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.
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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