Diabetes Management and Metabolic Control in School Aged Children

CHILDRENS HEALTH CARE, 35(4), 349363Copyright 2006, Lawrence Erlbaum Associates, Inc.

Diabetes Management andMetabolic Control in School-Age

Children With Type 1 Diabetes

Maureen A. FreyCarmen and Ann Adams Department of Pediatrics

Wayne State University

Deborah EllisDepartment of Psychiatry and Behavioral Neurosciences


Tom TemplinCollege of Nursing


Sylvie Naar-KingCarmen and Ann Adams Department of Pediatrics


James P. GutaiDepartment of Community Medicine


This study investigated the effect of mothers coping resources, cognitive resources,family stress, and demographic variables on diabetes management and the mediat-ing role of diabetes management on metabolic control among children with dia-betes. Mothers (N = 59) completed self-report measures. HbA1c was obtained fromthe medical records. Although cognitive resources, coping resources, and familystress accounted for 30% of the variance in diabetes management, the hypothesis of

Correspondence should be sent to Maureen A. Frey, Critical Care Medicine4134 CHM, 3901Beaubien, Detroit, MI 48201-2119. E-mail: [email protected]

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mediation was not supported. The only significant predictor of HbA1c was AfricanAmerican race/ethnicity. The findings identify modifiable targets for practice andhighlight the increased risk for poor metabolic control for minority children.

Type 1 diabetes, the most common endocrine disorder in the pediatric popula-tion, affects 1 in every 400 to 600 children and adolescents (National DiabetesInformation Clearing House, n.d.). Type 1 diabetes requires lifelong administrationof exogenous insulin, daily blood glucose testing, continual attention to dietaryintake, and increased physical activity to maintain adequate metabolic control.Although diabetes management and metabolic control have been extensively inves-tigated in adolescent populations, much less is known about predictors of diabetesmanagement and metabolic control in younger children. Yet the first years afterdiagnosis are critical in terms of establishing adequate patterns of diabetes manage-ment. The role of parents of young children with diabetes is also becoming increas-ingly important as intensive insulin therapy, with its higher level of complexity, isinitiated earlier in the course of the disease. In addition, because the microvascularcomplications of diabetes are directly related to duration of diagnosis, young chil-dren are at increased risk for such complications compared with those diagnosed ata later age (Donaghue et al., 2003). The lack of research with school-age and pread-olescent children with diabetes is a significant barrier to the development of healthpromotion and risk reduction interventions with this population. Accordingly, thepurpose of this study is to identify predictors of diabetes management among moth-ers of 6- to 13-year-old children with type 1 diabetes and subsequently to determinethe relationship between diabetes management and metabolic control.

Descriptive studies with children and adolescents show that younger childrenhave better metabolic control than do older children (Johnson et al., 1992; La Greca,Follansbee, & Skyler, 1990; Palta, Shen, Allen, Klein, & DAlessio, 1996). However,others have noted that many school-age and younger children show evidence ofless than adequate metabolic control. For example, Davis et al. (2001) reportedthat 43% of their sample had a glycohemoglobin level four standard deviationsabove the normal range, indicating fair to poor glycemic control. Garrison andcolleagues (Garrison, Biggs, & Williams, 1990) reported that 23% of their sampleshowed poor glycemic control for at least 1 year prior to study entry. In addition,29% of the sample showed fair to poor regimen adherence. These studies areimportant because all of the participants were younger than 11 years of age. Inaddition, there is evidence that patterns of metabolic control are established earlyin the course of the disease, often within 2 years of diagnosis (Forsander et al.,1998; Jacobson et al., 1990). Both cross-sectional and longitudinal studies showthat early patterns of metabolic control tend to remain stable over time (Seiffge-Krenke, & Stemmler, 2003).

Studies of school-age children with diabetes are limited compared with studies ofadolescents with diabetes (Anderson & Laffel, 1997). Family support for diabetes

350 FREY, ELLIS, TEMPLIN, NAAR-KING, GUTAI

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(Waller et al., 1986), family conflict (Jacobson et al., 1990), and family stress(Viner, McGrath, & Trudinger, 1996) have been related to metabolic control, andparent involvement with diabetes management has been related to adherence inchildren (La Greca et al., 1990). However, in view of the more primary role of thecaregiver in the management of diabetes at this age, caregiver factors might beparticularly important. Therefore, Davis and colleagues (2001) investigated par-enting style in mothers of 4- to 10-year-old children with diabetes. Parentalwarmth was a significant predictor of adherence. However, neither adherence norparental warmth was predictive of metabolic control. In examining maternal tem-perament variables related to young childrens adherence and metabolic control,Garrison et al. (1990) reported that maternal characteristics related to child out-comes in a differential manner. That is, maternal temperament was associatedwith metabolic control but not with adherence. Maternal temperament character-istics that were found to be associated with poor metabolic control were negativemood, high social withdrawal, high rigidity, and lower activity level. The authorsconcluded that further attention to individual characteristics of parents as predic-tors of diabetes management in children was warranted.

As has been found in studies of adolescents, studies with younger childrendemonstrate that diabetes management has not always been closely linked tometabolic control. In one of the few studies to include children younger than8 years of age, diabetes management, measured as a single-item parent rating ofthe extent to which You or your child follow the diabetes regimen, was notrelated to metabolic control (Auslander, Anderson, Bubb, Jung, & Santiago,1990). Johnson and associates (1992) also failed to show a relationship betweenadherence and metabolic control in school-age children despite considerablemethodological rigor and use of the well-established 24-Hour Recall Interview tomeasure adherence.

A significant gap in the research is lack of attention to the cognitive ability ofcaregivers. Diabetes management is complex and requires considerable problemsolving and decision making. The cognitive ability of adolescents is a criticalfactor in determining readiness for and ability to manage self-care (Brandt,1998). Likewise, the cognitive ability of caretakers may affect their ability tomake decisions regarding insulin adjustments or dietary changes. Cognitive abil-ity is also related to health literacy or to the ability to process and act on healthinformation. Parents with poor health literacy may struggle to carry out recom-mendations made by medical personnel for management of their childs care(Schillinger et al., 2002).

Some of the strongest and most consistent predictors of metabolic control inchildren and adolescents are the sociodemographic characteristics of race/ethnicity,family income, and family composition. Poor metabolic control is associatedwith minority status, low family income, and single-parent headed households(Auslander, Thompson, Dreitzer, White, & Santiago, 1997; Delamater, Albrecht,

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Postellon, & Gutai, 1991; Delamater et al., 1999; Harris, Greco, Wysocki,Elder-Danda, & White, 1999; Harris & Mertlich, 2003; Overstreet, Holmes,Dunlap, & Frentz, 1996; Thompson, Auslander, & White, 2001). However, in stud-ies in which the effects of race/ethnicity and socioeconomic status were controlled,the most significant risk factor for poor metabolic control was living in a single-parent household (Harris et al., 1999; Overstreet et al., 1996; Thompson et al.,2001). Auslander et al. (1990) also found that African American mothers reportedlower adherence to diet and frequency of blood glucose testing than did EuropeanAmerican mothers. However, there is limited research on other caregiver charac-teristics or behaviors that might account for differences in health outcome.

Kings Conceptual System (King, 1981, 1995) provides an ecological frameworkfor understanding and organizing factors that may influence diabetes manage-ment and metabolic control. The framework includes personal systems (individu-als), interpersonal systems (families and other small groups), and social systems(large community groups). It is interactions within and between the three systemsthat influence behavior. Behavior, in turn, influences health outcomes. Knowledgeof interactions comes from concepts such as stress, coping, resources, and health.The empirical literature has also suggested that these concepts are important inunderstanding diabetes management.

In summary, research with caregivers of school-age children is very limiteddespite the acknowledged importance of diabetes management and metaboliccontrol for this age group. Research has suggested that some parent characteris-tics and demographic factors may influence metabolic control directly and indi-rectly through management of diabetes. Accordingly, the aim of our study is toinvestigate the effects of family stress, mothers coping resources, mothers cog-nitive resources, and selected demographic variables on diabetes management in6- to 13-year-old children with type 1 diabetes. A secondary aim was to determineif diabetes management mediated the effects of family stress, mothers copingresources, mothers cognitive resources, and selected demographic variables onmetabolic control in this population.

METHOD

ParticipantsParticipants for this descriptive cross-sectional study were recruited from auniversity-affiliated pediatric diabetes clinic. Participants were seen clinically formedical visits by a multidisciplinary team at 3- to 4-month intervals. The sampleconsisted of 59 motherchild dyads who met the following eligibility criteria:child between 6 and 12.9 years of age, who had been diagnosed for at least 1 yearwith type I diabetes, and no known developmental delay or other chronic med-ical conditions. Given the young age of children in the sample, all children were


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managed by injected insulin rather than insulin pump. They were expected to testblood glucose three to four times per day and were provided with individualizedmeal plans. All maternalchild dyads were English speaking. Data were collectedat the time of a regularly scheduled clinic appointment with the assistance of atrained data collector. Sixty-three percent of the eligible individuals agreed to par-ticipate. The most frequent reason for nonparticipation was the extra time duringthe clinic appointment needed to complete the research measures. All data wereobtained by maternal report.

Characteristics of the participants for the total sample and by race/ethnicity areshown in Table 1. Approximately two thirds of the sample was of minorityrace/ethnicity: 27% were African American, 3% were American Indian, 15%were biracial, and 15% selected Other. Three mothers, 5% of the sample, didnot provide race/ethnic information. Overall, the demographics of the samplewere representative of the diverse urban population served by the clinic whereparticipants were recruited. Analyses of variance indicated no significant differ-ences between the racial/ethnic subsamples on mothers age, mothers education,or childs age. Pearson chi-square analyses indicated no significant differences inchilds gender. There was a difference in mothers marital status and familyincome. A significantly smaller percent of African American mothers (29%) weremarried compared with the European American (70%) or the Other (61%) group.Family income was also significantly lower for African Americans.

The mean age of the children was somewhat skewed toward the upper end ofthe eligibility age range, which is consistent with the typical age of onset of type 1diabetes. On average, children had been diagnosed with diabetes for 3 to 4 years.


TABLE 1Sample Characteristics by Race/Ethnicity

African European Characteristic Totala Americansb Americansc Otherd

ChildAge 9.45 (2.48) 10.15 (2.68) 9.49 (2.20) 9.17 (2.58)Female (%) 61 69 45 67Duration of 3.52 (2.53) 3.59 (2.88) 4.06 (2.92) 2.99 (1.82)

diabetesMother

Age 37.50 (5.80) 38.79 (7.02) 38.20 (5.56) 35.72 (4.93)Education (years) 13.56 (1.80) 13.19 (1.22) 14.30 (2.22) 13.18 (1.72)Married (%) 61 31 70 78Income (yearly) $32,198 ($16,293) $19,893 ($16,908) $38,525 ($13,629) $34,605 ($14,000)

Note. Values are mean (SD) or percentage.aN = 59. bn = 16 (27%). cn = 20 (34%). dn = 23 (39%).

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Measures

Family Inventory of Life Events and Changes (FILE; McCubbin & Patterson,1987). The FILE is a 71-item parent-report scale designed to assess both acuteand chronic stressful events in the family system over the previous 12 months.Positive responses are summed to form a total stress score. Higher scores indicatehigher levels of stress. Testretest reliability of .80 and evidence of good con-struct validity has been reported. The scale has been extensively used with diversepopulations.

The Shipley Institute of Living Scale. This 60-item parent-report scale iscomposed of two brief subtests: vocabulary and abstract thinking, which are cog-nitive resources correlated with general cognitive ability (Zackery, 1991). Respondentsselect words with similar meaning from a list and fill in numbers or letters to logi-cally complete given sequences (abstract thinking). The scale requires minimalassistance to complete and can be administered in small groups. High split-half(.84) and testretest (.78) reliability coefficients have been reported. The Shipleyscale (Zackery, 1991) correlates highly with full-scale IQ scores as obtained byinstruments such as the Wechsler Adult Intelligence ScaleRevised (Wechsler,1981). Total scores were used in our analyses.

The Coping Resources Inventory (CRI). This 60-item parent-reportscale quantifies the availability of five resources for coping: cognitive, social,emotional, spiritual/philosophical, and physical (Hammer & Marting, 1988).Responses are on a 4-point scale, ranging 1 (never), 2 (rarely), 3 (always), and4 (almost always), in the previous 6 months. Cronbachs alpha coefficients of .71to .84 for the subscales and .91 for the scale total have been reported. In this study,Cronbachs alpha coefficients ranged from .48 to .89 for the subscales and .92 forthe scale total. In this study, the total score was used in the analyses.

The Diabetes Management ScaleParent Report (DMSPR). TheDMSPR was used to measure diabetes management (Frey, Ellis, Naar-King, &Greger, 2004; Schilling, Grey, & Knafl, 2002). The scale taps blood glucose test-ing; insulin injections; dietary considerations; symptom management; exercise;and parent/adult supervision, assistance, and overall responsibility for manage-ment. Mothers were asked What percent of the time ? various componentsof management were completed in the past 3 months. The response scale rangedfrom 0% to 100%. Items are summed to obtain a total score; higher scores reflectmore completed diabetes care for the child. A previous study showed that theDMSPR was sensitive to differences in both age and level of metabolic control.Internal consistency of the DMSPR in this study was .78.


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Metabolic control. HbA1c was obtained at each visit as part of standardcare. The HbA1c value obtained at the time of study recruitment was used inanalysis. The range of HbA1c for the sample was 5.76% to 15.99%, with a meanof 9.00% (SD = 1.95%).

The demographic characteristics of race/ethnicity, family income, motherseducation (highest grade completed), and marital status were obtained bymothers self-report. Income and education were considered proxy measures ofsocioeconomic status.

Procedures

The study received human participants approval from the Institutional ReviewBoard. All mothers provided written informed consent, and children older than7 years of age gave verbal assent to participate. Most potential participants werenotified of the study by letter or by telephone prior to a scheduled medical appoint-ment, although some were approached in the waiting area at the time of theirappointment. Mothers completed the questionnaires in the clinic and received $10to compensate for additional time in the clinic.

Analytic ApproachWe used structural equation modeling (SEM) with AMOS Version 5.0 (Arbuckle& Wothke, 2003) to determine predictors of mothers diabetes management andto test for mediation. A manifest model was specified because all constructs weremeasured as single indicators. SEM is considered more appropriate than tradi-tional multivariate analyses for testing mediation because it allows both theassessment of goodness of fit of a specified model and the testing of each esti-mated path coefficient (Satorra & Saris, 1985). In the past, use of SEM wasrestricted to studies with large samples. However, bootstrap analysis allowsmodel testing with small samples by using the actual data to estimate standarderror (Shrout & Bolger, 2002). The theoretical model tested is shown in Figure 1and contains three exogenous variables and two endogenous variables. Wehypothesized that maternal cognitive resources, maternal coping resources, andfamily stress would be predictors of diabetes management. In this model, diabetesmanagement fully mediates the relationship between maternal and family vari-ables and metabolic control.

Demographic covariates were added to the SEM model empirically after ananalysis of residuals was performed. Potential covariates were family income,mothers education (highest grade completed), childs gender, childs race/ethnicity,and family composition. Family composition was dichotomized as single parent


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and two parent. Two-parent families included two biological parents, a stepparentand biological parent, and parents living with a partner. The contrasts used forrace/ethnicity were African American versus all others and European Americanversus all others. In this analysis, residual error for each endogenous variable(diabetes management and metabolic control) was first computed. The pattern ofcorrelation between each residual and each potential covariate was then exam-ined. Those covariates that were significantly related to the residuals and to theexogenous/predictor variables were included in the model to reduce bias andpotential confounding effects. Family income and African American race/ethnicitymet these criteria.

RESULTS

Bivariate correlations between variables are shown in Table 2. The demographicvariables of family income, family composition, and race/ethnicity were signifi-cantly intercorrelated indicating that African American children were more likelyto be from single-parent lower income families compared with White and otherrace/ethnic children. There were no significant correlations between the predictorvariables of mothers cognitive resources, mothers coping resources, and familystress. However, several demographic variables were significantly related to thepredictor variables. Race/ethnicity, family income, and mothers education weresignificantly related to mothers cognitive resources, and family income wasalso related to mothers coping resources. Mothers who reported lower cogni-tive resources also reported lower family income, lower education, and AfricanAmerican race/ethnicity. Family stress (r = .30, p = .022) and coping resources(r = .30, p = .009) were significantly associated with diabetes management anddiabetes management was significantly associated with metabolic control (r = .26,p = .047). Mothers who reported lower stress and higher coping resources also


MothersCognitiveResources

FamilyStress

MothersCopingResources

MothersDiabetesManagement

ChildsMetabolicControl

FIGURE 1 Theoretical model.

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TABL

E 2

Corre

latio

ns A

mon

g Varia

bles

Vari

able

Age

Race

/Eth

nici

tya

Dur

atio

nH

gA1c

Inco

me

Educ

atio

nM

arri

edb

FILE

Ship

leyCR

I

Child A

ge

Rac

e/et

hnic

ity.15

Dur

atio

n.37

**.02

HbA

1c.33

**.56

**.09

Mot

her

Inco

me

.02

.37

**.23

.40

**

Educ

atio

n (ye

ars)

.11

.13

.04

.03

.18

Mar

ried

.10

.30

**.17

.17

.52

**.08

FILE

.05

.06

.05

.10

.04

.17

.21

Ship

ley

.11

.34

**.28

**.10

.48

**.42

**.24

.14

CRI

.03

.16

< .0

1.04

.27

.09

.07

.10

.16

DM

SPR

.19

.10

.21

.26

*.11

.03

.10

.34

**.17

.30

**

Note

.FI

LE =

Fam

ily In

ven

tory

of L

ife E

vents

and

Cha

nges

; Shi

pley

= S

hipl

ey In

stitu

te o

f Liv

ing

Scal

e; C

RI =

Cop

ing

Reso

urce

s Inv

ento

ry; D

MS

PR =

Dia

bete

s Man

agem

ent S

cale

Par

ent R

epor

t.a A

frica

n Am

eric

an =

1,a

ll ot

hers

= 0

. bM

arrie

d =

1,al

l oth

ers =

0.

*p

< .0

5. *

*p

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Diabetes Management and Metabolic Control in School Aged Children