Exploratory Analysis of the Relationships Among Different Methods of Assessing Adherence and Glycemic Control in Youth With Type 1 Diabetes Mellitus

Child and Adolescent Psychiatry and Behavioral Medicine, Children's Hospital of Wisconsin, Milwaukee, WI 53201-1997, USA.
Health Psychology (Impact Factor: 3.59). 07/2011; 31(1):35-42. DOI: 10.1037/a0024704
Source: PubMed


The present study examined four methods of assessing diabetes adherence (self-report, diary measure, electronic monitoring, and provider rating) within a population of youth with Type I Diabetes Mellitus (T1DM).
Comparisons were conducted among the four methods of assessing diabetes adherence. Associations among the seven different measures of blood glucose monitoring (BGM) and HbA1c were examined. An exploratory stepwise regression analysis was conducted to determine the best predictors of glycemic control (i.e., Hemoglobin A1c; HbA1c) while controlling for relevant demographic variables.
The adherence measures appeared to be interrelated. The relationships between many of the BGM measures and HbA1c demonstrated a medium effect size. The Self Care Inventory (SCI) adjusted global score was the strongest predictor of HbA1c, even after taking the demographic variables into account.
The SCI is a robust, easy-to-use, and cost-efficient measure of adherence that has a strong relationship to HbA1c. Demographic variables are important to examine within the context of different methods of assessing adherence. The research methodology utilized to assess both general diabetes adherence and more specific behavioral measurements of BGM should be clearly documented in future studies to ensure accurate interpretation of results.

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    ABSTRACT: OBJECTIVE: Detriments in quality of life (QOL) may contribute to the common, costly decline in adolescents' type 1 diabetes management and control, yet we know little about how this might happen. METHODS: Participants were 150 adolescents (age 13-18) with type 1 diabetes and their parents. We constructed a latent QOL variable from a multi-informant, multi-domain assessment when participants entered the study. The QOL variable was examined in relation to prospective assessments of diabetes management (blood glucose monitoring frequency; BGM) and control (hemoglobin A1c). We used an indirect path model to test the links among these variables, using bias-corrected bootstrapping. RESULTS: Poorer QOL at baseline was indirectly linked with higher A1c at 12 months via less frequent BGM obtained at 6 months (b=-0.01, 95% CI=-0.025, -0.004, p<0.05). Older age (b=-0.32), longer diabetes duration (b=-0.07), and insulin delivery via injections versus the insulin pump (b=0.67) were covariates of less frequent BGM, and unmarried caregiver status was associated with higher A1c (b=-0.76), all ps<0.05. CONCLUSIONS: In this study, poorer QOL acted as a barrier to effective diabetes management, subsequently altering diabetes control. PRACTICE IMPLICATIONS: Efforts to monitor and enhance QOL may hold promise for improving adolescents' diabetes outcomes in the future.
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    ABSTRACT: Background: Electronic measures of adherence can be superior to patient report. In type 1 diabetes, frequency of blood glucose monitoring (BGM), as measured by patients' home blood glucose meters, has already been identified as a valid proxy of adherence. We present methodology to calculate adherence using insulin pump records and evaluate the reliability and validity of this methodology. Subjects and methods: Blood glucose meter data, insulin pump records, and corresponding hemoglobin A1c (HbA1c) levels were randomly gathered from clinical and research databases for 100 children and youths (referred to hereafter as youths) with type 1 diabetes (mean±SD age, 12.7±4.6 years). Youths' mean frequency of daily BGM was calculated. Additionally, we calculated a mean mealtime insulin bolus score (BOLUS): youths received 1 point each for a bolus between 0600 and 1000 h, 1100 and 1500 h, and 1600 and 2200 h (maximum of 1 point/meal or 3 points/day). Results: Simple correlations between youths' HbA1c level, age, frequency of BGM, and insulin BOLUS scores were all significant. Partial correlations and multiple regression analyses revealed that insulin BOLUS scores better explain variations in HbA1c levels than the electronically recorded frequency of daily blood glucose measures. Conclusions: Our procedures for calculating insulin BOLUS scores using insulin pump records demonstrate better concurrent validity with youths' HbA1c levels than that of the frequency of BGM with youths' HbA1c levels. Our analyses have shown that insulin bolus scoring was superior to the frequency of BGM in predicting youths' HbA1c levels.
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