ArticleTranslational psychiatry2022
Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life.
Article in Translational psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Feasibility and Acceptability of a Mobile App and Wearable Device for Collecting Mental Health Survey and Passively Sensed Data Among Health Care Workers in Kenya: Mixed Methods Pilot Study.JMIR mHealth and uHealth · 2026Article
- The effects of exercise on generalized anxiety symptoms in adults: a systematic review and meta-analysis.BMC sports science, medicine & rehabilitation · 2026Article
- Comparing training window selection methods for prediction in non-stationary time series.The British journal of mathematical and statistical psychology · 2026Article
- The Use of Artificial Intelligence for Personalized Treatment in Psychiatry.Current psychiatry reports · 2025Review
- Harnessing Digital Phenotyping for Early Self-Detection of Psychological Distress.Healthcare (Basel, Switzerland) · 2025Article
- Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review.Journal of medical Internet research · 2025Article
- Passive Smartphone Sensors for Detecting Psychopathology.JAMA network open · 2025Article
- Observational
- Integrating genome-wide information and wearable device data to explore the link of anxiety and antidepressants with pulse rate variability.Molecular psychiatry · 2025Article
- Neuro-Nutrition and Exercise Synergy: Exploring the Bioengineering of Cognitive Enhancement and Mental Health Optimization.Bioengineering (Basel, Switzerland) · 2025Review
- Forecasting mental states in schizophrenia using digital phenotyping data.PLOS digital health · 2025Article
- Understanding tinnitus symptom dynamics and clinical improvement through intensive longitudinal data.NPJ digital medicine · 2025Article
- Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.Depression and anxiety · 2025Article
- Relationships between depression, anxiety, and motivation in the real-world: Effects of physical activity and screentime.medRxiv : the preprint server for health sciences · 2024Article
- Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number.Translational psychiatry · 2024Article
- Review: Preventing Psychopathology in the Digital Age: Leveraging Technology to Target Coping and Emotion Regulation in Adolescents.JAACAP open · 2024Review
- Walk, talk, think, see and feel: harnessing the power of digital biomarkers in healthcare.NPJ digital medicine · 2024Article
- Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study.JMIR formative research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundGeneralized anxiety disorder (GAD) is a highly prevalent condition. Monitoring GAD symptoms requires substantial time, effort, and cost. The development of digital phenotypes of GAD may enable new scalable, timely, and inexpensive assessments of GAD symptoms.
methodThe current study used passive movement data collected within a large national cohort (N = 264) to assess GAD symptom severity.
resultsUsing one week of movement data, machine learning models accurately predicted GAD symptoms across a continuum (r = 0.511) and accurately detected those individuals with elevated GAD symptoms (AUC = 0.892, 70.0% Sensitivity, 95.5% Specificity, Brier Score = 0.092). Those with a risk score at the 90 LIMITATIONS: The current study examined the detection of GAD symptom severity rather than the prediction of GAD symptom severity across time. Furthermore, the instant sample of data did not include nighttime actigraphy, as participants were not asked to wear the actigraphs at night.
conclusionsThese results suggest that artificial intelligence can effectively utilize wearable movement data collected in daily life to accurately infer risk of GAD symptoms.
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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.