Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System
By Irit Hochberg, Guy Feraru, Mark Kozdoba, Shie Mannor, Moshe Tennenholtz, Elad Yom-Tov. 2016
198 citations recorded by the source corpus.
Background Regular physical activity is known to be beneficial for people with type 2 diabetes. Nevertheless, most of the people who have diabetes lead a sedentary lifestyle. Smartphones create new possibilities for helping people to adhere to their physical activity goals through continuous monitoring and communication, coupled with personalized feedback. Objective The aim of this study was to help type 2 diabetes patients increase the level of their physical activity. Methods We provided 27 sedentary type 2 diabetes patients with a smartphone-based pedometer and a personal plan for physical activity. Patients were sent short message service messages to encourage physical activity between once a day and once per week. Messages were personalized through a Reinforcement Learning algorithm so as to improve each participant’s compliance with the activity regimen. The algorithm was compared with a static policy for sending messages and weekly reminders. Results Our results show that participants who received messages generated by the learning algorithm increased the amount of activity and pace of walking, whereas the control group patients did not. Patients assigned to the learning algorithm group experienced a superior reduction in blood glucose levels (glycated hemoglobin [HbA1c]) compared with control policies, and longer participation caused greater reductions in blood glucose levels. The learning algorithm improved gradually in predicting which messages would lead participants to exercise. Conclusions Mobile phone apps coupled with a learning algorithm can improve adherence to exercise in diabetic patients. This algorithm can be used in large populations of diabetic patients to improve health and glycemic control. Our results can be expanded to other areas where computer-led health coaching of humans may have a positive impact. Summary of a part of this manuscript has been previously published as a letter in Diabetes Care, 2016.
Subfields: General Health Professions, Physiology, Human-Computer Interaction
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Citations in the corpus
- Reinforcement Learning in Healthcare: A Survey (2019, methodology): “AmI in healthcare domains. For example, RL has been used to adapt the intervention strategies of smart phones in order to recommend regular physical activity to people who suffer from diabetes type 2 [302], [303], or who have experienced a cardiac event and been in cardiac rehab [351], [352], [353].”
- Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities (2021, background): “In other healthcare contexts, reinforcement learning has recently demonstrated potential in steering behavioural interventions (Yom-Tov et al. 2017) and improving health outcomes.”
- Artificial Intelligence for Diabetes Management and Decision Support: Literature Review (2018, background): “Previously, Yom-Tov et al proposed a DSS based on a RL algorithm that automatically sends messages to patients who are following a personalized plan for physical exercise [131].”