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

JAMIA Open. 2023 May 2;6(2):ooad029. doi: 10.1093/jamiaopen/ooad029. eCollection 2023 Jul.


OBJECTIVE: To assess whether an individual’s degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device.

MATERIALS AND METHODS: 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.

RESULTS: We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5-7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 (P = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70.

DISCUSSION: In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct.

CONCLUSIONS: These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.

PMID:37143859 | PMC:PMC10152991 | DOI:10.1093/jamiaopen/ooad029


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