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Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System
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 monito...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654735/ https://www.ncbi.nlm.nih.gov/pubmed/29017988 http://dx.doi.org/10.2196/jmir.7994 |
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author | Yom-Tov, Elad Feraru, Guy Kozdoba, Mark Mannor, Shie Tennenholtz, Moshe Hochberg, Irit |
author_facet | Yom-Tov, Elad Feraru, Guy Kozdoba, Mark Mannor, Shie Tennenholtz, Moshe Hochberg, Irit |
author_sort | Yom-Tov, Elad |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5654735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-56547352017-10-31 Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System Yom-Tov, Elad Feraru, Guy Kozdoba, Mark Mannor, Shie Tennenholtz, Moshe Hochberg, Irit J Med Internet Res Original Paper 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. JMIR Publications 2017-10-10 /pmc/articles/PMC5654735/ /pubmed/29017988 http://dx.doi.org/10.2196/jmir.7994 Text en ©Elad Yom-Tov, Guy Feraru, Mark Kozdoba, Shie Mannor, Moshe Tennenholtz, Irit Hochberg. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.10.2017. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Yom-Tov, Elad Feraru, Guy Kozdoba, Mark Mannor, Shie Tennenholtz, Moshe Hochberg, Irit Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System |
title | Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System |
title_full | Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System |
title_fullStr | Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System |
title_full_unstemmed | Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System |
title_short | Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System |
title_sort | encouraging physical activity in patients with diabetes: intervention using a reinforcement learning system |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654735/ https://www.ncbi.nlm.nih.gov/pubmed/29017988 http://dx.doi.org/10.2196/jmir.7994 |
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