Robert P Hirten, Maria Suprun, Matteo Danieletto, Micol Zweig, Eddye Golden, Renata Pyzik, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Kyle Landell, Jovita Rodrigues, Erwin P Bottinger, Laurie Keefer, Dennis Charney, Girish N Nadkarni, Mayte Suarez-Farinas, Zahi A Fayad
{"title":"A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort.","authors":"Robert P Hirten, Maria Suprun, Matteo Danieletto, Micol Zweig, Eddye Golden, Renata Pyzik, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Kyle Landell, Jovita Rodrigues, Erwin P Bottinger, Laurie Keefer, Dennis Charney, Girish N Nadkarni, Mayte Suarez-Farinas, Zahi A Fayad","doi":"10.1093/jamiaopen/ooad029","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>P</i> = .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.</p><p><strong>Discussion: </strong>In a <i>post hoc</i> 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.</p><p><strong>Conclusions: </strong>These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/41/01/ooad029.PMC10152991.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JAMIA Open","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jamiaopen/ooad029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/7/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
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.