Communications of the ACM
Wearables Measure Workers' Resilience
Researchers examined physiological markers collected from Apple Watches.
Credit: Mount Sinai Health System
Applying machine learning models to data collected from wearable devices can identify a worker's degree of resilience and well-being, according to investigators at the Icahn School of Medicine at Mount Sinai.
The findings, reported in JAMIA Open, support collecting physiological metrics from wearable devices, such as the Apple Watch, as a way to monitor and assess psychological states remotely without requiring the completion of mental health questionnaires.
Resilience, or an individual's ability to overcome difficulty, is an important stress mitigator, reduces morbidity, and improves chronic disease management, the paper says.
The researchers analyzed a data set comprised of 329 health care workers enrolled at seven hospitals in New York City. Subjects wore an Apple Watch to measure heart rate variability and resting heart rate. Surveys were collected measuring resilience, optimism, and emotional support at baseline. The metrics collected were found to be predictive in identifying resilience or well-being states.
From Mount Sinai Health System
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