@Article{info:doi/10.2196/33329,
author="Kamath, Sowmya
and Kappaganthu, Karthik
and Painter, Stefanie
and Madan, Anmol",
title="Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study",
journal="JMIR Form Res",
year="2022",
month="Mar",
day="21",
volume="6",
number="3",
pages="e33329",
keywords="personalization; type 2 diabetes; recommendation; causal; observational; mobile health; machine learning; engagement; glycemic control; mHealth; recommender systems",
abstract="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. ",
issn="2561-326X",
doi="10.2196/33329",
url="https://formative.jmir.org/2022/3/e33329",
url="https://doi.org/10.2196/33329",
url="http://www.ncbi.nlm.nih.gov/pubmed/35311691"
}