Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study.

BACKGROUND: The use of remote data capture for monitoring blood glucose and supporting digital apps is becoming the norm in diabetes care. One common goal of such apps is to increase user awareness and engagement with their day-to-day health-related behaviors (digital engagement) in order to improve diabetes outcomes. However, we lack a deep understanding of the complicated association between digital engagement and diabetes outcomes. OBJECTIVE: This study investigated the association between digital engagement (operationalized as tagging of behaviors alongside glucose measurements) and the monthly average blood glucose level in persons with type 2 diabetes during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first 6 months, blood glucose levels will drop faster and further in patients with increased digital engagement and that difference in outcomes will persist for the remainder of the year. Finally, we hypothesize that disaggregated between- and within-person variabilities in digital engagement will predict individual-level changes in blood glucose levels. METHODS: This retrospective real-world analysis followed 998 people with type 2 diabetes who regularly tracked their blood glucose levels with the Dario digital therapeutics platform for chronic diseases. Subjects included "nontaggers" (users who rarely or never used app features to notice and track mealtime, food, exercise, mood, and location, n=585) and "taggers" (users who used these features, n=413) representing increased digital engagement. Within- and between-person variabilities in tagging behavior were disaggregated to reveal the association between tagging behavior and blood glucose levels. The associations between an individual's tagging behavior in a given month and the monthly average blood glucose level in the following month were analyzed for quasicausal effects. A generalized mixed piecewise statistical framework was applied throughout. RESULTS: Analysis revealed significant improvement in the monthly average blood glucose level during the first 6 months (t=-10.01, P<.001), which was maintained during the following 6 months (t=-1.54, P=.12). Moreover, taggers demonstrated a significantly steeper improvement in the initial period relative to nontaggers (t=2.15, P=.03). Additional findings included a within-user quasicausal nonlinear link between tagging behavior and glucose control improvement with a 1-month lag. More specifically, increased tagging behavior in any given month resulted in a 43% improvement in glucose levels in the next month up to a person-specific average in tagging intensity (t=-11.02, P<.001). Above that within-person mean level of digital engagement, glucose levels remained stable but did not show additional improvement with increased tagging (t=0.82, P=.41). When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels (t=1.30, P=.20). CONCLUSIONS: This study sheds light on the source of the association between user engagement with a diabetes tracking app and the clinical condition, highlighting the importance of within-person changes versus between-person differences. Our findings underscore the need for and provide a basis for a personalized approach to digital health.

Plasma concentrations of lipids during pregnancy and the risk of gestational diabetes mellitus: A longitudinal study.

BACKGROUND: Abnormal lipid profiles have been associated with gestational diabetes mellitus (GDM), but studies with longitudinal measures of lipids throughout pregnancy are sparse. The aim of the present study was to characterize longitudinal changes in lipid profiles throughout pregnancy and prospectively examine the associations of plasma lipid concentrations with risk of GDM. METHODS: This study was a nested case-control study including 107 GDM cases and 214 matched non-GDM controls from participants in the National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies - Singleton cohort. Blood samples were collected longitudinally at Gestational Weeks (GW) 10-14, 15-26 (fasting sample), 23-31, and 33-39. Plasma concentrations of triglycerides, total cholesterol, and high-density lipoprotein cholesterol (HDL-C) were measured by enzymatic assays. Low-density lipoprotein cholesterol (LDL-C) was calculated using Friedewald's formula. RESULTS: Plasma triglycerides, total cholesterol, and LDL-C increased as pregnancy progressed. At GW 10-14, the adjusted odds ratios (aORs) of GDM comparing the highest versus lowest quartile were 3.15 (95% confidence interval [CI] 1.38-7.15; P trend = 0.002) for triglycerides and 0.44 (95% CI 0.18-1.09; P trend = 0.045) for HDL-C. At GW 15-26, the aORs were 6.57 (95% CI 2.25-19.17; P trend = 0.001) for triglycerides and 0.23 (95% CI 0.08-0.63; P trend = 0.005) for HDL-C. No significant associations were observed for total cholesterol and LDL-C concentrations with risk of GDM. CONCLUSIONS: Higher plasma triglyceride and lower HDL-C concentrations in early and mid-pregnancy were significantly associated with a greater risk of GDM. Total cholesterol and LDL-C concentrations during pregnancy were not significantly associated with GDM risk.