JMIR Publications

ABSTRACT

Background:

The use of remote data capture for blood glucose monitoring and supporting digital applications (also known as “apps”) is becoming the norm in diabetes care. One common goal of such applications is to increase behavioral engagement in self-management. Our results show that identifying shorter- vs. longer-term impacts of behavioral engagement and understanding the dynamics of within-person trends are critical for effectively leveraging behavioral engagement to improve diabetic outcomes. 

Objective:

The present study investigates the association between behavioral engagement (operationalized here as tagging of behaviors alongside glucose measurements) and diabetes outcomes (in the form of monthly average blood glucose levels) in persons with type 2 diabetes (PWD) during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first six months, blood glucose levels will drop faster and further in patients with increased behavioral engagement, and that this difference in outcome will persist for the remainder of the year. Finally, we hypothesize that disaggregated between-person fluctuations in behavioral engagement will predict individual-level changes in blood glucose levels.

Methods:

This retrospective, real-world analysis follows 998 people with type 2 diabetes subjects who regularly tracked their blood glucose level with the Dario Digital Therapeutic for Chronic Disease.  Users included “non-taggers” (users who rarely or never used app features to notice and track food, exercise, mealtime, location and mood, N=585), and “taggers” (users who did use these features, N=413), representing increased behavioral engagement.  Within- and between-person variability 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 in the following month were analyzed for quasi-causal effects. A generalized mixed piecewise statistical framework was applied throughout.

Results:

Analysis revealed significant improvement in monthly average blood glucose during the first six months, which was maintained during the next six months. Moreover, taggers demonstrated significantly more improvement in the initial period relative to non-taggers. Additional findings include a within-user quasi-causal, nonlinear link between tagging behavior and glucose control improvement with a 1-month lag. More specifically, increased tagging behavior resulted in a 43% improvement in glucose levels, up to a person-specific average in tagging intensity. Above that within-person mean level of behavioral engagement, glucose levels remain stable but do not show additional improvement with increased tagging. When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels.

Conclusions:

Conclusions The current study sheds light on the source of the association between users’ engagement with a diabetes tracking app and their clinical condition, highlighting the importance of within-person changes vs. between-person differences. Our findings underscore the need for a personalized approach to digital health.  Clinical Trial: NA