Sleep data from COPD patients with wearable devices may help predict how likely they are to stay engaged with pulmonary rehabilitation, according to researchers at Rochester, Minn.-based Mayo Clinic.
The study, published in Mayo Clinic Proceedings: Digital Health, evaluated whether sleep metrics collected from wrist-worn devices could improve machine learning models predicting participation in a 12-week pulmonary rehabilitation. Researchers analyzed wearable-derived sleep measures — including duration, efficiency and fragmentation — alongside clinical information, finding that adding sleep data bettered the models’ ability to identify which patients would engage in the home-based activities.
“As a scientist and engineer, I wanted to explore how wearable data could improve the drop-out rates of remote pulmonary rehabilitation programs,” said study first author Stephanie Zawada, Ph.D., a Mayo Clinic research associate, in a March 26 news release. “By better understanding a patient’s day-to-day life, we can make more personalized and potentially more effective care plan recommendations.”
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