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J Pediatr Psychol. Jan 2013; 38(1): 18–29.
Published online Sep 29, 2012. doi:  10.1093/jpepsy/jss100
PMCID: PMC3695638
Changes in Executive Functioning and Self-Management in Adolescents With Type 1 Diabetes: A Growth Curve Analysis
Megan M. Miller, BA,corresponding author1 Jennifer M. Rohan, MA,2 Alan Delamater, PhD,3 Jennifer Shroff-Pendley, PhD,4 Lawrence M. Dolan, MD,5 Grafton Reeves, MD,6 and Dennis Drotar, PhD7
1Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, 2Department of Psychology, University of Cincinnati, 3Department of Pediatrics, University of Miami, 4Division of Behavioral Health, Alfred I. duPont Hospital for Children, 5Division of Endocrinology, Cincinnati Children's Hospital Medical Center, 6Division of Pediatric Endocrinology, Alfred I. duPont Hospital for Children, and 7Department of Psychology, University of Cincinnati
corresponding authorCorresponding author.
All correspondence concerning this article should be addressed to Megan M. Miller, BA, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave MLC 7039, Cincinnati, OH 45229, USA. E-mail: megan.miller_2/at/cchmc.org
Received February 29, 2012; Revised July 6, 2012; Accepted July 30, 2012.
Objective To investigate the relation of changes in executive functioning to changes in diabetes self-management in a 2-year prospective study of a sample of youth aged 9–11 years at baseline (n = 239) with type 1 diabetes and their maternal caregivers. Research Design and Methods Youth and maternal caregivers completed the Diabetes Self-Management Profile (DSMP) at baseline, 12 months, and 24 months. Maternal caregivers completed the Behavioral Rating Inventory of Executive Functioning (BRIEF) at the same time points to assess global executive functioning, and the domains of behavioral regulation and metacognition. Results Youth reported self-management decreased over time (p < .01) while behavioral regulation (e.g., the child’s ability to shift cognitive set and moderate emotions and behaviors via emotional control) increased (p < .05). Changes in behavioral regulation significantly predicted rate of change in youth-reported self-management (p < .01). Global executive functioning and metacognition (e.g., the child’s ability to monitor, initiate, plan, organize, and sustain future-oriented problem solving and working memory) did not change over time and did not predict changes in self-management. Moreover, executive functioning and self-management did not predict changes in HbA1c. Conclusions Positive changes in behavioral regulation may enhance self-management of type 1 diabetes during the transition to adolescence.
Keywords: behavioral regulation, executive functioning, metacognition, self-management, type 1 diabetes
Non-adherence to diabetes treatment regimens and less than optimal glycemic control, which have been consistently reported for pediatric patients with type 1 diabetes, puts children and adolescents at potential risk for future health complications (Delamater, 2006; Helgeson, Honcharuk, Becker, Escobar, & Siminerio, 2011; Hood, Peterson, Rohan, & Drotar, 2009). The increased prevalence of non-adherence and problematic self-management are of special concern in adolescents with type 1 diabetes because adolescents assume increasing responsibility for the management of their condition at a time of decreasing parental monitoring that occurs during this developmental transition (Anderson, Ho, Brackett, Finkelstein, & Laffel, 1997). Problematic self-management for type 1 diabetes and deterioration in glycemic control during adolescence have led to a research focus on the identification of individual differences in risk and protective factors that affect self-management. One such factor is executive functioning. Executive functioning is a multidimensional construct that encompasses the abilities to initiate, plan, organize, and sustain future-oriented problem solving in working memory, as well as the ability to shift cognitive set and modulate emotions and behavior via appropriate inhibitory control (Gioia, Isquith, Guy, & Kenworthy, 2000). Executive functioning includes two primary domains that measure related, but different, factors. These are behavioral regulation, defined as abilities to inhibit, shift, and sustain emotional control, and metacognition, defined as abilities to initiate, plan, organize, monitor, and working memory (Gioia et al., 2000).
The potential clinical relevance of executive functioning to facilitate adolescents’ management of type 1 diabetes stems from the multifaceted demands of diabetes treatment, which requires a range of competencies in executive functioning such as planning, problem solving, monitoring, organizing, as well as ongoing regulation of behavior and emotions to achieve effective insulin management, dietary intake, and exercise (Wing, Epstein, Nowalk, & Lamparski, 1986). Executive functioning is potentially relevant for diabetes management at various ages but may be particularly crucial during early adolescence when increased self-management for diabetes is required (Bagner, Williams, Geffken, Silverstein, & Storch, 2007). Young adolescents demonstrate potential vulnerabilities in cognitive capacities that could interfere with their management of the complex tasks involved in diabetes management (Wysocki, 2000). For example, recent research has described the neurobiological immaturity of younger adolescents in areas that are related to executive functioning, such as the ability to regulate strong emotion (Dahl, 2004), and increased reward seeking in the presence of peers (Steinberg, 2007), which could both interfere with diabetes management. For example, adolescents with problematic behavioral regulation might be more likely to get upset if their blood sugar levels are not within the targeted range and may cause them to avoid monitoring their blood sugar levels in the future. Children with less adequate behavioral regulation might also have difficulty inhibiting certain behaviors (e.g., activities with peers) that would interfere with their adherence to treatment. Such data underscore the need for studies of how changes in executive functioning among youth with type 1 diabetes that transition to adolescence relate to changes in their diabetes self-management.
Several studies have found a relation between executive functioning, as measured by the Behavioral Rating Inventory of Executive Functioning (BRIEF) (Gioia et al., 2000) treatment adherence, and/or self-management in children and adolescents with type 1 diabetes. Bagner et al. (2007) described a significant relation between overall executive functioning and adherence in a sample of youth (aged 8–19 years) with type 1 diabetes. Graziano et al. (2011) noted a significant relation among specific measures of executive functioning (e.g., emotional regulation), adherence, and glycemic control for male adolescents (aged 12–18 years). Finally, McNally, Rohan, Pendley, Delamater, & Drotar (2010) found that a higher level of executive functioning related to diabetes self-management also mediated the effects on glycemic control.
However, all these studies had limitations. The most important limitation was that the cross-sectional designs precluded description of changes in executive functioning over time and limited inferences that could be drawn concerning the impact of changes in executive functioning on diabetes management. Bagner et al. (2007) included a wide age range (8–19 years) and did not control for age-related differences in executive functioning. Moreover, children on insulin pump therapy were excluded, and glycemic control was not assessed. McNally et al.'s (2010) study of 9–11-year-olds used only the composite score of the BRIEF. The two subscales of the composite executive functioning score, behavioral regulation and metacognition, which assess different domains of executive functioning, were not analyzed in addition to the global executive functioning score.
To our knowledge, no study has assessed changes in executive functioning including behavioral regulation and metacognition and the relation to changes in diabetes self-management in a relatively homogeneous (by age) sample of youth studied at the onset of adolescence. Early adolescence was chosen as a focus of the study because it is an important time in the development of psychological autonomy and changes in parent–adolescent communication concerning decision making, and illness management (Steinberg & Silverberg, 1986). Research has indicated that psychological autonomy and diabetes-related autonomy undergo a rapid increase in early adolescence after a period of relative stability during the school-age period (Steinberg & Silverberg, 1986; Wysocki et al., 1996a,b). Young adolescents are also in the process of learning lifelong strategies of health behaviors, including diabetes self-management, which make this an opportune time for preventive intervention (Williams, Holmbeck, & Greenley, 2002).
To address these needs, the goals of our study were to (1) describe changes in executive functioning during two years in youth with type 1 diabetes, and (2) document the relation of change in executive functioning to changes in self-management and glycemic control. We studied overall executive functioning, as well as the two separate domains of executive functioning: (1) behavioral regulation (ability to shift cognitive set and moderate emotions and behaviors via emotional control), and (2) metacognition (ability to monitor, initiate, plan, and organize future-oriented problem solving and working memory). We were interested in determining whether these two factors of the multidimensional construct of executive functioning would have comparable relations with self-management and glycemic control.
Our primary hypotheses were that changes in overall executive functioning would predict changes in self-management. Moreover, we expected that the Behavioral Regulation Index and Metacognition Index of the BRIEF would each predict changes in self-management and glycemic control in adolescents with type 1 diabetes. More precisely, we expected that positive changes in behavioral regulation and metacognition would predict positive changes in self-management, which in turn would relate to better glycemic control.
Participants and Procedure
The initial participants studied at baseline were 239 children and young adolescents with type 1 diabetes and their caregivers who were followed at pediatric diabetes clinics at three university-affiliated medical centers in Cincinnati, Ohio; Wilmington, Delaware; and Miami, Florida. Each site’s Institutional Review Boards approved the study. Data were gathered as part of an ongoing longitudinal study that investigated psychological processes of diabetes self-management during early adolescence. Baseline executive functioning, self-management, and HbA1c results have been previously reported from this study (McNally et al., 2010). This current report is the first and only from this data set to focus on the prediction of changes in self-management from changes in executive functioning in the 2-year period post baseline.
Caregivers and children were recruited during a regularly scheduled outpatient clinic visit. Inclusion criteria included diagnosis of type 1 diabetes for at least a year, age 9–11 years, absence of potential secondary causes of type 1 diabetes diagnosis (e.g., glucocorticoid treatment, cystic fibrosis), English speaking, and having no known plans to move out of the area in the next three years. Exclusion criteria included current involvement in foster care, presence of severe psychiatric disorders that were evident from a patient’s history and would preclude participation in the study (e.g., psychosis) or comorbid chronic conditions (e.g., renal disease) that require burdensome ongoing treatment regimens, or diagnosis of mental retardation.
Eligible participants were identified and contacted by clinic personnel to ask about their interest in the study and then were approached by research staff who explained the study procedures. Of the 361 who were approached, 240 (66.5%) consented and participated. Reasons for not participating included the following: being too busy (n = 54), having no transportation (n = 3), and other (n = 64). Signed informed consent was obtained from a parent or legal guardian, written assent was obtained from 11-year-old children, and verbal assent was obtained from children aged < 11 years, according to the guidelines established by the local institutional review boards. After enrollment, one child was diagnosed with Mature Onset Diabetes of the Young (MODY) (Hattersley, Bruining, Shield, Njolstad, & Donaghue, 2006) and no longer treated with insulin, which required removal from the study and analysis.
Overall attrition from baseline to 2 years was 3.3% (n = 8). Reasons for attrition included the following: Child and/or family no longer interested in participating in research study (n = 2), family moving out of the area (n = 1), patient changed endocrinologists and the doctor was not affiliated with the hospital (n = 1), family felt too overwhelmed to participate in research study (n = 1), and family would not schedule research visit and were dropped by study coordinator (n = 3). Missing data owing to non-completion of visits included 13 participants at 1 year and 14 participants at 2 years. There were no significant differences (all p < .05) between those who participated in the 1- and 2-year follow-ups and those who did not complete the 1- and/or 2-year study visit with respect to baseline disease duration, age, race, income, household composition (one- vs. two-parent), child’s gender, insulin delivery method at baseline, 12 months, and 24 months, or HbA1c obtained at baseline, 6 months, and 18 months.
Demographic and medical characteristics of our sample at baseline through 2-year post baseline are shown in Table I. At 2 years post baseline, the sample had a mean of 12.62 years with a comparable percentage of females (53.8%) and males (46.2%). Annual household income ranged from $49 000 to $72 999 at baseline. The majority of participants were non-Hispanic Caucasian youth (75.6%), but there was a higher percentage of Hispanic Caucasians (13.3%) than typical type 1 diabetes studies. Recent studies of adolescents with type 1 diabetes (e.g., Helgeson et al., 2011; Ingerski, Anderson, Dolan, & Hood, 2010) had 0.1% Hispanic youth. The majority of the sample (69.2%) received insulin via subcutaneous insulin infusion (i.e., insulin pump or pod), which is a higher percentage than a number of recent studies (Helgeson et al., 2011; Ingerski et al., 2010). Pubertal status was assessed yearly based on a physical examination conducted by physicians or nurse practitioners (Marshall & Tanner, 1969, 1970). Tanner stage was rated on a scale of 1 to 5, where 1 indicated prepubertal and 5 indicated full pubertal status. At the 2-year follow-up, males averaged a tanner stage of 2.72 (1.18) and females 3.38 (1.02).
Table I.
Table I.
Demographic Characteristics From Baseline to 24 Months
Measures
Behavior Rating Inventory of Executive Functioning
Participants’ executive functioning was measured using the Behavior Rating Inventory of Executive Functioning (BRIEF), an 86-item parent report measure that assesses children’s executive functioning (Gioia et al., 2000). This measure provides an observation and measure of children’s day-to-day executive functioning that is less time-consuming than neuropsychological testing. The BRIEF includes 3-point Likert scale items that indicated the extent to which the child’s behavior never occurred, sometimes occurred, or occurred often. Higher scores mean less adequate executive functioning. The composite raw score for the Global Executive Composite (GEC) includes the Behavioral Regulation Index (e.g., the child’s ability to shift cognitive set and moderate emotions and behaviors via emotional control) and Metacognition Index (e.g., the child’s ability to monitor, initiate, plan, organize, and sustain future-oriented problem solving and working memory). The composite score was separated into the two domains for the analyses, as these two domains can be assessed in a separate, but clinically meaningful, way (Gioia, Isquith, Retzlaff, & Espy, 2002). A T score of ≥65 on any of the scales of the BRIEF is considered an elevated score (e.g., clinically significant). See Table II for descriptive statistics.
Table II.
Table II.
Descriptive Statistics for Primary Measures
Reliability of the BRIEF has been established (α = 0.80–0.98) for both clinical and normative samples (parent and teacher forms), and validity has been documented with other measures of behavioral and attentional functioning (Gioia et al., 2000). Standardized scores were used in the analyses. In our sample, the maternal reported (n = 236) Behavioral Regulation Index internal consistency was assessed using Cronbach’s α and was 0.94 at baseline, 0.95 at 12 months post baseline, and 0.94 at 24 months post baseline. Internal consistency for the Metacognition Index (maternal report, n = 236), based on Cronbach’s α, was 0.96 at baseline, 0.96 at 12 months post baseline, and 0.96 at 24 months post baseline.
Diabetes Self-Management Profile
The Diabetes Self-Management Profile (DSMP) is a 25-item structured interview, which was administered to the youth and primary caregiver to assess diabetes-related adherence behaviors during the prior three months (Harris et al., 2000). Questions were asked in an open-ended manner (i.e., “In the past three months, how often have you tested your blood sugar?”) and addressed the following domains: exercise, blood glucose monitoring, insulin administration, diet, and hypoglycemia management. The DSMP comprises both 3- to 5-point Likert scale items (e.g., 4 = Never or hardly ever, 3 = Seldom, 2 = Occasionally, 1 = Almost daily) and dichotomous items (yes, no) that are coded based on how the child or primary caregiver responded to the open-ended questions. If the child or the primary caregiver gives an answer that is in-between Likert scale items, the research assistant continues to probe the question to narrow down the answer to one of the Likert scale items. The overall self-management score was calculated by summing all answers, with higher scores denoting better self-management. The range of possible total scores is 0 to 88.
The DSMP total score has demonstrated good internal consistency (r = .76), moderate cross-informant validity for both parent and child report (r = .26), and strong inter-rater agreement (r = .94) (Harris et al., 2000). This measure has also demonstrated good predictive validity between parent- and child-reported adherence and self-management behaviors and glycemic control (Harris et al., 2000). In the present sample, internal consistency, analyzed using Cronbach’s α, was 0.60 at baseline, 0.64 at 12 months past baseline, and 0.67 at 24 months past baseline for the child version. Internal consistency was 0.64 at baseline, 0.65 at 12 months past baseline, and 0.61 at 24 months past baseline for the parent version.
Children were interviewed separately by trained research staff. All research assistants (RAs) had at least a bachelor’s degree. Several had a Master’s or a PhD degree. Training included a general meeting of investigators and site-specific training that included reading the procedure manual for the DSMP, practicing with another RA, observing an experienced RA conduct DSMPs, then being observed while conducting DSMPs, and then conducting them independently. Additional quality control procedures involved review of data forms at the central site and troubleshooting problems in administration by the study coordinator at the central site and RAs at different sites.
Glycemic Control
Glycated hemoglobin (HbA1c) provided an estimate of glycemic control during the previous 2–3 months. Blood samples were obtained every six months by finger stick during clinic visits and study visits. Samples from each site were shipped to a central laboratory for standardization purposes. Blood samples were analyzed using the TOSOH-G7 method (reference range 4.0–6.0%).
Parent Report on Child Autonomy
The Parent Report on Child Autonomy is a 28- or 38-item form (version administered depends on insulin regimen) that caregivers of children with type 1 diabetes rate on a 3-point Likert scale, the degree to which a particular diabetes task (i.e., blood sugar testing, injections, pump site changing) is a parent responsibility, shared responsibility, or a child responsibility (Anderson, Auslander, Jung, Miller, & Santiago, 1990; Weissberg-Benchell, Goodman, Lomaglio, & Zebracki, 2007). The range of possible total scores is 28 to 114. Higher scores indicate a higher degree of child autonomy in their diabetes management. Acceptable test–retest reliability and internal consistency have been reported in numerous studies that have used this instrument. In the present sample, internal consistency, analyzed using Cronbach’s α, was 0.95 at baseline, 0.91 at 12 months past baseline, and 0.96 at 24 months past baseline for the injection version. For the pump version, internal consistency was 0.89 at baseline, 0.90 at 12 months past baseline, and 0.89 at 24 months past baseline. Caregivers filled out the appropriate version for their child (pump vs. injection) at the baseline, 12-month, and 24-month time points.
Data Analytic Strategy
Unconditional and conditional growth curve models examined changes in the independent variables including global executive functioning, behavioral regulation, and metacognition, and primary dependent variables including diabetes self-management and HbA1c from baseline to 24 months. Individual growth curve models (or level 1) measured change over time, using a minimum of three data points, for each individual in the sample the entire sample, and summarized growth for the sample and for each individual using two terms: fitted intercept and fitted slope (Singer & Willett, 2003). In the conditional growth curve model (or level 2), static and/or dynamic predictors were included to determine whether change in the primary outcome of self-management could be predicted by one or more variables (e.g., meta-cognition and/or behavioral regulation) (Singer & Willett, 2003).
Time-varying covariates (e.g., age, duration, tanner stage, insulin method, and maternal caregiver education) were examined at the unconditional model level to determine whether these variables moderated changes in diabetes self-management and/or behavioral regulation from baseline to 24 months, and thus should be included in the final conditional model investigating how changes in behavioral regulation influence changes in diabetes self-management. It is not necessary to eliminate subjects with missing data from the analysis because growth curve analysis accounts for missing data in that the analysis makes use of whatever data are available for each individual subject (Singer & Willett, 2003).
Unconditional and conditional growth curve modeling was performed using SAS Proc Mixed (SAS Institute, 1990). Restricted maximum likelihood estimations were used to avoid biased estimates of the variance components. Unstructured covariance matrices were used to allow variances and covariances to vary across time points rather than to conform to a priori constraints (Singer & Willett, 2003).
The results of the unconditional growth curve models illustrating changes over time from baseline to 24 months for youth- and parent-reported self-management, global executive functioning, behavioral regulation, metacognition, and HbA1c are presented in Table III.
Table III.
Table III.
Growth Curve Models
Global Executive Functioning
Adolescents’ global executive functioning scores did not change from baseline to 2 years (p = .79).
Behavioral Regulation
Adolescents’ behavioral regulation scores significantly decreased (i.e., behavioral regulation improved) at a rate of 0.06 units per year from a mean score of 52.04 at baseline to 50.89 at 2 years (F (1,434) = 4.98, p < .05). To determine whether any of the proposed covariates should be included in the conditional model, each covariate was separately examined at the unconditional level. It was determined that age, duration, tanner stage, and insulin regimen did not relate to changes in behavioral regulation from baseline to 24 months (all were p > .05).
Metacognition
Adolescents’ metacognition scores did not change from baseline to 2 years (p = .33).
Youth-Reported Self-Management
Youth-reported self-management was 60.95 at baseline and significantly decreased at a rate of 0.07 units per year from baseline to 2 years (F (1,232) = 7.90, p ≤ .01). As with behavioral regulation, each covariate was separately examined at the unconditional level. It was determined that age, duration, tanner stage, and insulin regimen did not moderate changes in youth-reported self-management over time (all were p > .05).
Parent-Reported Self-Management
Parent-reported self-management was 64.92 at baseline and significantly decreased at a rate of 2.04 units per year from baseline to 2 years (F (1,225) = 53.62, p < .01).
Glycemic Control (HbA1c)
The mean HbA1c was 8.19 at baseline and significantly increased at a rate of 0.24 units per year from baseline to 8.55 at 2 years (F (1,228) = 17.21, p ≤ .0001).
Predicting Youth-Reported Self-Management From Maternal-Rated Executive Functioning
Global Executive Functioning
Results of the conditional growth curve model indicated that youth-reported self-management did significantly change over time (t (356) = −1.99, p < .05, d = −0.21). Contrary to prediction, global executive functioning did not predict the rate of change in youth-reported self-management (p = .20).
Behavioral Regulation
Results of the conditional growth curve model indicated that youth-reported self-management changed significantly over time, t (368) = −2.77, p < .01, d = −0.29. Moreover, consistent with our hypotheses, the rate of changes in maternal-rated behavioral regulation significantly predicted the rate of change in youth-reported self-management, t (382) = 2.06, p < .05, d = 0.21. Cross-sectionally, behavioral regulation and self-management were not significantly correlated at baseline (p > .05), or 24 months (p > .05), but they were significantly correlated at 12-month follow-up (p < .05). To determine how behavioral regulation related to self-management behaviors, we tested differences in youth-reported self-management behaviors between participants with better (i.e., T score <50) and worse (i.e., T score >50) behavioral regulation (based on median split), which are shown in Figure 1. As shown, those with better behavioral regulation had better self-management skills from baseline to 24 months compared with those with worse behavioral regulation.
Figure 1.
Figure 1.
Changes in diabetes self-management as a function of changes in behavioral regulation from baseline to 24 months.
Metacognition
Results of the conditional growth curve model indicated that youth-reported self-management did not change over time (p = .17) contrary to prediction. Metacognition did not predict the rate of change in youth-reported self-management (p = .51).
Predicting Parent-Reported Self-Management From Maternal-Rated Executive Functioning
Global Executive Functioning
Results of the conditional growth curve model indicated that parent-reported self-management did significantly change over time (t (340) = −2.00, p < .05, d = −0.22). However, contrary to prediction, global executive functioning did not predict the rate of change in parent-reported self-management (p = .48).
Behavioral Regulation
Results of the conditional growth curve model indicated that parent-reported self-management did change significantly over time (t (354) = −2.33, p < .05). Contrary to prediction, behavioral regulation did not predict the rate of change in parent-reported self-management (p = .34).
Metacognition
Results of the conditional growth curve model indicated that parent-reported self-management did not change over time (p = .08). Contrary to prediction, metacognition did not predict the rate of change in parent-reported self-management (p = .62).
Predicting Glycemic Control From Maternal-Rated Executive Functioning
As noted earlier, results of the unconditional growth curve model indicated that glycemic control (HbA1c) decreased over time. However, contrary to prediction, the rate of changes in behavioral regulation (p = .95) or metacognition did not predict the rate of change in HbA1c (p = .92).
Parental Involvement and Executive Functioning
Several post hoc analyses were conducted to clarify the meaning of findings obtained from hypotheses tests. It was possible that changes in metacognition did not relate to self-management because parents compensated for their youth’s weakness in metacognition by becoming more involved in their diabetes management. To evaluate this possibility, χ2 tests were conducted to determine whether there was a relationship between high vs. low parental involvement in diabetes management and high vs. low metacognition.
Results of the χ2 tests indicated that whether a child has high or low parental involvement did not differ according to what metacognition group the child was in at baseline (p = .39).
Predicting Executive Functioning From Self-Management
To determine whether diabetes self-management predicted behavioral regulation, we conducted a reverse growth curve analyses. Results of the conditional growth curve model indicated that behavioral regulation did not change significantly over time (p = .73). Youth-reported self-management did not predict the rate of change in behavioral regulation (p = .91).
To our knowledge, this is the first study to investigate changes in executive functioning and the relation to changes in self-management over time in a sample of youth who were in the process of transitioning to adolescence. The descriptive findings concerning executive functioning, which were unexpected, are noteworthy. For example, changes in the Behavioral Regulation Index of executive functioning, which includes items concerning shifting of cognitive sets, emotional regulation, and problem solving, suggesting that behavioral regulation improved during the 2-year follow-up. On the other hand, the Metacognition Index, which includes the abilities to initiate, plan, organize, and monitor, as well as working memory, did not demonstrate any positive change during the same period.
Taken together, our findings suggest that these differential rates of growth in executive functioning domains may have influenced changes in self-management for this sample of adolescents. As predicted, changes in behavioral regulation predicted changes in the overall level of diabetes self-management. Those with better behavioral regulation had better self-management overall in comparison with those with less adequate behavioral regulation. These findings are consistent with the direction of previous research concerning executive functioning in adolescents with type 1 diabetes (Bagner et al., 2007; Granziano et al., 2011; McNally et al., 2010) but extends these findings by predicting change in self-management for two years in a sample of young adolescents. These findings suggest that key components of behavioral regulation such as moderation of emotions and behaviors in response to change may influence the quality of adolescents’ management of their diabetes. It is also possible that negative changes in behavioral regulation may interfere with progression toward increased competence in diabetes management during children’s transition to adolescence. However, it is important to note that these scores in behavioral regulation were within the normative range (i.e., t < 65) for the measure.
Contrary to our prediction, the metacognition domain of executive functioning (e.g., planning and organizing) did not change over time nor did it predict diabetes self-management or glycemic control. It is possible that the absence of change in metacognition across the transition to adolescence limited the predictive power of this finding. One potential explanation for this finding relates to the limitations of measuring executive functioning based on parent report. Behaviors reflecting metacognition (e.g., planning, monitoring) may be less observable by parents than those involving behavioral regulation (e.g., emotional reactions, failure to inhibit behaviors).
Contrary to our expectation, changes in neither executive functioning nor self-management predict glycemic control. However, the relation between changes in self-management and glycemic control was in the hypothesized direction (i.e., better glycemic control associated with better self-management). Future research should expand the assessment of executive functioning to include teacher report, which may capture a more accurate account of daily metacognition tasks within the school setting rather than in the home with the parent report (Gioia et al., 2000). Moreover, a recently developed measure of the functional impact of executive functioning for children and adolescents (Barkley, 2012), which can be administered directly to adolescents as well as parents, has potential application for pediatric type 1 diabetes. In future research, it may be useful to identify which specific areas of executive functioning and relevant impact are associated with various key diabetes self-management behaviors (e.g., monitoring of blood glucose, insulin administration, and so forth).
Several other limitations should be considered in interpreting our findings. For example, demographics of our sample included a majority of Caucasian and highly educated families, which limits the generalizability of our findings. Future research should determine whether the present findings generalize to populations of adolescents with type 1 diabetes with a wider range of race and parental educational levels. On the other hand, our sample included a relatively high percentage of Hispanic youth who are underrepresented in research in type 1 diabetes (Helgeson et al., 2011; Ingerski et al., 2010). Additionally, the follow-up period of two years was relatively short. Future studies should study the developmental trajectory of executive functioning and relation to diabetes management across a longer period after the transition to adolescence. Finally, our recruitment rate and population sampling may have affected the findings by restricting the range of predictors and outcomes.
The effect sizes demonstrated in our findings were relatively small. For this reason, potential clinical implications are only suggestive and need to be established in future research. For example, studies that characterize executive functioning, especially behavioral regulation, among adolescents with type 1 diabetes who are having clinically significant problems in self-management and glycemic control would be instructive. Identification of specific deficits in behavioral regulation in this population would have implications for the development of interventions that are tailored to address specific deficits in self-management. For example, interventions that enhance behavioral regulation and help adolescents to (1) inhibit behaviors that disrupt self-management and (2) manage emotional reactions that interfere with diabetes management could extend the impact of coping skills interventions for children and adolescents with type 1 diabetes (Grey, Boland, Davidson, Li & Tamborlane, 2000).
Funding
The work reported in this article was funded by the National Institute of Diabetes and Digestive Disease (1R01 DK069486).
Acknowledgments
The assistance of Katie Wetterau, Erica Sood, Hanna Carpenter, Daniela Fernandez, Angeline Merzier, and Andrea Perry in conducting this study is gratefully acknowledged.
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