Citation
The relationship between adherence behaviors and glycemic control in childhood diabetes

Material Information

Title:
The relationship between adherence behaviors and glycemic control in childhood diabetes
Creator:
Spevack, Marika, 1944-
Publication Date:
Language:
English
Physical Description:
viii, 116 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Age groups ( jstor )
Calories ( jstor )
Children ( jstor )
Diabetes ( jstor )
Diabetes complications ( jstor )
Dosage ( jstor )
Glycemic control ( jstor )
Insulin ( jstor )
Parents ( jstor )
Type 1 diabetes mellitus ( jstor )
Blood Glucose Self-Monitoring ( mesh )
Diabetes Mellitus, Type I ( mesh )
Health Behavior ( mesh )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1987.
Bibliography:
Includes bibliographical references (leaves 110-114).
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Marika Spevack.

Record Information

Source Institution:
University of Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
20379913 ( OCLC )
ocm20379913
0030506071 ( ALEPH )

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Full Text











THE RELATIONSHIP BETWEEN ADHERENCE BEHAVIORS
AND GLYCEMIC CONTROL IN CHILDHOOD DIABETES














BY

MARIKA SPEVACK


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


UNIVERSITY OF FLORIDA

1987









ACKNOWLEDGEMENTS


I wish to take this opportunity to express special and

heart-felt thanks to Dr. Suzanne Johnson who has provided me

with more than the expected support of a Chair throughout

this investigation. Not only has her interest and input

energized this project and enhanced the quality of the

outcome, but she has been an inspiration, a role model and a

friend throughout this endeavor.

My appreciation and thanks go out to my committee,

Nathan Perry, Sheila Eyberg, William Riley, and Jon Shuster

who have been supportive throughout this endeavor.

Most of all, I would like to take this opportunity to

thank my best friend, confidant, and husband Avram who

tolerated bad moods, angst, ambition, and enthusiasm. I

would like to express deep respect, overwhelming love, and

gratitude for his unwavering encouragement and faith in my

ability to accomplish this project.















TABLE OF CONTENTS
Page

ACKNOWLEDGEMENTS......................................... ii

LIST OF TABLES ........ ................................. iv

ABSTRACT................................................ vi

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

METHOD................................................. 31

Sample Characteristics............................ 31
Measures.......................................... 31
Adherence Measures.............................. 31
Glycemic Control Measures....................... 39
Procedure......................................... 40

RESULTS................................................ 42

Reliability of Child Report....................... 42
Effect of Diabetes Camp on Adherence............. 60
Effects of Diabetes Camp on Glycemic Control..... 66
Relationship Between Adherence and Diabetic
Control......................................... 72
Adherence/GSP Relationship........................ 73
Hierarchical regression analyses................ 73
Categorical analyses............................ 78
Adherence/HbAlc Relationships..................... 81
Hierarchical regression analyses................ 81
Categorical analyses............................ 90

DISCUSSION.............................................. 95

Reliability of Child Report....................... 95
Effect of Diabetes Camp on Adherence.............. 98
Effect of Diabetes Camp on Glycemic Control...... 102
Relationship Between Adherence and Diabetic
Control......................................... 104
Limitations and Future Directions................ 107

REFERENCES ...... ...................................... 110

APPENDIX .............................................. 115

BIOGRAPHICAL SKETCH ................................... 116


iii














LIST OF TABLES

Table Page

1 Parent-Child Correlations for Total Sample
and by Age Group (Johnson, et al.)........... 23

2 Sample Characteristics........................ 32

3 Parent-Child Correlations for Total
Sample and by Age Group: Pre-camp............ 43

4 Parent-Child Correlations for Total
Sample and by Age Group: Immediately
Post-Camp ...................................... 45

5 Parent-Child Correlations for Total
Sample and by Age Group: Six
Weeks Post-Camp....................... ........ 47

6 Parent-Child Correlations for Total
Sample and by Age Group: Three
Months Post-Camp.............................. 49

7 Parent-Child Correlations for Total Sample
and by Age Group Collapsed Across Time....... 53

8 Parent-Child Correlations for Total Sample
Over Four Time Periods: Pre and Post-Camp... 56

9 Duncan's Multiple Range Tests on the
Adherence Measures Demonstrating Within
Subject Effects............................... 61

10 Means and Standard Deviations for
Adherence Measures at the 5 Time Periods..... 64

11 Means and Standard Deviations for Adherence
Measures with Age x Time Interaction at the
5 Time Periods ............................... 65

12 Means and Standard Deviations for Adherence
Measures that Remained Stable Over the
5 Time Periods............................... 67

13 Correlations Between Glycosylated
Hemoglobins and Serum Proteins................ 69








14 Correlations Between Adherence Measures
Within and Between Factors.................... 75

15 Average Correlations between Adherence
Factors....................................... 77

16 Predicting Post-camp HbAlc by Pre-camp HbAlc,
Age, Post-camp Injection Factor, and Pre-camp
Hbalc x Post-camp Injection Interaction...... 84

17 Predicting Post-camp HbAlc: Nonstandardized
Injection Beta Weights at Varying Levels of
Pre-camp HbAlc................................ 85

18 Pre-camp HbAlc x Post-camp Injection Factor:
Descriptive Characteristics................... 86

19 Predicting Post-camp HbAlc by Pre-camp HbAlc,
Age, Insulin Dose Change, Post-camp Injection
Factor, and Insulin Dose Change x Post-camp
Injection Interaction......................... 88

20 Predicting Post-camp Hbalc: Nonstandardized
Beta Weights at Varying Levels of Change in
Insulin Dose from Pre-camp to 3 Months
Post-camp..................................... 89

21 Pre- Post-camp Change in Insulin Dose x
Post-camp Injection Factor: Descriptive
Characteristics.............................. 91














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


THE RELATIONSHIP BETWEEN ADHERENCE BEHAVIORS
AND GLYCEMIC CONTROL IN CHILDHOOD DIABETES

BY

MARIKA SPEVACK

December 1987

Chairman: Suzanne Bennett Johnson, Ph.D.
Major Department: Clinical and Health Psychology


This study was designed to investigate the following

questions: 1) do children's diabetes management behaviors

change at summer camp; 2) does attendance at summer camp

affect diabetic children's glycemic control; 3) if there are

changes at camp, are they maintained at home; 4) is there a

relationship between adherence behavior and glycemic

control. Sixty-four 7- to 12.6-year old youngsters with

diabetes were followed before, during, and after attending a

2-week summer camp. Both their diabetes management behav-

iors and their glycemic control were monitored.

Children's diabetes management behaviors (i.e., injec-

tion regularity, injection interval, calories consumed,

eating frequency, exercise duration, exercise type, exercise

frequency, and glucose testing frequency) changed signifi-

cantly while they attended summer camp. There was also a








change in children's level of glycemic control as measured

by glycosylated serum protein levels. However, children

were in poorer glycemic control at the end of camp than at

the beginning. None of these camp-related changes were

maintained once the youngsters returned home. No clear

relationship between adherence behaviors during camp and

control could be established. However, post-camp metabolic

control was predicted by an interaction between change in

insulin dose (pre- to post-camp) and injection adherence

behaviors. Youngsters who had large increases in insulin

dose exhibited a deterioration in metabolic control when

they were more adherent. Youngsters who had large decreases

in insulin dose exhibited the opposite relationship: better

adherence was associated with better control. Further

inspection of this statistically significant interaction

indicated that pre- post-camp change in insulin dose

appeared to be initiated during camp. Camp related insulin

dose changes were maintained or enhanced once the children

went home. The group of children whose insulin was

increased during camp, were given further increases in

insulin dose once they returned home and may have been

experiencing the Somogyi phenomenon (rebounding) after camp

as a consequence of excessive increments in their insulin

dose. Home physicians, appeared to have used insulin

adjustments made during camp as a model of treatment and

continued the trend initiated by the camp physicians. It


vii







appears that high levels of adherence with inappropriate

insulin dose prescriptions can lead to poorer control.


viii














INTRODUCTION


Insulin dependent diabetes mellitus (IDDM) is a chronic

illness, the result of insufficient insulin production by

the pancreas. Onset occurs before age 30--most frequently

between the ages of 8 and 12 years--although it can occur at

any time from birth to adulthood (Blevins, 1979).

Under normal circumstances, insulin is secreted

directly into the blood-stream by the Islets of Langerhans

in the pancreas. This hormone plays a vital role in the

metabolism of food since it facilitates the movement of

glucose from the bloodstream into muscle and fat tissue for

energy. Glucose that is not used for energy is then

directed into storage (primarily into the liver) in the form

of glycogen and/or fat. Fat is an additional source of

energy which does not require insulin to be converted.

However, the energy expended in its production is

considerable, leaving a negligible amount for functional

use. Normally, the pancreas secretes insulin at rates that

maintain glucose levels in the bloodstream within a narrow

range. In youngsters with IDDM, the pancreas cannot perform

this function and glucose levels build up.

The absence of insulin prevents glucose usage, fat is

broken down and glucose levels remain high. The liver






2
converts some of the fat into ketones, which increase in the

blood and, at high levels, spill into the urine. In

addition, when the blood passes through the kidney, glucose

is filtered out and, at high levels, the extra glucose

spills over into the urine. The kidney attempts to filter

out the excess glucose and ketones from the blood in order

to maintain acid alkaline (Ph) levels within normal limits.

This results in increased urination and increased thirst.

However, the kidney can not keep up this level of activity.

Unchecked, ketones and acids build up in the blood and the

child develops diabetic ketoacidosis--a serious condition

requiring hospitalization. Furthermore, the loss of urine

water and the use of body fat for energy produce weight loss

(Travis, 1969). If this condition is untreated, coma and

death can result.

Presenting symptoms of IDDM include polyuria, poly-

dipsia, polyphagia, and weight loss. There is often a high

prevalence of infections. More subtle signs of the disorder

include pruritus (severe and protracted itching of the

skin), enuresis bedwettingg), vulvitis, irascible disposi-

tion, lassitude, and abrupt onset of visual difficulties

(Sussman, 1971). As yet, there is no cure for this disease.



The treatment of IDDM consists of the following:

1) Daily insulin injections--A child with IDDM

injects insulin at least once and as many as

four times daily for the rest of his/her life







because the pancreas does not resume insulin

production.

2) Controlled diet--Food intake must be adjusted

for content and synchronized with the

injections and exercise for optimal benefit.

The youngster must eat three meals a day as

well as one to three snacks per day,

preferably at regular intervals. Low fat

nutrition is encouraged and concentrated

sweets are to be avoided.

3) Regular exercise--An exercise regimen is

important for improving cardiovascular

functioning which facilitates insulin

circulation.

4) Glucose testing--Close monitoring of

glycemic control (approximately four

times per day preferably before meals)

is recommended so that meals, snacks,

and exercise can be appropriately ad-

justed. In addition, this information

is necessary for the physician to make

appropriate changes in regimen recom-

mendations.

The various components of the regimen are tailored to

the needs of each individual youngster. The goal of

treatment is to maintain blood glucose levels within a

reasonable range in order to promote normal growth and







development and to prevent complications. With growing

youngsters, this task is particularly difficult since their

nutritional and insulin requirements are changing as they

grow and injections administered once or twice a day cannot

imitate the synchronous pancreatic action of nondiabetic

individuals (Guthrie and Guthrie, 1977).

Complications associated with IDDM can be divided into

three categories: short-term, intermediate, and long-term.

1) Short-term (acute) consequences include

diabetic ketoacidosis (DKA), hypoglycemia and

hyperglycemia. Diabetic ketoacidosis can be

precipitated by an infection although stress

or insufficient insulin can also be a

precipitant. Symptoms include rapid respira-

tion, polydipsia, polyuria, vomiting and

nausea. Hypoglycemia (insulin reaction) can

be caused by overdoses of insulin, its

improper distribution, inadequate intake (or

delayed absorption) of food, or too much

exercise without a concurrent adjustment of

the food intake (Guthrie and Guthrie, 1977).

Increased perspiration, confusion, irri-

tability and inappropriate behavior are

common symptoms (Sussman, 1971). Hyper-

glycemia may result from too little insulin,

eating improperly, decreased exercise

(without concomitant decreased food intake),








emotional stress, and infection. The most

frequent symptoms are weakness, increased

thirst, frequent urination, dry mouth,

decreased appetite or polyphagia, nausea and

vomiting, abdominal pain, coma, and acetone

breath which occurs when ketone levels rise

in advanced stages. These acute consequences

are characteristic of youngsters in poor

control (Guthrie and Guthrie, 1977).

2) Intermediate consequences include delayed

growth and development, frequent infections,

and psychosocial problems. Delayed growth

and development in IDDM youngsters is not

uncommon and has been found to correlate most

clearly with those children in poor control

(Guthrie and Guthrie, 1977). Various kinds of

infections are also common in IDDM children.

Clinical observations and research suggest

that these are also more common in patients

in poor diabetes control (Guthrie and

Guthrie, 1977). Psychological problems are

associated with poor control although the

nature of the relationship between psycho-

logical problems and health status in

youngsters with diabetes is not entirely

clear (Johnson, 1985).







3) Long-term (chronic) consequences of the

disease include retinopathy and nephropathy.

Retinopathy is characterized by the formation

of new capillaries and duplication of small

veins, retinal detachment, preretinal and

vitreous hemorrhage, and fibrosis. Blindness

may be the result (Sussman, 1971). Nephropa-

thy (renal disease) is the leading cause of

death among people with IDDM (Blevins, 1979).

Complications occur 15-20 years after

diabetes onset. Scientific data suggest that

these diseases may be preventable if the

patient's diabetes is maintained in good

control (Guthrie and Guthrie, 1977).



Clearly, metabolic control, or lack thereof, is

implicated in all three categories of complications both

directly and indirectly. Although evidence that complica-

tions occur more frequently among patients in poor control

is mounting (Rifkin, 1978; Maxwell, Luft, Clark, and

Vinicor, 1982; Unger,1982; Johnson, 1984), it is difficult

to properly evaluate this literature since the definition of

"control" is so loosely posited and varies from investigator

to investigator. Moreover, a definitive link between

adherence to prescribed regimen (i.e. behavior) and control

has yet to be established.








Studies in this area are few and present numerous

methodological problems. For example, control and com-

pliance are often confused as when hemoglobin Alc (HbAlc), a

metabolic indicator of glycemic control, is taken to

represent compliance. In some cases, although both con-

structs are treated separately, the same person judges both,

introducing the possibility of a spurious association

between the two (i.e., the rater may believe compliance and

control are related and judge them accordingly).

The few studies in the area can be divided into two

categories. The first category is composed of correlational

studies which are concerned with establishing associations

between compliance behaviors and control. One such study by

Rainwater, Jackson and Burns (1982) tried to assess the

relationship between psychological variables, metabolic

control, and behavioral measures using 115 children between

8 and 17 years and their mothers. Their most noteworthy

finding was that Mother's Health Locus of Control (MHLC) was

significantly correlated with behavioral and metabolic

measures suggesting that mothers who scored external tended

to have better controlled children. That is, MHLC corre-

lated significantly r=-.58, p< .01, with total amount of

sugar spilled in a 24-hour urine sample collected from the

patient, the number of patient hospitalizations in the past

year, -.42, (p<.01), the number of insulin reactions

experienced by the patient in the past month r=.36 (p<.01),

and the number of hours the patient exercised per week






8
during the past 3 months r=.35 (p<.01). However, HbAlc did

not correlate significantly with MHLC or Mother's General

Locus of Control. Further, there appeared to be no sig-

nificant relationship between the author's diabetes control

indices (such as 24-hour urine, number of hospitalizations,

number of reactions, hours of exercise and HbAlc) and the

child's self-esteem (measured by the Piers-Harris Inventory)

or with the child's life change scores (measured with the

Coddington Scale for Life Events).

This study claimed to have used both behavioral

(defined by the authors as the number of hours exercised per

week, number of diabetes related hospitalizations in the

past year, and number of insulin reactions in the past

month, and number of times acetone was noted in the urine in

the past month) and metabolic (hemoglobin Alc, triglyce-

rides, cholesterol, fasting blood sugar, and total amount of

sugar spilled in the urine in a 24-hour period) measures

which the authors presumed to reflect diabetes control.

Glycosylated hemoglobin (HbAlc) is now widely accepted as

the single most reliable and adequate measure of glycemic

control. The authors' use of 24-hour urine as an index of

control is problematic for two reasons. Valid 24-hour

urines are difficult to collect even under controlled

conditions such as hospitals. In our experience, only

20-25% of all specimens solicited and collected were

complete collections. Glycosylated hemoglobin (HbAlc)

values are a more widely accepted measure of control than








24-hour urines and provide a more stable measure of control

over the preceding 3 months rather than just 24 hours.

Rainwater et al. did not report whether the 24-hour speci-

mens in their study were valid. Even if the 24-hour

collection was valid, it indirectly represents the young-

ster's metabolic control for a relatively brief period of

time. Since no relationship was shown between MHLC and

HbAlc, the most reliable indicator of metabolic control over

the past 3 months, the relationship between MHLC and the

24-hour collection data is difficult to interpret (i.e., why

would such a relationship exist for one measure of diabetes

control but not another?). Furthermore, the study did not

directly assess adherence behaviors. Rather, behavioral

measures, as defined by the authors, consisted of report by

family of the number of diabetes related hospitalizations in

the past year, average number of hours of exercise per week

over the past 3 months, number of insulin reactions in the

past month, and number of times acetone was noted in the

urine in the past month, thereby confusing adherence

behaviors with metabolic indicators and other sequelae of

poor control.

In another correlational study, LaGreca et al. (1982)

examined the relationship between metabolic control as

measured by hemoglobin Al and different aspects of diabetes

care such as degree of responsibility taken by the child,

level of knowledge, and degree of compliance as rated by

physicians following routine appointments with 40 IDDM







youngsters. Results of this study indicate that for

preadolescents there was no relationship between physicians

compliance ratings and metabolic control. For adolescents

the best predictor of control was the child's overall level

of compliance with the treatment regimen, especially with

eating regular snacks and carrying sugar for emergencies.

However, their findings suggest that regardless of com-

pliance ratings, the more responsibility assumed by the

youngster, the poorer their control.

It should be noted that the physician's four-point

rating of compliance was based on patient report (testing,

charting, administering insulin, eating proper foods, eating

regular snacks, carrying quick-acting sugar, and taking

sugar to treat reactions) as well as on whether the child

kept appointments. The reliability of these ratings was not

tested and it is unclear whether the physician's rating may

have been influenced by the patient's current level of

metabolic control. Furthermore, no information was provided

about the relative importance of any one aspect of the

regimen.

McCulloch et al. (1983) correlated dietary behaviors

such as accuracy of measuring carbohydrates and maintaining

consistent and regular eating patterns with HbA1 in adult

diabetics who kept 7-day diaries. Results indicated better

glycemic control in those persons who were more precise in

their measurements and compliant in the regularity of their

meals. There was, however, no attempt made to corroborate








the patients' self-reports, and nondietary aspects of the

treatment regimen were not studied.

One study that has attempted to address the more global

versus specific nature of the relationship between adherence

and metabolic control was conducted by Schafer, Glasgow,

McCaul, and Dreher (1983). Their 34 subjects were 12 to

14-year-olds with IDDM who completed questionnaires dealing

with regimen adherence for the past week as well as psycho-

social measures dealing with diabetes-specific family

behaviors, barriers to adherence, and family interactions.

They found that compliance was a highly specific construct;

the degree of adherence to one aspect of the IDDM regimen

was not related to adherence to the other aspects of the

regimen. Using multiple correlations, they also found that

glycosylated hemoglobin levels could be predicted from a

combination of three adherence measures (i.e., the extent to

which one's diet was followed, reported care measuring

insulin doses, and number of daily glucose tests). Unfor-

tunately, the reliability of the self-report compliance

measures was not assessed, nor was there any attempt to

verify their reports by actual measurement of home behaviors

or by corroboration by significant others. In addition, the

use of multiple regression with such a small sample and

such a large number of variables is likely to severely

overestimate the strength of the resultant R2.

In a more thorough study by some of the same inves-

tigators (Glasgow, McCaul, and Schafer, 1987), these same







issues were addressed with 93 adult IDDM outpatients.

Similar to the Schafer et al.(1983) study, this investiga-

tion explored adherence to various aspects of the diabetes

care regimen, the congruity of adherence of the different

behavioral components, and the relationship between adher-

ence and glycemic control as measured by glycosylated

hemoglobin. Subjects participated in three home interviews

during a 1 week period, completed psychosocial measures,

collected self-care measures on injections, glucose testing,

diet and physical activity. The entire procedure was

repeated for a subsample at two months and for the entire

sample at six months. Self-report information was organized

into five categories of adherence: insulin injections,

glucose testing, diet level, dietary adherence, and physical

activity. Glycemic control was measured with glycosylated

hemoglobin and percent negative glucose tests (both urine

and blood). Each adherence measure was compared to patient

report of regimen prescriptions. Their results indicated

that compliance was better for taking medication and for

glucose testing than for behaviors which required major

adjustments in routine (e.g., diet and exercise); adherence

with one component of the regimen did not necessarily

correlate with adherence to other regimen tasks. Compliance

behaviors were not stable over time although glycemic

control was relatively stable; no clear relationship between

adherence and glycemic control was established through

either bivariate or multivariate analyses. Although this







study is an improvement over most correlational studies,

there are some basic weaknesses. For example, no attempt

was made to verify actual diabetologists' prescriptions--

rather patient report of physician prescriptions were used

and comparisons were made to the self-reports or in some

instances to absolute levels of behaviors (although the

authors do not define "absolute levels," these are presum-

ably meant to be ideal levels). Similarly, no attempt was

made to corroborate any of the self-reports of adherence.

Moreover, in calculating dietary compliance, only evening

meals were used.

Intervention studies comprise the second category of

investigations in the area of compliance and control. These

studies concentrate on increasing compliance, assuming that

lack of adherence to the medical regimen can result in

serious diabetic complications. However, a number of these

studies seek to modify adherence but do not include measures

of control. In one such study Gross (1982) conducted a

multiple baseline intervention with four IDDM boys aged 10-

12 years to determine the utility of self-management

behaviors. The youngsters were selected based on parent

report that they failed to reliably perform their urine

tests the recommended four times per day. The boys received

six 1-hour long sessions (one per week) which consisted of

written lessons, discussions, modeling and role-playing. In

addition, the children were assigned a self-management

project which included collecting baseline data on the







frequency of their urine testing which was arbitrarily

designated by the investigator as the target behavior. The

youngsters chose rewards and self-delivered them contingent

on performing the target behavior and graphed their perfor-

mance. During the final phase, the subjects were taught

negotiating and contracting skills and in a meeting with the

experimenter, the child, and his parents a contract was

arbitrated. All contracts involved parental reinforcement

for continuation of self-management of urine testing.

Reliability data were collected by parents counting the

number of test tablets used on 1 day each week when the

child was not at home. The children were aware that the

parents would be checking but did not know when. No attempt

was made to monitor the accuracy of the children's urine

testing. Reliability was based on dividing the number of

agreements of parent and child report by agreement plus

disagreement and multiplying the quotient by 100. Reliabi-

lity averaged 80%. No data were reported on the rate of

compliance with the self-reward regimen. Pre- and post-

training tests on the principles and procedures of behavior

modification were conducted and 2- and 8-week follow-up data

were also collected. Children improved their rate of urine

testing from 9% to 74% of the time during the self-manage-

ment condition and at the 2-week follow-up. However, at the

8-week follow-up, two of the four families had discontinued

with the contract and those children's behavior had returned

to baseline levels.








Only one of the many necessary diabetes management

behaviors was targeted in this study and no attempt was made

to determine whether increasing the frequency of urine

testing had an effect on children's level of control.

An intervention study by Epstein et al. (1981) employed

parent training procedures. Twenty families were assigned

to one of three groups in a multiple baseline design.

Parents were instructed to use a point system and praise for

their youngsters' compliance with urine testing. Both

self-report (daily urine test results and adherence to urine

testing regimen) and biochemical measures (insulin dosage,

HbA1, plasma glucose, serum lipids) were used to assess

effects of the intervention. Epstein et al. went to

considerable lengths to determine the reliability of the

children's recordings (although only negative urine data

were used for analyses) by asking parents to test four

urines per week on a schedule determined by the experimen-

ters and without knowledge of the child's values. Reliabi-

lity was assessed for 91% of the treatment weeks and parents

and children agreed on the glucose concentration within a

one measurement interval range on each of the four reliabi-

lity tests during 83% of the weeks. Exact parent/child

agreement on the urine tests was not reported. The

reliability/validity of reported urine testing frequency was

measured by including predetermined quantities of inert,

placebo Clinitest tablets in each bottle. Parents were

informed about the correct number of placebos. At the end






16
of each week, parents were instructed to test the remaining

tablets in each bottle, and add the number they found to the

number the child reported finding. Comparisons of this

number to the actual number provided the measure of the

child's adherence to the regimen for that week. Parents and

children agreed on the number of marked items during 76% of

the weeks. Results showed a significant increase in the

number of tests performed and in the percent of urine tests

that were negative for glucose, although other metabolic

indices of diabetic control did not show improvement.

Epstein et al. concluded that behavioral techniques appear

useful for improving behavioral adherence to diabetes

regimen. However, in this study, increasing the number of

urine tests that were negative for glucose was not associ-

ated with improvement in other measures of diabetic control.

Kaplan, Chadwick and Schimmel (1985) randomly assigned

21 IDDM teenagers between 13 and 18 from middle class

backgrounds to either a daily social learning group or a

medical facts learning group when they entered a 3 week

summer program. The social learning group identified social

situations in which peer influence might prevent adherence

to diabetes regimen and as a group suggested appropriate

responses. Rehearsal exercises enacting problem situations

and their solutions were ultimately videotaped and then

filmed in a television studio. Guided practice with

reinforcement was used to develop their social skills. The

control group spent the same amount of time discussing








diabetes relevant medical information. These discussions

were videotaped and then filmed. Hemoglobin Al was drawn

during the program to measure metabolic control before the

program and at a 4-month reunion to measure metabolic

control since the program.

The groups were equivalent at the beginning of the

program. Hemoglobin A1 values at the reunion (obtained for

82% of those who provided an original sample) showed a

slight increase in values for the control group and a

contrasting decrease for the experimental group. Further-

more, the authors found a substantial correlation (r=-.78)

between self-reported self-care and diabetes control as

measured with HbA1 values. This study is an improvement

over earlier research in that intervention was directed at

many aspects of the treatment regimen instead of urine

testing alone. Furthermore, correlations between adherence

behaviors and control were provided.

However, the study's experimental and control groups

were small and restricted to white middle class adolescents.

The reliability of the self-report measures of compliance

was not assessed nor were parents surveyed to corroborate

the youngsters' report. Before treatment, the experimental

group was in slightly better diabetes control than the

control group (although the difference was not statistically

significant). At post-treatment, it was unclear whether the

slight improvement of the experimental group and the slight

deterioration of the controls was clearly related to the








study's intervention or was a natural continuation of each

group's pretreatment metabolic condition. Pre/post- changes

in diabetic control were not significant and there was no

assessment of and therefore no evidence that one group's

compliance was better than the other group's at post-treat-

ment. Consequently, the only evidence clearly in favor of

the experimental group was the post-treatment difference in

HbAI. Certainly, a replication of the study's findings is

needed if we are to have confidence that the post-treatment

HbAl differences were, in fact, due to the intervention

employed.

Schafer, Glasgow, and McCaul (1982) designed a multiple

baseline approach to assess the effectiveness of goal

setting and behavioral contracting to increase adherence and

control in three adolescents with IDDM. A self-monitoring

program of 1 week, introduced before goal setting, con-

stituted the baseline phase. Contracting was introduced

only if 90% compliance was not achieved. Compliance was

assessed by subjects records of relevant target activity

such as time, duration, etc. Reliability checks were made

periodically by mothers' independent monitoring. Diabetic

control was assessed with urine glucose tests performed and

recorded daily by subjects. Reliability checks were

conducted by having mothers spot-check the urine tests. Two

hour postprandial blood glucose levels were determined, and

24-hour urine glucose was collected at pretest, posttest,

and followup. Two of the subjects showed improvements in








compliance and control. The third was essentially unaf-

fected by the treatment.

Unfortunately, as mentioned above, the use of 24 hour

urines as indices of diabetic control is problematic since

they are difficult to collect even in hospital settings. In

addition, postprandial blood glucose levels measure control

at brief points in time and may not relate to the patient's

general level of diabetic control.

It is generally accepted that the issue of compliance

as it relates to level of control and possible complications

is vital to the area of chronic illness in general, and

diabetes in particular. However, results are mixed and

difficult to interpret. In order to establish whether there

is actually a causal link, methods of measuring the various

components must be improved. Clearly the major reasons for

this gap in the area of IDDM are that

1) The definition of control has varied among

investigators, and diabetologists have been

unable to reach a consensus on a precise

definition so that studies have used varying

measures of control (Spevack, Johnson,

Harkavy, Silverstein, Shuster, Rosenbloom,

and Malone, 1987). There have been recent

improvements in this situation with the

general acceptance of HbAlc, although some

investigators balk at using this as the sole

measure of control since it does not








accurately reflect variability in blood

glucose.

2) The measurement of adherence behaviors is

equally problematic. Little psychometric

data are provided as to the quality of the

measures used. Compliance and control are

often confused or the same rater judges both.

Compliance is often treated as a global

construct although there is mounting evidence

that it is complex, consisting of several

independent components. Studies focus on be-

haviors that do not relate directly to

control. For example, wearing a medic alert

bracelet is definitely important in case of

accident (Schafer et al., 1982). However,

wearing it may not have a direct impact on

glycemic control.

3) Correlational studies have been inconsistent

in demonstrating a relationship between com-

pliance and control. In any case, data from

these studies cannot be used to establish

causality.

4) The few intervention studies available

suggest that adherence may be improved.

However, improved adherence is not always

associated with improved control (Epstein and

Cluss, 1982). In some intervention studies,








only a single behavior is targeted for

intervention or selected for treatment by the

parent of the diabetic child. In such cases,

the adherence behaviors improved may have no

significant impact on diabetic control.

5) It is very difficult to study IDDM youngsters

under controlled conditions, where behavior

can be reliably measured, without interfering

with or disrupting the youngster's normal

behavior.

Many investigators have dealt with the first problem by

relying predominantly on the best single estimator of

control--namely, hemoglobin Alc--which is an indicator of

integrated plasma glucose levels over approximately 3

months. Consequently, to establish a link between adherenc-

e/behavior and control as measured by hemoglobin Alc, a 3-

month behavior monitoring system should be available.

Unfortunately, youngsters balk at keeping diaries even for

short intervals and even when they do keep such records,

reliability checks are problematic. To date the most

systematic procedure for measuring compliance has been

developed by Johnson, Silverstein, Rosenbloom, Carter, and

Cunningham (1986). A 24-hour recall interview technique was

used to assess daily management behaviors in four diabetes

relevant areas (diet, injection, exercise, and glucose

testing). Each child and his/her parent were interviewed

separately on three different occasions concerning the







child's diabetes management behaviors in the last 24 hours.

Interviews were conducted by phone and took approximately 20

minutes. Data from this procedure were used to measure 13

different diabetes management behaviors: injection regulari-

ty, injection interval, injection-meal timing, regularity of

injection-meal timing, calories consumed, percent calories:

fat, percent calories: carbohydrates, concentrated sweets,

eating frequency, exercise duration, exercise type, exercise

frequency, and glucose testing frequency. Pearson product

moment correlations for all 13 adherence measures were

conducted to asses parent-child agreement. The results are

presented in Table 1. Although all of the correlations were

statistically significant (p<.0001), a number of measures

differed in agreement depending on the age of the child with

the youngest children (6-9 years) showing poorest

parent/child agreement on measures involving time and the

10- to 12- and 13- to 15-year old groups showing the most

consistent parent/child agreement across all measures. The

oldest group (16-19 years) had highly variable parent/child

correlations. The authors attribute these findings to the

reduction of parental supervision during late adolescence

when children spend more time with peers outside of the

parents' purview. The 13 adherence measures were then

subjected to a principal component factor analysis resulting

in a five-factor solution accounting for 71.3% of the

variance. The five factors were rotated to simple structure

using the varimax procedure. Factor I (Exercise) consisted







TABLE 1
Parent-Child Correlations for Total Sample and by Age Group


Group 1
(6-9 Years)
r(n)
p <


Group 2
(10-12 Years)
r(n)


Group 3 Group 4
(13-15.6 Years) (16-19 Years)
r(n) r(n)
p < p <


Adherence Measure


Injection
Regularity

Injection
interval

Injection-
meal timing

Regularity of
injection-
meal timing

Calories
consumed

Percentage
calories-fat


.61(152)
(.0001)

.77(154)
(.0001)

.67(163)
(.0001)

.42(148)
(.0001)


.77(139)
(.0001)

.64(167)
(.0001)


.46(27)
(.02)

.36(29)
(.05)

.53(31)
(.002)

-.23(26)
(.26)


.79(30)
(.0001)

.50(31)
(.005)


.68(65)
(.0001)

.71(67)
(.0001)

.547(68)
(.0001)

.50(63)
(.0001)


.80(66)
(.0001)

.63(70)
(.0001)


.83(42)
(.0001)

.54(40)
(.003)

.60(44)
(.001)

.58(41)
(.0001)


.61(33)
(.0001)

.76(44)
(.0002)


-.04(18)
(.86)

.91(18)
(.0001)

.78(20)
(.0001)

.46(18)
(.05)


.92(10)
(.0002)

.60(22)
(.003)


Total
Sample
r(n)







TABLE 1
(continued)


Total Group 1 Group 2 Group 3 Group 4
Sample (6-9 Years) (10-12 Years) (13-15.6 Years) (16-19 Years)
r(n) r(n) r(n) r(n) r(n)
p < p < p < p < p <
Adherence Measure
Percentage .64(167) .44(31) .63(70) .81(44) .62(22)
calories- (.0001) (.01) (.0001) (.0001) (.002)
carbohydrate

Concentrated .62(167) .47(31) .71(70) .76(44) .12(22)
Sweets (.0001) (.007) (.0001) (.0001) .58

Eating .45(167) .67(31) .47(70) .54(44) .13.(22)
frequency (.0001) (.0001) (.0001) (.0002) (.57)

Exercise .59(167) .03(31) .96(70) .79(44) .47(22)
duration (.0001) (.87) (.0001) (.0001) (.03)

Exercise .54(167) .74(31) .99(70) .66(44) .32(22)
type (.0001) (.0001) (.0001) (.0001) (.15)

Exercise .62(167) .62(31) .72(70) .72(44) .32(22)
frequency (.0001) (.0002) (.0001) (.0001) (.13)

Glucose .78(164) .81(31) .73(68) .77(44) .37(21)
testing (.0001) (.0001) (.0001) (.0001) (.10)
frequency


Table from Johnson, et al.,


1986








of the three exercise measures, Factor II (Injection)

consisted of all four injection measures, Factor III (Diet

Type) was made up of measures of diet type, Factor IV

(Eating/Glucose Testing Frequency) included measures of

eating frequency and glucose testing frequency, and Factor V

(Diet Amount) included measures of total calories consumed

and the amount of concentrated sweets ingested. The Johnson

et al. (1986) results indicate that adherence to a diabetic

regimen consists of five independent constructs and should

not be viewed as a global construct.

Diabetes summer camps offer a natural but relatively

controlled environment. Here youngsters' behavior and

metabolic control could be easily monitored and both would

be expected to change. Such an environment would be

conducive to investigating relationships of compliance/

control. Diabetes summer camps have as their main goal to

create an environment in which IDDM youngsters can meet

other children with diabetes and learn to live more normally

with their disease. The focus of the program is usually to

provide a model for healthy living along with didactic

components designed to reinforce adherence skills and

increase understanding of the disease. It is a generally

accepted philosophy that research conducted at summer camps

must be nonintrusive and must not interfere with camper ac-

tivities and enjoyment. The major criticism leveled at camp

studies is that they constitute an unnatural environment

which itself affects the youngsters' behavior and level of








control. Whether this is a valid criticism and if so,

whether the effects are positive or negative, has never been

comprehensively evaluated. There is evidence that diabetes

camp may improve diabetic control although only one study

specifically addressed this issue (Strickland et al., 1984).

The Strickland et al. study examined changes in glycemic

control during a 2-week summer camp program by performing

pre-camp and post-camp values of fasting plasma glucose,

glycosylated hemoglobin, and glycosylated serum protein in a

group of 36 children. Their results suggested that there

was measurable improvement in diabetic control in some

children.

Similarly, only a few studies have addressed behavioral

changes associated with camp. For example, Stunkard and

Pestka (1962) examined physical activity at a 2-week Girl

Scout camp and found that there was significantly more

activity during camp compared with at-home behavior. The

youngsters studied did not have diabetes.

Most camp studies deal with psychological constructs.

Moffatt and Pless (1983) investigated changes in locus of

control in juvenile diabetic campers during a 3 week camp

experience and found that there were significant changes

toward internal locus of control.

Self-concept was scrutinized using the Tennessee Self-

Concept Scale with 26 IDDM adolescents (aged 13-17) attend-

ing an 8-day summer camp (Hoffman, Guthrie, Speelman, and

Childs, 1982). The authors report that self-concept may be








more resistent to change above the age of 14 and that

initial level of control was not a significant predictor of

self-concept change. The greatest change occurred in the

13- to 14-year old females who improved on self-concept

significantly. However, this study did not address the

issue of whether self-concept change was associated with

change in glycemic control.

In a more recent study, Scharf, Adams, and Leach (1987)

compared 45 IDDM adolescents aged 12 to 17 who attended a

residential diabetes summer camp, to IDDM youngsters who did

not attend camp on psychological functioning immediately

following the camp experience and at a 5 month follow- up.

They reported that campers' adjustment to diabetes, their

self-worth, and their parents' perceptions of their

behavioral competencies and general personality adjustment

did not change due to the camp experience. Moreover,

metabolic control as measured with HbAlc before camp did not

differentially influence the experimental or control

subjects in how they reacted to the camp experience or how

they adjusted to a chronic illness. No attempt was made to

assess whether the camp experience affected glycemic control

after camp.

The effects of stress on glycemic control were examined

at a 1982 Tennessee camp for diabetic children using 39

adolescents between the ages of 12 and 15 years (Hanson and

Pichert, 1986). The children were followed for 3 days while

insulin administration, dietary intake, exercise, self-








reported stress, and blood glucose levels (measured via

Chemstrip bG Reagent strips four times during the 24 hours

preceding each afternoon rest period during which stress

questionnaires were administered) were monitored. Results

suggested that negative cumulative stress, as perceived by

the youngsters, correlated significantly with blood glucose

levels. Interestingly, positive cumulative stress was

significantly and negatively correlated with girls' blood

glucose levels.

Educational experiences at camp were found to contri-

bute to the knowledge and performance of self-care techni-

ques in a study by Lebovitz, Ellis, and Skyler (1978).

Harkavy et al. (1983) found that knowledge increased in 12-

to 15-year old campers but not in 10- to 11-year olds.

Dorchy, Loeb, Mozin, Lemiere, and Ernould (1982) adminis-

tered a 27 question pre- and post-test to 63 IDDM youngsters

and found that regardless of age or duration of diabetes all

children ages 9 to 15 years benefit from the teaching of

theory and practical issues. The youngest children (9-10

years) showed progress in all areas while the oldest (13-15

years) made greater gains in the areas of nutrition and

insulin therapy.

There is, therefore, some evidence that the camp

experience may affect psychological factors such as locus of

control (Moffat and Pless, 1983) and self-esteem (Hoffman,

et al., 1982), knowledge about the disease (particularly in

12- to 15-year old youngsters, Harkavy et al., 1983), self-








care (Lebovitz et al., 1978), and physical activity levels

(Stunkard and Pestka, 1962). Moreover, there is some

evidence that metabolic control may improve during the camp

session (Strickland et al., 1984).

Improvements in adherence behaviors are assumed to

occur since camp provides exercise opportunities and the

youngsters' regimen is regulated regarding injections and

meals. There are, however, no empirical data documenting

improved adherence at camp nor attesting to the relationship

between such behavior change and improvements in metabolic

control. Further, it is unclear whether any behavioral

changes induced by the camp setting are maintained once the

child returns home.

Glycosylated serum proteins (GSP) differ from glyco-

sylated hemoglobins (GH) in that serum protein levels in the

blood change more rapidly than do hemoglobin values with

changes in blood glucose concentration (Mehl, Wenzel,

Russel, Gardner, and Merimee, 1983). Therefore, since

glycosylated serum proteins have been shown to accurately

reflect alteration of mean glycemic levels 1 to 2 weeks

prior to testing, it becomes feasible to address the issue

of whether adherence behaviors at camp are indeed related to

metabolic control at camp. This study will, therefore,

assess the following:

1) The effect of summer camp on children's

diabetes management behavior;








2) The effects of a diabetes summer camp

environment on children's diabetes control;

3) The length of time that effects of camp are

maintained--i.e., do changes or effects of

camp dissipate once the child returns home;

4) The relationship between adherence behavior

and level of diabetic control.

This proposed investigation represents a naturalistic

study in which the experience of camp is expected to

actually change both the children's behavior and their

levels of diabetic control thereby providing the opportunity

to monitor both. Therefore, the behavior and the levels of

control are being naturally manipulated by virtue of the

youngsters' attendance at camp, providing an ideal situation

to assess the extent to which changes in diabetic control

are associated with changes in behavior. Should such

changes be demonstrated, and their association be estab-

lished, it would constitute strong support for a relation-

ship between behavior and health status.














METHOD

Sample Characteristics

Participants were 64 IDDM youngsters who attended the

1985 North Florida Camp for Children and Youth with

Diabetes. The 1985 camp session lasted 2 weeks. One parent

of each youngster was also asked to participate. Informed

consent was obtained from all participants. Youngsters were

between the ages of 7-12 (with a mean age of 10.5 years).

There were 34 boys and 30 girls. Participating families

reported income in 5 categories: 1= $ 0 to $9,999, 2=

$10,000 to $19,999, 3= $20,000 to $29,999, 4= $30,000 to

$39,999, and 4= $40,000+. Of all participating families

14.5% were in category 1, 27.3% in 2, 23.6% in 3, 18.2% in

4, and 16.4% in 5. All but one (98.4%) of the youngsters

had had diabetes for at least 1 year with a range from .9

years to 9.1 years (Table 2). All but one of the youngsters

were Caucasian. Only two youngsters dropped out of the

study after camp.


Measures

Adherence Measures

Twenty-four hour recall interviews, conducted with

children and parents, were recorded on Diabetes Daily Record

forms to assess the youngsters' diabetes related







TABLE 2
Sample Characteristics


Total Group 1 Group 2 Group 3
Sample 7-9 10-11.4 11.5-12.6
Years Years Years


Sample Size 64 20 23 21

Sex: Males 34 13 13 8
Females 30 7 10 13

Family Income
in categories: 1 14.5% 0% 28.6% 11.1%
2 27.3% 31.3% 28.6% 22.2%
3 23.6% 25.0% 23.8% 22.2%
4 18.2% 18.8% 9.5% 27.8%
5 16.4% 25.0% 9.5% 16.7%

Age: Mean 10.5 8.5 10.8 12.2
Range 7.6-12.6 7.6-9.1 10.1-11.1 11.6-12.6
Stand. Dev. 1.57 .65 .36 .47


Duration: Mean 3.9 3.6 3.3 4.9
Range .9-9.1 1-5.1 .9-4.4 1.1-8.3
Stand. Dev. 2.67 2.11 2.18 3.37






33
behaviors before, during and after camp (see Appendix). The

interviews were conducted by telephone. When possible, two

of these interviews dealt with.weekdays and one with weekend

activities. Participants were told that the interviewer was

concerned with what actually transpired rather than what

they believe should have been done. Trained, undergraduate

research assistants conducted these interviews. Partici-

pants were asked to recall the previous day's activities in

a sequential manner beginning with when the child woke up

and ending with when he went to bed. All diabetes relevant

behaviors were recorded. Interviewers prompted for informa-

tion that may have been inadvertently omitted, such as,

mid-morning snacks, exercise, glucose testing, etc.

Youngsters and their parents were interviewed separately.

Using data obtained from the 24 hour recall interviews,

13 different adherence measures were quantified. For each

measure, a range of scores is possible with higher scores

indicating relative noncompliance and lower scores indicat-

ing relative compliance (Johnson et al., 1986):

1) Injection Regularity--this measure assessed

the degree to which injections were given at

the same time every day. It was calculated

by measuring the standard deviation of

injection times reported across the inter-

views.

2) Injection Interval--this measure assessed the

youngster's average deviation from an ideal








injection interval of 24 hours between a.m.

injections. For youngsters who take p.m.

injections, an ideal.shot interval was

arbitrarily defined as 10 hours between a.m.

and p.m. injections on the same day, and 14

hours between the p.m. injection and the a.m.

injection on the following day.

3) Injection-Meal Timing--this measure evaluated

the timing of injection in relationship to

meals. It was calculated by averaging the

number of minutes between taking an injection

and eating a meal. Sixty minutes were added

to this average so that youngsters taking

their injection 60 minutes before a meal

received an adherence score of zero. Young-

sters who, on the average, took their

injections 60 minutes to 1 minute before

meals received scores from 0 to .99. Young-

sters who usually took their injections at

the time of their meals or after eating

received scores of 1.0 or greater.

4) Regularity of Injection-Meal Timing--

this measure appraised the regularity of

intervals between injections and eating.

This was measured by calculating the

standard deviation of the Injection-Meal

Timing measure described above.








5) Calories Consumed--for each youngster, an

ideal number of total daily calories was

identified based on the youngster's age, sex,

and ideal weight for height. Ideal weight

for height was obtained from tables provided

in a standard textbook on childhood obesity

(Collipp, 1980). Ideal calorie consumption

per kilogram was calculated using standards

provided by the U.S. Public Health Service

(Anderson et al., 1981). Each child's ideal

total number of daily calories was then

subtracted from the youngster's reported

daily caloric consumption. Reported calorie

consumption was obtained from the diet

information obtained by interview. All diet

information was coded in exchange units from

which calorie estimates were derived (Franz,

1983). A high score on the calorie consumed

measure indicated that the youngster ate more

than his ideal total number of daily calo-

ries. A zero score indicated that the child's

actual calorie consumption equalled that of

his ideal calorie consumption. A negative

score indicated that the youngster ate less

than the ideal.

6) Percent Calories: Fat--based on the exchange

unit information collected from the 24-hour








recall interviews, the percent of total

calories consumed which consisted of fat was

calculated. Ideal fat consumption was based

on the lower limit (25%) recommended by the

American Diabetes Association (Nuttal and

Brunzall, 1979). Ideal fat consumption (25%)

was subtracted from actual fat consumption.

Scores above zero indicated that the child

consumed more than 25% of his calories in

fats. Scores below zero indicated that the

child's fat intake was less than 25% of his

total calories.

7) Percent Calories: Carbohydrates--this

measure was also based on the exchange unit

information collected from the 24-hour recall

interviews. Ideal carbohydrate consumption

was based on the upper limit (60%) recom-

mended by the American Diabetes Association

(Nuttal and Brunzall, 1979). In this case

actual carbohydrate consumption was sub-

tracted from the ideal (60%) so that scores

above zero indicated insufficient carbo-

hydrate ingestion. A score of zero indicated

the child's diet consisted of 60% carbo-

hydrates. Scores below zero indicated that

more than 60% of the calories consumed

consisted of carbohydrates. No measure of








protein consumption was developed since it

can be automatically determined by knowing

the child's fat and carbohydrate consumption.

8) Concentrated Sweets--forty calories of any

concentrated sweet was considered equivalent

to one concentrated sweet exchange unit. The

average number of these exchange units

consumed per day was calculated.

9) Eating Frequency--based on an ideal of six

meals/snacks per day, the percent of snacks

or meals not eaten across the interviews was

calculated and multiplied by 100. A high

score indicates that the child ate infre-

quently. A low score indicated frequent

eating occasions. A score of zero indicated

the child averaged six meals or snacks per

day.

10) Exercise Duration--the average amount of time

the child spent exercising during each

exercise occasion across the interviews was

calculated and a constant (1) was added to

avoid subsequent division by zero. The

reciprocal of this score was used so that low

scores indicated lengthy exercise and high

scores indicated little or no exercise.

11) Exercise Type--each exercise or activity was

given an energy expenditure rating (Katch and








McArdle, 1977). Higher ratings indicated

more strenuous exercise. The youngster's

average expenditure rating across the

interviews was calculated and a constant (1)

was added to avoid subsequent division by

zero. The reciprocal of this score served as

the measure of Exercise Type; low scores

indicated more strenuous exercise while high

scores indicated less strenuous exercise.

12) Exercise Frequency--the occurrence or

nonoccurrence of exercise on three occasions

per day--morning, afternoon, and evening--

was noted for each of the interviews. The

percent of nonoccurrence of exercise across

these occasions was used as an estimate of

exercise frequency. A score of zero indicated

that the child reported exercise on all

occasions. A score of 100 indicated no

reported exercise on any occasion.

13) Glucose Testing Frequency--the frequency of

glucose testing across the interviews was

calculated. Using an ideal testing frequency

of four times per day, the number of glucose

tests was divided by this ideal (e.g., 12 for

3 days) and multiplied by 100. The total was

subtracted from 100 so that high scores indi-

cated few glucose tests and low scores








indicated frequent glucose tests. A score of

zero indicated the youngster tested 4 times

per day. A score of 100 indicated no

reported glucose tests over the interviews.

Glycemic Control Measures

Glycosylated hemoglobin Alc levels were obtained at the

beginning of camp and at 3 months following camp. The

assays were performed at the Shands Teaching Hospital

Laboratory using a column chromatography method performed

with Glyco-Gel kits. The expected normal range for Glycosy-

lated Hemoglobin Alc (HbAlc) using this procedure was 3.5-

6.2% and represents an overall estimate of glycemic control

for a period of approximately 3 months prior to testing.

Glycosylated serum proteins (GSP) were obtained at the

beginning and end of the 2-week camp session to evaluate any

changes in glycemic control associated with the camp

experience. The assays were performed at the Shands

Teaching Hospital Laboratory using the Pierce GlycoTest in

vitro diagnostic kits which contain GLYCO-GEL Analytical

Columns. This is an affinity chromatography based method

used after serum is first dialyzed against normal saline to

prevent spurious elevations of GSP levels due to free

glucose (Kennedy, 1981). GSP represents the average

glycemic control for a period of 10 to 14 days before

testing. Normal ranges were not available. However, in this

analysis the control value for a nondiabetic subject ranged

from .47 to 1.1% with a mean of .79%.








Procedure

In the 2 weeks prior to attending diabetes summer camp,

each child and one of his parents were interviewed by phone

on three separate occasions using the 24-hour recall

technique described earlier. The interviews were recorded

on the Diabetes Daily Record (Appendix).

On the first or second day of camp, fasting blood was

obtained from each participant. Serum obtained from venous

samples was stored at -20 degrees centigrade to be assayed

for GSP at a later time. Heparinized blood samples were

submitted for HbAlc assays. During their 2-week camp

experience, each child and his counselor were interviewed on

three separate occasions (on 2 weekdays and 1 weekend day)

by trained interviewers who recorded diabetes management

behaviors for the previous day on the Diabetes Daily Record.

However, counselor interviews were discontinued as it became

clear that they had difficulty differentially remembering

the individual children's activities and food consumption.

During the last 2 days of camp, fasting blood was again

obtained for GSP analysis. Those children whose blood was

drawn on the first day of camp were drawn on the second to

last day of camp. Similarly, if their blood was first drawn

on the second day of camp, the second sample was obtained on

the last full day of camp.

Within the 2 weeks following camp, study participants

and their parents again participated in three telephone

interviews using the 24-hour recall technique. This






41

procedure was repeated at 6 and 12 weeks post-camp. At 12

weeks post-camp, a second HbAlc sample was obtained.














RESULTS


Reliability of Child Report

One possible measure of child report reliability is

parent-child agreement. Perfect agreement was not expected

since parents are not always able to observe everything the

child does. However, since counselors were unable to

provide useful data concerning the campers activities, the

youngsters were the only consistent reporters available and

the issue of their reliability in reporting their behaviors

was critical.

Estimates of agreement between parent and child were

calculated to indicate the reliability of the information

obtained from the youngsters at camp, using Pearson product

moment correlations. These analyses replicated the Johnson

et al. study (1986) and used their 13 adherence measures.

Agreement was assessed for the total sample and for three

age groups (7-9, 10-11.4, and 11.5-12.6) at four points in

time: pre-camp and post-camp on 3 separate occasions

(immediately after camp, 6 weeks after camp, and 3 months

after camp). These data are presented in Tables 3, 4, 5,

and 6.

For the pre-camp period, parent/child correlations for

the total sample were all significant at p < .02, except for

regularity of injection-meal timing. One or both of the two







TABLE 3
Parent-Child Correlations for Total
Pre-Camp


Sample and by Age Group


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
Adher< ence< p< pleasure
Adherence Measure


Injection
regularity

Injection
interval

Injection-
meal timing

Regularity of
injection-
meal timing

Calories
consumed

Percentage
calories-fat


.65(60)
(.0001)

.75(59)
(.0001)

.54(62)
(.0001)

.09(59)
(.5010)


.73(62)
(.0001)

.78(63)
(.0001)


.80(18)
(.0001)

.51(19)
(.0262)

-.03(19)
(.8885)

-.08(18)
(.7382)


.81(20)
(.0001)

.86(20)
(.0001)


.28(21)
(.2156)

.92(19)
(.0001)

.57(22)
(.0052)

-.08(20)
(.7450)


.62(22)
(.0023)

.64(22)
(.0013)


.82(21)
(.0001)

.64(21)
(.0018)

.75(21)
(.0001)

.42(21)
(.0526)


.84(20)
(.0001)

.73(21)
(.0002)






TABLE 3
(continued)


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
p < p < p < p <
Adherence Measure

Percentage .72(63) .80(20) .65(22) .69(21)
calories- (.0001) (.0001) (.0011) (.0005)
carbohydrate

Concentrated .63(63) .35(20) .71(22) .63(21)
sweets (.0001) (.1292) (.0002) (.0020)

Eating .72(63) .83(20) .64(22) .73(21)
frequency (.0001) (.0001) (.0013) (.0020)

Exercise .29(63) .68(20) .07(22) .96(21)
duration (.0211) (.0010) (.7603) (.0001)

Exercise .64(63) .76(20) .56(22) .62(21)
type (.0001) (.0001) (.0071) (.0029)

Exercise .68(63) .81(20) .50(22) .68(21)
frequency (.0001) (.0001) (.0190) (.0007)

Glucose .90(63) .89(20) .97(22) .68(21)
testing (.0001) (.0001) (.0001) (.0006)
frequency







TABLE 4
Parent-Child Correlations for Total Sample and by Age Group
Immediately Post-Camp


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
p < p < p < p <
Adherence Measure


Injection
regularity

Injection
interval

Injection-
meal timing

Regularity of
injection-
meal timing

Calories
consumed

Percentage
calories-fat


.54(55)
(.0001)

.73(56)
(.0001)

.68(57)
(.0001)

.44(54)
(.0008)


.62(59)
(.0001)

.74(60)
(.0001)


.54(15)
(.0381)

.67(16)
(.0045)

.31(18)
(.2152)

-.40(15)
(.1432)


.36(18)
(.1471)

.68(18)
(.0020)


.52(20)
(.0198)

.66(20)
(.0016)

.91(19)
(.0001)

.42(19)
(.0726)


.62(22)
(.0022)

.87(22)
(.0001)


.65(20)
(.0021)

.83(20)
(.0001)

.56(20)
(.0098)

.72(19)
(.0003)


.70(19)
(.0009)

.62(20)
(.0035)






TABLE 4
(continued)


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
p < p < p < p <
Adherence Measure

Percentage .75(60) .63(18) .85(22) .70(20)
calories- (.0001) (.0048) (.0001) (.0006)
carbohydrate

Concentrated .59(60) .58(18) .83(22) .34(20)
sweets (.0001) (.0108) (.0001) (.1415)

Eating .67(60) .77(18) .59(22) .63(20)
frequency (.0001) (.0002) (.0037) (.0026)

Exercise .21(60) .53(18) .21(22) .13(20)
duration (.1106) (.0234) (.3568) (.5965)

Exercise .64(60) .66(18) .50(22) .85(20)
type (.0001) (.0028) (.0171) (.0001)

Exercise .68(60) .67(18) .53(22) .92(20)
frequency (.0001) (.0024) (.0117) (.0001)

Glucose .78(60) .55(18) .92(22) .68(20)
testing (.0001) (.0171) (.0001) (.0009)
frequency







TABLE 5
Parent-Child Correlations for Total Sample and by Age Group
Six Weeks Post-Camp


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
p < P < p < p <
Adherence Measure


Injection
regularity

Injection
interval

Injection-
meal timing

Regularity of
injection-
meal timing

Calories
consumed

Percentage
calories-fat


.64(53)
(.0001)

.66(53)
(.0001)

.76(56)
(.0001)

.26(52)
(.0615)


.69(56)
(.0001)

.86(57)
(.0001)


.57(14)
(.0320)

.45(15)
(.0942)

.11(16)
(.6915)

.11(14)
(.7032)


.49(17)
(.0456)

.96(17)
(.0001)


.66(20)
(.0017)

.79(20)
(.0001)

.76(21)
(.0001)

.34(19)
(.1501)


.65(21)
(.0014)

.76(21)
(.0001)


.67(19)
(.0019)

.81(18)
(.0001)

.95(19)
(.0001)

.46(19)
(.0461)


.85(18)
(.0001)

.84(19)
(.0001)






TABLE 5
(continued)


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
P < p < p < p <
Adherence Measure
Percentage .83(57) .93(17) .70(21) .86(19)
calories- (.0001) (.0001) (.0004) (.0001)
carbohydrate

Concentrated .68(57) .92(17) .70(21) .55(19)
sweets (.0001) (.0001) (.0004) (.0141)

Eating .74(57) .62(17) .74(21) .85(19)
frequency (.0001) (.0080) (.0001) (.0001)

Exercise .30(57) .20(17) .61(21) .23(19)
duration (.0235) (.4428) (.0035) (.3383)

Exercise .56(57) .49(17) .63(21) .76(19)
type (.0001) (.0442) (.0021) (.0001)

Exercise .58(57) .40(17) .75(21) .70(19)
frequency (.0001) (.1150) (.0001) (.0009)

Glucose .91(57) .85(17) .96(21) .93(19)
testing (.0001) (.0001) (.0001) (.0001)
frequency






TABLE 6
Parent-Child Correlations for Total Sample and by Age Group
Three Months Post-Camp


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
p < p < p < p <
Adherence Measure


Injection
regularity

Injection
interval

Injection-
meal timing

Regularity of
injection-
meal timing

Calories
consumed

Percentage
calories-fat


.81(55)
(.0001)

.89(52)
(.0001)

.77(60)
(.0001)

.25(54)
(.0665)


.69(60)
(.0001)

.66(61)
(.0001)


.81(17)
(.0001)

.76(18)
(.0002)

.47(19)
(.0422)

.22(17)
(.4001)


.11(20)
(.6344)

.75(20)
(.0001)


.88(18)
(.0001)

.95(15)
(.0001)

.78(21)
(.0001)

-.11(17)
(.6710)


.64(21)
(.0016)

.62(21)
(.0026)


.74(20)
(.0002)

.93(19)
(.0001)

.86(20)
(.0001)

.38(20)
(.1011)


.75(19)
(.0002)

.65(20)
(.0019)






TABLE 6
(continued)


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
p < p < p < p <
Adherence Measure

Percentage .65(61) .68(20) .69(21) .65(20)
calories- (.0001) (.0009) (.0006) (.0017)
carbohydrate

Concentrated .43(61) .37(20) .25(21) .69(20)
sweets (.0005) (.1078) (.2666) (.0008)

Eating .80(61) .51(20) .85(21) .90(20)
frequency (.0001) (.0228) (.0001) (.0001)
Exercise .66(61) .68(20) .67(21) .77(20)
duration (.0001) (.0011) (.0009) (.0001)

Exercise .68(61) .51(20) .74(21) .77(20)
type (.0001) (.0227) (.0001) (.0001)

Exercise .71(61) .71(20) .69(21) .76(20)
frequency (.0001) (.0005) (.0006) (.0001)

Glucose .91(61) .89(20) .96(21) .85(20)
testing (.0001) (.0001) (.0001) (.0001)
frequency








younger groups exhibited poor correlations on measures

involving time (injection regularity, injection-meal timing,

regularity of injection-meal timing, and exercise duration).

However, the oldest children were significantly correlated

with their parents (p < .05) on all measures (Table 3).

Immediately after camp (Table 4), the parent/child

correlations for the total sample were all significant with

the exception of exercise duration. Injection-meal timing

and regularity of injection-meal timing were still problema-

tic for the 7- to 9-year olds. The 10- to 11.4-year olds

exhibited poor correlations on regularity of injection-meal

timing and exercise duration, while the oldest youngsters

were not significantly correlated with their parents on

measures of concentrated sweets and exercise duration (Table

4).

The 6 weeks and 3 months after camp interviews demonst-

rate a similar pattern. These results are presented in

Tables 5 and 6 respectively. Percentage calories: fat, per-

centage calories: carbohydrates, eating frequency, exercise

type, and glucose testing demonstrated significant correlat-

ions for all children. Regularity of injection-meal timing

and exercise duration demonstrated the weakest (nonsig-

nificant) correlations in each of the study periods except

for the last one in which parent/child agreement for

exercise duration improved (r=.66). However, parent/child

agreement for regularity of injection-meal timing remained

weak (r=.25).






52
In addition to the parent/child correlations calculated

for each time period, the interviews were also collapsed

across time periods to determine the overall correlations by

age group (Table 7). Parents and children's reports for the

total sample were significantly correlated for all 13

measures, ranging from r=.36 (p<.004) on regularity of

injection-meal timing to r=.92 (p<.0001) for glucose testing

frequency. Comparisons between independent correlations

using Fisher's z' transformation were conducted to assess

differences in the degree of parent/child agreement across

the three age groups (p < .05, Cohen and Cohen, 1983, pages

53-56). These tests revealed that on 5 of the 13 adherence

measures (injection interval, injection-meal timing,

calories consumed, exercise duration, and glucose testing

frequency) there were significant differences in the

parent/child correlations among the three age groups (see

Table 7). The 10- to 11.4-year old demonstrated the highest

agreement (r=.92) with their parents on the injection

interval measure while the 7- to 9-year olds' reports did

not correspond as well (r=.51). On the injection-meal timing

measure, the oldest youngsters' reports corresponded best

with their parents (r= .75), while the 7- to 9-year olds

demonstrated the worst parent/child agreement (r= -.03).

Similarly, on the calories consumed measure, the oldest

group exhibited the highest parent/child correlation

(r=.89) and the youngest group displayed the weakest

(r=.53). The 11.5- to 12.6-year olds again demonstrated the







TABLE 7
Parent-Child Correlations for Total Sample and by Age Group
Collapsed Across Time


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
pAdher< ence< p< pleasure
Adherence Measure


Injection
regularity

Injection *
interval

Injection- *
meal timing

Regularity of
injection-
meal timing

Calories *
consumed

Percentage
calories-fat


.81(63)
(.0001)

.75(59)
(.0001)

.69(63)
(.0001)

.36(63)
(.0049)


.81(62)
(.0001)

.91(63)
(.0001)


.75(20)
(.0001)

.51(19)
(.0262)

-.03(19)
(.8885)

-.08(18)
(.7141)


.53(20)
(.0163)

.96(20)
(.0001)


.88(22)
(.0001)

.92(19)
(.0001)

.57(22)
(.0052)

-.08(20)
(.7382)


.81(22)
(.0001)

.88(22)
(.0001)


.77(21)
(.0001)

.64(21)
(.0018)

.75(21)
(.0001)

.43(21)
(.0526)


.89(20)
(.0001)

.89(21)
(.0001)






TABLE 7
(continued)


Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
p < p < p < p <
Adherence Measure

Percentage .89(63) .96(20) .88(22) .84(21)
calories- (.0001) (.0001) (.0001) (.0001)
carbohydrate

Concentrated .69(63) .73(20) .80(22) .58(21)
sweets (.0001) (.0003) (.0001) (.0054)

Eating .83(63) .80(20) .77(22) .90(21)
frequency (.0001) (.0001) (.0001) (.0001)

Exercise .63(63) .68(20) .43(22) .86(21)
duration (.0001) (.0010) (.0457) (.0001)

Exercise .70(63) .75(20) .60(22) .73(21)
type (.0001) (.0001) (.0033) (.0002)

Exercise .76(63) .81(20) .63(22) .86(21)
frequency (.0001) (.0001) (.0016) (.0001)

Glucose .92(63) .86(20) .98(22) .84(21)
testing (.0001) (.0001) (.0001) (.0001)
frequency


* indicates significant differences by age group.








highest parent/child agreement (r=.86) on the exercise

duration measure, while the 10- to 11.4-year olds exhibited

the weakest correlation (r=.43). On the glucose testing

frequency measure, all groups' reports correlated highly

with their parents, however there was a significant dif-

ference between the 10- to 11.4-year olds' (r=.98) and the

11.5- to 12.6-year olds' (r=.84) parent/child correlations.

The correlations between parent and child report were

then collapsed across the age groups so that the parent/

child correlations for the total sample could be examined

across the four time periods of the study. A test of

compound symmetry was used to test for the equality of these

dependent correlations (Steiger, 1980). Results of these

analyses (Table 8) indicated that agreement between parent

and child did not differ significantly across time for 8 of

the 13 adherence measures: calories consumed; percent

calories: fat; percent calories: carbohydrates; eating

frequency; exercise duration; exercise type; exercise

frequency; and testing frequency. On measures of injection

regularity, injection interval, injection-meal timing,

regularity of injection-meal timing, and concentrated sweets

there were significant differences in the parent/child

correlations across two or more time periods. These

differences exhibited no consistent pattern. Injection

regularity, for example, exhibited the best parent/child

agreement at Time 5 (r=.81) and the worst at Time 3 (r=.54),

while concentrated sweets exhibited the highest parent/child







TABLE 8
Parent-Child Correlations for Total Sample
Over Four Time Periods: Pre and Post-Camp


Time 1 Time 3 Time 4 Time 5
Pre-Camp Post-Camp 6 wks Post-Camp 3 Mos Post-Camp
r(n) r(n) r(n) r(n)
pAdhere< p< p< pleasure
Adherence Measure


Injection *
regularity

Injection *
interval

Injection- *
meal timing

Regularity of *
injection-
meal timing

Calories
consumed

Percentage
calories-fat


.65(60)
(.0001)

.75(59)
(.0001)

.54(62)
(.0001)

.09(59)
(.5010)


.73(62)
(.0001)

.78(63)
(.0001)


.54(55)
(.0001)

.73(56)
(.0001)

.68(57)
(.0001)

.44(54)
(.0008)


.62(59)
(.0001)

.74(60)
(.0001)


.64(53)
(.0001)

.66(53)
(.0001)

.76(56)
(.0001)

.26(52)
(.0615)


.69(56)
(.0001)

.86(57)
(.0001)


.81(55)
(.0001)

.89(52)
(.0001)

.77(60)
(.0001)

.25(54)
(.0665)


.69(60)
(.0001)

.66(61)
(.0001)






TABLE 8
(continued)
Time 1 Time 3 Time 4 Time 5
Pre-Camp Post-Camp 6 wks Post-Camp 3 Mos Post-Camp
r(n) r(n) r(n) r(n)
P < p < p < p <
Adherence Measure

Percentage .72(63) .75(60) .83(57) .65(61)
calories- (.0001) (.0001) (.0001) (.0001)
carbohydrate

Concentrated .63(63) .59(60) .68(57) .43(61)
sweets (.0001) (.0001) (.0001) (.0005)

Eating .72(63) .67(60) .74(57) .80(61)
frequency (.0001) (.0001) (.0001) (.0001)
Exercise .29(63) .21(60) .30(57) .66(61)
duration (.0211) (.1106) (.0235) (.0001)

Exercise .64(63) .64(60) .56(57) .68(61)
type (.0001) (.0001) (.0001) (.0001)

Exercise .68(63) .68(60) .58(57) .71(61)
frequency (.0001) (.0001) (.0001) (.0001)

Glucose .90(63) .78(60) .91(57) .91(61)
testing (.0001) (.0001) (.0001) (.0001)
frequency


* indicates significant differences between correlations
across time periods.








agreement at Time 4 (r=.68) and the poorest concordance at

Time 5 (r=.43). Only the injection-meal timing measure

exhibited any evidence of a linear trend toward improvement

in parent/child agreement across time (see Table 8).

However, by the end of the study (Time 5) parents and

children were significantly correlated on 12 of the 13 ad-

herence measures (p< .0005). Only parent/child agreement

for the regularity of injection-meal timing remained weak.

Differences between parent and child report were

further analyzed with repeated measures ANOVA on each of the

13 adherence measures collapsed across all parent/child

interviews using two between subject factors: Age group (7-

to 9-year olds, 10- to 11.4-year olds, and 11.5- to 12.6-

year olds) and Sex, and one within subject factor: Respon-

dent (parent, child). Results indicated that there were few

differences between parent and child report. Simple main

effects for Respondent emerged on three measures. On the

injection-meal timing measure, parents reported an average

of 14 minutes while children reported an average of 19

minutes between injection and meal, F(1,56)=5.98, p < .02.

On the eating frequency measure parents reported that

children ate an average of 5.25 meals per day while children

reported eating an average of 5 times per day, F(1,56)=

25.29, p< .0001. On calories consumed parents reported that

their children ate approximately 92 calories more than the

recommended amount per day while children reported ingesting






59
approximately 13 calories less than the recommended amount

per day, F(1,55)=6.83, p < .01.

On measures of exercise type and exercise frequency a

Respondent x Sex interaction emerged F(1,56)=9.37 p < .003

and F(1,56)=7.28, p < .009 respectively. To determine the

source of the Respondent x Sex interactions, the data were

divided by sex and repeated measures ANOVA were conducted on

each of the two exercise measures using one within subject

factor (Respondent). For both exercise type and exercise

frequency, boys' reports did not significantly differ from

their parents' reports. In contrast, girls reported

participating in more strenuous and more frequent episodes

of exercise than did their parents, F(1,29)=11.99 p < .002,

and F(1,29)=5.90 p < .02 respectively. These interactions

were further examined using T tests to explore possible

differences between parents reports of boys' versus girls'

exercise type and exercise frequency; parents reported

significantly more strenuous and more frequent episodes of

exercise for boys compared to girls, t(62)=-3.35, p < .001.

In contrast, the children themselves did not report the same

male/female differences found in the parent data.

In summary, the children's reports correlated with

their parents reports in the expected direction, both across

time and across age groups. In addition, even when

differences between parent and child report did exist, they

were relatively small and of questionable clinical sig-

nificance. Since the youngsters' self-report data were






60
generally reliable, replicating reliability data reported in

a previous study (Johnson et al., 1986), and since the

counselor interview data were unusable, it was decided to

use only the children's report in the succeeding analyses.


Effect of Diabetes Camp on Adherence

To test for possible changes in children's management

behaviors during camp, a repeated measures ANOVA was

conducted on each of the 13 adherence measures using two

between subject factors--Age Group (7-9 years, 10-11 years,

11.5-12.6 years) and Sex (M,F) and one within subjects

factor--Time (Time 1: before camp, Time 2: during camp, Time

3: immediately after camp, Time 4: 6 weeks after camp, and

Time 5: 3 months after camp). Significant Time main effects

or interactions with Time emerged for 9 of the 13 adherence

measures (i.e., injection regularity, injection interval,

injection-meal timing, calories consumed, eating frequency,

exercise duration, exercise type, exercise frequency, and

glucose testing frequency). Duncan's Multiple Range Tests

performed on these measures revealed that in all cases

adherence behaviors at camp (Time 2) differed significantly

from one or more of the pre- or post-camp periods (Time 1,

Time 3, Time 4, and Time 5). These results are presented in

Table 9.

Simple main effects for Time emerged for behaviors as-

sociated with injection regularity F(4,164)=6.04, p < .0001,

injection-meal timing F(4,168)=2.83, p < .03, eating







TABLE 9
Duncan's Multiple Range Tests (p< .05) on the Adherence Measures
Demonstrating Within Subject Effects


Time Periods Compared
Pre-camp vs Post-camp Camp vs Pre- and Post-camp Post-camp
1 3 1 4 1 5 2 1 2 3 2 4 2 5 3 4 3 5 4 5
Adherence Measure


Injection
regularity

Injection 7-9yrs
interval 10-11.4yrs
11.5-12.6yrs


* *




* *

* *


Injection-
meal timing

Regularity of
injection-
meal timing


Calories 7-9yrs
consumed 10-11.4yrs
11.5-12.6yrs


* *
* *
* *


Percentage
calories-fat






TABLE 9
(continued)


Time Periods Compared
Pre-camp vs Post-camp Camp vs Pre- and Post-camp Post-camp
13 14 15 21 23 24 25 34 35 45
Adherence Measure

Percentage
calories-
carbohydrate

Concentrated
sweets

Eating *
frequency

Exercise # # *
duration

Exercise #* *
type

Exercise 7-9yrs # *
frequency 10-11.4yrs *
11.5-12.6yrs # # # *

Glucose # # *
testing
frequency


* indicates significant improvement.
# indicates significant deterioration.








frequency F(4,200)=26.15, p < .0001, exercise duration

F(4,200)=5.97, p < .0001, exercise type F(4,200)=101.08,

p < .0001, and testing frequency, F(4,200)=8.11 p < .0001.

In every instance children exhibited more compliant behavior

during camp (Table 10).

Repeated measures ANOVA revealed Time main effects as

well as Time x Age group interaction effects on 3 additional

adherence measures (Table 11): injection interval (Time

effect, F(4,140)=2.45, p < .05; Time x Age group interac-

tion, F(8,140) = 2.21, p < .03), calories consumed (Time

effect, F(4,196)=7.96, p < .0001; Time x Age group interac-

tion, F(8,196)=2.39, p < .02), and exercise frequency (Time

effect, F(4,200)=179, p < .0001; Time x Age group interac-

tion, F(8,200)=2.27, p < .02). However, subsequent analyses

indicated that injection interval was the only measure to

evidence differential effects at camp for different age

groups. That is, the oldest children (11.5-12.6 years)

reported significantly improved compliance during camp,

while the 7- to 9-year olds and the 10- to 11.4-year olds

reported no change on these measures between home and camp.

Time x Age group interactions for the calories consumed and

exercise frequency measures were due to differences between

the age groups at one of the five time periods rather than

to a differential effect of camp on the age groups. In

fact, all age groups exhibited a significant increase on

calories consumed during camp and all youngsters reported a






TABLE 10
Means and Standard Deviations for Adherence
Measures Demonstrating Simple Main Effects for Time


Time Period
Time 1 Time 2 Time 3 Time 4 Time 5
Means (SD) Means (SD) Means (SD) Means (SD) Means (SD)
(Interp) (Interp) (Interp) (Interp) (Interp)
Adherence Measure

Injection .47(.38) .27(.27) .50(.35) .46(.31) .68(.55)
regularity (minutes) (28.02) (15.92) (30.06) (27.60) (40.59)

Injection- .70(.50) .49(.44) .68(.37) .66(.47) .71(.46)
meal timing (minutes) (42.14) (29.65) (40.81) (39.52) (42.74)

Eating 15.35(12.62) 2.58(3.54) 15.31(13.55) 17.33(13.93) 16.26(14.67)
frequency (per day) (5.02) (5.86) (5.07) (4.97) (5.02)

Exercise .09(.14) .03(.01) .16(.23) .17(.24) .12(.18)
duration (minutes (20.03) (42.41) (14.44) (15.35) (14.07)
per occasion)

Exercise .97(.01) .94(.01) .98(.01) .97(..02) .97(.01)
type (.03) (.06) (.02) (.03) (.03)
(kilocalories/min)

Glucose 51.23(24.41) 45.80(12.78) 54.09(24.50) 59.18(26.09) 60.80(25.47)
testing (1.88) (2.17) (1.79) (1.58) (1.53)
frequency
(per day)






TABLE 11
Means and Standard Deviations for Adherence
Measures with Age x Time Interaction at the 5 Time Periods


Time Period
Time 1 Time 2 Time 3 Time 4 Time 5
Means(SD) Means(SD) Means(SD) Means(SD) Means(SD)
(Interp) (Interp) (Interp) (Interp) (Interp)
Adherence Measure

Injection 7-9yrs .43(.41) .46(.54) .63(.65) .98(.66) .45(.34)
interval (25.6) (27.6) (37.8) (58.8) (26.7)
(minutes) 10-11.4yrs .91(.87) 1.05(1.9) .94(.73) .79(.77) 1.31(1.2)
(54.8) (63.3) (56.4) (47.4) (78.5)
11.5-12.6yrs .79(.50) .24(.23) 1.28(1.0) .84(.42) 1.06(.94)
(47.1) (14.5) (76.54) (50.3) (63.5)
Calories 7-9yrs -7.4(489) 164.9(962) -4.1(619) 18.1(721) 277.1(480)
consumed
10-11.4yrs 100.0(843) 598.9(794) 228.5(610) 111.5(693) 297.4(726)

11.5-12.6yrs -307.8(612) 382.9(874) -327.9(533) -325.3(649) -396.9(550)

Exercise 7-9yrs 72.9(14.8) 30.8(11.7) 80.9(12.0) 74.8(12.0) 67.0(13.4)
frequency (1.8) (4.5) (1.3) (1.6) (2.0)
10-11.4yrs 73.7(11.1) 23.4(9.7) 74.2(19.5) 71.1(16.3) 74.6(13.8)
(1.6) (4.2) (1.6) (1.8) (.07)
11.5-12.6yrs 69.3(14.3) 28.4(16.7) 80.4(12.5) 81.2(11.1) 78.4(11.2)
(.10) (4.3) (.04) (04) (1.3)







significant increase in exercise frequency associated with

the camp experience (see Table 9 and 11).

Five Measures exhibited no significant change related

to the camp experience: regularity of injection-meal timing,

percentage calories: fat, percentage calories: carbohydrat-

es, and concentrated sweets (Table 12).

On the regularity of injection-meal timing measure,

there was also an Age group x Sex interaction, F(2,38)=3.41,

p < .04). Subsequent LSMEANS procedure revealed that the 10-

to 11.4-year old girls were significantly less adherent than

the 11.5- to 12.6-year old girls or the 10- to 11.4-year old

boys. No other main or interaction effects for sex emerged

for any other adherence measure.

Although it is clear that significant changes occurred

during camp for 9 of the 13 adherence behaviors, examination

of Table 9 demonstrates that these changes were not main-

tained. That is, Time 2 (camp) exhibited consistent

significant differences from Times 1, 3, 4, and 5. In

contrast there were fewer significant differences between

Time 1, 3, 4, and 5. When differences between these times

did occur, they were most often in the direction of poorer

adherence (see Table 9 and interpretations in Tables 10, 11,

and 12).



Effect of Diabetes Camp on Glycemic Control

This project used two measures to assess glycemic

control. Glycosylated hemoglobin (HbAlc) which provides an






TABLE 12
Means and Standard Deviations for Adherence
Measures that Remained Stable Over the 5 Time Periods


Time Period
Time 1 Time 2 Time 3 Time 4 Time 5
Means(SD) Means(SD) Means(SD) Means(SD) Means(SD)
(Interp) (Interp) (Interp) (Interp) (Interp)
Adherence Measure

Regularity of 18.68(19.3) 18.10(13.5) 19.49(18.2) 12.02(10.4) 18.20(19.22)
injection- (minutes)
meal timing

Percentage 23.67(6.9) 22.04(5.7) 24.53(7.2) 23.99(9.0) 22.73(6.5)
calories-fat (48.7) (47.0) (49.5) (48.9) (47.7)

Percentage 24.33(7.2) 23.35(6.0) 25.44(7.5) 24.76(8.8) 23.35(6.7)
calories- (35.7) (36.7) (34.6) (35.2) (36.7)
carbohydrate

Concentrated 1.52(1.9) 1.24(1.1) 1.33(1.8) 1.38(1.7) 1.56(1.7)
sweets (per day)






68
average estimation of glycemic control for a period of 2 to

4 months, and glycosylated serum protein (GSP) which

provides a measure of glycemic.control over a 10 to 14 day

period. In order to support the reliability of the laborat-

ory assays, Pearson product moment correlations were

performed (Table 13). It was expected that the highest GSP/

HbAlc correlation would occur between the pre-camp GSP

measure and the pre-camp HbAlc measure since they were drawn

at the same time and reflect some overlap in time periods.

This was found to be the case (r=.71, p < .0001, Table 13).

Further, it was expected that the pre-camp GSP measure would

correlate less well with the 3 month followup HbAlc since

these measures reflect entirely different time periods. In

fact, the correlation between pre-camp GSP and followup

HbAlc was markedly lower (r=.52, p < .0001). The lowest

GSP/Hbalc correlations were expected between post-camp GSP

and both pre-camp HbAlc and followup HbAlc, taken at 3

months after camp, since the GSP and HbAlc measures reflect

completely different time periods and completely different

settings (camp versus home). In fact, these correlations

(r=.43; r=.39) represent the lowest correlations in the

matrix depicted in Table 13. Overall, the pattern of

results depicted in Table 13 supports the reliability of our

glycemic control measures.

To assess possible changes in glycemic control due to

the camp experience,a repeated measures ANOVA was performed

on the GSP pre- and post-camp measures using 2 between











TABLE 13
Correlations Between Glycosylated Hemoglobins
and Serum Proteins


HbAIC GSP

3 months
pre-camp post-camp pre-camp post-camp
r(n) r(n) r(n) r(n)
p< p < p < p <


HbAlc :

pre-camp 1.00(63)
(.0000)

3 months .62(61) 1.00(62)
post-camp (.0001) (.0000)


GSP

pre-camp .71(53) .52(52) 1.00(54)
(.0001) (.0001) (.0000)

post-camp .43(60) .39(59) .78(54) 1.00(61)
(.0007) (.0020) (.0001) (.0000)






70
subjects factors (Age group and Sex), and 1 within subjects

factor (Time: pre- and post-camp). A significant main

effect for Time, F(1,50)=11.00, p < .0017, and a significant

main effect for Age group, F(2,50)=3.31, p < .0447 emerged.

Subsequent LSMEANS procedures revealed that the overall mean

GSP values increased from 5.63% to 6.44% and that the 7- to

9-year olds (5.40%) and the 11- to 11.4-year old youngsters

(5.23%) had significantly lower mean GSP values than the

11.5- to 12.6-year old youngsters (6.82%).

It was then hypothesized that children arriving at camp

at different levels of glycemic control may have been dif-

ferentially affected by the camp experience. Therefore,

the youngsters were assigned to good (GSP less than 4.5%),

moderate (GSP between 4.5 and 7.0%), and poor control (GSP

greater than 7.0%) categories based on groupings apparent

from a univariate procedure on the pre-camp GSP values.

Repeated measures ANOVA on the GSP values pre- and post-

camp was conducted using one between subjects factor (GSP

control group) and one within subjects factor (Time: pre-

and post-camp). Results indicated a significant main effect

for Time, F(1,51)=12.45, p < .0009, and a significant main

effect for GSP control group, F(2,51)=55.91, p < .0001.

However, the Time x GSP control group interaction was not

significant, indicating that the various control groups did

not react differentially to the camp experience.

To assess changes in HbAlc, a repeated measures

analysis of variance (ANOVA) was conducted on the HbAlc pre-








camp and at followup using 2 between subjects factors (Age

group and Sex), and 1 within subjects factor (Time). There

were no significant main effects for Time and no significant

interactions, indicating that the camp experience had no

perceivable lasting effect. However, it was hypothesized

that the youngsters may have demonstrated differences

depending on their glycemic level of control before camp.

Therefore, based on a univariate procedure performed on

HbAlc drawn at the beginning of camp, the children were

divided into good (HbAlc less than 8.4%), moderate (HbAlc

between 8.4 and 10.4%), and poor (HbAlc greater than 10.4%)

HbAlc control groups. A repeated measures ANOVA procedure

was conducted on the HbAlc (pre-camp and at followup) using

one between subjects factor (HbAlc control group) and one

within subjects factor (Time). Results demonstrated a

between subjects effect of HbAlc control group, F(2,58)-

=61.75 p < .0001, and a Time x HbAlc control group

interaction F(2,58)=4.81, p < .0117, suggesting that not all

three HbAlc control groups were equally stable on the HbAlc

measure at followup. Therefore, repeated measures ANOVA

procedures were conducted with each of the HbAlc control

groups separately using one within subjects factor (Time:

pre-camp and followup). Results from these analyses

indicated that children in good and poor control remained

stable, while youngsters in moderate control demonstrated a

significant time effect F(1,26)=11.09, p < 0026. Examina-

tion of the mean HbAlc values pre-camp and at followup for






72
each of these groups indicated that while the means for the

good and poor control groups remained stable (7.45 to 7.80%

and 11.36 to 10.72% respectively), the moderate control

group experienced a significant decrease going from 9.51% to

8.80%.

To determine if these changes in HbAlc levels were

associated with camp, a repeated measures ANOVA was con-

ducted on the GSP pre- and post-camp measures using one

between subjects factor (the HbAlc control groups) and one

within subject factor (Time). The result demonstrated a

significant main effect for Time, F(1,51)=8.31, p < .0058,

and a significant between subjects effect of HbAlc control

group, F(2,51)=7.51, p < .0014) but no HbAlc control group x

Time interaction. These results suggest that the improve-

ments noted in the moderate HbAlc control group from pre-

camp to 3 months post-camp did not appear to be the result

of changes associated with the camp experience.



Relationship between Adherence and Diabetic Control

This study explored the relationship between adherence

and diabetic control in two ways. First adherence behaviors

during camp were employed in hierarchical regression models

to predict to post-camp GSP, and adherence behaviors during

the 3 months following camp were used in hierarchical

regression models to predict to the followup HbAlc. Second,

categorical analyses, which involve the division of data into

logical groupings, were utilized to explore possible








associations between adherence and diabetic control for

differing categories of campers (e.g., those in good versus

moderate versus poor diabetic control).

Adherence/GSP Relationships

Hierarchical regression analyses

In order to explore possible relationships between

adherence behaviors and diabetic control as measured by GSP,

it was hypothesized that adherence measures during camp

would predict GSP values post-camp. Since pre- and post-

camp GSP measures were highly correlated (r=.78), pre-camp

GSP was entered first (R2=.69). To determine whether simple

patient characteristics would predict post-camp glycemic

control, age and duration of disease were added to the

model. Since no significant increase in the R2 occurred,

age and duration of disease were dropped from subsequent

models. Since increases in GSP levels as well as increases

in calories consumed were reported for all youngsters during

camp, calories consumed was added next to the model.

However, no improvement in the model's predictive power

occurred. Next, calories consumed before coming to camp was

added to control for pre-camp calorie consumption. The

addition of pre-camp caloric consumption did not enhance the

model's predictive power. Finally, it was hypothesized that

calories consumed during camp may affect post-camp GSP at

varying levels of pre-camp GSP control. Therefore a model

including pre-camp GSP, calories consumed during camp, and

the interaction of pre-camp GSP x calories consumed during








camp was tested. This model did not significantly enhance

the R2 above that offered by the original model using pre-

camp GSP as the sole predictor.variable.

Since the obvious hypothesis that increases in calories

consumed at camp was responsible for the post-camp GSP

increases was not confirmed, further analyses were conducted

to assess whether any of the other adherence measures may

have related to post-camp GSP. To do so, the data were

combined into adherence factors found by Johnson et al.,

(1986). However, Johnson et al.'s factors were based on a

combination of child and parent report whereas the data in

this study were based exclusively on child report. Accord-

ingly, to support the combination of the child report data

into factors, it was necessary to determine whether the

measures (based on child report) within each of the Johnson,

et al. factors were more highly correlated than measures

between factors. The intercorrelation matrix of child

reported pre-camp adherence measures was examined. The

correlations of measures within factors were averaged

(excluding the 1.0s) and compared to the average correlation

of measures between factors. Results indicated that similar

to Johnson et al.'s results, average correlations of

measures within factors were larger (ranging from .22 to

.96) than the average correlations of measures between

factors (ranging from .01 to .21, see Table 14). These

results supported the combination of the measures. There-

fore, measures were standardized to pre-camp measures and











TABLE 14
Correlations Between Adherence Measures
Within and Between Factors


Factor: Exercise Injection Diet Eat/Test Diet
Type Frequency Amount


Exercise .66

Injection .02 .22

Diet Type .12 .01 .96

Eat/Test -.03 .10 .21 .27
Frequency

Diet Amount -.11 -.06 .01 .01 .27






76
five factors were created based on the Johnson et al. (1986)

model. Factor 1 contained the three exercise measures,

Factor 2 was made up of all four injection measures, Factor

3 consisted of measures of diet type (percentage of calori-

es: carbohydrates and percentage calories: fat), Factor 4

contained measures of eating frequency and glucose testing

frequency, and Factor 5 included measures of calories

consumed and concentrated sweets. A factor score was

calculated by averaging the standardized scores of measures

loading highly on that factor. It was expected that the

average correlation between factors would be low. This was

found to be the case (Table 15) with correlations ranging

from r=.01 (between the injection factor and the diet type

factor) to r=.26 (between the diet type factor and the

eating/testing frequency factor). These correlations were

considered to support the relative independence of the

factors and justify their use in further exploration of the

adherence/control issue.

Hierarchical regression analyses were again used to

determine whether adherence behaviors during camp would

predict post-camp GSP. Therefore, each of the five

adherence factors reflecting camp behavior was added

separately to the original model which contained the pre-

camp GSP measure. None contributed significant additional

variance. Next, it was decided to control for adherence

behavior before coming to camp. Therefore, adherence before

camp and adherence during camp were added to the original












TABLE 15
Average Correlations between Adherence Factors


Factor: Exercise Injection Diet Eat/Test Diet
Type Frequency Amount


Exercise 1.00

Injection .03 1.00

Diet Type .14 .01 1.00

Eat/Test -.05 .19 .26 1.00
Frequency

Diet Amount -.16 -.14 -.02 .03 1.00








model. For example, the model controlling for exercise

behavior before coming to camp included pre-camp GSP,

exercise factor (before camp) and exercise factor (during

camp). None of these five models significantly enhanced the

predictive power of the pre-camp GSP prediction model.

Finally, it was hypothesized that insulin dosage during

camp might predict post-camp glycemic control. Repeated

measures ANOVA on the pre- and during-camp insulin dosage

with one within subject factor (Time) indicated that there

was a significant difference between dosage before camp

compared to dosage during camp F(1,57)=6.31, p < .01. The

average dose before camp was 32.2 units while the average

dose during camp was 27.2 units. Adding insulin dosage

during camp to pre-camp GSP did not increase the variance

accounted for by pre-camp GSP alone. A model which included

pre-camp GSP, dosage during camp, and insulin dosage before

camp was also tested. No significant increase in the R2 was

achieved. To determine whether children entering camp at

different levels of glycemic control would be differentially

affected, a model containing pre-camp GSP, insulin dosage

during camp, and an interaction of these two measures was

tested. This model did not strengthen the predictive power

of the original pre-camp GSP prediction model.

Categorical analyses

Categorical analysis involves the division of data into

categories based on logical groupings. In this study, the

data were grouped based on levels of glycemic control and








levels of adherence as well as change in glycemic control

and change in adherence.

Accordingly, it was first hypothesized that children

arriving at camp at different levels of diabetic control

would report different levels of adherence prior to attend-

ing camp. Therefore, based on a univariate procedure on the

pre-camp GSP measure, the youngsters were divided into three

GSP control categories. Pre-camp GSP of less than or equal

to 4.5 was considered good control, greater than 4.5 and

less than or equal to 7.0 was considered moderate control,

and greater than 7.0 was considered poor control. ANOVAs

were conducted on each of the five pre-camp adherence

factors using one between subject factor (GSP control

group). Results indicated no significant differences

between the groups on any of the adherence factors.

Next, it was postulated that the adherence/GSP control

relationship might be detected if youngsters were categori-

zed on their degree of adherence before coming to camp.

Theoretically, those children who had been more adherent

before coming to camp would also exhibit lower GSP values at

the beginning of camp. Therefore, based on the pre-camp

median score for each of the five adherence factors,

youngsters were assigned a score of 1 for being below the

median (adherent) and a score of 0 for being above the

median (nonadherent). Their scores for the five adherence

factors were summed and children were placed into three

adherence categories (0-1 was poor adherence, 2-3 was








moderate adherence, and 4-5 was good adherence). An ANOVA

was performed on the pre-camp GSP measure using one between

subject factor (Adherence group). Results indicated no

significant difference between the adherence groups on the

pre-camp GSP measure.

It was conjectured that an adherence/control relation-

ship might be better detected if the youngsters were divided

into categories of metabolic change. Presumably, those

children who exhibited an improvement in diabetic control

pre- to post-camp, might show an improvement in adherence

behaviors from pre- to during-camp. Such improvement in

adherence behaviors would not be expected for children whose

diabetic control stayed the same or deteriorated over the

camp experience. Consequently, post-camp GSP values were

subtracted from the pre-camp GSP values and based on

univariate analysis of the resulting difference, the

youngsters were divided into children who improved (dif-

ference greater or equal to .5), did not change (difference

less than .5 and greater or equal to -.4), and got worse

(difference less than -.4). A repeated measures ANOVA was

conducted on each of the pre- and during-camp adherence

factors as well as the calories consumed measure using one

between subject factor (GSP difference group) and one within

subject factor (Time). Results of these analyses indicated

only simple Time main effects for the exercise factor

F(1,59)=155.34, p < .0001, the injection factor F(1,55)=








15.51, p < .0002, the eating/testing frequency factor

F(1,59)=39.71, p < .0001, and the calories consumed measure

F(1,58)=10.93, p < .001. There was no Time x GSP difference

group interaction; youngsters who exhibited differential

changes in GSP pre- to post-camp did not exhibit differen-

tial changes in adherence pre- to during-camp.

Finally, it was conjectured that changes in adherence

might clarify the association between adherence and control.

It would seem that children who improved in adherence during

camp would exhibit a concomitant improvement in GSP control.

while these who did not improve were expected to exhibit

stable GSP values pre- to post-camp. Therefore, the

children were divided into change groups. Post-camp

adherence factors were subtracted from pre-camp adherence

factors to produce two groups (those who improved in

adherence and those who did not improve in adherence).

Repeated measures ANOVA was then performed on the GSP

measures using one within subject factor (Time, pre- and

post-camp) and one between subject factor (Change group).

Results revealed a simple Time main effect. The interaction

term was nonsignificant.



Adherence/HbAlc Relationships

Hierarchical regression analyses

To assess possible relationships between adherence and

glycemic control after camp, it was hypothesized that

adherence behaviors after camp would predict the followup








HbAlc. Since pre-camp and followup HbAlc values were

moderately correlated (r=.62), the pre-camp HbAlc was

entered into the hierarchical regression model first

(R2=.39). To determine whether simple patient charac-

teristics would enhance prediction of followup HbAlc, age

was added to this model. Age significantly increased the R2

to .47 and was retained in all subsequent models. Next,

duration of disease was added to this model, but did not

enhance the predictive power of the model and was dropped

from all subsequent analyses. Further analyses were

conducted to assess whether any of the adherence factors

were related to followup glycemic control. To do so, the

post-camp adherence measures were standardized to the pre-

camp adherence measures and combined into the five adherence

factors as previously described. Each of the five post-

camp adherence factors was then added to the model in five

separate regressions. None of the factors contributed sig-

nificant additional variance. It was conjectured that

controlling for adherence before camp might enhance the

predictive power of the model. Therefore, each adherence

factor pre-camp was added to the model containing pre-camp

HbAlc, age, and the same post-camp adherence factor. The

addition of the pre-camp adherence factor did not sig-

nificantly increase the R2 in any of the five models tested.

It was then postulated that the adherence factors post-camp

might predict post-camp HbAlc for various levels of glycemic

control at the outset of the study. Therefore, an








interaction term (adherence factor x pre-camp HbAlc) was

tested for each of the five adherence factors. Only the

model that included pre-camp HbAlc, age, post-camp injection

factor, and the pre-camp HbAlc x post-camp injection factor

interaction term proved to increase the R2 significantly

(R2=.51 see Table 16). The interaction between the pre-camp

HbAlc and injection was interpreted by calculating the

nonstandardized Beta weights for injection for varying pre-

camp HbAlc levels from 5.8 to 13.1 (the ranges of pre-camp

HbAlc found in this study's sample) using the equation from

Table 16 (as suggested by Cohen and Cohen, 1983). As is

apparent from Table 17, injection behaviors in children with

low pre-camp HbAlc (e.g., 5.8 to 7.0) was negatively

associated with post-camp HbAlc: less compliant behavior

(higher injection scores) was associated with lower post-

camp HbAlc (i.e., better diabetic control). At pre-camp

HbAlc levels between 8.0 and 9.0 the relationship between

injection behavior and post-camp HbAlc diminishes to zero.

For pre-camp HbAlc levels between 11.0 and 13.1 (poor

control) there was a positive association between injection

behaviors and post-camp diabetic control, i.e., less

adherent behaviors were associated with poorer control.

Table 18 depicts the characteristics of three groups divided

on the basis of the injection factor Beta weights depicted

in Table 17. Noteworthy was a relatively large increase in

insulin dosage for both groups where the Injection Beta

weights were large (good and poor control youngsters). Due










TABLE 16
Predicting Post-camp HbA1c by Pre-camp HbAlc,
Age, Post-camp Injection Factor,
and Pre-camp HbAlc x Post-camp Injection Interaction


Variables in model Beta weights p< R2 = .51


Pre-camp HbAlc .47445345 .0017

Age .28755174 .0142

Post-camp -4.07128891 .0510
Injection Factor

Pre-camp HbAlc x .45447440 .0487
Post-camp
Injection Factor












TABLE 17
Predicting Post-camp HbAlc:
Nonstandardized Injection
Beta Weights at Varying Levels
of Pre-camp HbAlc


Levels of
Pre-camp HbAlc


Injection Factor (Post-camp)
Beta Weights


5.8
7.0
8.0
9.0
10.0
11.0
12.0
13.1


-1.435
- .890
- .435
- .021
.473
.928
1.382
1.882


* Y= 1.33 + .47 (Pre-camp HbAlc) + .29 (Age)
4.07 (Post-camp Injection factor)
+ .45 (Pre-camp HbAlc x Post-camp Injection factor)











TABLE 18
Pre-camp HbAlc x Post-camp Injection Factor:
Descriptive Characteristics


Pre-camp HbAlc 5.8-7.0 8.0-10.0 11.0-13.0
Injection B Wts -1.4 to -.9 -.4 to .5 .9 to 1.9


N 8 37 5

Pre-camp HbAlc 6.5 8.9 11.6

Post-camp HbAlc 7.4 8.5 11.0

Age 9.9 10.5 11.2

Duration of disease 3.8 4.0 3.5

Injection factor .10 .01 .18
pre-camp

Injection factor .20 .34 .59
post-camp

Insulin dose 25.9 30.9 33.0
pre-camp

Insulin dose 21.8 27.9 29.4
during camp

Insulin dose 34.5 29.5 40.7
at followup

Change in insulin 8.6 1.4 7.7
dose between pre-
camp and followup








to this finding it seemed appropriate to consider whether

insulin dosage and change in insulin dose would enhance the

predictive power of the model that contained pre-camp HbAlc

and age. Insulin dosage at camp did not increase the R2.

To control for pre-camp insulin dose, dosage before camp was

added to the model with no increase in R2. Keeping in mind

the role of injection which had earlier been established, it

was added to the model. No significant increase of R2

resulted. It was then hypothesized that the changes in

injection behavior at various changes in insulin dose from

pre-camp to followup might differentially predict post-camp

HbAlc. Change in insulin dosage was calculated by sub-

tracting the total insulin dosage reported by the child on

the last interview (3 months post-camp) from total dosage

reported by the child during the last interview before

coming to camp. A model including pre-camp HbAlc, age, dif-

ference in dosage, injection factor, and difference in

dosage x injection factor interaction term was tested. This

model significantly increased the variance, R2=.57 (see

Table 19). This interaction was interpreted by calculating

the nonstandardized Beta weights for injection for various

units of change in dosage from -40 to +40 (the range of

total units change found in this study's sample) based on

the equation from Table 19. As is apparent from Table 20,

the greater the decrease in insulin dose, the higher the

injection factor Beta weight. At the insulin dose change of

-5 to +10 the relationship between injection and post-camp











TABLE 19
Predicting Post-camp HbAlc by
Pre-camp HbAlc, Age, Insulin Dose Change,
Post-camp Injection Factor, and
Insulin Dose Change x Post-camp Injection Interaction


Variables in model Beta weights p< R2 = .57


Pre-camp HbAlc .63400558 .0001

Age .24956679 .0321

Change in insulin .02784116 .0212
dose from pre-camp
to followup

Post-camp .21885475 .4495
Injection factor

Change in insulin x .06829848 .0065
Post-camp
Injection factor











TABLE 20
Predicting Post-camp HbAlc:
Nonstandardized Injection
Beta Weights at Varying Levels
of Change in Insulin Dose from
Pre-camp to 3 Months Post-camp


Levels of Change in Injection Factor (Post-camp)
Insulin Dose from Beta Weights
Pre-camp to Followup


-40 2.951
-30 2.268
-20 1.585
-10 .901
- 5 .560
0 .219
5 .123
10 .464
15 .806
20 1.147
30 1.830
40 2.513


* Y= .26 + .63 (Pre-camp HbAlc) + .25 (Age) + .03 (Insulin
Dose Change) + .22 (Post-camp Injection Factor)
.07 (Insulin Dose Change x Post-camp Injection Factor)








HbAlc approached zero. Increases in insulin dose were as-

sociated negatively with the injection factor Beta weight.

Table 21 depicts the characteristics of groups divided based

on the Beta weights for injection at varying levels of

change in insulin dose (Group 1 decreased insulin, Group 2

stayed the same, and Group 3 increased insulin dose). A

number of characteristics are noteworthy for the group of

children who reported the largest increases in insulin dose

during camp: children in this group were in the best HbAlc

control before camp; this group was the youngest of the

three groups; children in this group reported the greatest

post-camp compliance; this group was the only one that ex-

perienced an increase in HbAlc while the other two groups

demonstrated slight improvement in glycemic control at

followup.

The other four adherence factors were considered in

similar models to determine if their interaction with

insulin dose change would offer similar predictive power.

This was not the case.


Categorical analyses

Based on a univariate procedure, the pre-camp HbAlc

values were divided into control categories (less than

8.4=good, greater than 8.4 and less than 10.4=moderate, and

greater than 10.4=poor control). ANOVAs were conducted on

each of the five adherence factors using one between subject

factor (HbAlc control group). A group effect was detected

for the injection factor with the differences between the











TABLE 21
Pre- Post-camp Change in Insulin Dose x Post-camp
Injection Factor: Descriptive Characteristics


Change in Dose(CH) Ch <= -10 -10 < Ch < 15 Ch => 15
Injection B Wts 3.0 to .9 .6 to -.5 -.8 to -.2.5


N 13 28 9

Pre-camp HbAlc 9.3 8.7 8.1

Post-camp HbAlc 9.0 8.5 8.5

Age 10.6 10.7 9.9

Duration of disease 3.5 4.2 4.5

Injection factor .14 .18 .14
pre-camp

Injection factor .40 .47 .21
post-camp

Insulin dose 44.1 29.6 13.3
pre-camp

Insulin dose 25.5 30.3 24.4
during camp

Insulin dose 21.2 32.4 42.6
at followup

Change in insulin -23.7 2.8 29.3
dose between pre-
camp and followup








good and poor control groups F(2,57)=3.75, p < .03.

However, the group effect was in the opposite direction of

what was expected. That is, the good control group was

actually less adherent on the injection factor (.28) than

the poor control group (-.24).

The youngsters were then divided into control groups

based on a univariate procedure on the followup HbAlc (less

than 8.4=good, greater than 8.4 and less than or equal to

10.4 moderate, and greater than 10.4=poor). ANOVAS on the

adherence post-camp adherence factors using one between

subject factor (followup HbAlc control group) revealed no

differences. In other words, the pre-camp findings were not

replicated with the post-camp data.

Next, the adherence/control relationship was examined

by assessing whether control was associated with degrees of

adherence. Based on the median score on each of the five

adherence factors, youngsters were assigned a score of 1 for

being below the median of the pre-camp adherence factor

(adherent) and a score of 0 for being above the median of

the pre-camp adherence factor (nonadherent). Their scores

for the five adherence factors were summed and children were

paced into three adherence categories (0-1 was poor ad-

herence, 2-3 was moderate adherence, and 4-5 was good

adherence). Separate ANOVA procedures using one between

subject factor (Adherence group) on the pre-camp and the

followup HbAlc measures revealed that these groups did not




Full Text
92
good and poor control groups F(2,57)=3.75, p < .03.
However, the group effect was in the opposite direction of
what was expected. That is, the good control group was
actually less adherent on the injection factor (.28) than
the poor control group (-.24).
The youngsters were then divided into control groups
based on a univariate procedure on the followup HbA^c (less
than 8.4=good, greater than 8.4 and less than or equal to
10.4 moderate, and greater than 10.4=poor). ANOVAS on the
adherence post-camp adherence factors using one between
subject factor (followup HbA^c control group) revealed no
differences. In other words, the pre-camp findings were not
replicated with the post-camp data.
Next, the adherence/control relationship was examined
by assessing whether control was associated with degrees of
adherence. Based on the median score on each of the five
adherence factors, youngsters were assigned a score of 1 for
being below the median of the pre-camp adherence factor
(adherent) and a score of 0 for being above the median of
the pre-camp adherence factor (nonadherent). Their scores
for the five adherence factors were summed and children were
paced into three adherence categories (0-1 was poor ad
herence, 2-3 was moderate adherence, and 4-5 was good
adherence). Separate ANOVA procedures using one between
subject factor (Adherence group) on the pre-camp and the
followup HbA^c measures revealed that these groups did not


recall interviews, the percent of total
calories consumed which consisted of fat was
calculated. Ideal fat consumption was based
on the lower limit (25%) recommended by the
American Diabetes Association (Nuttal and
Brunzall, 1979). Ideal fat consumption (25%)
was subtracted from actual fat consumption.
Scores above zero indicated that the child
consumed more than 25% of his calories in
fats. Scores below zero indicated that the
child's fat intake was less than 25% of his
total calories.
Percent Calories: Carbohydratesthis
measure was also based on the exchange unit
information collected from the 24-hour recall
interviews. Ideal carbohydrate consumption
was based on the upper limit (60%) recom
mended by the American Diabetes Association
(Nuttal and Brunzall, 1979). In this case
actual carbohydrate consumption was sub
tracted from the ideal (60%) so that scores
above zero indicated insufficient carbo
hydrate ingestion. A score of zero indicated
the child's diet consisted of 60% carbo
hydrates. Scores below zero indicated that
more than 60% of the calories consumed
consisted of carbohydrates. No measure of


85
TABLE 17
Predicting Post-camp HbA^c:
Nonstandardized Injection
Beta Weights at Varying Levels
of Pre-camp HbA^c
Levels of
Pre-camp HbA^c
Injection Factor (Post-camp)
Beta Weights
5.8
-1.435
7.0
- .890
8.0
- .435
9.0
- .021
10.0
.473
11.0
.928
12.0
1.382
13.1
1.882
* Y= 1.33 + .47 (Pre-camp HbA1c) + .29 (Age)
- 4.07 (Post-camp Injection factor)
+ .45 (Pre-camp HbA^c x Post-camp Injection factor)


TABLE 5
Parent-Child Correlations for Total Sample and by Age Group
Six Weeks Post-Camp
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r (n)
P <
Group 2
(10-11.4 Years)
r (n)
P <
Group 3
(11.5-12.6 Years)
r (n)
P <
Injection
.64(53)
.57(14)
.66(20)
.67(19)
regularity
( 0001)
(.0320)
(.0017)
(.0019)
Injection
.66(53)
.45(15)
.79(20)
.81(18)
interval
(.0001)
(.0942)
(.0001)
(.0001)
Injection-
.76(56)
.11(16)
.76(21)
.95(19)
meal timing
(.0001)
(.6915)
(.0001)
( .0001)
Regularity of
.26(52)
.11(14)
.34(19)
.46(19)
injection-
meal timing
(.0615)
( .7032)
(.1501)
( .0461)
Calories
.69(56)
.49(17)
.65(21)
.85(18)
consumed
(.0001)
( 0456)
(.0014)
( .0001)
Percentage
.86(57)
.96(17)
.76(21)
.84(19)
calories-fat
(.0001)
( 0001)
(.0001)
( .0001)


6
3) Long-term (chronic) consequences of the
disease include retinopathy and nephropathy.
Retinopathy is characterized by the formation
of new capillaries and duplication of small
veins, retinal detachment, preretinal and
vitreous hemorrhage, and fibrosis. Blindness
may be the result (Sussman, 1971). Nephropa
thy (renal disease) is the leading cause of
death among people with IDDM (Blevins, 1979).
Complications occur 15-20 years after
diabetes onset. Scientific data suggest that
these diseases may be preventable if the
patient's diabetes is maintained in good
control (Guthrie and Guthrie, 1977).
Clearly, metabolic control, or lack thereof, is
implicated in all three categories of complications both
directly and indirectly. Although evidence that complica
tions occur more frequently among patients in poor control
is mounting (Rifkin, 1978; Maxwell, Luft, Clark, and
Vinicor, 1982; Unger,1982; Johnson, 1984), it is difficult
to properly evaluate this literature since the definition of
"control" is so loosely posited and varies from investigator
to investigator. Moreover, a definitive link between
adherence to prescribed regimen (i.e. behavior) and control
has yet to be established.


17
diabetes relevant medical information. These discussions
were videotaped and then filmed. Hemoglobin A^ was drawn
during the program to measure metabolic control before the
program and at a 4-month reunion to measure metabolic
control since the program.
The groups were equivalent at the beginning of the
program. Hemoglobin values at the reunion (obtained for
82% of those who provided an original sample) showed a
slight increase in values for the control group and a
contrasting decrease for the experimental group. Further
more, the authors found a substantial correlation (r=-.78)
between self-reported self-care and diabetes control as
measured with HbA^ values. This study is an improvement
over earlier research in that intervention was directed at
many aspects of the treatment regimen instead of urine
testing alone. Furthermore, correlations between adherence
behaviors and control were provided.
However, the study's experimental and control groups
were small and restricted to white middle class adolescents.
The reliability of the self-report measures of compliance
was not assessed nor were parents surveyed to corroborate
the youngsters' report. Before treatment, the experimental
group was in slightly better diabetes control than the
control group (although the difference was not statistically
significant). At post-treatment, it was unclear whether the
slight improvement of the experimental group and the slight
deterioration of the controls was clearly related to the


TABLE 7
(continued)
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r (n)
P <
Group 3
(11.5-12.6 Years)
r (n)
P <
Percentage
.89(63)
.96(20)
.88(22)
.84(21)
calories-
carbohydrate
( .0001)
( 0001)
(.0001)
(.0001)
Concentrated
.69(63)
.73(20)
.80(22)
. 58(21)
sweets
(.0001)
(.0003)
( .0001)
( .0054)
Eating
.83(63)
.80(20)
. 77 (22)
.90(21)
frequency
( .0001)
(.0001)
( .0001)
( .0001)
Exercise *
.63(63)
.68(20)
.43(22)
. 86(21)
duration
(.0001)
(.0010)
( .0457)
( .0001)
Exercise
.70(63)
.75(20)
.60(22)
.73(21)
type
(.0001)
(.0001)
(.0033)
( .0002)
Exercise
.76(63)
.81(20)
.63(22)
.86(21)
frequency
(.0001)
(.0001)
(.0016)
(.0001)
Glucose *
.92(63)
.86(20)
.98(22)
. 84(21)
testing
frequency
(.0001)
(.0001)
(.0001)
( .0001)
* indicates significant differences by age group.


19
compliance and control. The third was essentially unaf
fected by the treatment.
Unfortunately, as mentioned above, the use of 24 hour
urines as indices of diabetic control is problematic since
they are difficult to collect even in hospital settings. In
addition, postprandial blood glucose levels measure control
at brief points in time and may not relate to the patient's
general level of diabetic control.
It is generally accepted that the issue of compliance
as it relates to level of control and possible complications
is vital to the area of chronic illness in general, and
diabetes in particular. However, results are mixed and
difficult to interpret. In order to establish whether there
is actually a causal link, methods of measuring the various
components must be improved. Clearly the major reasons for
this gap in the area of IDDM are that
1) The definition of control has varied among
investigators, and diabetologists have been
unable to reach a consensus on a precise
definition so that studies have used varying
measures of control (Spevack, Johnson,
Harkavy, Silverstein, Shuster, Rosenbloom,
and Malone, 1987). There have been recent
improvements in this situation with the
general acceptance of HbA^c, although some
investigators balk at using this as the sole
measure of control since it does not


108
variability to establish clear relationships between
adherence and control. The use of a control group in
replicating this study might enhance variability, making it
possible to better examine adherence/control relationships.
The major implication of this study is that individual
differences need closer scrutiny in the area of juvenile
diabetes. To date, diabetic regimen recommendations are
formulistic and assume that control is a consistent con
struct that requires consistent behaviors by all youngsters.
Future research using multiple baseline design and varying
adherence behaviors one at a time under controlled condi
tions could possibly provide more information concerning
highly individualized relationships between adherence and
glycemic control. It is possible that children' biological
differences require more individual prescriptions. A recent
study conducted by Freund et al., (1986) suggests that
individual differences exist in symptom patterns of hyper
glycemic and hypoglycemic reactions. It is likely that
these differences extend into other biological reactions to
regimen components such as injection-meal timing, exercise,
and dietary intake. Adjustments to insulin dose made during
camp are made in response to changes in the camper's routine
(most children reported significant changes in behavior
during camp). This study clearly demonstrated that once
children return home their adherence behaviors revert back
to pre-camp levels. It stands to reason that insulin
requirements would then need to be readjusted to previous


TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ii
LIST OF TABLES iv
ABSTRACT vi
INTRODUCTION 1
METHOD 31
Sample Characteristics 31
Measures 31
Adherence Measures 31
Glycemic Control Measures 39
Procedure 40
RESULTS 42
Reliability of Child Report 42
Effect of Diabetes Camp on Adherence 60
Effects of Diabetes Camp on Glycemic Control 66
Relationship Between Adherence and Diabetic
Control 72
Adherence/GSP Relationship 73
Hierarchical regression analyses 73
Categorical analyses 78
Adherence/HbA^c Relationships 81
Hierarchical regression analyses 81
Categorical analyses 90
DISCUSSION 95
Reliability of Child Report 95
Effect of Diabetes Camp on Adherence 98
Effect of Diabetes Camp on Glycemic Control 102
Relationship Between Adherence and Diabetic
Control 104
Limitations and Future Directions 107
REFERENCES 110
APPENDIX 115
BIOGRAPHICAL SKETCH 116
iii


20
accurately reflect variability in blood
glucose.
2) The measurement of adherence behaviors is
equally problematic. Little psychometric
data are provided as to the quality of the
measures used. Compliance and control are
often confused or the same rater judges both.
Compliance is often treated as a global
construct although there is mounting evidence
that it is complex, consisting of several
independent components. Studies focus on be
haviors that do not relate directly to
control. For example, wearing a medic alert
bracelet is definitely important in case of
accident (Schafer et al., 1982). However,
wearing it may not have a direct impact on
glycemic control.
3) Correlational studies have been inconsistent
in demonstrating a relationship between com
pliance and control. In any case, data from
these studies cannot be used to establish
causality.
4) The few intervention studies available
suggest that adherence may be improved.
However, improved adherence is not always
associated with improved control (Epstein and
Cluss, 1982). In some intervention studies,


81
15.51, p < .0002, the eating/testing frequency factor
F(1,59)=39.71, p < .0001, and the calories consumed measure
F(1,58)=10.93, p < .001. There was no Time x GSP difference
group interaction; youngsters who exhibited differential
changes in GSP pre- to post-camp did not exhibit differen
tial changes in adherence pre- to during-camp.
Finally, it was conjectured that changes in adherence
might clarify the association between adherence and control.
It would seem that children who improved in adherence during
camp would exhibit a concomitant improvement in GSP control.
while these who did not improve were expected to exhibit
stable GSP values pre- to post-camp. Therefore, the
children were divided into change groups. Post-camp
adherence factors were subtracted from pre-camp adherence
factors to produce two groups (those who improved in
adherence and those who did not improve in adherence).
Repeated measures ANOVA was then performed on the GSP
measures using one within subject factor (Time, pre- and
post-camp) and one between subject factor (Change group).
Results revealed a simple Time main effect. The interaction
term was nonsignificant.
Adherence/HbAic Relationships
Hierarchical regression analyses
To assess possible relationships between adherence and
glycemic control after camp, it was hypothesized that
adherence behaviors after camp would predict the followup


22
child's diabetes management behaviors in the last 24 hours.
Interviews were conducted by phone and took approximately 20
minutes. Data from this procedure were used to measure 13
different diabetes management behaviors: injection regulari
ty, injection interval, injection-meal timing, regularity of
injection-meal timing, calories consumed, percent calories:
fat, percent calories: carbohydrates, concentrated sweets,
eating freguency, exercise duration, exercise type, exercise
freguency, and glucose testing freguency. Pearson product
moment correlations for all 13 adherence measures were
conducted to asses parent-child agreement. The results are
presented in Table 1. Although all of the correlations were
statistically significant (pc.0001), a number of measures
differed in agreement depending on the age of the child with
the youngest children (6-9 years) showing poorest
parent/child agreement on measures involving time and the
10- to 12- and 13- to 15-year old groups showing the most
consistent parent/child agreement across all measures. The
oldest group (16-19 years) had highly variable parent/child
correlations. The authors attribute these findings to the
reduction of parental supervision during late adolescence
when children spend more time with peers outside of the
parents' purview. The 13 adherence measures were then
subjected to a principal component factor analysis resulting
in a five-factor solution accounting for 71.3% of the
variance. The five factors were rotated to simple structure
using the varimax procedure. Factor I (Exercise) consisted


63
frequency F(4,200)=26.15, p < .0001, exercise duration
F(4,200)=5.97, p < .0001, exercise type F(4,200)=101.08,
p < .0001, and testing frequency, F(4,200)=8.11 p < .0001.
In every instance children exhibited more compliant behavior
during camp (Table 10).
Repeated measures ANOVA revealed Time main effects as
well as Time x Age group interaction effects on 3 additional
adherence measures (Table 11): injection interval (Time
effect, F(4,140)=2.45, p < .05; Time x Age group interac
tion, F(8,140) = 2.21, p < .03), calories consumed (Time
effect, F(4,196)=7.96, p < .0001; Time x Age group interac
tion, F(8,196)=2.39, p < .02), and exercise frequency (Time
effect, F(4,200)=179, p < .0001; Time x Age group interac
tion, F(8,200)=2.27, p < .02). However, subsequent analyses
indicated that injection interval was the only measure to
evidence differential effects at camp for different age
groups. That is, the oldest children (11.5-12.6 years)
reported significantly improved compliance during camp,
while the 7- to 9-year olds and the 10- to 11.4-year olds
reported no change on these measures between home and camp.
Time x Age group interactions for the calories consumed and
exercise frequency measures were due to differences between
the age groups at one of the five time periods rather than
to a differential effect of camp on the age groups. In
fact, all age groups exhibited a significant increase on
calories consumed during camp and all youngsters reported a


73
associations between adherence and diabetic control for
differing categories of campers (e.g., those in good versus
moderate versus poor diabetic control).
Adherence/GSP Relationships
Hierarchical regression analyses
In order to explore possible relationships between
adherence behaviors and diabetic control as measured by GSP,
it was hypothesized that adherence measures during camp
would predict GSP values post-camp. Since pre- and post
camp GSP measures were highly correlated (r=.78), pre-camp
GSP was entered first (R2=.69). To determine whether simple
patient characteristics would predict post-camp glycemic
control, age and duration of disease were added to the
model. Since no significant increase in the R2 occurred,
age and duration of disease were dropped from subsequent
models. Since increases in GSP levels as well as increases
in calories consumed were reported for all youngsters during
camp, calories consumed was added next to the model.
However, no improvement in the model's predictive power
occurred. Next, calories consumed before coming to camp was
added to control for pre-camp calorie consumption. The
addition of pre-camp caloric consumption did not enhance the
model's predictive power. Finally, it was hypothesized that
calories consumed during camp may affect post-camp GSP at
varying levels of pre-camp GSP control. Therefore a model
including pre-camp GSP, calories consumed during camp, and
the interaction of pre-camp GSP x calories consumed during


14 Correlations Between Adherence Measures
Within and Between Factors 75
15 Average Correlations between Adherence
Factors 77
16 Predicting Post-camp HbA^c by Pre-camp HbA^c,
Age, Post-camp Injection Factor, and Pre-camp
Hba^c x Post-camp Injection Interaction 84
17 Predicting Post-camp HbA^c: Nonstandardized
Injection Beta Weights at Varying Levels of
Pre-camp HbA^c 85
18 Pre-camp HbA^c x Post-camp Injection Factor:
Descriptive Characteristics 86
19 Predicting Post-camp HbA^c by Pre-camp HbA^c,
Age, Insulin Dose Change, Post-camp Injection
Factor, and Insulin Dose Change x Post-camp
Injection Interaction 88
20 Predicting Post-camp Hba^c: Nonstandardized
Beta Weights at Varying Levels of Change in
Insulin Dose from Pre-camp to 3 Months
Post-camp 89
21 Pre- Post-camp Change in Insulin Dose x
Post-camp Injection Factor: Descriptive
Characteristics 91
v


McArdle, 1977). Higher ratings indicated
more strenuous exercise. The youngster's
average expenditure rating across the
interviews was calculated and a constant (1)
was added to avoid subsequent division by
zero. The reciprocal of this score served as
the measure of Exercise Type; low scores
indicated more strenuous exercise while high
scores indicated less strenuous exercise.
12) Exercise Frequencythe occurrence or
nonoccurrence of exercise on three occasions
per daymorning, afternoon, and evening
was noted for each of the interviews. The
percent of nonoccurrence of exercise across
these occasions was used as an estimate of
exercise frequency. A score of zero indicated
that the child reported exercise on all
occasions. A score of 100 indicated no
reported exercise on any occasion.
13) Glucose Testing Frequencythe frequency of
glucose testing across the interviews was
calculated. Using an ideal testing frequency
of four times per day, the number of glucose
tests was divided by this ideal (e.g., 12 for
3 days) and multiplied by 100. The total was
subtracted from 100 so that high scores indi
cated few glucose tests and low scores


30
2) The effects of a diabetes summer camp
environment on children's diabetes control;
3) The length of time that effects of camp are
maintainedi.e., do changes or effects of
camp dissipate once the child returns home;
4) The relationship between adherence behavior
and level of diabetic control.
This proposed investigation represents a naturalistic
study in which the experience of camp is expected to
actually change both the children's behavior and their
levels of diabetic control thereby providing the opportunity
to monitor both. Therefore, the behavior and the levels of
control are being naturally manipulated by virtue of the
youngsters' attendance at camp, providing an ideal situation
to assess the extent to which changes in diabetic control
are associated with changes in behavior. Should such
changes be demonstrated, and their association be estab
lished, it would constitute strong support for a relation
ship between behavior and health status.


27
more resistent to change above the age of 14 and that
initial level of control was not a significant predictor of
self-concept change. The greatest change occurred in the
13- to 14-year old females who improved on self-concept
significantly. However, this study did not address the
issue of whether self-concept change was associated with
change in glycemic control.
In a more recent study, Scharf, Adams, and Leach (1987)
compared 45 IDDM adolescents aged 12 to 17 who attended a
residential diabetes summer camp, to IDDM youngsters who did
not attend camp on psychological functioning immediately
following the camp experience and at a 5 month follow- up.
They reported that campers' adjustment to diabetes, their
self-worth, and their parents' perceptions of their
behavioral competencies and general personality adjustment
did not change due to the camp experience. Moreover,
metabolic control as measured with HbA^c before camp did not
differentially influence the experimental or control
subjects in how they reacted to the camp experience or how
they adjusted to a chronic illness. No attempt was made to
assess whether the camp experience affected glycemic control
after camp.
The effects of stress on glycemic control were examined
at a 1982 Tennessee camp for diabetic children using 39
adolescents between the ages of 12 and 15 years (Hanson and
Pichert, 1986). The children were followed for 3 days while
insulin administration, dietary intake, exercise, self-


106
not prone to pubertal effects), were most adherent post
camp, had their insulin increased during camp (a trend which
was continued after camp), and had experienced an increase
in glycemic levels while the other two groups decreased (see
Table 21). This finding suggests that this group of
youngsters may have experienced increases in HbA^c due to
what is known as rebound hyperglycemia or the Somogyi
phenomenon. This phenomenon is thought to be precipitated
by overinsulinization which initially causes glycemic levels
to drop and then rebound to higher levels (Travis, et al.,
1987). Physicians often respond to the "highs" and prescri
be even more insulin. It is noteworthy that physicians used
the camp physicians as models and continued whatever dose
changes that had been made at camp. However, for those
youngsters whose insulin was increased at camp, substantial
additional increases in insulin dose were made after camp so
that by 3 months post-camp their dose had tripled (from an
average of 13.3 units before camp to 42.6 units at fol
lowup) It is possible that physicians caring for these
youngsters responded to their bouts of hyperglycemia by
prescribing increased insulin doses i.e., they failed to
detect possible rebound hyperglycemia in their children.
This hypothesis was further supported by the fact that this
group of children were in the best control at the outset of
this study and given their age, were not likely to be prone
to adolescent related increases in glycemic control.
Nevertheless, this was the only group that demonstrated an


UNIVERSITY OF FLORIDA
3 1262 08554 4079


109
levels. However, our data indicate that the physicians
caring for these youngsters at home tend to use camp insulin
changes as a model. This is understandable, since camp
physicians are usually pediatric endocrinologists while
physicians caring for these youngsters are pediatricians who
rarely have specialty training in diabetes. Therefore,
physicians at camp should take care to make recommendations
which would alert the pediatricians to this potential
problem. Insulin dose is at the crux of the diabetes
management since it has a powerful influence on control. It
is clear that if the insulin dose is not appropriate high
levels of adherence will cause worse control. It is
possible that some youngsters are not on the correct dose
which clearly presents a problem. This situation would
account for a curvilinear relationship between adherence
with injection behaviors and glycemic control.
It is noteworthy that the only effect of camp that was
maintained during the 3 month followup period was insulin
dose change. That is, the children's behaviors reverted to
pre-camp levels as did their overall glycemic control.
However, insulin dose changes initiated at camp were usually
maintained and even embellished once the children returned
home. It is therefore vital that camp physicians carefully
scrutinize their decision process in determining appropriate
insulin dose and that they diligently make recommendations
for insulin dose adjustment for physicians to follow once
the children return home.


21
only a single behavior is targeted for
intervention or selected for treatment by the
parent of the diabetic child. In such cases,
the adherence behaviors improved may have no
significant impact on diabetic control.
5) It is very difficult to study IDDM youngsters
under controlled conditions, where behavior
can be reliably measured, without interfering
with or disrupting the youngster's normal
behavior.
Many investigators have dealt with the first problem by
relying predominantly on the best single estimator of
controlnamely, hemoglobin A^cwhich is an indicator of
integrated plasma glucose levels over approximately 3
months. Consequently, to establish a link between adherenc
e/behavior and control as measured by hemoglobin A^c, a 3-
month behavior monitoring system should be available.
Unfortunately, youngsters balk at keeping diaries even for
short intervals and even when they do keep such records,
reliability checks are problematic. To date the most
systematic procedure for measuring compliance has been
developed by Johnson, Silverstein, Rosenbloom, Carter, and
Cunningham (1986). A 24-hour recall interview technique was
used to assess daily management behaviors in four diabetes
relevant areas (diet, injection, exercise, and glucose
testing). Each child and his/her parent were interviewed
separately on three different occasions concerning the


103
well as diabetic youngsters. Of course, this increased
insulin resistance had the most profound effect on diabetic
youngsters in whom hyperglycemia is likely to result.
To assess long term effects of the camp experience on
glycemic control, a repeated measures ANOVA of the glycosy
lated hemoglobins (HbA^c) collected at the beginning and at
the end of the study (3 months after camp) was conducted.
No significant differences were noted, suggesting that camp
effects were not maintained over a 3 month period. That is,
the increases in glycemic control noted at the end of camp
(increased GSP) were not noted in the followup HbA^c which
remained essentially the same. These findings are consis
tent with the correlations between the glycemic control
measures (between the beginning of camp and at 3 month
followup) which suggested moderate stability, and with the
data on adherence measures which also demonstrated marked
stability within the home environment.
To assess whether children at different levels of
glycemic control at the beginning of camp would exhibit
different effects, the children were grouped into HbA^c
control categories (good, moderate,and poor). Repeated
measures ANOVA revealed that although children in good and
poor control at the beginning of this study remained stable
over the study period, youngsters in moderate control were
significantly improved at followup. However, subsequent
analyses suggested that this improvement did not appear to
be the result of the camp experience.


10
youngsters. Results of this study indicate that for
preadolescents there was no relationship between physicians
compliance ratings and metabolic control. For adolescents
the best predictor of control was the child's overall level
of compliance with the treatment regimen, especially with
eating regular snacks and carrying sugar for emergencies.
However, their findings suggest that regardless of com
pliance ratings, the more responsibility assumed by the
youngster, the poorer their control.
It should be noted that the physician's four-point
rating of compliance was based on patient report (testing,
charting, administering insulin, eating proper foods, eating
regular snacks, carrying quick-acting sugar, and taking
sugar to treat reactions) as well as on whether the child
kept appointments. The reliability of these ratings was not
tested and it is unclear whether the physician's rating may
have been influenced by the patient's current level of
metabolic control. Furthermore, no information was provided
about the relative importance of any one aspect of the
regimen.
McCulloch et al. (1983) correlated dietary behaviors
such as accuracy of measuring carbohydrates and maintaining
consistent and regular eating patterns with HbA^ in adult
diabetics who kept 7-day diaries. Results indicated better
glycemic control in those persons who were more precise in
their measurements and compliant in the regularity of their
meals. There was, however, no attempt made to corroborate


I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
Professor of Statistics
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
Associate Professor of
Pathology
This dissertation was submitted to the Graduate Faculty
of the College of Health Related Professions and to the
Graduate School and was accepted as partial fulfillment
of the requirements for the degree of Doctor of Philo
sophy.
December 1987
Dean, College of Health
Related Professions
Dean, Graduate School


41
procedure was repeated at 6 and 12 weeks post-camp. At 12
weeks post-camp, a second HbA^c sample was obtained.


82
HbA^c. Since pre-camp and followup HbA^c values were
moderately correlated (r=.62), the pre-camp HbA^c was
entered into the hierarchical regression model first
(R2=.39). To determine whether simple patient charac
teristics would enhance prediction of followup HbAic, age
was added to this model. Age significantly increased the R2
to .47 and was retained in all subseguent models. Next,
duration of disease was added to this model, but did not
enhance the predictive power of the model and was dropped
from all subsequent analyses. Further analyses were
conducted to assess whether any of the adherence factors
were related to followup glycemic control. To do so, the
post-camp adherence measures were standardized to the pre
camp adherence measures and combined into the five adherence
factors as previously described. Each of the five post
camp adherence factors was then added to the model in five
separate regressions. None of the factors contributed sig
nificant additional variance. It was conjectured that
controlling for adherence before camp might enhance the
predictive power of the model. Therefore, each adherence
factor pre-camp was added to the model containing pre-camp
HbA^c, age, and the same post-camp adherence factor. The
addition of the pre-camp adherence factor did not sig
nificantly increase the R2 in any of the five models tested.
It was then postulated that the adherence factors post-camp
might predict post-camp HbAic for various levels of glycemic
control at the outset of the study. Therefore, an


39
indicated frequent glucose tests. A score of
zero indicated the youngster tested 4 times
per day. A score of 100 indicated no
reported glucose tests over the interviews.
Glycemic Control Measures
Glycosylated hemoglobin A^c levels were obtained at the
beginning of camp and at 3 months following camp. The
assays were performed at the Shands Teaching Hospital
Laboratory using a column chromatography method performed
with Glyco-Gel kits. The expected normal range for Glycosy
lated Hemoglobin A^c (HbA^c) using this procedure was 3.5-
6.2% and represents an overall estimate of glycemic control
for a period of approximately 3 months prior to testing.
Glycosylated serum proteins (GSP) were obtained at the
beginning and end of the 2-week camp session to evaluate any
changes in glycemic control associated with the camp
experience. The assays were performed at the Shands
Teaching Hospital Laboratory using the Pierce GlycoTest in
vitro diagnostic kits which contain GLYCO-GEL Analytical
Columns. This is an affinity chromatography based method
used after serum is first dialyzed against normal saline to
prevent spurious elevations of GSP levels due to free
glucose (Kennedy, 1981). GSP represents the average
glycemic control for a period of 10 to 14 days before
testing. Normal ranges were not available. However, in this
analysis the control value for a nondiabetic subject ranged
from .47 to 1.1% with a mean of .79%.


83
interaction term (adherence factor x pre-camp HbA^c) was
tested for each of the five adherence factors. Only the
model that included pre-camp HbA^c, age, post-camp injection
factor, and the pre-camp HbA^c x post-camp injection factor
interaction term proved to increase the R2 significantly
(R2=.51 see Table 16). The interaction between the pre-camp
HbA^c and injection was interpreted by calculating the
nonstandardized Beta weights for injection for varying pre
camp HbA^c levels from 5.8 to 13.1 (the ranges of pre-camp
HbA^c found in this study's sample) using the equation from
Table 16 (as suggested by Cohen and Cohen, 1983). As is
apparent from Table 17, injection behaviors in children with
low pre-camp HbA^c (e.g., 5.8 to 7.0) was negatively
associated with post-camp HbA^c: less compliant behavior
(higher injection scores) was associated with lower post
camp HbA^c (i.e., better diabetic control). At pre-camp
HbA^c levels between 8.0 and 9.0 the relationship between
injection behavior and post-camp HbA^c diminishes to zero.
For pre-camp HbA^c levels between 11.0 and 13.1 (poor
control) there was a positive association between injection
behaviors and post-camp diabetic control, i.e., less
adherent behaviors were associated with poorer control.
Table 18 depicts the characteristics of three groups divided
on the basis of the injection factor Beta weights depicted
in Table 17. Noteworthy was a relatively large increase in
insulin dosage for both groups where the Injection Beta
weights were large (good and poor control youngsters). Due


70
subjects factors (Age group and Sex), and 1 within subjects
factor (Time: pre- and post-camp). A significant main
effect for Time, F(1,50)=11.00, p < .0017, and a significant
main effect for Age group, F(2,50)=3.31, p < .0447 emerged.
Subsequent LSMEANS procedures revealed that the overall mean
GSP values increased from 5.63% to 6.44% and that the 7- to
9-year olds (5.40%) and the 11- to 11.4-year old youngsters
(5.23%) had significantly lower mean GSP values than the
11.5- to 12.6-year old youngsters (6.82%).
It was then hypothesized that children arriving at camp
at different levels of glycemic control may have been dif
ferentially affected by the camp experience. Therefore,
the youngsters were assigned to good (GSP less than 4.5%),
moderate (GSP between 4.5 and 7.0%), and poor control (GSP
greater than 7.0%) categories based on groupings apparent
from a univariate procedure on the pre-camp GSP values.
Repeated measures ANOVA on the GSP values pre- and post
camp was conducted using one between subjects factor (GSP
control group) and one within subjects factor (Time: pre-
and post-camp). Results indicated a significant main effect
for Time, F(1,51)=12.45, p < .0009, and a significant main
effect for GSP control group, F(2,51)=55.91, p < .0001.
However, the Time x GSP control group interaction was not
significant, indicating that the various control groups did
not react differentially to the camp experience.
To assess changes in HbA^c, a repeated measures
analysis of variance (ANOVA) was conducted on the HbA^c pre-


RESULTS
Reliability of Child Report
One possible measure of child report reliability is
parent-child agreement. Perfect agreement was not expected
since parents are not always able to observe everything the
child does. However, since counselors were unable to
provide useful data concerning the campers activities, the
youngsters were the only consistent reporters available and
the issue of their reliability in reporting their behaviors
was critical.
Estimates of agreement between parent and child were
calculated to indicate the reliability of the information
obtained from the youngsters at camp, using Pearson product
moment correlations. These analyses replicated the Johnson
et al. study (1986) and used their 13 adherence measures.
Agreement was assessed for the total sample and for three
age groups (7-9, 10-11.4, and 11.5-12.6) at four points in
time: pre-camp and post-camp on 3 separate occasions
(immediately after camp, 6 weeks after camp, and 3 months
after camp). These data are presented in Tables 3, 4, 5,
and 6.
For the pre-camp period, parent/child correlations for
the total sample were all significant at p < .02, except for
regularity of injection-meal timing. One or both of the two
42


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
THE RELATIONSHIP BETWEEN ADHERENCE BEHAVIORS
AND GLYCEMIC CONTROL IN CHILDHOOD DIABETES
BY
MARIKA SPEVACK
December 1987
Chairman: Suzanne Bennett Johnson, Ph-D.
Major Department: Clinical and Health Psychology
This study was designed to investigate the following
questions: 1) do children's diabetes management behaviors
change at summer camp; 2) does attendance at summer camp
affect diabetic children's glycemic control; 3) if there are
changes at camp, are they maintained at home; 4) is there a
relationship between adherence behavior and glycemic
control. Sixty-four 7- to 12.6-year old youngsters with
diabetes were followed before, during, and after attending a
2-week summer camp. Both their diabetes management behav
iors and their glycemic control were monitored.
Children's diabetes management behaviors (i.e., injec
tion regularity, injection interval, calories consumed,
eating frequency, exercise duration, exercise type, exercise
frequency, and glucose testing frequency) changed signifi
cantly while they attended summer camp. There was also a
vi


TABLE 1
Parent-Child Correlations for Total Sample and by Age Group
Adherence Measure
Total
Sample
r (n)
P <
Group 1
(6-9 Years)
r(n)
P <
Group 2
(10-12 Years)
r(n)
P <
Group 3
(13-15.6 Years)
r(n)
P <
Group 4
(16-19 Years
r(n)
P <
Injection
Regularity
.61(152)
(.0001)
.46(27)
(.02)
.68(65)
( .0001)
.83(42)
( .0001)
-.04(18)
(.86)
Injection
interval
.77(154)
(.0001)
. 36(29)
(.05)
.71(67)
( .0001)
.54(40)
(.003)
.91(18)
( .0001)
Injection-
meal timing
.67(163)
( .0001)
. 53(31)
(.002)
. 547 (68)
( .0001)
.60(44)
(.001)
. 78(20)
(.0001)
Regularity of
inj ection-
meal timing
. 42 (148)
(.0001)
-.23(26)
(.26)
.50(63)
( 0001)
. 58(41)
( .0001)
.46(18)
(.05)
Calories
consumed
.77(139)
(.0001)
.79(30)
(.0001)
.80(66)
( .0001)
.61(33)
( .0001)
.92(10)
( .0002)
Percentage
calories-fat
.64(167)
(.0001)
.50(31)
(.005)
.63 (70)
( 0001)
.76(44)
(.0002)
.60(22)
(.003)


51
younger groups exhibited poor correlations on measures
involving time (injection regularity, injection-meal timing,
regularity of injection-meal timing, and exercise duration).
However, the oldest children were significantly correlated
with their parents (p < .05) on all measures (Table 3).
Immediately after camp (Table 4), the parent/child
correlations for the total sample were all significant with
the exception of exercise duration. Injection-meal timing
and regularity of injection-meal timing were still problema
tic for the 7- to 9-year olds. The 10- to 11.4-year olds
exhibited poor correlations on regularity of injection-meal
timing and exercise duration, while the oldest youngsters
were not significantly correlated with their parents on
measures of concentrated sweets and exercise duration (Table
4)
The 6 weeks and 3 months after camp interviews demonst
rate a similar pattern. These results are presented in
Tables 5 and 6 respectively. Percentage calories: fat, per
centage calories: carbohydrates, eating frequency, exercise
type, and glucose testing demonstrated significant correlat
ions for all children. Regularity of injection-meal timing
and exercise duration demonstrated the weakest (nonsig
nificant) correlations in each of the study periods except
for the last one in which parent/child agreement for
exercise duration improved (r=.66). However, parent/child
agreement for regularity of injection-meal timing remained
weak (r=.25).


59
approximately 13 calories less than the recommended amount
per day, F(1,55)=6.83, p < .01.
On measures of exercise type and exercise frequency a
Respondent x Sex interaction emerged F(l,56)=9.37 p < .003
and F(1,56)=7.28, p < .009 respectively. To determine the
source of the Respondent x Sex interactions, the data were
divided by sex and repeated measures ANOVA were conducted on
each of the two exercise measures using one within subject
factor (Respondent). For both exercise type and exercise
frequency, boys' reports did not significantly differ from
their parents' reports. In contrast, girls reported
participating in more strenuous and more frequent episodes
of exercise than did their parents, F(1,29)=11.99 p < .002,
and F(1,29)=5.90 p < .02 respectively. These interactions
were further examined using T tests to explore possible
differences between parents reports of boys' versus girls'
exercise type and exercise frequency; parents reported
significantly more strenuous and more frequent episodes of
exercise for boys compared to girls, t(62)=-3.35, p < .001.
In contrast, the children themselves did not report the same
male/female differences found in the parent data.
In summary, the children's reports correlated with
their parents reports in the expected direction, both across
time and across age groups. In addition, even when
differences between parent and child report did exist, they
were relatively small and of questionable clinical sig
nificance. Since the youngsters' self-report data were


Calories Consumedfor each youngster, an
ideal number of total daily calories was
identified based on the youngster's age, sex,
and ideal weight for height. Ideal weight
for height was obtained from tables provided
in a standard textbook on childhood obesity
(Collipp, 1980). Ideal calorie consumption
per kilogram was calculated using standards
provided by the U.S. Public Health Service
(Anderson et al., 1981). Each child's ideal
total number of daily calories was then
subtracted from the youngster's reported
daily caloric consumption. Reported calorie
consumption was obtained from the diet
information obtained by interview. All diet
information was coded in exchange units from
which calorie estimates were derived (Franz,
1983). A high score on the calorie consumed
measure indicated that the youngster ate more
than his ideal total number of daily calo
ries. A zero score indicated that the child's
actual calorie consumption equalled that of
his ideal calorie consumption. A negative
score indicated that the youngster ate less
than the ideal.
Percent Calories: Fatbased on the exchange
unit information collected from the 24-hour


113
Rifkin, H. Why control diabetes? Medical Clinics of North
America, 1978, 62(4): 747-752.
Schafer, L.C., Glasgow, R.E., McCaul, K.D. Increasing the
adherence of diabetic adolescents. Journal of
Behavioral Medicine, 1982, 5, 353-362.
Schafer, L.C., Glasgow, R.E., McCaul, K.D., and Dreher, M.
Adherence to IDDM regimens: Relationship to psychoso
cial variables and metabolic control. Diabetes Care,
1983, 6(5): 493-498.
Scharf, Linda S., Adams, Kenneth M., and Leach, David C.
Importance of diabetes camp in adolescents'
psychological adjustment. Presented at the American
Psychological Association Meetings, Washington, D.C.,
August, 1987.
Spevack, Marika, Johnson, Suzanne B., Harkavy, Jill M. ,
Silverstein, Janet, Shuster, Jon, Rosenbloom, Arlan,
and Malone, John. Diabetologists' judgments of
diabetic control: Reliability and mathematical
simulation. Diabetes Care, 1987, 10(2): 217-224.
Steiger, James H. Tests for comparing elements of a
correlation matrix. Psychological Bulletin, 1980,
87(2): 245-251.
Strickland, A.L., McFarland, K.F., Murtiashaw, M.H.,
Thorpe, S.R.,and Baynes, J.W. Changes in blood protein
glycosylation during a diabetes summer camp. Diabetes
Care, 1984, (March-April), 7(2): 183-185.
Stunkard, Albert, and Pestka, Joan. The physical activity
of obese girls. American Journal of Diseases of
Children, 1962, 1CH: 116-121.
Sussman, K.E. (ed). Juvenile-type Diabetes and Its
Complications: Theoretical and Practical
Considerations. Springfield: Charles C. Thomas, 1971.
Travis, Luther B. An Instructional Aid on Juvenile Diabetes
Mellitus fifth edition. Galveston, Texas: University
of Texas Medical Branch, 1978.
Travis, Luther B., Brouhard, Ben H., and Schreiner, Barbara-
Jo. Diabetes Mellitus in Children and Adolescents.
Philadelphia: W.B. Saunders Company, 1987.
Unger, R.H. Benefits and risks of meticulous control of
diabetes. Medical Clinics of North America, 1982,
66(6): 1317-1324.


TABLE 5
(continued)
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r(n)
P <
Group 3
(11.5-12.6 Years)
r(n)
P <
Percentage
.83(57)
.93(17)
.70(21)
.86(19)
calories-
(.0001)
(.0001)
( .0004)
( 0001)
carbohydrate
Concentrated
.68(57)
.92(17)
.70(21)
.55(19)
sweets
(.0001)
( .0001)
(.0004)
(.0141)
Eating
.74(57)
.62(17)
.74(21)
.85(19)
frequency
(.0001)
(.0080)
(.0001)
(.0001)
Exercise
.30(57)
.20(17)
.61(21)
.23(19)
duration
(.0235)
(.4428)
(.0035)
( 3383)
Exercise
.56(57)
.49(17)
. 63(21)
.76(19)
type
(.0001)
(.0442)
( .0021)
( .0001)
Exercise
.58(57)
.40(17)
.75(21)
.70(19)
frequency
(.0001)
(.1150)
( .0001)
(.0009)
Glucose
.91(57)
.85(17)
.96(21)
.93(19)
testing
( .0001)
(.0001)
( 0001)
(.0001)
frequency


99
periods, three meals and three snacks, and prescribed times
for injections and testing per day. Repeated measures
ANOVA on each of the 13 adherence measures confirmed that 9
of the 13 were significantly different while the children
attended diabetes summer camp, particularly for those
behaviors that were regimented or scheduled by camp person
nel. Therefore, based on the children's report, diabetes
management behaviors of injection regularity, injection-meal
timing, calories consumed, eating frequency, exercise
duration, exercise type, exercise frequency, and testing
frequency were significantly different while the youngsters
attended camp. With the exception of calories consumed,
children were most adherent during camp. During camp, all
children consumed more than the suggested amount and
significantly more than at home. After camp all groups
returned to pre-camp levels. Although children appeared to
be more nonadherent on this measure during camp, increases
in dietary intake may be considered appropriate given the
significant increase in the youngsters' daily exercise. It
is interesting to note that proportions of carbohydrates and
fat to total calories and concentrated sweets remained
stable before, during, and after camp. This is presumably
due to the fact that the children were given free reign
during camp meals regarding quantity and selection of foods
and most likely selected the types of foods consistent with
their home diet, only in greater quantity.


TABLE 9
(continued)
Time
Periods
Compared
Pre-camp vs
Post-camp
Camp vs
Pre- and
Post-camp Post-camp
13 14
1 5
2 1
2 3 2 4
25 34 3545
Adherence Measure
Percentage
calories-
carbohydrate
Concentrated
sweets
Eating
freguency
*
* *
*
Exercise
duration
# #
* *
*
Exercise
type
#
*
* *
* *
Exercise 7-9yrs
#
*
* *
* *
frequency 10-11.4yrs
k
* *
*
11.5-12.6yrs
# #
#
*
* *
*
Glucose
testing
frequency
#
#
* *
* #
* indicates significant improvement.
# indicates significant deterioration.
o\


TABLE 8
Parent-Child Correlations for Total Sample
Over Four Time Periods: Pre and Post-Camp
Time 1 Time 3 Time 4 Time 5
Pre-Camp Post-Camp 6 wks Post-Camp 3 Mos Post-Camp
r(n) r(n) r(n) r(n)
P< P< P< P<
Adherence Measure
Injection *
regularity
.65(60)
( .0001)
.54(55)
(.0001)
.64(53)
( .0001)
.81(55)
( .0001)
Injection *
interval
.75(59)
( .0001)
.73(56)
(.0001)
. 66(53)
( .0001)
.89(52)
( .0001)
Injection- *
meal timing
.54(62)
(.0001)
.68(57)
(.0001)
.76(56)
( 0001)
.77(60)
( 0001)
Regularity of *
injection-
meal timing
.09(59)
(.5010)
.44 (54)
(.0008)
.26(52)
(.0615)
.25(54)
( .0665)
Calories
consumed
.73(62)
(.0001)
.62(59)
(.0001)
.69(56)
(.0001)
.69(60)
( .0001)
Percentage
calories-fat
.78(63)
(.0001)
.74(60)
(.0001)
.86(57)
( .0001)
. 66(61)
( .0001)
U1
CTi


40
Procedure
In the 2 weeks prior to attending diabetes summer camp,
each child and one of his parents were interviewed by phone
on three separate occasions using the 24-hour recall
technique described earlier. The interviews were recorded
on the Diabetes Daily Record (Appendix).
On the first or second day of camp, fasting blood was
obtained from each participant. Serum obtained from venous
samples was stored at -20 degrees centigrade to be assayed
for GSP at a later time. Heparinized blood samples were
submitted for HbA^c assays. During their 2-week camp
experience, each child and his counselor were interviewed on
three separate occasions (on 2 weekdays and 1 weekend day)
by trained interviewers who recorded diabetes management
behaviors for the previous day on the Diabetes Daily Record.
However, counselor interviews were discontinued as it became
clear that they had difficulty differentially remembering
the individual children's activities and food consumption.
During the last 2 days of camp, fasting blood was again
obtained for GSP analysis. Those children whose blood was
drawn on the first day of camp were drawn on the second to
last day of camp. Similarly, if their blood was first drawn
on the second day of camp, the second sample was obtained on
the last full day of camp.
Within the 2 weeks following camp, study participants
and their parents again participated in three telephone
interviews using the 24-hour recall technique. This


29
care (Lebovitz et al., 1978), and physical activity levels
(Stunkard and Pestka, 1962). Moreover, there is some
evidence that metabolic control may improve during the camp
session (Strickland et al., 1984).
Improvements in adherence behaviors are assumed to
occur since camp provides exercise opportunities and the
youngsters' regimen is regulated regarding injections and
meals. There are, however, no empirical data documenting
improved adherence at camp nor attesting to the relationship
between such behavior change and improvements in metabolic
control. Further, it is unclear whether any behavioral
changes induced by the camp setting are maintained once the
child returns home.
Glycosylated serum proteins (GSP) differ from glyco
sylated hemoglobins (GH) in that serum protein levels in the
blood change more rapidly than do hemoglobin values with
changes in blood glucose concentration (Mehl, Wenzel,
Russel, Gardner, and Merimee, 1983). Therefore, since
glycosylated serum proteins have been shown to accurately
reflect alteration of mean glycemic levels 1 to 2 weeks
prior to testing, it becomes feasible to address the issue
of whether adherence behaviors at camp are indeed related to
metabolic control at camp. This study will, therefore,
assess the following:
1) The effect of summer camp on children's
diabetes management behavior;


13
study is an improvement over most correlational studies,
there are some basic weaknesses. For example, no attempt
was made to verify actual diabetologists' prescriptions
rather patient report of physician prescriptions were used
and comparisons were made to the self-reports or in some
instances to absolute levels of behaviors (although the
authors do not define "absolute levels," these are presum
ably meant to be ideal levels). Similarly, no attempt was
made to corroborate any of the self-reports of adherence.
Moreover, in calculating dietary compliance, only evening
meals were used.
Intervention studies comprise the second category of
investigations in the area of compliance and control. These
studies concentrate on increasing compliance, assuming that
lack of adherence to the medical regimen can result in
serious diabetic complications. However, a number of these
studies seek to modify adherence but do not include measures
of control. In one such study Gross (1982) conducted a
multiple baseline intervention with four IDDM boys aged 10-
12 years to determine the utility of self-management
behaviors. The youngsters were selected based on parent
report that they failed to reliably perform their urine
tests the recommended four times per day. The boys received
six 1-hour long sessions (one per week) which consisted of
written lessons, discussions, modeling and role-playing. In
addition, the children were assigned a self-management
project which included collecting baseline data on the


33
behaviors before, during and after camp (see Appendix). The
interviews were conducted by telephone. When possible, two
of these interviews dealt with weekdays and one with weekend
activities. Participants were told that the interviewer was
concerned with what actually transpired rather than what
they believe should have been done. Trained, undergraduate
research assistants conducted these interviews. Partici
pants were asked to recall the previous day's activities in
a sequential manner beginning with when the child woke up
and ending with when he went to bed. All diabetes relevant
behaviors were recorded. Interviewers prompted for informa
tion that may have been inadvertently omitted, such as,
mid-morning snacks, exercise, glucose testing, etc.
Youngsters and their parents were interviewed separately.
Using data obtained from the 24 hour recall interviews,
13 different adherence measures were quantified. For each
measure, a range of scores is possible with higher scores
indicating relative noncompliance and lower scores indicat
ing relative compliance (Johnson et al., 1986):
1) Injection Regularitythis measure assessed
the degree to which injections were given at
the same time every day. It was calculated
by measuring the standard deviation of
injection times reported across the inter
views .
2) Injection Intervalthis measure assessed the
youngster's average deviation from an ideal


REFERENCES
Amiel, Stephanie A., Sherwin, Robert S., Simonson,
Donald C. Lauritano, Albert A., and Tamborlane,
William V. Impaired insulin action in puberty.
The New England Journal of Medicine, 1986, 315(4):
215-219.
Anderson, B., Miller, J., Auslander, W., and Santiago, J.
Family characteristics of diabetic adolescents:
Relationship to metabolic control. Diabetes Care,
1981, 4, 586-594.
Blevins, Dorothy R.. The Diabetic and Nursing Care. New
York: McGraw-Hill, 1979.
Clarke, William L., Snyder, Andrea L., and Nowacek, George.
Outpatient pediatric diabetesI. Current practices.
Journal of Chronic Diseases, 1985, 3_8(1): 85-90.
Cohen, Jacob and Cohen, Patricia. Applied Multiple
Regression/Correlation Analysis for the Behavioral
Sciences. New Jersey: Lawrence Erlbaum Associates,
1983.
Collipp, P. Childhood Obesity. Littleton, MA:PSG, 1980.
Dorchy H., Loeb, H., Mozin, M.J., Lemiere, B. and Ernould,
C. Vacation camps: Goals and needs. Pediatric and
Adolescent Endocrinology, 1982, 10: 161-165.
Ellenberg, Max and Rifkin, Harold (Eds.). Diabetes
Mellitus: Theory and Practice. 3rd Ed. New Hyde
Park, NY: Medical Examination Pub. Co., 1983.
Epstein, Leonard H., Beck,, Steven, Figueroa, Jorge, Farkas
Gary, Kazdin, Alan E., Daneman, Denis, and Becker,
Dorothy. The effects of targeting improvements in
urine glucose on metabolic control in children with
insulin dependent diabetes. Journal of Applied
Behavioral Analysis, 1981, 14: 365-375.
Epstein, Leonard H. and Cluss, P.A. A behavioral medicine
perspective on adherence to long term medical regimens
Journal of Consulting and Clinical Psychology, 1982,
50: 950-971.
110


7
Studies in this area are few and present numerous
methodological problems. For example, control and com
pliance are often confused as when hemoglobin Ale (HbAic), a
metabolic indicator of glycemic control, is taken to
represent compliance. In some cases, although both con
structs are treated separately, the same person judges both,
introducing the possibility of a spurious association
between the two (i.e., the rater may believe compliance and
control are related and judge them accordingly).
The few studies in the area can be divided into two
categories. The first category is composed of correlational
studies which are concerned with establishing associations
between compliance behaviors and control. One such study by
Rainwater, Jackson and Burns (1982) tried to assess the
relationship between psychological variables, metabolic
control, and behavioral measures using 115 children between
8 and 17 years and their mothers. Their most noteworthy
finding was that Mother's Health Locus of Control (MHLC) was
significantly correlated with behavioral and metabolic
measures suggesting that mothers who scored external tended
to have better controlled children. That is, MHLC corre
lated significantly r=-.58, p< .01, with total amount of
sugar spilled in a 24-hour urine sample collected from the
patient, the number of patient hospitalizations in the past
year, -.42, (p<.01), the number of insulin reactions
experienced by the patient in the past month r=.36 (pc.Ol),
and the number of hours the patient exercised per week


9
24-hour urines and provide a more stable measure of control
over the preceding 3 months rather than just 24 hours.
Rainwater et al. did not report whether the 24-hour speci
mens in their study were valid. Even if the 24-hour
collection was valid, it indirectly represents the young
ster's metabolic control for a relatively brief period of
time. Since no relationship was shown between MHLC and
HbA^c, the most reliable indicator of metabolic control over
the past 3 months, the relationship between MHLC and the
24-hour collection data is difficult to interpret (i.e., why
would such a relationship exist for one measure of diabetes
control but not another?). Furthermore, the study did not
directly assess adherence behaviors. Rather, behavioral
measures, as defined by the authors, consisted of report by
family of the number of diabetes related hospitalizations in
the past year, average number of hours of exercise per week
over the past 3 months, number of insulin reactions in the
past month, and number of times acetone was noted in the
urine in the past month, thereby confusing adherence
behaviors with metabolic indicators and other sequelae of
poor control.
In another correlational study, LaGreca et al. (1982)
examined the relationship between metabolic control as
measured by hemoglobin and different aspects of diabetes
care such as degree of responsibility taken by the child,
level of knowledge, and degree of compliance as rated by
physicians following routine appointments with 40 IDDM


TABLE 12
Means and Standard Deviations for Adherence
Measures that Remained Stable Over the 5 Time Periods
Adherence Measure
Time 1
Means(SD)
(Interp)
Time 2
Means(SD)
(Interp)
Time Period
Time 3 Time 4
Means(SD) Means(SD)
(Interp) (Interp)
Time 5
Means (SD)
(Interp)
Regularity of
injection- (minutes)
meal timing
18.68(19.3)
18.10(13.5)
19.49(18.2)
i 12.02(10.4)
18.20(19.22)
Percentage
calories-fat
23.67(6.9)
(48.7)
22.04(5.7)
(47.0)
24.53 (7.2)
(49.5)
23.99(9.0)
(48.9)
22.73(6.5)
(47.7)
Percentage
calories-
carbohydrate
24.33(7.2)
(35.7)
23.35(6.0)
(36.7)
25.44(7.5)
(34.6)
24.76(8.8)
(35.2)
23.35(6.7)
(36.7)
Concentrated
1.52(1.9)
1.24(1.1)
1.33(1.8)
1.38(1.7)
1.56(1.7)
sweets (per day)


25
of the three exercise measures, Factor II (Injection)
consisted of all four injection measures, Factor III (Diet
Type) was made up of measures of diet type, Factor IV
(Eating/Glucose Testing Frequency) included measures of
eating frequency and glucose testing frequency, and Factor V
(Diet Amount) included measures of total calories consumed
and the amount of concentrated sweets ingested. The Johnson
et al. (1986) results indicate that adherence to a diabetic
regimen consists of five independent constructs and should
not be viewed as a global construct.
Diabetes summer camps offer a natural but relatively
controlled environment. Here youngsters' behavior and
metabolic control could be easily monitored and both would
be expected to change. Such an environment would be
conducive to investigating relationships of compliance/
control. Diabetes summer camps have as their main goal to
create an environment in which IDDM youngsters can meet
other children with diabetes and learn to live more normally
with their disease. The focus of the program is usually to
provide a model for healthy living along with didactic
components designed to reinforce adherence skills and
increase understanding of the disease. It is a generally
accepted philosophy that research conducted at summer camps
must be nonintrusive and must not interfere with camper ac
tivities and enjoyment. The major criticism leveled at camp
studies is that they constitute an unnatural environment
which itself affects the youngsters' behavior and level of


16
of each week, parents were instructed to test the remaining
tablets in each bottle, and add the number they found to the
number the child reported finding. Comparisons of this
number to the actual number provided the measure of the
child's adherence to the regimen for that week. Parents and
children agreed on the number of marked items during 76% of
the weeks. Results showed a significant increase in the
number of tests performed and in the percent of urine tests
that were negative for glucose, although other metabolic
indices of diabetic control did not show improvement.
Epstein et al. concluded that behavioral techniques appear
useful for improving behavioral adherence to diabetes
regimen. However, in this study, increasing the number of
urine tests that were negative for glucose was not associ
ated with improvement in other measures of diabetic control.
Kaplan, Chadwick and Schimmel (1985) randomly assigned
21 IDDM teenagers between 13 and 18 from middle class
backgrounds to either a daily social learning group or a
medical facts learning group when they entered a 3 week
summer program. The social learning group identified social
situations in which peer influence might prevent adherence
to diabetes regimen and as a group suggested appropriate
responses. Rehearsal exercises enacting problem situations
and their solutions were ultimately videotaped and then
filmed in a television studio. Guided practice with
reinforcement was used to develop their social skills. The
control group spent the same amount of time discussing


91
TABLE 21
Pre- Post-camp Change in Insulin Dose x Post-camp
Injection Factor: Descriptive Characteristics
Change in Dose(CH)
Injection B Wts
Ch <= -10
3.0 to .9
-10 < Ch < 15
.6 to -.5
Ch =>
-.8 to -
N
13
28
9
Pre-camp HbA^c
9.3
8.7
8.1
Post-camp HbA^^c
9.0
8.5
8.5
Age
10.6
10.7
9.9
Duration of disease
3.5
4.2
4.5
Injection factor
pre-camp
- 14
. 18
- .14
Injection factor
post-camp
.40
.47
.21
Insulin dose
pre-camp
44.1
29.6
13.3
Insulin dose
during camp
25.5
30.3
24.4
Insulin dose
at followup
21.2
32.4
42.6
Change in insulin
dose between pre
camp and followup
-23.7
2.8
29.3


11
the patients' self-reports, and nondietary aspects of the
treatment regimen were not studied.
One study that has attempted to address the more global
versus specific nature of the relationship between adherence
and metabolic control was conducted by Schafer, Glasgow,
McCaul, and Dreher (1983). Their 34 subjects were 12 to
14-year-olds with IDDM who completed questionnaires dealing
with regimen adherence for the past week as well as psycho
social measures dealing with diabetes-specific family
behaviors, barriers to adherence, and family interactions.
They found that compliance was a highly specific construct;
the degree of adherence to one aspect of the IDDM regimen
was not related to adherence to the other aspects of the
regimen. Using multiple correlations, they also found that
glycosylated hemoglobin levels could be predicted from a
combination of three adherence measures (i.e., the extent to
which one's diet was followed, reported care measuring
insulin doses, and number of daily glucose tests). Unfor
tunately, the reliability of the self-report compliance
measures was not assessed, nor was there any attempt to
verify their reports by actual measurement of home behaviors
or by corroboration by significant others. In addition, the
use of multiple regression with such a small sample and
such a large number of variables is likely to severely
overestimate the strength of the resultant R2.
In a more thorough study by some of the same inves
tigators (Glasgow, McCaul, and Schafer, 1987), these same


TABLE 4
Parent-Child Correlations for Total Sample and by Age Group
Immediately Post-Camp
Adherence Measure
Total
Sample
r (n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r (n)
P <
Group 3
(11.5-12.6 Years)
r(n)
P <
Injection
.54(55)
.54(15)
. 52 (20)
.65(20)
regularity
(.0001)
(.0381)
( .0198)
(.0021)
Inj ection
.73(56)
.67(16)
.66(20)
.83 (20)
interval
(.0001)
(.0045)
( .0016)
( .0001)
Injection-
.68(57)
.31(18)
.91(19)
. 56(20)
meal timing
(.0001)
(.2152)
( 0001)
(.0098)
Regularity of
.44(54)
-.40(15)
.42(19)
.72(19)
injection-
meal timing
(.0008)
(.1432)
(.0726)
(.0003)
Calories
.62(59)
.36(18)
.62(22)
.70(19)
consumed
(.0001)
(.1471)
(.0022)
(.0009)
Percentage
.74(60)
.68(18)
.87(22)
.62(20)
calories-fat
(.0001)
(.0020)
(.0001)
(.0035)
in


93
differ in glycemic control either before camp or at
followup.
Next, the HbA^c measure at followup was subtracted from
the pre-camp HbA^c measure. It was hypothesized that
youngsters who exhibited a decrease in HbA^c at followup
would have demonstrated increased compliance, while those
whose HbA^c values increased would have demonstrated
decreased compliance. This difference score was subjected
to a univariate procedure so that youngsters could be
divided into three categories. Scores greater than or equal
to .5 were placed in the improved group, scores between .4
and -.4 were in the no change group, and scores less than or
equal to -.5 were in the worse group. Repeated measures
ANOVA on the adherence factors (based on the three inter
views conducted before camp and on the nine interviews
conducted during the 3 month followup period) using one
within subject factor (Time) and one between subject factor
(HbA^c control category) was conducted. Results indicated a
simple Time main effect for the injection factor F(l,58)=
13.15, p < .0006 and a simple Time main effect for the
eating/testing frequency factor F(1,59)=5.11, p < .03. No
Time x HbA^c change group interaction was detected.
Finally, post-camp adherence factor scores were
subtracted from pre-camp adherence factor scores and the
children were divided into change groups (improved adherence
and less adherent during followup). It was hypothesized
that improved adherence would be inversely related to the


emotional stress, and infection. The most
frequent symptoms are weakness, increased
thirst, frequent urination, dry mouth,
decreased appetite or polyphagia, nausea and
vomiting, abdominal pain, coma, and acetone
breath which occurs when ketone levels rise
in advanced stages. These acute consequences
are characteristic of youngsters in poor
control (Guthrie and Guthrie, 1977).
Intermediate consequences include delayed
growth and development, frequent infections,
and psychosocial problems. Delayed growth
and development in IDDM youngsters is not
uncommon and has been found to correlate most
clearly with those children in poor control
(Guthrie and Guthrie, 1977). Various kinds of
infections are also common in IDDM children.
Clinical observations and research suggest
that these are also more common in patients
in poor diabetes control (Guthrie and
Guthrie, 1977). Psychological problems are
associated with poor control although the
nature of the relationship between psycho
logical problems and health status in
youngsters with diabetes is not entirely
clear (Johnson, 1985).


28
reported stress, and blood glucose levels (measured via
Chemstrip bG Reagent strips four times during the 24 hours
preceding each afternoon rest period during which stress
questionnaires were administered) were monitored. Results
suggested that negative cumulative stress, as perceived by
the youngsters, correlated significantly with blood glucose
levels. Interestingly, positive cumulative stress was
significantly and negatively correlated with girls' blood
glucose levels.
Educational experiences at camp were found to contri
bute to the knowledge and performance of self-care techni
ques in a study by Lebovitz, Ellis, and Skyler (1978).
Harkavy et al. (1983) found that knowledge increased in 12-
to 15-year old campers but not in 10- to 11-year olds.
Dorchy, Loeb, Mozin, Lemiere, and Ernould (1982) adminis
tered a 27 question pre- and post-test to 63 IDDM youngsters
and found that regardless of age or duration of diabetes all
children ages 9 to 15 years benefit from the teaching of
theory and practical issues. The youngest children (9-10
years) showed progress in all areas while the oldest (13-15
years) made greater gains in the areas of nutrition and
insulin therapy.
There is, therefore, some evidence that the camp
experience may affect psychological factors such as locus of
control (Moffat and Pless, 1983) and self-esteem (Hoffman,
et al. 1982), knowledge about the disease (particularly in
12- to 15-year old youngsters, Harkavy et al., 1983), self-


change in children's level of glycemic control as measured
by glycosylated serum protein levels. However, children
were in poorer glycemic control at the end of camp than at
the beginning. None of these camp-related changes were
maintained once the youngsters returned home. No clear
relationship between adherence behaviors during camp and
control could be established. However, post-camp metabolic
control was predicted by an interaction between change in
insulin dose (pre- to post-camp) and injection adherence
behaviors. Youngsters who had large increases in insulin
dose exhibited a deterioration in metabolic control when
they were more adherent. Youngsters who had large decreases
in insulin dose exhibited the opposite relationship: better
adherence was associated with better control. Further
inspection of this statistically significant interaction
indicated that pre- post-camp change in insulin dose
appeared to be initiated during camp. Camp related insulin
dose changes were maintained or enhanced once the children
went home. The group of children whose insulin was
increased during camp, were given further increases in
insulin dose once they returned home and may have been
experiencing the Somogyi phenomenon (rebounding) after camp
as a consequence of excessive increments in their insulin
dose. Home physicians, appeared to have used insulin
adjustments made during camp as a model of treatment and
continued the trend initiated by the camp physicians. It
Vll


100
Changes noted in this study are consistent with the
only other study published that we were able to locate, that
attempted to look at effects of camp on behavior change
(Stunkard and Pestka (1962) Stunkard and Pestka monitored
the physical activity of 15 nondiabetic obese 10- to 13-year
old girls and compared them to matched non-obese nondiabetic
girls during and after a 2-week Girl Scout camp. Physical
activity was measured by means of a mechanical pedometer and
a significantly higher rate of activity at camp was noted
for both obese and nonobese girls. However, our study
represents a significant expansion of the Stunkard and
Pestka (1962) study. In this study, physical activity
included measures of frequency of participation, duration of
exercise episodes, and type of exercise as gauged by the
amount of kilocalories expended per minute. Moreover,
multiple other diabetes management behaviors were measured.
Of the three adherence measures (injection interval,
calories consumed, and exercise frequency) that demonstrated
a significant Time x Age group interaction only injection
interval demonstrated a differential effect of camp on older
versus younger children. That is, unlike the other eight
measures where children of all ages significantly changed at
camp, only the oldest group reported improvement at camp on
the injection interval measure. It is possible that this
result reflects the fact that the younger children's
difficulty with time related events was compounded by the
fact that during camp, events were scheduled by 'periods'


66
significant increase in exercise frequency associated with
the camp experience (see Table 9 and 11).
Five Measures exhibited no significant change related
to the camp experience: regularity of injection-meal timing,
percentage calories: fat, percentage calories: carbohydrat
es, and concentrated sweets (Table 12).
On the regularity of injection-meal timing measure,
there was also an Age group x Sex interaction, F(2,38)=3.41,
p < .04). Subsequent LSMEANS procedure revealed that the 10-
to 11.4-year old girls were significantly less adherent than
the 11.5- to 12.6-year old girls or the 10- to 11.4-year old
boys. No other main or interaction effects for sex emerged
for any other adherence measure.
Although it is clear that significant changes occurred
during camp for 9 of the 13 adherence behaviors, examination
of Table 9 demonstrates that these changes were not main
tained. That is, Time 2 (camp) exhibited consistent
significant differences from Times 1, 3, 4, and 5. In
contrast there were fewer significant differences between
Time 1, 3, 4, and 5. When differences between these times
did occur, they were most often in the direction of poorer
adherence (see Table 9 and interpretations in Tables 10, 11,
and 12).
Effect of Diabetes Camp on Glycemic Control
This project used two measures to assess glycemic
control. Glycosylated hemoglobin (HbAic) which provides an
0


78
model. For example, the model controlling for exercise
behavior before coming to camp included pre-camp GSP,
exercise factor (before camp) and exercise factor (during
camp). None of these five models significantly enhanced the
predictive power of the pre-camp GSP prediction model.
Finally, it was hypothesized that insulin dosage during
camp might predict post-camp glycemic control. Repeated
measures ANOVA on the pre- and during-camp insulin dosage
with one within subject factor (Time) indicated that there
was a significant difference between dosage before camp
compared to dosage during camp F(l,57)=6.31, p < .01. The
average dose before camp was 32.2 units while the average
dose during camp was 27.2 units. Adding insulin dosage
during camp to pre-camp GSP did not increase the variance
accounted for by pre-camp GSP alone. A model which included
pre-camp GSP, dosage during camp, and insulin dosage before
camp was also tested. No significant increase in the R2 was
achieved. To determine whether children entering camp at
different levels of glycemic control would be differentially
affected, a model containing pre-camp GSP, insulin dosage
during camp, and an interaction of these two measures was
tested. This model did not strengthen the predictive power
of the original pre-camp GSP prediction model.
Categorical analyses
Categorical analysis involves the division of data into
categories based on logical groupings. In this study, the
data were grouped based on levels of glycemic control and


72
each of these groups indicated that while the means for the
good and poor control groups remained stable (7.45 to 7.80%
and 11.36 to 10.72% respectively), the moderate control
group experienced a significant decrease going from 9.51% to
8.80%.
To determine if these changes in HbA^c levels were
associated with camp, a repeated measures ANOVA was con
ducted on the GSP pre- and post-camp measures using one
between subjects factor (the HbAic control groups) and one
within subject factor (Time). The result demonstrated a
significant main effect for Time, F(1,51)=8.31, p < .0058,
and a significant between subjects effect of HbA^c control
group, F(2,51)=7.51, p < .0014) but no HbA^c control group x
Time interaction. These results suggest that the improve
ments noted in the moderate HbA^c control group from pre
camp to 3 months post-camp did not appear to be the result
of changes associated with the camp experience.
Relationship between Adherence and Diabetic Control
This study explored the relationship between adherence
and diabetic control in two ways. First adherence behaviors
during camp were employed in hierarchical regression models
to predict to post-camp GSP, and adherence behaviors during
the 3 months following camp were used in hierarchical
regression models to predict to the followup HbA^c. Second,
categorical anlyses, which involve the division of data into
logical groupings, were utilized to explore possible


69
TABLE 13
Correlations Between Glycosylated Hemoglobins
and Serum Proteins
pre-camp
r (n)
P <
HbA1c
3 months
post-camp
r(n)
P <
pre-camp
r(n)
P <
GSP
post-camp
r(n)
P <
HbA^c :
pre-camp
3 months
post-camp
1.00(63)
( .0000)
. 62(61)
(.0001)
1.00(62)
( 0000)
GSP
pre-camp
.71(53)
(.0001)
. 52 (52)
( 0001)
1.00(54)
(.0000)
post-camp
.43(60)
. 39(59)
.78(54)
1.00(61)
(.0007)
( 0020)
( .0001)
(.0000)


60
generally reliable, replicating reliability data reported in
a previous study (Johnson et al., 1986), and since the
counselor interview data were unusable, it was decided to
use only the children's report in the succeeding analyses.
Effect of Diabetes Camp on Adherence
To test for possible changes in children's management
behaviors during camp, a repeated measures ANOVA was
conducted on each of the 13 adherence measures using two
between subject factorsAge Group (7-9 years, 10-11 years,
11.5-12.6 years) and Sex (M,F) and one within subjects
factorTime (Time 1: before camp, Time 2: during camp, Time
3: immediately after camp, Time 4: 6 weeks after camp, and
Time 5: 3 months after camp). Significant Time main effects
or interactions with Time emerged for 9 of the 13 adherence
measures (i.e., injection regularity, injection interval,
injection-meal timing, calories consumed, eating frequency,
exercise duration, exercise type, exercise frequency, and
glucose testing frequency). Duncan's Multiple Range Tests
performed on these measures revealed that in all cases
adherence behaviors at camp (Time 2) differed significantly
from one or more of the pre- or post-camp periods (Time 1,
Time 3, Time 4, and Time 5). These results are presented in
Table 9.
Simple main effects for Time emerged for behaviors as
sociated with injection regularity F(4,164)=6.04, p < .0001,
injection-meal timing F(4,168)=2.83, p < .03, eating


I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
tki
Suzanne (B. Johnsdr^j Chairman
Professor of Clinical and
Health Psychology
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
Nathan W. Perry
Professor of Clinical an*
Health Psychology
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
V )... Sheila M. EybergV
Professor of Clinical
Health Psychology


Ill
Franz, M. Exchanges for all Occasions. Minneapolis, M.N.
International Diabetes Center, 1983.
Freund, Anne., Johnson, Suzanne B., Rosenbloom, Arlan,
Alexander, B., Hansen, Carolyn A. Subjective Symptoms,
Blood Glucose Estimation, and Blood Glucose Concentra
tions in Adolescents with Diabetes. Diabetes Care,
1986, 9(3): 236-243.
Glasgow, Russell, E., McCaul, Kevin D., and Schafer,
Lorraine C. Self-care behaviors and glycemic control
in type I diabetes. Journal of Chronic Diseases, 1987,
40(5): 399-412.
Gross, Alan M. Self-management training and medication
compliance in children with diabetes. Child & Family
Behavior Therapy, 1982, 4_(2/3): 47-55.
Guthrie, Diana W. and Guthrie, Richard A. (eds). Nursing
Management of Diabetes Mellitus. Saint Louis: C.V.
Mosby Co., 1977.
Hanson, Stephanie L., and Pichert, James W. Perceived
stress and diabetes control in adolescents. Health
Psychology, 1986, 5(5): 439-452.
Harkavy, Jill, Johnson, Suzanne B., Silverstein, Janet,
Spillar, Rebecca, McCallum, Martha, and Rosenbloom,
Arlan. Who learns what at diabetes summer camp.
Journal of Pediatric Psychology, 1983, 8(2): 143-153.
Hoffman, Richard G., Guthrie, Diana, Speelman, Diana,
Childs, Lindy. Self-concept changes in Diabetic
Adolescents. Pediatric and Adolescent Endocrinology,
1982, 10: 32-38.
Johnson, Suzanne B. Knowledge, attitudes, and behavior:
Correlates of health in childhood diabetes. Clinical
Psychology Review, 1985, 4_, 503-524.
Johnson, Suzanne B. Diabetes Mellitus in Childhood.
Handbook of Pediatric Psychology, Don Routh (ed.). New
York: Guilford Publications, 1984.
Johnson, Suzanne B., Silverstein, Janet, Rosenbloom, Arlan,
Carter, Randy, and Cunningham, Walter. Assessing daily
management in childhood diabetes. Health Psychology,
1986, 5(6): 545-564.
Kaplan, Robert M., Chadwick, Michele W., and Schimmel,
Leslie E. Social learning intervention to promote
metabolic control in Type I diabetes mellitus: Pilot
experiment results. Diabetes Care, 1985, 8(2): 152-
155.


96
correlations of parent and child report before and after
camp (4 different time periods), it was decided that the
youngsters' report was generally reliable concerning
diabetes management behaviors.
Agreement was also assessed for three age groups (7- to
9-year olds, 10- to 11.4-year olds, and 11.5- to 12.6-year
olds). It was noted that only on 5 of the 13 adherence
measures (injection interval, injection-meal timing,
calories consumed, exercise duration, and glucose testing
frequency) were there significant differences among the age
groups. Of these, three of the measures dealt with time and
were more problematic for the youngest age groups. These
findings are consistent with the findings of Johnson et al.,
1986) who found that 6- to 9-year olds showed poor agreement
with parent report with measures involving time. This
finding was attributed to the difficulty this age group
usually experiences with complex mathematical concepts such
as time. On the calories consumed measures the oldest
children were most concordant while the youngest were the
least concordant. This finding is not consistent with the
findings of Johnson et al., (1986) and could represent a
sample selection bias. On the glucose testing frequency
measure, all groups were highly concordant with their
parents. Therefore, the significant differences between the
two older groups is of questionable clinical significance.
The data were collapsed across age groups so that
correlations at the four time periods could be compared.


84
TABLE 16
Predicting Post-camp HbA^^c by Pre-camp HbA]_c,
Age, Post-camp Injection Factor,
and Pre-camp HbAj^c x Post-camp Injection Interaction
Variables in model
Beta weights
P<
R2 = .51
Pre-camp HbA^^c
.47445345
.0017
Age
.28755174
. 0142
Post-camp
Injection Factor
-4.07128891
. 0510
Pre-camp HbA^^c x
Post-camp
Injection Factor
.45447440
.0487


97
Results suggested that parent/child agreement was stable
across time for 8 of the 13 adherence measures: calories
consumed; percent calories: fat; percent calories: car
bohydrates; eating freguency; exercise duration; exercise
type; exercise frequency; and testing frequency. Sig
nificant differences between at least two of the time
periods existed on measures of injection regularity,
injection-meal timing, regularity of injection-meal timing,
and concentrated sweets. Although 3 of the 5 correlations
were at their best at Time 5, there was no evidence for a
linear improvement with the possible exception of injection-
meal timing. However, only 3 of the 13 parent/child cor
relations (all dealing with the dietary measures) were at
their weakest at Time 5 while 8 of the 13 correlations were
at their best at Time 5. The relatively better concordance
between parents and children at followup could not be
attributed to a practice effect since a linear improvement
in the correlations was not noted. Rather, this effect may
be due to the fact that by Time 5, children were back in
school. As a consequence, parents may have been more
knowledgeable about their activities but not as aware of
what they were eating, since many children eat school
lunches. Overall, when considering the parent/child
correlations there was no evidence to suggest that infor
mation provided by the children deteriorated over the study
period.


75
TABLE 14
Correlations Between Adherence Measures
Within and Between Factors
Factor:
Exercise
Injection
Diet
Eat/Test
Diet
Type
Frequency
Amount
Exercise
. 66
Injection
. 02
.22
Diet Type
. 12
. 01
.96
Eat/Test
Frequency
-.03
. 10
.21
.27
Diet Amount
-.11
-.06
.01
. 01
.27


87
to this finding it seemed appropriate to consider whether
insulin dosage and change in insulin dose would enhance the
predictive power of the model that contained pre-camp HbA^c
and age. Insulin dosage at camp did not increase the R2.
To control for pre-camp insulin dose, dosage before camp was
added to the model with no increase in R2. Keeping in mind
the role of injection which had earlier been established, it
was added to the model. No significant increase of R2
resulted. It was then hypothesized that the changes in
injection behavior at various changes in insulin dose from
pre-camp to followup might differentially predict post-camp
HbA^c. Change in insulin dosage was calculated by sub
tracting the total insulin dosage reported by the child on
the last interview (3 months post-camp) from total dosage
reported by the child during the last interview before
coming to camp. A model including pre-camp HbA^c, age, dif
ference in dosage, injection factor, and difference in
dosage x injection factor interaction term was tested. This
model significantly increased the variance, R2=.57 (see
Table 19). This interaction was interpreted by calculating
the nonstandardized Beta weights for injection for various
units of change in dosage from -40 to +40 (the range of
total units change found in this study's sample) based on
the equation from Table 19. As is apparent from Table 20,
the greater the decrease in insulin dose, the higher the
injection factor Beta weight. At the insulin dose change of
-5 to +10 the relationship between injection and post-camp


76
five factors were created based on the Johnson et al. (1986)
model. Factor 1 contained the three exercise measures,
Factor 2 was made up of all four injection measures, Factor
3 consisted of measures of diet type (percentage of calori
es: carbohydrates and percentage calories: fat), Factor 4
contained measures of eating frequency and glucose testing
frequency, and Factor 5 included measures of calories
consumed and concentrated sweets. A factor score was
calculated by averaging the standardized scores of measures
loading highly on that factor. It was expected that the
average correlation between factors would be low. This was
found to be the case (Table 15) with correlations ranging
from r=.01 (between the injection factor and the diet type
factor) to r=.26 (between the diet type factor and the
eating/testing frequency factor). These correlations were
considered to support the relative independence of the
factors and justify their use in further exploration of the
adherence/control issue.
Hierarchical regression analyses were again used to
determine whether adherence behaviors during camp would
predict post-camp GSP. Therefore, each of the five
adherence factors reflecting camp behavior was added
separately to the original model which contained the pre
camp GSP measure. None contributed significant additional
variance. Next, it was decided to control for adherence
behavior before coming to camp. Therefore, adherence before
camp and adherence during camp were added to the original


TABLE 9
Duncan's Multiple Range Tests (p< .05) on the Adherence Measures
Demonstrating Within Subject Effects
Adherence Measure
Time Periods Compared
Pre-camp vs Post-camp Camp vs Pre- and Post-camp
13 14 15 21 23 24 25
Post-camp
3 4 3 5 4 5
Injection
regularity
Injection 7-9yrs
interval 10-11.4yrs
11.5-12.6yrs
Inj ection-
meal timing
Regularity of
inj ection-
meal timing
Calories 7-9yrs
consumed 10-11.4yrs
11.5-12.6yrs
Percentage
calories-fat
#
* * *
# #
* * *
* * *
# * # #
* *
* * *
ON


71
camp and at followup using 2 between subjects factors (Age
group and Sex), and 1 within subjects factor (Time). There
were no significant main effects for Time and no significant
interactions, indicating that the camp experience had no
perceivable lasting effect. However, it was hypothesized
that the youngsters may have demonstrated differences
depending on their glycemic level of control before camp.
Therefore, based on a univariate procedure performed on
HbA^c drawn at the beginning of camp, the children were
divided into good (HbA^c less than 8.4%), moderate (HbA^c
between 8.4 and 10.4%), and poor (HbA^c greater than 10.4%)
HbA^c control groups. A repeated measures ANOVA procedure
was conducted on the HbA^c (pre-camp and at followup) using
one between subjects factor (HbA^c control group) and one
within subjects factor (Time). Results demonstrated a
between subjects effect of HbA^c control group, F(2,58)-
=61.75 p < .0001, and a Time x HbA^c control group
interaction F(2,58)=4.81, p < .0117, suggesting that not all
three HbA^c control groups were equally stable on the HbAic
measure at followup. Therefore, repeated measures ANOVA
procedures were conducted with each of the HbA^c control
groups separately using one within subjects factor (Time:
pre-camp and followup). Results from these analyses
indicated that children in good and poor control remained
stable, while youngsters in moderate control demonstrated a
significant time effect F(1,26)=11.09, p < 0026. Examina
tion of the mean HbAic values pre-camp and at followup for


ACKNOWLEDGEMENTS
I wish to take this opportunity to express special and
heart-felt thanks to Dr. Suzanne Johnson who has provided me
with more than the expected support of a Chair throughout
this investigation. Not only has her interest and input
energized this project and enhanced the quality of the
outcome, but she has been an inspiration, a role model and a
friend throughout this endeavor.
My appreciation and thanks go out to my committee,
Nathan Perry, Sheila Eyberg, William Riley, and Jon Shuster
who have been supportive throughout this endeavor.
Most of all, I would like to take this opportunity to
thank my best friend, confidant, and husband Avram who
tolerated bad moods, angst, ambition, and enthusiasm. I
would like to express deep respect, overwhelming love, and
gratitude for his unwavering encouragement and faith in my
ability to accomplish this project.
11


77
TABLE 15
Average Correlations between Adherence Factors
Factor: Exercise Injection Diet Eat/Test Diet
Type Frequency Amount
Exercise
1.00
Injection
. 03
1.00
Diet Type
. 14
. 01
1.00
Eat/Test
Frequency
-.05
. 19
.26
1.00
Diet Amount
-.16
-.14
-.02
. 03


55
highest parent/child agreement (r=.86) on the exercise
duration measure, while the 10- to 11.4-year olds exhibited
the weakest correlation (r=.43). On the glucose testing
freguency measure, all groups' reports correlated highly
with their parents, however there was a significant dif
ference between the 10- to 11.4-year olds' (r=.98) and the
11.5- to 12.6-year olds' (r=.84) parent/child correlations.
The correlations between parent and child report were
then collapsed across the age groups so that the parent/
child correlations for the total sample could be examined
across the four time periods of the study. A test of
compound symmetry was used to test for the eguality of these
dependent correlations (Steiger, 1980). Results of these
analyses (Table 8) indicated that agreement between parent
and child did not differ significantly across time for 8 of
the 13 adherence measures: calories consumed; percent
calories: fat; percent calories: carbohydrates; eating
freguency; exercise duration; exercise type; exercise
freguency; and testing freguency. On measures of injection
regularity, injection interval, injection-meal timing,
regularity of injection-meal timing, and concentrated sweets
there were significant differences in the parent/child
correlations across two or more time periods. These
differences exhibited no consistent pattern. Injection
regularity, for example, exhibited the best parent/child
agreement at Time 5 (r=.81) and the worst at Time 3 (r=.54),
while concentrated sweets exhibited the highest parent/child


15
Only one of the many necessary diabetes management
behaviors was targeted in this study and no attempt was made
to determine whether increasing the frequency of urine
testing had an effect on children's level of control.
An intervention study by Epstein et al. (1981) employed
parent training procedures. Twenty families were assigned
to one of three groups in a multiple baseline design.
Parents were instructed to use a point system and praise for
their youngsters' compliance with urine testing. Both
self-report (daily urine test results and adherence to urine
testing regimen) and biochemical measures (insulin dosage,
HbAi, plasma glucose, serum lipids) were used to assess
effects of the intervention. Epstein et al. went to
considerable lengths to determine the reliability of the
children's recordings (although only negative urine data
were used for analyses) by asking parents to test four
urines per week on a schedule determined by the experimen
ters and without knowledge of the child's values. Reliabi
lity was assessed for 91% of the treatment weeks and parents
and children agreed on the glucose concentration within a
one measurement interval range on each of the four reliabi
lity tests during 83% of the weeks. Exact parent/child
agreement on the urine tests was not reported. The
reliability/validity of reported urine testing frequency was
measured by including predetermined quantities of inert,
placebo Clinitest tablets in each bottle. Parents were
informed about the correct number of placebos. At the end


37
protein consumption was developed since it
can be automatically determined by knowing
the child's fat and carbohydrate consumption.
8) Concentrated Sweetsforty calories of any
concentrated sweet was considered equivalent
to one concentrated sweet exchange unit. The
average number of these exchange units
consumed per day was calculated.
9) Eating Frequencybased on an ideal of six
meals/snacks per day, the percent of snacks
or meals not eaten across the interviews was
calculated and multiplied by 100. A high
score indicates that the child ate infre
quently. A low score indicated frequent
eating occasions. A score of zero indicated
the child averaged six meals or snacks per
day.
10) Exercise Durationthe average amount of time
the child spent exercising during each
exercise occasion across the interviews was
calculated and a constant (1) was added to
avoid subsequent division by zero. The
reciprocal of this score was used so that low
scores indicated lengthy exercise and high
scores indicated little or no exercise.
11) Exercise Typeeach exercise or activity was
given an energy expenditure rating (Hatch and


INTERVIEWER'S NAME:
Name:
For:
Today's date:
TESTING
For: Weekday
Weekend
INSLLIN INJECTION(S)
Ho many shots prescribed: 1234
r "r"
T une
l&C
T m*
~7>r
T i me
Tm~
Time
TM
Units
Type
Units
Type
Uni ts
Uni ts
Dose
Regular
Dose
Regular
Dose
Regular
Dose
Regular
NPH
NTH
NPH
NPH
Lente
Lente
Lente
Lente
Semi
Semi
Semi
Semi
Actrapid
Actrapid
Actrapid
Actrapid
Monotard
Monotard
Monotard
Monotard
Who gave shot
?
Who gave shot?
Who gave shot?
Who gave shot?
This parent
This parent
This parent
This parent
obs? Yes
NO
obs? Yes
No
ops? Yes
-No
obs ? ves
No
Pre-Breakfast
Method used:
2-drop/cs/other
Tester:
Parent observed?
Yes No
Time:
AM PM
Sugar:
<2% 2-6% >6%
Ketones:
N S M L
Chemstrip:
Pre-Lunch
Method use*
i:
2-drop/cs/other
Tester:
Parent obs'
irved?
Yes No
Time:
AM PM
Suqar:
<2% 2-6% >6%
Ketones:
N S M L
Chenistri p:
Pre-Supper
Method used:
2-drop/cs/other
Tester:
Parent observed?
Yes No
Time:
AM PM
Sugar:
<2% 2-6% >6%
Ketones:
N S M L
Chemstrip:
FOOD INTAKE
Pre-Be
d
Method used:
2-drop/cs/
other
Tester:
Parent observed:
Yes h
0
Time:
AM F*
M
Sugar:
<2k 1-6%
> 6%
Ketones:
N S M
L
Chemstrip:
Method used:
2-drop/cs/other
Tester:
Parent observed?
Yes No
Time:
AM PM
Sugar:
Ott o c v r C./0 l"Da) Q/o
Ketones:
N S M L
Cnemstrip:
BREAKFAST
SNACK
LUNCH
SNACK
SUPPER
SNACK
Time AM PM
Time AM PM
Time AM PM
Time AM PM

Time AM PM
Time AM PM
Parent Obs? Yes cT
Parent Obs? Yes No
Parent Obs? Yes No
Parent Obs? Yes No
Parent Obs? Yes No
Parent Obs? Yes No
Qty/
Size
Item
Qty/
Size
Item
Qty/
Si ze
Item
Qty/
Size
Item
Qty/
Si ze
Item
Qty/
Size
Item


i
:
EXERCISE
Morning
Afternoon
Evening
T i me AM PM
Tine AM PM
Time AM PM
This parent obs? Yes No
This parent obs? Yes No
This parent obs? Yes No
Activities How long?
Activities How long?
Activities How long?
Time AM PM
Time AM PM
Time AM PM
This parent obs? Yes No
This parent obs? Yes No
This parent cbs? Yes No
Activities How long.'
Activ:ties How long?
Activities How long?
COMMENTS
Was this a typical day
eating, exercise, i 1In
Yes
for you? (i .e.,
ess, stress, etc.)
No
Why?

-
EXTRA SNACK
Time AM PM
Parent Obs? Yes No
Qty/
Size
Item

115


74
camp was tested. This model did not significantly enhance
the R2 above that offered by the original model using pre
camp GSP as the sole predictor variable.
Since the obvious hypothesis that increases in calories
consumed at camp was responsible for the post-camp GSP
increases was not confirmed, further analyses were conducted
to assess whether any of the other adherence measures may
have related to post-camp GSP. To do so, the data were
combined into adherence factors found by Johnson et al.,
(1986). However, Johnson et al.'s factors were based on a
combination of child and parent report whereas the data in
this study were based exclusively on child report. Accord
ingly, to support the combination of the child report data
into factors, it was necessary to determine whether the
measures (based on child report) within each of the Johnson,
et al. factors were more highly correlated than measures
between factors. The intercorrelation matrix of child
reported pre-camp adherence measures was examined. The
correlations of measures within factors were averaged
(excluding the 1.0s) and compared to the average correlation
of measures between factors. Results indicated that similar
to Johnson et al.'s results, average correlations of
measures within factors were larger (ranging from .22 to
.96) than the average correlations of measures between
factors (ranging from .01 to .21, see Table 14). These
results supported the combination of the measures. There
fore, measures were standardized to pre-camp measures and


52
In addition to the parent/child correlations calculated
for each time period, the interviews were also collapsed
across time periods to determine the overall correlations by
age group (Table 7). Parents and children's reports for the
total sample were significantly correlated for all 13
measures, ranging from r=.36 (p<.004) on regularity of
injection-meal timing to r=.92 (pc.0001) for glucose testing
freguency. Comparisons between independent correlations
using Fisher's z' transformation were conducted to assess
differences in the degree of parent/child agreement across
the three age groups (p < .05, Cohen and Cohen, 1983, pages
53-56). These tests revealed that on 5 of the 13 adherence
measures (injection interval, injection-meal timing,
calories consumed, exercise duration, and glucose testing
freguency) there were significant differences in the
parent/child correlations among the three age groups (see
Table 7). The 10- to 11.4-year old demonstrated the highest
agreement (r=.92) with their parents on the injection
interval measure while the 7- to 9-year olds' reports did
not correspond as well (r=.51). On the injection-meal timing
measure, the oldest youngsters' reports corresponded best
with their parents (r= .75), while the 7- to 9-year olds
demonstrated the worst parent/child agreement (r= -.03).
Similarly, on the calories consumed measure, the oldest
group exhibited the highest parent/child correlation
(r=.89) and the youngest group displayed the weakest
(r=.53). The 11.5- to 12.6-year olds again demonstrated the


TABLE 11
Means and Standard Deviations for Adherence
Measures with Age x Time Interaction at the 5 Time Periods
Adherence Measure
Time 1
Means(SD)
(Interp)
Time 2
Means(SD)
(Interp)
Time Period
Time 3 Time 4
Means(SD) Means(SD)
(Interp) (Interp)
Time 5
Means(SD)
(Interp)
Injection 7-9yrs
. 4 3 (. 41)
.46(.54)
.63(.65)
.98(.66)
. 4 5 (. 3 4)
interval
(25.6)
(27.6)
(37.8)
(58.8)
(26.7)
(minutes) 10-11.4yrs
. 91(.87)
1.05(1.9)
.94(.73)
. 79 ( .77)
1.31(1.2)
(54.8)
(63.3)
(56.4)
(47.4)
(78.5)
11.5-12.6yrs
.79(.50)
.24(.23)
1.28(1.0)
.84(.42)
1.06(.94)
(47.1)
(14.5)
(76.54)
(50.3)
(63.5)
Calories 7-9yrs
consumed
-7.4(489)
164.9(962)
-4.1(619)
18.1(721)
277.1(480)
10-11.4yrs
100.0(843)
598.9(794)
228.5(610)
111.5(693)
297.4(726)
11.5-12.6yrs -
307.8(612)
382.9(874)
-327.9(533)
-325.3(649)
-396.9(550)
Exercise 7-9yrs
72.9(14.8)
30.8(11.7)
80.9(12.0)
74.8(12.0)
67.0(13.4)
frequency
(1.8)
(4.5)
(1.3)
(1.6)
(2.0)
10-11.4yrs
73.7(11.1)
23.4(9.7)
74.2(19.5)
71.1(16.3)
74.6(13.8)
(1.6)
(4.2)
(1.6)
(1.8)
(.07)
11.5-12.6yrs
69.3(14.3)
28.4(16.7)
80.4(12.5)
81.2(11.1)
78.4(11.2)
(.10)
(4.3)
(.04)
(04)
(1.3)
Ol


because the pancreas does not resume insulin
production.
3
2) Controlled dietFood intake must be adjusted
for content and synchronized with the
injections and exercise for optimal benefit.
The youngster must eat three meals a day as
well as one to three snacks per day,
preferably at regular intervals. Low fat
nutrition is encouraged and concentrated
sweets are to be avoided.
3) Regular exerciseAn exercise regimen is
important for improving cardiovascular
functioning which facilitates insulin
circulation.
4) Glucose testingClose monitoring of
glycemic control (approximately four
times per day preferably before meals)
is recommended so that meals, snacks,
and exercise can be appropriately ad
justed. In addition, this information
is necessary for the physician to make
appropriate changes in regimen recom
mendations .
The various components of the regimen are tailored to
the needs of each individual youngster. The goal of
treatment is to maintain blood glucose levels within a
reasonable range in order to promote normal growth and


appears that high levels of adherence with inappropriate
insulin dose prescriptions can lead to poorer control.
vm


LIST OF TABLES
Table Page
1 Parent-Child Correlations for Total Sample
and by Age Group (Johnson, et al.) 23
2 Sample Characteristics 32
3 Parent-Child Correlations for Total
Sample and by Age Group: Pre-camp 4 3
4 Parent-Child Correlations for Total
Sample and by Age Group: Immediately
Post-Camp 45
5 Parent-Child Correlations for Total
Sample and by Age Group: Six
Weeks Post-Camp 47
6 Parent-Child Correlations for Total
Sample and by Age Group: Three
Months Post-Camp 49
7 Parent-Child Correlations for Total Sample
and by Age Group Collapsed Across Time 53
8 Parent-Child Correlations for Total Sample
Over Four Time Periods: Pre and Post-Camp... 56
9 Duncan's Multiple Range Tests on the
Adherence Measures Demonstrating Within
Subject Effects 61
10 Means and Standard Deviations for
Adherence Measures at the 5 Time Periods 64
11 Means and Standard Deviations for Adherence
Measures with Age x Time Interaction at the
5 Time Periods 65
12 Means and Standard Deviations for Adherence
Measures that Remained Stable Over the
5 Time Periods 67
13 Correlations Between Glycosylated
Hemoglobins and Serum Proteins 69
IV


TABLE 2
Sample Characteristics
Total
Sample
Group 1
7-9
Years
Group 2
10-11.4
Years
Group 3
11.5-12.6
Years
Sample Size
64
20
23
21
Sex: Males
34
13
13
8
Females
30
7
10
13
Family Income
in categories: 1
14.5%
0%
28.6%
11.1%
2
27.3%
31.3%
28.6%
22.2%
3
23.6%
25.0%
23.8%
22.2%
4
18.2%
18.8%
9.5%
27.8%
5
16.4%
25.0%
9.5%
16.7%
Age: Mean
10.5
8.5
10.8
12.2
Range
7.6-12.6
7.6-9.1
10.1-11.1
11.6-12.6
Stand. Dev.
1.57
. 65
. 36
.47
Duration: Mean
3.9
3.6
3.3
4.9
Range
.9-9.1
1-5.1
.9-4.4
1.1-8.3
Stand. Dev.
2.67
2.11
2.18
3.37
U>
ro


107
increase in HbA^c by followup. It is possible that in
creased compliance in this instance (these were the most
compliant youngsters) would exacerbate the rebound cycle,
leading to deterioration in glycemic control.
When youngsters were categorized into control categor
ies based on pre-camp HbA^c values, it was found that there
was a negative relationship between control and injection
factor adherence. However, these results were not replicated
post-camp or pre-camp using GSP control groupings. Overall,
there was no support for a simple linear associations
between adherence and glycemic control at any point in this
investigation. These findings are consistent with those
reported by Glasgow et al, (1987) who could find no clear
relationship between adherence and glycemic control through
either bivariate or multivariate analyses.
Limitations and Future Directions
This study had several strengths and limitations. The
use of child report made it possible to monitor children
over a relatively extensive period of time spanning a
naturalistic imposition of behavior change relevant to
diabetes management. The advent of GSP measures made it
possible to assess diabetic control over a relatively short
period of time so that an association between behavior
change and glycemic control could be examined. However, the
narrow age range in this sample and the consistent change
produced by the camp experience did not afford enough


METHOD
Sample Characteristics
Participants were 64 IDDM youngsters who attended the
1985 North Florida Camp for Children and Youth with
Diabetes. The 1985 camp session lasted 2 weeks. One parent
of each youngster was also asked to participate. Informed
consent was obtained from all participants. Youngsters were
between the ages of 7-12 (with a mean age of 10.5 years).
There were 34 boys and 30 girls. Participating families
reported income in 5 categories: 1= $ 0 to $9,999, 2=
$10,000 to $19,999, 3= $20,000 to $29,999, 4= $30,000 to
$39,999, and 4= $40,000+. Of all participating families
14.5% were in category 1, 27.3% in 2, 23.6% in 3, 18.2% in
4, and 16.4% in 5. All but one (98.4%) of the youngsters
had had diabetes for at least 1 year with a range from .9
years to 9.1 years (Table 2). All but one of the youngsters
were Caucasian. Only two youngsters dropped out of the
study after camp.
Measures
Adherence Measures
Twenty-four hour recall interviews, conducted with
children and parents, were recorded on Diabetes Daily Record
forms to assess the youngsters' diabetes related
31


68
average estimation of glycemic control for a period of 2 to
4 months, and glycosylated serum protein (GSP) which
provides a measure of glycemic control over a 10 to 14 day
period. In order to support the reliability of the laborat
ory assays, Pearson product moment correlations were
performed (Table 13). It was expected that the highest GSP/
HbA^c correlation would occur between the pre-camp GSP
measure and the pre-camp HbA^c measure since they were drawn
at the same time and reflect some overlap in time periods.
This was found to be the case (r=.71, p < .0001, Table 13).
Further, it was expected that the pre-camp GSP measure would
correlate less well with the 3 month followup HbA^c since
these measures reflect entirely different time periods. In
fact, the correlation between pre-camp GSP and followup
HbAiC was markedly lower (r=.52, p < .0001). The lowest
GSP/Hba^c correlations were expected between post-camp GSP
and both pre-camp HbA^c and followup HbA^c, taken at 3
months after camp, since the GSP and HbA^c measures reflect
completely different time periods and completely different
settings (camp versus home). In fact, these correlations
(r=.43; r=.39) represent the lowest correlations in the
matrix depicted in Table 13. Overall, the pattern of
results depicted in Table 13 supports the reliability of our
glycemic control measures.
To assess possible changes in glycemic control due to
the camp experience,a repeated measures ANOVA was performed
on the GSP pre- and post-camp measures using 2 between


14
frequency of their urine testing which was arbitrarily
designated by the investigator as the target behavior. The
youngsters chose rewards and self-delivered them contingent
on performing the target behavior and graphed their perfor
mance. During the final phase, the subjects were taught
negotiating and contracting skills and in a meeting with the
experimenter, the child, and his parents a contract was
arbitrated. All contracts involved parental reinforcement
for continuation of self-management of urine testing.
Reliability data were collected by parents counting the
number of test tablets used on 1 day each week when the
child was not at home. The children were aware that the
parents would be checking but did not know when. No attempt
was made to monitor the accuracy of the children's urine
testing. Reliability was based on dividing the number of
agreements of parent and child report by agreement plus
disagreement and multiplying the quotient by 100. Reliabi
lity averaged 80%. No data were reported on the rate of
compliance with the self-reward regimen. Pre- and post
training tests on the principles and procedures of behavior
modification were conducted and 2- and 8-week follow-up data
were also collected. Children improved their rate of urine
testing from 9% to 74% of the time during the self-manage
ment condition and at the 2-week follow-up. However, at the
8-week follow-up, two of the four families had discontinued
with the contract and those children's behavior had returned
to baseline levels.


TABLE 1
(continued)
Adherence Measure
Total
Sample
r (n)
P <
Group 1
(6-9 Years)
r(n)
P <
Group 2
(10-12 Years)
r(n)
P <
Group 3
(13-15.6 Years)
r(n)
P <
Group 4
(16-19 Years)
r(n)
P <
Percentage
calories-
carbohydrate
.64(167)
(.0001)
.44(31)
(.01)
.63(70)
(.0001)
.81(44)
( .0001)
.62(22)
(.002)
Concentrated
Sweets
.62(167)
(.0001)
.47(31)
(.007)
.71(70)
( .0001)
.76(44)
( 0001)
.12 (22)
. 58
Eating
frequency
.45(167)
(.0001)
. 67(31)
( .0001)
. 47 (70)
(.0001)
.54(44)
(.0002)
. 13.(22)
(.57)
Exercise
duration
.59(167)
(.0001)
.03(31)
(.87)
.96(70)
(.0001)
.79(44)
(.0001)
.47(22)
(.03)
Exercise
type
.54(167)
(.0001)
.74(31)
( .0001)
.99(70)
(.0001)
.66(44)
(.0001)
.32(22)
(.15)
Exercise
frequency
.62(167)
(.0001)
. 62(31)
(.0002)
.72(70)
( .0001)
.72(44)
( .0001)
.32(22)
(.13)
Glucose
testing
frequency
.78(164)
(.0001)
.81(31)
(.0001)
.73(68)
(.0001)
.77(44)
( 0001)
.37 (21)
(.10)
Table from Johnson, et al.,
1986
fo


12
issues were addressed with 93 adult IDDM outpatients.
Similar to the Schafer et al.(1983) study, this investiga
tion explored adherence to various aspects of the diabetes
care regimen, the congruity of adherence of the different
behavioral components, and the relationship between adher
ence and glycemic control as measured by glycosylated
hemoglobin. Subjects participated in three home interviews
during a 1 week period, completed psychosocial measures,
collected self-care measures on injections, glucose testing,
diet and physical activity. The entire procedure was
repeated for a subsample at two months and for the entire
sample at six months. Self-report information was organized
into five categories of adherence: insulin injections,
glucose testing, diet level, dietary adherence, and physical
activity. Glycemic control was measured with glycosylated
hemoglobin and percent negative glucose tests (both urine
and blood). Each adherence measure was compared to patient
report of regimen prescriptions. Their results indicated
that compliance was better for taking medication and for
glucose testing than for behaviors which required major
adjustments in routine (e.g., diet and exercise); adherence
with one component of the regimen did not necessarily
correlate with adherence to other regimen tasks. Compliance
behaviors were not stable over time although glycemic
control was relatively stable; no clear relationship between
adherence and glycemic control was established through
either bivariate or multivariate analyses. Although this


34
injection interval of 24 hours between a.m.
injections. For youngsters who take p.m.
injections, an ideal shot interval was
arbitrarily defined as 10 hours between a.m.
and p.m. injections on the same day, and 14
hours between the p.m. injection and the a.m.
injection on the following day.
3) Injection-Meal Timingthis measure evaluated
the timing of injection in relationship to
meals. It was calculated by averaging the
number of minutes between taking an injection
and eating a meal. Sixty minutes were added
to this average so that youngsters taking
their injection 60 minutes before a meal
received an adherence score of zero. Young
sters who, on the average, took their
injections 60 minutes to 1 minute before
meals received scores from 0 to .99. Young
sters who usually took their injections at
the time of their meals or after eating
received scores of 1.0 or greater.
4) Regularity of Injection-Meal Timing
this measure appraised the regularity of
intervals between injections and eating.
This was measured by calculating the
standard deviation of the Injection-Meal
Timing measure described above.


105
pre-camp adherence was detected among these groups.
Similarly, when children were grouped based on change in
glycemic control no differential change in adherence by
group was detected. When children were categorized based on
their level of adherence before coming to camp, it was found
that the GSP levels of the highly adherent children did not
differ from the GSP levels of nonadherent children.
Similarly, analyses based on groups that either improved or
declined in adherence during camp did not reveal any
associations between adherence and glycemic control.
To evaluate the relationship between adherence and
control after camp, the adherence measures derived from the
nine interviews conducted after camp were combined into the
five adherence factors. Hierarchical regression analyses
revealed that the model that best predicted the followup
HbA^c measure of glycemic control contained the pre-camp
HbA^c, age, (increasing age has been associated with changes
in insulin absorption, Amiel et al., 1986), insulin dose
differences (pre-camp minus during-camp dose), post-camp
injection factor, and an insulin dose difference x post
camp injection factor interaction term (to control for
insulin dose changes). When the children were divided into
groups based on the Beta weights for the post-camp injection
factor for varying levels of insulin dose change pre- to
post-camp, it was found that the children who had the
largest increase in insulin dose at followup had arrived at
camp in the best control, were the youngest (and possibly


TABLE 3
(continued)
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r (n)
P <
Group 3
(11.5-12.6 Years)
r (n)
P <
Percentage
. 72 (63)
. 80(20)
. 65(22)
.69 (21)
calories-
carbohydrate
( 0001)
(.0001)
( .0011)
( .0005)
Concentrated
.63(63)
.35(20)
.71(22)
.63(21)
sweets
( 0001)
( 1292)
(.0002)
( .0020)
Eating
.72(63)
.83(20)
.64(22)
.73(21)
freguency
( 0001)
( .0001)
(.0013)
( .0020)
Exercise
.29(63)
.68(20)
.07(22)
.96(21)
duration
( .0211)
(.0010)
(.7603)
( .0001)
Exercise
.64(63)
.76(20)
.56(22)
.62 (21)
type
( 0001)
(.0001)
(.0071)
( 0029)
Exercise
.68(63)
.81(20)
.50(22)
.68(21)
freguency
( 0001)
(.0001)
(.0190)
(.0007)
Glucose
.90(63)
.89(20)
.97(22)
.68(21)
testing
frequency
( .0001)
(.0001)
(.0001)
(.0006)
4^


79
levels of adherence as well as change in glycemic control
and change in adherence.
Accordingly, it was first hypothesized that children
arriving at camp at different levels of diabetic control
would report different levels of adherence prior to attend
ing camp. Therefore, based on a univariate procedure on the
pre-camp GSP measure, the youngsters were divided into three
GSP control categories. Pre-camp GSP of less than or egual
to 4.5 was considered good control, greater than 4.5 and
less than or egual to 7.0 was considered moderate control,
and greater than 7.0 was considered poor control. ANOVAs
were conducted on each of the five pre-camp adherence
factors using one between subject factor (GSP control
group). Results indicated no significant differences
between the groups on any of the adherence factors.
Next, it was postulated that the adherence/GSP control
relationship might be detected if youngsters were categori
zed on their degree of adherence before coming to camp.
Theoretically, those children who had been more adherent
before coming to camp would also exhibit lower GSP values at
the beginning of camp. Therefore, based on the pre-camp
median score for each of the five adherence factors,
youngsters were assigned a score of 1 for being below the
median (adherent) and a score of 0 for being above the
median (nonadherent). Their scores for the five adherence
factors were summed and children were placed into three
adherence categories (0-1 was poor adherence, 2-3 was


112
Katch, F. and McArdle, W. Nutrition, weight control and
exercise. Boston:Houghton-Mifflin, 1977.
Kennedy, Allan L. and Merimee, Thomas J. Glycosylated serum
protein and hemoglobin A]_, levels to measure control of
glycemia. Annals of Internal Medicine, 1981, 95,
56-58.
LaGreca, Annette M., Follansbee, Donna, Skyler, and Jay S.
Behavioral aspects of diabetes management in children
and adolescents. Paper presented at the American
Psychological Association Meetings, Washington, D.C.,
1982.
Lebovitz, Franzine L. Ellis III, George J., and Skyler, Jay
S. Performance of technical skills of diabetes
management: Increased independence after a camp
experience. Diabetes Care, 1978, 1(1): 23-26.
Maxwell, D.R., Luft, F.C., Clark, C.M., and Vinicor, F.
Diabetes mellitus: New approaches to complications.
Journal of the Indiana State Medical Association, 1982,
March, 75, 184-186.
McCulloch, D.K., Young, R.J., Steel, J.M., Wilson, E.M.,
Prescott, R.J., and Duncan, L.J. Effect of dietary
compliance on metabolic control in insulin-dependent
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37(4): 287-292.
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Merimee, Thomas J. Comparison of two indices of
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serum protein and hemoglobin. Diabetes Care, 1983,
6(1): 34-39.
Moffatt, M.E.K. and Pless, I.B. Locus of control in
juvenile diabetic campers: changes during camp, and
relationship to camp staff assessments. Journal of
Pediatrics, 1983, 103: 146-150
Nuttal, F.Q. and Brunzall, J.D. Principles of nutrition and
dietary recommendations for individuals with diabetes
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tion. Journal of the American Dietetic Association,
1979, 75(5): 527-530.
Rainwater, Nancy, Jackson, Ginny G., and Burns, Kenton L.
Relationships among psychological, metabolic, and
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Psychological Association, Washington, D.C., August,
1982 .


THE RELATIONSHIP BETWEEN ADHERENCE BEHAVIORS
AND GLYCEMIC CONTROL IN CHILDHOOD DIABETES
BY
MARIKA SPEVACK
DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1987

ACKNOWLEDGEMENTS
I wish to take this opportunity to express special and
heart-felt thanks to Dr. Suzanne Johnson who has provided me
with more than the expected support of a Chair throughout
this investigation. Not only has her interest and input
energized this project and enhanced the guality of the
outcome, but she has been an inspiration, a role model and a
friend throughout this endeavor.
My appreciation and thanks go out to my committee,
Nathan Perry, Sheila Eyberg, William Riley, and Jon Shuster
who have been supportive throughout this endeavor.
Most of all, I would like to take this opportunity to
thank my best friend, confidant, and husband Avram who
tolerated bad moods, angst, ambition, and enthusiasm. I
would like to express deep respect, overwhelming love, and
gratitude for his unwavering encouragement and faith in my
ability to accomplish this project.
11

TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ii
LIST OF TABLES iv
ABSTRACT vi
INTRODUCTION 1
METHOD 31
Sample Characteristics 31
Measures 31
Adherence Measures 31
Glycemic Control Measures 39
Procedure 40
RESULTS 42
Reliability of Child Report 42
Effect of Diabetes Camp on Adherence 60
Effects of Diabetes Camp on Glycemic Control 66
Relationship Between Adherence and Diabetic
Control 72
Adherence/GSP Relationship 73
Hierarchical regression analyses 73
Categorical analyses 78
Adherence/HbA^c Relationships 81
Hierarchical regression analyses 81
Categorical analyses 90
DISCUSSION 95
Reliability of Child Report 95
Effect of Diabetes Camp on Adherence 98
Effect of Diabetes Camp on Glycemic Control 102
Relationship Between Adherence and Diabetic
Control 104
Limitations and Future Directions 107
REFERENCES 110
APPENDIX 115
BIOGRAPHICAL SKETCH 116
iii

LIST OF TABLES
Table Page
1 Parent-Child Correlations for Total Sample
and by Age Group (Johnson, et al.) 23
2 Sample Characteristics 32
3 Parent-Child Correlations for Total
Sample and by Age Group: Pre-camp 4 3
4 Parent-Child Correlations for Total
Sample and by Age Group: Immediately
Post-Camp 45
5 Parent-Child Correlations for Total
Sample and by Age Group: Six
Weeks Post-Camp 47
6 Parent-Child Correlations for Total
Sample and by Age Group: Three
Months Post-Camp 49
7 Parent-Child Correlations for Total Sample
and by Age Group Collapsed Across Time 53
8 Parent-Child Correlations for Total Sample
Over Four Time Periods: Pre and Post-Camp... 56
9 Duncan's Multiple Range Tests on the
Adherence Measures Demonstrating Within
Subject Effects 61
10 Means and Standard Deviations for
Adherence Measures at the 5 Time Periods 64
11 Means and Standard Deviations for Adherence
Measures with Age x Time Interaction at the
5 Time Periods 65
12 Means and Standard Deviations for Adherence
Measures that Remained Stable Over the
5 Time Periods 67
13 Correlations Between Glycosylated
Hemoglobins and Serum Proteins 69
IV

14 Correlations Between Adherence Measures
Within and Between Factors 75
15 Average Correlations between Adherence
Factors 77
16 Predicting Post-camp HbA^c by Pre-camp HbA^c,
Age, Post-camp Injection Factor, and Pre-camp
Hba^c x Post-camp Injection Interaction 84
17 Predicting Post-camp HbA^c: Nonstandardized
Injection Beta Weights at Varying Levels of
Pre-camp HbA^c 85
18 Pre-camp HbA^c x Post-camp Injection Factor:
Descriptive Characteristics 86
19 Predicting Post-camp HbA^c by Pre-camp HbA^c,
Age, Insulin Dose Change, Post-camp Injection
Factor, and Insulin Dose Change x Post-camp
Injection Interaction 88
20 Predicting Post-camp Hba^c: Nonstandardized
Beta Weights at Varying Levels of Change in
Insulin Dose from Pre-camp to 3 Months
Post-camp 89
21 Pre- Post-camp Change in Insulin Dose x
Post-camp Injection Factor: Descriptive
Characteristics 91
v

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
THE RELATIONSHIP BETWEEN ADHERENCE BEHAVIORS
AND GLYCEMIC CONTROL IN CHILDHOOD DIABETES
BY
MARIKA SPEVACK
December 1987
Chairman: Suzanne Bennett Johnson, Ph-D.
Major Department: Clinical and Health Psychology
This study was designed to investigate the following
questions: 1) do children's diabetes management behaviors
change at summer camp; 2) does attendance at summer camp
affect diabetic children's glycemic control; 3) if there are
changes at camp, are they maintained at home; 4) is there a
relationship between adherence behavior and glycemic
control. Sixty-four 7- to 12.6-year old youngsters with
diabetes were followed before, during, and after attending a
2-week summer camp. Both their diabetes management behav¬
iors and their glycemic control were monitored.
Children's diabetes management behaviors (i.e., injec¬
tion regularity, injection interval, calories consumed,
eating frequency, exercise duration, exercise type, exercise
frequency, and glucose testing frequency) changed signifi¬
cantly while they attended summer camp. There was also a
vi

change in children's level of glycemic control as measured
by glycosylated serum protein levels. However, children
were in poorer glycemic control at the end of camp than at
the beginning. None of these camp-related changes were
maintained once the youngsters returned home. No clear
relationship between adherence behaviors during camp and
control could be established. However, post-camp metabolic
control was predicted by an interaction between change in
insulin dose (pre- to post-camp) and injection adherence
behaviors. Youngsters who had large increases in insulin
dose exhibited a deterioration in metabolic control when
they were more adherent. Youngsters who had large decreases
in insulin dose exhibited the opposite relationship: better
adherence was associated with better control. Further
inspection of this statistically significant interaction
indicated that pre- post-camp change in insulin dose
appeared to be initiated during camp. Camp related insulin
dose changes were maintained or enhanced once the children
went home. The group of children whose insulin was
increased during camp, were given further increases in
insulin dose once they returned home and may have been
experiencing the Somogyi phenomenon (rebounding) after camp
as a consequence of excessive increments in their insulin
dose. Home physicians, appeared to have used insulin
adjustments made during camp as a model of treatment and
continued the trend initiated by the camp physicians. It
Vll

appears that high levels of adherence with inappropriate
insulin dose prescriptions can lead to poorer control.
vm

INTRODUCTION
Insulin dependent diabetes mellitus (IDDM) is a chronic
illness, the result of insufficient insulin production by
the pancreas. Onset occurs before age 30—most frequently
between the ages of 8 and 12 years—although it can occur at
any time from birth to adulthood (Blevins, 1979) .
Under normal circumstances, insulin is secreted
directly into the blood-stream by the Islets of Langerhans
in the pancreas. This hormone plays a vital role in the
metabolism of food since it facilitates the movement of
glucose from the bloodstream into muscle and fat tissue for
energy. Glucose that is not used for energy is then
directed into storage (primarily into the liver) in the form
of glycogen and/or fat. Fat is an additional source of
energy which does not require insulin to be converted.
However, the energy expended in its production is
considerable, leaving a negligible amount for functional
use. Normally, the pancreas secretes insulin at rates that
maintain glucose levels in the bloodstream within a narrow
range. In youngsters with IDDM, the pancreas cannot perform
this function and glucose levels build up.
The absence of insulin prevents glucose usage, fat is
broken down and glucose levels remain high. The liver
1

2
converts some of the fat into ketones, which increase in the
blood and, at high levels, spill into the urine. In
addition, when the blood passes through the kidney, glucose
is filtered out and, at high levels, the extra glucose
spills over into the urine. The kidney attempts to filter
out the excess glucose and ketones from the blood in order
to maintain acid alkaline (Ph) levels within normal limits.
This results in increased urination and increased thirst.
However, the kidney can not keep up this level of activity.
Unchecked, ketones and acids build up in the blood and the
child develops diabetic ketoacidosis—a serious condition
requiring hospitalization. Furthermore, the loss of urine
water and the use of body fat for energy produce weight loss
(Travis, 1969). If this condition is untreated, coma and
death can result.
Presenting symptoms of IDDM include polyuria, poly¬
dipsia, polyphagia, and weight loss. There is often a high
prevalence of infections. More subtle signs of the disorder
include pruritus (severe and protracted itching of the
skin), enuresis (bedwetting), vulvitis, irascible disposi¬
tion, lassitude, and abrupt onset of visual difficulties
(Sussman, 1971). As yet, there is no cure for this disease.
The treatment of IDDM consists of the following:
1) Daily insulin injections—A child with IDDM
injects insulin at least once and as many as
four times daily for the rest of his/her life

because the pancreas does not resume insulin
production.
3
2) Controlled diet—Food intake must be adjusted
for content and synchronized with the
injections and exercise for optimal benefit.
The youngster must eat three meals a day as
well as one to three snacks per day,
preferably at regular intervals. Low fat
nutrition is encouraged and concentrated
sweets are to be avoided.
3) Regular exercise—An exercise regimen is
important for improving cardiovascular
functioning which facilitates insulin
circulation.
4) Glucose testing—Close monitoring of
glycemic control (approximately four
times per day preferably before meals)
is recommended so that meals, snacks,
and exercise can be appropriately ad¬
justed. In addition, this information
is necessary for the physician to make
appropriate changes in regimen recom¬
mendations .
The various components of the regimen are tailored to
the needs of each individual youngster. The goal of
treatment is to maintain blood glucose levels within a
reasonable range in order to promote normal growth and

development and to prevent complications. With growing
youngsters, this task is particularly difficult since their
nutritional and insulin requirements are changing as they
grow and injections administered once or twice a day cannot
imitate the synchronous pancreatic action of nondiabetic
individuals (Guthrie and Guthrie, 1977) .
Complications associated with IDDM can be divided into
three categories: short-term, intermediate, and long-term.
1) Short-term (acute) consequences include
diabetic ketoacidosis (DKA), hypoglycemia and
hyperglycemia. Diabetic ketoacidosis can be
precipitated by an infection although stress
or insufficient insulin can also be a
precipitant. Symptoms include rapid respira¬
tion, polydipsia, polyuria, vomiting and
nausea. Hypoglycemia (insulin reaction) can
be caused by overdoses of insulin, its
improper distribution, inadequate intake (or
delayed absorption) of food, or too much
exercise without a concurrent adjustment of
the food intake (Guthrie and Guthrie, 1977).
Increased perspiration, confusion, irri¬
tability and inappropriate behavior are
common symptoms (Sussman, 1971). Hyper¬
glycemia may result from too little insulin,
eating improperly, decreased exercise
(without concomitant decreased food intake),

emotional stress, and infection. The most
frequent symptoms are weakness, increased
thirst, frequent urination, dry mouth,
decreased appetite or polyphagia, nausea and
vomiting, abdominal pain, coma, and acetone
breath which occurs when ketone levels rise
in advanced stages. These acute consequences
are characteristic of youngsters in poor
control (Guthrie and Guthrie, 1977).
Intermediate consequences include delayed
growth and development, frequent infections,
and psychosocial problems. Delayed growth
and development in IDDM youngsters is not
uncommon and has been found to correlate most
clearly with those children in poor control
(Guthrie and Guthrie, 1977). Various kinds of
infections are also common in IDDM children.
Clinical observations and research suggest
that these are also more common in patients
in poor diabetes control (Guthrie and
Guthrie, 1977). Psychological problems are
associated with poor control although the
nature of the relationship between psycho¬
logical problems and health status in
youngsters with diabetes is not entirely
clear (Johnson, 1985).

6
3) Long-term (chronic) consequences of the
disease include retinopathy and nephropathy.
Retinopathy is characterized by the formation
of new capillaries and duplication of small
veins, retinal detachment, preretinal and
vitreous hemorrhage, and fibrosis. Blindness
may be the result (Sussman, 1971). Nephropa¬
thy (renal disease) is the leading cause of
death among people with IDDM (Blevins, 1979).
Complications occur 15-20 years after
diabetes onset. Scientific data suggest that
these diseases may be preventable if the
patient's diabetes is maintained in good
control (Guthrie and Guthrie, 1977).
Clearly, metabolic control, or lack thereof, is
implicated in all three categories of complications both
directly and indirectly. Although evidence that complica¬
tions occur more frequently among patients in poor control
is mounting (Rifkin, 1978; Maxwell, Luft, Clark, and
Vinicor, 1982; Unger,1982; Johnson, 1984), it is difficult
to properly evaluate this literature since the definition of
"control" is so loosely posited and varies from investigator
to investigator. Moreover, a definitive link between
adherence to prescribed regimen (i.e. behavior) and control
has yet to be established.

7
Studies in this area are few and present numerous
methodological problems. For example, control and com¬
pliance are often confused as when hemoglobin Ale (HbAic), a
metabolic indicator of glycemic control, is taken to
represent compliance. In some cases, although both con¬
structs are treated separately, the same person judges both,
introducing the possibility of a spurious association
between the two (i.e., the rater may believe compliance and
control are related and judge them accordingly).
The few studies in the area can be divided into two
categories. The first category is composed of correlational
studies which are concerned with establishing associations
between compliance behaviors and control. One such study by
Rainwater, Jackson and Burns (1982) tried to assess the
relationship between psychological variables, metabolic
control, and behavioral measures using 115 children between
8 and 17 years and their mothers. Their most noteworthy
finding was that Mother's Health Locus of Control (MHLC) was
significantly correlated with behavioral and metabolic
measures suggesting that mothers who scored external tended
to have better controlled children. That is, MHLC corre¬
lated significantly r=-.58, p< .01, with total amount of
sugar spilled in a 24-hour urine sample collected from the
patient, the number of patient hospitalizations in the past
year, -.42, (p<.01), the number of insulin reactions
experienced by the patient in the past month r=.36 (pc.Ol),
and the number of hours the patient exercised per week

8
during the past 3 months r=.35 (p<.01). However, HbA^c did
not correlate significantly with MHLC or Mother's General
Locus of Control. Further, there appeared to be no sig¬
nificant relationship between the author's diabetes control
indices (such as 24-hour urine, number of hospitalizations,
number of reactions, hours of exercise and HbAic) and the
child's self-esteem (measured by the Piers-Harris Inventory)
or with the child's life change scores (measured with the
Coddington Scale for Life Events).
This study claimed to have used both behavioral
(defined by the authors as the number of hours exercised per
week, number of diabetes related hospitalizations in the
past year, and number of insulin reactions in the past
month, and number of times acetone was noted in the urine in
the past month) and metabolic (hemoglobin A^c, triglyce¬
rides, cholesterol, fasting blood sugar, and total amount of
sugar spilled in the urine in a 24-hour period) measures
which the authors presumed to reflect diabetes control.
Glycosylated hemoglobin (HbA^c) is now widely accepted as
the single most reliable and adeguate measure of glycemic
control. The authors' use of 24-hour urine as an index of
control is problematic for two reasons. Valid 24-hour
urines are difficult to collect even under controlled
conditions such as hospitals. In our experience, only
20-25% of all specimens solicited and collected were
complete collections. Glycosylated hemoglobin (HbA^c)
values are a more widely accepted measure of control than

9
24-hour urines and provide a more stable measure of control
over the preceding 3 months rather than just 24 hours.
Rainwater et al. did not report whether the 24-hour speci¬
mens in their study were valid. Even if the 24-hour
collection was valid, it indirectly represents the young¬
ster's metabolic control for a relatively brief period of
time. Since no relationship was shown between MHLC and
HbA^c, the most reliable indicator of metabolic control over
the past 3 months, the relationship between MHLC and the
24-hour collection data is difficult to interpret (i.e., why
would such a relationship exist for one measure of diabetes
control but not another?). Furthermore, the study did not
directly assess adherence behaviors. Rather, behavioral
measures, as defined by the authors, consisted of report by
family of the number of diabetes related hospitalizations in
the past year, average number of hours of exercise per week
over the past 3 months, number of insulin reactions in the
past month, and number of times acetone was noted in the
urine in the past month, thereby confusing adherence
behaviors with metabolic indicators and other sequelae of
poor control.
In another correlational study, LaGreca et al. (1982)
examined the relationship between metabolic control as
measured by hemoglobin and different aspects of diabetes
care such as degree of responsibility taken by the child,
level of knowledge, and degree of compliance as rated by
physicians following routine appointments with 40 IDDM

10
youngsters. Results of this study indicate that for
preadolescents there was no relationship between physicians
compliance ratings and metabolic control. For adolescents
the best predictor of control was the child's overall level
of compliance with the treatment regimen, especially with
eating regular snacks and carrying sugar for emergencies.
However, their findings suggest that regardless of com¬
pliance ratings, the more responsibility assumed by the
youngster, the poorer their control.
It should be noted that the physician's four-point
rating of compliance was based on patient report (testing,
charting, administering insulin, eating proper foods, eating
regular snacks, carrying quick-acting sugar, and taking
sugar to treat reactions) as well as on whether the child
kept appointments. The reliability of these ratings was not
tested and it is unclear whether the physician's rating may
have been influenced by the patient's current level of
metabolic control. Furthermore, no information was provided
about the relative importance of any one aspect of the
regimen.
McCulloch et al. (1983) correlated dietary behaviors
such as accuracy of measuring carbohydrates and maintaining
consistent and regular eating patterns with HbA^ in adult
diabetics who kept 7-day diaries. Results indicated better
glycemic control in those persons who were more precise in
their measurements and compliant in the regularity of their
meals. There was, however, no attempt made to corroborate

11
the patients' self-reports, and nondietary aspects of the
treatment regimen were not studied.
One study that has attempted to address the more global
versus specific nature of the relationship between adherence
and metabolic control was conducted by Schafer, Glasgow,
McCaul, and Dreher (1983). Their 34 subjects were 12 to
14-year-olds with IDDM who completed questionnaires dealing
with regimen adherence for the past week as well as psycho¬
social measures dealing with diabetes-specific family
behaviors, barriers to adherence, and family interactions.
They found that compliance was a highly specific construct;
the degree of adherence to one aspect of the IDDM regimen
was not related to adherence to the other aspects of the
regimen. Using multiple correlations, they also found that
glycosylated hemoglobin levels could be predicted from a
combination of three adherence measures (i.e., the extent to
which one's diet was followed, reported care measuring
insulin doses, and number of daily glucose tests). Unfor¬
tunately, the reliability of the self-report compliance
measures was not assessed, nor was there any attempt to
verify their reports by actual measurement of home behaviors
or by corroboration by significant others. In addition, the
use of multiple regression with such a small sample and
such a large number of variables is likely to severely
overestimate the strength of the resultant R2.
In a more thorough study by some of the same inves¬
tigators (Glasgow, McCaul, and Schafer, 1987), these same

12
issues were addressed with 93 adult IDDM outpatients.
Similar to the Schafer et al.(1983) study, this investiga¬
tion explored adherence to various aspects of the diabetes
care regimen, the congruity of adherence of the different
behavioral components, and the relationship between adher¬
ence and glycemic control as measured by glycosylated
hemoglobin. Subjects participated in three home interviews
during a 1 week period, completed psychosocial measures,
collected self-care measures on injections, glucose testing,
diet and physical activity. The entire procedure was
repeated for a subsample at two months and for the entire
sample at six months. Self-report information was organized
into five categories of adherence: insulin injections,
glucose testing, diet level, dietary adherence, and physical
activity. Glycemic control was measured with glycosylated
hemoglobin and percent negative glucose tests (both urine
and blood). Each adherence measure was compared to patient
report of regimen prescriptions. Their results indicated
that compliance was better for taking medication and for
glucose testing than for behaviors which required major
adjustments in routine (e.g., diet and exercise); adherence
with one component of the regimen did not necessarily
correlate with adherence to other regimen tasks. Compliance
behaviors were not stable over time although glycemic
control was relatively stable; no clear relationship between
adherence and glycemic control was established through
either bivariate or multivariate analyses. Although this

13
study is an improvement over most correlational studies,
there are some basic weaknesses. For example, no attempt
was made to verify actual diabetologists' prescriptions—
rather patient report of physician prescriptions were used
and comparisons were made to the self-reports or in some
instances to absolute levels of behaviors (although the
authors do not define "absolute levels," these are presum¬
ably meant to be ideal levels). Similarly, no attempt was
made to corroborate any of the self-reports of adherence.
Moreover, in calculating dietary compliance, only evening
meals were used.
Intervention studies comprise the second category of
investigations in the area of compliance and control. These
studies concentrate on increasing compliance, assuming that
lack of adherence to the medical regimen can result in
serious diabetic complications. However, a number of these
studies seek to modify adherence but do not include measures
of control. In one such study Gross (1982) conducted a
multiple baseline intervention with four IDDM boys aged 10-
12 years to determine the utility of self-management
behaviors. The youngsters were selected based on parent
report that they failed to reliably perform their urine
tests the recommended four times per day. The boys received
six 1-hour long sessions (one per week) which consisted of
written lessons, discussions, modeling and role-playing. In
addition, the children were assigned a self-management
project which included collecting baseline data on the

14
frequency of their urine testing which was arbitrarily
designated by the investigator as the target behavior. The
youngsters chose rewards and self-delivered them contingent
on performing the target behavior and graphed their perfor¬
mance. During the final phase, the subjects were taught
negotiating and contracting skills and in a meeting with the
experimenter, the child, and his parents a contract was
arbitrated. All contracts involved parental reinforcement
for continuation of self-management of urine testing.
Reliability data were collected by parents counting the
number of test tablets used on 1 day each week when the
child was not at home. The children were aware that the
parents would be checking but did not know when. No attempt
was made to monitor the accuracy of the children's urine
testing. Reliability was based on dividing the number of
agreements of parent and child report by agreement plus
disagreement and multiplying the quotient by 100. Reliabi¬
lity averaged 80%. No data were reported on the rate of
compliance with the self-reward regimen. Pre- and post¬
training tests on the principles and procedures of behavior
modification were conducted and 2- and 8-week follow-up data
were also collected. Children improved their rate of urine
testing from 9% to 74% of the time during the self-manage¬
ment condition and at the 2-week follow-up. However, at the
8-week follow-up, two of the four families had discontinued
with the contract and those children's behavior had returned
to baseline levels.

15
Only one of the many necessary diabetes management
behaviors was targeted in this study and no attempt was made
to determine whether increasing the frequency of urine
testing had an effect on children's level of control.
An intervention study by Epstein et al. (1981) employed
parent training procedures. Twenty families were assigned
to one of three groups in a multiple baseline design.
Parents were instructed to use a point system and praise for
their youngsters' compliance with urine testing. Both
self-report (daily urine test results and adherence to urine
testing regimen) and biochemical measures (insulin dosage,
HbAi, plasma glucose, serum lipids) were used to assess
effects of the intervention. Epstein et al. went to
considerable lengths to determine the reliability of the
children's recordings (although only negative urine data
were used for analyses) by asking parents to test four
urines per week on a schedule determined by the experimen¬
ters and without knowledge of the child's values. Reliabi¬
lity was assessed for 91% of the treatment weeks and parents
and children agreed on the glucose concentration within a
one measurement interval range on each of the four reliabi¬
lity tests during 83% of the weeks. Exact parent/child
agreement on the urine tests was not reported. The
reliability/validity of reported urine testing frequency was
measured by including predetermined quantities of inert,
placebo Clinitest tablets in each bottle. Parents were
informed about the correct number of placebos. At the end

16
of each week, parents were instructed to test the remaining
tablets in each bottle, and add the number they found to the
number the child reported finding. Comparisons of this
number to the actual number provided the measure of the
child's adherence to the regimen for that week. Parents and
children agreed on the number of marked items during 76% of
the weeks. Results showed a significant increase in the
number of tests performed and in the percent of urine tests
that were negative for glucose, although other metabolic
indices of diabetic control did not show improvement.
Epstein et al. concluded that behavioral techniques appear
useful for improving behavioral adherence to diabetes
regimen. However, in this study, increasing the number of
urine tests that were negative for glucose was not associ¬
ated with improvement in other measures of diabetic control.
Kaplan, Chadwick and Schimmel (1985) randomly assigned
21 IDDM teenagers between 13 and 18 from middle class
backgrounds to either a daily social learning group or a
medical facts learning group when they entered a 3 week
summer program. The social learning group identified social
situations in which peer influence might prevent adherence
to diabetes regimen and as a group suggested appropriate
responses. Rehearsal exercises enacting problem situations
and their solutions were ultimately videotaped and then
filmed in a television studio. Guided practice with
reinforcement was used to develop their social skills. The
control group spent the same amount of time discussing

17
diabetes relevant medical information. These discussions
were videotaped and then filmed. Hemoglobin A^ was drawn
during the program to measure metabolic control before the
program and at a 4-month reunion to measure metabolic
control since the program.
The groups were equivalent at the beginning of the
program. Hemoglobin values at the reunion (obtained for
82% of those who provided an original sample) showed a
slight increase in values for the control group and a
contrasting decrease for the experimental group. Further¬
more, the authors found a substantial correlation (r=-.78)
between self-reported self-care and diabetes control as
measured with HbA^ values. This study is an improvement
over earlier research in that intervention was directed at
many aspects of the treatment regimen instead of urine
testing alone. Furthermore, correlations between adherence
behaviors and control were provided.
However, the study's experimental and control groups
were small and restricted to white middle class adolescents.
The reliability of the self-report measures of compliance
was not assessed nor were parents surveyed to corroborate
the youngsters' report. Before treatment, the experimental
group was in slightly better diabetes control than the
control group (although the difference was not statistically
significant). At post-treatment, it was unclear whether the
slight improvement of the experimental group and the slight
deterioration of the controls was clearly related to the

18
study's intervention or was a natural continuation of each
group's pretreatment metabolic condition. Pre/post- changes
in diabetic control were not significant and there was no
assessment of and therefore no evidence that one group's
compliance was better than the other group's at post-treat¬
ment. Consequently, the only evidence clearly in favor of
the experimental group was the post-treatment difference in
HbAi. Certainly, a replication of the study's findings is
needed if we are to have confidence that the post-treatment
HbA^ differences were, in fact, due to the intervention
employed.
Schafer, Glasgow, and McCaul (1982) designed a multiple
baseline approach to assess the effectiveness of goal
setting and behavioral contracting to increase adherence and
control in three adolescents with IDDM. A self-monitoring
program of 1 week, introduced before goal setting, con¬
stituted the baseline phase. Contracting was introduced
only if 90% compliance was not achieved. Compliance was
assessed by subjects records of relevant target activity
such as time, duration, etc. Reliability checks were made
periodically by mothers' independent monitoring. Diabetic
control was assessed with urine glucose tests performed and
recorded daily by subjects. Reliability checks were
conducted by having mothers spot-check the urine tests. Two
hour postprandial blood glucose levels were determined, and
24-hour urine glucose was collected at pretest, posttest,
and followup. Two of the subjects showed improvements in

19
compliance and control. The third was essentially unaf¬
fected by the treatment.
Unfortunately, as mentioned above, the use of 24 hour
urines as indices of diabetic control is problematic since
they are difficult to collect even in hospital settings. In
addition, postprandial blood glucose levels measure control
at brief points in time and may not relate to the patient's
general level of diabetic control.
It is generally accepted that the issue of compliance
as it relates to level of control and possible complications
is vital to the area of chronic illness in general, and
diabetes in particular. However, results are mixed and
difficult to interpret. In order to establish whether there
is actually a causal link, methods of measuring the various
components must be improved. Clearly the major reasons for
this gap in the area of IDDM are that
1) The definition of control has varied among
investigators, and diabetologists have been
unable to reach a consensus on a precise
definition so that studies have used varying
measures of control (Spevack, Johnson,
Harkavy, Silverstein, Shuster, Rosenbloom,
and Malone, 1987). There have been recent
improvements in this situation with the
general acceptance of HbA^c, although some
investigators balk at using this as the sole
measure of control since it does not

20
accurately reflect variability in blood
glucose.
2) The measurement of adherence behaviors is
equally problematic. Little psychometric
data are provided as to the quality of the
measures used. Compliance and control are
often confused or the same rater judges both.
Compliance is often treated as a global
construct although there is mounting evidence
that it is complex, consisting of several
independent components. Studies focus on be¬
haviors that do not relate directly to
control. For example, wearing a medic alert
bracelet is definitely important in case of
accident (Schafer et al., 1982). However,
wearing it may not have a direct impact on
glycemic control.
3) Correlational studies have been inconsistent
in demonstrating a relationship between com¬
pliance and control. In any case, data from
these studies cannot be used to establish
causality.
4) The few intervention studies available
suggest that adherence may be improved.
However, improved adherence is not always
associated with improved control (Epstein and
Cluss, 1982). In some intervention studies,

21
only a single behavior is targeted for
intervention or selected for treatment by the
parent of the diabetic child. In such cases,
the adherence behaviors improved may have no
significant impact on diabetic control.
5) It is very difficult to study IDDM youngsters
under controlled conditions, where behavior
can be reliably measured, without interfering
with or disrupting the youngster's normal
behavior.
Many investigators have dealt with the first problem by
relying predominantly on the best single estimator of
control—namely, hemoglobin A^c—which is an indicator of
integrated plasma glucose levels over approximately 3
months. Consequently, to establish a link between adherenc¬
e/behavior and control as measured by hemoglobin A^c, a 3-
month behavior monitoring system should be available.
Unfortunately, youngsters balk at keeping diaries even for
short intervals and even when they do keep such records,
reliability checks are problematic. To date the most
systematic procedure for measuring compliance has been
developed by Johnson, Silverstein, Rosenbloom, Carter, and
Cunningham (1986). A 24-hour recall interview technique was
used to assess daily management behaviors in four diabetes
relevant areas (diet, injection, exercise, and glucose
testing). Each child and his/her parent were interviewed
separately on three different occasions concerning the

22
child's diabetes management behaviors in the last 24 hours.
Interviews were conducted by phone and took approximately 20
minutes. Data from this procedure were used to measure 13
different diabetes management behaviors: injection regulari¬
ty, injection interval, injection-meal timing, regularity of
injection-meal timing, calories consumed, percent calories:
fat, percent calories: carbohydrates, concentrated sweets,
eating freguency, exercise duration, exercise type, exercise
freguency, and glucose testing freguency. Pearson product
moment correlations for all 13 adherence measures were
conducted to asses parent-child agreement. The results are
presented in Table 1. Although all of the correlations were
statistically significant (pc.0001), a number of measures
differed in agreement depending on the age of the child with
the youngest children (6-9 years) showing poorest
parent/child agreement on measures involving time and the
10- to 12- and 13- to 15-year old groups showing the most
consistent parent/child agreement across all measures. The
oldest group (16-19 years) had highly variable parent/child
correlations. The authors attribute these findings to the
reduction of parental supervision during late adolescence
when children spend more time with peers outside of the
parents' purview. The 13 adherence measures were then
subjected to a principal component factor analysis resulting
in a five-factor solution accounting for 71.3% of the
variance. The five factors were rotated to simple structure
using the varimax procedure. Factor I (Exercise) consisted

TABLE 1
Parent-Child Correlations for Total Sample and by Age Group
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(6-9 Years)
r(n)
P <
Group 2
(10-12 Years)
r(n)
P <
Group 3
(13-15.6 Years)
r(n)
P <
Group 4
(16-19 Years
r(n)
P <
Injection
Regularity
.61(152)
( .0001)
.46(27)
(.02)
.68(65)
( .0001)
.83(42)
(.0001)
-.04(18)
(.86)
Injection
interval
.77(154)
( .0001)
. 36(29)
(.05)
.71(67)
( .0001)
.54(40)
(.003)
.91(18)
( .0001)
Injection-
meal timing
.67(163)
( .0001)
. 53(31)
(.002)
. 547 (68)
( .0001)
.60(44)
(.001)
. 78(20)
(.0001)
Regularity of
inj ection-
meal timing
. 42 (148)
(.0001)
-.23(26)
(.26)
.50(63)
( . 0001)
. 58(41)
( .0001)
.46(18)
(.05)
Calories
consumed
.77(139)
(.0001)
.79(30)
(.0001)
.80(66)
( .0001)
.61(33)
( .0001)
.92(10)
( .0002)
Percentage
calories-fat
.64(167)
(.0001)
.50(31)
(.005)
.63 (70)
( . 0001)
.76(44)
(.0002)
.60(22)
(.003)

TABLE 1
(continued)
Adherence Measure
Total
Sample
r (n)
P <
Group 1
(6-9 Years)
r(n)
P <
Group 2
(10-12 Years)
r(n)
P <
Group 3
(13-15.6 Years)
r(n)
P <
Group 4
(16-19 Years)
r(n)
P <
Percentage
calories-
carbohydrate
.64(167)
(.0001)
.44(31)
(.01)
.63(70)
(.0001)
.81(44)
( .0001)
.62(22)
(.002)
Concentrated
Sweets
.62(167)
(.0001)
.47(31)
(.007)
.71(70)
( .0001)
.76(44)
( . 0001)
.12 (22)
. 58
Eating
frequency
.45(167)
(.0001)
. 67(31)
( .0001)
. 47 (70)
(.0001)
.54(44)
(.0002)
. 13.(22)
(.57)
Exercise
duration
.59(167)
(.0001)
.03(31)
(.87)
.96(70)
(.0001)
.79(44)
(.0001)
.47(22)
(.03)
Exercise
type
.54(167)
(.0001)
.74(31)
( .0001)
.99(70)
(.0001)
.66(44)
(.0001)
.32(22)
(.15)
Exercise
frequency
.62(167)
(.0001)
. 62(31)
(.0002)
.72(70)
( .0001)
.72(44)
( .0001)
.32(22)
(.13)
Glucose
testing
frequency
.78(164)
(.0001)
.81(31)
(.0001)
.73(68)
(.0001)
.77(44)
( . 0001)
.37 (21)
(.10)
Table from Johnson, et al.,
1986
fo

25
of the three exercise measures, Factor II (Injection)
consisted of all four injection measures, Factor III (Diet
Type) was made up of measures of diet type, Factor IV
(Eating/Glucose Testing Frequency) included measures of
eating frequency and glucose testing frequency, and Factor V
(Diet Amount) included measures of total calories consumed
and the amount of concentrated sweets ingested. The Johnson
et al. (1986) results indicate that adherence to a diabetic
regimen consists of five independent constructs and should
not be viewed as a global construct.
Diabetes summer camps offer a natural but relatively
controlled environment. Here youngsters' behavior and
metabolic control could be easily monitored and both would
be expected to change. Such an environment would be
conducive to investigating relationships of compliance/
control. Diabetes summer camps have as their main goal to
create an environment in which IDDM youngsters can meet
other children with diabetes and learn to live more normally
with their disease. The focus of the program is usually to
provide a model for healthy living along with didactic
components designed to reinforce adherence skills and
increase understanding of the disease. It is a generally
accepted philosophy that research conducted at summer camps
must be nonintrusive and must not interfere with camper ac¬
tivities and enjoyment. The major criticism leveled at camp
studies is that they constitute an unnatural environment
which itself affects the youngsters' behavior and level of

26
control. Whether this is a valid criticism and if so,
whether the effects are positive or negative, has never been
comprehensively evaluated. There is evidence that diabetes
camp may improve diabetic control although only one study
specifically addressed this issue (Strickland et al., 1984).
The Strickland et al. study examined changes in glycemic
control during a 2-week summer camp program by performing
pre-camp and post-camp values of fasting plasma glucose,
glycosylated hemoglobin, and glycosylated serum protein in a
group of 36 children. Their results suggested that there
was measurable improvement in diabetic control in some
children.
Similarly, only a few studies have addressed behavioral
changes associated with camp. For example, Stunkard and
Pestka (1962) examined physical activity at a 2-week Girl
Scout camp and found that there was significantly more
activity during camp compared with at-home behavior. The
youngsters studied did not have diabetes.
Most camp studies deal with psychological constructs.
Moffatt and Pless (1983) investigated changes in locus of
control in juvenile diabetic campers during a 3 week camp
experience and found that there were significant changes
toward internal locus of control.
Self-concept was scrutinized using the Tennessee Self-
Concept Scale with 26 IDDM adolescents (aged 13-17) attend¬
ing an 8-day summer camp (Hoffman, Guthrie, Speelman, and
Childs, 1982). The authors report that self-concept may be

27
more resistent to change above the age of 14 and that
initial level of control was not a significant predictor of
self-concept change. The greatest change occurred in the
13- to 14-year old females who improved on self-concept
significantly. However, this study did not address the
issue of whether self-concept change was associated with
change in glycemic control.
In a more recent study, Scharf, Adams, and Leach (1987)
compared 45 IDDM adolescents aged 12 to 17 who attended a
residential diabetes summer camp, to IDDM youngsters who did
not attend camp on psychological functioning immediately
following the camp experience and at a 5 month follow- up.
They reported that campers' adjustment to diabetes, their
self-worth, and their parents' perceptions of their
behavioral competencies and general personality adjustment
did not change due to the camp experience. Moreover,
metabolic control as measured with HbA^c before camp did not
differentially influence the experimental or control
subjects in how they reacted to the camp experience or how
they adjusted to a chronic illness. No attempt was made to
assess whether the camp experience affected glycemic control
after camp.
The effects of stress on glycemic control were examined
at a 1982 Tennessee camp for diabetic children using 39
adolescents between the ages of 12 and 15 years (Hanson and
Pichert, 1986). The children were followed for 3 days while
insulin administration, dietary intake, exercise, self-

28
reported stress, and blood glucose levels (measured via
Chemstrip bG Reagent strips four times during the 24 hours
preceding each afternoon rest period during which stress
questionnaires were administered) were monitored. Results
suggested that negative cumulative stress, as perceived by
the youngsters, correlated significantly with blood glucose
levels. Interestingly, positive cumulative stress was
significantly and negatively correlated with girls' blood
glucose levels.
Educational experiences at camp were found to contri¬
bute to the knowledge and performance of self-care techni¬
ques in a study by Lebovitz, Ellis, and Skyler (1978).
Harkavy et al. (1983) found that knowledge increased in 12-
to 15-year old campers but not in 10- to 11-year olds.
Dorchy, Loeb, Mozin, Lemiere, and Ernould (1982) adminis¬
tered a 27 question pre- and post-test to 63 IDDM youngsters
and found that regardless of age or duration of diabetes all
children ages 9 to 15 years benefit from the teaching of
theory and practical issues. The youngest children (9-10
years) showed progress in all areas while the oldest (13-15
years) made greater gains in the areas of nutrition and
insulin therapy.
There is, therefore, some evidence that the camp
experience may affect psychological factors such as locus of
control (Moffat and Pless, 1983) and self-esteem (Hoffman,
et al. , 1982), knowledge about the disease (particularly in
12- to 15-year old youngsters, Harkavy et al., 1983), self-

29
care (Lebovitz et al., 1978), and physical activity levels
(Stunkard and Pestka, 1962). Moreover, there is some
evidence that metabolic control may improve during the camp
session (Strickland et al., 1984).
Improvements in adherence behaviors are assumed to
occur since camp provides exercise opportunities and the
youngsters' regimen is regulated regarding injections and
meals. There are, however, no empirical data documenting
improved adherence at camp nor attesting to the relationship
between such behavior change and improvements in metabolic
control. Further, it is unclear whether any behavioral
changes induced by the camp setting are maintained once the
child returns home.
Glycosylated serum proteins (GSP) differ from glyco¬
sylated hemoglobins (GH) in that serum protein levels in the
blood change more rapidly than do hemoglobin values with
changes in blood glucose concentration (Mehl, Wenzel,
Russel, Gardner, and Merimee, 1983). Therefore, since
glycosylated serum proteins have been shown to accurately
reflect alteration of mean glycemic levels 1 to 2 weeks
prior to testing, it becomes feasible to address the issue
of whether adherence behaviors at camp are indeed related to
metabolic control at camp. This study will, therefore,
assess the following:
1) The effect of summer camp on children's
diabetes management behavior;

30
2) The effects of a diabetes summer camp
environment on children's diabetes control;
3) The length of time that effects of camp are
maintained—i.e., do changes or effects of
camp dissipate once the child returns home;
4) The relationship between adherence behavior
and level of diabetic control.
This proposed investigation represents a naturalistic
study in which the experience of camp is expected to
actually change both the children's behavior and their
levels of diabetic control thereby providing the opportunity
to monitor both. Therefore, the behavior and the levels of
control are being naturally manipulated by virtue of the
youngsters' attendance at camp, providing an ideal situation
to assess the extent to which changes in diabetic control
are associated with changes in behavior. Should such
changes be demonstrated, and their association be estab¬
lished, it would constitute strong support for a relation¬
ship between behavior and health status.

METHOD
Sample Characteristics
Participants were 64 IDDM youngsters who attended the
1985 North Florida Camp for Children and Youth with
Diabetes. The 1985 camp session lasted 2 weeks. One parent
of each youngster was also asked to participate. Informed
consent was obtained from all participants. Youngsters were
between the ages of 7-12 (with a mean age of 10.5 years).
There were 34 boys and 30 girls. Participating families
reported income in 5 categories: 1= $ 0 to $9,999, 2=
$10,000 to $19,999, 3= $20,000 to $29,999, 4= $30,000 to
$39,999, and 4= $40,000+. Of all participating families
14.5% were in category 1, 27.3% in 2, 23.6% in 3, 18.2% in
4, and 16.4% in 5. All but one (98.4%) of the youngsters
had had diabetes for at least 1 year with a range from .9
years to 9.1 years (Table 2). All but one of the youngsters
were Caucasian. Only two youngsters dropped out of the
study after camp.
Measures
Adherence Measures
Twenty-four hour recall interviews, conducted with
children and parents, were recorded on Diabetes Daily Record
forms to assess the youngsters' diabetes related
31

TABLE 2
Sample Characteristics
Total
Sample
Group 1
7-9
Years
Group 2
10-11.4
Years
Group 3
11.5-12.6
Years
Sample Size
64
20
23
21
Sex: Males
34
13
13
8
Females
30
7
10
13
Family Income
in categories: 1
14.5%
0%
28.6%
11.1%
2
27.3%
31.3%
28.6%
22.2%
3
23.6%
25.0%
23.8%
22.2%
4
18.2%
18.8%
9.5%
27.8%
5
16.4%
25.0%
9.5%
16.7%
Age: Mean
10.5
8.5
10.8
12.2
Range
7.6-12.6
7.6-9.1
10.1-11.1
11.6-12.6
Stand. Dev.
1.57
. 65
. 36
.47
Duration: Mean
3.9
3.6
3.3
4.9
Range
.9-9.1
1-5.1
.9-4.4
1.1-8.3
Stand. Dev.
2.67
2.11
2.18
3.37
U>
ro

33
behaviors before, during and after camp (see Appendix). The
interviews were conducted by telephone. When possible, two
of these interviews dealt with weekdays and one with weekend
activities. Participants were told that the interviewer was
concerned with what actually transpired rather than what
they believe should have been done. Trained, undergraduate
research assistants conducted these interviews. Partici¬
pants were asked to recall the previous day's activities in
a sequential manner beginning with when the child woke up
and ending with when he went to bed. All diabetes relevant
behaviors were recorded. Interviewers prompted for informa¬
tion that may have been inadvertently omitted, such as,
mid-morning snacks, exercise, glucose testing, etc.
Youngsters and their parents were interviewed separately.
Using data obtained from the 24 hour recall interviews,
13 different adherence measures were quantified. For each
measure, a range of scores is possible with higher scores
indicating relative noncompliance and lower scores indicat¬
ing relative compliance (Johnson et al., 1986):
1) Injection Regularity—this measure assessed
the degree to which injections were given at
the same time every day. It was calculated
by measuring the standard deviation of
injection times reported across the inter¬
views .
2) Injection Interval—this measure assessed the
youngster's average deviation from an ideal

34
injection interval of 24 hours between a.m.
injections. For youngsters who take p.m.
injections, an ideal shot interval was
arbitrarily defined as 10 hours between a.m.
and p.m. injections on the same day, and 14
hours between the p.m. injection and the a.m.
injection on the following day.
3) Injection-Meal Timing—this measure evaluated
the timing of injection in relationship to
meals. It was calculated by averaging the
number of minutes between taking an injection
and eating a meal. Sixty minutes were added
to this average so that youngsters taking
their injection 60 minutes before a meal
received an adherence score of zero. Young¬
sters who, on the average, took their
injections 60 minutes to 1 minute before
meals received scores from 0 to .99. Young¬
sters who usually took their injections at
the time of their meals or after eating
received scores of 1.0 or greater.
4) Regularity of Injection-Meal Timing—
this measure appraised the regularity of
intervals between injections and eating.
This was measured by calculating the
standard deviation of the Injection-Meal
Timing measure described above.

Calories Consumed—for each youngster, an
ideal number of total daily calories was
identified based on the youngster's age, sex,
and ideal weight for height. Ideal weight
for height was obtained from tables provided
in a standard textbook on childhood obesity
(Collipp, 1980). Ideal calorie consumption
per kilogram was calculated using standards
provided by the U.S. Public Health Service
(Anderson et al., 1981). Each child's ideal
total number of daily calories was then
subtracted from the youngster's reported
daily caloric consumption. Reported calorie
consumption was obtained from the diet
information obtained by interview. All diet
information was coded in exchange units from
which calorie estimates were derived (Franz,
1983). A high score on the calorie consumed
measure indicated that the youngster ate more
than his ideal total number of daily calo¬
ries. A zero score indicated that the child's
actual calorie consumption equalled that of
his ideal calorie consumption. A negative
score indicated that the youngster ate less
than the ideal.
Percent Calories: Fat—based on the exchange
unit information collected from the 24-hour

recall interviews, the percent of total
calories consumed which consisted of fat was
calculated. Ideal fat consumption was based
on the lower limit (25%) recommended by the
American Diabetes Association (Nuttal and
Brunzall, 1979). Ideal fat consumption (25%)
was subtracted from actual fat consumption.
Scores above zero indicated that the child
consumed more than 25% of his calories in
fats. Scores below zero indicated that the
child's fat intake was less than 25% of his
total calories.
Percent Calories: Carbohydrates—this
measure was also based on the exchange unit
information collected from the 24-hour recall
interviews. Ideal carbohydrate consumption
was based on the upper limit (60%) recom¬
mended by the American Diabetes Association
(Nuttal and Brunzall, 1979). In this case
actual carbohydrate consumption was sub¬
tracted from the ideal (60%) so that scores
above zero indicated insufficient carbo¬
hydrate ingestion. A score of zero indicated
the child's diet consisted of 60% carbo¬
hydrates. Scores below zero indicated that
more than 60% of the calories consumed
consisted of carbohydrates. No measure of

37
protein consumption was developed since it
can be automatically determined by knowing
the child's fat and carbohydrate consumption.
8) Concentrated Sweets—forty calories of any
concentrated sweet was considered equivalent
to one concentrated sweet exchange unit. The
average number of these exchange units
consumed per day was calculated.
9) Eating Frequency—based on an ideal of six
meals/snacks per day, the percent of snacks
or meals not eaten across the interviews was
calculated and multiplied by 100. A high
score indicates that the child ate infre¬
quently. A low score indicated frequent
eating occasions. A score of zero indicated
the child averaged six meals or snacks per
day.
10) Exercise Duration—the average amount of time
the child spent exercising during each
exercise occasion across the interviews was
calculated and a constant (1) was added to
avoid subsequent division by zero. The
reciprocal of this score was used so that low
scores indicated lengthy exercise and high
scores indicated little or no exercise.
11) Exercise Type—each exercise or activity was
given an energy expenditure rating (Hatch and

McArdle, 1977). Higher ratings indicated
more strenuous exercise. The youngster's
average expenditure rating across the
interviews was calculated and a constant (1)
was added to avoid subsequent division by
zero. The reciprocal of this score served as
the measure of Exercise Type? low scores
indicated more strenuous exercise while high
scores indicated less strenuous exercise.
12) Exercise Frequency—the occurrence or
nonoccurrence of exercise on three occasions
per day—morning, afternoon, and evening—
was noted for each of the interviews. The
percent of nonoccurrence of exercise across
these occasions was used as an estimate of
exercise frequency. A score of zero indicated
that the child reported exercise on all
occasions. A score of 100 indicated no
reported exercise on any occasion.
13) Glucose Testing Frequency—the frequency of
glucose testing across the interviews was
calculated. Using an ideal testing frequency
of four times per day, the number of glucose
tests was divided by this ideal (e.g., 12 for
3 days) and multiplied by 100. The total was
subtracted from 100 so that high scores indi¬
cated few glucose tests and low scores

39
indicated frequent glucose tests. A score of
zero indicated the youngster tested 4 times
per day. A score of 100 indicated no
reported glucose tests over the interviews.
Glycemic Control Measures
Glycosylated hemoglobin A^c levels were obtained at the
beginning of camp and at 3 months following camp. The
assays were performed at the Shands Teaching Hospital
Laboratory using a column chromatography method performed
with Glyco-Gel kits. The expected normal range for Glycosy¬
lated Hemoglobin A^c (HbA^c) using this procedure was 3.5-
6.2% and represents an overall estimate of glycemic control
for a period of approximately 3 months prior to testing.
Glycosylated serum proteins (GSP) were obtained at the
beginning and end of the 2-week camp session to evaluate any
changes in glycemic control associated with the camp
experience. The assays were performed at the Shands
Teaching Hospital Laboratory using the Pierce GlycoTest in
vitro diagnostic kits which contain GLYCO-GEL Analytical
Columns. This is an affinity chromatography based method
used after serum is first dialyzed against normal saline to
prevent spurious elevations of GSP levels due to free
glucose (Kennedy, 1981). GSP represents the average
glycemic control for a period of 10 to 14 days before
testing. Normal ranges were not available. However, in this
analysis the control value for a nondiabetic subject ranged
from .47 to 1.1% with a mean of .79%.

40
Procedure
In the 2 weeks prior to attending diabetes summer camp,
each child and one of his parents were interviewed by phone
on three separate occasions using the 24-hour recall
technique described earlier. The interviews were recorded
on the Diabetes Daily Record (Appendix).
On the first or second day of camp, fasting blood was
obtained from each participant. Serum obtained from venous
samples was stored at -20 degrees centigrade to be assayed
for GSP at a later time. Heparinized blood samples were
submitted for HbA^c assays. During their 2-week camp
experience, each child and his counselor were interviewed on
three separate occasions (on 2 weekdays and 1 weekend day)
by trained interviewers who recorded diabetes management
behaviors for the previous day on the Diabetes Daily Record.
However, counselor interviews were discontinued as it became
clear that they had difficulty differentially remembering
the individual children's activities and food consumption.
During the last 2 days of camp, fasting blood was again
obtained for GSP analysis. Those children whose blood was
drawn on the first day of camp were drawn on the second to
last day of camp. Similarly, if their blood was first drawn
on the second day of camp, the second sample was obtained on
the last full day of camp.
Within the 2 weeks following camp, study participants
and their parents again participated in three telephone
interviews using the 24-hour recall technique. This

41
procedure was repeated at 6 and 12 weeks post-camp. At 12
weeks post-camp, a second HbA^c sample was obtained.

RESULTS
Reliability of Child Report
One possible measure of child report reliability is
parent-child agreement. Perfect agreement was not expected
since parents are not always able to observe everything the
child does. However, since counselors were unable to
provide useful data concerning the campers activities, the
youngsters were the only consistent reporters available and
the issue of their reliability in reporting their behaviors
was critical.
Estimates of agreement between parent and child were
calculated to indicate the reliability of the information
obtained from the youngsters at camp, using Pearson product
moment correlations. These analyses replicated the Johnson
et al. study (1986) and used their 13 adherence measures.
Agreement was assessed for the total sample and for three
age groups (7-9, 10-11.4, and 11.5-12.6) at four points in
time: pre-camp and post-camp on 3 separate occasions
(immediately after camp, 6 weeks after camp, and 3 months
after camp). These data are presented in Tables 3, 4, 5,
and 6.
For the pre-camp period, parent/child correlations for
the total sample were all significant at p < .02, except for
regularity of injection-meal timing. One or both of the two
42

TABLE 3
Parent-Child Correlations for Total Sample and by Age Group
Pre-Camp
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r (n)
P <
Group 2
(10-11.4 Years)
r(n)
P <
Group
(11.5-12.6
r (n)
P <
Injection
.65(60)
.80(18)
. 28(21)
. 82(21)
regularity
( . 0001)
(.0001)
(.2156)
( .0001)
Injection
.75(59)
.51(19)
.92(19)
. 64 (21)
interval
(.0001)
(.0262)
(.0001)
( .0018)
Injection-
.54(62)
-.03(19)
. 57 (22)
.75(21)
meal timing
(.0001)
( .8885)
(.0052)
(.0001)
Regularity of
.09(59)
-.08(18)
-.08(20)
.42(21)
injection-
meal timing
(.5010)
( .7382)
(.7450)
(.0526)
Calories
.73(62)
.81(20)
. 62 (22)
.84(20)
consumed
(.0001)
(.0001)
(.0023)
( .0001)
Percentage
.78(63)
.86(20)
.64(22)
.73(21)
calories-fat
(.0001)
(.0001)
(.0013)
(.0002)

TABLE 3
(continued)
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r (n)
P <
Group 3
(11.5-12.6 Years)
r (n)
P <
Percentage
. 72 (63)
. 80(20)
. 65(22)
.69 (21)
calories-
carbohydrate
( . 0001)
(.0001)
(.0011)
( .0005)
Concentrated
.63(63)
.35(20)
.71(22)
.63(21)
sweets
( . 0001)
( . 1292)
(.0002)
( . 0020)
Eating
.72(63)
.83(20)
.64(22)
.73(21)
freguency
( . 0001)
( .0001)
(.0013)
( . 0020)
Exercise
.29(63)
.68(20)
.07(22)
.96(21)
duration
( .0211)
(.0010)
(.7603)
( .0001)
Exercise
.64(63)
.76(20)
.56(22)
.62 (21)
type
( . 0001)
(.0001)
(.0071)
( . 0029)
Exercise
.68(63)
.81(20)
.50(22)
.68 (21)
freguency
( . 0001)
(.0001)
(.0190)
(.0007)
Glucose
.90(63)
.89(20)
.97(22)
.68(21)
testing
frequency
( .0001)
(.0001)
(.0001)
(.0006)
4^

TABLE 4
Parent-Child Correlations for Total Sample and by Age Group
Immediately Post-Camp
Adherence Measure
Total
Sample
r (n)
P <
Group 1
(7-9 Years)
r (n)
P <
Group 2
(10-11.4 Years)
r (n)
P <
Group
(11.5-12.6
r(n)
P <
Injection
.54(55)
.54(15)
. 52 (20)
.65(20)
regularity
(.0001)
(.0381)
( .0198)
(.0021)
Inj ection
.73(56)
.67(16)
.66(20)
.83 (20)
interval
(.0001)
(.0045)
( .0016)
( .0001)
Injection-
.68(57)
.31(18)
.91(19)
. 56(20)
meal timing
(.0001)
(.2152)
( . 0001)
(.0098)
Regularity of
.44(54)
-.40(15)
.42(19)
.72(19)
injection-
meal timing
(.0008)
(.1432)
(.0726)
(.0003)
Calories
.62(59)
.36(18)
.62(22)
.70(19)
consumed
(.0001)
(.1471)
(.0022)
(.0009)
Percentage
.74(60)
.68(18)
.87(22)
.62(20)
calories-fat
(.0001)
(.0020)
(.0001)
(.0035)

TABLE 4
(continued)
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r(n)
P <
Group 3
(11.5-12.6 Years)
r(n)
P <
Percentage
.75(60)
. 63(18)
.85(22)
.70(20)
calories-
carbohydrate
( .0001)
(.0048)
(.0001)
( . 0006)
Concentrated
.59(60)
.58(18)
.83(22)
.34 (20)
sweets
( .0001)
(.0108)
(.0001)
( . 1415)
Eating
.67(60)
.77(18)
.59(22)
.63(20)
frequency
(.0001)
(.0002)
(.0037)
( . 0026)
Exercise
.21(60)
.53(18)
.21(22)
.13(20)
duration
(.1106)
(.0234)
(.3568)
( . 5965)
Exercise
.64(60)
.66(18)
.50(22)
.85(20)
type
(.0001)
(.0028)
(.0171)
( . 0001)
Exercise
.68(60)
.67(18)
.53(22)
. 92 (20)
frequency
(.0001)
(.0024)
( .0117)
( .0001)
Glucose
.78(60)
.55(18)
.92(22)
. 68(20)
testing
frequency
(.0001)
(.0171)
(.0001)
(.0009)

TABLE 5
Parent-Child Correlations for Total Sample and by Age Group
Six Weeks Post-Camp
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r (n)
P <
Group 2
(10-11.4 Years)
r (n)
P <
Group 3
(11.5-12.6 Years)
r (n)
P <
Injection
.64(53)
.57(14)
.66(20)
. 67(19)
regularity
( . 0001)
(.0320)
(.0017)
(.0019)
Injection
.66(53)
.45(15)
.79(20)
.81(18)
interval
(.0001)
(.0942)
(.0001)
(.0001)
Injection-
.76(56)
.11(16)
.76(21)
.95(19)
meal timing
(.0001)
(.6915)
(.0001)
( .0001)
Regularity of
.26(52)
.11(14)
.34(19)
.46(19)
injection-
meal timing
(.0615)
( .7032)
(.1501)
( .0461)
Calories
.69(56)
.49(17)
.65(21)
.85(18)
consumed
(.0001)
( . 0456)
(.0014)
( .0001)
Percentage
.86(57)
.96(17)
.76(21)
.84(19)
calories-fat
(.0001)
( . 0001)
(.0001)
( .0001)

TABLE 5
(continued)
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r (n)
P <
Group 3
(11.5-12.6 Years)
r(n)
P <
Percentage
.83(57)
.93(17)
.70(21)
.86(19)
calories-
(.0001)
(.0001)
( .0004)
( . 0001)
carbohydrate
Concentrated
.68(57)
.92(17)
.70(21)
.55(19)
sweets
(.0001)
( .0001)
(.0004)
(.0141)
Eating
.74(57)
.62(17)
.74(21)
.85(19)
frequency
(.0001)
(.0080)
( . 0001)
(.0001)
Exercise
.30(57)
.20(17)
.61(21)
.23(19)
duration
(.0235)
(.4428)
( .0035)
( . 3383)
Exercise
.56(57)
.49(17)
. 63(21)
.76(19)
type
(.0001)
(.0442)
( .0021)
( .0001)
Exercise
.58(57)
.40(17)
.75(21)
.70(19)
frequency
(.0001)
(.1150)
( .0001)
(.0009)
Glucose
.91(57)
.85(17)
.96(21)
.93(19)
testing
(.0001)
(.0001)
( . 0001)
(.0001)
frequency

TABLE 6
Parent-Child Correlations for Total Sample and by Age Group
Three Months Post-Camp
Total Group 1 Group 2 Group
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6
r(n) r(n) r(n) r(n)
P< P< P< P<
Adherence Measure
Injection
regularity
.81(55)
(.0001)
.81(17)
(.0001)
.88(18)
(.0001)
.74(20)
(.0002)
Injection
interval
. 89(52)
(.0001)
.76(18)
(.0002)
.95(15)
(.0001)
.93(19)
(.0001)
Injection-
meal timing
.77(60)
( .0001)
.47(19)
(.0422)
.78(21)
( .0001)
.86(20)
( .0001)
Regularity of
injection-
meal timing
.25(54)
(.0665)
.22(17)
(.4001)
-.11(17)
( . 6710)
.38(20)
(.1011)
Calories
consumed
.69(60)
(.0001)
.11(20)
(.6344)
. 64(21)
(.0016)
.75(19)
( .0002)
Percentage
calories-fat
.66(61)
( .0001)
.75(20)
(.0001)
.62(21)
(.0026)
. 65(20)
(.0019)
3
Years)

TABLE 6
(continued)
Adherence Measure
Total
Sample
r (n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r(n)
P <
Group 3
(11.5-12.6 Years)
r(n)
P <
Percentage
. 65(61)
.68(20)
. 69(21)
. 65(20)
calories-
carbohydrate
( . 0001)
(.0009)
( .0006)
(.0017)
Concentrated
.43(61)
.37(20)
.25(21)
.69(20)
sweets
(.0005)
(.1078)
( .2666)
(.0008)
Eating
.80(61)
.51(20)
.85(21)
.90(20)
frequency
(.0001)
(.0228)
(.0001)
(.0001)
Exercise
.66(61)
.68(20)
. 67(21)
.77(20)
duration
(.0001)
(.0011)
(.0009)
(.0001)
Exercise
.68(61)
.51(20)
.74(21)
.77(20)
type
(.0001)
(.0227)
( .0001)
( .0001)
Exercise
.71(61)
.71(20)
. 69(21)
.76(20)
frequency
(.0001)
(.0005)
( .0006)
( . 0001)
Glucose
.91(61)
.89(20)
.96(21)
.85(20)
testing
frequency
(.0001)
(.0001)
( .0001)
(.0001)
U1
o

51
younger groups exhibited poor correlations on measures
involving time (injection regularity, injection-meal timing,
regularity of injection-meal timing, and exercise duration).
However, the oldest children were significantly correlated
with their parents (p < .05) on all measures (Table 3).
Immediately after camp (Table 4), the parent/child
correlations for the total sample were all significant with
the exception of exercise duration. Injection-meal timing
and regularity of injection-meal timing were still problema¬
tic for the 7- to 9-year olds. The 10- to 11.4-year olds
exhibited poor correlations on regularity of injection-meal
timing and exercise duration, while the oldest youngsters
were not significantly correlated with their parents on
measures of concentrated sweets and exercise duration (Table
4) •
The 6 weeks and 3 months after camp interviews demonst¬
rate a similar pattern. These results are presented in
Tables 5 and 6 respectively. Percentage calories: fat, per¬
centage calories: carbohydrates, eating frequency, exercise
type, and glucose testing demonstrated significant correlat¬
ions for all children. Regularity of injection-meal timing
and exercise duration demonstrated the weakest (nonsig¬
nificant) correlations in each of the study periods except
for the last one in which parent/child agreement for
exercise duration improved (r=.66). However, parent/child
agreement for regularity of injection-meal timing remained
weak (r=.25).

52
In addition to the parent/child correlations calculated
for each time period, the interviews were also collapsed
across time periods to determine the overall correlations by
age group (Table 7). Parents and children's reports for the
total sample were significantly correlated for all 13
measures, ranging from r=.36 (p<.004) on regularity of
injection-meal timing to r=.92 (pc.0001) for glucose testing
freguency. Comparisons between independent correlations
using Fisher's z' transformation were conducted to assess
differences in the degree of parent/child agreement across
the three age groups (p < .05, Cohen and Cohen, 1983, pages
53-56). These tests revealed that on 5 of the 13 adherence
measures (injection interval, injection-meal timing,
calories consumed, exercise duration, and glucose testing
freguency) there were significant differences in the
parent/child correlations among the three age groups (see
Table 7). The 10- to 11.4-year old demonstrated the highest
agreement (r=.92) with their parents on the injection
interval measure while the 7- to 9-year olds' reports did
not correspond as well (r=.51). On the injection-meal timing
measure, the oldest youngsters' reports corresponded best
with their parents (r= .75), while the 7- to 9-year olds
demonstrated the worst parent/child agreement (r= -.03).
Similarly, on the calories consumed measure, the oldest
group exhibited the highest parent/child correlation
(r=.89) and the youngest group displayed the weakest
(r=.53). The 11.5- to 12.6-year olds again demonstrated the

TABLE 7
Parent-Child Correlations for Total Sample and by Age Group
Collapsed Across Time
Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
P< P< P< P<
Adherence Measure
Injection
regularity
.81(63)
(.0001)
.75(20)
(.0001)
. 88 (22)
( .0001)
. 77(21)
( . 0001)
Injection *
interval
.75(59)
(.0001)
.51(19)
(.0262)
.92(19)
(.0001)
.64 (21)
(.0018)
Injection- *
meal timing
.69(63)
(.0001)
-.03(19)
(.8885)
.57(22)
(.0052)
.75(21)
(.0001)
Regularity of
inj ection-
meal timing
.36(63)
(.0049)
-.08(18)
(.7141)
-.08(20)
(.7382)
.43(21)
( .0526)
Calories *
consumed
.81(62)
(.0001)
.53(20)
(.0163)
.81(22)
( .0001)
.89(20)
( .0001)
Percentage
calories-fat
.91(63)
(.0001)
.96(20)
(.0001)
.88(22)
( .0001)
.89(21)
( .0001)
U>

TABLE 7
(continued)
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r (n)
P <
Group 3
(11.5-12.6 Years)
r (n)
P <
Percentage
.89(63)
.96(20)
.88(22)
.84(21)
calories-
carbohydrate
( .0001)
( .0001)
(.0001)
(.0001)
Concentrated
.69(63)
.73(20)
.80(22)
. 58(21)
sweets
(.0001)
(.0003)
( .0001)
(.0054)
Eating
.83(63)
.80(20)
. 77 (22)
.90(21)
frequency
( .0001)
(.0001)
( .0001)
( .0001)
Exercise *
.63(63)
.68(20)
.43(22)
. 86(21)
duration
(.0001)
(.0010)
( .0457)
( .0001)
Exercise
.70(63)
.75(20)
.60(22)
.73(21)
type
(.0001)
(.0001)
(.0033)
( .0002)
Exercise
.76(63)
.81(20)
.63(22)
.86(21)
frequency
(.0001)
(.0001)
(.0016)
(.0001)
Glucose *
.92(63)
.86(20)
.98(22)
. 84(21)
testing
frequency
(.0001)
(.0001)
(.0001)
( .0001)
* indicates significant differences by age group.

55
highest parent/child agreement (r=.86) on the exercise
duration measure, while the 10- to 11.4-year olds exhibited
the weakest correlation (r=.43). On the glucose testing
freguency measure, all groups' reports correlated highly
with their parents, however there was a significant dif¬
ference between the 10- to 11.4-year olds' (r=.98) and the
11.5- to 12.6-year olds' (r=.84) parent/child correlations.
The correlations between parent and child report were
then collapsed across the age groups so that the parent/
child correlations for the total sample could be examined
across the four time periods of the study. A test of
compound symmetry was used to test for the eguality of these
dependent correlations (Steiger, 1980). Results of these
analyses (Table 8) indicated that agreement between parent
and child did not differ significantly across time for 8 of
the 13 adherence measures: calories consumed; percent
calories: fat; percent calories: carbohydrates; eating
freguency; exercise duration; exercise type; exercise
freguency; and testing freguency. On measures of injection
regularity, injection interval, injection-meal timing,
regularity of injection-meal timing, and concentrated sweets
there were significant differences in the parent/child
correlations across two or more time periods. These
differences exhibited no consistent pattern. Injection
regularity, for example, exhibited the best parent/child
agreement at Time 5 (r=.81) and the worst at Time 3 (r=.54),
while concentrated sweets exhibited the highest parent/child

TABLE 8
Parent-Child Correlations for Total Sample
Over Four Time Periods: Pre and Post-Camp
Time 1 Time 3 Time 4 Time 5
Pre-Camp Post-Camp 6 wks Post-Camp 3 Mos Post-Camp
r(n) r(n) r(n) r(n)
p< p< p< p<
Adherence Measure
Injection *
regularity
.65(60)
( .0001)
.54(55)
( .0001)
.64(53)
( .0001)
.81(55)
( .0001)
Injection *
interval
.75(59)
( .0001)
.73(56)
( .0001)
.66(53)
( .0001)
.89(52)
( .0001)
Injection- *
meal timing
.54(62)
(.0001)
.68(57)
( .0001)
.76(56)
( . 0001)
.77(60)
( . 0001)
Regularity of *
injection-
meal timing
.09(59)
(.5010)
.44(54)
(.0008)
.26(52)
(.0615)
.25(54)
( .0665)
Calories
consumed
.73(62)
(.0001)
.62(59)
(.0001)
.69(56)
(.0001)
.69(60)
( .0001)
Percentage
calories-fat
.78(63)
(.0001)
.74(60)
(.0001)
.86(57)
( .0001)
. 66(61)
( .0001)

TABLE 8
(continued)
Adherence Measure
Time 1
Pre-Camp
r(n)
P <
Time 3
Post-Camp
r(n)
P <
Time 4
6 wks Post-Camp
r (n)
P <
Time 5
3 Mos Post-Camp
r (n)
P <
Percentage
.72(63)
.75(60)
.83(57)
. 65(61)
calories-
carbohydrate
(.0001)
(.0001)
( .0001)
(. 0001)
Concentrated *
.63(63)
. 59(60)
.68(57)
.43(61)
sweets
(.0001)
(.0001)
( .0001)
( . 0005)
Eating
.72(63)
.67(60)
.74(57)
.80(61)
frequency
(.0001)
(.0001)
( .0001)
( .0001)
Exercise
.29(63)
.21(60)
.30(57)
. 66(61)
duration
(.0211)
(.1106)
( .0235)
( . 0001)
Exercise
.64(63)
.64(60)
.56(57)
. 68(61)
type
(.0001)
( .0001)
(.0001)
( .0001)
Exercise
.68(63)
. 68(60)
.58(57)
.71(61)
frequency
(.0001)
( .0001)
( .0001)
( . 0001)
Glucose
.90(63)
.78(60)
.91(57)
.91(61)
testing
frequency
(.0001)
( .0001)
( .0001)
( .0001)
* indicates significant differences between correlations
across time periods. y,

58
agreement at Time 4 (r=.68) and the poorest concordance at
Time 5 (r=.43). Only the injection-meal timing measure
exhibited any evidence of a linear trend toward improvement
in parent/child agreement across time (see Table 8).
However, by the end of the study (Time 5) parents and
children were significantly correlated on 12 of the 13 ad¬
herence measures (p< .0005). Only parent/child agreement
for the regularity of injection-meal timing remained weak.
Differences between parent and child report were
further analyzed with repeated measures ANOVA on each of the
13 adherence measures collapsed across all parent/child
interviews using two between subject factors: Age group (7-
to 9-year olds, 10- to 11.4-year olds, and 11.5- to 12.6-
year olds) and Sex, and one within subject factor: Respon¬
dent (parent, child). Results indicated that there were few
differences between parent and child report. Simple main
effects for Respondent emerged on three measures. On the
injection-meal timing measure, parents reported an average
of 14 minutes while children reported an average of 19
minutes between injection and meal, F(1,56)=5.98, p < .02.
On the eating frequency measure parents reported that
children ate an average of 5.25 meals per day while children
reported eating an average of 5 times per day, F(l,56)=
25.29, p< .0001. On calories consumed parents reported that
their children ate approximately 92 calories more than the
recommended amount per day while children reported ingesting

59
approximately 13 calories less than the recommended amount
per day, F(1,55)=6.83, p < .01.
On measures of exercise type and exercise frequency a
Respondent x Sex interaction emerged F(l,56)=9.37 p < .003
and F(1,56)=7.28, p < .009 respectively. To determine the
source of the Respondent x Sex interactions, the data were
divided by sex and repeated measures ANOVA were conducted on
each of the two exercise measures using one within subject
factor (Respondent). For both exercise type and exercise
frequency, boys' reports did not significantly differ from
their parents' reports. In contrast, girls reported
participating in more strenuous and more frequent episodes
of exercise than did their parents, F(1,29)=11.99 p < .002,
and F(1,29)=5.90 p < .02 respectively. These interactions
were further examined using T tests to explore possible
differences between parents reports of boys' versus girls'
exercise type and exercise frequency; parents reported
significantly more strenuous and more frequent episodes of
exercise for boys compared to girls, t(62)=-3.35, p < .001.
In contrast, the children themselves did not report the same
male/female differences found in the parent data.
In summary, the children's reports correlated with
their parents reports in the expected direction, both across
time and across age groups. In addition, even when
differences between parent and child report did exist, they
were relatively small and of questionable clinical sig¬
nificance. Since the youngsters' self-report data were

60
generally reliable, replicating reliability data reported in
a previous study (Johnson et al., 1986), and since the
counselor interview data were unusable, it was decided to
use only the children's report in the succeeding analyses.
Effect of Diabetes Camp on Adherence
To test for possible changes in children's management
behaviors during camp, a repeated measures ANOVA was
conducted on each of the 13 adherence measures using two
between subject factors—Age Group (7-9 years, 10-11 years,
11.5-12.6 years) and Sex (M,F) and one within subjects
factor—Time (Time 1: before camp, Time 2: during camp, Time
3: immediately after camp, Time 4: 6 weeks after camp, and
Time 5: 3 months after camp). Significant Time main effects
or interactions with Time emerged for 9 of the 13 adherence
measures (i.e., injection regularity, injection interval,
injection-meal timing, calories consumed, eating frequency,
exercise duration, exercise type, exercise frequency, and
glucose testing frequency). Duncan's Multiple Range Tests
performed on these measures revealed that in all cases
adherence behaviors at camp (Time 2) differed significantly
from one or more of the pre- or post-camp periods (Time 1,
Time 3, Time 4, and Time 5). These results are presented in
Table 9.
Simple main effects for Time emerged for behaviors as¬
sociated with injection regularity F(4,164)=6.04, p < .0001,
injection-meal timing F(4,168)=2.83, p < .03, eating

TABLE 9
Duncan's Multiple Range Tests (p< .05) on the Adherence Measures
Demonstrating Within Subject Effects
Adherence Measure
Time Periods Compared
Pre-camp vs Post-camp Camp vs Pre- and Post-camp
13 14 15 21 23 24 25
Post-camp
3 4 3 5 4 5
Injection
regularity
Injection 7-9yrs
interval 10-11.4yrs
11.5-12.6yrs
Inj ection-
meal timing
Regularity of
inj ection-
meal timing
Calories 7-9yrs
consumed 10-11.4yrs
11.5-12.6yrs
Percentage
calories-fat
#
* * * *
# #
* * * *
* * * *
# * * * # #
* * *
* * * *
CTi

TABLE 9
(continued)
Time
Periods
Compared
Pre-camp vs
Post-camp
Camp vs
Pre- and
Post-camp
Post-camp
13 14
1 5
2 1
2 3 2 4
2 5
3 4 3 5 4 5
Adherence Measure
Percentage
calories-
carbohydrate
Concentrated
sweets
Eating
freguency
*
* *
*
Exercise
duration
# #
* *
*
Exercise
type
#
*
* *
*
*
Exercise 7-9yrs
#
*
* *
*
*
frequency 10-11.4yrs
•k
* *
*
11.5-12.6yrs
# #
#
*
* *
*
Glucose
testing
frequency
#
#
* *
*
#
* indicates significant improvement.
# indicates significant deterioration.
o\
to

63
frequency F(4,200)=26.15, p < .0001, exercise duration
F(4,200)=5.97, p < .0001, exercise type F(4,200)=101.08,
p < .0001, and testing frequency, F(4,200)=8.11 p < .0001.
In every instance children exhibited more compliant behavior
during camp (Table 10).
Repeated measures ANOVA revealed Time main effects as
well as Time x Age group interaction effects on 3 additional
adherence measures (Table 11): injection interval (Time
effect, F(4,140)=2.45, p < .05; Time x Age group interac¬
tion, F(8,140) = 2.21, p < .03), calories consumed (Time
effect, F(4,196)=7.96, p < .0001; Time x Age group interac¬
tion, F(8,196)=2.39, p < .02), and exercise frequency (Time
effect, F(4,200)=179, p < .0001; Time x Age group interac¬
tion, F(8,200)=2.27, p < .02). However, subsequent analyses
indicated that injection interval was the only measure to
evidence differential effects at camp for different age
groups. That is, the oldest children (11.5-12.6 years)
reported significantly improved compliance during camp,
while the 7- to 9-year olds and the 10- to 11.4-year olds
reported no change on these measures between home and camp.
Time x Age group interactions for the calories consumed and
exercise frequency measures were due to differences between
the age groups at one of the five time periods rather than
to a differential effect of camp on the age groups. In
fact, all age groups exhibited a significant increase on
calories consumed during camp and all youngsters reported a

TABLE 10
Means and Standard Deviations for Adherence
Measures Demonstrating Simple Main Effects for Time
Adherence Measure
Time 1
Means (SD)
(Interp)
Time 2
Means (SD)
(Interp)
Time Period
Time 3
Means (SD)
(Interp)
Time 4
Means (SD)
(Interp)
Time 5
Means (SD)
(Interp)
Injection
.47(.38)
.27(.27)
.50(.35)
. 46 (.31)
.68(.55)
regularity (minutes)
(28.02)
(15.92)
(30.06)
(27.60)
(40.59)
Injection-
.70(.50)
.49 ( . 44)
.68(.37)
.66(.47)
.71(.46)
meal timing (minutes)
(42.14)
(29.65)
(40.81)
(39.52)
(42.74)
Eating 15.
35(12.62) 2
.58(3.54) 15
.31(13.55) 17
. 33 (13.93)
16.26(14.67)
freguency (per day)
(5.02)
(5.86)
(5.07)
(4.97)
(5.02)
Exercise
09(.14)
.03(.01)
.16 ( . 23)
.17(.24)
. 12(.18)
duration (minutes
per occasion)
(20.03)
(42.41)
(14.44)
(15.35)
(14.07)
Exercise
97(.01)
.94(.01)
.98(.01)
.97(..02)
.97(.01)
type
(kilocalories/min)
(.03)
(.06)
(.02)
(.03)
(.03)
Glucose 51.
23(24.41) 45
.80(12.78) 54
.09(24.50) 59
.18(26.09)
60.80(25.47)
testing
frequency
(per day)
(1.88)
(2.17)
(1.79)
(1.58)
(1.53)
as

TABLE 11
Means and Standard Deviations for Adherence
Measures with Age x Time Interaction at the 5 Time Periods
Adherence Measure
Time 1
Means(SD)
(Interp)
Time 2
Means(SD)
(Interp)
Time Period
Time 3 Time 4
Means(SD) Means(SD)
(Interp) (Interp)
Time 5
Means(SD)
(Interp)
Injection 7-9yrs
. 4 3 (. 41)
.46(.54)
.63(.65)
.98(.66)
. 4 5 (. 3 4)
interval
(25.6)
(27.6)
(37.8)
(58.8)
(26.7)
(minutes) 10-11.4yrs
. 91(.87)
1.05(1.9)
.94(.73)
.79( .77)
1.31(1.2)
(54.8)
(63.3)
(56.4)
(47.4)
(78.5)
11.5-12.6yrs
.79(.50)
.24(.23)
1.28(1.0)
.84(.42)
1.06(.94)
(47.1)
(14.5)
(76.54)
(50.3)
(63.5)
Calories 7-9yrs
consumed
-7.4(489)
164.9(962)
-4.1(619)
18.1(721)
277.1(480)
10-11.4yrs
100.0(843)
598.9(794)
228.5(610)
111.5(693)
297.4(726)
11.5-12.6yrs -
307.8(612)
382.9(874)
-327.9(533)
-325.3(649)
-396.9(550)
Exercise 7-9yrs
72.9(14.8)
30.8(11.7)
80.9(12.0)
74.8(12.0)
67.0(13.4)
frequency
(1.8)
(4.5)
(1.3)
(1.6)
(2.0)
10-11.4yrs
73.7(11.1)
23.4(9.7)
74.2(19.5)
71.1(16.3)
74.6(13.8)
(1.6)
(4.2)
(1.6)
(1.8)
(.07)
11.5-12.6yrs
69.3(14.3)
28.4(16.7)
80.4(12.5)
81.2(11.1)
78.4(11.2)
(.10)
(4.3)
(.04)
(04)
(1.3)
Ol

66
significant increase in exercise frequency associated with
the camp experience (see Table 9 and 11).
Five Measures exhibited no significant change related
to the camp experience: regularity of injection-meal timing,
percentage calories: fat, percentage calories: carbohydrat¬
es, and concentrated sweets (Table 12).
On the regularity of injection-meal timing measure,
there was also an Age group x Sex interaction, F(2,38)=3.41,
p < .04). Subsequent LSMEANS procedure revealed that the 10-
to 11.4-year old girls were significantly less adherent than
the 11.5- to 12.6-year old girls or the 10- to 11.4-year old
boys. No other main or interaction effects for sex emerged
for any other adherence measure.
Although it is clear that significant changes occurred
during camp for 9 of the 13 adherence behaviors, examination
of Table 9 demonstrates that these changes were not main¬
tained. That is, Time 2 (camp) exhibited consistent
significant differences from Times 1, 3, 4, and 5. In
contrast there were fewer significant differences between
Time 1, 3, 4, and 5. When differences between these times
did occur, they were most often in the direction of poorer
adherence (see Table 9 and interpretations in Tables 10, 11,
and 12).
Effect of Diabetes Camp on Glycemic Control
This project used two measures to assess glycemic
control. Glycosylated hemoglobin (HbAic) which provides an

TABLE 12
Means and Standard Deviations for Adherence
Measures that Remained Stable Over the 5 Time Periods
Adherence Measure
Time 1
Means(SD)
(Interp)
Time 2
Means(SD)
(Interp)
Time Period
Time 3 Time 4
Means(SD) Means(SD)
(Interp) (Interp)
Time 5
Means (SD)
(Interp)
Regularity of
injection- (minutes)
meal timing
18.68(19.3)
18.10(13.5)
19.49(18.2)
i 12.02(10.4)
18.20(19.22)
Percentage
calories-fat
23.67(6.9)
(48.7)
22.04(5.7)
(47.0)
24.53 (7.2)
(49.5)
23.99(9.0)
(48.9)
22.73(6.5)
(47.7)
Percentage
calories-
carbohydrate
24.33(7.2)
(35.7)
23.35(6.0)
(36.7)
25.44(7.5)
(34.6)
24.76(8.8)
(35.2)
23.35(6.7)
(36.7)
Concentrated
1.52(1.9)
1.24(1.1)
1.33(1.8)
1.38(1.7)
1.56(1.7)
sweets (per day)

68
average estimation of glycemic control for a period of 2 to
4 months, and glycosylated serum protein (GSP) which
provides a measure of glycemic control over a 10 to 14 day
period. In order to support the reliability of the laborat¬
ory assays, Pearson product moment correlations were
performed (Table 13). It was expected that the highest GSP/
HbA^c correlation would occur between the pre-camp GSP
measure and the pre-camp HbA^c measure since they were drawn
at the same time and reflect some overlap in time periods.
This was found to be the case (r=.71, p < .0001, Table 13).
Further, it was expected that the pre-camp GSP measure would
correlate less well with the 3 month followup HbA^c since
these measures reflect entirely different time periods. In
fact, the correlation between pre-camp GSP and followup
HbAiC was markedly lower (r=.52, p < .0001). The lowest
GSP/Hba^c correlations were expected between post-camp GSP
and both pre-camp HbA^c and followup HbA^c, taken at 3
months after camp, since the GSP and HbA^c measures reflect
completely different time periods and completely different
settings (camp versus home). In fact, these correlations
(r=.43; r=.39) represent the lowest correlations in the
matrix depicted in Table 13. Overall, the pattern of
results depicted in Table 13 supports the reliability of our
glycemic control measures.
To assess possible changes in glycemic control due to
the camp experience,a repeated measures ANOVA was performed
on the GSP pre- and post-camp measures using 2 between

69
TABLE 13
Correlations Between Glycosylated Hemoglobins
and Serum Proteins
pre-camp
r (n)
P <
HbA1c
3 months
post-camp
r(n)
P <
pre-camp
r(n)
P <
GSP
post-camp
r(n)
P <
HbA^c :
pre-camp
3 months
post-camp
1.00(63)
( .0000)
. 62(61)
(.0001)
1.00(62)
(.0000)
GSP
pre-camp
.71(53)
(.0001)
.52(52)
(.0001)
1.00(54)
( .0000)
post-camp
.43(60)
.39(59)
.78(54)
1.00(61)
(.0007)
(.0020)
( .0001)
(.0000)

70
subjects factors (Age group and Sex), and 1 within subjects
factor (Time: pre- and post-camp). A significant main
effect for Time, F(1,50)=11.00, p < .0017, and a significant
main effect for Age group, F(2,50)=3.31, p < .0447 emerged.
Subsequent LSMEANS procedures revealed that the overall mean
GSP values increased from 5.63% to 6.44% and that the 7- to
9-year olds (5.40%) and the 11- to 11.4-year old youngsters
(5.23%) had significantly lower mean GSP values than the
11.5- to 12.6-year old youngsters (6.82%).
It was then hypothesized that children arriving at camp
at different levels of glycemic control may have been dif¬
ferentially affected by the camp experience. Therefore,
the youngsters were assigned to good (GSP less than 4.5%),
moderate (GSP between 4.5 and 7.0%), and poor control (GSP
greater than 7.0%) categories based on groupings apparent
from a univariate procedure on the pre-camp GSP values.
Repeated measures ANOVA on the GSP values pre- and post¬
camp was conducted using one between subjects factor (GSP
control group) and one within subjects factor (Time: pre-
and post-camp). Results indicated a significant main effect
for Time, F(1,51)=12.45, p < .0009, and a significant main
effect for GSP control group, F(2,51)=55.91, p < .0001.
However, the Time x GSP control group interaction was not
significant, indicating that the various control groups did
not react differentially to the camp experience.
To assess changes in HbA^c, a repeated measures
analysis of variance (ANOVA) was conducted on the HbA^c pre-

71
camp and at followup using 2 between subjects factors (Age
group and Sex), and 1 within subjects factor (Time). There
were no significant main effects for Time and no significant
interactions, indicating that the camp experience had no
perceivable lasting effect. However, it was hypothesized
that the youngsters may have demonstrated differences
depending on their glycemic level of control before camp.
Therefore, based on a univariate procedure performed on
HbA^c drawn at the beginning of camp, the children were
divided into good (HbA^c less than 8.4%), moderate (HbA^c
between 8.4 and 10.4%), and poor (HbA^c greater than 10.4%)
HbA^c control groups. A repeated measures ANOVA procedure
was conducted on the HbA^c (pre-camp and at followup) using
one between subjects factor (HbA^c control group) and one
within subjects factor (Time). Results demonstrated a
between subjects effect of HbA^c control group, F(2,58)-
=61.75 , p < .0001, and a Time x HbA^c control group
interaction F(2,58)=4.81, p < .0117, suggesting that not all
three HbA^c control groups were equally stable on the HbAic
measure at followup. Therefore, repeated measures ANOVA
procedures were conducted with each of the HbA^c control
groups separately using one within subjects factor (Time:
pre-camp and followup). Results from these analyses
indicated that children in good and poor control remained
stable, while youngsters in moderate control demonstrated a
significant time effect F(1,26)=11.09, p < . 0026. Examina¬
tion of the mean HbAic values pre-camp and at followup for

72
each of these groups indicated that while the means for the
good and poor control groups remained stable (7.45 to 7.80%
and 11.36 to 10.72% respectively), the moderate control
group experienced a significant decrease going from 9.51% to
8.80%.
To determine if these changes in HbA^c levels were
associated with camp, a repeated measures ANOVA was con¬
ducted on the GSP pre- and post-camp measures using one
between subjects factor (the HbAic control groups) and one
within subject factor (Time). The result demonstrated a
significant main effect for Time, F(1,51)=8.31, p < .0058,
and a significant between subjects effect of HbA^c control
group, F(2,51)=7.51, p < .0014) but no HbA^c control group x
Time interaction. These results suggest that the improve¬
ments noted in the moderate HbA^c control group from pre¬
camp to 3 months post-camp did not appear to be the result
of changes associated with the camp experience.
Relationship between Adherence and Diabetic Control
This study explored the relationship between adherence
and diabetic control in two ways. First adherence behaviors
during camp were employed in hierarchical regression models
to predict to post-camp GSP, and adherence behaviors during
the 3 months following camp were used in hierarchical
regression models to predict to the followup HbA^c. Second,
categorical anlyses, which involve the division of data into
logical groupings, were utilized to explore possible

73
associations between adherence and diabetic control for
differing categories of campers (e.g., those in good versus
moderate versus poor diabetic control).
Adherence/GSP Relationships
Hierarchical regression analyses
In order to explore possible relationships between
adherence behaviors and diabetic control as measured by GSP,
it was hypothesized that adherence measures during camp
would predict GSP values post-camp. Since pre- and post¬
camp GSP measures were highly correlated (r=.78), pre-camp
GSP was entered first (R2=.69). To determine whether simple
patient characteristics would predict post-camp glycemic
control, age and duration of disease were added to the
model. Since no significant increase in the R2 occurred,
age and duration of disease were dropped from subsequent
models. Since increases in GSP levels as well as increases
in calories consumed were reported for all youngsters during
camp, calories consumed was added next to the model.
However, no improvement in the model's predictive power
occurred. Next, calories consumed before coming to camp was
added to control for pre-camp calorie consumption. The
addition of pre-camp caloric consumption did not enhance the
model's predictive power. Finally, it was hypothesized that
calories consumed during camp may affect post-camp GSP at
varying levels of pre-camp GSP control. Therefore a model
including pre-camp GSP, calories consumed during camp, and
the interaction of pre-camp GSP x calories consumed during

74
camp was tested. This model did not significantly enhance
the R2 above that offered by the original model using pre¬
camp GSP as the sole predictor variable.
Since the obvious hypothesis that increases in calories
consumed at camp was responsible for the post-camp GSP
increases was not confirmed, further analyses were conducted
to assess whether any of the other adherence measures may
have related to post-camp GSP. To do so, the data were
combined into adherence factors found by Johnson et al.,
(1986). However, Johnson et al.'s factors were based on a
combination of child and parent report whereas the data in
this study were based exclusively on child report. Accord¬
ingly, to support the combination of the child report data
into factors, it was necessary to determine whether the
measures (based on child report) within each of the Johnson,
et al. factors were more highly correlated than measures
between factors. The intercorrelation matrix of child
reported pre-camp adherence measures was examined. The
correlations of measures within factors were averaged
(excluding the 1.0s) and compared to the average correlation
of measures between factors. Results indicated that similar
to Johnson et al.'s results, average correlations of
measures within factors were larger (ranging from .22 to
.96) than the average correlations of measures between
factors (ranging from .01 to .21, see Table 14). These
results supported the combination of the measures. There¬
fore, measures were standardized to pre-camp measures and

75
TABLE 14
Correlations Between Adherence Measures
Within and Between Factors
Factor:
Exercise
Injection
Diet
Eat/Test
Diet
Type
Frequency
Amount
Exercise
. 66
Injection
. 02
.22
Diet Type
. 12
. 01
.96
Eat/Test
Frequency
-.03
. 10
.21
.27
Diet Amount
-.11
-.06
.01
. 01
.27

76
five factors were created based on the Johnson et al. (1986)
model. Factor 1 contained the three exercise measures,
Factor 2 was made up of all four injection measures, Factor
3 consisted of measures of diet type (percentage of calori¬
es: carbohydrates and percentage calories: fat), Factor 4
contained measures of eating frequency and glucose testing
frequency, and Factor 5 included measures of calories
consumed and concentrated sweets. A factor score was
calculated by averaging the standardized scores of measures
loading highly on that factor. It was expected that the
average correlation between factors would be low. This was
found to be the case (Table 15) with correlations ranging
from r=.01 (between the injection factor and the diet type
factor) to r=.26 (between the diet type factor and the
eating/testing frequency factor). These correlations were
considered to support the relative independence of the
factors and justify their use in further exploration of the
adherence/control issue.
Hierarchical regression analyses were again used to
determine whether adherence behaviors during camp would
predict post-camp GSP. Therefore, each of the five
adherence factors reflecting camp behavior was added
separately to the original model which contained the pre¬
camp GSP measure. None contributed significant additional
variance. Next, it was decided to control for adherence
behavior before coming to camp. Therefore, adherence before
camp and adherence during camp were added to the original

77
TABLE 15
Average Correlations between Adherence Factors
Factor: Exercise Injection Diet Eat/Test Diet
Type Frequency Amount
Exercise
1.00
Injection
. 03
1.00
Diet Type
. 14
. 01
1.00
Eat/Test
Frequency
-.05
. 19
.26
1.00
Diet Amount
-.16
-.14
-.02
. 03

78
model. For example, the model controlling for exercise
behavior before coming to camp included pre-camp GSP,
exercise factor (before camp) and exercise factor (during
camp). None of these five models significantly enhanced the
predictive power of the pre-camp GSP prediction model.
Finally, it was hypothesized that insulin dosage during
camp might predict post-camp glycemic control. Repeated
measures ANOVA on the pre- and during-camp insulin dosage
with one within subject factor (Time) indicated that there
was a significant difference between dosage before camp
compared to dosage during camp F(l,57)=6.31, p < .01. The
average dose before camp was 32.2 units while the average
dose during camp was 27.2 units. Adding insulin dosage
during camp to pre-camp GSP did not increase the variance
accounted for by pre-camp GSP alone. A model which included
pre-camp GSP, dosage during camp, and insulin dosage before
camp was also tested. No significant increase in the R2 was
achieved. To determine whether children entering camp at
different levels of glycemic control would be differentially
affected, a model containing pre-camp GSP, insulin dosage
during camp, and an interaction of these two measures was
tested. This model did not strengthen the predictive power
of the original pre-camp GSP prediction model.
Categorical analyses
Categorical analysis involves the division of data into
categories based on logical groupings. In this study, the
data were grouped based on levels of glycemic control and

79
levels of adherence as well as change in glycemic control
and change in adherence.
Accordingly, it was first hypothesized that children
arriving at camp at different levels of diabetic control
would report different levels of adherence prior to attend¬
ing camp. Therefore, based on a univariate procedure on the
pre-camp GSP measure, the youngsters were divided into three
GSP control categories. Pre-camp GSP of less than or egual
to 4.5 was considered good control, greater than 4.5 and
less than or egual to 7.0 was considered moderate control,
and greater than 7.0 was considered poor control. ANOVAs
were conducted on each of the five pre-camp adherence
factors using one between subject factor (GSP control
group). Results indicated no significant differences
between the groups on any of the adherence factors.
Next, it was postulated that the adherence/GSP control
relationship might be detected if youngsters were categori¬
zed on their degree of adherence before coming to camp.
Theoretically, those children who had been more adherent
before coming to camp would also exhibit lower GSP values at
the beginning of camp. Therefore, based on the pre-camp
median score for each of the five adherence factors,
youngsters were assigned a score of 1 for being below the
median (adherent) and a score of 0 for being above the
median (nonadherent). Their scores for the five adherence
factors were summed and children were placed into three
adherence categories (0-1 was poor adherence, 2-3 was

80
moderate adherence, and 4-5 was good adherence). An ANOVA
was performed on the pre-camp GSP measure using one between
subject factor (Adherence group). Results indicated no
significant difference between the adherence groups on the
pre-camp GSP measure.
It was conjectured that an adherence/control relation¬
ship might be better detected if the youngsters were divided
into categories of metabolic change. Presumably, those
children who exhibited an improvement in diabetic control
pre- to post-camp, might show an improvement in adherence
behaviors from pre- to during-camp. Such improvement in
adherence behaviors would not be expected for children whose
diabetic control stayed the same or deteriorated over the
camp experience. Consequently, post-camp GSP values were
subtracted from the pre-camp GSP values and based on
univariate analysis of the resulting difference, the
youngsters were divided into children who improved (dif¬
ference greater or equal to .5), did not change (difference
less than .5 and greater or equal to -.4), and got worse
(difference less than -.4). A repeated measures ANOVA was
conducted on each of the pre- and during-camp adherence
factors as well as the calories consumed measure using one
between subject factor (GSP difference group) and one within
subject factor (Time). Results of these analyses indicated
only simple Time main effects for the exercise factor
F(1,59)=155.34, p < .0001, the injection factor F(l,55)=

81
15.51, p < .0002, the eating/testing frequency factor
F(1,59)=39.71, p < .0001, and the calories consumed measure
F(1,58)=10.93, p < .001. There was no Time x GSP difference
group interaction; youngsters who exhibited differential
changes in GSP pre- to post-camp did not exhibit differen¬
tial changes in adherence pre- to during-camp.
Finally, it was conjectured that changes in adherence
might clarify the association between adherence and control.
It would seem that children who improved in adherence during
camp would exhibit a concomitant improvement in GSP control.
while these who did not improve were expected to exhibit
stable GSP values pre- to post-camp. Therefore, the
children were divided into change groups. Post-camp
adherence factors were subtracted from pre-camp adherence
factors to produce two groups (those who improved in
adherence and those who did not improve in adherence).
Repeated measures ANOVA was then performed on the GSP
measures using one within subject factor (Time, pre- and
post-camp) and one between subject factor (Change group).
Results revealed a simple Time main effect. The interaction
term was nonsignificant.
Adherence/HbAic Relationships
Hierarchical regression analyses
To assess possible relationships between adherence and
glycemic control after camp, it was hypothesized that
adherence behaviors after camp would predict the followup

82
HbA^c. Since pre-camp and followup HbA^c values were
moderately correlated (r=.62), the pre-camp HbA^c was
entered into the hierarchical regression model first
(R2=.39). To determine whether simple patient charac¬
teristics would enhance prediction of followup PíbA^c, age
was added to this model. Age significantly increased the R2
to .47 and was retained in all subseguent models. Next,
duration of disease was added to this model, but did not
enhance the predictive power of the model and was dropped
from all subsequent analyses. Further analyses were
conducted to assess whether any of the adherence factors
were related to followup glycemic control. To do so, the
post-camp adherence measures were standardized to the pre¬
camp adherence measures and combined into the five adherence
factors as previously described. Each of the five post¬
camp adherence factors was then added to the model in five
separate regressions. None of the factors contributed sig¬
nificant additional variance. It was conjectured that
controlling for adherence before camp might enhance the
predictive power of the model. Therefore, each adherence
factor pre-camp was added to the model containing pre-camp
HbA^c, age, and the same post-camp adherence factor. The
addition of the pre-camp adherence factor did not sig¬
nificantly increase the R2 in any of the five models tested.
It was then postulated that the adherence factors post-camp
might predict post-camp HbAic for various levels of glycemic
control at the outset of the study. Therefore, an

83
interaction term (adherence factor x pre-camp HbA^c) was
tested for each of the five adherence factors. Only the
model that included pre-camp HbA^c, age, post-camp injection
factor, and the pre-camp HbA^c x post-camp injection factor
interaction term proved to increase the R2 significantly
(R2=.51 see Table 16). The interaction between the pre-camp
HbA^c and injection was interpreted by calculating the
nonstandardized Beta weights for injection for varying pre¬
camp HbA^c levels from 5.8 to 13.1 (the ranges of pre-camp
HbA^c found in this study's sample) using the equation from
Table 16 (as suggested by Cohen and Cohen, 1983). As is
apparent from Table 17, injection behaviors in children with
low pre-camp HbA^c (e.g., 5.8 to 7.0) was negatively
associated with post-camp HbA^c: less compliant behavior
(higher injection scores) was associated with lower post¬
camp HbA^c (i.e., better diabetic control). At pre-camp
HbA^c levels between 8.0 and 9.0 the relationship between
injection behavior and post-camp HbA^c diminishes to zero.
For pre-camp HbA^c levels between 11.0 and 13.1 (poor
control) there was a positive association between injection
behaviors and post-camp diabetic control, i.e., less
adherent behaviors were associated with poorer control.
Table 18 depicts the characteristics of three groups divided
on the basis of the injection factor Beta weights depicted
in Table 17. Noteworthy was a relatively large increase in
insulin dosage for both groups where the Injection Beta
weights were large (good and poor control youngsters). Due

84
TABLE 16
Predicting Post-camp HbA^^c by Pre-camp HbA]_c,
Age, Post-camp Injection Factor,
and Pre-camp HbAj^c x Post-camp Injection Interaction
Variables in model
Beta weights
P<
R2 = .51
Pre-camp HbA^c
.47445345
.0017
Age
.28755174
. 0142
Post-camp
Injection Factor
-4.07128891
. 0510
Pre-camp HbA^^c x
Post-camp
Injection Factor
.45447440
. 0487

85
TABLE 17
Predicting Post-camp HbA^c:
Nonstandardized Injection
Beta Weights at Varying Levels
of Pre-camp HbA^c
Levels of
Pre-camp HbA^c
Injection Factor (Post-camp)
Beta Weights
5.8
-1.435
7.0
- .890
8.0
- .435
9.0
- .021
10.0
.473
11.0
.928
12.0
1.382
13.1
1.882
* Y= 1.33 + .47 (Pre-camp HbA1c) + .29 (Age)
- 4.07 (Post-camp Injection factor)
+ .45 (Pre-camp HbA^c x Post-camp Injection factor)

86
TABLE 18
Pre-camp HbA1c x Post-camp Injection Factor:
Descriptive Characteristics
Pre-camp HbA^^c
Injection B Wts
5.8-7.0
1.4 to -.9
8.0-10.0
-.4 to .5
11.0-13.0
.9 to 1.9
N
8
37
5
Pre-camp HbA^^c
6.5
8.9
11.6
Post-camp HbA-LC
7.4
8.5
11.0
Age
9.9
10.5
11.2
Duration of disease
3.8
4.0
3.5
Injection factor
pre-camp
. 10
. 01
- .18
Injection factor
post-camp
.20
. 34
.59
Insulin dose
pre-camp
25.9
30.9
33.0
Insulin dose
during camp
21.8
27.9
29.4
Insulin dose
at followup
34.5
29.5
40.7
Change in insulin
dose between pre¬
camp and followup
8.6
- 1.4
7.7

87
to this finding it seemed appropriate to consider whether
insulin dosage and change in insulin dose would enhance the
predictive power of the model that contained pre-camp HbA^c
and age. Insulin dosage at camp did not increase the R2.
To control for pre-camp insulin dose, dosage before camp was
added to the model with no increase in R2. Keeping in mind
the role of injection which had earlier been established, it
was added to the model. No significant increase of R2
resulted. It was then hypothesized that the changes in
injection behavior at various changes in insulin dose from
pre-camp to followup might differentially predict post-camp
HbA^c. Change in insulin dosage was calculated by sub¬
tracting the total insulin dosage reported by the child on
the last interview (3 months post-camp) from total dosage
reported by the child during the last interview before
coming to camp. A model including pre-camp HbA^c, age, dif¬
ference in dosage, injection factor, and difference in
dosage x injection factor interaction term was tested. This
model significantly increased the variance, R2=.57 (see
Table 19). This interaction was interpreted by calculating
the nonstandardized Beta weights for injection for various
units of change in dosage from -40 to +40 (the range of
total units change found in this study's sample) based on
the equation from Table 19. As is apparent from Table 20,
the greater the decrease in insulin dose, the higher the
injection factor Beta weight. At the insulin dose change of
-5 to +10 the relationship between injection and post-camp

88
TABLE 19
Predicting Post-camp HbA^c by
Pre-camp HbAj^c, Age, Insulin Dose Change,
Post-camp Injection Factor, and
Insulin Dose Change x Post-camp Injection Interaction
Variables in model Beta weights p<
R2 = .57
Pre-camp HbA^^c
.63400558
. 0001
Age
.24956679
.0321
Change in insulin
dose from pre-camp
to followup
.02784116
. 0212
Post-camp
Injection factor
.21885475
. 4495
Change in insulin x
Post-camp
Injection factor
- .06829848
. 0065

89
TABLE 20
Predicting Post-camp HbA^c:
Nonstandardized Injection
Beta Weights at Varying Levels
of Change in Insulin Dose from
Pre-camp to 3 Months Post-camp
Levels of Change in
Insulin Dose from
Pre-camp to Followup
Injection Factor (Post-camp)
Beta Weights
-40
2.951
-30
2.268
-20
1.585
-10
.901
- 5
. 560
0
.219
5
- . 123
10
- .464
15
- .806
20
- 1.147
30
- 1.830
40
- 2.513
* Y= .26 + .63 (Pre-camp HbA^c) + .25 (Age) + .03 (Insulin
Dose Change) + .22 (Post-camp Injection Factor)
- .07 (Insulin Dose Change x Post-camp Injection Factor)

90
HbA^c approached zero. Increases in insulin dose were as¬
sociated negatively with the injection factor Beta weight.
Table 21 depicts the characteristics of groups divided based
on the Beta weights for injection at varying levels of
change in insulin dose (Group 1 decreased insulin, Group 2
stayed the same, and Group 3 increased insulin dose). A
number of characteristics are noteworthy for the group of
children who reported the largest increases in insulin dose
during camp: children in this group were in the best HbA^c
control before camp; this group was the youngest of the
three groups; children in this group reported the greatest
post-camp compliance; this group was the only one that ex¬
perienced an increase in HbA^c while the other two groups
demonstrated slight improvement in glycemic control at
followup.
The other four adherence factors were considered in
similar models to determine if their interaction with
insulin dose change would offer similar predictive power.
This was not the case.
Categorical analyses
Based on a univariate procedure, the pre-camp HbAiC
values were divided into control categories (less than
8.4=good, greater than 8.4 and less than 10.4=moderate, and
greater than 10.4=poor control). ANOVAs were conducted on
each of the five adherence factors using one between subject
factor (HbAic control group). A group effect was detected
for the injection factor with the differences between the

91
TABLE 21
Pre- Post-camp Change in Insulin Dose x Post-camp
Injection Factor: Descriptive Characteristics
Change in Dose(CH)
Injection B Wts
Ch <= -10
3.0 to .9
-10 < Ch < 15
.6 to -.5
Ch =>
-.8 to -
N
13
28
9
Pre-camp HbA^c
9.3
8.7
8.1
Post-camp HbA^^c
9.0
8.5
8.5
Age
10.6
10.7
9.9
Duration of disease
3.5
4.2
4.5
Injection factor
pre-camp
- . 14
. 18
- .14
Injection factor
post-camp
.40
.47
.21
Insulin dose
pre-camp
44.1
29.6
13.3
Insulin dose
during camp
25.5
30.3
24.4
Insulin dose
at followup
21.2
32.4
42.6
Change in insulin
dose between pre¬
camp and followup
-23.7
2.8
29.3

92
good and poor control groups F(2,57)=3.75, p < .03.
However, the group effect was in the opposite direction of
what was expected. That is, the good control group was
actually less adherent on the injection factor (.28) than
the poor control group (-.24).
The youngsters were then divided into control groups
based on a univariate procedure on the followup HbA^c (less
than 8.4=good, greater than 8.4 and less than or equal to
10.4 moderate, and greater than 10.4=poor). ANOVAS on the
adherence post-camp adherence factors using one between
subject factor (followup HbA^c control group) revealed no
differences. In other words, the pre-camp findings were not
replicated with the post-camp data.
Next, the adherence/control relationship was examined
by assessing whether control was associated with degrees of
adherence. Based on the median score on each of the five
adherence factors, youngsters were assigned a score of 1 for
being below the median of the pre-camp adherence factor
(adherent) and a score of 0 for being above the median of
the pre-camp adherence factor (nonadherent). Their scores
for the five adherence factors were summed and children were
paced into three adherence categories (0-1 was poor ad¬
herence, 2-3 was moderate adherence, and 4-5 was good
adherence). Separate ANOVA procedures using one between
subject factor (Adherence group) on the pre-camp and the
followup HbA^c measures revealed that these groups did not

93
differ in glycemic control either before camp or at
followup.
Next, the HbA^c measure at followup was subtracted from
the pre-camp HbA^c measure. It was hypothesized that
youngsters who exhibited a decrease in HbA^c at followup
would have demonstrated increased compliance, while those
whose HbA^c values increased would have demonstrated
decreased compliance. This difference score was subjected
to a univariate procedure so that youngsters could be
divided into three categories. Scores greater than or equal
to .5 were placed in the improved group, scores between .4
and -.4 were in the no change group, and scores less than or
equal to -.5 were in the worse group. Repeated measures
ANOVA on the adherence factors (based on the three inter¬
views conducted before camp and on the nine interviews
conducted during the 3 month followup period) using one
within subject factor (Time) and one between subject factor
(HbA^c control category) was conducted. Results indicated a
simple Time main effect for the injection factor F(l,58)=
13.15, p < .0006 and a simple Time main effect for the
eating/testing frequency factor F(1,59)=5.11, p < .03. No
Time x HbA^c change group interaction was detected.
Finally, post-camp adherence factor scores were
subtracted from pre-camp adherence factor scores and the
children were divided into change groups (improved adherence
and less adherent during followup). It was hypothesized
that improved adherence would be inversely related to the

94
post-camp HbA^c while decreased adherence would be asso¬
ciated with increased HbA^c at followup. Repeated measures
ANOVA was then conducted on the two HbA^c measures using one
within subject factor (Time, pre-camp and followup) and one
between subject factor (Change group). Results revealed
only a Time main effect.

DISCUSSION
This study was designed to address the following
questions: 1) does attendance at a diabetes summer camp
alter children's diabetes management behaviors, 2) does
attendance at a diabetes summer camp influence children's
glycemic control, 3) if there are behavioral or metabolic
changes associated with camp, are they maintained once the
children return home, 4) is there a relationship between
adherence behavior and diabetic control. Results relevant
to each of these four questions will first be discussed. A
summary of the findings and a discussion of the study's
limitations and implications of its results will follow.
Reliability of Child Report
In order to address this question, youngsters and their
parents were interviewed prior to and after attendance at a
diabetes summer camp. While at camp, counselor report was
expected to corroborate and compliment the children's
reports. However, when counselor report was found to be
unusable, the reliability of the youngsters' self-report had
to be established in order to gauge whether any changes in
diabetes management behaviors occurred at camp. The results
of this portion of the study replicated the findings of
Johnson et. al., 1986. Based on Pearson product moment
95

96
correlations of parent and child report before and after
camp (4 different time periods), it was decided that the
youngsters' report was generally reliable concerning
diabetes management behaviors.
Agreement was also assessed for three age groups (7- to
9-year olds, 10- to 11.4-year olds, and 11.5- to 12.6-year
olds). It was noted that only on 5 of the 13 adherence
measures (injection interval, injection-meal timing,
calories consumed, exercise duration, and glucose testing
frequency) were there significant differences among the age
groups. Of these, three of the measures dealt with time and
were more problematic for the youngest age groups. These
findings are consistent with the findings of Johnson et al.,
1986) who found that 6- to 9-year olds showed poor agreement
with parent report with measures involving time. This
finding was attributed to the difficulty this age group
usually experiences with complex mathematical concepts such
as time. On the calories consumed measures the oldest
children were most concordant while the youngest were the
least concordant. This finding is not consistent with the
findings of Johnson et al., (1986) and could represent a
sample selection bias. On the glucose testing frequency
measure, all groups were highly concordant with their
parents. Therefore, the significant differences between the
two older groups is of questionable clinical significance.
The data were collapsed across age groups so that
correlations at the four time periods could be compared.

Results suggested that parent/child agreement was stable
across time for 8 of the 13 adherence measures: calories
97
consumed; percent calories: fat; percent calories: car¬
bohydrates; eating freguency; exercise duration; exercise
type; exercise frequency; and testing frequency. Sig¬
nificant differences between at least two of the time
periods existed on measures of injection regularity,
injection-meal timing, regularity of injection-meal timing,
and concentrated sweets. Although 3 of the 5 correlations
were at their best at Time 5, there was no evidence for a
linear improvement with the possible exception of injection-
meal timing. However, only 3 of the 13 parent/child cor¬
relations (all dealing with the dietary measures) were at
their weakest at Time 5 while 8 of the 13 correlations were
at their best at Time 5. The relatively better concordance
between parents and children at followup could not be
attributed to a practice effect since a linear improvement
in the correlations was not noted. Rather, this effect may
be due to the fact that by Time 5, children were back in
school. As a consequence, parents may have been more
knowledgeable about their activities but not as aware of
what they were eating, since many children eat school
lunches. Overall, when considering the parent/child
correlations there was no evidence to suggest that infor¬
mation provided by the children deteriorated over the study
period.

98
In addition, differences between child and parent
report on the 13 adherence measures were analyzed using
repeated measures ANOVA using two between subject factor
(Age group, Sex) and one within subject factor (Respondent).
Results demonstrated that there were few significant
differences between parent and child report. Differences
that were detected indicated that on measures of exercise
type and frequency, parents' and daughters' reports differed
in that girls reported themselves as being more compliant
than their parents did. No such difference was detected for
boys. In addition parents reported their sons as being more
compliant than their daughters while there was no sig¬
nificant difference between girls' and boys' reports on
these measures. There were differences between parent and
child report on measures of injection-meal timing, eating
frequency, and calories consumed. However in all instances
these differences were of questionable clinical signifi¬
cance.
Since the children's reports were significantly
correlated with parent report both across time and across
age groups it was felt that their reports were sufficiently
reliable to be utilized in assessing diabetes relevant adh¬
erence behaviors before, during, and after camp.
Effect of Diabetes Camp on Adherence
It was expected that compliance would increase at camp
since daily schedules revolved around six fixed exercise

99
periods, three meals and three snacks, and prescribed times
for injections and testing per day. Repeated measures
ANOVA on each of the 13 adherence measures confirmed that 9
of the 13 were significantly different while the children
attended diabetes summer camp, particularly for those
behaviors that were regimented or scheduled by camp person¬
nel. Therefore, based on the children's report, diabetes
management behaviors of injection regularity, injection-meal
timing, calories consumed, eating frequency, exercise
duration, exercise type, exercise frequency, and testing
frequency were significantly different while the youngsters
attended camp. With the exception of calories consumed,
children were most adherent during camp. During camp, all
children consumed more than the suggested amount and
significantly more than at home. After camp all groups
returned to pre-camp levels. Although children appeared to
be more nonadherent on this measure during camp, increases
in dietary intake may be considered appropriate given the
significant increase in the youngsters' daily exercise. It
is interesting to note that proportions of carbohydrates and
fat to total calories and concentrated sweets remained
stable before, during, and after camp. This is presumably
due to the fact that the children were given free reign
during camp meals regarding quantity and selection of foods
and most likely selected the types of foods consistent with
their home diet, only in greater quantity.

100
Changes noted in this study are consistent with the
only other study published that we were able to locate, that
attempted to look at effects of camp on behavior change
(Stunkard and Pestka (1962) . Stunkard and Pestka monitored
the physical activity of 15 nondiabetic obese 10- to 13-year
old girls and compared them to matched non-obese nondiabetic
girls during and after a 2-week Girl Scout camp. Physical
activity was measured by means of a mechanical pedometer and
a significantly higher rate of activity at camp was noted
for both obese and nonobese girls. However, our study
represents a significant expansion of the Stunkard and
Pestka (1962) study. In this study, physical activity
included measures of frequency of participation, duration of
exercise episodes, and type of exercise as gauged by the
amount of kilocalories expended per minute. Moreover,
multiple other diabetes management behaviors were measured.
Of the three adherence measures (injection interval,
calories consumed, and exercise frequency) that demonstrated
a significant Time x Age group interaction only injection
interval demonstrated a differential effect of camp on older
versus younger children. That is, unlike the other eight
measures where children of all ages significantly changed at
camp, only the oldest group reported improvement at camp on
the injection interval measure. It is possible that this
result reflects the fact that the younger children's
difficulty with time related events was compounded by the
fact that during camp, events were scheduled by 'periods'

101
rather than actual time. In addition, younger children were
probably more closely supervised while at home and in that
respect, camp did not represent a significant change.
Moreover, close scrutiny of the data provided by the 10- to
11.4-year olds revealed that the mean injection interval of
63.30 minutes was the result of several youngsters' inac¬
curate report of injection times. In any case it is likely
that the younger children's adherence on the injection
interval measure at camp was underestimated in this study.
Overall, the camp experience was highly consistent for
all youngsters regardless of age and sex. It is possible
that camp could have had a differential effect on older
children. However, this study sample was comprised of a
relatively young sample so that age differences demonstrated
in other studies which include adolescent samples were not
found in this study (Johnson, 1984).
Although it is clear that changes occurred during camp
for 9 of the 13 adherence behaviors, it is apparent these
changes were not maintained after camp. It appears that the
environment, whether it be camp or home, exerts a powerful
influence on children's daily diabetes management behaviors.
While children may practice more adherent behaviors during
camp, these behaviors have not yet become habitual and the
child's home environment does not offer the necessary
structure to continue these gains once the camp experience
is over.

102
Effect of Diabetes Camp on Glycemic Control
Repeated measures ANOVA on glycosylated serum proteins
(GSP) collected at the beginning and at the end of camp
indicated that GSP levels were significantly changed over
the 2-week camp period. Post-camp GSP values were sig¬
nificantly higher than pre-camp values, indicating that the
youngsters were in poorer diabetic control at the end of
camp compared to the beginning. This finding was not
consistent with the finding of Strickland, et al.,(1984) who
reported that GSP values decreased over a 2 week camp
experience. However, Strickland et al., used a smaller
sample with a larger age range (thirty-six 7- to 15-year
olds), did not report how many children were in each age
group and did not provide details of the camp experience.
Although the improvement in GSP reported in their study was
significant, it was small in actual value (decreasing from
.83 to .77%). It is conceivable that participants' charac¬
teristics were different in the two studies, that the camp
experiences were not equivalent, or that camp has no consis¬
tent effect on GSP. Since there are so few studies on this
topic, it is difficult to ascertain the possible causes of
these differing results.
Older campers (11.5 to 12.6 years) had the highest mean
GSP values throughout the study. This may be due to the
fact that this group was entering puberty. In a recent
study Amiel et al., (1986) demonstrated that puberty was
associated with increased insulin resistance in normal as

103
well as diabetic youngsters. Of course, this increased
insulin resistance had the most profound effect on diabetic
youngsters in whom hyperglycemia is likely to result.
To assess long term effects of the camp experience on
glycemic control, a repeated measures ANOVA of the glycosy¬
lated hemoglobins (HbA^c) collected at the beginning and at
the end of the study (3 months after camp) was conducted.
No significant differences were noted, suggesting that camp
effects were not maintained over a 3 month period. That is,
the increases in glycemic control noted at the end of camp
(increased GSP) were not noted in the followup HbA^c which
remained essentially the same. These findings are consis¬
tent with the correlations between the glycemic control
measures (between the beginning of camp and at 3 month
followup) which suggested moderate stability, and with the
data on adherence measures which also demonstrated marked
stability within the home environment.
To assess whether children at different levels of
glycemic control at the beginning of camp would exhibit
different effects, the children were grouped into HbA^c
control categories (good, moderate,and poor). Repeated
measures ANOVA revealed that although children in good and
poor control at the beginning of this study remained stable
over the study period, youngsters in moderate control were
significantly improved at followup. However, subsequent
analyses suggested that this improvement did not appear to
be the result of the camp experience.

104
Relationship Between Adherence and Diabetic Control
Since this study clearly documented that children were
generally more adherent while they were at camp except for
calories consumed, hierarchical regressions were used to
test the hypothesis that higher post-camp GSP levels were a
reflection of significant increases in caloric intake during
camp by all youngsters. This hypothesis was not confirmed.
It should be kept in mind that although the significant
increase in caloric intake could be construed as noncom¬
pliance, there was a concomitant increase in exercise
behaviors probably necessitating increased food intake. A
series of hierarchical regression models was unable to
establish a relationship between any of the adherence
behaviors and glycemic control during a 2 week camp ex¬
perience. Rather, the best predictor of post-camp GSP was
the pre-camp GSP (R2=.69). The pre-camp/post-camp GSP
correlation was exceptionally high, (r=.83) which we suspect
is close to the level of reliability of the GSP measure. If
so, there would be little room for other factors to enhance
this prediction model. It is possible that the camp experi¬
ence was too brief and too consistent (everybody changed
significantly at camp) to provide sufficient variability to
accurately test adherence/control relationships using
multiple regression. If so, the association between
adherence and control could be better tested using categori¬
cal analyses. However, when children were divided into pre¬
camp GSP control categories, no significant differences in

105
pre-camp adherence was detected among these groups.
Similarly, when children were grouped based on change in
glycemic control no differential change in adherence by
group was detected. When children were categorized based on
their level of adherence before coming to camp, it was found
that the GSP levels of the highly adherent children did not
differ from the GSP levels of nonadherent children.
Similarly, analyses based on groups that either improved or
declined in adherence during camp did not reveal any
associations between adherence and glycemic control.
To evaluate the relationship between adherence and
control after camp, the adherence measures derived from the
nine interviews conducted after camp were combined into the
five adherence factors. Hierarchical regression analyses
revealed that the model that best predicted the followup
HbA^c measure of glycemic control contained the pre-camp
HbA^c, age, (increasing age has been associated with changes
in insulin absorption, Amiel et al., 1986), insulin dose
differences (pre-camp minus during-camp dose), post-camp
injection factor, and an insulin dose difference x post¬
camp injection factor interaction term (to control for
insulin dose changes). When the children were divided into
groups based on the Beta weights for the post-camp injection
factor for varying levels of insulin dose change pre- to
post-camp, it was found that the children who had the
largest increase in insulin dose at followup had arrived at
camp in the best control, were the youngest (and possibly

106
not prone to pubertal effects), were most adherent post¬
camp, had their insulin increased during camp (a trend which
was continued after camp), and had experienced an increase
in glycemic levels while the other two groups decreased (see
Table 21). This finding suggests that this group of
youngsters may have experienced increases in HbA^c due to
what is known as rebound hyperglycemia or the Somogyi
phenomenon. This phenomenon is thought to be precipitated
by overinsulinization which initially causes glycemic levels
to drop and then rebound to higher levels (Travis, et al.,
1987). Physicians often respond to the "highs" and prescri¬
be even more insulin. It is noteworthy that physicians used
the camp physicians as models and continued whatever dose
changes that had been made at camp. However, for those
youngsters whose insulin was increased at camp, substantial
additional increases in insulin dose were made after camp so
that by 3 months post-camp their dose had tripled (from an
average of 13.3 units before camp to 42.6 units at fol¬
lowup) . It is possible that physicians caring for these
youngsters responded to their bouts of hyperglycemia by
prescribing increased insulin doses i.e., they failed to
detect possible rebound hyperglycemia in their children.
This hypothesis was further supported by the fact that this
group of children were in the best control at the outset of
this study and given their age, were not likely to be prone
to adolescent related increases in glycemic control.
Nevertheless, this was the only group that demonstrated an

107
increase in HbA^c by followup. It is possible that in¬
creased compliance in this instance (these were the most
compliant youngsters) would exacerbate the rebound cycle,
leading to deterioration in glycemic control.
When youngsters were categorized into control categor¬
ies based on pre-camp HbA^c values, it was found that there
was a negative relationship between control and injection
factor adherence. However, these results were not replicated
post-camp or pre-camp using GSP control groupings. Overall,
there was no support for a simple linear associations
between adherence and glycemic control at any point in this
investigation. These findings are consistent with those
reported by Glasgow et al, (1987) who could find no clear
relationship between adherence and glycemic control through
either bivariate or multivariate analyses.
Limitations and Future Directions
This study had several strengths and limitations. The
use of child report made it possible to monitor children
over a relatively extensive period of time spanning a
naturalistic imposition of behavior change relevant to
diabetes management. The advent of GSP measures made it
possible to assess diabetic control over a relatively short
period of time so that an association between behavior
change and glycemic control could be examined. However, the
narrow age range in this sample and the consistent change
produced by the camp experience did not afford enough

108
variability to establish clear relationships between
adherence and control. The use of a control group in
replicating this study might enhance variability, making it
possible to better examine adherence/control relationships.
The major implication of this study is that individual
differences need closer scrutiny in the area of juvenile
diabetes. To date, diabetic regimen recommendations are
formulistic and assume that control is a consistent con¬
struct that requires consistent behaviors by all youngsters.
Future research using multiple baseline design and varying
adherence behaviors one at a time under controlled condi¬
tions could possibly provide more information concerning
highly individualized relationships between adherence and
glycemic control. It is possible that children' biological
differences require more individual prescriptions. A recent
study conducted by Freund et al., (1986) suggests that
individual differences exist in symptom patterns of hyper¬
glycemic and hypoglycemic reactions. It is likely that
these differences extend into other biological reactions to
regimen components such as injection-meal timing, exercise,
and dietary intake. Adjustments to insulin dose made during
camp are made in response to changes in the camper's routine
(most children reported significant changes in behavior
during camp). This study clearly demonstrated that once
children return home their adherence behaviors revert back
to pre-camp levels. It stands to reason that insulin
requirements would then need to be readjusted to previous

109
levels. However, our data indicate that the physicians
caring for these youngsters at home tend to use camp insulin
changes as a model. This is understandable, since camp
physicians are usually pediatric endocrinologists while
physicians caring for these youngsters are pediatricians who
rarely have specialty training in diabetes. Therefore,
physicians at camp should take care to make recommendations
which would alert the pediatricians to this potential
problem. Insulin dose is at the crux of the diabetes
management since it has a powerful influence on control. It
is clear that if the insulin dose is not appropriate high
levels of adherence will cause worse control. It is
possible that some youngsters are not on the correct dose
which clearly presents a problem. This situation would
account for a curvilinear relationship between adherence
with injection behaviors and glycemic control.
It is noteworthy that the only effect of camp that was
maintained during the 3 month followup period was insulin
dose change. That is, the children's behaviors reverted to
pre-camp levels as did their overall glycemic control.
However, insulin dose changes initiated at camp were usually
maintained and even embellished once the children returned
home. It is therefore vital that camp physicians carefully
scrutinize their decision process in determining appropriate
insulin dose and that they diligently make recommendations
for insulin dose adjustment for physicians to follow once
the children return home.

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protein and hemoglobin A]_, levels to measure control of
glycemia. Annals of Internal Medicine, 1981, 95,
56-58.
LaGreca, Annette M., Follansbee, Donna, Skyler, and Jay S.
Behavioral aspects of diabetes management in children
and adolescents. Paper presented at the American
Psychological Association Meetings, Washington, D.C.,
1982.
Lebovitz, Franzine L. , Ellis III, George J., and Skyler, Jay
S. Performance of technical skills of diabetes
management: Increased independence after a camp
experience. Diabetes Care, 1978, 1(1): 23-26.
Maxwell, D.R., Luft, F.C., Clark, C.M., and Vinicor, F.
Diabetes mellitus: New approaches to complications.
Journal of the Indiana State Medical Association, 1982,
March, 75, 184-186.
McCulloch, D.K., Young, R.J., Steel, J.M., Wilson, E.M.,
Prescott, R.J., and Duncan, L.J. Effect of dietary
compliance on metabolic control in insulin-dependent
diabetes. Human Nutrition. Applied Nutrition, 1983,
37(4): 287-292.
Mehl, Thomas D., Wenzel, S.E., Russell, B., Gardner, D., and
Merimee, Thomas J. Comparison of two indices of
glycemic control in diabetic subjects: glycosylated
serum protein and hemoglobin. Diabetes Care, 1983,
6(1): 34-39.
Moffatt, M.E.K. and Pless, I.B. Locus of control in
juvenile diabetic campers: changes during camp, and
relationship to camp staff assessments. Journal of
Pediatrics, 1983, 103: 146-150
Nuttal, F.Q. and Brunzall, J.D. Principles of nutrition and
dietary recommendations for individuals with diabetes
mellitus:1979 report of the American Diabetes Associa¬
tion. Journal of the American Dietetic Association,
1979, 75(5): 527-530.
Rainwater, Nancy, Jackson, Ginny G., and Burns, Kenton L.
Relationships among psychological, metabolic, and
behavioral measures in juvenile diabetes. Paper
presented at the 90th annual meeting of the Amercian
Psychological Association, Washington, D.C., August,
1982 .

113
Rifkin, H. Why control diabetes? Medical Clinics of North
America, 1978, 62(4): 747-752.
Schafer, L.C., Glasgow, R.E., McCaul, K.D. Increasing the
adherence of diabetic adolescents. Journal of
Behavioral Medicine, 1982, 5, 353-362.
Schafer, L.C., Glasgow, R.E., McCaul, K.D., and Dreher, M.
Adherence to IDDM regimens: Relationship to psychoso¬
cial variables and metabolic control. Diabetes Care,
1983, 6(5): 493-498.
Scharf, Linda S., Adams, Kenneth M., and Leach, David C.
Importance of diabetes camp in adolescents'
psychological adjustment. Presented at the American
Psychological Association Meetings, Washington, D.C.,
August, 1987.
Spevack, Marika, Johnson, Suzanne B., Harkavy, Jill M.,
Silverstein, Janet, Shuster, Jon, Rosenbloom, Arlan,
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Steiger, James H. Tests for comparing elements of a
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87(2): 245-251.
Strickland, A.L., McFarland, K.F., Murtiashaw, M.H.,
Thorpe, S.R.,and Baynes, J.W. Changes in blood protein
glycosylation during a diabetes summer camp. Diabetes
Care, 1984, (March-April), 7(2): 183-185.
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of obese girls. American Journal of Diseases of
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Sussman, K.E. (ed). Juvenile-type Diabetes and Its
Complications: Theoretical and Practical
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Travis, Luther B. An Instructional Aid on Juvenile Diabetes
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Travis, Luther B. , Brouhard, Ben H., and Schreiner, Barbara-
Jo. Diabetes Mellitus in Children and Adolescents.
Philadelphia: W.B. Saunders Company, 1987.
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diabetes. Medical Clinics of North America, 1982,
66(6): 1317-1324.

XldNSddY

INTERVIEWER'S NAME:
TESTING
Name:
Today's date:
For:
For: Weekday
Weekend
INSLLIN INJECTION(S)
Ho« many shots prescribed: 1 2 3 4
"—w ■ '
¡ ime
l&C
T
T i me
Tm~
Time
TM
Units
Lype
Units
Type
Uni ts
Units
~IxEi
Dose
Regular
Dose
Regular
Dose
Regular
Dose
Regular
NPH
NF'H
NPH
NPH
Lente
Lente
Lente
Lente
Semi
Semi
Semi
Semi
Actrapid
Actrapid
Actrapid
Actrapid
Monotard
Monotard
Monotard
Monotard
Who gave shot
?
Who gave shot?
Who gave shot?
Who gave shot?
This parent
This parent
This parent
This parent
obs? Yes
NO
obs? Yes
No
ops? Yes
JKo
obs ? ves
No
Pre-Breakfast
Method used:
2-drop/cs/other
Tester:
Parent observed?
Yes No
Time:
AM PM
Sugar:
<2% 2-6% >6%
Ketones:
N S M L
Chemstrip:
Pre-Lunch
Method use*
i:
2-drop/cs/other
Tester:
Parent obs'
irved?
Yes No
Time:
AM PM
Suqar:
<2% 2-6% >6%
Ketones:
N S M L
Chenistri p:
Pre-Supper
Method used:
2-drop/cs/other
Tester:
Parent observed?
Yes No
Time:
AM PM
Sugar:
<2% 2-6% >6%
Ketones:•
N S M L
Chemstrip:
FOOD INTAKE
Pre-Be
d
Method used:
2-drop/cs/
other
Tester:
Parent observed:
Yes Mo
Time:
AM P
M
Sugar:
<2* 1-6%
> 6*
Ketones:
N S M
L
Chemstrip:
Method used:
2-drop/cs/other
Tester:
Parent observed?
Yes No
Time:
AM PM
Sugar:
Ott £ V r C /o l* Ja) Ü/o
Ketones:
N S M L
Cnemstrip:
BREAKFAST
SNACK
LUNCH
SNACK
SUPPER
SNACK
Time AM PM
Time AM PM
Time AM PM
Time AM PM
—
Time AM PM
Time AM PM
Parent Obs? Yes No
Parent Obs? Yes No
Parent Obs? Yes No
Parent Obs? Yes No
Parent Obs? Yes No
Parent Obs? Yes No
Qty/
Size
Item
Qty/
Size
Item
Qty/
Si ze
Item
Qty/
Size
Item
Qty/
Si ze
Item
Qty/
Size
Item
•
•
i
:
EXERCISE
Morning
Afternoon
Eveninq
T i me AM PM
Tine AM PM
Time AM PM
This parent obs? Yes No
This parent obs? Yes No
This parent obs? Yes No
Activities How long?
Activities How long?
Activities How long?
Time AM PM
Time AM PM
Time AM PM
This parent obs? Yes No
This parent obs? Yes No
This parent cbs? Yes No
Activities How long?
Activities How long?
Activities How long?
COMMENTS
Was this a typical day
eating, exercise, i 1In
Yes
for you? (i .e.,
ess, stress, etc.)
No
Why?
•
EXTRA SNACK
Time AM PM
Parent Obs? Yes No
Qty/
Size
Item
•
115

BIOGRAPHICAL SKETCH
Born in Budapest, Hungary, on February 20, 1944, Marika
E. Spevack (nee Kessler) and her family emigrated to
Montreal, Canada, where she grew up and married. After 10
years of travel and the advent of 4 daughters, she decided
to pursue a degree in education and earned a Master of
Education degree from the University of Florida in 1980.
After a brief teaching experience, Marika became involved in
psychological research and was encouraged by her family and
inspired by her employer and mentor to pursue an advanced
degree in clinical psychology. From 1982-1987, Marika
attended the University of Florida's graduate program in
clinical psychology where she earned her M.S. in May 1985.
She plans to complete the requirements in time to graduate
with a Ph.D. in December 1987.
116

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
â– o-nKj
Suzanne (B. Johnsdr^j Chairman
Professor of Clinical and
Health Psychology
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
Cj
Nathan W. Perry
Professor of Clinical an*
Health Psychology
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
\ )...Iy -1 ( > «.o
Sheila M. EybergV
Professor of Clinical
Health Psychology

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
Professor of Statistics
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
Associate Professor of
Pathology
This dissertation was submitted to the Graduate Faculty
of the College of Health Related Professions and to the
Graduate School and was accepted as partial fulfillment
of the requirements for the degree of Doctor of Philo¬
sophy.
December 1987
Dean, College of Health
Related Professions
Dean, Graduate School

UNIVERSITY OF FLORIDA
3 1262 08554 4079



TABLE 7
Parent-Child Correlations for Total Sample and by Age Group
Collapsed Across Time
Total Group 1 Group 2 Group 3
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6 Years)
r(n) r(n) r(n) r(n)
P< P< P< P<
Adherence Measure
Injection
regularity
.81(63)
(.0001)
.75(20)
(.0001)
. 88 (22)
( .0001)
. 77(21)
( 0001)
Injection *
interval
.75(59)
(.0001)
.51(19)
(.0262)
.92(19)
(.0001)
.64 (21)
(.0018)
Injection- *
meal timing
.69(63)
(.0001)
-.03(19)
(.8885)
.57(22)
(.0052)
.75(21)
(.0001)
Regularity of
inj ection-
meal timing
.36(63)
(.0049)
-.08(18)
(.7141)
-.08(20)
(.7382)
.43(21)
(.0526)
Calories *
consumed
.81(62)
(.0001)
.53(20)
(.0163)
.81(22)
( .0001)
.89(20)
( .0001)
Percentage
calories-fat
.91(63)
(.0001)
.96(20)
(.0001)
.88(22)
( .0001)
.89(21)
( .0001)
U>


TABLE 6
(continued)
Adherence Measure
Total
Sample
r (n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r(n)
P <
Group 3
(11.5-12.6 Years)
r(n)
P <
Percentage
. 65(61)
.68(20)
. 69(21)
. 65(20)
calories-
carbohydrate
( 0001)
(.0009)
( .0006)
(.0017)
Concentrated
.43(61)
.37(20)
.25(21)
.69(20)
sweets
(.0005)
(.1078)
( .2666)
(.0008)
Eating
.80(61)
.51(20)
.85(21)
.90(20)
frequency
(.0001)
(.0228)
(.0001)
(.0001)
Exercise
.66(61)
.68(20)
. 67(21)
.77(20)
duration
(.0001)
(.0011)
(.0009)
(.0001)
Exercise
.68(61)
.51(20)
.74(21)
.77(20)
type
(.0001)
(.0227)
( .0001)
( .0001)
Exercise
.71(61)
.71(20)
. 69(21)
.76(20)
frequency
(.0001)
(.0005)
( .0006)
( 0001)
Glucose
.91(61)
.89(20)
.96(21)
.85(20)
testing
frequency
(.0001)
(.0001)
( .0001)
(.0001)
U1
o


THE RELATIONSHIP BETWEEN ADHERENCE BEHAVIORS
AND GLYCEMIC CONTROL IN CHILDHOOD DIABETES
BY
MARIKA SPEVACK
DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1987


104
Relationship Between Adherence and Diabetic Control
Since this study clearly documented that children were
generally more adherent while they were at camp except for
calories consumed, hierarchical regressions were used to
test the hypothesis that higher post-camp GSP levels were a
reflection of significant increases in caloric intake during
camp by all youngsters. This hypothesis was not confirmed.
It should be kept in mind that although the significant
increase in caloric intake could be construed as noncom
pliance, there was a concomitant increase in exercise
behaviors probably necessitating increased food intake. A
series of hierarchical regression models was unable to
establish a relationship between any of the adherence
behaviors and glycemic control during a 2 week camp ex
perience. Rather, the best predictor of post-camp GSP was
the pre-camp GSP (R2=.69). The pre-camp/post-camp GSP
correlation was exceptionally high, (r=.83) which we suspect
is close to the level of reliability of the GSP measure. If
so, there would be little room for other factors to enhance
this prediction model. It is possible that the camp experi
ence was too brief and too consistent (everybody changed
significantly at camp) to provide sufficient variability to
accurately test adherence/control relationships using
multiple regression. If so, the association between
adherence and control could be better tested using categori
cal analyses. However, when children were divided into pre
camp GSP control categories, no significant differences in


8
during the past 3 months r=.35 (p<.01). However, HbA^c did
not correlate significantly with MHLC or Mother's General
Locus of Control. Further, there appeared to be no sig
nificant relationship between the author's diabetes control
indices (such as 24-hour urine, number of hospitalizations,
number of reactions, hours of exercise and HbAic) and the
child's self-esteem (measured by the Piers-Harris Inventory)
or with the child's life change scores (measured with the
Coddington Scale for Life Events).
This study claimed to have used both behavioral
(defined by the authors as the number of hours exercised per
week, number of diabetes related hospitalizations in the
past year, and number of insulin reactions in the past
month, and number of times acetone was noted in the urine in
the past month) and metabolic (hemoglobin A^c, triglyce
rides, cholesterol, fasting blood sugar, and total amount of
sugar spilled in the urine in a 24-hour period) measures
which the authors presumed to reflect diabetes control.
Glycosylated hemoglobin (HbA^c) is now widely accepted as
the single most reliable and adeguate measure of glycemic
control. The authors' use of 24-hour urine as an index of
control is problematic for two reasons. Valid 24-hour
urines are difficult to collect even under controlled
conditions such as hospitals. In our experience, only
20-25% of all specimens solicited and collected were
complete collections. Glycosylated hemoglobin (HbA^c)
values are a more widely accepted measure of control than


18
study's intervention or was a natural continuation of each
group's pretreatment metabolic condition. Pre/post- changes
in diabetic control were not significant and there was no
assessment of and therefore no evidence that one group's
compliance was better than the other group's at post-treat
ment. Consequently, the only evidence clearly in favor of
the experimental group was the post-treatment difference in
HbAi. Certainly, a replication of the study's findings is
needed if we are to have confidence that the post-treatment
HbA^ differences were, in fact, due to the intervention
employed.
Schafer, Glasgow, and McCaul (1982) designed a multiple
baseline approach to assess the effectiveness of goal
setting and behavioral contracting to increase adherence and
control in three adolescents with IDDM. A self-monitoring
program of 1 week, introduced before goal setting, con
stituted the baseline phase. Contracting was introduced
only if 90% compliance was not achieved. Compliance was
assessed by subjects records of relevant target activity
such as time, duration, etc. Reliability checks were made
periodically by mothers' independent monitoring. Diabetic
control was assessed with urine glucose tests performed and
recorded daily by subjects. Reliability checks were
conducted by having mothers spot-check the urine tests. Two
hour postprandial blood glucose levels were determined, and
24-hour urine glucose was collected at pretest, posttest,
and followup. Two of the subjects showed improvements in


26
control. Whether this is a valid criticism and if so,
whether the effects are positive or negative, has never been
comprehensively evaluated. There is evidence that diabetes
camp may improve diabetic control although only one study
specifically addressed this issue (Strickland et al., 1984).
The Strickland et al. study examined changes in glycemic
control during a 2-week summer camp program by performing
pre-camp and post-camp values of fasting plasma glucose,
glycosylated hemoglobin, and glycosylated serum protein in a
group of 36 children. Their results suggested that there
was measurable improvement in diabetic control in some
children.
Similarly, only a few studies have addressed behavioral
changes associated with camp. For example, Stunkard and
Pestka (1962) examined physical activity at a 2-week Girl
Scout camp and found that there was significantly more
activity during camp compared with at-home behavior. The
youngsters studied did not have diabetes.
Most camp studies deal with psychological constructs.
Moffatt and Pless (1983) investigated changes in locus of
control in juvenile diabetic campers during a 3 week camp
experience and found that there were significant changes
toward internal locus of control.
Self-concept was scrutinized using the Tennessee Self-
Concept Scale with 26 IDDM adolescents (aged 13-17) attend
ing an 8-day summer camp (Hoffman, Guthrie, Speelman, and
Childs, 1982). The authors report that self-concept may be


102
Effect of Diabetes Camp on Glycemic Control
Repeated measures ANOVA on glycosylated serum proteins
(GSP) collected at the beginning and at the end of camp
indicated that GSP levels were significantly changed over
the 2-week camp period. Post-camp GSP values were sig
nificantly higher than pre-camp values, indicating that the
youngsters were in poorer diabetic control at the end of
camp compared to the beginning. This finding was not
consistent with the finding of Strickland, et al.,(1984) who
reported that GSP values decreased over a 2 week camp
experience. However, Strickland et al., used a smaller
sample with a larger age range (thirty-six 7- to 15-year
olds), did not report how many children were in each age
group and did not provide details of the camp experience.
Although the improvement in GSP reported in their study was
significant, it was small in actual value (decreasing from
.83 to .77%). It is conceivable that participants' charac
teristics were different in the two studies, that the camp
experiences were not equivalent, or that camp has no consis
tent effect on GSP. Since there are so few studies on this
topic, it is difficult to ascertain the possible causes of
these differing results.
Older campers (11.5 to 12.6 years) had the highest mean
GSP values throughout the study. This may be due to the
fact that this group was entering puberty. In a recent
study Amiel et al., (1986) demonstrated that puberty was
associated with increased insulin resistance in normal as


2
converts some of the fat into ketones, which increase in the
blood and, at high levels, spill into the urine. In
addition, when the blood passes through the kidney, glucose
is filtered out and, at high levels, the extra glucose
spills over into the urine. The kidney attempts to filter
out the excess glucose and ketones from the blood in order
to maintain acid alkaline (Ph) levels within normal limits.
This results in increased urination and increased thirst.
However, the kidney can not keep up this level of activity.
Unchecked, ketones and acids build up in the blood and the
child develops diabetic ketoacidosisa serious condition
requiring hospitalization. Furthermore, the loss of urine
water and the use of body fat for energy produce weight loss
(Travis, 1969). If this condition is untreated, coma and
death can result.
Presenting symptoms of IDDM include polyuria, poly
dipsia, polyphagia, and weight loss. There is often a high
prevalence of infections. More subtle signs of the disorder
include pruritus (severe and protracted itching of the
skin), enuresis (bedwetting), vulvitis, irascible disposi
tion, lassitude, and abrupt onset of visual difficulties
(Sussman, 1971). As yet, there is no cure for this disease.
The treatment of IDDM consists of the following:
1) Daily insulin injectionsA child with IDDM
injects insulin at least once and as many as
four times daily for the rest of his/her life


TABLE 6
Parent-Child Correlations for Total Sample and by Age Group
Three Months Post-Camp
Total Group 1 Group 2 Group
Sample (7-9 Years) (10-11.4 Years) (11.5-12.6
r(n) r(n) r(n) r(n)
P< P< P< P<
Adherence Measure
Injection
regularity
.81(55)
(.0001)
.81(17)
(.0001)
.88(18)
(.0001)
.74(20)
(.0002)
Injection
interval
. 89(52)
(.0001)
.76(18)
(.0002)
.95(15)
(.0001)
.93(19)
(.0001)
Injection-
meal timing
.77(60)
( .0001)
.47(19)
(.0422)
.78(21)
( .0001)
.86(20)
( .0001)
Regularity of
injection-
meal timing
.25(54)
(.0665)
.22(17)
(.4001)
-.11(17)
( 6710)
.38(20)
(.1011)
Calories
consumed
.69(60)
(.0001)
.11(20)
(.6344)
. 64(21)
(.0016)
.75(19)
( .0002)
Percentage
calories-fat
.66(61)
( .0001)
.75(20)
(.0001)
.62(21)
(.0026)
. 65(20)
(.0019)
3
Years)


80
moderate adherence, and 4-5 was good adherence). An ANOVA
was performed on the pre-camp GSP measure using one between
subject factor (Adherence group). Results indicated no
significant difference between the adherence groups on the
pre-camp GSP measure.
It was conjectured that an adherence/control relation
ship might be better detected if the youngsters were divided
into categories of metabolic change. Presumably, those
children who exhibited an improvement in diabetic control
pre- to post-camp, might show an improvement in adherence
behaviors from pre- to during-camp. Such improvement in
adherence behaviors would not be expected for children whose
diabetic control stayed the same or deteriorated over the
camp experience. Consequently, post-camp GSP values were
subtracted from the pre-camp GSP values and based on
univariate analysis of the resulting difference, the
youngsters were divided into children who improved (dif
ference greater or equal to .5), did not change (difference
less than .5 and greater or equal to -.4), and got worse
(difference less than -.4). A repeated measures ANOVA was
conducted on each of the pre- and during-camp adherence
factors as well as the calories consumed measure using one
between subject factor (GSP difference group) and one within
subject factor (Time). Results of these analyses indicated
only simple Time main effects for the exercise factor
F(1,59)=155.34, p < .0001, the injection factor F(l,55)=


TABLE 4
(continued)
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r(n)
P <
Group 2
(10-11.4 Years)
r(n)
P <
Group
(11.5-12.6
r(n)
P <
Percentage
.75(60)
. 63(18)
.85(22)
.70(20)
calories-
carbohydrate
( .0001)
(.0048)
(.0001)
( 0006)
Concentrated
.59(60)
.58(18)
.83(22)
.34 (20)
sweets
( .0001)
(.0108)
(.0001)
( 1415)
Eating
.67(60)
.77(18)
.59(22)
.63(20)
frequency
(.0001)
(.0002)
(.0037)
( .0026)
Exercise
.21(60)
.53(18)
.21(22)
.13(20)
duration
(.1106)
(.0234)
(.3568)
( 5965)
Exercise
.64(60)
.66(18)
.50(22)
.85(20)
type
(.0001)
(.0028)
(.0171)
( 0001)
Exercise
.68(60)
.67(18)
.53(22)
. 92 (20)
frequency
(.0001)
(.0024)
( .0117)
( 0001)
Glucose
.78(60)
.55(18)
.92(22)
. 68(20)
testing
frequency
(.0001)
(.0171)
( .0001)
(.0009)


98
In addition, differences between child and parent
report on the 13 adherence measures were analyzed using
repeated measures ANOVA using two between subject factor
(Age group, Sex) and one within subject factor (Respondent).
Results demonstrated that there were few significant
differences between parent and child report. Differences
that were detected indicated that on measures of exercise
type and frequency, parents' and daughters' reports differed
in that girls reported themselves as being more compliant
than their parents did. No such difference was detected for
boys. In addition parents reported their sons as being more
compliant than their daughters while there was no sig
nificant difference between girls' and boys' reports on
these measures. There were differences between parent and
child report on measures of injection-meal timing, eating
frequency, and calories consumed. However in all instances
these differences were of questionable clinical signifi
cance.
Since the children's reports were significantly
correlated with parent report both across time and across
age groups it was felt that their reports were sufficiently
reliable to be utilized in assessing diabetes relevant adh
erence behaviors before, during, and after camp.
Effect of Diabetes Camp on Adherence
It was expected that compliance would increase at camp
since daily schedules revolved around six fixed exercise


88
TABLE 19
Predicting Post-camp HbA^c by
Pre-camp HbAj^c, Age, Insulin Dose Change,
Post-camp Injection Factor, and
Insulin Dose Change x Post-camp Injection Interaction
Variables in model Beta weights p<
R2 = .57
Pre-camp HbA^^c
.63400558
. 0001
Age
.24956679
.0321
Change in insulin
dose from pre-camp
to followup
.02784116
. 0212
Post-camp
Injection factor
.21885475
. 4495
Change in insulin x
Post-camp
Injection factor
- .06829848
. 0065


APPENDIX


development and to prevent complications. With growing
youngsters, this task is particularly difficult since their
nutritional and insulin requirements are changing as they
grow and injections administered once or twice a day cannot
imitate the synchronous pancreatic action of nondiabetic
individuals (Guthrie and Guthrie, 1977).
Complications associated with IDDM can be divided into
three categories: short-term, intermediate, and long-term.
1) Short-term (acute) consequences include
diabetic ketoacidosis (DKA), hypoglycemia and
hyperglycemia. Diabetic ketoacidosis can be
precipitated by an infection although stress
or insufficient insulin can also be a
precipitant. Symptoms include rapid respira
tion, polydipsia, polyuria, vomiting and
nausea. Hypoglycemia (insulin reaction) can
be caused by overdoses of insulin, its
improper distribution, inadequate intake (or
delayed absorption) of food, or too much
exercise without a concurrent adjustment of
the food intake (Guthrie and Guthrie, 1977).
Increased perspiration, confusion, irri
tability and inappropriate behavior are
common symptoms (Sussman, 1971). Hyper
glycemia may result from too little insulin,
eating improperly, decreased exercise
(without concomitant decreased food intake),


90
HbA^c approached zero. Increases in insulin dose were as
sociated negatively with the injection factor Beta weight.
Table 21 depicts the characteristics of groups divided based
on the Beta weights for injection at varying levels of
change in insulin dose (Group 1 decreased insulin, Group 2
stayed the same, and Group 3 increased insulin dose). A
number of characteristics are noteworthy for the group of
children who reported the largest increases in insulin dose
during camp: children in this group were in the best HbA^c
control before camp; this group was the youngest of the
three groups; children in this group reported the greatest
post-camp compliance; this group was the only one that ex
perienced an increase in HbA^c while the other two groups
demonstrated slight improvement in glycemic control at
followup.
The other four adherence factors were considered in
similar models to determine if their interaction with
insulin dose change would offer similar predictive power.
This was not the case.
Categorical analyses
Based on a univariate procedure, the pre-camp HbAiC
values were divided into control categories (less than
8.4=good, greater than 8.4 and less than 10.4=moderate, and
greater than 10.4=poor control). ANOVAs were conducted on
each of the five adherence factors using one between subject
factor (HbA^c control group). A group effect was detected
for the injection factor with the differences between the


89
TABLE 20
Predicting Post-camp HbA^c:
Nonstandardized Injection
Beta Weights at Varying Levels
of Change in Insulin Dose from
Pre-camp to 3 Months Post-camp
Levels of Change in
Insulin Dose from
Pre-camp to Followup
Injection Factor (Post-camp)
Beta Weights
-40
2.951
-30
2.268
-20
1.585
-10
.901
- 5
. 560
0
.219
5
- 123
10
- .464
15
- .806
20
- 1.147
30
- 1.830
40
- 2.513
* Y= .26 + .63 (Pre-camp HbA^c) + .25 (Age) + .03 (Insulin
Dose Change) + .22 (Post-camp Injection Factor)
- .07 (Insulin Dose Change x Post-camp Injection Factor)


INTRODUCTION
Insulin dependent diabetes mellitus (IDDM) is a chronic
illness, the result of insufficient insulin production by
the pancreas. Onset occurs before age 30most frequently
between the ages of 8 and 12 yearsalthough it can occur at
any time from birth to adulthood (Blevins, 1979) .
Under normal circumstances, insulin is secreted
directly into the blood-stream by the Islets of Langerhans
in the pancreas. This hormone plays a vital role in the
metabolism of food since it facilitates the movement of
glucose from the bloodstream into muscle and fat tissue for
energy. Glucose that is not used for energy is then
directed into storage (primarily into the liver) in the form
of glycogen and/or fat. Fat is an additional source of
energy which does not require insulin to be converted.
However, the energy expended in its production is
considerable, leaving a negligible amount for functional
use. Normally, the pancreas secretes insulin at rates that
maintain glucose levels in the bloodstream within a narrow
range. In youngsters with IDDM, the pancreas cannot perform
this function and glucose levels build up.
The absence of insulin prevents glucose usage, fat is
broken down and glucose levels remain high. The liver
1


DISCUSSION
This study was designed to address the following
questions: 1) does attendance at a diabetes summer camp
alter children's diabetes management behaviors, 2) does
attendance at a diabetes summer camp influence children's
glycemic control, 3) if there are behavioral or metabolic
changes associated with camp, are they maintained once the
children return home, 4) is there a relationship between
adherence behavior and diabetic control. Results relevant
to each of these four questions will first be discussed. A
summary of the findings and a discussion of the study's
limitations and implications of its results will follow.
Reliability of Child Report
In order to address this question, youngsters and their
parents were interviewed prior to and after attendance at a
diabetes summer camp. While at camp, counselor report was
expected to corroborate and compliment the children's
reports. However, when counselor report was found to be
unusable, the reliability of the youngsters' self-report had
to be established in order to gauge whether any changes in
diabetes management behaviors occurred at camp. The results
of this portion of the study replicated the findings of
Johnson et. al., 1986. Based on Pearson product moment
95


101
rather than actual time. In addition, younger children were
probably more closely supervised while at home and in that
respect, camp did not represent a significant change.
Moreover, close scrutiny of the data provided by the 10- to
11.4-year olds revealed that the mean injection interval of
63.30 minutes was the result of several youngsters' inac
curate report of injection times. In any case it is likely
that the younger children's adherence on the injection
interval measure at camp was underestimated in this study.
Overall, the camp experience was highly consistent for
all youngsters regardless of age and sex. It is possible
that camp could have had a differential effect on older
children. However, this study sample was comprised of a
relatively young sample so that age differences demonstrated
in other studies which include adolescent samples were not
found in this study (Johnson, 1984).
Although it is clear that changes occurred during camp
for 9 of the 13 adherence behaviors, it is apparent these
changes were not maintained after camp. It appears that the
environment, whether it be camp or home, exerts a powerful
influence on children's daily diabetes management behaviors.
While children may practice more adherent behaviors during
camp, these behaviors have not yet become habitual and the
child's home environment does not offer the necessary
structure to continue these gains once the camp experience
is over.


TABLE 10
Means and Standard Deviations for Adherence
Measures Demonstrating Simple Main Effects for Time
Adherence Measure
Time 1
Means (SD)
(Interp)
Time 2
Means (SD)
(Interp)
Time Period
Time 3
Means (SD)
(Interp)
Time 4
Means (SD)
(Interp)
Time 5
Means (SD)
(Interp)
Injection
.47(.38)
.27(.27)
.50(.35)
. 46 (.31)
.68(.55)
regularity (minutes)
(28.02)
(15.92)
(30.06)
(27.60)
(40.59)
Injection-
.70(.50)
.49 ( 44)
.68(.37)
.66(.47)
.71(.46)
meal timing (minutes)
(42.14)
(29.65)
(40.81)
(39.52)
(42.74)
Eating 15.
35(12.62) 2
.58(3.54) 15
.31(13.55) 17
. 33 (13.93)
16.26(14.67)
freguency (per day)
(5.02)
(5.86)
(5.07)
(4.97)
(5.02)
Exercise
09(.14)
.03(.01)
.16 ( 23)
.17(.24)
. 12(.18)
duration (minutes
per occasion)
(20.03)
(42.41)
(14.44)
(15.35)
(14.07)
Exercise
97(.01)
.94(.01)
.98(.01)
.97(..02)
.97(.01)
type
(kilocalories/min)
(.03)
(.06)
(.02)
(.03)
(.03)
Glucose 51.
23(24.41) 45
.80(12.78) 54
.09(24.50) 59
.18(26.09)
60.80(25.47)
testing
frequency
(per day)
(1.88)
(2.17)
(1.79)
(1.58)
(1.53)
as


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94
post-camp HbA^c while decreased adherence would be asso
ciated with increased HbA^c at followup. Repeated measures
ANOVA was then conducted on the two HbA^c measures using one
within subject factor (Time, pre-camp and followup) and one
between subject factor (Change group). Results revealed
only a Time main effect.


58
agreement at Time 4 (r=.68) and the poorest concordance at
Time 5 (r=.43). Only the injection-meal timing measure
exhibited any evidence of a linear trend toward improvement
in parent/child agreement across time (see Table 8).
However, by the end of the study (Time 5) parents and
children were significantly correlated on 12 of the 13 ad
herence measures (p< .0005). Only parent/child agreement
for the regularity of injection-meal timing remained weak.
Differences between parent and child report were
further analyzed with repeated measures ANOVA on each of the
13 adherence measures collapsed across all parent/child
interviews using two between subject factors: Age group (7-
to 9-year olds, 10- to 11.4-year olds, and 11.5- to 12.6-
year olds) and Sex, and one within subject factor: Respon
dent (parent, child). Results indicated that there were few
differences between parent and child report. Simple main
effects for Respondent emerged on three measures. On the
injection-meal timing measure, parents reported an average
of 14 minutes while children reported an average of 19
minutes between injection and meal, F(1,56)=5.98, p < .02.
On the eating frequency measure parents reported that
children ate an average of 5.25 meals per day while children
reported eating an average of 5 times per day, F(l,56)=
25.29, p< .0001. On calories consumed parents reported that
their children ate approximately 92 calories more than the
recommended amount per day while children reported ingesting


TABLE 3
Parent-Child Correlations for Total Sample and by Age Group
Pre-Camp
Adherence Measure
Total
Sample
r(n)
P <
Group 1
(7-9 Years)
r (n)
P <
Group 2
(10-11.4 Years)
r(n)
P <
Group
(11.5-12.6
r (n)
P <
Inj ection
.65(60)
.80(18)
. 28(21)
. 82(21)
regularity
( 0001)
( .0001)
(.2156)
( .0001)
Injection
.75(59)
.51(19)
.92(19)
. 64 (21)
interval
(.0001)
(.0262)
(.0001)
(.0018)
Injection-
.54(62)
-.03(19)
. 57 (22)
.75(21)
meal timing
(.0001)
( .8885)
(.0052)
(.0001)
Regularity of
.09(59)
-.08(18)
-.08(20)
.42(21)
injection-
meal timing
(.5010)
( .7382)
(.7450)
(.0526)
Calories
.73(62)
.81(20)
. 62 (22)
.84(20)
consumed
(.0001)
(.0001)
(.0023)
( .0001)
Percentage
.78(63)
.86(20)
.64(22)
.73(21)
calories-fat
(.0001)
(.0001)
(.0013)
(.0002)
3
Years)


BIOGRAPHICAL SKETCH
Born in Budapest, Hungary, on February 20, 1944, Marika
E. Spevack (nee Kessler) and her family emigrated to
Montreal, Canada, where she grew up and married. After 10
years of travel and the advent of 4 daughters, she decided
to pursue a degree in education and earned a Master of
Education degree from the University of Florida in 1980.
After a brief teaching experience, Marika became involved in
psychological research and was encouraged by her family and
inspired by her employer and mentor to pursue an advanced
degree in clinical psychology. From 1982-1987, Marika
attended the University of Florida's graduate program in
clinical psychology where she earned her M.S. in May 1985.
She plans to complete the requirements in time to graduate
with a Ph.D. in December 1987.
116


86
TABLE 18
Pre-camp HbA1c x Post-camp Injection Factor:
Descriptive Characteristics
Pre-camp HbAj^c
Injection B Wts
5.8-7.0
1.4 to -.9
8.0-10.0
-.4 to .5
11.0-13.0
.9 to 1.9
N
8
37
5
Pre-camp HbA^^c
6.5
8.9
11.6
Post-camp HbA-LC
7.4
8.5
11.0
Age
9.9
10.5
11.2
Duration of disease
3.8
4.0
3.5
Injection factor
pre-camp
. 10
. 01
- .18
Injection factor
post-camp
.20
. 34
.59
Insulin dose
pre-camp
25.9
30.9
33.0
Insulin dose
during camp
21.8
27.9
29.4
Insulin dose
at followup
34.5
29.5
40.7
Change in insulin
dose between pre
camp and followup
8.6
- 1.4
7.7


TABLE 8
(continued)
Adherence Measure
Time 1
Pre-Camp
r(n)
P <
Time 3
Post-Camp
r(n)
P <
Time 4
6 wks Post-Camp
r (n)
P <
Time 5
3 Mos Post-Camp
r (n)
P <
Percentage
.72(63)
.75(60)
.83(57)
. 65(61)
calories-
carbohydrate
(.0001)
(. 0001)
( .0001)
(. 0001)
Concentrated *
.63(63)
. 59(60)
.68(57)
.43(61)
sweets
(.0001)
(.0001)
( .0001)
( 0005)
Eating
.72(63)
.67(60)
.74(57)
.80(61)
frequency
(.0001)
(.0001)
( .0001)
( .0001)
Exercise
.29(63)
.21(60)
.30(57)
. 66(61)
duration
(.0211)
(.1106)
( .0235)
( 0001)
Exercise
.64(63)
.64(60)
.56(57)
. 68(61)
type
(.0001)
( .0001)
(.0001)
( .0001)
Exercise
.68(63)
. 68(60)
.58(57)
.71(61)
frequency
(.0001)
( .0001)
( .0001)
( 0001)
Glucose
.90(63)
.78(60)
.91(57)
.91(61)
testing
frequency
(.0001)
( .0001)
( .0001)
( .0001)
* indicates significant differences between correlations
across time periods. y,