ArticleScientific data2024
A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Introducing openESM: A database of openly available experience sampling datasets.Behavior research methods · 2026Article
- Machine learning for scalable obstructive sleep apnea risk screening using digital phenotyping from wearable devices and clinical scales.Scientific reports · 2026Article
- Smartphone-based digital phenotyping for detection of high-risk depression and anxiety in Korean community settings.Internet interventions · 2026Article
- Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Exploration of wearable sensor measures associated with panic attacks differs across mental health conditions.Frontiers in digital health · 2026Article
- Digital phenotyping using smartphones could help steer mental health treatment.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study.Scientific data · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
This study investigated the utilization of digital phenotypes and machine learning algorithms to predict impending panic symptoms in patients with mood and anxiety disorders. A cohort of 43 patients was monitored over a two-year period, with data collected from smartphone applications and wearable devices. This research aimed to differentiate between the day before panic (DBP) and stable days without symptoms. With RandomForest, GradientBoost, and XGBoost classifiers, the study analyzed 3,969 data points, including 254 DBP events. The XGBoost model demonstrated performance with a ROC-AUC score of 0.905, while a simplified model using only the top 10 variables maintained an ROC-AUC of 0.903. Key predictors of panic events included evaluated Childhood Trauma Questionnaire scores, increased step counts, and higher anxiety levels. These findings indicate the potential of machine learning algorithms leveraging digital phenotypes to predict panic symptoms, thereby supporting the development of proactive and personalized digital therapies and providing insights into real-life indicators that may exacerbate panic symptoms in this population.
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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.