective at preventing type 1 diabetes. Therefore as results indicated, many mothers of children at risk for developing type 1 diabetes reported engaging in behaviors that foster lifestyle changes in their children such as healthier diets and increased exercise. Since researchers do not know the exact triggers of type 1 diabetes, when conducting natural history studies or prevention trials these behavioral changes need to be monitored. In the future researchers may find that certain environmental influences or lifestyle choices may indeed trigger an immunological process leading to type 1 diabetes Similarly researchers may find that healthy diet and physical activity may be protective factors for developing type 1 diabetes. In the meantime these results suggest that participation in genetic screening and risk identification may have promoted healthier lifestyles for these children, through increased health surveillance, healthier eating, and increased physical activity. These behavioral outcomes are important to consider in the context of ethical debates about newborn screening for type 1 diabetes. Critics argue that screening creates undue burden and causes distress in at risk families Staunch opponents to screening

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122 advocate that without a definitive prevention strategy, population screening should not be conducted at all. Further if screening is conducted in only within a research context, families should not be notified of results (Friedman Ross 2003) Results suggest that mothers report healthy changes, and very few mothers are reportedly engaging in maladaptive or inappropriate behaviors suggestive of overprotectiveness or undue parental burden. Although this cannot be confirmed as this study did not explicitly examine the negative or positive impact of these changes on families quality of life. However data collected in this study on the duration of behaviors suggests that most mothers who reported initiating a behavior continued their efforts consistently over time. Perhaps if certain behaviors were burdensome, mothers would have discontinued them Furthermore, the benefits of increased health surveillance and healthy lifestyles are obvious in this population. Increased health surveillance may aid families in earlier detection and diagnosis should their child eventually develop diabetes Objective 2: To Assess the Predictors of Reported Maternal Behavior Change as a Result of Genetic Screening for Type 1 Diabetes Results of regression analyses were generally consistent with Baum et al's Stress Disease Risk Coping Model (1997); however this model was not intended to address issues pertaining to other family members and family dynamics (other than medical history) -variables that other researchers believe are key in understanding the psychological impact of genetic testing results (Rolland 1999 ; Tercyak 2000). While this model was not intended to pertain to behavior changes in parents in response to a child s risk it appear s that this model fit well with current data a nd was useful in interpreting results of the current study. To date the Stress Disease Risk Coping Model

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123 has not been formally tested and therefore, future studies are needed to further develop and generate empirical and statistical support of this model. In every model, the presence of a first degree relative with diabetes also predicted greater likelihood of behavior change. Most of these relatives had type 1 diabetes and in nearly half of these families, the relative was the mother herself. Mothers, who live with the disease and understand its severity may be more inclined to take actions to prevent it from developing in their offspring. Additionally, having a close relative with diabetes is concrete evidence and a visible sign of genetic predisposition This may increase one's perceived risk above and beyond results of screening for genetic (HLA) markers, and increase the likelihood of preventative actions These findings are consistent with Baum et al's (1997) model in which family history is considered a personal variable associated with risk appraisal. It is difficult to compare findings of the current study which was conducted in the general population, with other published genetic screening studies as most involved selected samples of participants with family histories of a particular disease. However in other health screening studies Marteau & Lerman (2001) suggest that family history has an inconsistent relationship to behavior change. For example in studies on heart disease, those who perceived a family history were no more likely to engage in risk reducing behavior (Becker & Levine 1987) and in a separate survey 15 % of those with a family history of heart disease perceived a sense of fatalism (Hunt et al., 2001) which may in part explain why more people do not change their behavior if they perceive themselves as greater risk (Marteau & Lerman 2001) However it should be noted that these studies

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124 were on adults about their own health risks and behavior, and it is possible that parents may be more active to prevent disease in a child. As opposed to family history, actual risk, as determined by genetic screening, was not a significant predictor of behavioral change. While this study did not contain a control group to directly test this, it is possible that mothers of children with first-degree relatives may have engaged in these behavior changes regardless of whether screening had occurred. To explore this issue future studies should include a sample of families with a first-degree relative with diabetes whose children did not undergo genetic screerung As hypothesized perceived risk, anxiety, coping, and information seeking were all predictors of behavior change. These findings support the Baum et al. s (1997) Stress Disease Risk Coping Model in which health behavior changes are impacted by both affective and cognitive variables, including psychological distress, coping resources personal factors (i. e ., information seeking). Mothers who perceived their child to be at greater risk were more likely to report engaging in behavior change, consistent with previous studies on breast cancer risk and mammography screening (Meiser et al., 2000, Ritvo et al. 2002). It seems logical that mothers who perceive their child to be more susceptible to a disease would be more likely to take action to reduce their risk as hypothesized in several models of health behavior (Leventhal 1970; Rosenstock 1974) Pierce et al. (1999) found that those who hypothesized their risk to be high were also more likely to worry. It is possible that mothers who perceived their child s risk to be higher would be more likely to feel anxious and thus, more likely to initiate behavior change.

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125 In this study, actual risk was not a significant predictor which is similar to findings of Aiken et al. (1994), in which perceived risk above and beyond actual risk predicted behavior change. These results suggest that in predicting behavior, it was more important how mothers viewed their child's risk than the risk itself. However, it is important to keep in mind that all children in this sample were identified as at increased risk for type 1 diabetes (as compared to the general population) to begin with. If this study had included a wider range of risk groups (i.e., protected to extremely high risk), actual risk may have been a predictor. Additionally, it should be noted that the highest level of actual risk ("extremely high risk") identified through screening signifies the child has a 20% probability of developing diabetes Anxiety at the time of the current interview was also a significant predictor of behavior change, even when controlling for initial anxiety. Congruent with Johnson & Tercyak (1995) and Hendrieckx et al. (2002), mothers who reported greater diabetes specific worry and anxiety were more likely to report engaging in preventative behaviors. This lends further support to studies of other populations suggesting that a person's affective response (i.e., distress) to a health threat is related to whether they adopt health protective behaviors (e.g., Diefenbach et al., 1999). Furthermore, it supports the Stress Disease Risk Coping Model, which theorizes that a person's stress response will in tum influence modifications in health behaviors (Baum et al., 1997). Again, consistent with Baum et al. (1997), mothers who reported using more coping strategies at the 4-month follow-up interview were more likely to report taking preventative action. Engaging in behaviors viewed as potentially risk-reducing may represent another method of coping with a health threat. As hypothesi ze d the total score

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126 from the Ways of Coping Checklist-Revised (WCC-R) and three of its coping scales were significant predictors of behavior change. Active coping, as characterized by seeking social support and problem-focused coping constructs were both significant predictors. Somewhat surprising passive coping as exemplified by the construct of wishful thinking, was also a significant predictor of behavior change. When all three coping scales were entered into the summary model, only wishful thinking remained significant. Based on analyses of items in this scale it appears that the construct includes items that reflect optimism but also a desire for the problem to "go away." Mothers who wished the problem would resolve and felt optimistic about the possibilities were more likely to engage in preventative behaviors despite the lack of available recommendations As hypothesi zed, information seeking was related to behavior change, more so than other predictor variables, such as anxiety risk perception, and coping style. Mothers who reported having more sources of information regarding type 1 diabetes were more likely to report engaging in behavior change. Mothers in this study reported following advice given to them by others and it is not surprising that more information was related to greater likelihood of taking actions. Information seeking can be viewed as a form of active coping when dealing with a health threat. Seeking out more information is a proactive step much like changing one's behavior. Additionally, most of the information in the media as well as data collected in this study on information from family and friends appear to be directed at type 2 diabetes and healthy lifestyle changes The more information that mothers receive about diabetes they more likely they may be to apply available recommendations to their at risk child.

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127 Contrary to the original hypothesis, perceived control was not a significant predictor nor did it demonstrate any significant interaction effects with other predictor variables. This was incongruent with Baum et al.(1997) because perceived control was originally considered part of the appraisal process described as "risk perception." Our findings are in contrast to Hendrieckx et al (2002) who found that adult participants who perceived greater internal control were more likely to report intentions for behavior change. It should be noted that Hendrieckx (2002) explored adults' intentions for their own behavior, rather than the current study of mothers' reported behavior changes related to their children. Another unexpected finding from this study was that participation in the PANDA study (Part II) did not predict to whether behavior change occurred. Study participation could be viewed as health surveillance behavior in and of itself but it was surprising to find that this was not be related to other types of health surveillance or changes in other behavior domains. However in interacting with participants PANDA study staff remind mothers there are no current recommendations for prevention of type 1 diabetes so continued contact with staff would not necessarily increase the likelihood of behavioral change. Objective 3: To Assess Psychological Effects (i.e., Anxiety) of Maternal Behavior Change Over Time Contrary to the original hypothesis behavior change did not appear to decrease anxiety over time. Mothers who reported behavior change and perceived greater control were more anxious at time of the current interview. In essence behavior change did not decrease anxiety over time it actually was associated with higher levels of anxiety as compared to tho s e who did not reported behavior chan g e and who perceived less control.

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128 These findings are consistent with Pierce et al. (1999) who found that parents who thought prevention was possible were more likely to worry about their children developing type 2 diabetes It is somewhat surprising that mothers who perceived greater control were more anxious. Perhaps when mothers feel they have control, it engenders a sense of increased responsibility for their child's health and therefore increased anxiety. Similarly it may also be that there is stress that accompanies believing that there is something one ought to be doing to prevent diabetes in one s children Sociodemographic predictors of anxiety at the current interview above and beyond initial anxiety included child s age and family history of diabetes. Mothers of children who were younger and who had relatives with diabetes were more anxious at the current interview. Previous studies within the larger PANDA population are consistent with these findings that family history is a significant predictor (Johnson et al. submitted). This may reflect the relationship between anxiety and elevated perceived risk, as may be present when there is a positive family history. Current data indicated a significant correlation between composite scores of perceived risk and anxiety as have previous studies using data from the PANDA study (Johnson et al. submitted). The lack of other significant sociodemographic predictors as found in Johnson et al. (submitted) when predicting to earlier assessments of anxiety (i.e PANDA Part ID interviews), may be due to the homogeneity of this current sample length of time since notification and controlling for the effect of initial anxiety.

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129 Objective 4: To Compare Reported Behavior Change between Mothers of Children Genetically at Risk for Developing Type 1 Diabetes with Mothers of Children in the Diabetes Prevention Trial Who Were ICA+, and Therefore, at Even Greater Risk for Diabetes Onset In terms of reported behavior there were relatively few differences between these two samples of mothers. As hypothesized mothers of children enrolled in the DPT-1 trial were more likely to report at least one behavior change, but the difference only approached significance, with 43% of mothers in the DPT-1 sample versus 33% in the PANDA sample. Data collected from DPT-1 participants themselves indicated that 54% reported at least one behavior modification, which was slightly higher than maternal report (Johnson, 2002). The prevalence of reported behavior change in the DPT-1 sample is comparable to rates reported in other studies of people who are ICA+. Johnson and Tercyak (1995) found 52% of children and 25% of adults reported engaging in prevention efforts. This may be explained by the difference in actual risk between ICA+ samples, including the DPT-1 (Johnson, 2002) and Johnson & Tercyak (1995), and genetic screening samples, such as this PANDA sample. Those who are ICA+ are at significantly greater risk of developing type 1 diabetes (i.e., approximately 45% chance of developing diabetes), versus those who are identified through genetic screening. The highest risk classification, "extreme ly high risk" signifies having a 20% chance of developing diabetes Additionally, while the rates of behavior change overall did not differ between the current sample and the DPT-1 sample, the difference may also reflect differences in anxiety, as previous studies have found that mothers of ICA+ children report higher levels of anxiety than mothers of genetically at risk children (Johnson et al., submitted) Mothers who were more anxious may indeed be more likely to report

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130 engaging in prevention efforts, as found in this study (see Objective 2) and this may also be true of the DPT-1 study population. In analyses of individual items, mothers in both samples more frequently endorsed changes in diet and physical activity, consistent with previous studies (Johnson, 2002; Johnson & Tercyak, 1995). Differences between the two samples included mothers in the DPT-1 trial more often reporting feeding their children less regular soda, less sweet food, and more diet or sugar-free drinks. However mothers in the current sample more often reported feeding their children less juice Additionally, mothers in the DPT-1 sample reported more often administering vitamins and insulin. It is not surprising that administering insulin was more prevalent in the DPT-1 study as presumably all mothers understood the purpose of the study was to determine if early administration of insulin was a effective prevention method. Additionally, 45% of children were enrolled in the experimental arm of the study in which they received to begin with, and therefore, it may have been more readily available. Increased use of vitamins may be explained by the use of a paper questionnaire. In our survey, many mothers frequently reported administering vitamins to their children but when asked again by the interviewer if this was because of their child's diabetes risk they no longer endorsed this item. With a paper survey as used in the DPT1 no one is there to remind the mothers these practices are intended to only reflect specific efforts to prevent diabetes Strengths and Limitations The proposed study design has severa l strengths. Hendrieckx et al. (2002) was the first study to systematically explore behavioral efforts to prevent type I diabetes in at risk individuals However outcomes were measured as intentions, not behavior changes and assessment occurred prior to screening. In contrast this proposed study was

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131 conducted several years post-notification and examined actual reported behavior change. The study design also permitted linkage to data collected during previous interviews. This allowed for prospective statistical analyses to interpret temporal relationships between variables and predictors of reported behavior change, including the relative contributions of personal affective and cognitive appraisal variables (e.g., psychological distress risk perception, perceived control, sociodemographics). Behavioral outcomes in this proposed study sample were also compared with those of mothers of children at even greater risk for developing type 1 diabetes who entered a prevention trial (DPT-1) Additionally our study's design permitted analyses of both recognition and recall data through the use of open-ended and forced choice questions. This was possible due to the retrospective nature of our study. While results indicated that recognition (i.e. forced choice items) yielded a higher rate of behavior change and more detailed information, the recognition method if used prospectively in prevention trials might prompt behavior change in participants For example, providing mothers with a list of possible behaviors may inadvertently encourage mothers to initiate behaviors they otherwise might not otherwise engaged in. Consequently, any subsequent data collected on behavior may not be valid as it was not an action mothers engaged in on their own. Researchers should be cautious about using a recognition approach since a long list of forced choice item s may increase social desirability. The study provided important guidance as to which domains of behavior change are most commonly reported and should be targeted for more expensive observational data collection Indeed this study has shown a large proportion of mothers reported at least some behavior change as a result of genetic screening Health surveillance and

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132 behaviors characterized as healthy lifestyle changes were most commonly reported. These are behaviors that could be operationally defined and tracked in future research studies This study has several limitations. First, nearly all of the behavior change data collected were based on self report and may be subject to social desirability bias. Whether mothers had their child participate in PANDA blood-draws was the only observational behavioral data available. These data indicated mothers were largely accurate in their report of participation suggesting their responses in the rest of the interview may also be valid. Mothers were more likely to report not participating in blood draws when they actually had than inaccurately stating they had participated when they had not. Additionally the rates of behavior change were consistent with other studies suggesting that mothers were not overly endorsing practices (Hendieck:x et al., 2002; Johnson 2002, Johnson & Carmichael 2000). The apparent lack of social desirability may have been a direct result of our efforts to use language in the interview to minimize such bias reiterating at several points in the interview that there were no right or wrong answers, nor are their known methods to prevent the development of type 1 diabetes However as with all self report data social desirability is a factor to consider. The second study limitation was the lack of representativeness of the study's s ample To som e e xt e nt this was unavoidable as a significant proportion o f mothers were unable to be contacted despite multiple attempts by staff. In this sample, minorities were under-represented even more so than even in the original PANDA sample. Mothers who completed the current interview as compared to mothers who were unable to be contacted were significantly older more educated had higher family incomes and were

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133 more likely to be manied Additionally, they were more accurate in their reported understanding of their child s risk. Most of these mothers had completed previous interviews, suggested a higher level of concern and motivation. The lack of socioeconomic variability in this sample is a weakness of this study as results may not be generalizable to PANDA participants as a whole or to the general population. Additionally it is possible that some demographic variables may predict behavior change, although, this was not apparent due to the restricted range in the current sample. In terms of genetic risk this sample contained few mothers of children who were at extremely high risk although the prevalence rate in the current study is representative of the entire PANDA study popula6on. The current study did not include participants who were at low risk for developing diabetes to use as a comparison group. This was not feasible at the present time as PANDA only longitudinally follows children at moderate, high or extremely high risk. However gathering data on those in lower risk groups could be useful information in future studies. Third measures used in this study were largely new and were not psychometrically sound. Predictor variables often had too few items (i.e. perceived control perceive risk) with moderate levels of internal consistency. It is important in future studies to develop better measures for these constructs by adding more items conducting in depth pilot testing and/or create new measures which specifically relate to genetic screening In terms of the study's outcome measure most of the behavior interview was new with many items adapted from a similar survey used in only one previous study. Thus it was difficult to determine an accurate data analysis plan a priori. Results indicated there was satisfactory internal consistency for only two of the beha v ior

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134 domains Additionally behavioral data was not normalJy distributed and therefore, fine grained analyses including continuous behavior scores that reflected duration and frequency could not be successfully analyzed Despite these limitations, this study represents an important exploratory step in understanding maternal reactions to the news a child is at risk for a disease with no known method to prevent disease onset. This information is critical in efforts to develop the best risk communication methods, provide families with appropriate support in response to risk communication design natural history studies of disease onset and progression and design clinical trials aimed at disease prevention. Implications and Directions for Future Research This study provides unique contributions to the literature in several ways. First very few studies have examined the behavioral impact of genetic screening, especially non-health surveillance behaviors (e.g. diet, exercise, stress reduction). Second, this study improves our understanding of the public s awareness of diabetes including the lack of discrimination between types 1 and 2, as well as beliefs regarding diabetes prevention efforts. Many of behaviors reported by mothers in this sample were consistent with recommend a tions for type 2 diabetes suggesting the medical community s efforts to increase awareness of the importance of a healthy lifestyle have been effective. However many mothers believed they could prevent their child from developing type 1 diabetes when the re is currently no evidence to support this belief. These studies highlight the continued need to keep the public informed of current medical research and provide education regarding the distinctions between types of diabetes Furthermore it speaks to providing participants in genetic screening and natural history studies with more detailed information re g arding the nature of type 1 diabetes.

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135 Data collected in this study allowed us to gain a better understanding mothers advice-seeking behavior. A significant proportion of mothers received advice from a medical professional ; however, a sizable portion also received advice from family and friends. While its is unclear exactly how this advice may have influenced behavior in this study data indicated that nearly all mothers followed advice given to them by family or friends while slightly less followed the advice given to them by medical professionals Future studies are needed in this area to explore how people seek out medical advice and which sources are trusted the most for accurate information While it was not a focus of this study results from open-ended questions indicated several mothers relied on prayer to help prevent their child from developing diabetes. The role of religion in coping with a health threat particularly as it may pertain to predictive genetic testing and counseling, may be an area worthy of further exploration. Finally it is crucial when designing clinical trials, particularly natural history and prevention studies to understand the behavior changes that may occur in individuals everyday lives in response to a health threat. Unless carefully monitored such behavior modifications could threaten the internal validity of natural history studies and prevention trials The scientific community is still unclear regarding effective prevent i on methods for type 1 dia bet e s and it i s possible some of the measures taken by these mothers to prevent diabetes from developing in their children may ultimately be determined to be beneficial or harmful. It is clear that genetics is only part of the story. Environmental triggers which could include behavioral factors such as diet or exercise habits do p l ay a role. However scientists do not yet know what those environmental triggers are

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136 highlighting the importance of the continued study of attempted behavior change in response to risk notification.

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APPENDIX STRUCTURED TELEPHONE INTERVIEW Sample Script for Male Child Date : ___________ Hello, is (say mom s name) there please? This is _____ (principal investigator or research assistant) and I'm calling from the Diabetes Research Office at the University of Florida. You might remember that your son ...i.lfil'. child s name) participated in the PANDA study in which his blood was tested to see ifhe had any genes that might put him at risk for developing type I diabetes You were told about his results and then we talked with you over the phone several times to ask you some questions about your feelings regarding your child s involvement in this study. Does this sound familiar to you? It has been awhile since we last talked with you but if you don t mind I'd like to ask you a few more questions. Like the other times we have talked with you all the information is kept private and confidential -we won't tell anyone else and you can refuse to answer any question at any time. For this interview, it does not matter if you brought you child in for more blood draws or not. If you feel uncomfortable answering a question, feel free to skip that item. You are free to discontinue your participation in this interview at any point in time In doing this interview, there are no direct benefits to you. But, to thank you for helping us we would like to send you in the mail a $5 gift certificate to either Target or Publix your choice. The interview should take about 15-20 minutes Would you be interested in participating? Is now a good time for you? (If not find a good time. If mom r e fus e s int e rvi e w thank h e r for h e r e arli e r participation in PANDA and pre vious int e rvi e ws) To get started, I would like to make sure that we have current basic information about you and your family You may remember these same questions from before and I would like to know if anything has changed Family's mailing address (to be used solely for mailing gift certificates) Street Address: ____________________ City: ___________ State: _____ Zip Code: ______ Parents Marital Status: ______ !=single parent, involved father 2 = sin g le parent, non involved father 3=married 4 =s eparated 5=divorced How far did you go in school? (mom):____ father: ____ l = some high school 4 = graduated from college/trade school 2 = graduated HS/GED 5 =some graduate school or professional program 3=some college or trade s c hool 6=g raduated from graduate or professional school # of children in the family: ____ ,(_...fi""'rs = t--=c =h=ild"'-?'-. Y...__,_,/N...,_,) 137

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138 Annual Total Family Income : ____ 1=0-10,000 4 = 30 001-40 000 7=60,00 1 -70,000 2=10, 001-20 000 5=40,001-50 000 8=70,001-80,000 3=20,001-30,000 6=50 001-60,000 9=80 001-90 000 l 0=90 001-100,000 l 1=100 000+ Does anyone in yo ur son's fami l y have di a b etes? (T O INTERVIEWER: If there is family hi sto ry reported in earlier interviews, say: "Does anyone else in your son's fami ly besides ________ [list relations as previo usl y r e ported] h ave diabetes now? (include information regardin g th e mother s esti mating of diabetic control and how problematic the disease has b ee n for all diabetic famil y m e mb e rs including those from b efo r e) Relationship to child (circle) Type Age at onset Live with you? Duration Control* Problems ** Mother Fat her Fu ll S i blin g Full Sibling Half Sibling m -gra ndm ot her m -gran d father m -grt. grand mother m -grt.grandfather p -gra ndmother p gra ndfath e r p grt.grandmother p-grt. grandfather ( other) Note. Indicate if they DON'T KNOW.*l=excellent 2=good 3=fair 4=poor S=DK **l=none 2=a few3=several 4 =a lot S=DK The PANDA s tudy coordinator might have to ld you t h at yo u r so n was more or less atri sk for developing diabetes tha n other peop l e in the genera l population I'm goi n g t o read so me categories a nd give yo u so me numbers, and I want yo u to tell m e which risk category yo u were told your son was in Okay? Were you told your b aby was at: (circle one) 1. Very low risk (-1/5 000 ) 4 Moderate risk (-2/100 ) 2. Low risk (-1/600) 5 High risk (-5 or 10/100 ) 3. Intermediate risk (-1/125) 6 Extrem e ly hi g h ri sk (-1/5) 7 Don' t kn o w/don t remember What are yo u r thoughts o n ha vi ng had your so n tested at birth for diabetes ris k ? ( check one) D Glad you participated in the s tudy D Not s ur e abo ut yo ur p articipa t ion in the stud y D Wish yo u h ad not participated in the st ud y What do yo u think will happ e n to your c hild in re gar d s t o d eve l oping diabetes? (c heck one) D My c hild w ill d eve lop diabetes in the near future D My c h i ld will eventually devel o p diabet es but not for a l o n g time from now D My c h ild will not ever devel o p diabetes D I am un ur e what will happen

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139 How do you think your son s risk compares to other children for developing diabetes? (circle one) I Much lower 2 Somewhat lower 3 About the same 4 Somewhat higher 5 Much higher How often do you worry that your son will get diabetes? (circl e on e ) 0 Never 1 Rarely 2 Sometimes 3 Often 4 Very often I want you to think about your son's risk for developing diabetes I'm going to describe some feelings to you and I want you tell me how you feel right now at this moment about the situation. GIVE STAI -SFlO Would you say you currently feel: ( circle one for each) Not at all calm somewhat calm moderately calm very calm Not at all secure Not at all tense Not at all at-ease somewhat secure s omewhat tense s omewhat at -ease moderately secure moderately tense moderately at ease You are presently worrying over possible misfortunes : Not at all somewhat moderately very secure very tense very at -ease very much Not at all frightened somewhat frightened moderately frightened very frightened Not at all comfortable somewhat comfortable moderately comfortable very comfortable Not at all nervous Not at all relaxed Not at all worried s omewhat nervous somewhat relaxed somewhat worried moderately nervou s moderately relaxed moderately worried INFORMATION SEEKING very nervous very relaxed very worried Did you talk to your family doctor or pediatrician about your son's diabetes risk screening results? DYes DNo IF YES: Did you get any advice from your ph y sician ? D Yes DNo What was the advice ?

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140 Did you tak e the a d vice? D Yes Did you t alk about yo ur c hild s ri sk with family member s o r friends? D Yes Who? D Child's grandparent D Spouse D Other family member or friend who has diabetes D Other family member or friend who does not have diabetes D Other (please specify) ___________ Did you get a n y a d vice fro m them ? D Yes D No From w h o m did yo u ge t a dvi ce? D Child's grandparent D Spouse D Other family member or friend who has diabetes DNo DNo D Other famil y member or friend who does not have diabetes D Other (please specify ) __________ ( i f a n y of a b ove c h ecke d ) What was the a d vice from _______ (#1 li sted)? Did you t a k e the a d vice? D Yes D No What was the a d vice from ______ (#2 listed)? Did you take the advice? D Yes D No What was the a d vice fro m ________ ( # 3 listed) ? Did yo u take th e adv i ce? D Yes D i d yo u search the Internet for information on yo ur c hild s risk? DNo D Yes DNo Did you read a book or other l iterature regardi n g diabetes a nd di abetes risk ? DYes DNo Did yo u watc h diabetes related programming on te l evisio n ( i e news stor i es)? D Yes D No Are t h e r e a ny ot h er thing s I did not mention that yo u did in orde r to get m ore information about diabetes a n d/or your c h i ld s r i s k? ........................................................................

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141 Right now, doctors and scientists don't know how to stop type 1 diabetes from developing. But even so people may have different ideas about what might work or not. Some people might think there is something special you can do to stop your son from getting type 1 diabetes. Other people might think there is nothing you can do We are interested in knowing how you have responded to the news that you son is at risk for diabetes. This information may help in future research Whether you did something or did nothing is equally important information There are no right or wrong answers. We want you to be as honest as possible Did you do anything special to try to prevent diabetes in your son? OYes ONo How much do you agree or disagree with the following statement: "I can do something to reduce my son s risk for developing type I diabetes ? D Strong Disagree D Disagree D Neutral D Agree D Strongly Agree IF AGREE/STRONGLY AGREE: What types of things do you think you can do to try to prevent your child from developing type I diabetes? How much do you agree or disagree with the following statement: "Medic a l professionals c a n do something to reduce my son's risk for developing type I diabetes"? D Strong Disagree D Disagree D Neutral D Agree D Strongly Agree IF AGREE/STRONGLY AGREE: What types of things do you think medical professionals can do to try to prevent your child from developing type I diabetes? H o w much do you a gr e e o r di sagree with the followin g statement: It i s up to chance or fate whether my s on develop s type I diabetes"? D Strong Disagree D Disagree D Neutral D Agree D Strongly Agree ****************************************************************************************** Now I am goin g t o get a little more d e tail e d in the question s I will be a skin g y o u A gain I want you to keep in mind that there are no ri g ht or wron g answers. We jus t want to know about anythin g y o u have tried or are doin g specifically to try to prevent type I diabetes from developing in your son.

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142 DIET: Have you done anything different with your son's diet or eating patterns to prevent him from developing diabetes? (list verbatim below and check off corresponding items below followed b y questions in right-hand column) I am going to list off other things you may not have mentioned or tho ught of right now a n d I want you to tell me if you have done them The responses to these questions are j ust yes or no. D IET/EATING PATTERNS # Q UESTI O N IF YES, ASK THE F OLLOWING ... A Is this something you have been 1. Have you fed your son N O YES doing ever since you fou n d out about N O YES less candy, cookies, your son's risk? (ask (ask C) cake and other sweet B &C) foods? B Is this something you did early on NO YES and then stopped OR Did you start doing this just recently ? N O YES C During the time you were doing t h is, was it D O ccasionall y D A l ways/Nea rl y Every day A Is t h is something you have been 2. Have you fed your son N O YES doing ever since you found out about N O YES less regular soda and your son's risk? (ask (ask C) sweet dri nks? B&C) B. Is this something you did early on NO YES and then stopped OR Did you start doing this just recent l y? NO YES C. During the time you were doing this was it D O ccasiona ll y D A l ways/Nearly Everyday A. Is this something you have been 3. Have you fed your son NO YES doing ever since you found out about NO YES more diet soda and your son's risk? (ask (ask C) sugar free drinks ? B &C) B. Is this something you did earl y on NO YES and then stopped O R Did yo u start doing this j ust recently ? NO YES C. During the time you were doing this was it D O ccasi onall y D A l ways/Nearly Everyday

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143 A. Is this something you have been 4. Have you given your NO YES doing ever since you found out about NO YES son less juice to drink ? your son's risk? (ask (ask C) B &C) B. Is t h is someth in g you did earl y on N O YES and then stopped OR Did you start doin g this just recently? NO YES C. During the time you were doing this, was it D Occasionall y D Alwavs/Nearlv Evervdav A. Is this something you have been 5. Have you given your NO YES d o in g ever since you found out about NO YES son more juice to drink? your son's risk ? (as k (ask C) B &C) B Is this something you did early o n NO YES and then stopped OR Did you start doing t h is just recent l y? NO YES C. During the time you were doing this was it D Occa si onall y D Alwavs!Nearlv Everv da v A Is this so methin g you have been 6. Have you tried to get NO YES doing ever since you found out about NO YES your son to lose your son's r i sk? (ask (ask C) weight? B &C) B. Is t hi s something you did early on NO YES and then stoppe d OR Did you start doing this just recently? NO YES C. Durin g the time yo u were doin g this was it D Occasionall y D Alwavs/Nearlv Everv da v A Is this something you have been 7 Have you tried to get NO YES doing eve r si nce you fo und out abo ut NO YES your so n to gai n your son's risk? (ask (ask C) weight? B &C) B. I s this so m ethi n g yo u did early on NO YES and then stopped OR Did yo u start doing this just recent l y? NO YES C. During the time you were doin g this was it D Occasionally D A l wavs/Near l v Everv da v

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144 A. Is this something you have been 8. Ha ve you fed your son NO YES doing ever since you found out about NO YES more ofte n than you your son's risk? (ask ( ask C) otherwise would have ? B &C) B. Is this something you did early on NO YES and then stopped OR Did you start doing this just recently ? NO YES C. During the time you were doing this, was it D Occasionally D Alwavs/Nearlv Everyday A. Is this something you have been 9. Have you fed your son NO YES doing ever since you found out about NO YES les s often than you your son's risk? (ask ( ask C) otherwise would have ? B &C) B Is this something you did early on NO YES and then stopped OR Did you start d oi ng this just recently? NO YES C. During the time you were doing this was it D Occasionally D A l wavs/Near l v Everyday By how long? (in 10. Did you introduce solid NO YES months) foods, such as baby food, table food, or How old was your child when he was cereal, earlier than you first given solid foods? had planned? (in months ) 11. Did you introduce solid NO YES By how long? (i n foods, such as baby months ) food, table food, or cereal, later than you How old was your child when he was had planned? first given solid foods? (in months) 12 Did you delay giving NO YES By how long ? (in your son cow's milk? m o nths ) How old was your c hild when he was first given cow's milk? ( in months )

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145 A. I s thi s so methin g you ha ve been 1 3 Have you avo id e d NO YES doing ever since you found out a bout NO YES givi n g your so n cow's your so n's risk? (ask B & C) (ask C) milk altoget her ? B. I s this so mething you did early on NO YES a nd then s topped OR Did yo u s tart doing this just recently? NO YES C. During the time you were doing this, was it 0 Occasionally 0 Always/Nearly Everyday A. I s thi s so methin g you ha ve been 14. H ave yo u encouraged NO YES doing ever s ince you found o ut about NO YES your so n to eat m ore yo ur so n's risk? (ask B & C) (ask C) th a n you other wise would h ave? B. Is thi s so mething yo u did early o n NO YES a nd then s t o pped OR Did you s tart d o in g this jus t recently ? NO YES C. Durin g the time you were doin g thi s was it 0 Occasionally 0 Always/Nearly Evervdav A. I s this somethin g you have been 15. Did you encourage your NO YES d o in g ever s ince you found o ut a b o ut NO YES so n to eat le ss than yo u yo ur so n's ris k ? (ask B & C) (ask C) oth e rwise wo uld h ave? B. I s this so meth i n g yo u did early o n NO YES a nd then stopped OR Did yo u star t d oing this just recently? NO YES C. Durin g the time you were doin g this, wa s it 0 Occasionally 0 Always/Nearly Evervdav How l o n g did yo u b reastfeed your 16. Did yo u br eas tfeed your NO YES so n ? son? m o nth s / years (c ircl e o n e) Was your de c i s i o n to br eastfee d him influenced by his risk sta tu s for NO YES d eve l op in g type I di abetes? Did you bre astfee d yo ur son for a l o n ge r o r s hort e r time than yo u expec t ed because of h is risk? (circl e o ne ) Longer Shorter Neither About how mu c h l o nger/ s h orte r ? m ont h s/years (circ l e one)

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146 PHYSICAL ACTIVITY/PHYSICAL STRESS: Have you done anything different with your son's physical activity patterns to pre ve nt him from developing diabetes? (list verbatim b elow and c h eck off corresponding items below followed by questions in right-hand column) I am going to list off other things you may not have mentioned or thought of right now and I want you to tell me if you have done them The resp o nses to these questions are just yes or no PHYSICAL ACTIVITY/PHYSICAL STRESS # QUESTION IF YES, ASK THE FOLLOWING ... A. Is this something you have been 1. Have you encouraged yo ur NO YES doing ever since you found out about NO YES son to be active doing your son's risk? (ask (ask C) something physic a l every B &C) day? B. Is this something you did early on NO YES and then stopped OR Did you s tart doing this just recently? NO YES C. During the time you were doing this, was it D Occasionally D Always/Nearly Everyday A Is this something you have been 2. Have you encouraged yo ur NO YES doing ever since you found out about NO YES son to exercise more your son's risk? (ask (ask C) often? B&C) B. Is this something you did early on NO YES and then stopped OR Did you start doing this just recently? NO YES C. During the time you were doing this, was it D Occasionally D A l ways/Near l y Everyday A. Is this something you have been 3. Have you encouraged your NO YES doing ever since you found out about NO YES son t o be less active so that your so n's ri sk? (ask (ask C) he would not get tired ? B &C) B. Is thi s so mething you did early o n YES and then stopped OR NO Did you start doin g this just recently ? YES NO C. During the time you were doing thi s, was it D Occasionally D Always/Nearly Everyday

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147 A. Is this something yo u h ave been 4 When pl ayi n g hard h ave NO YES doing ever si nce yo u found out about NO YES yo u encouraged your son your son's r isk? ( ask (ask C) rest m o re so that he would B &C) not overdo it ? B. Is thi s so mething yo u did early o n YES and then stopped OR NO Did yo u star t d oi n g th is just recently? YES NO C. During the time you wer e d oi n g this was it c::J Occasionally c::J Always/Nearly Everyday HEALTHY LIFESTYLE/EMOTIONAL STRESS: Have yo ur d o ne a n yt hin g differently to lower yo ur so n's stre ss in order t o prevent him from developin g diab e tes? (list verba tim below and check off cor r esponding items b el ow foll owed by questions in right-hand column) I am going t o list off o ther things yo u ma y not h ave mentio ned or thought of right now and I wa nt you to tell me i f you h ave done them. The respon ses to these que s ti ons are jus t yes or no. HEAL THY LIFESTYLE/EMOTIONAL STRESS # QUESTION IF YES, ASK THE FOLLOWING ... A. Is this so methin g you ha ve been doing l. Have yo u encouraged NO YES ever si nce you found out about your so n s NO YES yo ur so n to get more risk? (ask ( ask C) rest? B&C) B. I s thi s so methin g yo u did early on and NO YES then sto pped OR Did yo u star t d o in g thi s just recently ? NO YES C. Durin g the time you were doing this was it c::J Occasionally c::J A l ways/Nearly Everyday A. I s thi s so methin g you have been d o in g 2. Have yo u active l y tried NO YES ever si nce yo u fo und o ut a b o ut your so n's NO YES to l ower your son's stres s risk? ( ask ( ask C) l eve l ? B&C) B. I s this somethin g you did early o n a nd NO YES then s topped OR Did yo u star t d o in g this j u s t recently? NO YES C. Durin g the time yo u were d o ing this, was it c::J Occasionally c::J A l ways/Nearly Everyday

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148 A. Is this something you have been doing 3. When your son gets NO YES ever since you found out about your son's NO YES upset, have you tried risk? (ask (ask C) harder to take his B &C) attention away from the situation or get him B. Is this something you did early on and NO YES focused on something then stopped OR different? Did you start doing this just recently? NO YES C. During the time you were doing this, was it D Occasionally D Always/Nearly Everyday A. Is this something you have been doing 4 Have you tried to keep NO YES ever since you found out about your son s NO YES your son away from risk? ( ask (as k C) situations that you felt B&C) might upset him? B. Is this something you did early on and NO YES then stopped OR Did you start doing this just recently ? NO YES C. During the time you were doing this, was it D Occasionally D Always/Nearly Everyday MEDICAL SURVEILLANCE: Have you done anything to monitor or keep an eye on your son's risk of developing diabetes? (list verbatim b elow and check off corresponding items below followed by questions in right hand column) I am going to list off other things you may not have mentioned or thought of right now and I want you to tell me if you have done them The responses to these questions are just yes or no MEDICAL SURVEILLANCE # QUESTION IF YES, ASK THE FOLLOWING ... A. Is this something you have been doing l. Have you taken your NO YES ever since you found out about your son s NO YES son to doctor's visits risk? (as k (ask C) more frequently? B&C) B. Is this something you did early on and NO YES then stopped OR Did you start doing this just recently? NO YES C. During the time you were doin g this, was it D Occasionally D Always/Nearly Everyday

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149 A. I s thi s so methin g you ha ve been d oi ng 2. Have yo u c heck e d yo ur NO YES ever since you found out about your so n's NO YES so n s blood gl u cose risk? ( ask ( ask C) le vel a t h ome? B&C) B. I s thi s so methin g you did ear l y on a nd NO YES then s t o pp e d OR Did yo u star t d o in g this just recently? NO YES C. Durin g the time you were doin g this was it D Occasionally D Always/Nearly Everyday A. I s this so mething you have been doi ng 3 Have you h ad your NO YES ever si n ce yo u fo und o ut abo ut yo ur so n's NO YES ped iatricia n check your risk? (askB (as k C) so n's bl ood g luco se &C) l eve l a t th eir office? B. I s thi s so me t hin g you did early on a nd NO YES then sto pp e d OR D i d yo u s tart d oing this jus t recently? NO YES C. Durin g the time yo u were doin g this, was it D Occasionally D Always/Nearlv Evervdav A. I s this so methin g you h ave been d oi n g 4 Have yo u h ad your NO YES ever si n ce yo u found out a b o ut yo ur so n's NO YES so n's blood drawn to ri s k ? (ask (ask C) te st for a u toantibodies B &C) as p ar t of PANDA study? B. Is this so methin g you did ear ly on and NO YES then stoppe d OR Did yo u sta rt d o in g this just recently? NO YES What kinds of things have you looked 5. Have yo u watc hed for NO YES for? s i g ns in your so n that yo u think may b e r e l ate d to symptoms of NO YES type I diabetes? A. Is this so methin g you h ave been doing ( ask (as k C) eve r si n ce yo u fo und out a bout your so n's B&C) risk? B. I s thi s somet hin g you did ear l y o n a nd NO YES then sto pped OR Did yo u star t doing this j u st recently ? NO YES C. During the time yo u we r e d oing this was it D Occasionally D Always/Nearlv Evervdav

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150 MEDICATIONS: Have you given your son any pills or medications to prevent him from developing diabetes? If yes what kinds? (list verbatim b e low and check off corresponding items below followed by questions in right-hand column) I am going to list off other things you may not have mentioned or thought of right now and I want you to tell me if you have done them. The responses to these questions are just yes or no MEDICATIONS # QUESTION IF YES, ASK THE FOLLOWING ... A. ls this something you have been l. Have you given your son NO YES doin g ever since you found out about NO YES vitamins? your son's risk? ( ask ( ask C) B&C) B. Is this something you did early on NO YES and then stopped OR Did you start doing this just recently? NO YES C. During the time you were doing thi s, was it D Occasionally D Always/Nearly Everyday A. ls this something you have been 2 Have you given your so n any NO YES doin g ever s ince you found out about NO YES medications for diabetes such your son's risk? (ask ( ask C) as glucophage or insulin? B &C) B. Is this something you did early on NO YES a nd then stopped OR Did you start d oi n g this just recently? NO YES C. Durin g the time you were doing this was it D Occasionally D Always/Nearly Everyday A. Is this so mething you have been 3. Have you given your son NO YES doing ever since you found out about NO YES in s ulin shots at hom e? your son's risk? ( ask ( ask C) B&C) B. Is this something you did early on NO YES and then stop ped OR Did you start doing this just rece ntly ? NO YES C. Durin g the time yo u were doin g thi s, was it D Occasionally D Always/Nearly Everyday

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151 A. Is this something you have been 4. Have you given your son NO YES doing ever since you found out about NO YES herbal supplements? your son's risk? ( ask (as k C) B&C) B. Is this something you did early on YES and then stopped OR NO Did you start doing this just recently? YES NO C. During the time you were doing this was it 0 Occasionally 0 Always/Nearly Everyday A. Is this something you have been 5. Have you given your son NO YES doing ever since you found out about NO YES nicotinamide? your son's risk? (a sk ( ask C) B&C) B. Is this something you did early on and then stopped OR YES Did you start doing this just recently? NO YES C. During the time you were doing NO this, was it 0 Occasionally 0 Always/Nearly Everyday PROTECTIVE BEHAVIORS: Have you actively done anything special to lower your son's chances of getting sick in order to prevent him from developing diabetes? (list ve rbatim b e low and check off corresponding items below followed b y questions in righthand co lumn) I am going to list off other thing s you may not have mentioned or thought of right now and I want you to tell me if you have done them. The re s ponses to these questions are just yes or no PROTECTIVE BERA VIORS # QUESTION IF YES, ASK THE FOLLOWING ... A. Is this something you have been l. Have you worked harder to NO YES doing ever since you found out about NO YES protect your so n from your son s risk? (ask (as k C) germs? B&C) B. Is this something you did early on NO YES and then stopped OR Did you start doin g this just recently? NO YES C. During the time you were doing this was it 0 Occasionally 0 Always/Nearly Everyday

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152 A. Is this something you have been 2. Have you kept your son out NO YES doing ever since you found out about NO YES of daycare with other your son's risk? ( ask ( ask C) children to protect him B&C) from germs? B. Is this something you did early on NO YES and then stopped OR Did you start doing this just recently? NO YES C. During the time you were doing this was it D Occasionally D Always/Nearly Everyday A. Is this something you have been 3 Have you increased your NO YES doing ever since you fo und out about NO YES son's exposure to other your son's risk? ( ask ( ask C) children ( i.e., put him in B&C) d aycare with other c hildren ) to b oost his B. Is thi s something you did early on NO YES immunity? and then stopped OR Did you start doin g this just recentl y? NO YES C. During the time you were doing this was it D Occasionally D Always/Nearly Evervdav A. Is this somethin g yo u have been 4. Have you had your son NO YES doing ever since yo u fo und out about NO YES wash his hands more your son's risk? (ask ( ask C) often? B &C) B. Is thi s something you did early on NO YES and then stoppe d OR Did you start doing this just recently? NO YES C. During the time you were doing this was it D Occasionally D Always/Nearlv Evervdav A. Is this something yo u have been 5. Have you limited your NO YES doin g ever since you found out about NO YES son's exposure to other your son s risk? ( ask ( ask C) c hildren because you were B &C) worried he might ge t sick and in c rease his risk? B. Is this some thin g yo u did early o n NO YES and then stopped OR Did you start doing this jus t r ecently? NO YES C. During the time you were d oi ng thi s, was it D Occasionally D Always/Nearly Everyday

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153 A. Is this something you have been 6. Have you limited your NO YES doing ever since you found out about N O YES son's exposure to toba cco your son's risk? (as k (ask C) smoke (i.e., cigarettes, B&C) cigars)? B. Is this something you did early on N O YES and then stopped O R Did you start doing this just recently? N O YES C During the time you were doing t h is, was it 0 O ccas i o n a ll y 0 A l ways/Near l y Eve r y d ay A Is this somet h ing you have been 7. Have you actively try to NO YES doing ever since you found out about N O YES reduce your son s contact your son's risk? ( ask (ask C) with harmful chemicals B &C) (i e ., pollution, food additives)? B. Is this something you did early on N O YES and then stopped OR Did you start doing this just recently? N O YES C. Duri n g the time yo u were doing this was it 0 O ccas i o n a ll y 0 A l ways/Nearly Every d ay 8 Have you delayed N O YES immunizations ( i e., DPT, MMR) for your son? Is there anything else that we have not covered that you have done or are still doing to try t o prevent type I diabetes from d eve l oping in your child? We have come to the end of the interview I know I have asked yo u about a lot of things today I want to say again that unfortunately scientists and doctors still do not know exact l y what causes type I diabetes in children. Because of this, we don't have any specific recommendations to give you and the questions I just asked you should not be considered to be thing s you s hould or should not be doing As in all matters of health, the best we can suggest is maintaining a healthy lifestyle for your child, including a balanced diet, activity, and rest. Thank you so much for your time and helpin g us with our s tudy. We will be ma i ling yo u a gift certificate in the next few weeks If you would l ike, we would also be willing to send you a summary of our resu l ts once the study is co m pleted

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160 Johnson, S. B. (2001). Screening programs to identify children at risk for diabetes mellitus: Psychological impact on children and parents. Journal of Pediatric Endocrinology and Metabolism, 14, S653-659. Johnson, S. B. (2002 May) Participant Experiences in the DPT-1: Preliminary Results Invited Presentation to NIH Type 1 TrialNet Study Group. Bethesda, MD. Johnson, S B ., Baughcum, A.E., Carmichael, S.K., She J., & Schatz, D.A. (submitted) Maternal anxiety associated with newborn genetic screening for type 1 diabetes. Diabetes Care Johnson, S. B., & Carmichael, S. K. (2000). At-risk for diabetes: Coping with the news Journal of Clinical Ps y chology in M e dical S e ttings 7, 69-78 Johnson S B Riley W J. Hansen C. A. & Nurick, M.A. (1990). Psychological impact of islet-cell antibody screening Preliminary results. Diabetes Care 13 93-97. Johnson, S B & Tercyak K. P. (1995). Psychological impact of islet cell antibody screening for IDDM on children, adults and their family members Diab e tes Care, 18, 1370-1372. Juengst E T. (1995). The ethics of prediction: genetic risk and the physician patient relationship. G e nome Science and T e chnology, 1, 21-36 Karges W. Hammond-McKibben D. Cheung R K. Visconti M., Shibuya N ., Kemp D. Dosch H.M. (1997) Immunological aspects of nutritional diabetes prevention in NOD mice: a pilot study for the cow's milk based IDDM prevention trial. Diab e tes 46, 557-564 Kash K. M. Holland J.C., Halper, M. S., & Miller, D. G. (1992). Psychological distress and surveillance behaviors in women with a family history of breast cancer. Journal of the National Canc e r Institute 84 24-30 Kessler S. (1989) Psychological aspects of genetic counseling: VI. A critical review of the literature dealing with education and reproduction. Ameri can Jo u rnal of Medical G e n e tics 34 340-353. Knip M (1998). Prediction and prevention of type 1 diabetes. A c ta P e dia trica, 425 S5462. Kovacs M. Finkelstein, R ., Feinberg T L., Crouse N ., M ., Paulaskas S. & Pollock, M. (1985) Initi a l psychologic responses of parents to the diagnosis of insulin dependent diabetes mellitus in their children. Diab e t e s Care, 8 568-575.

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161 Kupila A., Muona, P. Simell, T., Arvilommi, P., Savolainen H., Hamalainen, A.M., Korhonen, S., Kimpimaki, T., Sjoroos, M., Ilonen, J. Knip, M., & Simell, 0. (2001). Feasibility of genetic and immunological prediction of type 1 diabetes in a population-based birth cohort. Diabetologia, 44, 290-297 Kyvik, K. 0., Green, A. & Beck-Nielson H. (1995) Concordance rates of insulin dependent mellitus: A population based study of young Danish twins. BMJ, 311, 913917. Landin-Olsson, M. Palmer, J. P., Lemmark, A., Blom, L., Sundkvist, G. Nystrom, L., & Dahlquist, G. (1992). Predictive value of islet cell and insulin autoantibodies for type 1 (insulin-dependent) diabetes mellitus in a population-based study of newly-diagnosed diabetic and matched control children. Diab e tologia, 35, 10681073. Laporte, R E. (1995). Patterns of disease: Diabetes mellitus and the rest. BMJ, 310, 545-546 Lazarus R. S. & Folkman, S. (1984). Stress appraisal, and coping. New York: Springer. Lerman C. (1997). Psychological aspects of genetic testing: Introduction to the special issue. Health Ps ychology, 16, 37 Lerman, C., Croyle, R. T. Tercyak, K. P & Hamann, H. (2002) Genetic testing: Psychological aspects and implications. Journal of Clinical and Consulting Psychology, 70, 784-797 Lerman, C., Daly, M Masny A., & Balshem A. (1994a). Attitudes about genetic testing for breast-ovarian cancer susceptibility. Journal of Clinical On cology, 12, 843850. Lerman C. Daly, M. Sands, C., Balshem, A., Lustbader E., Heggan, T., Goldstein, L. James J., & Engstrom, P. (1993). Mammography adherence and psychological distress among woman at risk for breast cancer. Journal of the National Cancer Institute, 85 1074-1080 Lerman, C., Hughes, C., Croyle, R. T., Main D ., Durham, C., Snyder, C., Bonney A. Lynch J. F. Narod S. A., & Lynch, H. T. (2000). Prophylactic surgery decisions and surveillance practices one year following BRCA 1/2 testing. Preventive Medicine, 31, 75-80 Lerman C ., Kash, K., & Stefanek, M. (1994b). Younger women at increased risk for breast cancer: Perceived risk, psychological well-being and surveillance beha v ior. Monographs of th e National Cancer Institute, 16, 171-176.

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BIOGRAPIIlCAL SKETCH Amy E. Baughcum is currently in the doctoral program through the Department of Clinical and Health Psychology at the University of Florida specializing in child clinical/pediatric psychology. Her primary research interests to date include the psychological impact of genetic screening, early childhood feeding practices, and childhood obesity Her primary clinical interests are focused on working with chronically and terminally ill children and their families. Ms. Baughcum completed her master s degree in clinical psychology in 2001 in the Department of Clinical and Health Psychology at the University of Florida. Her master s thesis was titled Maternal Feeding Practices and Child Overweight in Preschool Children. Ms. Baughcum completed her bachelor's degree in psychology at Williams College in Williamstown, Massachusetts in 1997 Ms. Baughcum is originally from the Washington DC metropolitan area. In the fall of 2 003 Ms. Baughcum began her clinical internship year specializing in pediatric psychology at Columbus Children's Hospital in Columbus, Ohio. After internship she plans to complete a postdoctoral fellowship and pursue an academic career as a pediatric psychologist. 170

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I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Phil ophy. essor Medical Humanities and Social Sciences Florida State University, Tallahassee I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and uality, as dissertation for the degree of Doctor of Philos y. Alex ndra L. Quittner, C hair Professor of Clinical and Health Psychology I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philo~~ Samuel F. ears Associate Professor of Clinical and Health Psychology I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate in scope and quality, as a dissertation for the degree of Doctor of Philosophy. F ~ Professor of Psychology This dissertation was submitted to the Graduate Faculty of the College of Agricultural and Life Sciences and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy. August 2004 J;;~ G ZrauL Dean, College of Health Professions Dean, Grad

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