Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System
Elad Yom‐Tov, Guy Feraru, Mark Kozdoba, Shie Mannor, Moshe Tennenholtz, Irit Hochberg
Microsoft (Israel) Technion – Israel Institute of Technology Rambam Health Care Campus
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
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.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
生物医学Mobile Health and mHealth Applications
Physical Activity and Health · Innovative Human-Technology Interaction
参考文献 20
此处列出前 3 条
引用本文 11
按被引量排序,此处列出前 3 条