ArticleJAMIA open2023
A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort.
Article in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Frailty, physiological reserve, and surgical resilience: toward a dynamic stress-recovery model.Aging clinical and experimental research · 2026Review
- The Endocannabinoid-Microbiota-Neuroimmune Super-System: A Unifying Feedback Architecture for Systems Resilience, Collapse Trajectories, and Precision Feedback Medicine.International journal of molecular sciences · 2025Review
- Multilevel and multimodal biobehavioral methods in the study of stress, trauma, and resilience: a systems approach.Journal of neural transmission (Vienna, Austria : 1996) · 2025Review
- Resilience and brain health in global populations.Nature medicine · 2025Review
- Physiological Data Collected From Wearable Devices Identify and Predict Inflammatory Bowel Disease Flares.Gastroenterology · 2025Observational
- Real-time monitoring of military health and readiness: a perspective on future research.Frontiers in digital health · 2025Article
- Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review.Journal of medical Internet research · 2024Article
- Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study.JMIR mental health · 2024Article
- Understanding the impact of sisu on workforce and well-being: A machine learning-based analysis.Heliyon · 2024Article
Corrections and comments
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Authors and funding
17 authors.
Funding
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 ( Discussion: In a Conclusions: These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.