Feasibility of Electronic Health Record Assessment of 6 Pediatric Type 1 Diabetes Self-management Habits and Their Association With Glycemic Outcomes

Abstract

Importance: A low-burden electronic health record (EHR) workflow has been devised to systematize the collection and validation of 6 key diabetes self-management habits: (1) checks glucose at least 4 times/day or uses continuous glucose monitor (CGM); (2) gives at least 3 rapid-acting insulin boluses per day; (3) uses insulin pump; (4) delivers boluses before meals; (5) reviewed glucose data since last clinic visit, and (6) has changed insulin doses since the last clinic visit.

Objective: To describe the performance of these habits and examine their association with hemoglobin A1c (HbA1c) levels and time in range (TIR).

Design, setting, and participants: This cross-sectional study included individuals with known type 1 diabetes who were seen in a US pediatric diabetes clinic in 2019.

Main outcomes and measures: Habit performance, total habit score (sum of 6 habits per person), HbA1c levels, and TIR.

Results: Of 1344 patients, 1212 (609 [50.2%] males; 66 [5.4%] non-Hispanic Black; 1030 [85.0%] non-Hispanic White; mean [SD] age, 15.5 [4.5] years) were included, of whom 654 (54.0%) were using CGM and had a TIR. Only 105 patients (8.7%) performed all 6 habits. Habit performance was lower among older vs younger patients (age ≥18 years vs ≤12 years: 17 of 411 [4.1%] vs 57 of 330 [17.3%]; P < .001), Black vs White patients (3 [4.5%] vs 95 [9.2%]; P < .001), those with public vs private insurance (14 of 271 [5.2%] vs 91 of 941 [9.7%]; P < .001), and those with lower vs higher parental education levels (<college degree vs ≥college degree: 35 of 443 [7.9%] vs 66 of 574 [11.5%]; P < .001). After adjustment for demographic characteristics and disease duration, for every 1-unit increase in total habit score, we found a mean (SE) 0.6% (0.05) decrease in HbA1c among all participants and a mean (SE) 2.86% (0.71) increase in TIR among those who used CGMs. Multiple regression models revealed that performing each habit was associated with a significantly lower HbA1c level (habit 1: -0.16% [95% CI, -1.91% to -1.37%]; habit 2: -1.01% [-1.34% to -0.69%]; habit 3: -0.71% [95% CI, -0.93% to -0.49%]; habit 4: -0.97% [95% CI, -1.21% to -0.73%]; habit 5: -0.44% [95% CI, -0.71% to -0.17%]; habit 6: -0.75% [95% CI, -0.96% to -0.53%]; all P < .001). There were differences in HbA1c according to race, insurance, and parental education, but these associations were attenuated with the inclusion of the 6 habits, which had more robust associations with HbA1c levels than the demographic characteristics.

Conclusions and relevance: These findings suggest that a focus on increasing adherence to the 6 habits could be critical for improving disparities in glycemic outcomes; these metrics have been adopted by the Type 1 Diabetes Exchange Quality Improvement Collaborative for continuous quality improvement.

Figure.. Hemoglobin A_1c (HbA_1c) Levels and Time in Range (TIR) by Each Habit and Total Habit Score

A, Comparisons between those who performed and did not perform each habit were statistically significant (P < .001) for all habits. C, Comparisons between those who performed and did not perform habits 2, 3, 4, and 6 were statistically significant (habit 2, P = .006; habit 3, P = .03; habit 4, P < .001; and habit 6, P = .01). CGM indicates continuous glucose monitor. To convert HbA_1c levels to proportion of total hemoglobin, multiply by 0.01.