Estimating Recovery in Cardiac Rehabilitation Using Mobile Health Technology and Personalized Machine Learning
Estimating Recovery in Cardiac Rehabilitation Using Mobile Health Technology and Personalized Machine Learning
The goal of this study is to collect wearable sensor data that the investigators hypothesize will be useful in future algorithm development for monitoring recovering of participants enrolled in cardiac rehabilitation programs. Participants will be provided one or more smartwatches and/or smart rings to wear during a 14 week study which includes 2 weeks of baseline data collection and a 12-week, 36-session cardiac rehabilitation program. They will capture a pre-assessment 6 minute walk tests and a post-assessment 6 minute walk test. They will otherwise conduct standard cardiac rehabilitation program procedures and our sensors will capture data and sleep information throughout the study period.
The goal of this study is to collect wearable sensor data that the investigators hypothesize will be useful in future algorithm development for monitoring the recovery of participants enrolled in cardiac rehabilitation programs. Participants will be provided one or more smartwatches and/or smart rings to wear during a 14 week study which includes 2 weeks of baseline data collection and a 12-week, 36-session cardiac rehabilitation program. They will capture a pre-assessment 6 minute walk tests and a post-assessment 6 minute walk test. They will otherwise conduct standard cardiac rehabilitation program procedures and our sensors will capture data and sleep information throughout the study period.
This work will use data from cardiac rehabilitation, captured from smartwatches, and data from rest/sleep, captured from smartwatches and smartrings, to see if there are trends in recovery that can be modeled. Recovery is measured as an improvement in 6 minute walk test performance over the course of cardiac rehabilitation.
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