A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study

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 d...

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Bibliographic Details
Published in:Scientific data 2024-11, Vol.11 (1), p.1264-10, Article 1264
Main Authors: Jang, Sooyoung, Sun, Tai hui, Shin, Seunghyun, Lee, Heon-Jeong, Shin, Yu-Bin, Yeom, Ji Won, Park, Yu Rang, Cho, Chul-Hyun
Format: Article
Language:English
Subjects:
692/53/2421
692/53/2423
Adult
Algorithms
Anxiety disorders
Anxiety Disorders - diagnosis
Children
Data Descriptor
Female
Humanities and Social Sciences
Humans
Learning algorithms
Longitudinal Studies
Machine Learning
Male
multidisciplinary
Panic Disorder - diagnosis
Phenotype
Phenotypes
Phenotyping
Prospective Studies
Science
Science (multidisciplinary)
Smartphone
Wearable Electronic Devices
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Summary: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.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-04147-6