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Further Examination ofa Structured AdherenceInterview of Diabetes forChildren, Adolescents, andParentsAdam B. Lewin , Eric A. Storch , Gary R. Geffken ,Amanda D. Heidgerken , Laura B. Williams & JanetH. SilversteinPublished online: 07 Jun 2010.

To cite this article: Adam B. Lewin , Eric A. Storch , Gary R. Geffken , Amanda D.Heidgerken , Laura B. Williams & Janet H. Silverstein (2005) Further Examination ofa Structured Adherence Interview of Diabetes for Children, Adolescents, and Parents,Children's Health Care, 34:2, 149-164, DOI: 10.1207/s15326888chc3402_5

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Further Examination of a StructuredAdherence Interview of Diabetes forChildren, Adolescents, and Parents

Adam B. LewinDepartments of Clinical & Health Psychology and Psychiatry

University of Florida

Eric A. StorchDepartments of Psychiatry and Pediatrics

University of Florida

Gary R. GeffkenDepartments of Clinical & Health Psychology, Psychiatry, and Pediatrics

University of Florida

Amanda D. HeidgerkenDepartment of Psychiatry

University of Florida

Laura B. WilliamsDepartments of Clinical & Health Psychology and Psychiatry

University of Florida

Janet H. SilversteinDepartment of Pediatrics

University of Florida

This study evaluated the factor structure of the Diabetes Self-Management Profile(DSMP), a structured interview for diabetes regimen adherence for children withtype 1 diabetes. Study aims included a detailed examination of parent–child agree-ment in ratings of adherence. The DSMP was administered to 121 children and their

CHILDREN’S HEALTH CARE, 34(2), 149–164Copyright © 2005, Lawrence Erlbaum Associates, Inc.

Requests for reprints should be sent to Adam B. Lewin, Department of Clinical & Health Psychol-ogy, University of Florida, Box 100165, Gainesville, FL 32610–0165. E-mail: alewin@phhp.ufl.edu

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parents during routine visits to a tertiary care diabetes clinic. Confirmatory factoranalysis of the rationally derived five subscales yielded poor fit indexes. Subsequentexploratory factor analysis supported a two-factor solution for both the parent andchild DSMP adherence ratings with factors named Food and Insulin Schedule Ad-herence and Adherence to Blood Sugar Testing and Adjustments. The internal con-sistency of the factors was acceptable, and predictive validity was supported vis-à-vispositive correlations with metabolic control (HbA1c). This factor structure appearsto provide a brief yet reliable and valid framework for assessing adherence and pre-dicting metabolic control in children. In addition, parent–child agreement varied as afunction of age. However, poor metabolic control did not relate to higher par-ent–child disagreement.

Metabolic control is usually achievable only if the patients demonstrate adher-ence to their diabetes regimens. Frequency of blood glucose monitoring hasbeen shown to be the best predictor of metabolic control, indicating the impor-tance of patient compliance for good blood glucose levels. Because of this,health care providers have sought an accurate and reliable measure of assessingpediatric diabetes adherence. Haynes (1979) defined compliance as “the extentto which a person’s behavior coincides with medical or health advice” (pp. 1–2).Johnson (1993) maintained that defining adherence (or compliance) with a med-ical regimen is difficult due to the multitude and complexity of the regime be-haviors. In addition, regimen adherence with diabetes is not a unitary construct.Extant research indicates that adherence with one component of diabetes treat-ment (e.g., blood glucose testing) is not predictive of the adherence to other as-pects of the treatment regimen (e.g., diet; Glasgow, McCaul, & Schafer, 1987;Johnson et al., 1992; Johnson, Silverstein, Rosenbloom, Carter, & Cunningham,1986; Kavanagh, Gooley, & Wilson, 1993). Adding to the obfuscation, there isno universal agreement on explicit standards for measuring adherence. Methodsrange on a continuum from direct to indirect (La Greca, 1990). Skinner (1938)defined a behavior as “what an [individual] is doing—or more accurately what isobserved by another organism to be doing” (p. 6). Adherence and nonadherenceare behavioral components of diabetes management. Intuitively, the most accu-rate method of assessment should be observation of diabetes care behaviors.However, the behavioral observation approach is often unobtainable and imprac-tical given that the complexity of the diabetes treatment regime requires scrutinyof numerous behaviors over extended periods of observation.

Indirect measures of adherence behaviors offer an alternative method of assess-ment.Oneapproach involvesobtainingpatient self-reportsorparental reportsonad-herence (Anderson, Auslander, Jung, Miller, & Santiago, 1990). La Greca (1990)cautioned generalizations from self-reports, due to extensive variability in their de-tail and comprehensiveness. Johnson et al. (1986) developed a 24-hr recall interviewto quantify adherence behaviors. Interviewers ask patients and parents to describe

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diabetes-management behaviors during the previous 24 hr over a series of three20-min structured phone interviews. Thirteen adherence indexes are obtained, sev-eral of which include glucose testing, insulin administration, diet, and exercise con-structs. Significant correlations between parent and child reports have been docu-mented in separate analyses with correlations ranging from .09 to .94 (Freund,Johnson, Silverstein, & Thomas, 1991; Johnson et al., 1986; Spevack, Johnson, &Riley, 1991). Although there are advantages to this interview (e.g., minimization ofrecall errors, high reliability, and the ability to capture a nonreactive example of thechild’s adherence), the 24-hr recall interview is difficult to implement in a clinicalsetting, due to its reliance on multiple interviews (three child, three parent) and com-plex scoring system (McNabb, 1997). Required resources to conduct interviews andanalyze data (e.g., time and labor) are often not available to healthcare providers. Inaddition, despite the comprehensiveness of the 24-hr recall interview, strong associ-ations between adherence and HbA1c were not supported in a longitudinal analysisof 186 children using the measure (Johnson et al., 1992).

The Self-Care Adherence Inventory (SCAI; Hanson, Henggeler, & Burghen1987), a semistructured clinician-rated interview to assess adherence with type 1diabetes treatment regimen, is an alternative to the 24-hr recall interview (Hansonet al., 1996; Hanson et al., 1987). The interview is administered to the patient by anindividual familiar with the requirements of the diabetes regimen. The SCAI con-tent areas include glucose testing, dietary behaviors, insulin adjustment, andhypoglycemia preparedness. The authors found that the SCAI related to HbA1c allthree times the measure was administered (rp = –0.28, –0.25, and –0.20, p < .001).

Harris et al. (2000) refined the SCAI and developed the Diabetes Self-Manage-ment Profile (DSMP). The DSMP is a 23-item structured interview that assessesfive areas of diabetes management: insulin administration and dose adjustment,blood glucose monitoring, exercise, diet, and management of hypoglycemia. Asingle version of the measure was developed for administration to children withtype 1 diabetes, their parents, or the parent and child together. The DSMP consid-ers recent advances in diabetes treatment, such as rapid acting insulin and carbohy-drate management. The DSMP is designed to assess diabetes self-managementover the preceding 3 months. One hundred five children (ages 6–15; M = 11.6, SD= 1.2 years) with type 1 diabetes participated in the initial psychometric investiga-tion of the DSMP. The sample was predominately White; 49% of the patients werefemale. The majority of the sample comprised families reporting middle- to uppermiddle-class occupations (66 families reported parental occupation/education asCEOs, physicians, attorneys, or PhDs).

In the Harris et al. (2000) study, children 11 years of age and younger were in-terviewed together with their parents (in a single DSMP administration). For ado-lescents 12 years of age and older, there were 26 parents and adolescents inter-viewed separately, using the identical version of the DSMP. The authors reportedno significant difference (n = 26) between parent and child administrations. The

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remaining 27 adolescents were interviewed independently and were informed thattheir parents would not be interviewed. DSMP scores for adolescents did not sig-nificantly differ between the condition where (a) their parent received an inde-pendent DSMP administration and (b) they were told the parent would not be giventhe DSMP. Data also suggested that adolescent’s self-reports did not differ whenthey were interviewed separately from their parents (n = 26).

Internal consistency for the DSMP Total score (Cronbach’s α = .76 for both par-ent and child administrations) was acceptable. However, Harris et al. (2000) re-ported that alphas for each of the subscales (e.g., Exercise, Blood Glucose Testing)were less than .50, suggesting that the subscales may be unreliable when imple-mented separately (Cronbach, 1951). In addition, subscale correlations betweensubscale and DSMP Total scores were marginal to good, ranging from .54 (DoseAdjustment) to.87 (Diet). At 3 months, test–retest reliability was investigated with32 families whose treatment regimens were left unchanged during the period.Pearson correlations were acceptable for the total score (r = .67) but marginal foreach of the subscales (range is r = .34 for Dose Adjustment to r = .47 forHypoglycemia). Interrater agreement was excellent for 28 families (r = .94 for theDSMP Total; subscale scores = .85–.97).

The authors examined predictive validity by correlating the DSMP Total Scorewith HbA1c, an index of glycemic control over the previous 2 to 3 months (Ameri-can Diabetes Association [ADA], 2003). Pearson’s correlations between HbA1cand the DSMP Total score were significant (r = –.28, p < .01). Similarly, threeDSMP subscales correlated significantly with HbA1c (r = –.37 for Blood GlucoseTesting, r = –.25 for Insulin, and r = –.27 for Diet). Lewin et al. (2006) also foundstrong relations between the DSMP Total score and HbA1c (r = –.47, p < .01; r =–.41, p < .01 for child and parent report of adherence, respectively).

Although the initial properties of this measure appear promising, to our knowl-edge no further investigations of the DSMP psychometric properties have been re-ported. Furthermore, in the initial investigation of this measure, DSMP items wereassigned to subscale based on content, without an examination of the overall factorstructure. Given the poor internal consistency of each DSMP subscale, further ex-amination appears warranted. In addition, the initial validation of the DSMP wasconducted with population from a relatively high socioeconomic status (SES). It ispossible that findings would not generalize to lower SES families.

In addition, in the initial investigation of the DSMP (Harris et al., 2000), theDSMP was administered to the parent and child together as a single administra-tion for 50% of families participating. As such, both (a) independent parental re-ports of child adherence and (b) parent–child agreement was available for only25% of the overall sample (n = 26). Moreover, parent–child agreement is onlyexamined among one age group (adolescents). Given that parent–child agree-ment among ratings of adherence varies with age (Johnson et al., 1986), a moredetailed examination may be useful. Johnson (1993) reported that younger chil-dren show poorer parent–child agreement on adherence than older children. In

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addition, it seems likely that older adolescents’ adherence reports may differfrom their parents’ reports, given increasing independence and management ofdiabetes-related responsibilities. Taken together, parent–child agreement on in-dependent ratings of adherence may be curvilinear, with poorer relations be-tween both younger children and older adolescents. Finally, to our knowledge,no previous research has investigated parent–child agreement as a function ofmetabolic control. It appears reasonable to expect that children in extremelypoor metabolic control may be more discrepant from their parents in reports ofadherence than children in better metabolic control.

Therefore, in this study we sought to replicate the initial investigation of this in-strument with a population of children and adolescents with type 1 diabetes and toextend its use in families from lower SES backgrounds. In addition, we desired amore comprehensive investigation of the agreement between parent and child re-ports of adherence. To date, the Harris et al. (2000) study is the only psychometricinvestigation of the DSMP. Given the potential utility of the DSMP in evaluatingadherence behaviors in a medical setting, combined with the increased complexityof the diabetes regimen, a supplemental formal evaluation of the reliability and va-lidity of this measure is important to insure accurate assessment. Therefore, thisstudy was designed with the following specific objectives:

1. To examine the proposed five-factor model proposed by Harris and col-leagues (2000), using confirmatory factor analysis for both youth and par-ent ratings.

2. To examine the internal consistency and interscale correlations of theDSMP scores in a sample characterized as having a low SES.

3. To determine the predictive validity of the DSMP scores on the basis of therelationship between the DSMP total and subscale scores with HbA1c.

4. To investigate parent–child agreement on the DSMP Total and subscalescores. More specifically, we planned to examine where parent–childagreement varied as a function of both (a) the child’s age and (b) the child’smetabolic control (HbA1c). We expected that younger children’s and olderadolescents’ reports of adherence would be more discrepant from their par-ents’ reports. In addition, we hypothesized that children who are in ex-tremely poor metabolic control will be more discrepant in their reports ofadherence than children in good metabolic control.

METHOD

Participants and Procedure

Participants were 121 children and adolescents with type 1 diabetes and their care-givers, recruited from an outpatient pediatric endocrinology clinic at a university

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affiliated medical center. The sample consisted of 59 boys and 62, girls ages 7.9 to18.4 years (M = 9.1, SD = 2.0). The ethnic distribution of participants was 78.5%White, 10.7% African American, 5.8% Hispanic, 3.3% Native American, and1.6% representing other ethnic groups. The majority of the caregivers participatingin the study were mothers (80.7%), followed by fathers (14.3%) and grandparents(5.0%). The typical child participating in this study was from a family of below-av-erage SES (Hollingshead, 1975). Caregivers and children were recruited based onthe following study inclusion criteria: ages 8 to 18 years, diagnosed with type 1 di-abetes for at least 1 year, living with and accompanied by their primary caregiver,and no evidence of mental retardation. Of families approached, 121 agreed to par-ticipate in the study and 8 declined. Signed informed consent and assent, approvedby the Institutional Review Board, was obtained from each participant. The DSMPstructured interview took the parent and child approximately 15 min each to com-plete and was administered by trained research assistants. Children and their care-givers were interviewed separately by singular, trained research assistants. Trainednursing staff conducted an HbA1c test, collected by a finger-stick, as part of eachpatient’s routine clinic visit.

Measures

DSMP. The DSMP (Harris et al., 2000) is a structured interview consisting of23 questions, with an administration time of approximately 15 min. Versions withidentical item content were administered to both child and parent. For parents, theword you was replaced with your child, for example, “How often do you/(doesyour child) check your/(his/her) blood sugar daily?” These administrations arehereafter referred to as the DSMP–C and DSMP–P, respectively, for clarity in datapresentation. Questions assess five areas of diabetes management over the past 3months: insulin administration and dose adjustment, blood glucose monitoring,exercise, diet, and management of hypoglycemia. Participants’ responses to eachinterview item are rated on a 0 to 4 scale, with higher numbers indicating better ad-herence. A total adherence score is calculated from the sum of all scores. Investiga-tors found good internal consistency (Cronbach’s α = .76) and interobserver agree-ment (94%) for the DSMP. The predictive validity (r = –.28, p < .01) indicates thatthe measure only accounts for 7.8% of the variance in metabolic control (HbA1c;Harris et al., 2000).

Metabolic control. Metabolic control is a biological measurement of healthstatus operationalized via the glycated hemoglobin A1c laboratory test(GHb/HbA1c). HbA1c provides an estimate of metabolic control over the previous2 to 3 months (ADA, 2003). For this study, blood samples were analyzed using aBayer DCA 2000+ (Bayer Healthcare, Tarrytown, NY).

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Data Analysis

Data analysis of DSMP–C and DSMP–P administrations were each conductedseparately. A confirmatory factor analysis was conducted using AMOS 4.0(Arbuckle & Worthke, 1999) to explore the hypothesized five-factor structure ofthe DSMP. In the event that the data did not adequately fit the hypothesized model,we planned to conduct exploratory factor analyses in SPSS, Version 12 (2001).The internal consistencies of the DSMP scores were evaluated using Cronbach’salpha coefficient (Cronbach, 1951). Hierarchical multiple regression was used toassess the predictive validity of the DSMP. Fisher’s r to z tests were used to exam-ine the magnitude of the correlations between parent–child reports of adherencebased on age and HbA1c differences. For purposes of comparison, participantswere divided into three groups, preadolescents (ages 7–11), younger adolescents(ages 12–15), and older adolescents (ages 16–18).

RESULTS

Confirmatory Models

The data for the DSMP–C and DSMP–P were each examined for outliers and vio-lations to the assumptions of multivariate normality. Univariate normality was as-sessed yielding acceptable measures of skewness and kurtosis (Kline, 1998). Thedata did not meet multivariate normality as assessed through AMOS procedures(parent, kurtosis = 138.50, critical ratio for skewness = 22.28; child, kurtosis =62.33, critical ratio for skewness = 10.07). Given that both confirmatory and ex-ploratory factor analysis are relatively robust to violations of multivariate normal-ity, we proceeded with a maximum likelihood solution (Floyd & Widaman, 1995).

Confirmatory factor analyses were conducted separately for the parent andchild ratings. The hypothesized five-factor child model resulted in a poor model fit,χ2(220, N = 119) = 348.10, p < .001, with additional fit indexes confirming this(Joreskog-Sorbom Goodness of Fit Index = .81, Bentler Comparative Fit Index =.71; Standardized Root Mean Squared Residual = .07). The hypothesized five-fac-tor model for the parent ratings was unable to meet statistical minimization re-quirements using structural equation modeling, thus indicating extreme poor fit tothe data.

Exploratory Models

Given the poor fit of the theorized five-factor model for the parent and child ratings,exploratory factoranalyseswereconducted to furtherexamine the factor structureofthe ratings. Separate analyses were conducted on the child and parent ratings usingprincipal axis factor analysis with varimax rotation. An examination of the initial so-

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lutions revealed nine factors with Eigenvalues greater than 1.0 for the child ratingsand eight factors with Eigenvalues greater than 1.0 for the parent ratings. Items thathad factor loadings less than .30 (four child ratings and seven parent ratings) anddemonstrated less than .1 difference between factor loadings (two child ratings, twoparent rating) were eliminated from the analysis. The remaining items were factoranalyzed, resulting in two-factor structures for both the child and parent ratings.

Results of the final exploratory factor analysis of the child ratings indicated thepresence of a 10-item Food and Insulin Schedule Adherence (FISA) factor(Eigenvalue = 4.10; 11.4% of the variance) and a 7-item Adherence to Blood SugarTesting and Adjustments (ABST) factor (Eigenvalue = 2.34; 10.5% of the variance),together accounting for 21.9% of the variance. Rotated factor loading and initialcommunality estimates for child adherence ratings are provided in Table 1. Thetwo-factor model of parent ratings accounted for 21% of the variance, with an 8-itemFISA factor (Eigenvalue = 3.91; 11.8% of the variance) and a 6-item ABST factor(Eigenvalue = 2.32; 9.2% of the variance). Table 2 indicates corresponding rotatedfactor loadings and initial communality estimates for parent adherence ratings.

Internal Consistency

For the child ratings, internal consistency was acceptable for the FISA factor (.70)and ABST factor (.74). For the parent ratings, internal consistency was acceptable

156 LEWIN ET AL.

TABLE 1Rotated Factor Loadings and Initial Communality Coefficients

for Child Ratings

Item Factor 1 Factor 2 Communality

Delays meals .71 .50Skips meals .73 .53Adjusts insulin when meals skipped .59 .42Eats more than on diet plan .33 .22Eats less than on diet plan .45 .27Delays shots .56 .45Remembers to take shots .35 .32Frequency of exercise .33 .24Adjustment to more exercise .56 .49Adjustment to less exercise .40 .47Sugar available .40 .26Identification .33 .22Adjusts insulin when eats more .50 .34Adjusts insulin when eats less .34 .28Blood testing frequency .54 .48Adherence to physician recommendation of blood testing .48 .39Insulin adherence .47 .29

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for the FISA factor (.70) and ABST factor (.68). Internal consistencies for all itemsincluded in the two-factor models for the child and parent measure were accept-able (α = .75 and .72, respectively).

Table 3 presents the correlations among the DSMP–C and DSMP–P factor rat-ings. Overall, correlations between the factor ratings were modest suggesting thatthe factors measure related, but conceptually distinct constructs.

Convergent Validity

Hierarchical regression analyses were conducted to examine the relations betweenthe DSMP factors (for parents and children administrations individually) andHbA1c. In Step 1, demographic variables (age, gender, ethnicity) were entered intothe model; in Step 2, the DSMP factors were entered. Results for the DSMP–C indi-cated that controlling for demographic variables, the factors significantly predicted28.8% of the variance in HbA1c. Similarly, the factors of DSMP–P predicted 34.3%of the variance in HbA1c after controlling for ethnicity, age, and gender. Data arepresented in Tables 4 and 5 for child and parent administrations, respectively.

Parent–Child Agreement

The relations between the parent and child administrations presented by child agegroup are found in Table 6. For older adolescents (age ≥ 16, n = 27) the parent–ado-lescent agreement was lower than the overall sample. To determine whether par-ent–child agreement differed as a function of the child’s age, Fisher’s r to z tests

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TABLE 2Rotated Factor Loadings and Initial Communality Coefficients

for Parent Ratings

Item Factor 1 Factor 2 Communality

Adjustment to more exercise .39 .15Adjustment to less exercise .57 .33Measurement of diet .49 .24Eats more .41 .26Adjusts insulin when eats more .53 .28Blood testing frequency .57 .36Adherence to physician recommendation of blood testing .53 .32Insulin adherence .57 .32Delays meals .36 .16Skips meals .46 .23Eats food not on diet plan .42 .21Delays shots .68 .48Takes less than prescribed insulin .38 .15Remembers to take shots .80 .65

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158

TABLE 3Pearson Product–Moment Correlations Among DSMP Scores and HbA1c

1 2 3 4 5 6 7

1. Child DSMP total 1.00 .76*** .79*** .56*** .44*** .43*** –.60***2. Child factor 1 1.00 .26** .46*** .49*** .22* –.45***3. Child factor 2 1.00 .42*** .23* .44*** –.50***4. Parent DSMP total 1.00 .77*** .72*** –.54***5. Parent factor 1 1.00 .24** –.49***6. Parent factor 2 1.00 –.41***7. HbA1c 1.00

Note. DSMP = Diabetes Self-Management Profile; HbA1c = Hemoglobin A1c; Factor 1 = Foodand Insulin Schedule Adherence factor; Factor 2 = Adherence to Blood Sugar Testing and Adjustmentsfactor.

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

TABLE 4Hierarchical Regression Analysis Predicting HbA1c From Child Ratings of

Adherence

Step Variables R2 ∆R2 F β

1 Demographics .089 3.76*Child age .154*Gender .060Ethnicity –.039

2 Adherence factors (DSMP–C) .377 .288 13.7***Food and Insulin Schedule Adherence factor –.342***Blood Sugar Testing and Adjustments factor –.367***

Note. All standardized regression coefficients are from the final block of the regression. HbA1c =Hemoglobin A1c; DSMP–C = Diabetes Self-Management Profile–Children.

*p < .05. ***p < .001.

TABLE 5Hierarchical Regression Analysis Predicting HbA1c From Parent Ratings

of Adherence

Step Variables R2 ∆R2 F β

1 Demographics .089 3.76*Child age .112*Gender .016Ethnicity .048

2 Adherence factors (DSMP–P) .343 .254 11.92***Food and Insulin Schedule Adherence factor –.375***Blood Sugar Testing and Adjustments factor –.300***

Note. All standardized regression coefficients are from the final block of the regression. HbA1c =Hemoglobin A1c; DSMP–P = Diabetic Self-Management Profile–Parent.

*p < .05. ***p < .001.

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were used to compare the magnitude of correlations. For instance, Fisher’s r to ztests showed a significant difference in the magnitude of the correlations of par-ent–child agreement between older adolescents and the overall sample for DSMPtotal adherence (z = 2.5, p < .001), the FISA factor (z = 1.4, p < .08), but not for theABTA factor (z = .2, p = .41). Similarly, among younger children (preadolescents,age ≥ 12, n = 27) the parent–adolescent agreement was also lower that the sampleoverall. Fisher’s r to z tests also showed a significant difference in the magnitude ofthe correlations between older adolescents and the overall sample for DSMP totaladherence (z = 1.4, p < .007), the FISA factor (z = 2.4, p < .001) but not for theABTA factor (z = .8, p = .20). However, for younger adolescents (12 < age < 16, n =67) the parent–adolescent agreement was equivalent to the overall sample. Withregard to the direction of the effect, across ages, child reports of adherence tendedto be slightly higher than parent reports.

Parent–child agreement among children with extremely poor metabolic control(HbA1c ≥ 11.0, n = 19) was significantly better than among children in good meta-

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TABLE 6Age Group Comparison: Pearson Product–Moment Correlations Among

Parent–Child DSMP Administrations

DSMP Factor PreadolescentsaYounger

AdolescentsbOlder

Adolescentsc

DSMP total score .30 .72*** .06Factor 1 (FISA) –.01 .58*** .22Factor 2 (ABTA) .28 .54*** .40*

Note. DSMP = Diabetes Self-Management Profile; FISA = Food and Insulin Schedule Adherencefactor; ABTA = Adherence to Blood Sugar Testing and Adjustment factor.

aAges 7 to 11 (n = 27). bAges 12 to 15 (n = 67). cAges 16 to 18 (n = 27).*p < .05. ***p < .001.

TABLE 7Metabolic Health Comparison: Pearson Product–Moment Correlations

Among Parent–Child DSMP Administrations

DSMP FactorPoor Metabolic

Control HbA1c > 11aGood Metabolic

Control HbA1c < 7.5b

DSMP total score .74** .39Factor 1 (FISA) .35 .26Factor 2 (ABTA) .69** .22

Note. DSMP = Diabetes Self-Management Profile; HbA1c = Hemoglobin A1c; FISA = Food andInsulin Schedule Adherence factor; ABTA = Adherence to Blood Sugar Testing and Adjustment factor.

an = 19. bn = 22.**p < .01.

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bolic control (HbA1c ≤ 7.5, n = 22). Fisher’s r to z tests showed a significant differ-ence in the magnitude of the correlations between adolescents in poor metaboliccontrol and those in good control for both DSMP total adherence (z = 1.6, p < .05)and the ABTA factor (z = 1.9, p < .03) but not for the FISA factor (z = –.2, p = .4).These relations are presented in Table 7. It is notable that even for children in ex-tremely poor metabolic control, reports of adherence (DSMP total) remainedhighly negatively correlated with HbA1c for both children (r = –.66, p < .001) andparents (r = –.53, p < .05).

DISCUSSION

The primary objective of this study was to examine the reliability and factor struc-ture of the DSMP for both child and parent administrations. Confirmatory factoranalysis of the five proposed content areas for adherence did not adequately fit thedata for child or parent versions of this measure. Given the inadequate fit indexesof both models, exploratory factor analyses were used to identify alternative, morerepresentative depictions of the DSMP–C/P factor structures. For the child ratings,a two-factor solution was identified. One factor consisted of items pertaining todaily schedules related to nutrition (e.g., “eats more than on diet plan” or “skipsmeals”) and insulin injections (e.g., “delays shots” or “remembers to take shots”).This factor was labeled FISA. The other factor included items related to blood glu-cose monitoring, exercise, and performing insulin adjustments (e.g., “blood-test-ing frequency” or “adjustment when eats more”). This factor was labeled ABTA. Atwo-factor solution was also identified for the parent version of the DSMP. Contentof these factors was similar to the child version, also producing both a FISA and anABTA factor.

Our findings suggest that both parent and child ratings of adherence capturedwithin the DSMP may be more concisely and efficiently assessed according to thistwo-factor structure rather than the scoring framework originally proposed. It isalso notable that this framework, although similar, is not identical for parent andchild administrations. The DSMP–C/P factors both demonstrated acceptable inter-nal consistency. Further, the moderate correlations among factors for each scalesuggest that, although related, each factor assesses relatively distinct constructs.

The pattern of correlations among the DSMP–C/P scores with HbA1c is note-worthy. First, FISA and ABTA factor scores for both parent and child versionswere inversely correlated with HbA1c with relations of large to medium effectsizes (Cohen, 1977). In other words, both parents and children who reportedgreater regimen adherence had better diabetes metabolic control, as measured bya standardized laboratory assay. In addition, the regression analysis indicatedthat each factor significantly predicts unique variance in HbA1c. However,DSMP–C and DSMP–P total scores also correlated with metabolic control with

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relations of a large effect size. This suggests that, although the two-factor ver-sion of the DSMP–C and DSMP–P may be more efficient to administer (17- and14-item versions vs. the entire 23-item interview), the total DSMP score consist-ing of items included in the two-factor model is both clinically and empiricallyuseful as a strong predictor of metabolic control in children. The abbreviatedtwo-factor version may be more useful for research protocols or duringtime-limited assessments.

The data supported our hypothesis that the agreement between parent and childreports of adherence varies as a function of the child’s age. Younger children weremore discrepant from their parents in reports of adherence. This finding is consis-tent with previous findings (Freund et al., 1991; Johnson et al., 1986). It is also no-table that the lowest parent–child correlation is on the DSMP FISA. This is consis-tent with Johnson’s (1993) hypothesis that younger children have more difficultiesreliably reporting time and schedule based adherence behaviors. It is also likelythat (a) younger children have more difficulties remembering and reporting spe-cific adherence behaviors and (b) parents maintain more responsibility for routinediabetes care. Similarly, our data show a larger disparity between parent and childreports of adherence among the older adolescents. This finding is, as expected, be-cause of increased independence at this age. Adolescents older than age 16 aremore likely to be out of their parents’ proximity and have increased responsibilityfor their own diabetes management.

However, our hypothesis that parent–child agreement on reports of adherenceamong children in extremely poor metabolic control would be significantly lowerthan that among children in good control was not supported. Parent–child agree-ment among children in poor metabolic control was significantly stronger. To ourknowledge, no previous study has reported parent–child agreement (on report ofdiabetes adherence) as a function of the child’s metabolic control. Nevertheless, itis reasonable to hypothesize that health care professionals strongly inform patientswith poor metabolic control (and their parents) about the importance of adherenceand the risks associated with poor metabolic control (e.g., retinopathy,nephropathy, and heart disease). Likewise, patients in poor control are typicallyscheduled for more frequent visits, allowing for these messages to be reiteratedfrequently. These reminders of poor adherence may relate to our finding of strongparent–child agreement (on reports of adherence) among patients in poor meta-bolic control. This suggests that poor metabolic control is due less likely to par-ents’ awareness of their children’s level of adherence and more likely to to otherfactors such as the family’s inability to promote, or maintain, adherence to the pre-scribed diabetes regimen.

Overall, however, the relatively low correlation between parent and child rat-ings of adherence among the total sample is also of interest. Despite strong predic-tive validity and essentially identical item content, relations between parent andchild factors were less than in previous parent–child comparisons of similar adher-

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ence behaviors (Freund et al., 1991; Johnson et al., 1986; Spevack et al., 1991).One explanation for this discrepancy is the methodology of assay. For example,Freund and colleagues (1991) conducted nine adherence ratings over a 3-monthperiod, as compared to a single administration of the DSMP in this study. Never-theless, ratings of adherence using the DSMP produced stronger correlations withHbA1c despite the less complex administration and lower parent–child agreement.

Several limitations of this study deserve comment. First, the DSMP items maynot generalize adequately to more recently developed types of insulin. For exam-ple, certain questions related to timing/delays of meals are likely less important forpatients using newer insulin regimens (e.g., glargine) or the insulin pump. Second,although predictive validity was demonstrated, conclusions regarding the con-struct validity are limited in this investigation as no other measures of adherencewere provided. With regard to the exploratory factor analysis, a noteworthy limita-tion is that some of the factor loading are of relatively low order and together, thetwo factors account for only 21.9% and 21% of the variance for the child and par-ent DSMP administrations, respectively. Finally, there is the possibility that childrespondents attempted to appear favorably during the DSMP administration. How-ever, Harris et al. (2000) found no differences in child responses on the DSMPwhen children were interviewed with and without their caregivers.

IMPLICATIONS FOR PRACTICE

An important implication of this study includes the demonstration of reliabilityand validity of the DSMP in a population characterized as low SES. Given thatclinical research populations of children with type 1 diabetes are often of lowerSES backgrounds, the extension of the DSMP psychometric properties to familiesfrom varying economic backgrounds is noteworthy. In addition, our results suggestthat the DSMP child and parent versions may be better conceptualized as two-fac-tor models, in addition to the rationally derived five scale scores. Again, this factorstructure represents a shorter, more focused, and time efficient manner for healthcare providers to assess adherence with the diabetes regimen for patients with type1 diabetes. It is worth reiterating that these findings do not impinge on the validityof the overall DSMP score containing all items. Given the revised factor structureand item content, it will be important to assess the factorial stability, temporal reli-ability, and interrater reliability of the DSMP factors in future analyses. Other fu-ture methodological improvements based on this research include the developmentof age-based normative data based for reports of adherence. In addition, given re-cent advances in diabetes treatment (e.g., pump therapy), it will be important formeasures to be developed that examine diabetes related adherence specific to theseforms of treatment. Also, research evaluating the psychometric properties of awritten, patient-report version of the DSMP may allow for more efficient adminis-

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trations. Finally, this research suggests that parents’ and children’s agreement onreports of regimen adherence varies with age. This information has implicationsfor clinicians when identifying which informants (parents, children, or both)should be involved in assessments of diabetes adherence.

ACKNOWLEDGMENT

We thank Kenneth M. Gelfand for his assistance with this project.

REFERENCES

American Diabetes Association. (2003). Standards of medical care for patients with diabetes mellitus.Diabetes Care, 26, S33–S50.

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

Arbuckle, J. K., & Wothke, W. W. (1999). Amos 4.0 user’s guide. Chicago: Small Water Corp.Cohen, J. (1977). Statistical power analysis for the behavioral sciences. New York: Academic.Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16,

297–334.Floyd, F. J, & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical as-

sessment instruments. Psychological Assessment, 7, 251– 256.Freund, A., Johnson, S. B., Silverstein, J., & Thomas, J. (1991). Assessing daily management of child-

hood diabetes using 24-hr recall interviews: Reliability and stability. Health Psychology, 10,200–208.

Glasgow, R. E., McCaul, K. D., & Schafer, L. C. (1987). Self-care behaviors and glycemic control intype 1 diabetes. Journal of Chronic Diseases, 40, 399–412.

Hanson, C. L., De Guire, M. J., Schinkel, A. M., Kolterman, O. G., Goodman, J. P., & Buckingham, B.A. (1996). Self-care behaviors in insulin-dependent diabetes: Evaluative tools and their associationswith glycemic control. Journal of Pediatric Psychology, 21, 467–482.

Hanson, C. L., Henggeler, S. W., & Burghen, G. A. (1987). Social competence and parental support asmediators of the link between stress and metabolic control in adolescents with insulin-dependent dia-betes mellitus. Journal of Consulting and Clinical Psychology, 55, 529–533.

Harris, M. A., Wysocki, T., Sadler, M., Wilkinson, K., Harvey, L. M., Buckloh, L. M., et al. (2000). Val-idation of a structured interview for the assessment of diabetes self-management. Diabetes Care, 23,1301–1304.

Haynes, R. B. (1979). Introduction. In R. B. Haynes, D. W. Taylor, & D. L. Sackett (Eds.), Compliancein health care (pp. 1–10). Baltimore: Johns Hopkins University Press.

Hollingshead, A. (1975). Four factor index of social status. New Haven, CT: Yale University Press.Johnson, S. B. (1993). Chronic diseases of childhood: Assessing compliance with complex medical

regimens. In N. A. Krasnegor, L. Epstein, S. B. Johnson, & S. J. Yaffer (Eds.), Developmental as-pects of health compliance behavior (pp. 77– 89). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Johnson, S. B., Kelly, M., Henretta, J. C., Cunningham, W. R., Tomer, A., & Silverstein, J. H. (1992). Alongitudinal analysis of adherence and health status in childhood diabetes. Journal of Pediatric Psy-chology, 17, 537–553.

ADHERENCE DSMP 163

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tuck

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rsity

] at

13:

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9 O

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014

Johnson, S. B., Silverstein, J., Rosenbloom, A., Carter, R., & Cunningham, W. (1986). Assessing dailymanagement in childhood diabetes. Health Psychology, 9, 493–501.

Kavanagh, D. J., Gooley, S., & Wilson, P. H. (1993). Prediction of adherence and control in diabetes.Journal of Behavioral Medicine, 16, 509–522.

Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford.La Greca, A. M. (1990). Issues in adherence with pediatric regimens. Journal of Pediatric Psychology,

15, 423–436.Lewin, A. B., Heidgerken, A. D., Geffken, G. R., Williams, L. B., Storch, E. A., Gelfand, K. M., et al.

(2006). The relation between family factors and metabolic control: The rold of diabetes adherence.Journal of Pediatric Psychology, 31, 1–10.

McNabb, W. L. (1997). Adherence in diabetes: Can we define it and can we measure it? Diabetes Care,20, 215–218.

Skinner, B. F. (1938). The behavior of organisms. Acton, MA: Copley.Spevack, M., Johnson, S. B., & Riley, W. (1991). The effect of diabetes camp on adherence behaviors

and glycemic control. In J. H. Johnson & S. B. Johnson (Eds.), Advances in child health psychology(pp. 285–292). Gainesville: University of Florida Press.

SPSS, Inc. (2001). SPSS for Windows, Version 11.0.1 [Computer program]. Chicago: Author.

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