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

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES JAMIA Open Pub Date : 2023-05-02 eCollection Date: 2023-07-01 DOI:10.1093/jamiaopen/ooad029
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
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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.

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利用可穿戴设备数据确定复原力的机器学习方法:对观察队列的分析。
目的评估是否可以通过可穿戴设备被动收集的生理指标来确定个人的心理承受能力:本研究对 "勇士手表研究 "数据集进行了二次分析,该数据集是由纽约市 7 家医院的医护人员组成的前瞻性队列。受试者在参与研究期间一直佩戴 Apple Watch。我们收集了调查问卷,测量基线时的复原力、乐观情绪和情感支持:我们评估了 329 名受试者(平均年龄 37.4 岁,37.1% 为男性)的数据。在所有测试组中,梯度提升机(GBM)和极端梯度提升模型在高复原力与低复原力预测方面表现最佳,以康纳-戴维森复原力量表-2的中位数6分(四分位间范围=5-7)为分层,AUC为0.60。在预测连续变量抗逆力时,多元线性模型的相关性为 0.24(P = 0.029),测试数据的 RMSE 为 1.37。此外,还对由复原力、乐观和情感支持组成的积极心理结构进行了评估。斜向随机森林法在估算中位数为 32.5 的高分与低分综合得分时表现最佳,AUC 为 0.65,灵敏度为 0.60,特异度为 0.70:讨论:在事后分析中,应用于可穿戴设备收集的生理指标的机器学习模型在识别复原力状态和积极心理结构方面具有一定的预测能力:这些发现支持在专门研究中进一步评估从被动收集的可穿戴设备数据中得出的心理特征。
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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
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