%0 Journal Article
%@ 2561-326X
%I JMIR Publications
%V 6
%N 3
%P e33329
%T Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study
%A Kamath,Sowmya
%A Kappaganthu,Karthik
%A Painter,Stefanie
%A Madan,Anmol
%+ Teladoc Health, 2 Manhattanville Rd, Purchase, NY, 10577, United States, 1 3123307236, spainter@teladoc.com
%K personalization
%K type 2 diabetes
%K recommendation
%K causal
%K observational
%K mobile health
%K machine learning
%K engagement
%K glycemic control
%K mHealth
%K recommender systems
%D 2022
%7 21.3.2022
%9 Original Paper
%J JMIR Form Res
%G English
%X Background: Diabetes management is complex, and program personalization has been identified to enhance engagement and clinical outcomes in diabetes management programs. However, 50% of individuals living with diabetes are unable to achieve glycemic control, presenting a gap in the delivery of self-management education and behavior change. Machine learning and recommender systems, which have been used within the health care setting, could be a feasible application for diabetes management programs to provide a personalized user experience and improve user engagement and outcomes. Objective: This study aims to evaluate machine learning models using member-level engagements to predict improvement in estimated A1c and develop personalized action recommendations within a remote diabetes monitoring program to improve clinical outcomes. Methods: A retrospective study of Livongo for Diabetes member engagement data was analyzed within five action categories (interacting with a coach, reading education content, self-monitoring blood glucose level, tracking physical activity, and monitoring nutrition) to build a member-level model to predict if a specific type and level of engagement could lead to improved estimated A1c for members with type 2 diabetes. Engagement and improvement in estimated A1c can be correlated; therefore, the doubly robust learning method was used to model the heterogeneous treatment effect of action engagement on improvements in estimated A1c. Results: The treatment effect was successfully computed within the five action categories on estimated A1c reduction for each member. Results show interaction with coaches and self-monitoring blood glucose levels were the actions that resulted in the highest average decrease in estimated A1c (1.7% and 1.4%, respectively) and were the most recommended actions for 54% of the population. However, these were found to not be the optimal interventions for all members; 46% of members were predicted to have better outcomes with one of the other three interventions. Members who engaged with their recommended actions had on average a 0.8% larger reduction in estimated A1c than those who did not engage in recommended actions within the first 3 months of the program. Conclusions: Personalized action recommendations using heterogeneous treatment effects to compute the impact of member actions can reduce estimated A1c and be a valuable tool for diabetes management programs in encouraging members toward actions to improve clinical outcomes. 
%M 35311691
%R 10.2196/33329
%U https://formative.jmir.org/2022/3/e33329
%U https://doi.org/10.2196/33329
%U http://www.ncbi.nlm.nih.gov/pubmed/35311691