Machine learning predicts frailty outcomes in populations using wearable device data, suggesting new healthcare applications.
This approach of wearable activity data-based prediction of frailty offers a surrogate (proxy or estimate) for determining frailty metrics in a scalable manner. It can also be used to determine adverse outcomes such as hospitalizations and mortality, allowing frailty to be used as a metric in other studies or medical practices.
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Culos et al. (2026) studied this question.
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