ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2021
Identifying Mobile Sensing Indicators of Stress-Resilience.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 42 citations in OpenAlex.
- Veteran Monitoring Initiative for Noninvasive Physiology and Depression (V-MIND) Exploring Physical Activity and Mental Health in UK Veterans: Protocol for an Observational Digital Phenotyping Study.JMIR research protocols · 2026Article
- Evaluation of the relationship between resilience and empathy in medical professionals using structural equation modeling.Frontiers in psychology · 2026Article
- Understanding resilience in medical interns through ecological momentary assessments, predictive modelling, and topic analysis.Communications medicine · 2025Article
- Analyzing Global Research Trends on Medical Resident Burnout and Physical Activity: A Bibliometric Analysis (2005-2025).Healthcare (Basel, Switzerland) · 2025Review
- The Moderating Effect of Atypical Events on the Relationship Between Heart Rate and Stress in Medical Residents Working in an Intensive Care Unit: Longitudinal, Observational Daily Diary Study.JMIR formative research · 2025Observational
- 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
- Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review.JAMIA open · 2025Review
- Article
- Climbing the ladder: a ranking approach to burnout prediction.Frontiers in digital health · 2025Article
- Toward burnout prevention with Bayesian mixed-effects regression analysis of longitudinal data from wearables: a preliminary study.Frontiers in digital health · 2025Article
- Exploring emotions beyond the laboratory: A review of emotional and physiological ecological momentary assessment methods in children and youth.Psychophysiology · 2024Review
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review.Journal of medical Internet research · 2024Article
- Capturing the College Experience: A Four-Year Mobile Sensing Study of Mental Health, Resilience and Behavior of College Students during the Pandemic.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling Study.JMIR AI · 2023Article
- Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians.Proceedings of the ACM on human-computer interaction · 2022Article
- Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies.PloS one · 2022Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Resident physicians (residents) experiencing prolonged workplace stress are at risk of developing mental health symptoms. Creating novel, unobtrusive measures of resilience would provide an accessible approach to evaluate symptom susceptibility without the perceived stigma of formal mental health assessments. In this work, we created a system to find indicators of resilience using passive wearable sensors and smartphone-delivered ecological momentary assessment (EMA). This system identified indicators of resilience during a medical internship, the high stress first-year of a residency program. We then created density estimation approaches to predict these indicators before mental health changes occurred, and validated whether the predicted indicators were also associated with resilience. Our system identified resilience indicators associated with physical activity (step count), sleeping behavior, reduced heart rate, increased mood, and reduced mood variability. Density estimation models were able to replicate a subset of the associations between sleeping behavior, heart rate, and resilience. To the best of our knowledge, this work provides the first methodology to identify and predict indicators of resilience using passive sensing and EMA. Researchers studying resident mental health can apply this approach to design resilience-building interventions and prevent mental health symptom development.
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What OpenQuestion holds
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.