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Family Factors, Adherence, and Metabolic Control in Youth with Type 1 Diabetes

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Title:
Family Factors, Adherence, and Metabolic Control in Youth with Type 1 Diabetes
Creator:
DUKE, DANNY C. ( Author, Primary )
Copyright Date:
2008

Subjects

Subjects / Keywords:
Adolescents ( jstor )
Child psychology ( jstor )
Children ( jstor )
Demography ( jstor )
Diabetes ( jstor )
Diabetes complications ( jstor )
Insulin ( jstor )
Parents ( jstor )
Pediatrics ( jstor )
Type 1 diabetes mellitus ( jstor )

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University of Florida
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University of Florida
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Copyright Danny C. Duke. Permission granted to University of Florida to digitize and display 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.
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3/1/2007

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FAMILY FACTORS, ADHERENCE, AND METABOLIC CONTROL INT YOUTH WITH
TYPE 1 DIABETES.














By

DANNY C. DUKE


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2006




























Copyright 2006

by

Danny C. Duke



































To my family, whose sacrifices made my education possible.
















ACKNOWLEDGMENTS

I would like to express my gratitude to my mentor and chair, Dr. Gary R. Geffken. Also,

my appreciation goes to Adam Lewin and Laura Bimbo, for their assistance with data collection;

and to Dr. Janet Silverstein, Division Chief of Pediatric Endocrinology, for allowing us to recruit

families from the pediatric diabetes clinic. Finally, thanks are due to my committee members, Dr.

Sheila Eyberg, Dr. Michael Perri, and Dr. William Perlstein.




















TABLE OF CONTENTS


page

ACKNOWLEDGMENT S .............. .................... iv


LI ST OF T ABLE S ............_ ..... ..__ .............. vii..


LIST OF FIGURES ............_...... .__ ..............viii...


AB STRAC T ................ .............. ix


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


Overview of Diabetes .............. ..... .... ........ .. ........ ... ...................
Treatment and the Diabetes Control and Complications Trial (DCCT) ................... ....2
Measures of Adherence ................ .............. .................. ...............4
Measures of Metabolic Control ................... ................... .. ........ ....... ........7
Relationships between Family Factors, Adherence, and Metabolic Control ................7
Diabetes Specific Family Measures............... ..........................1
Externalizing, Diabetes Specific Family Functioning, Adherence and Metabolic
Control .............. ...............14....
Sum m ary ................. ...............14.......... ......


2 RE SEARCH QUE ST IONS .............. ............... 17....


Question 1: Family Factors Predicting Metabolic Control ................... .. ................ ..17
Question 2: Adherence Mediating between Diabetes-Specific Family Factors and
M etabolic Control. ............. ....... .......... .. .. ... .. ........1

Question 3: Family factors mediating between child externalizing and metabolic
control. ............. ............. .... .. .. .... .........1

Question 4: Adherence mediating child externalizing behaviors and metabolic
control? ................ ...............18.................


3 M ETHODS .............. ...............22....


Participants and Settings............... ...............22
Procedure .............. ...............22....
M measures ................... ........... ...............23.......

Demographic Questionnaire ................. ...............23.................












Diabetes-Specific Family Measures ................. ...............23................
Diabetes Family Behavior Scale (DFB S) ................. ................ ....._.23
Diabetes Family Behavior Checklist (DFBC). ................. ............. .......24
Diabetes Family Responsibility Questionnaire (DFRQ) ................... ...........24
Child Behavior............... .... .. .............2
Child Behavior Checklist .............. ...............25....
Measurement of Adherence .....__ ................. ........._._ ......... 2
Measure of Metabolic Control .....__ ................. .........__ ........ 2


4 RE SULT S .............. ...............27....


5 DI SCUS SSION ................. ...............44...............


Interpretation of Results ................. ..... ... .............4
Limitations, Implications, and Future Directions .....__ ................ ............._.._47


REFERENCES .............. ...............50....


BIOGRAPHICAL SKETCH ............. .............56......

















LIST OF TABLES


Table page

4-1 Demographic Characteristics. ............. ...............36.....

4-2 Means and Standard Deviations for Age, HbAlc, Duration of Diabetes, and Income........37

4-3 Intercorrelations between HbAlc, Diabetes Family Measures, Adherence, and
Externalizing. ............. ...............38.....

4-4 Hierarchical Regression Analysis Predicting HbAlc from Diabetes-Related Family
Factors and Adherence. .............. ...............39....

4-5 Hierarchical Regression Analysis Predicting HbAlc from Parent and Child Report of
Adherence ........... ..... .._ ...............40...

4-6 Mediation Regression Analysis Predicting HbAlc: Adherence Mediating the
Relationship between Family Factors (DFBC) and Metabolic Control (HbAlc): Final
Block of the Regression. ............. ...............41.....

4-7 Mediation Regression Analysis Predicting HbAlc: Family Factors (DFBC) Mediating
the Relationship between Child Extemnalizing and Metabolic Control (HbAlc): Final
Block of the Regression. ............. ...............42.....

4-8 Mediation Regression Analysis Predicting HbAlc : Adherence Mediating the
Relationship between Child Extemnalizing and Metabolic Control (HbAlc): Final Block
of the Regression. ................. ...............43.............

















LIST OF FIGURES

Figure page

2-1 Theoretical model of adherence mediating family factors and metabolic control. ..............19

2-2 Theoretical model of family factors mediating externalizing and metabolic control. .........20

2-3 Theoretical model of adherence mediating externalizing and metabolic control. ..............21

4-1 The relationship between family factors (DFBC) and metabolic control (HbAlc)
partially mediated by adherence (Parent DSMP). .............. ...............33....

4-2 The relationship between externalizing and metabolic control (HbAlc) fully mediated
by family factors (DFBC). ............. ...............34.....

4-3 The relationship between externalizing and metabolic control (HbAlc) fully mediated
by adherence (Parent DSMP). .............. ...............35....
















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

FAMILY FACTORS, ADHERENCE, AND METABOLIC CONTROL INT YOUTH
WITH TYPE 1 DIABETES

By

Danny C. Duke

December 2006

Chair: Gary Geffken
Major: Psychology

Considerable research has examined the relationship of family variables and

adherence to metabolic control in pediatric populations with type 1 diabetes, with most

studies finding that these constructs were only weakly related to metabolic control.

Children and adolescents with type 1 diabetes are often noncompliant with their

prescribed treatment regimens. Clinical evidence suggests that poor adherence leads to

poor metabolic control and conversely that compliance with a diabetes treatment regimen

predicts good metabolic control. Inadequate metabolic control leads to physiological

complications (e.g., retinopathy, neuropathy, nephropathy) at an earlier age. Our study

examined the ability of a combined battery of diabetes-specific family functioning

measures (e.g., family responsibility, parental support, guidance, warmth, and criticism

specific to diabetes care) to predict metabolic control (health status). One hundred twenty

children and parents, who presented in a pediatric diabetes clinic, completed four

measures of diabetes-specific family functioning, and separate adherence interviews.









Overall, combined with demographic variables, these measures predicted approximately

44% of the variance in metabolic control. Examination of parent and child reports of

adherence showed that, while controlling for demographic variables, parent report

accounted for 27.4% of the variance in metabolic control, while child report did not add

significantly to the variance accounted for in metabolic control beyond parent report.

Adherence was found to partially mediate the relationship between child metabolic

control and perceptions of parental nagging, criticizing, and arguing about diabetes

specific tasks. Family variables were found to partially mediate the relationship between

externalizing and metabolic control and adherence was found to fully mediate the

relationship between child externalizing and metabolic control. Our data suggest that

reports of adherence behaviors are strongly related to a child's health status independently

and through important pathways of family functioning. These data suggest that an

increased and comprehensive focus on assessing and monitoring family behaviors related

to adherence is critical for optimizing health-status outcomes.















CHAPTER 1
INTTRODUCTION

Overview of Diabetes

The incidence of diabetes in children and adolescents is increasing worldwide

(Diabetes Control and Complications Trial Research Group, 1986; EURODIAB ACE

Study Group, 2000; Onkamo et al., 1999). This disease adds significantly to public

health care costs and profoundly affects individual quality of life, having treatment

demands that are often complex and unrelenting. Diabetes is one of the most common

chronic diseases in school-aged children, affecting about 151,000 young people in the

United States, or about 1 in every 400 to 500 youth under 20 years of age. With the peak

incidence of type 1 diabetes occurring at puberty (Feingold & Funk, 1997), the

prevalence of type 1 diabetes in the U. S. is increasing with approximately 30,000 new

cases of type 1 diabetes diagnosed annually (Engelgau & Geiss, 2000).

Pancreatic beta cells are insulin-producing bodies within the pancreas that are

responsible for producing the insulin necessary for the body's metabolism of glucose.

Type 1 diabetes is thought to result from an autoimmune process that leads to the

destruction of the insulin producing beta cells. The typically acute onset develops over a

period of a few days to weeks. The percentage of beta cell destruction ranges from 60 to

80% at the time of clinical symptom presentation (National Diabetes Data Group, 1995;

Notkins & Lernmark, 2001). Without insulin, cells are unable to metabolize sugars

present in the bloodstream. As a means of compensating for the body's lack of insulin

production, individuals with diabetes require insulin via subcutaneous inj sections.









Additionally, maintaining near normal blood-glucose levels requires intensive and often-

complex management, including frequent blood-glucose monitoring and regular exercise,

and dietary and insulin adjustments based on blood-glucose levels (American Diabetes

Association, 2003).

Treatment and the Diabetes Control and Complications Trial (DCCT)

To avoid periods of elevated blood sugar (hyperglycemia), the American Diabetes

Association (ADA) guidelines recommend that individuals with type 1 diabetes maintain

blood glucose levels of 80 tol20 mg/dL. Hyperglycemia can lead to long-term

complications, including retinopathy, hypertension, neuropathy, nephropathy, and

cardiovascular disease (ADA, 2003). Profound hyperglycemia associated with

insufficient insulin can result in diabetic ketoacidosis (DKA), a lethal condition if not

promptly treated with insulin and fluids (Feingold & Funk, 1997). The Diabetes Control

and Complications Trial (DCCT) identified an empirical relationship between intensive

insulin therapy and reduced complications (Diabetes Control and Complications Trial

Research Group, 1987; 1993; 1994). This randomized, controlled clinical trial showed

that for patients with type 1 diabetes, the risk of the development and progression of

retinopathy, nephropathy, and neuropathy is reduced 50 to 75% by intensive treatment

regimens when compared to conventional treatment regimens. For example, Zhang,

Krzentowski, Albert, & Lefebvre (2001) found that, among patients in the DCCT (ages

13 to 39) with mean HbAlc < 6.87% (good control), 90% remained free of retinopathy.

However, in patients with a mean HbAlc > 9.49% (poor control), complications

developed in 57% of the patients. In a study of 195 adolescents, participants who were

assigned an intensive therapy regimen (administration of three or more inj sections per day

or use of an insulin pump, adjustment of insulin based on blood-glucose levels, at least









four blood-glucose tests per day, diet, and exercise) showed delayed onset and slower

progression of retinopathy compared to individuals in a conventional therapy (one or two

insulin inj sections and self monitoring of blood glucose at least twice per day).

Additionally, intensive therapy also reduced the risk of the development of

microalbuminuria, which is a precursor to the development of diabetes-related

nephropathy (Diabetes Control and Complications Research Group, 1994).

To prevent long-term complications, the DCCT research group recommended

intensive insulin and monitoring therapy for adults and adolescents as young as 13 years

old (Diabetes Control and Research Group, 1993, 1994). While intensive therapy reduces

the risk of diabetes-related complications and high HbAlc levels, it is a complex and

demanding regimen for children and families. Physicians typically prescribe complex

daily regimens that include insulin inj sections, blood-glucose monitoring, diet and

exercise. It is expected that these behavioral adjustments be integrated into the patient' s

daily routine. Data indicate that children and adolescents are often noncompliant with

many of these recommendations. A nine-year follow-up study by Kovacs and colleagues

(1992) established that 45% of adolescents engage in some significant form of

noncompliance during adolescence. These findings were before the publication of DCCT

research, before intensive insulin therapy was routinely prescribed. As children and

adolescent medical regimens become more intense and complex, understanding

adherence becomes an increasingly important facet of treatment.

Past research has often failed to account for the behavioral contributions of the

child to reciprocal parent-child interactions necessary to administer such complex

treatment regimens. There are many problematic child behaviors that may contribute to










poor regimen adherence. Achenbach (1991) categorized childhood problems into the two

broad dimensions of internalizing and externalizing. Internalizing encompasses behavior

problems characterized by anxiety, depression, and social withdrawal, while externalizing

behavior problems are characterized by aggression and antisocial behavior. Limited

research has examined relationships between child psychopathology, family functioning

and metabolic control. Work by Northam et al. (2005) found that in a sample of

adolescents with diabetes, those with high blood sugars had higher levels of externalizing

behaviors than those with good and low blood sugars. Conversely, Cohen et al. (2003)

found that lack of internalizing behaviors significantly predicted adherence in an

economically disadvantaged population.

Measures of Adherence

Haynes (1979) defined compliance as the extent to which a person' s behavior

coincides with medical or health advice. Johnson (1993) maintains that defining

adherence (or compliance) with the childhood diabetes regimen is difficult due to the

multitude and complexity of the regimen behaviors. This has hindered the development

of an accurate and reliable measure of adherence for youth with diabetes.

Adherence to diabetes treatment regimens is multidimensional, with research

demonstrating that a patient' s behavior with regard to one aspect of the diabetes regimen

is not predictive of the same patient' s behavior in relation to other components of the

regimen (Glasgow, McCaul, & Schafer, 1987; Johnson et al 1986; Kavanagh, Gooley, &

Wilson, 1993). Adding to the obfuscation, there is not universal agreement on explicit

standards for measuring adherence. Methods range on a continuum from direct to indirect

(La Greca, 1990). Intuitively, the most accurate method of assessment would be

observation of diabetes care behavior. However, the behavioral observational approach is










impractical for the clinical scientist practitioner since the complexity of the diabetes

treatment regimen would require intense scrutiny of numerous behaviors over extended

periods of observation. Indirect measures of adherence behavior offer an alternative

means of assessment. One approach involves obtaining patient self-reports or parental

reports of adherence (Anderson, Auslander, Jung, Miller, & Santiago, 1990). La Greca

(1990) cautions about generalizations from self-reports due to extensive variability in

their detail and comprehensiveness. Johnson, Silverstein, Rosenbloom, Carter, and

Cunningham (1986) developed a 24-hour recall interview to quantify adherence

behaviors. Interviewers ask patients and parents, over a series of three 20-minute

structured phone interviews, to describe diabetes-management behaviors during the

previous 24-hour period. Statistical analyses resulted in indices of adherence. Broadly

speaking these indices covered different aspects of glucose testing, insulin administration,

diet, and exercise constructs. Significant correlations between parent and child report was

documented in separate analyses (Freund, Johnson, Silverstein, & Thomas, 1991;

Johnson et al., 1986; Spevack, Johnson, & Riley, 1991). Advantages of the interview

include a minimization of recall errors, improved reliability, and the ability to capture a

non-reactive example of the child' s adherence. However, the 24-hour recall interview is

difficult to implement in a clinical setting, due to its reliance on multiple interviews (3

child, 3 parent) and complex scoring system (McNabb, 1997). The resources required to

conduct interviews and analyze data (e.g., time and manpower) are not typically available

to healthcare providers. Additionally, despite the comprehensiveness of the 24-hour recall

interview, strong associations between adherence and HbAlc were not supported in a










longitudinal analysis of approximately 200 children using the measure (Johnson, Kelly,

Henretta, Cunningham, Tomer, & Silverstein, 1992).

The Self-Care Adherence Inventory (SCAI) (Hanson, Henggeler, & Burghen

1987), a semi-structured interview that assesses adherence to type 1 diabetes treatment

regimens, provides an alternative to the 24-hour recall interview (Hanson et al., 1987;

Hanson, DeGuire, Schinkel, Kolterman, Goodman, & Buckingham, 1996). The interview

is administered to the patient by an individual familiar with the requirements of the

diabetes regimen. The SCAI content areas include glucose testing, dietary behaviors,

insulin adjustment, and hypoglycemia preparedness. The authors found that the SCAI

related to HbAlc on three separate administrations

(partial r = -0.28, -0.25, and -0.20, p < 0.001).

The SCAI was further refined and the Diabetes Self Management Profile (DSMP)

was developed by Harris and colleagues (2000). The DSMP consists of structuredd

interviews of children/adolescents and parents with diabetes, consisting of 23 questions,

that assess five areas of diabetes management, including: insulin administration/dose

adjustment, blood-glucose monitoring, exercise, diet, and management of hypoglycemia.

The DSMP is designed to access diabetes self-management over the preceding three

months. Investigators found good internal consistency and inter-observer agreement

(Harris et al., 2000). The predictive validity (r = -0.28, p < 0.01) for metabolic control

with the DSMP was consistent with data from the SCAI, however, the measure was

shown to only account for 7.8% of the variance in metabolic control (HbAlc) (Harris et

al., 2000).









Measures of Metabolic Control

While adherence is a behavioral construct, metabolic control is a biological assay

of health status. The widely accepted standard of measurement for metabolic control is

the glycated hemoglobin Alc test (GHB/HbAlc). HbAlc is a measure of glucose bound

to specific hemoglobin molecules within red blood cells. The amount of glucose bound to

such hemoglobin molecules is directly proportional to the concentration of glucose in the

blood. Therefore, HbAlc provides an estimate of the average blood glucose

concentration. Since red blood cells live for approximately 90 days this provides an

estimate over the preceding 8 tol2 weeks. The DCCT observed an average HbAlc of

7.2% for individuals in the intensive treatment group compared to 9.0% in the

conventional treatment group, indicating that individuals in the intensive insulin therapy

group were under better metabolic control.

Relationships between Family Factors, Adherence, and Metabolic Control

Despite the Eindings from the DCCT, most studies fail to aind high or even

moderate correlations between regimen adherence and diabetes metabolic control

(Glascow et al., 1987; Hanson et al., 1987; Hanson et al., 1996; Harris et al., 2000;

Johnson et al, 1986, 1992, 1993, 1994; Kavanagh et al., 1993; Schafer, McCaul, &

Glasgow, 1986; Spevack et al., 1991). An explanation for this discrepancy might result

from examination of the instrument used to measure adherence. Most studies of

adherence have measured an individual's behavior in relation to an ideal treatment

regimen, and have not examined variations from individual prescriptions (La Greca,

1990). Additionally, if a patient' s prescribed treatment regimen was ineffective, perfect

adherence would not be expected to relate to good metabolic control (Johnson, 1992).

Currently, there is no specific, standardized set of self-care behaviors applicable to all









children with diabetes. It is difficult to measure congruity of behaviors that are not

uniformly defined in operational terms (McNabb, 1997). Johnson (1993) maintains that

only weak relationships exist between metabolic control and behavior. Nevertheless,

clinical evidence indicates adherence is a significant predictor of metabolic control. For

example, data from a residential treatment program for youth in poor metabolic control

demonstrate a significant mean reduction in HbAlc when the treatment regimen is

delivered in a controlled environment (Geffken, Lewis, Johnson, Silverstein,

Rosenbloom, & Monaco, 1997). Glascow et al. (1987) suggested that the relationship

between adherence and metabolic control is not always straightforward, and regimen

adherence must be examined in the context of other factors. Anderson and Coyne (1993)

argue that examination of adherence in chronically ill children must include the parent' s

involvement in treatment tasks. When considering the complexity of diabetes

management, it is intuitive to consider that familial relationships and parental behaviors

will relate to child health status.

The relationships between family functioning, diabetes adherence and metabolic

control have been the focus of several analyses (Anderson et al., 1990; Lewin et al.,

2006; Liss, Waller, Kennard, McIntire, Capra, Stephens, 1998; McKelvey et al., 1993;

Schafer et al., 1986; Waller et al., 1986). For example, Hauser and colleagues (1990)

found that children's perceptions of family conflict, as measured by the Family

Environmental Scale (FES), were the strongest predictor of poor adherence with multiple

diabetes treatment regimen components (r = 0.50,

p < 0.001). In addition, the study suggested that child and parent perception of family

cohesion was related to both improved adherence, and comprehensive levels of patient









adherence. Likewise, a study found that child report of parent-child conflict (measured by

the Parent-Child Scales) contributes to the unique variance in predicting both adherence

(r = 0.50, p < 0.001) and metabolic control (r = 0.31, p < 0.05) (Miller-Johnson, Emery,

Marvin, Clarke, Lovinger, & Martin, 1994). Similarly, Hanson, De Guire, Schinkle,

Henggeler, and Burghen (1992) found that both disease-specific and general family

factors related to treatment adherence. Another study by Leonard et al, (2002) using the

Youth Self-report, found that individuals who reported higher levels of attention

problems, and higher levels of aggressive and delinquent behaviors reported worse

metabolic control as measured by HbAlc. However, there is some controversy in the

literature regarding the optimal method for measuring family interaction. Several studies

indicate that general measures of family functioning (general family stress, conflict,

support, etc.), such as the Family Environmental Scale (FES) and the Family Assessment

Measure (Skinner, Steinhauser, & Santa-Barbara, 1983), do not correlate with metabolic

control (Hauser et al., 1990; Liss et al., 1998; Schafer, Glasgow, McCaul, & Dreher,

1983).

It is possible that certain weaknesses in family functioning are distinct from

general family function, being limited to the issues and concerns specific to dealing with

the demands of diabetes. Measures of family functioning that are specifically designed

for families of children with diabetes (e.g., family support specifically related to diabetes

care) may be more sensitive in detecting relationships between family functioning and

metabolic control. While the FES and other general measures of family interaction may

assess characteristics such as family organization, the DFB S includes assessments of

diabetes specific family dimensions, e.g. organization specific to diabetes management.









For example, Liss et al. (1998) found that children hospitalized with DKA and their

parents reported less diabetes family support than did a clinic control group. However,

the Family Assessment Measure (a non-diabetes-specific measure of family functioning)

detected no differences in family functioning between the DKA and clinic control groups.

Additionally, Schafer et al. (1983) found that diabetes-specific family measures were

more predictive of adherence than were more general measures of family interaction

(mean correlation between general measures of family interaction and adherence M~<

0. 10; mean correlation between diabetes-specific measures and adherence M~= 0. 18. The

Diabetes-Specific Family Behavior Scale (DFBS; Waller et al., 1986), the Diabetes

Family Behavior Checklist (DFBC; Schafer et al., 1983), and the Diabetes Family

Responsibility Questionnaire (DFRQ; Anderson et al., 1990) are three measures of

diabetes-specific family support. There follows a description of these three measures

specifically developed to assess diabetes-specific family interaction.

Diabetes Specific Family Measures.

Waller and colleagues (1986) developed the Diabetes-Specific FamFFFFFFFF~~~~~~~~~ily Behavior

Scale of child/adolescent report of diabetes-specific family behaviors. The study

investigated whether the child' s overall perception of family diabetes-specific support

predicted metabolic control. The scale developed by Waller and colleagues (1986) was

based on the assumption that diabetes specific family behaviors consisted of three distinct

child perceived dimensions: parental warmth/caring, parental guidance/control about

diabetes, and diabetes related problem solving. Children and adolescents, ages 7 to 17,

completed the measure with results indicating that, for children under age 13 (n = 20),

there was a significant association (r = 0.50, p < 0.001) between the child's perception of

family-guidance/control and metabolic control (HbAlc), the same finding was not










present for older adolescents. Examples of DFB S items that correlated significantly with

metabolic control were as follows:

Whether the parent watches while the child tests for blood sugar (r = 0.55)
Whether the parent writes down blood sugar tests (r = 0.52)
Whether the child has someone in the family to talk to about diabetes (r = 0.55)

The correlation between metabolic control and warmth/caring was 0.36, p < 0.03 across

the entire age span of 7 to 17 years. Waller et al (1986) found the problem solving scale

did not account for a significant portion of the variance in metabolic control. Further

examination of the DFB S by McKelvey and colleagues (1993) replicated the correlation

between family guidance/control and diabetes metabolic control. However, the child

report of guidance/control accounted for only 1.4% of the variance in HbAlc and

problem-solving was again not related to metabolic control.

The Diabetes FamFFFFFFFF~~~~~~~~~ily Responsibility Questionnaire (Anderson et al. 1990) is a

questionnaire that measures mother and child perceptions of division of responsibility for

diabetes tasks related to treatment. In this study the measure was administered to both

children/adolescents (age 6-21) and their mothers. Responses for each item could be

classified into one of three possible patterns from the dyad scores: (1) perfect agreement

(both mother and child agree as to how the responsibility is shared), (2) overlap

(disagreement where both mother and child claim responsibility), and (3) no

responsibility (neither mother nor child report responsibility for a task). Factor analysis

revealed a three-factor solution representing these domains: general health management

tasks, regimen tasks, and social presentation of diabetes. The investigators found that

mother-child dyad scores indicating that no one takes responsibility for diabetes

management, and lower adherence scores based on parental report explained 13.4% of









the variance in HbAlc. Parent-child dyad scores indicating that neither the parent nor the

child takes responsibility for diabetes management correlated significantly with

metabolic control (r = 0.32, p < 0.001). A study by Wysocki and colleagues found that

children and adolescents reporting increased diabetes management responsibilities

relative their developmental level on a composite index of the DFRQ and the Diabetes

Independence Survey demonstrated less treatment adherence and marginally worse

metabolic control (Wysocki, Linschlid, Taylor, Yeates, Hough, & Naglieri, 1996).

Schafer and colleagues (1983) developed the Diabetes Family Behavior Checklist

(DFBC) to assess patient and family member report of supportive (positive) and

unsupportive (negative) diabetes-specific family behaviors. Administration of the DFBC

to 34 adolescents demonstrated significant relationships between negative DFBC scores

and adolescent's self-reported adherence to diet, care in measuring insulin, and frequency

of glucose testing.

However in a subsequent administration of the DFBC by Schafer and colleagues

(1986) wherein adolescents (ages 12 to 19) and adults (ages 20 and older) completed the

measures, negative family behaviors (related to glucose testing, insulin inj sections, and

diet) were inversely correlated with adherence in adults, but neither positive nor negative

DFBC scores were significantly related to adherence in adolescents. Reports of

unsupportive DFBC scores (criticism, nagging, argumentative statements) were

marginally correlated (r = 0.24, p < 0. 10) with metabolic control in adults but not in

adolescents. However, it may be that this study lacked statistical power to detect such a

relationship, as only 18 adolescents were included in this sample. Nevertheless, Hanson

and colleagues (1987) suggested that the measure was the best instrument available for









assessing parental support of the diabetes treatment regimen, despite the need for larger

sample sizes to demonstrate its predictive validity.

In a study evaluating a parent-adolescent teamwork approach to diabetes

management, families in the intervention group reported significantly less conflict

(measured by the DFBC) and were in better metabolic control (Anderson, Ho, Brackett,

& Laffel, 1999). In addition, Schafer et al (1983) demonstrated a significant relationship

between negative DFBC scores and adolescent's self-reported adherence.

Across all three diabetes-specific measures of family functioning were child

perception of parental support or lack of support of the diabetes treatment regimen.

McKelvey et al (1993) noted appropriate parent supervision of diabetes care tasks, as

measured in the DFRQ, may be closely related to the diabetes regimen guidance/control

dimension of the DFBS. The assessment of supportive and unsupportive parent behaviors

in the DFBC focuses on the same dimension of parent involvement in the disease

treatment. Children who perceive their parents as supportive of their diabetes care have

better metabolic control; children who perceive parents as negative, critical, and

unsupportive of their diabetes management are in poorer metabolic control, as are

families where no one takes responsibility for diabetes management. While the

relationships of the DFBC, DFRQ, and DFBS to metabolic control are small, in several

cases, the diabetes-specific family factors explain larger percentages of the variance in

metabolic control than adherence measures (SCAI, DSMP, 24-hour recall). Lewin et al

(2006) suggest that when examined concurrently, these measures may delineate a

construct of diabetes specific family factors related to adherence. Diabetes-specific









family functioning is a critical construct to evaluate to optimize metabolic control and

adherence outcomes (Anderson et al., 1997).

Externalizing, Diabetes Specific Family Functioning, Adherence and Metabolic
Control

The Child Behavior Checklist (CBCL) was first published in 1978 (Achenbach &

Edelbrock, 1978). The CBCL has become a widely used standardized parent report

questionnaire for 4- to 18-year-olds that exhibits excellent psychometric properties

(Achenbach, 1991). The CBCL provides separate scores for Intemnalizing and

Externalizing. These broad band psychological problems have been identified repeatedly

in multivariate analyses (Achenbach, 1991) and reflect a distinction between depressed,

fearful, and inhibited over-controlled behaviors known as Internalizing, and aggressive,

oppositional, and antisocial under controlled behaviors known as Extemnalizing. There

exists very little work that has examined the relationships between child

psychopathology, family functioning, adherence and metabolic control. Our clinical

experience has led us to hypothesize that externalizing behavior has important

relationships to diabetes specific family functioning and adherence in explaining

metabolic control in youth with type 1 diabetes.

Summary

Recent research has found that more intensive diabetes regimens are related to

better metabolic control and that worse metabolic control leads to increased diabetes

related complications at an earlier age of onset in children and adolescents. Accordingly,

physicians have prescribed increasingly more demanding regimens. Clinicians believe

that the extent to which patients follow treatment recommendations is integral in

producing positive health outcomes. However, most existing psychological research only









weakly supports these assumptions. Prior research suggests the absence of a strong

relationship between behavior and health status in children and adolescents with type 1

diabetes. Methodological problems (e.g. the measurement of adherence, the complexity

and quality of the medical regimen, disease specific verses general measures of family

functioning, and the reliability of health-status indicators) are often cited as explanations

for these Eindings. Additionally, multiple studies fail to show strong relationships

between individual diabetes-specific family factors related to adherence and metabolic

control .

McKelvey and colleagues (1993) suggested that incorporating the DFBC and

DFRQ scales with the DFBS might demonstrate a stronger connection between family

adherence and HbAlc. To date, only Lewin and colleagues (2006) have examined the

relationship between this combination of diabetes-specific family-related adherence

variables and metabolic control with encouraging results. Lewin et al (2006) found 34%

of the variance in metabolic control was accounted for by diabetes-specific family factors

after accounting for demographic variables. Further they hypothesized and successfully

demonstrated a mediation model wherein adherence partially mediated the relationship

between a composite of diabetes-specific family variables, and metabolic control.

Limited research has examined the contribution of children' s externalizing behavior to

metabolic control. The first aim of this investigation was to replicate the Eindings of

Lewin et al (2006) by accounting for a significant portion of the variance in metabolic

control through the use of diabetes-specific measures of family functioning related to

disease management. The second aim was to replicate a model wherein adherence

partially mediates the relationship between diabetes-specific family variables and









metabolic control. The third aim was to examine a proposed mediation model wherein

family factors mediate the relationship between externalizing and metabolic control. Our

fourth and final aim was to examine a proposed mediation model wherein adherence

mediates the relationship between externalizing and metabolic control.















CHAPTER 2
RESEARCH QUESTIONS

Question 1: Family Factors Predicting Metabolic Control

Does a combination of diabetes-specific family functioning measures predict significant

variance in metabolic control in children and adolescents with type 1 diabetes?

Metabolic control will be predicted by the following adherence-related family

variables: both parent and child deny responsibility for diabetes care tasks (DFRQ); child

perception of the parent providing guidance/control over diabetes care (DFBS); child

perception of parental warmth and caring surrounding the diabetes treatment regimen

(DFBS), and child perceptions of parental nagging, critical, and argumentative behavior

related to diabetes management (DFBC).

Question 2: Adherence Mediating between Diabetes-Specific Family Factors and
Metabolic Control.

Does adherence mediate the relationship between diabetes-specific family factors and

metabolic control in children and adolescents with type 1 diabetes?

Our study tested a model that proposes that diabetes-specific family factors affect

metabolic control through pathways of adherence. Adherence is expected to mediate the

relationship between diabetes-specific family factors and metabolic control (Figure 2-1).

Question 3: Family factors mediating between child externalizing and metabolic
control.

Do diabetes-specific family factors mediate the relationship between externalizing and

metabolic control?










Our study tested a model that proposes that children's externalizing behaviors

affect metabolic control through pathways of family factors. It is expected that child

externalizing behaviors will mediate the relationship between family factors and

metabolic control

(Figure 2-2).

Question 4: Adherence mediating child externalizing behaviors and metabolic
control?

Does adherence mediate the relationship between child externalizing and metabolic

control?

Finally, our study tested a model that proposes that child externalizing affects

metabolic control through pathways of adherence. It is expected that adherence will

mediate the relationship between externalizing behaviors and metabolic control (Figure

2-3).

















Adherence




Figure 2-1. Theoretical model of adherence mediating family factors and metabolic
control .


Family
Factors


Metabolic
Control











Externalizing
Behaviors


Metabolic
Control


Figure 2-2. Theoretical model of family factors mediating externalizing and metabolic
control .


Family
Factors












Externalizing
B ehavi ors


Metabolic
Control


Adherence


Figure 2-3. Theoretical model of adherence mediating externalizing and metabolic
control .















CHAPTER 3
IVETHOD S

Participants and Settings

Participants were 120 children with type 1 diabetes and their parents, attending an

outpatient pediatric diabetes clinic affiliated with the University of Florida in Gainesville,

Florida. The sample was composed of 51 boys and 69 girls, ages 8.25 to 18.75 (M~=

13.92, SD = 2.71). The ethnic distribution was 72.5% Caucasian, 15.0% African

American, 10.0% Hispanic, and 2.5% representing other ethnic groups. More mothers

(78.3%) participated in the study compared to fathers (13.3%) and other parents (8.4%).

The mean family income was 46.6K with a standard deviation of 29. 1K.

Procedure

Parents and children were recruited based on the following study inclusion

criteria: eight to eighteen years of age, a diagnosis of type 1 diabetes for at least 6

months, living with and accompanied by their primary caretaker, no evidence of mental

retardation. Of families approached, 82.8% agreed to participate. A signed informed

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

obtained from each participant. Both parents and children completed self-report

questionnaires and one face-to-face structured interview while waiting for their scheduled

appointment. Trained graduate or undergraduate psychology students conducted

interviews individually with both caregiver and child of approximately 10-15 minutes in

length. Instructions were given to both for completing the self-report measures. Self-

report measures were typically completed within 30-40 minutes. Children and









adolescents were prompted to answer questions honestly and were reminded that no one

else in their family would see their answers. Blood samples for measuring HbAlc were

drawn by trained clinic staff as part of each patient' s routine visit.

All research assistants underwent extensive training with the first author in the

administration of these measures, which included: (1) attending an instructional meeting,

(2) observing 3 administrations of the measures, (3) administering the measures 3 times

with in vivo observation and supervision.

Measures

Demographic Questionnaire

Parents were administered a brief demographics questionnaire to assess the

child's age, sex, race, age at onset of diabetes, family structure, parental education,

occupation and estimated income.

Diabetes-Specific Family Measures

Diabetes Family Behavior Scale (DFBS).

The DFB S is a measure of perceived family support for children and adolescents

with type 1 diabetes (Waller et al., 1986). This 60-item questionnaire has three subscales

(warmth/caring, guidance/control, and problem solving) that are used to determine the

child' s overall perception of family support. Scores from the guidance/control and

warmth/caring subscales correlated with metabolic control (.50 and .36, respectively), but

the problem-solving subscale did not (.08). Test-retest reliability coefficients for the

warmth/caring and guidance/control subscales are good (.79 and .83, respectively), but

not so for the problem-solving subscale (.51). Analyses indicate stronger reliability with

younger children (under age 13) that have been attributed to developmental differences

(McKelvey, Waller, North, et al, 1993). Children and adolescents completed the warmth









and caring, and the guidance and control scales of this measure. Overall, the DFB S has

good internal consistency (alpha coefficient of 0.82). Cronbach's a in our study was

acceptable (a = .74).

Diabetes Family Behavior Checklist (DFBC).

This instrument is a measure of both supportive and non-supportive behaviors by

the family and their effects on the child's diabetes self-care regimen (Schafer et al.,

1986). This forced-response, 16-item scale is designed for ages 12 to 64 and consists of a

summative response scale from 1 (never) to 5 (at least once a day). Summary scores are

generated for both the positive/supportive and negative/unsupportive domains. A positive

summary score can range from 9 to 45 and the negative summary scale can range from 7

to 35. Schafer and colleagues (1986) found that negative behaviors of family members

reported by the adults with diabetes were inversely correlated with adherence (glucose

testing, diet, and insulin injections). Parents and, children and adolescents completed this

measure. Cronbach's alpha coefficients range from .67 to .80 for the positive/supportive

scale and .74 to .82 for the negative/unsupportive scale (Schafer, personal

communication, 1998). Internal consistency for this sample was acceptable

(a = .64).

Diabetes Family Responsibility Questionnaire (DFRQ).

The Diabetes Family Responsibility Questionnaire is used to assess the family

sharing of responsibilities for diabetes treatment (Anderson et al., 1990). Both the parent

and child independently complete this measure consisting of reading 17 statements

concerning diabetes management tasks and indicating which family member has the

responsibility for the specific task. A parent-child dyadic score is calculated to determine










patterns of agreement and disagreement within the dyad. Factor analyses of this 17-item

measure indicate three domains of 1) general health management tasks, 2) regimen tasks,

and 3) social presentation of diabetes. Internal consistencies for the three subscales were

acceptable, ranging from .69 to .85 (Anderson et al., 1990). For this sample internal

consistency was acceptable (a = .75).

Child Behavior

Child Behavior Checklist

Achenbach and Edelbrock first published the Child Behavior Checklist (CBCL) in

1978 and 1979. The CBCL has become a widely used standardized 118-item parent

report questionnaire for 4-18 years olds that exhibits excellent psychometric properties

(Achenbach, 1991). It was designed to assess the behavioral problems and social

competencies of children 4 to 18 years of age. The CBCL groups 20 competence items

into 11 Problem Scales (including eight Syndrome Scales) and four Competence Scales.

The CBCL also yields two broadband, higher order psychopathology scales, internalizing

and externalizing. The CBCL is widely used in both clinical and research settings

because of its demonstrated reliability and validity, ease of administration and scoring,

and applicability to clinical, nonclinical, and cross-cultural samples (Achenbach, 1991;

Cohen, Gotlieb, Kershner, & Wehrspann, 1985; Drotar, Stein, & Perrin, 1995; Sandberg,

Meyer-Bahlburg, &Yager, 1991). Cronbach's a in our study were strong (u = .94)

Measurement of Adherence

The DSMP is a refined version of the SCAI (Harris et al., 2000). The DSMP is a

structured interview, consisting of 23 questions, with 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. To minimize response bias, the interviewer

begins with a statement indicating that imperfect diabetes management is common.

Additionally, tasks for which nonadherence is more readily admitted (e.g. exercise) are

assessed first. Investigators found good internal consistency (Cronbach's alpha = 0.76)

and inter-observer agreement (94%). Parents and children were interviewed separately.

Internal consistency was found to be acceptable in our study for both parents (a = .86)

and children (a = .65).

Measure of Metabolic Control

During regularly scheduled appointments, patients in the Outpatient Diabetes

Clinic have their HbAlc routinely checked as part of their normal check-up. Blood

samples were analyzed using a Bayer DCA 2000+. HbAlc for individuals without

diabetes typically ranges between 4%-6.5% on this instrument.















CHAPTER 4
RESULTS

Preliminary analyses were conducted to test for relations between demographic

variables and study variables (HbAlc, adherence, and family functioning) for purposes of

control in subsequent hierarchical multiple regression. HbAlc was correlated with child's

age (r = .23,

p < .01), duration with diabetes (r = .27, p < .01), and family income (r = -. 16, p = .09).

Child age was significantly correlated to the DFBC (r = .25, p < .01), to the DFBS

guidance and control subscale (r = -.57, p < .01), and the parent DSMP (r = -.37, p < .01).

Family income was found to be significantly correlated to both the child (r = .22, p < .05)

and parent DSMP (r = .21, p < .05). Duration of diabetes was significantly related to

HbAlc (r = .27, p < .01) and to parent DSMP (r = -.26, p < .01). All other demographic

variables were not significantly related to measures of diabetes-specific family

functioning, parent reports of child externalizing, or to reports of adherence.

Table 4-1 provides the sample characteristics on sex and ethnicity and Table 4-2

reports means, standard deviations, and ranges for child's age, HbAlc, duration with

diabetes, and income. The mean age and duration with diabetes for children in our study

was approximately 13.92 (SD = 2.7) and 4.80 (SD = 3.81) years, respectively. Mean

income was 46.0K (SD = 29.1K), and the mean HbAlc for the sample was 8.93% (SD =

1.99%), which is in the elevated range. No sex differences were found in HbAlc (t (120)

= 1.17, p = 0. 19, mean for boys: 9. 18% (SD = 1.77%); mean for girls: 8.75% (SD =

2. 13%). Likewise, no sex-differences were identified on measures of diabetes-specific










family functioning or self-report of adherence (oc = .05). Intercorrelations between

variables are found in table 3. All subsequent regression analyses were assessed for

collinearity by calculating tolerance and variance inflation factors (VIF) and no

significant collinearity was identified (Bowerman and O'Connell, 1990).

Study Aim 1: Regression of Metabolic Control (HbAlc) on Family Functioning and
Adherence

To examine the relation between multiple measures of diabetes-specific family

factors and metabolic control, we conducted a hierarchical multiple linear regression. We

expected that when examined simultaneously, measures of adherence-related diabetes-

specific family factors would be predictive of significant variance in metabolic control.

To control for the influence of the child' s age, family income, and duration with diabetes,

we entered these variables into the regression equation in steps 1 and 2. To partition out

the variance shared by duration of diabetes and child's age these variables were

controlled in separate blocks, with child age and estimate of family income controlled in

the first block and duration of diabetes controlled in the second block. Combined, child

age and family income accounted for 6.7% of the variance in metabolic control, F (2,

102) = 3.69, p < .05. The unique variance accounted for by duration of diabetes was

5.3%, F (3, 101) = 4.62, p < .01. Considering that there were no a priori suppositions

regarding the magnitude of the relationship between each family functioning variable and

metabolic control, each family predictor was entered simultaneously into block 3 of the

regression. Combined, the four diabetes-specific family functioning variables (i.e.

parental warmth and caring (DFBS), guidance and control (DFBS), critical and negative

parenting (DFBC), and no-responsibility for the treatment regimen (DFRQ), explain an

additional 13.3% of the variance in metabolic control (HbAlc), F (7, 97) = 4.70, p < .01.









Lastly, child and parent report of adherence were entered into the model, which

combined accounted for 18.6% of the variance in metabolic control, F (9, 95) = 8.27, p <

.01. All together, the model predicted 43.9% of the variance in metabolic control

(HbAlc). The final model demonstrated significant contributions from child report of

critical, negative, unsupportive parents, and parent report of adherence (Table 4-4).

Study Aim 2: Does Adherence Mediate the Relationship Between Diabetes-specific
Family Factors and Metabolic Control?

To determine whether child report of adherence accounted for unique variance in

metabolic control beyond that of parent report, a regression equation was calculated

controlling for demographic variables in blocks one and two, with parent and child report

of adherence entered in the third and fourth blocks respectively. Parent report of

adherence explained 27.4% of the variance in metabolic control (HbAlc), F (4, 101) =

16.48, p < .01. Child report failed to account for significant variance in metabolic control

above and beyond that accounted for by parent report, explaining only .6% of the

variance in metabolic control. The complete model for both parent and child reports of

adherence, while controlling for demographic variables, accounted for 40. 1% of the

variance in metabolic control (See table 4-5). Since warmth and caring, guidance and

control, lack of responsibility, and child report of adherence, failed to add significantly to

the model in this and previous analyses, these variables were excluded from subsequent

analyses.

Next, Baron and Kenny's (1986) guidelines for mediation were followed to test

the influence of the remaining diabetes-specific family factor (DFBC) on metabolic

control via adherence. The following criteria are necessary for mediation: (I) the

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









predictor should be significantly associated with the mediator (adherence), (III) the

mediator should be associated with the outcome variable (metabolic control), and (IV)

lastly, the addition of the mediator to the full model should significantly reduce the

relationship between the predictor and outcome variable. Baron and Kenny's first through

third criteria were met in previous analyses, see table 3. To examine Baron and Kenny's

next criteria, family functioning (DFBC) was entered after demographic variables, and

parent report of adherence was entered into the final block of the regression. The family

functioning variable (DFBC) was found to account for 12.6% of variance in metabolic

control (HbAlc), while parent report of adherence (DSMP) added 18.6% to the model.

Finally, family functioning was entered into the model while controlling for adherence

and demographic variables. The final model accounted for 40.2% of the variance in

metabolic control (Table 4-6). To examine the significance of change in the path

coefficient, a Sobel's z-score (Sobel, 1988) was calculated, z = -6.70, p < .001, and was

found to be significant, meeting Baron and Kenny's criteria for mediation. The path

coefficient between family factors and metabolic control remained significant while

controlling for adherence, thereby indicating partial mediation. Standardized coefficient

of Family Factors on Metabolic Control equals .204 for direct and .201 for indirect effect

(figure 4-4).

Study Aim 3: Regression Analysis to determine if Family Factors (DFBC) mediates
the relationship between Externalizing and Metabolic Control (HbAlc).

Baron and Kenny's (1986) guidelines for mediation were followed to test the

influence of child externalizing behaviors on metabolic control (HbAlc) via diabetes

specific family factors (DFBC). Child externalizing was entered into the regression, after









controlling for demographic variables, and was found to account for 5.6% of the variance

in metabolic control, F= 5.40,

p < .001. Next, family factors (DFBC) were entered into the model and were found to

account for 8.6% of the variance in metabolic control, F = 7. 11, p < .001. Finally, child

externalizing was entered into the model while controlling for family factors (DFBC) and

was found to account for a non-significant portion of the variance in metabolic control,

meeting criteria for full mediation (Table 4-7). To examine the significance of change in

the path coefficient, a Sobel's z-score (Sobel, 1988) was calculated, z = 2.67, p < .01, and

was found to be significant, meeting Baron and Kenny's criteria for mediation. The path

coefficient between externalizing and metabolic control became non-significant while

controlling for family factors, thereby indicating full mediation. The standardized

coefficients of externalizing on HbAlc equals .161 for direct, and .1 10 for indirect effect

(figure 4-5).

Study Aim 4: Regression Analysis to Determine if Adherence (Parent DSMP)
Mediates the Relationship Between Externalizing Behaviors and Metabolic Control
(HbA 1c).

Baron and Kenny's (1986) guidelines for mediation were followed to test the

influence of child externalizing behaviors on metabolic control via adherence. Child

externalizing was entered into the regression, after controlling for demographic variables,

and was found to account for 5.6% of the variance in metabolic control, F = 5.40, p < .01.

Next, parent reports of adherence (DSMP) were entered into the model and were found to

account for 22.2% of the variance in metabolic control, F = 13.24, p < .001. Finally,

externalizing was entered into the model while controlling for adherence and accounted

for a non-significant portion of the variance in metabolic control, indicating full









mediation (Table 4-8). To examine the significance of change in the path coefficient,

Sobel's z-score (Sobel, 1988) was calculated, z = 3.66,

p < .001, and was found to be significant, meeting Baron and Kenny's criteria for

mediation. Since the path coefficient between externalizing and metabolic control was

reduced to a non-significant effect while controlling for adherence, full mediation was

indicated. Standardized coefficient of DFBC on HbAlc equals .059 for direct and .212

for indirect effect (figure 4-6).























.405***

(.204*)


Type of Mediation ----- Partial
Sobel z-value ------------ -6.69526, p =.000001

Standardized coefficient of DFBC on HbAlc:
Direct: .204
Indirect: .201


.374***


-.614***
(-.538***)


Figure 4-1. The relationship between family factors (DFBC) and metabolic control
(HbAlc) partially mediated by adherence (Parent DSMP).












Type of Mediation ---- Full
Sobel z-value ---------- 2.671389, p =.007554

Standardized coefficient of Externalizing on HbAlc:
Direct: .161
Indirect: .110


.271**

(. 161)


Outcome
]Variable:
HbAlc




.405***
(.355***)


.310***


Figure 4-2. The relationship between externalizing and metabolic control (HbAlc) fully
mediated by family factors (DFBC).























.271**

(.059)


Type of Mediation ---- Full
Sobel z-value ----------- 3.66278, p =.000249

Standardized coefficient of Externalizing on HbAlc:
Direct: .059
Indirect: .212


-.614***
(-.593***)


Figure 4-3. The relationship between externalizing and metabolic control (HbAlc) fully
mediated by adherence (Parent DSMP).










Table 4-1. Demographic characteristics


Category

Male
Female

Caucasian
African American
Hispanic
Other


Percentage


42.5
57.5

72.5
15.0
10.0
2.5


Ethnicity







37


Table 4-2. Means and Standard Deviations for Age, HbAlc, Duration of Diabetes, and

Income .


Mean
8.93
13.92
57.54
46005.06


SD
1.99
2.71
45.71
29089.61


Minimum
5.00
8.25
6.00
6000.00


Maximum
>14.00
18.75
270.00
155000.00


HbAlc (%)
Age
Duration (Months)
Income











Table 4-3. Intercorrelations among HbAlc, Diabetes Family Measures, Adherence, and
Externalizing.


Variable
1 HbAlc.
2 Child adherence report
3 Parent adherence report
4 Warmth and caring
5 Guidance and control
6 No responsibility
7 Critical parenting
8 Child externalizing


1


2 3
-.378** -.614**
-.501**


-.1i
1.
.1


4
113 -.01
08 .0:
79* .1:
-.1S


5
81 -
73 .0
28 .0
93* -.0
-.1(


6 7 8
006 .405** .271**
)04 -.279* -.269**
)06 -.374** -.358**
49 -.324** -.320**
93* -.225* -.095
-.119 .005
-.310**


*p <. 05, **p< .01










Table 4-4. Hierarchical Regression Analysis Predicting HbAlc from Diabetes-Related
Family Factors and Adherence.

Step Variables R2 R2 ~


.067


.067


1


3.6


;9*
.066
-.023
0*
.124*
12**
.058
.062
-.018
.224**
74**
-.058
-466**


Child Age
Income
2 .121 .053 6.1
Duration of diabetes
3 .253 .133 4.3
Warmth and caring (DFBS)
Guidance and control (DFBS)
Lack of responsibility (DFRQ)
Critical parenting (DFBC)
4 .439 .186 15.
Child reported adherence (DSMP)
Parent reported adherence (DSMP)

All standardized regression coefficients are from the final block of the equation.

*p < .05, ** p < .01, *** p < .001










Table 4-5. Hierarchical Regression Analysis Predicting HbAlc from Parent and Child
Report of Adherence.

Step Variables R2 2R F1
1 .067 .067 3.69*
Child Age .066
Income -.023
2 .121 .053 6.10*
Duration of diabetes .124
3 .395 .274 45.79*
Parent report of adherence -.520
4 .401 .006 1.05
Child report of adherence

All standardized regression coefficients are from the final block of the equation.
*p < .05, ** p < .01, *** p < .001










Table 4-6. Mediation Regression Analysis Predicting HbAlc: Adherence Mediating the
Relationship between Family Factors (DFBC) and Metabolic Control
(HbAlc): Final Block of the Regression.

Step Variables R2 2R F1
1 .067 .067 3.69*
Child Age .030
Income -.025
2 .121 .053 6.10*
Duration of diabetes .126
3 .371 .274 45.79**
Parent report of adherence (DSMP) -.492**
4 .402 .035 6.20**
Critical parenting (DFBC) .206


*p < .05, ** p < .01










Table 4-7. Mediation Regression Analysis Predicting HbAlc: Family Factors (DFBC)
Mediating the Relationship Between Child Externalizing and Metabolic
Control (HbAlc): Final Block of the Regression.


AR2
.067


Step


Variables


3.69*



6.10*


.067


Child Age
Income


.103
-.085

.215*

.320**


Duration of diabetes

Family Factors (DFBC)

Externalizing (CBCL)


.245

.262


16.72**


.017


2.28


.138


*p < .05, ** p < .01







43


Table 4-8. Mediation Regression Analysis Predicting HbAlc : Adherence Mediating the
Relationship Between Child Externalizing and Metabolic Control (HbAlc):


Final Block of the Regression.


Variables


AR2
.067



.053


Step


3.69*



6.10*


.067



.121


Child Age
Income


.066
-.023


Duration of diabetes


.274 45.79**


Parent report of adherence
(DSMP)

Externalizing (CBCL)


.492**


.398


.003


.062


*p < .05, ** p < .0















CHAPTER 5
DISCUSSION

Interpretation of Results

The purpose of our study was to examine how behavioral variables including

adherence to the diabetes treatment regimen, child externalizing, and family functioning

specific to diabetes management related to metabolic control. First, we investigated the

relationship between a combination of diabetes-related family factors and metabolic

control. Regression analysis indicated that, taken together, diabetes-related family factors

accounted for 13.3% of the variance in metabolic control after controlling for

demographic variables. In addition, it is important to note that reports of adherence

explained significant variance in metabolic control. Our study replicated Lewin et al's

findings of diabetes related family factors and adherence significantly predicting

metabolic control.

Another goal of our study was to replicate adherence mediating the relationship

between diabetes-specific family factors and metabolic control. The proposed mediating

model suggests that negative family functioning processes have a deleterious impact on

children's adherence behaviors and subsequent metabolic control. This model posits that

the child's perception of critical and negative parenting around the diabetes treatment

regimen is related to adherence to a treatment regimen. Overall, children who reported

more negative and critical relationships with their parents were in worse metabolic

control and such relationships are mediated by poor adherence. We believe it is likely

that there is a reciprocal relationship between critical parenting and poor diabetes










regimen adherence, with families becoming trapped in a coercive cycle or struggle for

control (Patterson, 1974). An adolescent's nonadherent behaviors may elicit parent

criticism, which in turn can lead to further adolescent rebellion toward adherence.

Regression analysis supported our third hypothesis that adherence mediates the

relationship between externalizing and metabolic control. The mediating model suggests

that children's externalizing behaviors have deleterious affects on children's adherence

behaviors and subsequent metabolic control. This model suggests that externalizing

behaviors interfere with good metabolic control and compliance with the prescribed

treatment regimen. Overall, children with higher reported externalizing behaviors were in

worse metabolic control. Parental difficulty dealing with externalizing behaviors such as

stubbornness and arguing, are unlikely to help with a child's adherence. Poor adherence

may fuel the parent and child battleground. Difficulties with adherence to complex and

demanding treatment regimens may exacerbate general child oppositionality and further

thwart any support parents might provide in management aimed at better metabolic

control .

Finally, regression analysis supported our fourth hypothesis; that family factors

mediate the relationship between externalizing and metabolic control. The mediating

model suggests that externalizing is related to children's perceptions of critical and

negative parenting around diabetes and its relationship to poor metabolic control. This

model suggests that parent reported externalizing behaviors are linked to child reported

perceptions of negative parenting that includes criticizing, nagging, and arguing with the

child about diabetes related treatment tasks, leading to worsened metabolic control that

are shown, in aim three, to be largely through pathways of adherence. Overall children









with higher parent reported externalizing behaviors were in worse metabolic control.

Child externalizing behaviors may precipitate reciprocal negative behaviors from parents,

leading to increased family conflict that disrupts adherence processes and subsequent

metabolic control. It was shown that younger children who experienced parental warmth

had improved metabolic control (Davis et al, 2001), suggesting the reciprocal to be true.

Our study did not identify a significant association between child ratings of

parental guidance and control and metabolic control. However, given our sample mean

age of 13.92 years, this finding is not inconsistent with the extant literature, which

suggests that in adolescent populations, the relationship between parental

guidance/control and HbAlc is weaker (McKelvey et al., 1993; Waller et al., 1986). As

most adolescents strive for autonomy, parents may have less influence regarding whether

or not they attempt to provide guidance and control. This pattern suggests that while the

DFBS Guidance and Control Scale may have examined content independent from the

other scales, the construct was not related to metabolic control with this sample.

Additionally, our study did not find that the DFBS warmth and caring or responsibility

subscale was significantly related to metabolic control.

Consistent with Lewin et al (2006), significant relationships were found between

children reporting more critical, negative, unsupportive relationships with their parents

(regarding their diabetes management) and worse metabolic control. Also, parent and

child reports of adherence were strongly associated with metabolic control. Child report

of adherence did not significantly add to the variance accounted for in metabolic control

by parent report. Parent report of adherence accounted for 27.4% of the variance in

metabolic control, above and beyond demographic variables. However, the relationship is









reduced to 18.6% when family factors are controlled. The complete model did account

for 43.9% of the variance in metabolic control while controlling for demographic

variables.

Limitations, Implications, and Future Directions

Before implications of our study and speculations on future research are

discussed, it is important to note limitations of our study. Due to the correlational nature

of the data, causal relationships cannot be implied. However, ethically, a controlled

analysis could not be implemented since children cannot be randomly assigned to

supportive and unsupportive families. Secondly, families reporting to this clinic are

largely of low economic status and sponsored by state-funded insurance (State of Florida

Children's Medical Services). Results may not generalize to higher SES families. Third,

while children are informed that no parent or physician will see their results and are

encouraged to be as accurate as possible, there is the potential for report-bias on the

questionnaires and interview. Finally, administration of general family functioning

measures along with diabetes-specific questionnaires would have allowed for more

comprehensive comparisons with other similar studies.

Our study identified that reports of adherence are significantly related to

metabolic control. The use of adherence interviews may an important method to identify

those patients who are at risk for poor metabolic control. Such a preventative approach

may lead to more timely interventions, avoiding some of the risk associated with poorly

controlled diabetes. A reduction in HbAlc by 1% was associated with a 15 to 30%

decrease in the risk of microvascular and neuropathic complications of diabetes,

highlighting the significance of this difference (Diabetes Control and Complications

Research Group, 1987; ADA, 2003).









It is not a new concept, in a clinical context, that child behaviors may have

important implications for medical treatment, however an evidence-based examination of

this concept through research is still important. Our study highlighted the importance of

considering children's externalizing behaviors as they relate to other family factors,

children's adherence to their treatment regimens and metabolic control.

Importantly, adherence interviews used in our study can be completed in a

clinical setting, require minimal training to score and administer, and need little time to

complete. These brief screeners can identify families for behavioral health interventions.

Additionally, as there is variation in the complexity of the prescribed treatment regimen

that is largely clinically determined by the patient's ability to manage such regimens, it is

logical to posit that screening for behavioral suitability to the more complex regimens

would increase the success of more complex regimens.

Our data suggest that family behavior is significantly related to a child's health

status, especially when multiple aspects of family functioning related to regimen

behaviors are considered in making the assessment. Therefore, when identifying barriers

to adherence, clinicians should assess child perceptions of parental negativity, and child

externalizing behaviors in addition to assessing disease management behaviors.

The results of our study should also be considered during interventions for

children with poor metabolic control. Specifically, behavioral family-systems such as that

developed by Wysocki et al, (2006) seem most beneficial for these problems. Instead of

focusing exclusively on improving specific adherence behaviors, therapy should address

instrumental processes, such as improving family communication patterns and reducing

factors that promote and maintain argumentative interaction patterns specific to diabetes










management. Parent training should facilitate parenting described by Baumrind (1991) as

authoritative rather than authoritarian, indulgent or neglectful. In addition to a solid

understanding of issues surrounding diabetes treatment, therapists require process skills

to optimally address adherence problems in the context of improving family dynamics

related to diabetes management.

It is appropriate to conclude our study by reviewing the powerful finding that

parent report of adherence explained 27.4% of the variance in metabolic control, after

controlling for demographic variables, without directly measuring diabetes knowledge.

Congruent with clinical observations, adherence was highly related to metabolic control,

highlighting the importance of clinical assessments of regimen-specific adherence

behaviors and family factors to optimize metabolic control.

Future research should further examine the family functioning, adherence, and

metabolic control relations, identified in the mediation model from our study. For

example, this model may contribute to intervention studies of these relationships. Given

that improved health status and reduced diabetes-related complications are the clinical

goals, the aim should be to develop family models of adherence that optimize metabolic

control. Future analyses should examine other factors that might relate to diabetes family

functioning. For example, parents who already perceive themselves as overwhelmed

might be less likely to be supportive of and responsible for their child's diabetes

treatment regimen. Other examples include parent and child, anxiety and depression.

Lastly, as interactive patterns of behavior are unlikely to be unidirectional, future studies

should examine potential reciprocal interaction patterns between family variables,

adherence behaviors, and metabolic control that are specific to diabetes.
















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BIOGRAPHICAL SKETCH

Danny C. Duke was born on October 7, 1954 in Redlands, California. The oldest of

three children, he grew up living in Yucaipa and later in Placerville, California, where he

graduated from El Dorado High School in 1972. He owned a successful landscape

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

2004. He has been married to his lovely wife (Vicki) since 1973 and they have two

children (Erin and Ryan). He earned his B.A degree in psychology from California State

University, Sacramento in 2002. He plans to complete his Ph.D. in Clinical Psychology

and to pursue his clinical and research interests in pediatric psychology and teaching.




Full Text

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FAMILY FACTORS, ADHERENCE, AND METABOLIC CONTROL IN YOUTH WITH TYPE 1 DIABETES. By DANNY C. DUKE A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Danny C. Duke

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To my family, whose sacrifices made my education possible.

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iv ACKNOWLEDGMENTS I would like to express my grat itude to my mentor and chair, Dr. Gary R. Geffken. Also, my appreciation goes to Adam Le win and Laura Bimbo, for their as sistance with data collection; and to Dr. Janet Silverstein, Di vision Chief of Pediatric Endocri nology, for allowing us to recruit families from the pediatric diabetes clinic. Finally, thanks are due to my committee members, Dr. Sheila Eyberg, Dr. Michael Perri, and Dr. William Perlstein.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES.........................................................................................................viii ABSTRACT....................................................................................................................... ix CHAPTER 1 INTRODUCTION........................................................................................................1 Overview of Diabetes...................................................................................................1 Treatment and the Diabetes Contro l and Complications Trial (DCCT).......................2 Measures of Adherence................................................................................................4 Measures of Metabolic Control....................................................................................7 Relationships between Family Factors, Adherence, and Metabolic Control................7 Diabetes Specific Family Measures............................................................................10 Externalizing, Diabetes Sp ecific Family Functioning, Adherence and Metabolic Control....................................................................................................................14 Summary.....................................................................................................................14 2 RESEARCH QUESTIONS........................................................................................17 Question 1: Family Factors Pr edicting Metabolic Control.........................................17 Question 2: Adherence Mediating between Diabetes-Specific Family Factors and Metabolic Control...................................................................................................17 Question 3: Family factors mediating be tween child externalizing and metabolic control.....................................................................................................................17 Question 4: Adherence mediating child externalizing behaviors and metabolic control?....................................................................................................................18 3 METHODS.................................................................................................................22 Participants and Settings.............................................................................................22 Procedure....................................................................................................................22 Measures.....................................................................................................................23 Demographic Questionnaire................................................................................23

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vi Diabetes-Specific Family Measures....................................................................23 Diabetes Family Behavior Scale (DFBS).....................................................23 Diabetes Family Behavior Checklist (DFBC)..............................................24 Diabetes Family Responsibi lity Questionnaire (DFRQ)..............................24 Child Behavior.....................................................................................................25 Child Behavior Checklist.............................................................................25 Measurement of Adherence.................................................................................25 Measure of Metabolic Control.............................................................................26 4 RESULTS...................................................................................................................27 5 DISCUSSION.............................................................................................................44 Interpretation of Results.............................................................................................44 Limitations, Implications, and Future Directions.......................................................47 REFERENCES..................................................................................................................50 BIOGRAPHICAL SKETCH.............................................................................................56

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vii LIST OF TABLES Table page 4-1 Demographic Characteristics...............................................................................................3 6 4-2 Means and Standard Deviations for Age, HbA1c, Duration of Diabetes, and Income........37 4-3 Intercorrelations between HbA1c, Diab etes Family Measures, Adherence, and Externalizing.................................................................................................................. ......38 4-4 Hierarchical Regression Analysis Predic ting HbA1c from Diabetes-Related Family Factors and Adherence.........................................................................................................3 9 4-5 Hierarchical Regression Analysis Predic ting HbA1c from Parent and Child Report of Adherence...................................................................................................................... .......40 4-6 Mediation Regression Analysis Pred icting HbA1c: Adherence Mediating the Relationship between Family Factors (DFBC) and Metabolic Cont rol (HbA1c): Final Block of the Regression.......................................................................................................4 1 4-7 Mediation Regression Analysis Predicting HbA1c: Family Factors (DFBC) Mediating the Relationship between Child Externalizi ng and Metabolic Cont rol (HbA1c): Final Block of the Regression.......................................................................................................4 2 4-8 Mediation Regression Analysis Predicting HbA1c : Adherence Mediating the Relationship between Child Externalizing a nd Metabolic Control (HbA1c): Final Block of the Regression.............................................................................................................. ....43

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viii LIST OF FIGURES Figure page 2-1 Theoretical model of adherence mediati ng family factors and metabolic control...............19 2-2 Theoretical model of family factors medi ating externalizing and metabolic control..........20 2-3 Theoretical model of adherence mediat ing externalizing and metabolic control................21 4-1 The relationship between family factor s (DFBC) and metabolic control (HbA1c) partially mediated by ad herence (Parent DSMP).................................................................33 4-2 The relationship between externalizing a nd metabolic control (HbA1c) fully mediated by family factors (DFBC)....................................................................................................34 4-3 The relationship between externalizing and me tabolic control (HbA1c) fully mediated by adherence (Parent DSMP)...............................................................................................35

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ix Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science FAMILY FACTORS, ADHERENCE, AND METABOLIC CONTROL IN YOUTH WITH TYPE 1 DIABETES By Danny C. Duke December 2006 Chair: Gary Geffken Major: Psychology Considerable research has examined the relationship of fa mily variables and adherence to metabolic control in pediatric popu lations with type 1 diabetes, with most studies finding that these c onstructs were only weakly re lated to metabolic control. Children and adolescents with type 1 diab etes are often noncompliant with their prescribed treatment regimens Clinical evidence suggests th at poor adherence leads to poor metabolic control and conversely that co mpliance with a diabetes treatment regimen predicts good metabolic contro l. Inadequate metabolic c ontrol leads to physiological complications (e.g., retinopathy, neuropathy, nephropathy) at an earlier age. Our study examined the ability of a combined battery of diabetes-specific family functioning measures (e.g., family responsibility, parent al support, guidance, warmth, and criticism specific to diabetes care) to predict metabolic control (health status). One hundred twenty children and parents, who presented in a pe diatric diabetes clinic, completed four measures of diabetes-specific family func tioning, and separate adherence interviews.

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x Overall, combined with dem ographic variables, these measures predicted approximately 44% of the variance in metabolic control. Examination of parent and child reports of adherence showed that, while controlling for demographic variables, parent report accounted for 27.4% of the variance in metabolic control, while chil d report did not add significantly to the variance accounted for in metabolic c ontrol beyond parent report. Adherence was found to partially mediate the relationship between child metabolic control and perceptions of parental nagging, criticizing, and arguing about diabetes specific tasks. Family variab les were found to partially me diate the relationship between externalizing and metabolic control and adherence was found to fully mediate the relationship between child externalizing and metabolic control. Our data suggest that reports of adherence behaviors are strongly rela ted to a child's health status independently and through important pathways of family functioning. These data suggest that an increased and comprehensive focus on assessin g and monitoring family behaviors related to adherence is critical for optimizing health-status outcomes.

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1 CHAPTER 1 INTRODUCTION Overview of Diabetes The incidence of diabetes in children and adolescents is increasing worldwide (Diabetes Control and Complications Tr ial Research Group, 1986; EURODIAB ACE Study Group, 2000; Onkamo et al., 1999). This disease adds signi ficantly to public health care costs and profoundl y affects individual quality of life, having treatment demands that are often complex and unrelenting. Diabetes is one of the most common chronic diseases in school-aged childr en, affecting about 151,000 young people in the United States, or about 1 in every 400 to 500 youth under 20 years of age. With the peak incidence of type 1 diabetes occurr ing at puberty (Feingold & Funk, 1997), the prevalence of type 1 diabetes in the U.S. is increasing with approximately 30,000 new cases of type 1 diabetes diagnosed annually (Engelgau & Geiss, 2000). Pancreatic beta cells are insulin-produci ng bodies within the pancreas that are responsible for producing the insulin necessa ry for the bodyÂ’s metabolism of glucose. Type 1 diabetes is thought to result from an autoimmune process that leads to the destruction of the insulin produc ing beta cells. The typically acute onset develops over a period of a few days to weeks. The percentage of beta cell destruction ranges from 60 to 80% at the time of clinical symptom presen tation (National Diabetes Data Group, 1995; Notkins & Lernmark, 2001). Without insulin, cells are unable to metabolize sugars present in the bloodstream. As a means of compensating for the bodyÂ’s lack of insulin production, individuals with diabetes require insulin via subcutaneous injections.

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2 Additionally, maintaining near normal blood-glucose levels requires intensive and oftencomplex management, including frequent blood -glucose monitoring and regular exercise, and dietary and insulin adjustments based on blood-glucose levels (American Diabetes Association, 2003). Treatment and the Diabetes Contro l and Complications Trial (DCCT) To avoid periods of elevated blood sugar (hyperglycemia), the American Diabetes Association (ADA) guidelines recommend that i ndividuals with type 1 diabetes maintain blood glucose levels of 80 to120 mg/dL. Hyperglycemia can lead to long-term complications, including retinopathy, hypertension, neuropathy, nephropathy, and cardiovascular disease (ADA, 2003). Pr ofound hyperglycemia associated with insufficient insulin can result in diabetic ke toacidosis (DKA), a le thal condition if not promptly treated with insulin and fluids (Feingold & Funk, 1997). The Diabetes Control and Complications Trial (DCCT) identified an empirical relationship between intensive insulin therapy and reduced complications (Diabetes Control and Complications Trial Research Group, 1987; 1993; 1994). This randomize d, controlled clinical trial showed that for patients with type 1 diabetes, the risk of the development and progression of retinopathy, nephropathy, and neuropathy is reduced 50 to 75% by intensive treatment regimens when compared to conventional treatment regimens. For example, Zhang, Krzentowski, Albert, & Lefebvre (2001) found that, among patients in the DCCT (ages 13 to 39) with mean HbA1c < 6.87% (good co ntrol), 90% remained free of retinopathy. However, in patients with a mean HbA1 c > 9.49% (poor control), complications developed in 57% of the patients. In a study of 195 adolescents, participants who were assigned an intensive therapy regimen (administr ation of three or more injections per day or use of an insulin pump, adjustment of in sulin based on blood-glucose levels, at least

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3 four blood-glucose tests per day, diet, and ex ercise) showed delayed onset and slower progression of retinopathy compared to individu als in a conventional therapy (one or two insulin injections and self monitoring of blood glucose at least twice per day). Additionally, intensive therapy also redu ced the risk of the development of microalbuminuria, which is a precursor to the development of diabetes-related nephropathy (Diabetes Control and Complications Research Group, 1994). To prevent long-term complications, th e DCCT research group recommended intensive insulin and monitoring therapy for adults and adolescents as young as 13 years old (Diabetes Control and Research Group, 1993, 1994). While intensive therapy reduces the risk of diabetes-related complications and high HbA1c levels, it is a complex and demanding regimen for children and families. Physicians typically prescribe complex daily regimens that include insulin inj ections, blood-glucose monitoring, diet and exercise. It is expected that these behavioral adjustments be integrated into the patientÂ’s daily routine. Data indicate that children and adolescents are often noncompliant with many of these recommendations. A nine-yea r follow-up study by Kovacs and colleagues (1992) established that 45% of adolescents engage in some significant form of noncompliance during adolescence. These finding s were before the publication of DCCT research, before intensive insulin therapy was routinely prescribed. As children and adolescent medical regimens become more intense and complex, understanding adherence becomes an increasingly important facet of treatment. Past research has often failed to account for the behaviora l contributions of the child to reciprocal parent-child interactio ns necessary to administer such complex treatment regimens. There are many problematic child behaviors that may contribute to

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4 poor regimen adherence. Achenbach (1991) categorized childhood problems into the two broad dimensions of internalizing and extern alizing. Internalizing encompasses behavior problems characterized by anxiet y, depression, and social wit hdrawal, while externalizing behavior problems are characterized by aggr ession and antisocial behavior. Limited research has examined relationships betw een child psychopathology, family functioning and metabolic control. Work by Northam et al. (2005) found that in a sample of adolescents with diabetes, those with high bl ood sugars had higher levels of externalizing behaviors than those with good and low blood sugars. Conversely, Cohen et al. (2003) found that lack of internalizing behaviors significantly predicted adherence in an economically disadvantaged population. Measures of Adherence Haynes (1979) defined compliance as the extent to which a personÂ’s behavior coincides with medical or health advi ce. Johnson (1993) maintains that defining adherence (or compliance) with the childhood di abetes regimen is difficult due to the multitude and complexity of the regimen beha viors. This has hindered the development of an accurate and reliable measure of adherence for youth with diabetes. Adherence to diabetes treatment regime ns is multidimensional, with research demonstrating that a patientÂ’s be havior with regard to one as pect of the diabetes regimen is not predictive of the same patientÂ’s beha vior in relation to ot her components of the regimen (Glasgow, McCaul, & Schafer, 1987; Johnson et al 1986; Kavanagh, Gooley, & Wilson, 1993). Adding to the obfuscation, there is not universal agreement on explicit standards for measuring adherence. Methods ra nge on a continuum from direct to indirect (La Greca, 1990). Intuitively, the most accurate method of assessment would be observation of diabetes care be havior. However, the behavior al observational approach is

PAGE 15

5 impractical for the clinical scientist practit ioner since the complexity of the diabetes treatment regimen would require intense scru tiny of numerous behaviors over extended periods of observation. Indirect measures of adherence behavior offer an alternative means of assessment. One approach involves obtaining patient self-re ports or parental reports of adherence (Anderson, Auslander, Jung, Miller, & Santia go, 1990). La Greca (1990) cautions about generaliza tions from self-reports due to extensive variability in their detail and comprehens iveness. Johnson, Silverstei n, Rosenbloom, Carter, and Cunningham (1986) developed a 24-hour re call interview to quantify adherence behaviors. Interviewers ask patients and parents, over a series of three 20-minute structured phone interviews, to describe diabetes-management behaviors during the previous 24-hour period. Statis tical analyses resulted in i ndices of adherence. Broadly speaking these indices covered different aspect s of glucose testing, insulin administration, diet, and exercise constructs. Significant correlations betwee n parent and child report was documented in separate analyses (F reund, Johnson, Silverstein, & Thomas, 1991; Johnson et al., 1986; Spevack, Johnson, & Rile y, 1991). Advantages of the interview include a minimization of recall errors, improve d reliability, and the ability to capture a non-reactive example of the childÂ’s adherence. However, the 24-hour recall interview is difficult to implement in a clinical setting, du e to its reliance on multiple interviews (3 child, 3 parent) and complex scoring system (McNabb, 1997). The resources required to conduct interviews and analyze data (e.g., tim e and manpower) are not typically available to healthcare providers. Add itionally, despite the comprehens iveness of the 24-hour recall interview, strong associations between adherence and HbA1 c were not supported in a

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6 longitudinal analysis of approximately 200 children using the measure (Johnson, Kelly, Henretta, Cunningham, Tome r, & Silverstein, 1992). The Self-Care Adherence Inventory (SCAI) (Hanson, Henggeler, & Burghen 1987), a semi-structured interview that assesse s adherence to type 1 diabetes treatment regimens, provides an alternative to the 24-hour recall interview (Hanson et al., 1987; Hanson, DeGuire, Schinkel, Kolterman, Goodman, & Buckingham, 1996). The interview is administered to the patient by an indi vidual familiar with the requirements of the diabetes regimen. The SCAI content areas in clude glucose testing, dietary behaviors, insulin adjustment, and hypoglycemia prepar edness. The authors found that the SCAI related to HbA1c on three se parate administrations (partial r = -0.28, -0.25, and -0.20, p < 0.001). The SCAI was further refined and the Diab etes Self Management Profile (DSMP) was developed by Harris and colleagues (2000) The DSMP consists of structured interviews of children/adolescents and parent s with diabetes, consisting of 23 questions, that assess five areas of diabetes manage ment, including: insulin administration/dose adjustment, blood-glucose monitoring, exerci se, diet, and management of hypoglycemia. The DSMP is designed to access diabetes self-management over the preceding three months. Investigators found good internal c onsistency and inter-observer agreement (Harris et al., 2000). Th e predictive validity ( r = -0.28, p < 0.01) for metabolic control with the DSMP was consistent with data from the SCAI, however, the measure was shown to only account for 7.8% of the variance in metabolic control (HbA1c) (Harris et al., 2000).

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7 Measures of Metabolic Control While adherence is a behavioral construct, metabolic control is a biological assay of health status. The widely accepted standa rd of measurement for metabolic control is the glycated hemoglobin A1c test (GHB/HbA 1c). HbA1c is a measure of glucose bound to specific hemoglobin molecules within re d blood cells. The amount of glucose bound to such hemoglobin molecules is directly proportion al to the concentration of glucose in the blood. Therefore, HbA1c provides an es timate of the average blood glucose concentration. Since red blood cells live fo r approximately 90 days this provides an estimate over the preceding 8 to12 weeks. Th e DCCT observed an average HbA1c of 7.2% for individuals in the intensive tr eatment group compared to 9.0% in the conventional treatment group, indicating that i ndividuals in the inte nsive insulin therapy group were under better metabolic control. Relationships between Family Factor s, Adherence, and Metabolic Control Despite the findings from the DCCT, most studies fail to find high or even moderate correlations between regimen a dherence and diabetes metabolic control (Glascow et al., 1987; Hans on et al., 1987; Hanson et al ., 1996; Harris et al., 2000; Johnson et al, 1986, 1992, 1993, 1994; Kavanagh et al., 1993; Schafer, McCaul, & Glasgow, 1986; Spevack et al., 1991). An expl anation for this discrepancy might result from examination of the instrument used to measure adherence. Most studies of adherence have measured an individualÂ’s be havior in relation to an ideal treatment regimen, and have not examined variations from individual pres criptions (La Greca, 1990). Additionally, if a patientÂ’s prescribed treatment regime n was ineffective, perfect adherence would not be expected to rela te to good metabolic control (Johnson, 1992). Currently, there is no specific, standardized set of self-care behaviors applicable to all

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8 children with diabetes. It is difficult to measure congruity of behaviors that are not uniformly defined in operational terms (McNabb, 1997). Johnson (1993) maintains that only weak relationships exist between meta bolic control and behavior. Nevertheless, clinical evidence indicates adherence is a si gnificant predictor of metabolic control. For example, data from a residential treatmen t program for youth in poor metabolic control demonstrate a significant mean reduction in HbA1c when the treatment regimen is delivered in a controlled environment (Geffken, Lewis, Johnson, Silverstein, Rosenbloom, & Monaco, 1997). Glascow et al. (1987) suggested that the relationship between adherence and metabolic control is not always straightforward, and regimen adherence must be examined in the context of other factors. Anderson and Coyne (1993) argue that examination of adherence in chronically ill children must include the parentÂ’s involvement in treatment tasks. When c onsidering the complexity of diabetes management, it is intuitive to consider that familial relationships and parental behaviors will relate to child health status. The relationships between family functio ning, diabetes adherence and metabolic control have been the focus of several an alyses (Anderson et al ., 1990; Lewin et al., 2006; Liss, Waller, Kennard, McIntire, Capra, Stephens, 1998; McKelvey et al., 1993; Schafer et al., 1986; Waller et al., 1986). For example, Hauser and colleagues (1990) found that childrenÂ’s perceptions of family conflict, as measured by the Family Environmental Scale (FES), were the strongest predictor of poor adherence with multiple diabetes treatment regimen components ( r = 0.50, p < 0.001). In addition, the study suggested that child and parent perception of family cohesion was related to both im proved adherence, and comprehensive levels of patient

PAGE 19

9 adherence. Likewise, a study f ound that child report of parent-child co nflict (measured by the Parent-Child Scales) contributes to the unique variance in predicting both adherence ( r = 0.50, p < 0.001) and metabolic control ( r = 0.31, p < 0.05) (Miller-Johnson, Emery, Marvin, Clarke, Lovinger, & Martin, 1994). Similarly, Hanson, De Guire, Schinkle, Henggeler, and Burghen (1992) found that both disease-specific and general family factors related to treatment adherence. A nother study by Leonard et al, (2002) using the Youth Self-report, found that individuals who reported higher levels of attention problems, and higher levels of aggressive and delinquent behaviors reported worse metabolic control as measured by HbA1c. Ho wever, there is some controversy in the literature regarding the optimal method for meas uring family interaction. Several studies indicate that general measures of family functioning (gener al family stress, conflict, support, etc.), such as the Family Environm ental Scale (FES) and the Family Assessment Measure (Skinner, Steinhauser, & Santa-Barb ara, 1983), do not correlate with metabolic control (Hauser et al ., 1990; Liss et al., 1998; Schafer, Glasgow, McCaul, & Dreher, 1983). It is possible that certain weaknesses in family functioning are distinct from general family function, being limited to the i ssues and concerns specific to dealing with the demands of diabetes. Measures of family functioning that are specifically designed for families of children with diabetes (e.g., fa mily support specifically related to diabetes care) may be more sensitive in detecting relationships between family functioning and metabolic control. While the FES and other ge neral measures of family interaction may assess characteristics such as family orga nization, the DFBS includes assessments of diabetes specific family dimensions, e.g. or ganization specific to diabetes management.

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10 For example, Liss et al. (1998) found that children hospitalized with DKA and their parents reported less diabetes family support than did a clinic control group. However, the Family Assessment Measure (a non-diabetes -specific measure of family functioning) detected no differences in family functioni ng between the DKA and clinic control groups. Additionally, Schafer et al. ( 1983) found that diabetes-speci fic family measures were more predictive of adherence than were mo re general measures of family interaction (mean correlation between general measures of family interaction and adherence M < 0.10; mean correlation between diabetes -specific measures and adherence M = 0.18. The Diabetes-Specific Family Behavior Scale (DFBS; Waller et al ., 1986), the Diabetes Family Behavior Checklist (DFBC; Schafe r et al., 1983), and the Diabetes Family Responsibility Questionnaire (DFRQ; Anders on et al., 1990) are three measures of diabetes-specific family support. There follo ws a description of these three measures specifically developed to assess diab etes-specific family interaction. Diabetes Specific Family Measures. Waller and colleagues (1986) developed the Diabetes-Specific Family Behavior Scale of child/adolescent report of diabetes -specific family behaviors. The study investigated whether the child Â’s overall perception of fam ily diabetes-specific support predicted metabolic control. The scale de veloped by Waller and colleagues (1986) was based on the assumption that diabetes specific fa mily behaviors consisted of three distinct child perceived dimensions: parental warm th/caring, parental guidance/control about diabetes, and diabetes related problem solv ing. Children and adolescents, ages 7 to 17, completed the measure with results indi cating that, for children under age 13 ( n = 20), there was a significant association ( r = 0.50, p < 0.001) between the childÂ’s perception of family-guidance/control and metabolic co ntrol (HbA1c), the same finding was not

PAGE 21

11 present for older adolescents. Examples of DF BS items that correlated significantly with metabolic control were as follows: Whether the parent watches while the child tests for blood sugar ( r = 0.55) Whether the parent writes down blood sugar tests ( r = 0.52) Whether the child has someone in the fa mily to talk to about diabetes ( r = 0.55) The correlation between metabolic control and warmth/caring was 0.36, p < 0.03 across the entire age span of 7 to 17 years. Walle r et al (1986) found the problem solving scale did not account for a significan t portion of the variance in metabolic control. Further examination of the DFBS by McKelvey and co lleagues (1993) replic ated the correlation between family guidance/control and diabetes metabolic control However, the child report of guidance/control accounted for only 1.4% of the variance in HbA1c and problem-solving was again not re lated to metabolic control. The Diabetes Family Responsibility Questionnaire (Anderson et al. 1990) is a questionnaire that measures mother and child perceptions of division of responsibility for diabetes tasks related to treatment. In this study the measure was administered to both children/adolescents (age 6-21) and their mo thers. Responses for each item could be classified into one of three possible pattern s from the dyad scores: (1) perfect agreement (both mother and child agree as to how th e responsibility is shared), (2) overlap (disagreement where both mo ther and child claim re sponsibility), and (3) no responsibility (neither mother nor child report responsibility fo r a task). Factor analysis revealed a three-factor solu tion representing these domains: general health management tasks, regimen tasks, and social presentati on of diabetes. The investigators found that mother-child dyad scores indicating that no one takes responsibility for diabetes management, and lower adherence scores ba sed on parental report explained 13.4% of

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12 the variance in HbA1c. Parent-child dyad scor es indicating that neit her the parent nor the child takes responsibility for diabetes ma nagement correlated significantly with metabolic control ( r = 0.32, p < 0.001). A study by Wysocki and colleagues found that children and adolescents reporting increased diabetes management responsibilities relative their developmental level on a com posite index of the DFRQ and the Diabetes Independence Survey demonstrated less tr eatment adherence and marginally worse metabolic control (Wysocki, Linschlid, Taylor, Yeates, Hough, & Naglieri, 1996). Schafer and colleagues (1983) developed the Diabetes Family Behavior Checklist (DFBC) to assess patient and family memb er report of supportive (positive) and unsupportive (negative) diabetes-specific fam ily behaviors. Administration of the DFBC to 34 adolescents demonstrated significant re lationships between negative DFBC scores and adolescentÂ’s self-reported adherence to diet, care in measuring insulin, and frequency of glucose testing. However in a subsequent administration of the DFBC by Schafer and colleagues (1986) wherein adolescents (ages 12 to 19) and adults (ages 20 and older) completed the measures, negative family behaviors (related to glucose testing, insulin injections, and diet) were inversely correlated with adherence in adults, but neither positive nor negative DFBC scores were significantly related to adherence in adolescents. Reports of unsupportive DFBC scores (cri ticism, nagging, argumentative statements) were marginally correlated ( r = 0.24, p < 0.10) with metabolic contro l in adults but not in adolescents. However, it may be that this st udy lacked statistical pow er to detect such a relationship, as only 18 adoles cents were included in this sample. Nevertheless, Hanson and colleagues (1987) suggested that the meas ure was the best instrument available for

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13 assessing parental support of the diabetes treatment regimen, despite the need for larger sample sizes to demonstrate its predictive validity. In a study evaluating a parent-adolescen t teamwork approach to diabetes management, families in the interventi on group reported significantly less conflict (measured by the DFBC) and were in better metabolic control (Anderson, Ho, Brackett, & Laffel, 1999). In addition, Scha fer et al (1983) demonstrated a significant relationship between negative DFBC scores and adol escentÂ’s self-reported adherence. Across all three diabetes-specific measures of family functioning were child perception of parental support or lack of support of the diabetes treatment regimen. McKelvey et al (1993) noted a ppropriate parent supervision of diabetes care tasks, as measured in the DFRQ, may be closely relate d to the diabetes regimen guidance/control dimension of the DFBS. The assessment of supportive and unsupportive parent behaviors in the DFBC focuses on the same dimension of parent involvement in the disease treatment. Children who perceive their parent s as supportive of thei r diabetes care have better metabolic control; children who percei ve parents as nega tive, critical, and unsupportive of their diabetes management ar e in poorer metabolic control, as are families where no one takes responsibility for diabetes management. While the relationships of the DFBC, DFRQ, and DFBS to metabolic control are small, in several cases, the diabetes-specific family factors ex plain larger percentage s of the variance in metabolic control than adherence measures (SCAI, DSMP, 24-hour recall). Lewin et al (2006) suggest that when examined conc urrently, these measures may delineate a construct of diabetes specific family fact ors related to adherence. Diabetes-specific

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14 family functioning is a critical construct to evaluate to optimize metabolic control and adherence outcomes (Anderson et al., 1997). Externalizing, Diabetes Specific Family Functioning, Adherence and Metabolic Control The Child Behavior Checklist (CBCL) wa s first published in 1978 (Achenbach & Edelbrock, 1978). The CBCL has become a wide ly used standardized parent report questionnaire for 4to 18-year-olds that exhibits excellent ps ychometric properties (Achenbach, 1991). The CBCL provides sepa rate scores for Internalizing and Externalizing. These broad band psychological pr oblems have been identified repeatedly in multivariate analyses (Achenbach, 1991) a nd reflect a distinction between depressed, fearful, and inhibited over-c ontrolled behaviors known as Internalizing, and aggressive, oppositional, and antisocial under controlled behaviors known as Externalizing. There exists very little work that has exam ined the relationships between child psychopathology, family functioning, adherenc e and metabolic control. Our clinical experience has led us to hypothesize that externalizing behavior has important relationships to diabetes specific family functioning and adherence in explaining metabolic control in youth with type 1 diabetes. Summary Recent research has found that more intens ive diabetes regimens are related to better metabolic control and that worse metabolic control leads to increased diabetes related complications at an ear lier age of onset in children and adolescents. Accordingly, physicians have prescribed increasingly mo re demanding regimens. Clinicians believe that the extent to which patients follow treatment recommendati ons is integral in producing positive health outcomes. However, most existing psychological research only

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15 weakly supports these assumptions. Prior research suggests the absence of a strong relationship between behavior a nd health status in children and adolescents with type 1 diabetes. Methodological problems (e.g. the meas urement of adherence, the complexity and quality of the medical regimen, disease sp ecific verses general measures of family functioning, and the reliability of health-status indicators) are often cited as explanations for these findings. Additionally, multiple st udies fail to show strong relationships between individual diabetes-specific family factors related to a dherence and metabolic control. McKelvey and colleagues (1993) sugges ted that incorporating the DFBC and DFRQ scales with the DFBS might demons trate a stronger connection between family adherence and HbA1c. To date, only Lewin and colleagues (2006) have examined the relationship between this co mbination of diabetes-specific family-related adherence variables and metabolic contro l with encouraging results. Lewin et al (2006) found 34% of the variance in metabolic control was accoun ted for by diabetes-specific family factors after accounting for demographic variables. Further they hypothesized and successfully demonstrated a mediation model wherein adhe rence partially mediated the relationship between a composite of diabetes-specific fa mily variables, and metabolic control. Limited research has examined the contributi on of childrenÂ’s extern alizing behavior to metabolic control. The first aim of this i nvestigation was to rep licate the findings of Lewin et al (2006) by accounti ng for a significant portion of the variance in metabolic control through the use of diab etes-specific measures of fa mily functioning related to disease management. The second aim was to replicate a model wherein adherence partially mediates the relationship between diabetes-specific family variables and

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16 metabolic control. The third aim was to examine a proposed mediation model wherein family factors mediate the relationship between externalizing and metabolic control. Our fourth and final aim was to examine a pr oposed mediation model wherein adherence mediates the relationship between exte rnalizing and metabolic control.

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17 CHAPTER 2 RESEARCH QUESTIONS Question 1: Family Factors Predicting Metabolic Control Does a combination of diabetes-specific fa mily functioning measures predict significant variance in metabolic control in children and adolescents with type 1 diabetes? Metabolic control will be predicted by the following adherence-related family variables: both parent and child deny respons ibility for diabetes care tasks (DFRQ); child perception of the parent providing guidance /control over diabetes care (DFBS); child perception of parental warmth and caring surrounding the diabetes treatment regimen (DFBS), and child perceptions of parental nagging, critical, and argumentative behavior related to diabetes management (DFBC). Question 2: Adherence Mediating between Diabetes-Specific Family Factors and Metabolic Control. Does adherence mediate the relationship betw een diabetes-specific family factors and metabolic control in children and adolescents with type 1 diabetes? Our study tested a model that proposes that diabetes-specific family factors affect metabolic control through pathwa ys of adherence. Adherence is expected to mediate the relationship between diabetes-specific family factors and metabolic control (Figure 2-1). Question 3: Family factors mediating be tween child externalizing and metabolic control. Do diabetes-specific family factors mediat e the relationship between externalizing and metabolic control?

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18 Our study tested a model that proposes th at childrenÂ’s exte rnalizing behaviors affect metabolic control through pathways of family factors. It is expected that child externalizing behaviors will mediate the relationship between family factors and metabolic control (Figure 2-2). Question 4: Adherence mediating child externalizing behaviors and metabolic control? Does adherence mediate the relationship be tween child externalizing and metabolic control? Finally, our study tested a model that pr oposes that child externalizing affects metabolic control through path ways of adherence. It is expected that adherence will mediate the relationship between externalizi ng behaviors and metabolic control (Figure 2-3).

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19 Figure 2-1. Theoretical model of adherence mediating family factors and metabolic control. Family Factors Adherence Metabolic Control

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20 Figure 2-2. Theoretical model of family fact ors mediating externalizing and metabolic control. Externalizing Behaviors Family Factors Metabolic Control

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21 Figure 2-3. Theoretical model of adheren ce mediating externa lizing and metabolic control. Externalizing Behaviors Adherence Metabolic Control

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22 CHAPTER 3 METHODS Participants and Settings Participants were 120 children with type 1 diabetes and their parents, attending an outpatient pediatric diabetes clinic affiliated with the University of Florida in Gainesville, Florida. The sample was composed of 51 boys and 69 girls, ages 8.25 to 18.75 ( M = 13.92, SD = 2.71). The ethnic distribution wa s 72.5% Caucasian, 15.0% African American, 10.0% Hispanic, and 2.5% repres enting other ethnic groups. More mothers (78.3%) participated in the study compared to fathers (13.3%) and other parents (8.4%). The mean family income was 46.6K w ith a standard deviation of 29.1K. Procedure Parents and children were recruited based on the following study inclusion criteria: eight to eighteen years of age, a di agnosis of type 1 diab etes for at least 6 months, living with and accompanied by their primary caretaker, no evidence of mental retardation. Of families approached, 82.8% ag reed to participate. A signed informed consent, approved by the Institutional Review Board of the University of Florida, was obtained from each participant. Both pa rents and children completed self-report questionnaires and one face-to-face structured interview while waiting for their scheduled appointment. Trained graduate or unde rgraduate psychology students conducted interviews individually with both caregiver and child of a pproximately 10-15 minutes in length. Instructions were given to both fo r completing the self-report measures. Selfreport measures were typically comple ted within 30-40 minutes. Children and

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23 adolescents were prompted to answer questi ons honestly and were reminded that no one else in their family would see their answer s. Blood samples for measuring HbA1c were drawn by trained clinic staff as part of each patientÂ’s routine visit. All research assistants underwent extensiv e training with the first author in the administration of these measures, which include d: (1) attending an instructional meeting, (2) observing 3 administrations of the measur es, (3) administering the measures 3 times with in vivo observation and supervision. Measures Demographic Questionnaire Parents were administered a brief dem ographics questionnaire to assess the childÂ’s age, sex, race, age at onset of diab etes, family structure, parental education, occupation and estimated income. Diabetes-Specific Family Measures Diabetes Family Behavior Scale (DFBS). The DFBS is a measure of perceived family support for children and adolescents with type 1 diabetes (Waller et al., 1986). This 60-item que stionnaire has three subscales (warmth/caring, guidance/control, and problem solving) that are used to determine the childÂ’s overall perceptio n of family support. Scores from the guidance/control and warmth/caring subscales correlated with meta bolic control (.50 and .36, respectively), but the problem-solving subscale did not (.08). Te st-retest reliability coefficients for the warmth/caring and guidance/control subscal es are good (.79 and .83, respectively), but not so for the problem-solving subscale (.51). Analyses indicate stronger reliability with younger children (under age 13) that have been attributed to developmental differences (McKelvey, Waller, North, et al, 1993). Child ren and adolescents completed the warmth

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24 and caring, and the guidance and control scales of this meas ure. Overall, the DFBS has good internal consistency (alpha coefficient of 0.82). CronbachÂ’s in our study was acceptable ( = .74). Diabetes Family Behavi or Checklist (DFBC). This instrument is a measure of bot h supportive and non-supp ortive behaviors by the family and their effects on the childÂ’s di abetes self-care regimen (Schafer et al., 1986). This forced-response, 16-item scale is de signed for ages 12 to 64 and consists of a summative response scale from 1 (never) to 5 (at least once a day). Summary scores are generated for both the positive/supportive and negative/unsupportive domains. A positive summary score can range from 9 to 45 and th e negative summary scale can range from 7 to 35. Schafer and colleagues (1986) found that negative behaviors of family members reported by the adults with diabetes were in versely correlated with adherence (glucose testing, diet, and insulin injec tions). Parents and, children an d adolescents completed this measure. CronbachÂ’s alpha coefficients ra nge from .67 to .80 for the positive/supportive scale and .74 to .82 for the negative /unsupportive scale (S chafer, personal communication, 1998). Internal consistency for this sample was acceptable ( = .64). Diabetes Family Responsibili ty Questionnaire (DFRQ). The Diabetes Family Responsibility Ques tionnaire is used to assess the family sharing of responsibilities for diabetes trea tment (Anderson et al., 1990). Both the parent and child independently complete this m easure consisting of reading 17 statements concerning diabetes management tasks and indicating which family member has the responsibility for the specific task. A parent-child dyadic scor e is calculated to determine

PAGE 35

25 patterns of agreement and disagreement within the dyad. Factor analys es of this 17-item measure indicate three domains of 1) general h ealth management tasks, 2) regimen tasks, and 3) social presentation of diabetes. Intern al consistencies for the three subscales were acceptable, ranging from .69 to .85 (Anderson et al., 1990). For this sample internal consistency was acceptable ( = .75). Child Behavior Child Behavior Checklist Achenbach and Edelbrock first published the Child Behavior Checklist (CBCL) in 1978 and 1979. The CBCL has become a widely used standardized 118-item parent report questionnaire for 4–18 years olds that exhibits excellent ps ychometric properties (Achenbach, 1991). It was designed to assess the be havioral problems and social competencies of children 4 to 18 years of age. The CBCL groups 20 competence items into 11 Problem Scales (including eight Syndrome Scales ) and four Competence Scales The CBCL also yields two broadband higher order psychopathology scales, internalizing and externalizing The CBCL is widely used in both clinical and research settings because of its demonstrated reliability and validity, ease of administration and scoring, and applicability to clinical, nonclinical, and cross-cu ltural samples (Achenbach, 1991; Cohen, Gotlieb, Kershner, & Wehrspann, 1985; Drotar, Stein, & Perrin, 1995; Sandberg, Meyer-Bahlburg, &Yager, 1991). Cronbach’s in our study were strong ( = .94) Measurement of Adherence The DSMP is a refined version of the SC AI (Harris et al., 2000). The DSMP is a structured interview, consisting of 23 que stions, with an administration time of approximately 15 to 20 minutes. Questions asse ss five areas of diabetes management, including: insulin administration/dose adju stment, blood-glucose monitoring, exercise,

PAGE 36

26 diet, and management of hypoglycemia. To minimize response bias, the interviewer begins with a statement indicating that imperfect diabetes management is common. Additionally, tasks for which nonadherence is more readily admitted (e.g. exercise) are assessed first. Investigator s found good internal consistenc y (CronbachÂ’s alpha = 0.76) and inter-observer agreement (94%). Parents and children were interviewed separately. Internal consistency was found to be acceptable in our study for both parents ( = .86) and children ( = .65). Measure of Metabolic Control During regularly scheduled appointments, patients in the Outpatient Diabetes Clinic have their HbA1c routinely checked as part of their normal check-up. Blood samples were analyzed using a Bayer DC A 2000+. HbA1c for i ndividuals without diabetes typically ranges between 4%-6.5% on this instrument.

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27 CHAPTER 4 RESULTS Preliminary analyses were conducted to test for relations between demographic variables and study variables (HbA1c, adherenc e, and family functioning) for purposes of control in subsequent hierarchical multiple regression. HbA1c was correlated with childÂ’s age ( r = .23, p < .01), duration with diabetes ( r = .27, p < .01), and family income ( r = -.16, p = .09). Child age was significantly correlated to the DFBC ( r = .25, p < .01), to the DFBS guidance and cont rol subscale ( r = -.57, p < .01), and the parent DSMP ( r = -.37, p < .01). Family income was found to be signif icantly correlated to both the child ( r = .22, p < .05) and parent DSMP ( r = .21, p < .05). Duration of diabetes wa s significantly related to HbA1c ( r = .27, p < .01) and to parent DSMP ( r = -.26, p < .01). All other demographic variables were not significan tly related to measures of diabetes-specific family functioning, parent reports of child externa lizing, or to reports of adherence. Table 4-1 provides the sample characteris tics on sex and ethnicity and Table 4-2 reports means, standard deviations, and ra nges for childÂ’s age, HbA1c, duration with diabetes, and income. The mean age and duratio n with diabetes for children in our study was approximately 13.92 ( SD = 2.7) and 4.80 ( SD = 3.81) years, respectively. Mean income was 46.0K (SD = 29.1K), and the mean HbA1c for the sample was 8.93% ( SD = 1.99%), which is in the elevated range. No sex differences were found in HbA1c ( t (120) = 1.17, p = 0.19, mean for boys: 9.18% ( SD = 1.77%); mean for girls: 8.75% ( SD = 2.13%). Likewise, no sex-differences were iden tified on measures of diabetes-specific

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28 family functioning or self-report of adherence ( = .05). Intercorrelations between variables are found in table 3. All subseque nt regression analyses were assessed for collinearity by calculating tolerance and va riance inflation f actors (VIF) and no significant collinearity was identified (Bowerman and OÂ’Connell, 1990). Study Aim 1: Regression of Metabolic Co ntrol (HbA1c) on Family Functioning and Adherence To examine the relation between multiple measures of diabetes-specific family factors and metabolic control, we conducted a hierarchical multiple linear regression. We expected that when examined simultaneousl y, measures of adhere nce-related diabetesspecific family factors would be predictive of significant variance in metabolic control. To control for the influence of the childÂ’s ag e, family income, and duration with diabetes, we entered these variables into the regressi on equation in steps 1 and 2. To partition out the variance shared by duration of diabetes and childÂ’s age these variables were controlled in separate blocks, with child age and estimate of family income controlled in the first block and duration of diabetes cont rolled in the second block. Combined, child age and family income accounted for 6.7% of the variance in metabolic control, F (2, 102) = 3.69, p < .05. The unique variance accounted for by duration of diabetes was 5.3%, F (3, 101) = 4.62, p < .01. Considering that there we re no a priori suppositions regarding the magnitude of the relationship between each family functioning variable and metabolic control, each family predictor was entered simultaneously into block 3 of the regression. Combined, the four diabetes-spe cific family functioning variables (i.e. parental warmth and caring (DFBS), guidance and control (DFBS), cr itical and negative parenting (DFBC), and no-responsibility for the treatment regimen (DFRQ), explain an additional 13.3% of the variance in metabolic control (HbA1c), F (7, 97) = 4.70, p < .01.

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29 Lastly, child and parent report of adhere nce were entered into the model, which combined accounted for 18.6% of the variance in meta bolic control, F (9, 95) = 8.27, p < .01. All together, the model predicted 43.9% of the vari ance in metabolic control (HbA1c). The final model demonstrated signi ficant contributions from child report of critical, negative, unsupportiv e parents, and parent report of adherence (Table 4-4). Study Aim 2: Does Adherence Mediate the Relationship Between Diabetes-specific Family Factors and Metabolic Control? To determine whether child report of a dherence accounted for unique variance in metabolic control beyond that of parent report, a regression e quation was calculated controlling for demographic variables in blocks one and two, with parent and child report of adherence entered in the third and fourth blocks resp ectively. Parent report of adherence explained 27.4% of the variance in metabolic control (HbA1c), F (4, 101) = 16.48, p < .01. Child report failed to account for significant variance in metabolic control above and beyond that accounted for by pare nt report, explaining only .6% of the variance in metabolic control. The complete model for both parent and child reports of adherence, while controlling for demographic variables, accounted for 40.1% of the variance in metabolic contro l (See table 4-5). Since warm th and caring, guidance and control, lack of responsibility, and child repor t of adherence, failed to add significantly to the model in this and previous analyses, th ese variables were excl uded from subsequent analyses. Next, Baron and KennyÂ’s (1986) guidelines for mediation were followed to test the influence of the remaining diabetes-sp ecific family factor (DFBC) on metabolic control via adherence. The following crit eria are necessary for mediation: (I) the predictor (DFBC) should be significantly asso ciated with the outco me (HbA1c), (II) the

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30 predictor should be significantly associated with the mediator (adherence), (III) the mediator should be associated with the outco me variable (metabolic control), and (IV) lastly, the addition of the me diator to the full model s hould significantly reduce the relationship between the pred ictor and outcome variable. Baron and KennyÂ’s first through third criteria were met in previous analys es, see table 3. To examine Baron and KennyÂ’s next criteria, family functi oning (DFBC) was entered after demographic variables, and parent report of adherence was entered into the final block of the regression. The family functioning variable (DFBC) was found to account for 12.6% of variance in metabolic control (HbA1c), while parent report of adherence (DSMP) added 18.6% to the model. Finally, family functioning was entered into the model while cont rolling for adherence and demographic variables. The final m odel accounted for 40.2% of the variance in metabolic control (Table 4-6). To examine the significance of change in the path coefficient, a SobelÂ’s z-scor e (Sobel, 1988) was calculated, z = -6.70, p < .001, and was found to be significant, meeting Baron and KennyÂ’s criteria for mediation. The path coefficient between family factors and meta bolic control remained significant while controlling for adherence, ther eby indicating partial mediatio n. Standardized coefficient of Family Factors on Metabolic Control equals .204 for direct and .201 for indirect effect (figure 4-4). Study Aim 3: Regression Analysis to dete rmine if Family Factors (DFBC) mediates the relationship between Externalizing and Metabolic Control (HbA1c). Baron and KennyÂ’s (1986) guidelines for me diation were followed to test the influence of child externaliz ing behaviors on metabolic c ontrol (HbA1c) via diabetes specific family factors (DFBC). Child external izing was entered into the regression, after

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31 controlling for demographic variables, and was found to account for 5.6% of the variance in metabolic control, F = 5.40, p < .001. Next, family factors (DFBC) were entered into the mode l and were found to account for 8.6% of the varian ce in metabolic control, F = 7.11, p < .001. Finally, child externalizing was entered into the model while controlling for family factors (DFBC) and was found to account for a non-significant portion of the variance in metabolic control, meeting criteria for full mediation (Table 4-7) To examine the significance of change in the path coefficient, a SobelÂ’s zscore (Sobel, 1988) was calculated, z = 2.67, p < .01, and was found to be significant, meeting Baron and KennyÂ’s criteria for mediation. The path coefficient between externalizing and meta bolic control became non-significant while controlling for family factor s, thereby indicating full me diation. The standardized coefficients of externalizing on HbA1c equals .161 for direct, and .110 for indirect effect (figure 4-5). Study Aim 4: Regression Analysis to Determine if Adherence (Parent DSMP) Mediates the Relationship Between Externalizing Behaviors and Metabolic Control (HbA1c). Baron and KennyÂ’s (1986) guidelines for me diation were followed to test the influence of child externaliz ing behaviors on metabolic control via adherence. Child externalizing was entered into the regression, after controlling for demographic variables, and was found to account for 5.6% of the variance in metabolic control, F = 5.40, p < .01. Next, parent reports of adherence (DSMP) we re entered into the model and were found to account for 22.2% of the varian ce in metabolic control, F = 13.24, p < .001. Finally, externalizing was entered into the model while controlling for adherence and accounted for a non-significant portion of the variance in metabol ic control, indicating full

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32 mediation (Table 4-8). To examine the signif icance of change in the path coefficient, SobelÂ’s z-score (Sobel, 1988) was calculated, z = 3.66, p < .001, and was found to be significant, meeting Baron and KennyÂ’s criteria for mediation. Since the path coefficient between externalizing and me tabolic control was reduced to a non-significant effect while co ntrolling for adherence, full mediation was indicated. Standardized coefficient of DFBC on HbA1c equals .059 for direct and .212 for indirect effect (figure 4-6).

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33 Figure 4-1. The relationship between fam ily factors (DFBC) and metabolic control (HbA1c) partially mediated by adherence (Parent DSMP). Type of Mediation ----Partial Sobel z-value ------------6.69526, p = .000001 Standardized coefficient of DFBC on HbA1c: Direct: .204 Indirect: .201 .405*** Independent Variable: DFBC (.204*) Outcome Variable: HbA1c .374*** -.614*** (-.538***) Mediating Variable: Adherence

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34 Figure 4-2. The relationship between extern alizing and metabolic control (HbA1c) fully mediated by family factors (DFBC). Type of Mediation ---Full Sobel z-value ---------2.671389, p = .007554 Standardized coefficient of Externalizing on HbA1c: Direct: .161 Indirect: .110 .271** Independent Variable: Externalizing (.161) Outcome Variable: HbA1c .310*** .405*** (.355***) Mediating Variable: DFBC

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35 Figure 4-3. The relationship between externaliz ing and metabolic control (HbA1c) fully mediated by adherence (Parent DSMP). Type of Mediation ---Full Sobel z-value ----------3.66278, p = .000249 Standardized coefficien t of Externalizing on HbA1c: Direct: .059 Indirect: .212 .271** Independent Variable: Externalizing (.059) Outcome Variable: HbA1c -.358*** -.614*** (-.593***) Mediating Variable: Adherence

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36 Table 4-1. Demographic characteristics Category N Percentage Sex Male 51 42.5 Female 69 57.5 Ethnicity Caucasian 87 72.5 African American 18 15.0 Hispanic 12 10.0 Other 3 2.5

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37 Table 4-2. Means and Standa rd Deviations for Age, HbA1c, Duration of Diabetes, and Income. Mean SD Minimum Maximum HbA1c (%) 8.93 1.99 5.00 >14.00 Age 13.92 2.71 8.25 18.75 Duration (Months) 57.54 45.71 6.00 270.00 Income 46005.06 29089.61 6000.00 155000.00

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38 Table 4-3. Intercorrelations among HbA1c, Di abetes Family Measures, Adherence, and Externalizing. Variable 1 2 3 4 5 6 7 8 1 HbA1c. -.378** -.614** -.113 -.081 -.006 .405** .271** 2 Child adherence report .501** .108 .073 .004 -.279* -.269** 3 Parent adherence report .179* .128 .006 -.374** -.358** 4 Warmth and caring .193* -.049 -.324** -.320** 5 Guidance and control .193* -.225* -.095 6 No responsibility .119 .005 7 Critical parenting .310** 8 Child externalizing *p <. 05, **p < .01

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39 Table 4-4. Hierarchical Regr ession Analysis Predicting Hb A1c from Diabetes-Related Family Factors and Adherence. Step Variables R2 R2 F 1 .067 .067 3.69* Child Age .066 Income -.023 2 .121 .053 6.10* Duration of diabetes .124* 3 .253 .133 4.32** Warmth and caring (DFBS) .058 Guidance and control (DFBS) .062 Lack of responsibility (DFRQ) -.018 Critical parenting (DFBC) .224** 4 .439 .186 15.74** Child reported adherence (DSMP) -.058 Parent reported adherence (DSMP) -.466** All standardized regression coefficients are from the final block of the equation. *p < .05, ** p < .01, *** p < .001

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40 Table 4-5. Hierarchical Regr ession Analysis Predicting HbA1c from Parent and Child Report of Adherence. Step Variables R2 R2 F 1 .067 .067 3.69* Child Age .066 Income -.023 2 .121 .053 6.10* Duration of diabetes .124 3 .395 .274 45.79* Parent report of adherence -.520 4 .401 .006 1.05 Child report of adherence All standardized regression coefficients are from the final block of the equation. *p < .05, ** p < .01, *** p < .001

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41 Table 4-6. Mediation Regression Analysis Pr edicting HbA1c: Adhe rence Mediating the Relationship between Family Factor s (DFBC) and Metabolic Control (HbA1c): Final Block of the Regression. *p < .05, ** p < .01 Step Variables R2R2F 1 .067 .067 3.69* Child Age .030 Income -.025 2 .121 .053 6.10* Duration of diabetes .126 3 .371 .274 45.79** Parent report of adherence (DSMP) -.492** 4 .402 .035 6.20** Critical parenting (DFBC) .206

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42 Table 4-7. Mediation Regres sion Analysis Predicting HbA1 c: Family Factors (DFBC) Mediating the Relationship Between Child Externalizing and Metabolic Control (HbA1c): Final Bl ock of the Regression. *p < .05, ** p < .01 Step Variables R2R2F 1 .067 .067 3.69* Child Age .103 Income -.085 2 .121 .053 6.10* Duration of diabetes .215* 3 .245 .125 16.72** Family Factors (DFBC) .320** 4 .262 .017 2.28 Externalizing (CBCL) .138

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43 Table 4-8. Mediation Regressi on Analysis Predicting HbA1c : Adherence Mediating the Relationship Between Child Externaliz ing and Metabolic Control (HbA1c): Final Block of the Regression. *p < .05, ** p < .0Step Variables R2R2F 1 .067 .067 3.69* Child Age .066 Income -.023 2 .121 .053 6.10* Duration of diabetes .124 3 .371 .274 45.79** Parent report of adherence (DSMP) -.492** 4 .398 .003 .55 Externalizing (CBCL) .062

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44 CHAPTER 5 DISCUSSION Interpretation of Results The purpose of our study was to examine how behavioral va riables including adherence to the diabetes treatment regimen, child externalizing, and family functioning specific to diabetes management related to me tabolic control. First, we investigated the relationship between a combination of diabet es-related family factors and metabolic control. Regression analysis in dicated that, taken together, di abetes-related family factors accounted for 13.3% of the variance in me tabolic control afte r controlling for demographic variables. In addition, it is im portant to note that reports of adherence explained significant variance in metabolic cont rol. Our study replicated Lewin et alÂ’s findings of diabetes related family fact ors and adherence significantly predicting metabolic control. Another goal of our study wa s to replicate adherence mediating the relationship between diabetes-specific family factors a nd metabolic control. The proposed mediating model suggests that negative family functioni ng processes have a deleterious impact on childrenÂ’s adherence behaviors and subsequent metabolic control. This model posits that the childÂ’s perception of critical and nega tive parenting around th e diabetes treatment regimen is related to adherence to a treat ment regimen. Overall, children who reported more negative and critical relationships w ith their parents were in worse metabolic control and such relationships are mediated by poor adherence. We believe it is likely that there is a reciprocal relationship between critical parenting and poor diabetes

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45 regimen adherence, with families becoming tr apped in a coercive cycle or struggle for control (Patterson, 1974). An adolescentÂ’s nonadherent behaviors may elicit parent criticism, which in turn can lead to further adolescent rebellion toward adherence. Regression analysis supported our third hypothesis that adherence mediates the relationship between externalizing and metabol ic control. The mediating model suggests that childrenÂ’s externalizing behaviors have deleterious a ffects on childrenÂ’s adherence behaviors and subsequent metabolic control. This model suggests that externalizing behaviors interfere with good metabolic cont rol and compliance w ith the prescribed treatment regimen. Overall, children with highe r reported externalizing behaviors were in worse metabolic control. Parent al difficulty dealing with exte rnalizing behaviors such as stubbornness and arguing, are unlikely to help with a childÂ’s adherence. Poor adherence may fuel the parent and child battleground. Difficulties with adherence to complex and demanding treatment regimens may exacerbate general child oppositionality and further thwart any support parents might provide in management aimed at better metabolic control. Finally, regression analysis supported our fourth hypothesis; th at family factors mediate the relationship between externalizi ng and metabolic control. The mediating model suggests that externalizing is related to childrenÂ’s perceptions of critical and negative parenting around diabetes and its re lationship to poor meta bolic control. This model suggests that parent reported externa lizing behaviors are li nked to child reported perceptions of negative parent ing that includes criticizi ng, nagging, and arguing with the child about diabetes related treatment tasks, leading to worsened metabolic control that are shown, in aim three, to be largely thr ough pathways of adherence. Overall children

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46 with higher parent reported externalizing be haviors were in worse metabolic control. Child externalizing behaviors ma y precipitate reciprocal nega tive behaviors from parents, leading to increased family conflict that disrupts adherence proc esses and subsequent metabolic control. It was shown that younge r children who experienced parental warmth had improved metabolic control (D avis et al, 2001), suggesting the reciprocal to be true. Our study did not identify a significant a ssociation between child ratings of parental guidance and control and metabolic control. However, given our sample mean age of 13.92 years, this finding is not incons istent with the exta nt literature, which suggests that in adolescent populations the relationship between parental guidance/control and HbA1c is weaker (McK elvey et al., 1993; Waller et al., 1986). As most adolescents strive for autonomy, parent s may have less influence regarding whether or not they attempt to provide guidance and c ontrol. This pattern suggests that while the DFBS Guidance and Control Scale may have examined content independent from the other scales, the construct was not related to metabolic control with this sample. Additionally, our study did not find that the DFBS warmth and caring or responsibility subscale was significantly rela ted to metabolic control. Consistent with Lewin et al (2006), significant relati onships were found between children reporting more critical, negative, un supportive relationships with their parents (regarding their diabetes management) and wo rse metabolic control. Also, parent and child reports of adherence were strongly associated with metabolic control. Child report of adherence did not significantly add to th e variance accounted for in metabolic control by parent report. Parent report of adhere nce accounted for 27.4% of the variance in metabolic control, above and beyond demographi c variables. However, the relationship is

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47 reduced to 18.6% when family factors are controlled. The complete model did account for 43.9% of the variance in metabolic control while controlling for demographic variables. Limitations, Implications, and Future Directions Before implications of our study and speculations on future research are discussed, it is important to note limitations of our study. Due to th e correlational nature of the data, causal relations hips cannot be implied. Howe ver, ethically, a controlled analysis could not be implemented since children cannot be randomly assigned to supportive and unsupportive families. Secondly, fa milies reporting to this clinic are largely of low economic status and sponsored by state-funded insurance (State of Florida ChildrenÂ’s Medical Services). Results may not generalize to higher SES families. Third, while children are informed that no parent or physician will see their results and are encouraged to be as accurate as possible, there is the potential for report-bias on the questionnaires and interview. Finally, administration of general family functioning measures along with diabetes-specific quest ionnaires would have allowed for more comprehensive comparisons with other similar studies. Our study identified that reports of a dherence are signifi cantly related to metabolic control. The use of adherence inte rviews may an important method to identify those patients who are at risk for poor metabo lic control. Such a preventative approach may lead to more timely interventions, avoidi ng some of the risk associated with poorly controlled diabetes. A reduction in HbA1c by 1% was associated with a 15 to 30% decrease in the risk of microvascular a nd neuropathic complications of diabetes, highlighting the significance of this diffe rence (Diabetes Control and Complications Research Group, 1987; ADA, 2003).

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48 It is not a new concept, in a clinical context, that child behaviors may have important implications for medi cal treatment, however an evidence-based examination of this concept through research is still important. Our study hi ghlighted the importance of considering childrenÂ’s externalizing behaviors as they relate to other family factors, childrenÂ’s adherence to their treatment regimens and metabolic control. Importantly, adherence interviews used in our study can be completed in a clinical setting, require minima l training to score and administer, and need little time to complete. These brief screeners can identify fami lies for behavioral heal th interventions. Additionally, as there is variation in the comp lexity of the prescribed treatment regimen that is largely clinically dete rmined by the patientÂ’s ability to manage such regimens, it is logical to posit that screening for behavioral suitability to the more complex regimens would increase the success of more complex regimens. Our data suggest that family behavior is significantly related to a childÂ’s health status, especially when multiple aspects of family functioning related to regimen behaviors are considered in making the assessm ent. Therefore, when identifying barriers to adherence, clinicians should assess child perceptions of parental negativity, and child externalizing behaviors in addition to a ssessing disease management behaviors. The results of our study should also be considered during interventions for children with poor metabolic control. Specificall y, behavioral family-systems such as that developed by Wysocki et al, (2006) seem most beneficial for these problems. Instead of focusing exclusively on improving specific adhe rence behaviors, therapy should address instrumental processes, such as improvi ng family communication patterns and reducing factors that promote and mainta in argumentative interaction patterns specific to diabetes

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49 management. Parent training should facilitate parenting described by Baumrind (1991) as authoritative rather than aut horitarian, indulgent or neglec tful. In addition to a solid understanding of issues surroundi ng diabetes treatment, therap ists require process skills to optimally address adherence problems in the context of improving family dynamics related to diabetes management. It is appropriate to conclude our st udy by reviewing the powerful finding that parent report of adherence e xplained 27.4% of the variance in metabolic control, after controlling for demographic variables, wit hout directly measuring diabetes knowledge. Congruent with clinical observations, adhere nce was highly related to metabolic control, highlighting the importance of clinical a ssessments of regimen-specific adherence behaviors and family factors to optimize metabolic control. Future research should further examine the family functioning, adherence, and metabolic control relations, identified in the mediation model from our study. For example, this model may contribute to interv ention studies of thes e relationships. Given that improved health status and reduced diab etes-related complicati ons are the clinical goals, the aim should be to develop family m odels of adherence that optimize metabolic control. Future analyses should examine other f actors that might relate to diabetes family functioning. For example, parents who alrea dy perceive themselves as overwhelmed might be less likely to be supportive of a nd responsible for thei r childÂ’s diabetes treatment regimen. Other examples include parent and child, a nxiety and depression. Lastly, as interactive patterns of behavior are unlikely to be unidirectional, future studies should examine potential reciprocal interac tion patterns between family variables, adherence behaviors, and metabolic cont rol that are specific to diabetes.

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56 BIOGRAPHICAL SKETCH Danny C. Duke was born on October 7, 1954 in Redlands, California. The oldest of three children, he grew up living in Yucaipa and later in Placer ville, California, where he graduated from El Dorado High School in 1972. He owned a successful landscape contracting business operating in the greater Sacramento, California area from 1973 until 2004. He has been married to his lovely wi fe (Vicki) since 1973 and they have two children (Erin and Ryan). He earned his B.A degree in psychology from California State University, Sacramento in 2002. He plans to complete his Ph.D. in Clinical Psychology and to pursue his clinical and research in terests in pediatric psychology and teaching.