Citation
Multivariate Assessment of Adherence and Glycemic Control in Youth with Type 1 Diabetes

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Title:
Multivariate Assessment of Adherence and Glycemic Control in Youth with Type 1 Diabetes
Creator:
Duke, Danny
Place of Publication:
[Gainesville, Fla.]
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (61 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Psychology
Clinical and Health Psychology
Committee Chair:
Geffken, Gary R.
Committee Members:
Johnson, James H.
Wiens, Brenda A.
Storch, Eric
Graduation Date:
8/8/2009

Subjects

Subjects / Keywords:
Business executives ( jstor )
Child psychology ( jstor )
Diabetes ( jstor )
Glycemic control ( jstor )
Mathematical variables ( jstor )
Modeling ( jstor )
Parenting ( jstor )
Parents ( jstor )
Pediatrics ( jstor )
Type 1 diabetes mellitus ( jstor )
Clinical and Health Psychology -- Dissertations, Academic -- UF
adherence, adolescent, child, compliance, family, glycemic, hba1c, type, youth
Genre:
Electronic Thesis or Dissertation
born-digital ( sobekcm )
Psychology thesis, Ph.D.

Notes

Abstract:
The considerable research examining family variables and their relationship to adherence and glycemic control in pediatric populations with type 1 diabetes (T1D) has generally identified only weak or inconsistent relationships between adherence and glycemic control. Even though youth with T1D are often nonadherent to their prescribed treatment regimens, the multiple determinants and ecological complexity of these behaviors make assessing nonadherence a challenging proposition. Adding to the difficulty, many adolescents experience hormonal changes during puberty that may cause insulin resistance, obfuscating the relationship between adherence and glycemic control (HbA1c). Although difficult to identify, the clinical evidence and large studies of intensive T1D management support the presence of a strong relationship between improved adherence and metabolic control. To improve ecological validity, our study included measures of diabetes-specific family functioning, medically-related parent stress, youth cognitive functioning, youth behaviors (internalizing and externalizing), and measures of adherence. One hundred-fifty-one youth and their caregivers completed parent and child measures, and separate structured adherence interviews, while only youth completed blood draws for HbA1c assay. Path analysis was used to model the interrelationships among these variables and their combined ability to predict HbA1c in a sample of youth with T1D. For the final model, chi-square was not significant (X2 = 9.2 (17, N=151), p = .93), indicating excellent model fit. Analysis of the model yielded an R-squared for glycemic control (HbA1c) of .59, while the R-squared for adherence was .48. Child behavior significantly predicted adherence (Beta = -.37, p < .001), while adherence significantly predicted HbA1c (Beta = -.78, p < .001). The latent construct representing adherence, mediated the relationship between critical parenting and HbA1c, and adherence mediated the relationship between duration of diabetes and HbA1c. Child behavior also mediated the relationship between metacognition and parent stress. These findings suggest that an increased and comprehensive focus on assessing and monitoring family behaviors related to adherence is critical for optimizing health outcomes. Additional implications are discussed and directions for future research are presented. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2009.
Local:
Adviser: Geffken, Gary R.
Statement of Responsibility:
by Danny Duke.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Duke, Danny. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Classification:
LD1780 2009 ( lcc )

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Diabetes-Specific Assessments of Family Functioning................................................. 31
Diabetes Family Behavior Checklist (DFBC) ................................................ 31
D iabetes Fam ily B behavior Scale (D FB S) ................................................................ 32
Diabetes Family Responsibility Questionnaire (DFRQ).................................... 32
M easu res of A dh eren ce .................................................................................................. 32
Diabetes Self-Management Profile (DSMP).................................. .............. 32
M measure of G lycem ic Control............ ................................. .................. .............. 33
Glycem ic control (HbA lo) .................................................................... 33
D ata A analysis ......... .................................................... 33
Path Analysis ................ ........ .................. 33
Structural Equation M odeling ........................................... .......................... 34
M odel fit ......... ............ .................................................................. ....... 36
M ediation............................ ........... .............. 37
Analyses..................................... ..... .............. 37

3 RESULTS .......................................... .... .............. 38

G en eral A im s ......................................................... ......... ..... 3 8
S p ecific A im s ...............................39..............................
Post H oc A analyses. ..................................................................... 40

4 D ISC U SSIO N ......... ............. ...................................................................... ...... 47

LIST OF REFERENCES ................................. ........................... ............ 52

BIOGRAPHICAL SKETCH ............................................ .......... ....... 61



























6









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Goals included examining the nature and treatment of T1D with a focus on family

variables related to its management. The purpose of this investigation is to integrate and extend

existing empirical knowledge through the use of multivariate modeling techniques that more

accurately represent real-world complexity. The present study will utilize a structural equation

modeling (SEM) approach to evaluate family variables expected to influence adherence to T1D

treatment regimens and health outcome in a clinical sample of youth with T1D. Specifically, this

model will examine latent constructs represented by measures of the parent-child relationship

regarding diabetes management, and adherence. This model will also examine the unique and

combined contributions of measures representing parent stress, youth executive functioning,

problem youth behaviors, and family functioning specific to diabetes management, to predicting

adherence and glycemic control in youth with T1D (Figure 1-1).

Background and Significance

Type 1 diabetes (T1D) is one of the most common chronic diseases of school-aged youth,

affecting approximately 1 in every 400 to 600 youth under 20 years of age (National Institute of

Diabetes and Digestive and Kidney Diseases, 2005). Historically, research has examined various

characteristics of the disease (e.g., severity, time since diagnosis), of the patient (e.g., self-

efficacy, disease knowledge), and of the family (e.g., family conflict, parental support) that have

been hypothesized to predict management of T1D in pediatric patients. Despite an extensive

history of research in this area, the identification of strong predictors of adherence and

subsequent glycemic control has been limited. This may be because adherence to T1D treatment

regimens is complex, often with contributions from multiple interrelated factors. Past difficulty

accounting for variance in adherence and glycemic control may be partially due to the use of

limited models used to assess complex relationships. In attempts at a parsimonious explanation,

researchers have often used simple correlational models. More recently, the state of T1D









Aim 3 The manifest variable Critical Parenting was the only variable remaining from

the hypothesized latent construct Diabetes Related Parenting. Alone, Critical Parenting

accounted for significant variance in the latent variables Child Behavior (p = .34, p < .001), and

Adherence (P = -.38, p < .001; Figure 2-1)

Aim 4 The squared multiple correlations for the latent construct Adherence equaled

.476. Adherence accounted for a significant portion of the variance in HbAlc (DSMP; 1 = -.78, p

<.001).

Specific Aims

Aim 5 To test Baron and Kenny's first criteria for mediation, the relationship between

the latent construct of Child Behavior and HbAlc was tested and was found to be non-

significant, therefore, the first criteria for mediation was not met and further analysis

discontinued.

Aim 6 Nested modeling of mediation processes revealed that the relationship between

Critical Parenting and HbAlc was fully mediated by Adherence (Figure 2-2).

Aim 7 After meeting Baron and Kenny's first two criteria were met, the third criteria

was examined and the relationship between youth externalizing behavior and adherence did not

significantly change with the addition of parent stress to the model, therefore, the criterion

necessary for mediation were not met.

Aim 8 To test Baron and Kenny's first criteria for mediation, the relationship between

the manifest variable Metacognition and the latent construct Adherence was tested and found to

be non-significant, therefore, the first criteria for mediation was not met and further analysis

discontinued.









costs due to hospital admissions, length of stay, and other health care expenditures (Sclar, Skaer,

Chin, Okamoto, & Gill, 1991; Swanson, Hull, Bartus, & Schweizer, 1992). In 2002, the average

cost of healthcare expenses for each person in the United States was $5,440. In that year, there

were 800 million medical encounters. Research suggests that a significant portion of the

healthcare advice and prescriptions dispensed in these encounters was wasted. Fifteen percent of

total medical costs in this country and 25% of the total Medicare budget are spent on patients

with diabetes, mostly for treating the complications of the disease (Davidson, 1998; Pfeifer,

1998). The annual cost to the healthcare system caused by non-adherence is estimated to be as

high as 300 billion dollars (DeMatteo, 2004).

Given the high costs and the increased risk of life-threatening consequences due to

nonadherence, understanding the multiple determinants of ineffective illness management is

essential. In addition to the individual biological and psychological factors that influence

adherence to a T1D treatment regimen, patient behaviors typically occur within the context of

the family system (Wolpert & Anderson, 2001). Surprisingly, research regarding the family

system and its influence on adherence to T1D treatment regimens is relatively limited.

Past Research

Some progress has been made in T1D research in the last two decades. Increased

attention has been afforded adolescence and the developmental factors affecting adaptation to

T1D diagnoses. For example, research has examined the impact of biological growth hormone

factors, adolescent cognitive factors, and family influences on metabolic control and

psychosocial outcomes in adolescents (Anderson, 1995). Investigations have become

increasingly more theory-driven and are beginning to test competing psychosocial theories

regarding adaptation to a T1D diagnosis (Glasgow & Anderson, 1995). Some findings have

become well established, such as the multidimensional nature of regimen adherence (Johnson,









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medicine: The second decade. Journal of Consulting and Clinical Psychology, 70, 611-
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Approaches, pitfalls and new technologies. Medicine and Health Rhode Island, 86 (4).

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was related to family factors, adherence and glycemic control. Moreover, prospective research

has shown that in the 10 years following diagnosis with diabetes, 27% of youths with T1D

experienced an episode of major depression while 13% experienced an anxiety disorder (Kovacs,

Goldston, Obrosky, & Bonar, 1997). Additionally, Leonard et al. (2002) found that participants

with elevated attention problems, and aggressive and delinquent behaviors reported higher

HbAc1 relative to those without such problems. It is intuitive to expect that the presence of

externalizing behaviors and a resulting pattern of conflict with caregivers would interfere with

adherence to the complex task of managing T1D. Interestingly, in a study by Cohen, Lumley,

Naar-King, Partridge, and Cakan (2004) internalizing behavior problems significantly predicted

lower HbA1c. Problem child behaviors may also be related to executive functioning in youth

engaged in complex and demanding adherence processes.

Youth Executive Functioning

Executive functioning is a term that refers to a broad set of abilities associated with the

brain's frontal lobes. Executive functioning dimensions include the ability to plan, self-monitor,

and use working memory. Given that diabetes self-management requires significant planning,

organization, and self-monitoring, executive functioning is crucial to successful performance of

T1D self-care tasks. The role of executive functioning may prove especially important in youth

prescribed intensive T1D treatment regimens, given that these treatments demand increased

problem-solving abilities, planning, and organizational skill than traditional T1D self-

management routines (Anderson, 2003, Lorenz, et al. 1996; DCCT, 1993).

In a study by Cook, Herold, Edidin, and Briars (2002), adolescents with T1D, who were

randomly assigned to a 6-week problem-solving intervention demonstrated improvements in

problem-solving and glycemic control at post-treatment assessment. Hill-Briggs (2003)

conducted a review of the current literature pertaining to problem solving and T1D that found









1992: Kurtz, 1990). Glasgow and Anderson (1994) suggested that much of the research

regarding diabetes management has been "stuck" in a static design and analysis paradigm of

cross-sectional correlational studies of relatively small convenience samples. These studies often

use simple bivariate analyses that were appropriate at some point in the evolution of the diabetes

adherence literature, but are now rarely informative given the current state of knowledge. In

particular, pediatric psychology could benefit from theories of adherence that focus on how

family interactions impact on the youth's adaptation to and coping with the demands of

adherence (Glasgow & Anderson, 1995). It is widely recognized that parent behaviors are a

critical influence on the adherence behavior of their youth with diabetes (Anderson & Auslander,

1980). However, few theoretical models have been offered that describe how family interactions

support or interfere with the child's adaptation to diabetes treatment demands. Glasgow and

Anderson (1995) have called for the use of structural equation modeling (SEM) or alternate

approaches that are capable of identifying the independent and combined predictive utility of

demographic, medical, developmental, and behavioral factors related to diabetes management.

The literature has identified multiple psychosocial and family related variables that influence

adherence to T1D treatment regimens. The combination of these variables into a comprehensive

model, could further inform our understanding of the relationship between complex

environment, adherence and glycemic control.

Theory

Ecological Systems Theory

Social ecological theory has been proposed as a useful framework for pediatric

psychology research in general (Brown, 2002) as well as for predicting illness-management

behaviors across the life span (Gonder-Frederick, Cox, & Ritterband, 2002). Social ecological

theory (Bronfenbrenner, 1979) posits that complex problem behaviors, such as poor illness









adherence research has evolved to using more complex regression techniques to evaluate

moderation and mediation effects. Given the complexity and interrelatedness of family factors

found to predict adherence, more comprehensive and ecologically valid models are overdue.

Type 1 Diabetes (T1D)

Although T1D has a peak age of onset in middle childhood, diagnosis can be made as late

as middle adulthood. Type 1 diabetes results from the autoimmune destruction of the insulin

producing pancreatic islet cells (beta cells). This cell destruction eventually stops the pancreatic

production of insulin. Insulin is a hormone that regulates glucose metabolism and plays a vital

role in growth, activity, wound healing, and brain function. Without insulin, energy from food

cannot be converted into a usable form. Although the exact pathogenesis is unknown, the

manifestation of T1D is thought to result from a combination of genetic predisposition and

environmental factors that serve as catalysts (American Diabetes Association [ADA], 2005;

Wysocki, Greco, & Buckloh, 2003). Given the absence of insulin produced by the body,

glycemic control becomes a critical aspect of disease management.

It is generally accepted that monitoring blood glucose levels is an essential aspect of

managing diabetes. Self-monitoring provides information about current blood glucose levels and

a means of determining individual responses to insulin, and the influence of diet and exercise, on

blood glucose levels. Monitoring provides information that allows the patient to achieve tighter

blood glucose control, with a goal range of between 80 and 120. However blood glucose values

are variable and the values present between testing are unknown. A more accurate long-term

estimation of glycemic control is the HbA1c test. The HbA1c test estimates how much glucose is

attached to hemoglobin cells. Since red blood cells have a 120 day life span, the HbA1~ test

results are assumed to indicate the blood glucose concentration over the previous 120 day period

(Davidson, 1998).









CHAPTER 1
INTRODUCTION

Overview

Type 1 diabetes mellitus (T1D) is a chronic condition, typically diagnosed in childhood or

adolescence. Research indicates that more intensive diabetes regimens for youth result in

improved glycemic control (HbAlo) and decreased long-term health risk (Diabetes Control and

Complications Trial Research Group [DCCT], 1993, 1994, 2005). These findings established a

widely accepted scientific basis for treatment with the goal of achieving glycemic control as

close to normal as possible. Accordingly, physicians have increasingly prescribed more intense

and complex treatment regimens. However, even with advances in diabetes management,

achieving ideal control has proved a challenging goal, particularly for children and adolescents

(Gruppuso, 2003). These regimens typically require the administration of multiple daily

injections, glucose and dietary monitoring, carbohydrate estimation, dietary restrictions, and

regular physical activity. Families and youth diagnosed with T1D face the unrelenting challenge

of implementing a complex and time-consuming treatment regimen to effectively manage their

condition. The increased use of these complex regimens has led to significant overall

improvements in glycemic control (Svoren et al., 2007). Nevertheless, youth non-adherence

remains a prevalent problem (Ellis, Naar-King, Frey, Rowland & Greger, 2003; Kovacs,

Goldston, Obrosky, & Iyengar, 1992; Weissberg-Benchell et al., 1995) that can have serious

long-term health consequences that include kidney disease, retinopathy, eye problems, and

neuropathy (DCCT, 1993, 1994, 2005; Clark & Lee, 1995). Additionally, it has been estimated

that two-thirds of individuals with diabetes will eventually die from heart disease (ADA, 2006).

Extreme nonadherence can also lead to diabetic ketoacidosis (DKA), a relatively immediate and

life-threatening condition (Golden, Herold, & Orr, 1985; Trachtenbarg, 2005).









T1D Duration


Critical Parenting


Externalizing


Model fit: X2 = 9.2 (17, N=151), p


Figure 2-1 Structural Model









most studies have established a relationship between increased problem-solving skills and better

self-management behaviors. However, in their review the relationship between problem-solving

and glycemic control was not clearly delineated. In a recent pilot study, Alioto and Janusz

(2004) found that for pediatric patients in an intensive treatment condition, executive functioning

played more of a role in T1D self-management than did other cognitive abilities, such as math

achievement or general intelligence. Within a broader sample of T1D patients using both

conventional and intensive regimens, similar findings have also been found. For example,

parent-reported child executive functioning on the BRIEF was significantly related to diabetes

management, with poorer executive functioning associated with decreased adherence (Bagner,

Williams, Geffken, Storch, & Silverstein, 2007). Given the preliminary nature of the research

examining executive functioning and T1D treatment outcomes, more studies are needed in this

area. In particular, this relationship has not been adequately investigated within the context of

complex family relationships.

Family Variables

A body of research has investigated the relationship between family variables and

outcomes in pediatric patients with T1D. Studies have found that patients experiencing higher

levels of family conflict are more nonadherent or have decreased glycemic control (Hauser et al.,

1990; Miller-Johnson et al., 1994). Anderson and colleagues (1990) also demonstrated that

disagreements between parents and children regarding responsibility for T1D related tasks

predicted worsened glycemic control. Weaker and inconsistent relationships between other

family characteristics such as warmth, discipline, parenting, and adherence or glycemic control

have also been found (Hauser et al., 1990; Miller-Johnson et al., 1994). Overall, these findings

suggest that family factors play an important role in the management of T1D.









acceptable to good internal consistency (.74 to .82; Schafer, personal communication, 1998). For

the present study only the youth report was used.

Diabetes Family Behavior Scale (DFBS)

The DFBS is a self-report measure of perceived family support for youth with T1D

(Waller et al., 1986). Given the aims of this study, only the "warmth and caring" and "guidance

and control" subscales were used. The warmth/caring subscale (DFBS-WC) has shown adequate

internal consistency (a = 0.79; McKelvey et al., 1993) as has the guidance and control subscale

(DFBS-GC) (c = 0.76; Lewin et al., 2006).

Diabetes Family Responsibility Questionnaire (DFRQ)

The DFRQ is a measure of family sharing of responsibility for diabetes treatment

(Anderson et al., 1990) that is completed by both parent and youth. The scale has shown

marginal to good internal consistency (.69 to .85) for the three subscales (Anderson et al., 1990).

This measure yields parent, child, and no-responsibility scores. For this study, only the no-

responsibility scores will be used (i.e., the scores indicating that neither parent nor the child

assumes responsibility).

Measures of Adherence

Diabetes Self-Management Profile (DSMP).

The DSMP is a structured interview, consisting of 23 questions having an administration

time of approximately 15 to 20 minutes. Questions assess five areas of diabetes management,

including: insulin administration/dose adjustment, blood-glucose monitoring, exercise, diet, and

management of hypoglycemia. The scale has shown adequate to good internal consistency (a =

.76) and inter-observer agreement (94%; Harris et al., 2000). Interviews were administered by

trained research assistants to both parent and child regarding the child's management of their

T1D









DISCUSSION

This study was an attempt to more closely model real-world ecological complexity.

Although the importance of the included variables have been independently validated, this is the

first known study to simultaneously examine the combined interrelationships among child

behavior, metacognition, parent stress, critical parenting, adherence and HbA1c. For our first

aim, the model exhibited excellent fit to the data, supporting the validity of the proposed

interrelationships. The model's validity was also supported by accounting by yielding a

significant R-squared for HbA1,. In addition, the effect sizes for the latent constructs of

Adherence and Child Behavior further confirmed the validity of the model. Congruent with

intuition and expectations, Child Behavior predicted Parent Stress, i.e. increases in problematic

Child Behavior increased parental stress. In addition, increased Child Behaviors predicted

decreased Adherence. Child behaviors are integral to the effective implementation of complex

T1D management tasks. Child internalizing and externalizing symptoms are likely to increase

parental stress directly as well as through interference with diabetes management tasks

(Adherence). In support of clinical observations, Adherence significantly predicted HbA1c, such

that better adherence predicted decreased HbA1,. Although expected, Parent Stress did not

predict Critical Parenting, suggesting that the tendency to use punitive and criticizing control

strategies occurs independent of stress. Metacognition significantly predicted HbA1~ and Child

Behavior. Considering that the Metacognition Index is comprised of the ability to initiate tasks,

working memory, the ability to plan and organize, materials organization, and self-monitoring,

this is a logical finding. These abilities are clearly related to both the parent's perception of the

youth's behavior and to in vivo control of HbA1,. It was surprising that Metacognition was not

significantly related to Adherence. It could be that those with better cognitive functioning

compensate in ways that are unmeasured in this study.









relationships or that our measure of metacognition is too general to adequately measure the

specific cognitive requirements of adherence to T1D treatment regimens.

Post hoc analyses found that the relationship between duration of T1D and HbAlc was

mediated by Adherence. This suggests that as Duration of Diabetes increased, Adherence

decreased which caused HbAlc values to rise. This may be partially due to the increasing

maturational demands and individuation processes previously posited, but may also be in part

due to weakening of resolve over time and a need for the youth to feel normal. Clinically,

children often express that they often pretend they don't have T1D. This likely serves a self-

protective function, but often leads to ineffective glycemic control (HbAlc). The final post hoc

analysis found that Child Behavior mediated the relationship between Metacognition and Parent

Stress. This is a logical and intuitive finding that suggests parent perception of metacognitive

functioning and their evaluation of their child's behaviors are congruent, such that they perceive

youth decreased ability to use metacognitive strategies as related to behavior problems, which in

turn led to parent stress.

It is important to note limitations of this study. First, due to the cross-sectional nature of

data collection, statements regarding causality and directionality can only be inferred. The

causal implications of future research could be strengthened by utilizing a longitudinal approach

to examining factors influencing adherence. Second, model development approaches,

necessarily post-hoc, and having been created based on the uniqueness of a population (in this

case largely rural and low SES), may not generalize to other settings. Third, while participants

were informed that no parent or physician would see their results and were encouraged to be as

truthful and accurate as possible, there exists potential for reporting bias on questionnaires and

during interviews. Fourth, this sample was relative small for path analysis. A larger sample may









1989). Previous research has found that parents of children with chronic illnesses such as

diabetes tend to report higher levels of general stress when compared to parents of healthy

children (Hauenstein, et al., 1989). More specifically, parents of children with diabetes

experience greater marital distress (Quittner et al., 1998) and greater stress related to mealtime

behaviors (Wysocki, et al., 1989). Although the presence of increased stress in parents of

children with T1D has been well documented, the implication of this finding in terms of

adherence and glycemic control has not been adequately researched. Given the strong

associations between family functioning, diabetes adherence, and health status measures, the

effect of pediatric parenting stress on these constructs calls for empirical evaluation (Lewin et al.,

2005; Lewin et al., 2006).

Within pediatric populations, parenting stress has historically been measured using general

measures of stress (e.g., Parenting Stress Index; PSI; Abidin, 1995). However, in more recent

years researchers have begun to consider the importance of measuring disease-related stress

experienced by parents of children with chronic diseases, termed "pediatric parenting stress"

(Streisand, Braniecki, Tercyak, & Kazak, 2001). Therefore, emphasis has been placed on

assessing specific disease-related parenting stress. The Pediatric Inventory for Parents (PIP;

Streisand, et al., 2001) is one such measure designed to assess the stress of parents of children

with chronic diseases. Within pediatric T1D populations, pediatric parenting stress has been

related to important issues such as parents' ability to learn disease-management skills (Gillis,

1993) and the child's ability to engage in successful diabetes management (Auslander et al.,

1997; Hanson et al., 1996). Although parenting stress was not significantly related to glycemic

control in a recent study, the authors noted that there might be "aspects of the diabetes regimen

itself that causes parent stress" (Streisand, Swift, Wickmark, Chen, & Holmes, 2005, p. 518).









TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ............................ ............................................................................. 4

LIST O F TA B LE S .................................................................................. 7

L IS T O F F IG U R E S ................................................................... ........................ .......... ...... 8

A B S T R A C T ......... .. ..................... ............................................................. 9

1 IN TR O D U C TIO N ............. .................................... ............... .... ....... ..... 11

Overview ....................................... ............................. ...... .............. 11
B background and Significance ................................................... ................................. 12
T ype 1 D diabetes (T 1D ) .... ............................ .... ............................ .............. 13
A d h e re n c e ......................................................................................................... 1 4
Past Research ......................................................................... ......... 16
T theory ...................................................................... 17
Ecological Systems Theory .......................... ........ .................... 17
Coercion M odel ......................................... 18
M iscarried Helping ................................... ........................... .............. 18
Variables Related to Adherence to T1D Treatment Regimens................................ 19
D em graphic variables ............................................................. .............. 19
Illness-specific variables.............................. ........ ...................... ...... .... .. 19
P rental stress ....................................................................... 19
C hild B behavior P problem s ........................................................... .............. 2 1
Youth Executive Functioning .................................. .............................. 22
F am ily V ariab les.......................................................................................... 2 3
R research A im s ...................................................................................................... 24
G general A im s ............................................................................................... 24
S p ecific aim s................................................................................................ 2 4

2 M E T H O D ......................................................................................................... .............. ...... 2 8

P a rtic ip a n ts ................................................................................................ ........... ...... 2 8
Procedure ........................................ .................... 28
M easu res ................................................... ......... ..... 2 9
D em graphic Inform ation............................. .............. 29
P aren't M easu res ..................................................................... 2 9
Child Measures ........................................................ .............. 30
Behavior Checklist (CBCL: Achenbach, 1991 ................... .......................... .. 30
Behavior Rating Inventory of Executive Functioning (BRIEF; Gioia, et al.,
2 0 0 0 ) ................................................................................................................... 3 0









Aim 9 The validity of this test was predicated on step one of Aim 8, therefore the first

criteria for mediation for this analysis was not met and further analysis discontinued.

Post Hoc Analyses.

Given the strong relationships among Critical Parenting, Adherence, and HbAlc (Table

2-1), post hoc analyses were conducted to examine whether Adherence mediated the relationship

between Critical Parenting and HbAlc. This analysis met Baron and Kenny's criteria, indicating

mediation processes. Adherence mediated the relationship between Duration of T1D and HbAlc

(Figure 2-3). Additionally, given the presence of strong relationships among Child Behavior,

Metacognition, and Parent Stress (Table 2-1), mediation analyses were conducted and a

significant mediation relationship was identified, wherein Child Behavior mediated the

relationship between Metacognition and Parent Stress (Figure 2-4).









Patterson, G. R. (1995). Coercion as a basis for early age of onset for arrest. In J. McCord (Ed.),
Coercion and Punishment in Long-Term Perspectives, (pp. 81-105). New York;
Cambridge University Press.

Patterson, G. R., Reid, J. B., & Dishion, T. (1992). AntisocialBoys. Eugene, OR: Castilia.

Pfeifer, M. A. (1998) Chronic Complications of Diabetes: An Overview. In: Funnell, M. M.,
(ed.) A Core Curriculum for Diabetes Education. 3rd ed. Chicago, Il: American
Association of Diabetes Educators; 1998: 659-675.

Quittner, A. L., Opipari, L. C., Espelage, D. L., Carter, B., Eid, N., & Eigen, H. (1998). Role
strain in couples with and without a child with a chronic illness: associations with marital
satisfaction, intimacy, and daily mood. Health Psychology, 17, 112-124.

Rapoff, M. A. (1999). Adherence to Pediatric Medical Regimens. New York: Kluwer
Academic/Plenum Publishers.

Rapoff, M. A. (2002). Assessing and enhancing adherence to medical regimens forjuvenile
rheumatoid arthritis. Pediatric Annals, 31, 373-379.

Reid, J. B., Patterson, G. R., & Snyder, J. (2002). Antisocial Behavior in Children and
Adolescents: A Developmental Analysis and Model for Intervention. Washington, DC:
American Psychological Association.

Schafer, L. C., McCaul, K. D., & Glasgow, R. E. (1986). Supportive and nonsupportive family
behaviors: Relationships to adherence and metabolic control in persons with type 1
diabetes. Diabetes Care, 9, 179-185.

Sclar, D. A., Skaer, t. 1., Chin, A., Okamoto, M. P., & Gill, M. A. (1991). Utility of a transdermal
delivery system for antihypersensitive therapy: Part 1. American Journal of Medicine, 91,
50S-56S.

Streisand, R., Braniecki, S., Tercyak, K. P., & Kazak, A. E. (2001). Childhood illness-related
parenting stress: The pediatric inventory for parents. Journal of Pediatric Psychology, 26,
155-162.

Streisand, R., Swift, E., Wickmark, T., Chen, R., & Holmes, C. S. (2005). Pediatric parenting
stress among parents of children with type 1 diabetes: The role of self-efficacy,
responsibility, and fear. Journal of Pediatric Psychology, 30, 513-521.

Svoren, B. M., Volkening, L. K., Butler, D. A., Moreland, E. C., Anderson, B. J., Laffel, L. M.
(2007). Temporal trends in the treatment of pediatric type 1 diabetes and impact on acute
outcomes. Journal ofPediatrics, 150, 279-85.

Swanson, M., Hull, D., Bartus, S., & Schweizer, R. (1992). Economic impact of noncompliance
in kidney transplant recipients. Transplantation Proceedings, 24, 27-32.









testing whether a model with latent variables (family factors and adherence) fits existing,

previously collected data.

Mediation

Baron and Kenny's (1986) guidelines for mediation will be followed to assess for

mediation effects. The following criteria are necessary for mediation: (I) the predictor should be

significantly associated with the outcome, (II) the predictor should be significantly associated

with the mediator, (III) the mediator should be associated with the outcome variable (with the

predictor accounted for), and (IV) lastly, the addition of the mediator to the full model should

significantly reduce the relationship between the predictor and criterion variable.

Analyses

Before testing the proposed model, the data was systematically screened for missing values

and mean subscale values were used to replace missing data points. To examine for the presence

of outliers in the data, Mahalanobis distance (Mahalanobis, 1936) was calculated for each

variable; no significant outliers were identified. Means, standard deviations, and correlation

matrices were calculated to examine the interrelationships among the model's variables (Table 2-

2). Mutivariate assumptions of normality were examined by calculating skewness and kurtosis

values for all variables. The variables Internalizing, Externalizing, and the Metacognition Index

were found to be positively skewed and/or kurtotic skewnesss or kurtosis > 1). Logarithmic

transformations (loglO) were conducted, which successfully normalized the distributions of these

variables. After transformations, skewness and kurtosis values were within the range of

acceptable normality (-1.0 to 1.0) for all variables. A model development approach was

conducted using AMOS 7 to construct the model. The maximum likelihood method estimated

model fit. Standardized path coefficients and their associated significance were calculated

(Figure 2-1). Non-significant pathways were eliminated from the model for parsimony and for

37



































2009 Danny C. Duke









Lewin, A. B., Heidgerken, A. D., Geffken, G. R., Williams, L. B., Storch, E. A., Gelfand, K. M.,
& Silverstein, J. H. (2006). The relation between family factors and metabolic control:
The role of diabetes adherence. Journal of Pediatric Psychology, 31, 174-183.

Lewin, A. B., Storch, E. A., Silverstein, J. H., Baumeister, A. L., Stawser, M. S., & Geffken, G.
R. (2005). Validation of the Pediatric Inventory for Parents in mothers of children with
type 1 diabetes: An examination of parenting stress, anxiety, and childhood
psychopathology. Families, Systems, & Health, 23, 56-65.

Lorenz, R. A., Jannasch, K., Bubb, J., Kramer, J., Davis, D., et al. (1996). Changing behavior:
Practical lessons from the Diabetes Control and Complications Trial. Diabetes Care, 19,
648-652.

Madsbad, S., McNair, P., Faber, O. K., Binder, C., Christiansen, C., & Transbol J. (1980). Beta-
cell function and metabolic control in insulin treated diabetics. Acta Endocrinology, 43,
196-200.

Mahalanobis, P. C. (1936). On the generalized distance in statistics. Proceedings of the National
Institute of Science ofIndia, 12, 49-55.

McCaul, K. D., Glasgow, R. E., & Schafer, L. C. (1987). Diabetes regimen behaviors. Predicting
adherence. Medical Care, 25, 868-881.

McKelvey, J., Waller, D. A., North, A. J., Marks, J. F., Schreiner, B., Travis, L. B., et al. (1993).
Reliability and validity of the Diabetes Family Behavior Scale. The Diabetes Educator,
19, 125-132.

Meyers, L. S., Gamst, G., & Guarino, A. J. (2006). AppliedMultivariate Research: Design and
Interpretation. Thousand Oaks, CA: Sage Publications.

Miller-Johnson, S., Emery, R. E., Marvin, R. S., Clarke, W., Lovinger, R., & Martin, M. (1994).
Parent-child relationships and the management of insulin-dependent diabetes mellitus.
Journal Counseling and Clinical Psychology, 62, 603-610.

Northam, E. A., Matthews, L. K., Anderson, P. J., Cameron, F. J., & Werther, G.A. (2005).
Psychiatric morbidity and health outcome in Type 1 diabetes perspectives from a
prospective longitudinal study. Diabetic Medicine, 22, 152-157.

National Institute of Diabetes and Digestive and Kidney Diseases (2005). National diabetes
statisticsfact sheet: General information and national estimates on diabetes in the United
States. Bethesda, MD: U.S. Department of Health and Human Services, National Institute
of Health, 2005.

Patterson, G. R. (1982). Coercive family processes. Eugene, OR: Castalia.









Anderson, B. J., Coyne, J. C. (1991). 'Miscarried helping' in families of children and adolescents
with chronic diseases. In: Johnson, J. H., Johnson, S. B. (eds), Advances in ChildHealth
Psychology. Gainesville: University of Florida Press.

Auslander, W. F. Anderson, B. J., Bubb, J., Jung, K. C., & Santiago, J. V. (1990). Risk factors
to health in diabetic children: A progressive study from diagnosis. Health and Social
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Auslander, W. F., Thompson, S., Dreitzer, D., White, N. H. & Santiago, J. V. (1997). Mothers'
satisfaction with medical care: Perceptions of racism, family stress, and medical
outcomes in children with diabetes. Health & Social Work, 22, 190-199.

Bagner, D. B., Williams, L. B., Geffken, G. R., & Storch, E. A. (2007). The relationship
between executive functioning and adherence in pediatric patients with type 1 diabetes.
Children's Health Care, 36, 169-179.

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with medication nonadherence in asthmatic children. Journal ofAsthma, 35, 347-353.

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical considerations. Journal of
Personality and Social Psychology, 52, 1173-1182.

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ecology of pediatric psychology. Journal of Pediatric Psychology, 2 7, 191-201.

Byrne, B. M. (1998). Structural Equation lA ,,eling n/ ith LISREL, PRELIS and SIMPLIS: Basic
Concepts, Applications, and Programming. Mahwah, New Jersey: Lawrence Erlbaum
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Barbara N. Byrne (2001). Structural Equation b 1deling / ith AMOS. Rahwah, NJ: Lawrence
Erlbaum Associates, 2001.

Chisholm, V., Atkinson, L., Donaldson, C., Noyes, K., Payne, A., & Kelnar, C. (2006).
Predictors of treatment adherence in young children with type 1 diabetes. Journal of
Advanced Nursing, 57, 482-493.

Chui, M., Deer, M., Bennett, S., Tu, W., Oury, S., Brater, D., et al. (2003). Association between
adherence to diuretic therapy and health care utilization in patients with heart failure.
Pharmacotherapy, 23, 326-332.









LIST OF REFERENCES


Abidin, R. R. (1995). Parenting Stress Index -Manual. (3rd ed.) Charlottesville, VA: Pediatric
Psychology Press.

Achenbach, T. M. (1991). Manual for the Child Behavior Checklist 4/18 and 1991 Profile.
Burlington: University of Vermont Department of Psychiatry.

Alioto, A. & Janusz, J. A. (2004). The role of executive functioning in adolescents' self-
management of type 1 diabetes. Poster presented at the Child Health Conference.
Charleston, SC.

American Diabetes Association. (2006). Redefining diabetes control. Diabetes &
Cardiovascular Disease Review, 1, 1-8.

American Diabetes Association (2005). Standards of medical care in diabetes (Position
Statement). Diabetes Care, 28, (Suppl. 1), S4-S36.

American Diabetes Association (1997). Position statement: Standards of medical care for
patients with diabetes mellitus. Diabetes Care, 20, 22-31.

Amiel, S. A., Sherwin, R. S., Simonson, D. C., Lauritano, A. A., & Tamborlane, W. V. (1986).
Impaired insulin action in puberty: A contributing factor to poor glycemic control in
adolescents with diabetes. New England Journal of Medicine, 315, 215-219.

Anderson, B. J. (2003). Who Benefits From Intensive Therapy in Type 1 Diabetes? A fresh
perspective, more questions, and hope. Diabetes Care, 26, 2204-2206.

Anderson, B. J. (2004). Family conflict and diabetes management in youth: Clinical lessons from
child development and diabetes research. Diabetes Spectrum, 17, 22-26.

Anderson, B. J. (1995). Childhood and adolescent psychological development in relation to
diabetes mellitus. In C. Kelnar (Ed.), Childhood andAdolescent Diabetes (pp. 107-119).
London: Chapman and Hall.

Anderson, B. J., Auslander, W. F., Jung, K. C., Miller, J. P., & Santiago, J. V. (1990). Assessing
family sharing of diabetes responsibilities. Journal of Pediatric Psychology, 15, 477-492.

Anderson, B. J., Auslander, W. F. (1980). Research on diabetes management and the family: A
critique. Diabetes Care, 3, 696-702.

Anderson, B. J., Brackett, J., Ho, J., & Laffel, L. M. (1999). An office-based intervention to
maintain parent-adolescent teamwork in diabetes management: Impact on parent
involvement, family conflict, and subsequent glycemic control. Diabetes Care, 22, 713-
721.










For our second aim, the demographic variable duration of diabetes added significantly to

the model. The presence of a relationship between the duration of T1D and adherence is well

established in the literature (Chisholm et al., 2006; Johnson, & Meltzer, 2002). The increasingly

complex social and academic demands inherent through the developmental period are likely to

increase the risk of nonadherence. In particular, adolescence is a period of escalating

responsibility and increased risk-taking. The process of individuation from parents and the

accompanying sense of invincibility (Elkind, 1967) are also likely to interfere with the complex

management of type 1 diabetes for many youth.

Although for aim 3, a latent construct comprised of a cluster of family factors related to

diabetes management was expected, critical parenting emerged as the only meaningful predictor

of this proposed construct. Considering the mean participant age of 13.58 years, this finding is

0 not inconsistent with the extant literature, which suggests that in adolescent populations the

relationship between parental guidance/control and HbA1c is not as strong (McKelvey et al.,

1993; Waller et al., 1986). Adolescents are more likely to be seeking autonomy and less likely to

seek or accept guidance and control from adults. Critical parenting was found to be a strong

predictor of both adherence and child behavior. This is congruent with expectations and in

keeping with Patterson's (1982) coercion model. A related alternate explanation is miscarried

helping proposed by Anderson and Coyne (1991). A key difference between these is who

precipitates the coercive cycle. In the coercion model it is generally considered to be a reciprocal

process, but in miscarried helping it is the parent who precipitates conflict. Although this pattern

is considered reciprocal, it is most likely initiated by the parent and its effectiveness as a strategy

learned by the child over time. Such maladaptive strategies are likely to have a generational









management, are determined by multiple difficulties within the systems in which the child and

family are embedded. Within the overarching context of social ecological theory, other

theoretical perspectives may be of some utility in explaining specific interaction patterns within a

family.

Coercion Model

Other influences on adherence are the effects of diabetes-related family factors, such as

critical parenting, around diabetes care tasks (Anderson, 2004; Ellis et al., 2007; Hansen &

Onikull-Ross, 1990; Laffel et al., 2003; Lewin et al., 2006). Critical parenting may affect

adherence and glycemic control through processes explained by Patterson's (1982) coercion

model. This model posits a process of behavioral contingencies that explain how parent and

youth behaviors mutually influence each other in ways that increase the likelihood that the

youth's aggressive behavior will increase while parental control over such behaviors will

decrease (Patterson, 1995; Patterson, Reid, & Dishion, 1992; Reid, Patterson, & Snyder, 2002).

These interchanges are characterized by parental demands for compliance, the child's refusal to

comply, his or her escalating complaints, and finally the parent's capitulation. This theoretical

perspective suggests that parenting and youth behaviors are reciprocal (Fite, Colder, Lochman, &

Wells, 2006), yet may be precipitated by parent or youth. The presence of such a coercive cycle

around diabetes management tasks is likely to interfere with adherence to treatment regimens

and subsequent glycemic control.

Miscarried Helping

A related construct, that is similar to Patterson's (1982) coercion model, is the concept of

miscarried helping proposed by Anderson and Coyne (1991). In miscarried helping the

caregiver's behaviors (good intentions) result in interpersonal conflicts between youth with

chronic health problems and their parents, further polarizing the two parties, putting the health of









MULTIVARIATE ASSESSMENT OF ADHERENCE AND GLYCEMIC CONTROL IN
YOUTH WITH TYPE 1 DIABETES





















By

DANNY C. DUKE


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

2009









6. Proposed is to examine whether a hypothesized relationship between family factors and

HbA1c is mediated by adherence.

7. Proposed is to examine whether a hypothesized relationship between youth externalizing

behavior and adherence is mediated by parent stress.

8. Proposed is to examine whether a hypothesized relationship between metacognition and

adherence is mediated by youth behaviors.

9. Proposed is to examine whether a hypothesized relationship between metacognition and

adherence is mediated by critical parenting.
















Independent Variable
(Critical Parenting)


Mediating Variable
(Adherence)


Figure 2-2 Adherence mediating the relationship between critical parents (DFBC) and HbA1c


.198**


(.066)


S-.782***
(-.726***)


xx









parent experiences managing stress (PIP-D). Higher scores indicate greater parenting stress.

Past empirical investigations have found that PIP total scores are highly correlated with the

Parenting Stress Index-Short Form, a general, non-illness specific measure of parenting stress

(Streisand et al., 2001). High internal consistencies for the PIP subscales have been

demonstrated for parents of children with T1D (PIP-F a = .94; PIP-D a = .94-.95; Lewin et al.,

2005; Streisand et al., 2005).

Child Measures

Behavior Checklist (CBCL: Achenbach, 1991

The CBCL is a widely used, standardized, 118-item parent-report questionnaire for 4-18

years olds that exhibits excellent psychometric properties (Achenbach, 1991). Each item is

scored 0 if 'not true', 1 if 'somewhat true' and 2 if 'very true' for the child. Designed to assess

behavioral problems and social competencies of youth 4 18 years of age, the CBCL yields two

broadband, higher order psychopathology scales, internalizing and externalizing. The sum of the

scores on all items produces a total score that gives an overall measure of the child's

emotional/behavioral adjustment (Achenbach, 1991; Cohen, Gotlieb, Kershner, & Wehrspann,

1985; Drotar, Stein, & Perrin, 1995). For the present study only the internalizing and

externalizing subscales were used.

Behavior Rating Inventory of Executive Functioning (BRIEF; Gioia, et al., 2000).

The BRIEF is a widely used and validated parent report measure, which is designed to

examine "children's everyday executive skills in natural settings" (Donders, 2002). Primary

caregivers typically complete the BRIEF, an 86-item parent-report measure designed to assess 8

domains of executive functioning. Although the BRIEF includes 86 items, only 72 items are

used in the calculation of the scale and composite scores. The additional questions are

considered to be items of clinical interest. The executive functioning domains measured by the

30









LIST OF TABLES

Table page

2-1 D em graphics ........................................................................................................................ 45

2-2 Correlations ................... ................... .............................6









models: a measurement model and a structural model. The measurement model represents the

degree to which the indicator variables capture the essence of the latent factor. It is basically

confirmatory factor analysis for each latent variable. It is called a measurement model because

the indicator variables are measured variables used to give us some indication of the intangible,

unmeasured latent construct (Meyers et al., 2006). The structural model is akin to path analysis

in that we are looking at the causal relationships between the major variables of interest in the

theory. Causal connections can be drawn between latent variables. For example, deficits in

child executive functioning may cause decreased adherence that then causes reduced glycemic

control. Once a model is proposed, (i.e., relationships between the variables have been

hypothesized) a correlation/covariance matrix is created. The estimates of the relationships

between the variables in the model are calculated using the maximum likelihood procedure. The

model is then compared with the relationships (the correlation/covariance matrix) of the actual or

observed data. SEM assesses how well the predicted interrelationships between the variables

match the interrelationships between the actual and observed variables. It has the capability to

assess both the measurement model (how well the measured variables define their respective

construct) and the structural model (how well the latent constructs relate to each other)

simultaneously. If the two matrices (the one based on the hypothesized model and the one

derived from the actual data) are consistent with one another, then the structural equation model

can be considered a credible explanation for the hypothesized relationships (Meyers et al., 2006).

In short, the technique allows a hypothesized model to be tested statistically, in a simultaneous

analysis of all the variables in the model, in order to determine if the model's covariance matrix

is consistent with the collected data. SEM is confirmatory, provides explicit estimates of

measurement errors, and can incorporate not only observed but also unobserved (latent) variables











Table 2-2 Correlations


M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. ChildAge 13.58 2.93 -.082 .150 -.238** .028 -.019 -.077 .090 -.026 -.373** -.078 -.187* -.136 .148
2. Family
2. Family 48.2K 34.7K .012 -.140 -.016 -.156 -.095 -.162 .034 .109 -.067 .204* .246** -.181*
income
3. T1D
3.T1 58.66 43.43 .079 .025 .039 -.021 .081 -.056 .076 -.066 .259** -.114 .266**
Duration
4. Parent
4.Parent 185.75 53.72 .316** .419** .368** .140 -.137 .006 -.085 -.204* .031 .144
Stress
5. Meta-
et33.89 20.88 .460** .344** .187* -.131 -.075 -.025 -.192* -.189* -.008
cognition
6. Internal- 7.74 7.01 .618** .335** -.258** -.138 -.097 -.299** -.202* .180*
izng
7. External- 8.38 8.26 .618** -.200* -.100 -.015 -.323** -.323** .176*
izng
8. Critical
ti15.08 5.55 -192 -.094 -.102 -.384** -.331 .418**
- Parenting
9. Warmth &
52.93 10.18 -.080 -.104 .132 .063 .030
Caring
10. Guidance
43.88 8.66 .028 .054 .043 -.061
& Control
11. Critical
2.22 2.14 .043 -.052 .022
Parenting
12. Adherence-
12.Adherence- 57.14 11.35 .470** -.550**
Parent
13. Adherence-
57.98 9.64 -.362**
Youth
14. HbA1c 8.83 1.92

** Correlation is significant at the .01 level.
Correlation is significant at the .05 level.









Table 2-1 Demographic Characteristics


Category N %

Sex

Male 61 40.4

Female 90 59.6

Ethnicity

Caucasian 109 72.2

African American 23 15.3

Hispanic 15 9.9

Other 4 2.6









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Understanding Multivariate Statistics (pp. 65-98). Washington, DC: American
Psychological Association.

Kovacs, M., Finkelstein, R., Feinberg, T. L., Crouse-Novak, M., Paulauskas, S., & Pollock, M.
(1985). Initial psychological responses of parents to the diagnosis of insulin-dependent
diabetes mellitus in their children. Diabetes Care, 8, 568-575.

Kovacs, M., Goldston, D., Obrosky, D. S., & Bonar, L. K. (1997). Psychiatric disorders in
youths with IDDM: Rates and risk factors. Diabetes Care, 20, 36-44.

Kovacs, M., Goldston, D., Obrosky, D. S., & Iyengar, S. (1992). Prevalence and predictors of
pervasive noncompliance with medical treatment among youths with insulin dependent
diabetes mellitus. Journal of the American Academy of Child and Adolescent Psychiatry,
31, 1112-1119.

Kurtz, S. (1990). Adherence to diabetes regimens: Empirical status and clinical applications.
The Diabetes Educator, 16, 50-56.

Laffel, L. M. B., Connell, A, Vangsness, L., Goebel-Fabbri, A., Mansfield, A., & Anderson, B. J.
(2003). General quality of life in youth with typel diabetes. Diabetes Care, 26, 3067-
3073.

La Greca, A. M., Follansbee, D. M., & Skyler, J. S. (1990). Developmetnal and behavioral
aspects of diabetes management in youngsters. Children's Health Care, 19, 132-139.

Lasagna, L., & Hunt, P. B. (1991). Health care, research, and regulatory impact of
noncompliance. In J.A Cramer & B. Spilker (Eds.), Patient Compliance in Medical
Practice and Clinical Trials (pp. 393-403). New York: Raven.

Leonard, B. J., Yuh-Pyng, J., Savik, K., Plumbo, P. M., & Christensen, R. (2002). Psychosocial
factors associated with levels of metabolic control in youth with type 1 diabetes. Journal
of Pediatric Nursing, 17, 28-37.

Lernmark, B., Persson, B., Fisher, L., & Rydelius, P. A. (1999). Symptoms of depression are
important to psychological adaptation and metabolic control in children with diabetes
mellitus. Diabetic Medicine, 16, 14-22.









METHOD


Participants

Participants were 153 youths with T1D and their primary caregivers, recruited from a

tertiary Pediatric Endocrinology Clinic affiliated with the University of Florida. Inclusion

criterion for youth participation were (a) aged 8-18 years, (b) the presence of T1D for at least six

months duration, (c) living with and accompanied by their primary caregiver, (d) no other

chronic medical conditions (e.g., cystic fibrosis), and (e) both child and primary caregiver ability

to read and complete study measures (e.g., English-speaking, no mental retardation).

The current sample consisted of 92 girls and 61 boys aged 8.0 to 18.75 years (M=

13.6 years, SD = 3.1). The ethnic/racial distribution of participants was 72.5% Caucasian, 15%

African American; 9.8% Hispanic, and 2.6% indicating membership in other ethnic/racial groups

(Table 2-1). Participants were from predominantly two-parent families (67.4%) with mothers

reporting as primary caregivers (75.8%), followed by fathers (16.3%) and other caregivers

(7.9%). Participants' average duration of T1D was 4.86 years (SD = 3.60, range = 0.5-17.0

years). The average participant HbAc1 assay was 8.83% (SD = 1.92; Table 2-2), which is higher

that the recommended target range of < 7%. Approximately 64.1% of children in the study

experienced at least one episode of DKA post-diagnosis and 24.1% of children experienced 2 or

more episodes of DKA (range 0-10 episodes).

Procedure

Participants were recruited during their regular visits to the pediatric endocrinology clinic

as part of a larger project entitled "Assessment of Adherence and Metabolic Control in Youths

with Type 1 Diabetes." Clinic endocrinologists or nurses identified patients meeting inclusion

criteria and trained research staff approached patients and explained the study. Informed

consent, approved by the University of Florida Institutional Review Board, was obtained from

28















Independent Variable
(Metacognition)


.146*


(.061)


.472***


Mediating Variable
(Child Behavior)


.525***
(.459***)


Figure 2-4 Child Behavior mediating the relationship between Metacognition and Parent Stress


Outcome Variable
(Parent Stress)









The American Diabetes Association's position statement on the standards of medical care

for patients with diabetes mellitus mandated that treatment include lowering blood glucose levels

to or to near normal in all patients (ADA, 1997). The American Diabetes Association (ADA,

2005) further recommended that adequate glycemic control for those with diabetes is

maintaining an HbA1c of less than 7%. However, the most recent National Health and Nutrition

Examination Survey suggested that 60% of persons with diabetes fail to meet this goal.

A number of factors have the potential to affect glycemic control. In the period after

diagnosis, children often go through a honeymoon period, during which their beta cells are still

producing insulin to varying degrees (Madsbad, McNair, & Faber, 1980). The duration of this

period is typically 18-24 months, during which glycemic control may be relatively easy to attain.

However, after residual pancreatic activity subsides, maintaining effective glycemic control

becomes significantly more challenging (Delamater, 2000). Puberty is another critical period

wherein biological factors influence glycemic control. Studies suggest that the worsening of

glycemic control often seen during puberty is in part due to decreased insulin sensitivity (Amiel,

Sherwin, Simonson, Lauritano, & Tamborlane, 1986; Bloch, Clemens, & Sperling, 1987).

Additionally, racial disparities have also been demonstrated, with African-American youth

generally having worse glycemic control than Caucasian youth (Auslander, Anderson, Bubb,

Jung, & Santiago, 1990; Auslander, Thompson, Dreitzer, White, & Santiago, 1997; Delamater,

Albrecht, Postellon, & Gutai, 1991). Adherence to an individually prescribed treatment regimen

is critical to the management of T1D.

Adherence

Nonadherence to a wide range of therapeutic regimens is common (Chui et al., 2003;

Farber et al., 2003), with nonadherence to pediatric medical regimens estimated to be about 50%

(Rapoff, 2002; Rapoff, 1999). Despite the known health complications, nonadherence among









Measure of Glycemic Control

Glycemic control (HbA1c)

HbA1c is a biological assay of health status operationalized via a glycosylated hemoglobin

HbA1c test. HbA1c provides an estimate of blood glucose levels over the preceding 2-3 months

(American Diabetes Association, 2003). Normal HbA1c ranges from 4% to 6%. Patients

routinely have their blood drawn and HbA1c checked as part of their regularly scheduled

appointments. Blood samples were analyzed by qualified and experienced technicians using a

Bayer DCA 2000+.

Data Analysis

Path Analysis

Path analysis is a general term used to describe causal modeling. These models are

comprised of measured variables as some data must have been collected before the model can

actually be processed. Traditionally, the label path analysis has been used to describe a causal

model with only measured variables, whereas the label "structural equation modeling" (SEM) is

most often used to describe a causal model that includes latent variables. By using path analysis

the researcher is able to evaluate explicitly hypothesized and often relatively complex causal

(predictive) relationships between the variables represented in their data (Klem, 1995). The

steps researchers take in conducting path analysis include the following:

Draw out the interrelationships of the variables in the form of a diagram.

Indicate the hypothesized strength and direction of each variable's presumed
effect on each other.

Perform the analysis yielding the path coefficients for each path.

Compare the obtained path coefficients with the hypothesized paths strengths and
directions.









Goldston, Kovacs, Obrosky, and Iyengar (1995) found that greater life disruption predicted

decreased glycemic control, and the relationship between life stress and glycemic control was

partially mediated by adherence. In contrast, Hanson, Henggeler, Harris, Burghen, and Moore

(1989) found support for a relationship between stress and reduced glycemic control, but not

between stress and nonadherence. Other studies have found no association between stress and

glycemic control (e.g., Hauenstein et al, 1989). For parents of children with T1D, stress also

appears to be closely linked with child behavior problems. A recent study detailing a psychology

consult service for children with T1D in a tertiary care clinic documented a high incidence of

distress and internalizing disorders in these patients (Gelfand et al., 2004). These inconsistent

findings may be due to the fact that "stress" is an overly general construct, and more specific

variables need to be studied. Studies are needed that further elucidate the relationships among

pediatric parenting stress, glycemic control, and adherence behaviors in patients with T1D.

Child Behavior Problems

Numerous family studies have shown that problematic family interactions are associated

with ineffective illness management and worsened health outcomes in Type 1 diabetes (e.g.,

Anderson, Brackett, Ho, & Laffel, 1999; Wysocki et al., 1996). Although there is limited

research examining the relationships between mental health and adherence behaviors, studies

with predominantly Caucasian samples of adolescents have found that both internalizing and

externalizing symptoms have been associated with decreased metabolic control in adolescents

(Leonard, Jan, Savik, Plumbo, & Christensen, 2002; Lernmark, Persson, Fisher, & Rydelius,

1999). Recent work has found that the presence of youth externalizing behaviors is associated

with decreased glycemic control. For example, Northam et al. (2005) found that in a sample of

adolescents with T1D, those with high blood sugars had increased levels of externalizing

behaviors compared to those with low blood sugars. Duke et al. (2008) found that externalizing









BRIEF include the ability to solve problems flexibly (Shift scale), anticipate future events and

set goals (Plan/Organize scale), the ability to control impulses (Inhibit scale), the modulation of

emotional responses (Emotional Control scale), the ability to start a task (Initiate scale), the

aptitude to retain information in one's mind for the completion of a task (Working Memory

scale), the ability to keep materials orderly (Organization of Materials scale), and the ability to

assess performance during or after a task (Monitor scale). The BRIEF also includes three

indices: the Behavior Regulation Index (BRI; including the Inhibit, Shift, and Emotional Control

scales), the Metacognition Index (MI; including the Initiate, Working Memory, Plan/Organize,

Organization of Materials, and Monitor scales), and the Global Executive Composite (GEC;

including all scales). BRIEF raw scores range from 0 to 238, with higher scores indicating

poorer executive functioning. In addition, the BRIEF includes the Inconsistency and Negativity

scales, which are used as measures of response validity. Reliabilities for the BRIEF subscales

and indices are satisfactory in both clinical and normative samples (a = .80-.98). Test-retest

reliability across clinical scales was r = .81. Previous research suggests the use of the MI, BRI,

and GEC indices, as these were most strongly related to adherence in a pediatric TID sample

(Bagner et al., 2007). Given the presence of alternate corresponding measures, only the MI was

used in this study.

Diabetes-Specific Assessments of Family Functioning.

Diabetes Family Behavior Checklist (DFBC)

The DFBC is a measure of both supportive and unsupportive family behaviors related to

the diabetes regimen, completed by both parents and youth (Schafer et al., 1986). Given the

aims of this study only the seven-item negative/unsupportive subscale was used; henceforth

referred to as the 'critical parenting' scale of the DFBC. The critical parenting scale has shown









component (i.e. learned from parents and passed on to their children through experiential and

modeling processes).

For aim 4, Adherence accounted for a significant portion of the variance in HbA1,.

Although this is an intuitive finding, past research has often found inconsistent or weak

relationships between adherence, family factors and glycemic control. It is likely that the use of

diabetes specific measures of adherence and critical parenting strengthened this study's ability to

detect these relationships.

For aim 5, Child Behavior was related to adherence, but it was not related to HbA1,.

Although we expected Adherence to relate directly to HbA1c, in this study child behavior was

related to adherence, while having little relationship to HbA1c. Given the mean age of 13.58 in

this sample, it may be that insulin resistance, corresponding to the onset of puberty, obfuscated

the relationship between Adherence and HbA1c.

For aim 6, Adherence mediated the relationship between Critical Parenting and HbA1~.

Youth, who reported more critical caregiver behavior regarding diabetes management (Critical

Parenting), experienced worsened HbA1c. Further, the significantly mediated relationship

implied that Critical Parenting caused decreased Adherence which in turn caused worsened

HbA1,. This suggests that youth resistance to critical (coercive) parents may manifest as non-

adherence to their treatment regimen leading to worsened glycemic control (HbAic).

For aim 7, the hypothesized relationship between Parent Stress and Adherence was not

identified. It may be that Parent Stress did not manifest in ways that interfere with the youths'

adherence. For aims 8 and 9 the hypothesized relationship between Metacognition and

Adherence was not present. It may be that this model lacked the power necessary to detect these









Evaluate how well the causal (predictive) model fits the data based on the results
of the analysis.

Causal modeling, in the context of path analysis, results from synthesizing the outcome of

several prediction analyses.

Path analysis is a general term used to describe causal modeling. These models are

comprised of measured variables as some data must have been collected before the model can

actually be processed. Traditionally, the label path analysis has been used to describe a causal

model with only measured variables, whereas the label "structural equation modeling" (SEM) is

most often used to describe a causal model that includes latent variables. By using path analysis

the researcher is able to evaluate explicitly hypothesized and often relatively complex causal

(predictive) relationships between the variables represented in their data (Klem, 1995). The

steps researchers take in conducting path analysis include the following:

Draw out the interrelationships of the variables in the form of a diagram.

Indicate the hypothesized strength and direction of each variable's presumed
effect on each other.

Perform the analysis yielding the path coefficients for each path.

Compare the obtained path coefficients with the hypothesized paths strengths and
directions.

Evaluate how well the causal (predictive) model fits the data based on the results
of the analysis.

Causal modeling, in the context of path analysis, results from synthesizing the outcome of

several prediction analyses.

Structural Equation Modeling

SEM can be thought of as a union between confirmatory factor analysis and path analysis

(Meyers, Gamst, & Guarino, 2006). This is because in SEM there are really two types of









Tebbi, C. K. (1993). Treatment compliance in childhood and adolescence. Cancer, 71 (Suppl.),
3441-3449.

Trachtenbarg, D. E. (2005). Diabetic Ketoacidosis. American Family Physician, 71, 1705-1714.

Waller, D. A., Chipman, J. J., Hardy, B. W., Hightower, M. S., North, A. J., Williams, S. B., &
Babick, A. J. (1986). Measuring diabetes-specific family support and its relation to
metabolic control: A preliminary report. Journal of the American Academy of Child
Psychiatry, 25, 415-418.

Weissberg-Benchell, J., Glasgow, A. M., Tynan, W. D., Wirtz, P., Turek, J., & Ward, J. (1995).
Adolescent Diabetes Management and Mismanagement. Diabetes Care. 18, 77-82.

Wolpert, H. A., Anderson, B. J. (2001). Management of diabetes: Are doctors framing the
benefits from the wrong perspective? British Medical Journal, 323, 994-996.

Wysocki, T., Greco, P., & Buckloh, L. M. (2003). Childhood diabetes in psychological
context. In M. C. Roberts (Ed.), Handbook of Pediatric Psychology, Third Edition. New
York: Guilford Press.

Wysocki, T., Huxtable, K., Linscheid, T. R., & Wayne, W. (1989). Adjustment to diabetes
mellitus in preschoolers and their mothers. Diabetes Care, 12, 524-529.

Wysocki, T., Taylor, A., Hough, B. S., Linscheid, T. R., Yeates, K. O., & Naglieri, J. A. (1996).
Deviation from developmentally appropriate self-care autonomy: Association with
diabetes outcome. Diabetes Care, 19, 119-125.









the legal guardian of all participants while youth provided assent. The recruitment rate was

approximately 85%, which is similar to past research recruitment within this clinic (Lewin et al.,

2005). The most commonly cited reason for declining participation was the time commitment

required to complete study measures. Most families completed the questionnaires in

approximately 45 minutes. Children and their caregivers were interviewed separately regarding

adherence to prescribed T1D treatment and independently completed study questionnaires.

Families received a $10 gift card as an honorarium for their participation. Blood samples for

assessing patient's glycemic control (HbAlo) were obtained by nursing staff as part of the

patients' regular clinic visit.

Measures

Demographic Information

The patient's primary caregiver completed a demographic information form that included

data such as age, sex, socioeconomic status (education and occupation), and duration of T1D.

Parent Measures

The Pediatric Inventory for Parents (PIP; Streisand, et al., 2001) is a parent-report

questionnaire designed to measure parenting stress related to caring for a child with a chronic

illness. The PIP consists of 42 items that assess the frequency and intensity of parenting stress.

Four domains that may be affected by parenting stress due to chronic illness are included: 1)

communication (e.g., with their child, partner, or medical team), 2) parent's emotional

functioning (e.g., impact of the child's illness on their sleep and mood), 3) child's medical care

(e.g., adhering to the medical regimen), and 4) parent's role functioning (e.g., ability to work or

care for other children). Parents responded by endorsing items on a 5-point Likert scale ranging

from "never" (1) to "very often" (5) and how often the event occurred during the past week. The

PIP yields two subscales assessing the frequency of stress (PIP-F) and the level of difficulty the

29









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

MULTIVARIATE ASSESSMENT OF ADHERENCE AND GLYCEMIC CONTROL IN
YOUTH WITH TYPE 1 DIABETES


By

Danny C. Duke

August 2009

Chair: Gary Geffken
Major: Psychology

The considerable research examining family variables and their relationship to adherence

and glycemic control in pediatric populations with type 1 diabetes (T1D) has generally identified

only weak or inconsistent relationships between adherence and glycemic control. Even though

youth with T1D are often nonadherent to their prescribed treatment regimens, the multiple

determinants and ecological complexity of these behaviors make assessing nonadherence a

challenging proposition. Adding to the difficulty, many adolescents experience hormonal

changes during puberty that may cause insulin resistance, obfuscating the relationship between

adherence and glycemic control (HbAil). Although difficult to identify, common sense, clinical

evidence, and large studies of intensive T1D management support the presence of a strong

relationship between improved adherence and metabolic control.

To improve ecological validity, our study included measures of diabetes-specific family

functioning, medically-related parent stress, youth cognitive functioning, youth behaviors

(internalizing and externalizing), and measures of adherence. One hundred-fifty-one youth and

their caregivers completed parent and child measures, and separate structured adherence

interviews, while only youth completed blood draws for HbAl1 assay. Path analysis was used to

































To my family, whose support made my education possible.









BIOGRAPHICAL SKETCH

Danny was born in 1954 in Redlands, California. The oldest of three children, he spent

his early youth living in Yucaipa and later in Placerville, California, where he graduated from El

Dorado High School in 1972. He has been married to his beautiful wife Vicki since 1973, and

they have two children, Erin and Ryan. He owned and operated a successful landscape

contracting business that operated in the greater Sacramento, California area from 1973 until

2004. He earned his B.A degree in psychology from California State University, Sacramento in

2002 and plans to complete his Ph.D. in clinical psychology and to pursue teaching, clinical, and

research interests in pediatric psychology.





















Mediating Variable





Figure 1-2 Mediation Model









LIST OF FIGURES

Figure page

1-1 Conceptual m odel of possible relationships ........................................ ........ .............. 26

1-2 M ed iatio n m o d el .... ..................................................................................... ............ .. 2 7

2 -1 Stru ctu ral m o d el ..................................................................................... 4 1

2-2 Adherence mediating critical parents (DFBC) and HbA .............................................. 42

2-3 Adherence mediating duration of T1D and HbA ................................ ...... ... .......... 43

2-4 Child behavior mediating metacognition and parent stress ............................................. 44









have identified smaller effects, reduced type 2 errors, and identified hypothesized mediation

processes.

Within these limitations, this study found important interrelationships between parenting,

parent stress, adherence, youth behaviors, youth metacognition, and HbA1,. These findings

suggest that clinical evaluation of these factors may inform interventions designed for non-

adherent youth with T1D. A preventative approach to intervention for youth may lead to timely

and specifically targeted interventions, thereby reducing the risk associated with poorly

controlled diabetes. Of particular importance is ameliorating ineffective parental responses to

child behavior problems that may promote and maintain argumentative interaction patterns

regarding diabetes management. Continued intervention research is critical for expanding the

effectiveness of psychological interventions with youth with T1D. Modeling of parenting, parent

stress, adherence, cognitive functioning, child behaviors, and glycemic control warrant ongoing

research as these and similar complex models may further inform intervention studies. In

particular, an important avenue of investigation should be examining causes of critical parenting.

Future research should also target other predictors of glycemic control that are potentially

mediated by adherence, such as parent and youth depression or anxiety.













Emton


Inhibit -----




Shift -------




Emotional
Control ----




Initiate -----


t. Working
S Memory ----




Plan/
Organize ----





Organization


Monitor ----


Figure 1-1. Conceptual model of possible relationships









Research Aims

The goal of this proposal is to investigate a multivariate model of adherence that more

accurately reflects real world complexity. This model will assess the contributions of parent

stress related to their child's diabetes, youth behavior problems, youth executive functioning, and

family factors that include youth perception of diabetes specific critical parenting, warmth and

caring, and guidance and control, and parent/child responsibility for adherence tasks. The

relationship of these variables to adherence and glycemic control in pediatric populations having

T1D will be evaluated using multivariate methods.

General Aims.

1. A path analysis will simultaneously examine the unique contributions of parent and child

variables (family factors) to predicting adherence and glycemic control (Figure 1-1).

2. Using correlation procedures, preliminary analyses will assess demographic variables for

making significant contributions to the model.

3. The latent construct of "diabetes related parenting" will be examined by assessing the

contributions of measures representing critical parenting, parental warmth, guidance and

control, and shared responsibility for diabetes tasks.

4. The latent construct of adherence will be included in the model by assessing the

contributions from measures of blood glucose testing, insulin administration, diet, and

exercise.

Specific aims

The presence of mediation processes (Figure 1-2) will be evaluated using procedures

outlined by Baron and Kenny (1986), as follows:

5. Proposed is to examine whether the hypothesized relationship between youth behaviors

and HbA1c is mediated by adherence.









in the analysis. The data analysis will be carried out using SPSS for Windows Version 15.0 and

AMOS 7.

Model fit

Several indices will be used to measure the fit of the models tested. The statistics most

often reported are Chi-square (X2), CMIN/DF (chi square/degree of freedom ratio), comparative

fit index (CFI), and root mean square error of approximation (RMSEA; Byrne, 2001). The X2

statistic is an overall test of how well the hypothesized model fits the data and a significant X2

indicates a model that does not fit the data well. Because the X2 statistic assumes multivariate

normality and is affected by large sample size (i.e. a model with relatively good fit for a large

dataset can still be rejected), additional indices of fit such as Comparative Fit Index (CFI), Root

Square Mean Square Error of Approximation (RMSEA) and CMIN/DF (chi square/degree of

freedom ratio) must be used to make a judgment regarding the fit of the model. CFI should have

a value over 0.90 (Hoyle & Pante, 1995). Interpretation of the RMSEA is often considered

according to the following; 0 = perfect fit; < .05 = close fit; .05 to .08 = fair fit; .08 to .10 = poor

fit; and > .10 = poor fit (Byrne, 2001). CMIN/DF is a measure of relative chi-square, also called

normal chi-square. It is the chi-square fit index divided by degrees of freedom, in an attempt to

make it less dependent on sample size. A chi-square/df ratio larger than 2 indicates an

inadequate fit (Byrne, 1989). These goodness-of-fit statistics only give information regarding

the model's lack of fit to the data used. They cannot assess if a model is plausible, hence it is

important to construct a model that is based on knowledge gained from empirical research and/or

theory. Before testing the whole model, constructs such as family factors, and adherence should

be independently validated. Since path analysis is confirmatory and can incorporate not only

observed but also unobserved (latent) variables in the analysis, this method is most applicable for
















Independent Variable
(Duration of T D)


Mediating Variable
(Adherence)


Figure 2-3 Adherence mediating the relationship between duration of T1D and HbA1c


.145*


(-.009)


S-.787***
(-.726***)


xx









model the interrelationships among these variables and their combined ability to predict HbA1c in

a sample of youth with T1D. For the final model, chi-square was not significant (X2 = 9.2 (17,

N=151), p = .93), indicating excellent model fit. Analysis of the model yielded an R-squared for

glycemic control (HbAi,) of .59, while the R-squared for adherence was .48. Child behavior

significantly predicted adherence (Beta = -.37, p < .001), while adherence significantly predicted

HbA1~ (Beta = -.78, p < .001). The latent construct representing adherence, mediated the

relationship between critical parenting and HbA1c, and adherence mediated the relationship

between duration of diabetes and HbA1c. Child behavior also mediated the relationship between

metacognition and parent stress. These findings suggest that an increased and comprehensive

focus on assessing and monitoring family behaviors related to adherence is critical for

optimizing health outcomes. Additional implications are discussed and directions for future

research are presented.









ACKNOWLEDGMENTS

I extend my sincere gratitude to my mentor, chair, and honorable friend, Dr. Gary R.

Geffken. I also extend thanks to Dr. Eric Storch for his invaluable guidance. Also, my

appreciation goes to Dr. Adam Lewin and Dr. Laura Williams, for their assistance on this

project. Finally, thanks are due to my committee members, Dr. James Johnson, and Dr. Brenda

Weins.









children and adolescents with diabetes is particularly prevalent (Ellis, et al., 2003; Weissberg-

Benchell et al., 1995). Many pediatric patients with T1D are nonadherent in regard to various

aspects of T1D management. For example, most patients reported taking their insulin injections;

however, 10% reported administering the wrong dose, 20% reported giving the injection at the

incorrect time, and 19% reported having difficulty adhering to their physician's

recommendations regarding adjusting their insulin dose (Delamater, Applegate, Eidson, &

Nemery, 1998). Higher rates of nonadherence are typically reported for other diabetes treatment

components. An evaluation of blood glucose testing found that 31% of pediatric patients do not

adhere to the recommended timing or frequency of this aspect of their treatment regimen.

Parents have reported that 48% of adolescents with T1D do not adhere to their recommended

eating practices (Delamater et al., 1998). A nine-year follow-up study of newly diagnosed T1D

patients determined that 45% of adolescents were nonadherent to their treatment regimen

(Kovacs, Goldston, Obrosky, & Iyengar, 1992). The consequences to the individual due to poor

adherence include not only health outcomes, but missed school days and compromised quality of

life (Bender, Milgrom, Rand, & Ackerson, 1998). Overall, adequate adherence to a T1D

diabetes treatment regimen is a problematic proposal for many pediatric patients.

Although the importance of adherence to the clinical management of T1D is critical, the

impact of nonadherence also affects the quality of data obtained from scientific studies, in

particular, clinical trials of medical treatment efficacy (Tebbi, 1993). Less than optimal

adherence during clinical trials may obscure the efficacy of medical treatment or cause erroneous

conclusions to be made regarding the effectiveness of a treatment or its dosage (Lasagna & Hunt,

1991). Although there has been little research on the financial implications of nonadherence in

pediatric populations, nonadherence in adult populations has been linked to increased medical









clarity of presentation. Indices used in this analysis included chi-square goodness-of-fit

statistics, CMIN/DF (chi square/degree of freedom ratio), the comparative fit index (CFI), and

the root mean square error of approximation (RMSEA; Hair et al., 1998; Jaccard & Wan, 1996).

Nested models were evaluated for meeting Baron and Kenny's (1986) mediation criteria using

standardized estimates of path coefficients.

RESULTS

The DFBS variables, Guidance and Control, and Warmth and Caring; and the DFRQ

variable, Shared Responsibility, were not significantly related to other variables of interest

(Table 2-2) and therefore did not add significantly to the model. In addition, DSMP child reports

of adherence did not add significantly to the model beyond that of DSMP parent report alone,

therefore these variables were excluded from further analyses.

General Aims

Aim 1 The chi-square statistic indicating goodness of fit was non-significant, X2 = 9.2

(17, N=151), p = .93, suggesting excellent model fit. An examination of additional indices

(CMIN= .541, CFI = 1.0, RMSEA= .000) further confirmed model fit. All path coefficients in

the final model were significant (p < .01) and indicative of practical significance (P > .30; Figure

2-1). The squared multiple correlations for the outcome variable HbAlc equaled .59. Adherence

significantly predicted HbAlc, (P = -.78, p < .001) and Child Behavior significantly predicted

Adherence (P = -.37,p < .001) and Parent Stress (P = .51, p < .001; Figure 2-1). Surprisingly,

Parent Stress was not significantly related to Critical Parenting. Metacognition significantly

predicted HbAlc (P = -.21,p = .004) and Child Behavior (P = .53,p < .001; Figure 2-1).

Aim 2 The inclusion of the demographic variables family income and child age did not

add significantly to the model beyond that of Duration of T1D. Duration of T1D was

significantly related to adherence (P = .32, p < .001), and was therefore included in the analysis.

38









the youth at greater risk (Anderson & Coyne, 1991). A key difference in perspectives is that

when considering miscarried helping, it is the parent's good intentions that precipitate conflict.

Variables Related to Adherence to T1D Treatment Regimens

Demographic variables

Patient and family correlates of adherence have included child age, parent education, work

status, number of children in the home, and socioeconomic status. Younger children with T1D

are more likely to be adherent to medication regimens than adolescents (Anderson, Auslander,

Jung, Miller, & Santiago, 1990; LaGreca, Follansbee, & Skyler, 1990). Lower socioeconomic

status (SES), race, and lower parental education levels have also been correlated with

nonadherence in children with T1D (Auslander, Thompson, Dreitzer, White & Santiago, 1997).

Relationships have also been found between adherence and family composition, with single

parent households found to be related to decreased adherence (Auslander, et al., 1997).

Illness-specific variables

In addition to demographic variables, several illness and treatment variables are also

related to adherence in pediatric T1D. Greater symptom severity and longer illness duration

have been found to be related to decreased adherence to pediatric T1D treatment regimens

(Rapoff, 1999). In addition, nonadherent pediatric patients with T1D show an association

between increased health problems and hospitalization for DKA (Geffken et al., 1997).

Additionally, patients having more complex regimens are less adherent to their prescribed

regimen (McCaul, Glasgow, & Schafer, 1987).

Parental stress

In general, higher levels of stress and decreased psychological functioning have been

consistently documented in children with T1D and their parents, especially mothers (Hauenstein,

Marvin, Snyder, & Clarke, 1989; Kovacs et al., 1985; Wysocki, Huxtable, Linscheid, & Wayne,




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