This study, published in the Journal of Medical Internet Research, explored how smartphones create new possibilities for helping people with type diabetes to adhere to their physical activity goals through continuous monitoring and communication, coupled with personalised feedback.
Patients were sent short message service messages to encourage physical activity between once a day and once per week. Messages were personalised 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 showed 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.
The study concluded that 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. The results can be expanded to other areas where computer-led health coaching of humans may have a positive impact.
Yom-Tov E, Feraru G, Kozdoba M, Mannor S, Tennenholtz M, Hochberg I
Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System
J Med Internet Res 2017;19(10):e338