TY  - JOUR
AU  - Kamath, Sowmya
AU  - Kappaganthu, Karthik
AU  - Painter, Stefanie
AU  - Madan, Anmol
PY  - 2022
DA  - 2022/3/21
TI  - Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study
JO  - JMIR Form Res
SP  - e33329
VL  - 6
IS  - 3
KW  - personalization
KW  - type 2 diabetes
KW  - recommendation
KW  - causal
KW  - observational
KW  - mobile health
KW  - machine learning
KW  - engagement
KW  - glycemic control
KW  - mHealth
KW  - recommender systems
AB  - 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. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2022/3/e33329
UR  - https://doi.org/10.2196/33329
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35311691
DO  - 10.2196/33329
ID  - info:doi/10.2196/33329
ER  -