Researchers at the Icahn School of Medicine at Mount Sinai have found that applying machine learning models to passively collected data from wearables can identify patients' degree of wellbeing and resilience.
The study, published in JAMIA Open analysed data from the Warrior Watch Study. Here, 329 healthcare workers in New York City wore an Apple Watch Series 4 or 5 and completed surveys on resilience, optimism, and emotional support. Data collected was predictive in identifying resilience or well-being states, supporting further assessment of psychological characteristics from passively collected wearable data. Researchers intend to further evaluate this technique in other patient populations to improve applicability. Further research is also needed to refine the algorithm and to improve its applicability in a broader range of disease areas.
Researchers have noted that wearable devices could improve access to mental health services for all people. “Wearables provide a means to continually collect information about an individual’s physical state. Our results provide insight into the feasibility of assessing psychological characteristics from this passively collected data,” said first author Robert P. Hirten, MD, Clinical Director, Hasso Plattner Institute for Digital Health at Mount Sinai. “To our knowledge, this is the first study to evaluate whether resilience, a key mental health feature, can be evaluated from devices such as the Apple Watch.”
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