New York City-based Mount Sinai researchers used machine learning models and the Apple Watch to identify a patient's degree of resilience and well-being.
Researchers used machine learning models to assess Apple Watch data from 329 healthcare workers every time there was a change in time between each heartbeat, or heart rate variability.
The aim was to see if machine learning and wearables could distinguish a person's degree of resilience and psychological well-being, according to a May 2 press release from Mount Sinai.
The metrics collected were found to be predictive in identifying resilience or well-being states, as researchers stated that using a person's individual heart rate variability could determine a person's degree of resilience.
The findings suggest that wearables can be used as a way to monitor and assess psychological states and have the possibility to automate these processes without requiring patients to complete mental health questionnaires.
The full study was published May 2 in JAMIA Open.