ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2016
Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data.
Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03184389 (Sense2Stop Mobile Sensor Data to Knowledge), which is not on this map. Cited by 37 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.
Sense2Stop Mobile Sensor Data to Knowledge: Preliminary Study of the Effects of Wearable Sensors and Smartphone Apps on Real Time Stress and Smoking in Recently Quit Smokers
Who cites it
37 citing papers in PubMed, 98 citations in OpenAlex.
- Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention.Contemporary clinical trials · 2021Trial
- Examining the Use of Consumer Wearable Devices and Digital Tools for Stress Measurement in College Students: Scoping Review of Methods.JMIR mHealth and uHealth · 2026Article
- Development of a Heart Rate Variability Based Ambulatory Stress Detection Model for Clinical Populations.Applied psychophysiology and biofeedback · 2026Article
- Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review.JAMIA open · 2025Review
- Online learning in bandits with predicted context.Proceedings of machine learning research · 2024Article
- Predicting Obsessive-Compulsive Disorder Events in Children and Adolescents in the Wild using a Wearable Biosensor (Wrist Angel): Protocol for the Analysis Plan of a Nonrandomized Pilot Study.JMIR research protocols · 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
- Health Outcomes by Neighborhood (HON): Effects of Neighborhood, Social Instability, and Health Factors on 12-Month Trajectories of Substance-Use Disorder Symptoms.Substance use & misuse · 2023Article
- mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022Article
- The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations.Frontiers in psychiatry · 2022Article
- mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study.JMIR mental health · 2021Article
- Article
- Using novel mobile sensors to assess stress and smoking lapse.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2020Article
- Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial Sensors.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2020Article
- Evaluating the Reproducibility of Physiological Stress Detection Models.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2020Article
- Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data.Sensors (Basel, Switzerland) · 2020Article
- Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures.Sensors (Basel, Switzerland) · 2020Article
- Stress Tracker-Detecting Acute Stress From a Trackpad: Controlled Study.Journal of medical Internet research · 2020Article
- Technologies for Opioid Use Disorder Management: Mobile App Search and Scoping Review.JMIR mHealth and uHealth · 2020Article
- THE STRATIFIED MICRO-RANDOMIZED TRIAL DESIGN: SAMPLE SIZE CONSIDERATIONS FOR TESTING NESTED CAUSAL EFFECTS OF TIME-VARYING TREATMENTS.The annals of applied statistics · 2020Article
Corrections and comments
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Authors and funding
12 authors at 7 institutions in 1 country.
Funding
Abstract
Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the duration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes.
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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.