ArticleJMIR mHealth and uHealth2019
Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
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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
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review.JMIR mHealth and uHealth · 2024Pooled it
- From Who experiences stress to When It Occurs: Revisiting Lazarus' transactional model of stress using an ecological momentary assessment framework.PLOS digital health · 2026Article
- Digital phenotyping of affect and stress in emerging adults.Frontiers in digital health · 2026Article
- Differential associations of passively sensed behaviors with in-vivo depression symptoms.Journal of psychopathology and clinical science · 2025Article
- Personalised modelling of routine variability and affective states.NPJ digital medicine · 2025Article
- Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders.The International journal of eating disorders · 2025Review
- Modern Smart Gadgets and Wearables for Diagnosis and Management of Stress, Wellness, and Anxiety: A Comprehensive Review.Healthcare (Basel, Switzerland) · 2025Review
- Forecasting mental states in schizophrenia using digital phenotyping data.PLOS digital health · 2025Article
- Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.Depression and anxiety · 2025Article
- Characterizing Stress Processes by Linking Big Five Personality States, Traits, and Day-to-Day Stressors.Journal of research in personality · 2024Article
- Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life.Translational psychiatry · 2022Article
- Fluctuations in behavior and affect in college students measured using deep phenotyping.Scientific reports · 2022Article
- An exploration in remote blood pressure management: Application of daily routine pattern based on mobile data in health management.Fundamental research · 2022Article
- Emerging wearable technology applications in gastroenterology: A review of the literature.World journal of gastroenterology · 2021Review
- Conditional Entropy: A Potential Digital Marker for Stress.Entropy (Basel, Switzerland) · 2021Article
- Predictors of health-related quality of life in musculoskeletal disease patients: a longitudinal analysis.Therapeutic advances in musculoskeletal disease · 2021Article
- What Are We Measuring When We Evaluate Digital Interventions for Improving Lifestyle? A Scoping Meta-Review.Frontiers in public health · 2021Article
- Digital phenotyping for mental health of college students: a clinical review.Evidence-based mental health · 2020Review
- Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study.Journal of medical Internet research · 2020Article
- Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study.JMIR mHealth and uHealth · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundStress levels among college students have been on the rise for the last few decades. Currently, rates of reported stress among college students are at an all-time high. Traditionally, the dominant way to assess stress levels has been through pen-and-paper surveys.
objectiveThe aim of this study is to use passive sensing data collected via mobile phones to obtain a rich and potentially less-biased source of data that can be used to help better understand stressors in the college experience.
methodsWe used a mobile sensing app, StudentLife, in tandem with a pictorial mobile phone-based measure of stress, the Mobile Photographic Stress Meter, to investigate the situations and contexts that are more likely to precipitate stress.
resultsUsing recently developed methods for handling high-dimensional longitudinal data, penalized generalized estimating equations, we identified a set of mobile sensing features (absolute values of beta >0.001 and robust z>1.96) across the domains of social activity, movement, location, and ambient noise that were predictive of student stress levels.
conclusionsBy combining recent statistical methods and mobile phone sensing, we have been able to study stressors in the college experience in a way that is more objective, detailed, and less intrusive than past research. Future work can leverage information gained from passive sensing and use that to develop real-time, targeted interventions for students experiencing a stressful time.
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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.