A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort.

Link: https://doi.org/10.1093/jamiaopen/ooad029
Authors: Hirten, Robert P; Suprun, Maria; Danieletto, Matteo; Zweig, Micol; Golden, Eddye; Pyzik, Renata; Kaur, Sparshdeep; Helmus, Drew; Biello, Anthony; Landell, Kyle; Rodrigues, Jovita; Bottinger, Erwin P; Keefer, Laurie; Charney, Dennis; Nadkarni, Girish N; Suarez-Farinas, Mayte; Fayad, Zahi A

Abstract: To assess whether an individual’s degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device. Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline. These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.