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<p>Healthcare workers (HCWs) in emergency departments face significant mental health risk due to chronic stressors and repeated trauma, yet symptom underreporting and bias in self-reports hinder accurate assessments. Expressive flexibility, the ability to dynamically modulate and recover from stressor-related changes in emotional arousal as reflected in observable behavior, has been linked to resilience. This NIH-funded study (R01HL156134) utilized digital phenotyping and computer vision to analyze dynamic facial expressivity during video-recorded interviews about work-related stressful situations with 240 HCWs (278 assessments). Participants additionally completed validated questionnaires to assess burnout, PTSD, depression, anxiety, and resilience. Latent profile analysis revealed two clinical phenotypes: At-risk (57.6%) and Resilient/Adaptive (42.4%). Machine learning models demonstrated high classification performance (accuracy = 0.83 ± 0.06, F1-score = 0.87 ± 0.05). Our findings indicate that digital biomarkers of temporal facial dynamics may serve as objective behavioral proxies of expressive flexibility, potentially capturing dynamics consistent with underlying stress-regulatory processes. These findings highlight their potential to improve identification of resilience-related phenotypes and support well-being and mental health in HCWs.</p>

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Bouncing back from stress: objective markers of expressive flexibility and resilience in emergency healthcare workers using computer vision

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月15日

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  • Charlotte E. Hilberdink,
  • Yiwen Zhao,
  • Scott McKernan,
  • Sapir Gershov,
  • Victoria Mueller,
  • Stephen P. Wall,
  • Katharina Schultebraucks

摘要

Healthcare workers (HCWs) in emergency departments face significant mental health risk due to chronic stressors and repeated trauma, yet symptom underreporting and bias in self-reports hinder accurate assessments. Expressive flexibility, the ability to dynamically modulate and recover from stressor-related changes in emotional arousal as reflected in observable behavior, has been linked to resilience. This NIH-funded study (R01HL156134) utilized digital phenotyping and computer vision to analyze dynamic facial expressivity during video-recorded interviews about work-related stressful situations with 240 HCWs (278 assessments). Participants additionally completed validated questionnaires to assess burnout, PTSD, depression, anxiety, and resilience. Latent profile analysis revealed two clinical phenotypes: At-risk (57.6%) and Resilient/Adaptive (42.4%). Machine learning models demonstrated high classification performance (accuracy = 0.83 ± 0.06, F1-score = 0.87 ± 0.05). Our findings indicate that digital biomarkers of temporal facial dynamics may serve as objective behavioral proxies of expressive flexibility, potentially capturing dynamics consistent with underlying stress-regulatory processes. These findings highlight their potential to improve identification of resilience-related phenotypes and support well-being and mental health in HCWs.