We examined the validity and reliability of a new scoring system to measure adherence to insulin use via insulin pump download reports. Specifically, we coded the frequency of mealtime insulin boluses. Inter-rater reliability for our BOLUS score was very good, suggesting the operational definitions we provided to code insulin use were clear and easy to apply to insulin pump records. Current validity of our BOLUS scores were confirmed based on high and significant simple and partial correlations between youths' BOLUS scores and HbA1c levels. In addition, analyses demonstrated a more robust relation between youths' BOLUS score and HbA1c level than between youths' frequency of daily BGM and HbA1c level.
Multiple measures of treatment adherence have been studied in youths with T1DM.3
Although several self-report measures examine BGM frequency, the only objective measure of BGM frequency is that obtained from glucometer downloads. Glucometer-derived BGM frequency has been shown to correlate with glycemic control among children and adolescents, with a medium effect size.5
In one study, a decrease of one blood glucose check per day was associated with a 1.26% increase in HbA1c level.5
In contrast, self-report measures, although helpful in the absence of objective monitoring data, can be problematic because they are often time-intensive and require in-depth knowledge of the assessment tool by providers.3
In addition, they widely are considered less precise than glucometer-derived data,9
although glucometer-derived data can also suffer reliability issues10
and may not fully capture all available data if multiple glucometers are used by youth, and at least one recent study found that the Self-Care Inventory predicted glycemic control better than glucometer-derived monitoring frequency.3
Notably absent from the pediatric T1DM literature are any objective measures of treatment adherence based upon actual patient self-delivery of medication (i.e., insulin). With the increased adoption of continuous subcutaneous insulin infusion by youths with T1DM, adherence measures based on insulin bolusing are becoming more relevant and more applicable to a broad population. To develop our measure, we focused on the frequency of youths' mealtime insulin boluses because mealtimes occur daily and tend to follow a predictable schedule. In addition, studies have demonstrated that missed mealtime boluses can have a devastating impact on youths' glycemic control.11–13
Burdick et al.11
determined that youths who missed at least one mealtime bolus per week had an average HbA1c level that was 0.8% higher than youths who did not miss any mealtime boluses. Likewise, Olinder et al.12
reported average HbA1c levels 0.8% higher for youths who missed mealtime insulin boluses compared with youths who did not miss mealtime boluses, and another study found youths who skipped mealtime insulin boluses had an average HbA1c of 8.67%.13
There is also a study showing that missed snack time insulin boluses may be common for some youth with type 1 diabetes and related to daily episodes of glycemic excursion.14
However, the occurrence, frequency, and timing of snacks, characteristics that significantly complicate the creation of an operational definition of a “snack bolus,” are highly variable. Thus, we elected not to include snacks in our final measure. We also elected not to use a daily sum of all available boluses (e.g., food plus correction boluses) because the frequency and timing of correction boluses would be highly variable among youths and need to be tied to blood glucose data, which would complicate the calculation of an insulin bolus score. However, as a next step it may be useful to directly compare our mealtime BOLUS score and a “daily sum” BOLUS score with regard to their relative associations to HbA1c level.
We believe this study contributes to the literature in the following ways. First, to our knowledge, this is the first example of a specific coding strategy for calculating adherence based on insulin pump records. Specifically, we now present a simple-to-calculate, easily deployable, objective measure of adherence to mealtime insulin use. Second, because our new adherence measure uses insulin pump data, we potentially remove inaccuracy that might be introduced if patients use multiple glucometers throughout the day and/or do not enter glucose values into their insulin pump. Our BOLUS score can be calculated based on data downloaded from a single device without requiring additional patient-driven data entry. Third, our data suggest that for every 1 point increase in youths' insulin bolus score, there was a 1.5 unit (%) decrease in youths' HbA1c levels (full model and BOLUS-only model, ). In contrast, every 1 point increase in youths' frequency of daily BGM was associated with only a 0.19 unit (%) decrease in HbA1c level (BGM-only model, ). This comparison suggests that measuring mealtime insulin bolus use is superior to the frequency of daily BMG in explaining variations in youths' HbA1c levels. Finally, our results have practical implications for clinical research and management, where identifying a robust and objective adherence measure is important when examining behavioral changes and measuring outcomes.
Some strengths of the present study are its use of a relatively large and heterogeneous sample (i.e., age 1–19 years, any level of glycemic control) and our decision to compare our new adherence measure with frequency of daily BGM, one of the gold standards of adherence in T1DM. Some of the limitations of this study include its exclusive focus on youths using an insulin pump for daily insulin administration. Although insulin pump therapy is becoming more widely adopted among youths, our specific methodology for calculating the frequency of mealtime insulin boluses will not be applicable to youths receiving insulin via injections or an insulin pen, as these administration routes would require patient self-reports of insulin use. Second, our methodology could be criticized because our mealtime windows sum to 12
h, suggesting we may have been too liberal in our definition of mealtimes. This issue was carefully considered by the research team. Although we wanted to ensure that we provided individual mealtime periods that were sufficiently long enough to include most youths, we also wanted to focus primarily on mealtime insulin use versus snack time or correction boluses. To determine each mealtime window, we examined the default mealtimes used by insulin pump companies, we surveyed pump records from youths, and we sought recommendations from our colleagues. As a follow-up study, it may be useful to see if applying tighter individual mealtimes yields different relationships with HbA1c level when coding insulin pump records. Third, the study design was cross-sectional, precluding any speculation about causality and leaving unanswered the question of whether the association between BOLUS scores and glycemic control is stable over time. As further support of the validity of our insulin BOLUS score, it would be helpful to use longitudinal data to see whether the relationship with HbA1c level is stable over time and to see if youths' BOLUS scores can reliably predict future HbA1c levels.
To summarize, objective proxy measures of adherence are helpful for clinical research and management.4
In T1DM, frequency of daily BGM has been used as a proxy measure of adherence and has been found to relate negatively with youths' HbA1c level.3,5–7
Here, we present new methodology for calculating a proxy measure of adherence using insulin pump records. Our methodology focuses on calculating a mealtime insulin bolus score (BOLUS) based on the mean frequency of mealtime boluses per day. Our data demonstrate that this measure correlates negatively with youths' HbA1c level and positively with youths' BGM. In addition, our analyses suggest that youths' BOLUS scores may be superior to frequency of BGM events in explaining variance in youths' HbA1c levels. We conclude that the BOLUS score is likely a better measure of adherence than frequency of daily BGM.