US
Urmimala Sarkar
3 records found
1
Effectiveness of a Digital Health Intervention Leveraging Reinforcement Learning
Results From the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation (DIAMANTE) Randomized Clinical Trial
Background: Digital and mobile health interventions using personalization via reinforcement learning algorithms have the potential to reach large number of people to support physical activity and help manage diabetes and depression in daily life. Objective: The Diabetes and Menta
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Adaptive learning algorithms to optimize mobile applications for behavioral health
Guidelines for design decisions
Objective: Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many c
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MHealth app using machine learning to increase physical activity in diabetes and depression
Clinical trial protocol for the DIAMANTE Study
Introduction Depression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target
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