Paperdoi:10.2196/33063
Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study
JMIR Medical Informatics 10(2):e33063
TL;DR
Fifty-nine people with panic disorder wore a Garmin Vívosmart 4 for one year while also reporting questionnaires and environmental exposures. Machine learning models predicted panic attacks over a 7-day window with accuracy from 67.4% to 81.3%, with random forest performing best.
- Form factor: wrist
Abstract
Background
A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and impairing the quality of life. A prediction model for PAs could help clinicians and patients monitor, control, and carry out early intervention for recurrent PAs, enabling more personalized treatment for panic disorder (PD).
Objective
This study aims to provide a 7-day PA prediction model and determine the relationship between a future PA and various features, including physiological factors, anxiety and depressive factors, and the air quality index (AQI).
Methods
We enrolled 59 participants with PD (Diagnostic and Statistical Manual of Mental Disorders, 5th edition, and the Mini International Neuropsychiatric Interview). Participants used smartwatches (Garmin Vívosmart 4) and mobile apps to collect their sleep, heart rate (HR), activity level, anxiety, and depression scores (Beck Depression Inventory [BDI], Beck Anxiety Inventory [BAI], State-Trait Anxiety Inventory state anxiety [STAI-S], State-Trait Anxiety Inventory trait anxiety [STAI-T], and Panic Disorder Severity Scale Self-Report) in their real life for a duration of 1 year. We also included AQIs from open data. To analyze these data, our team used 6 machine learning methods: random forests, decision trees, linear discriminant analysis, adaptive boosting, extreme gradient boosting, and regularized greedy forests.
Results
For 7-day PA predictions, the random forest produced the best prediction rate. Overall, the accuracy of the test set was 67.4%-81.3% for different machine learning algorithms. The most critical variables in the model were questionnaire and physiological features, such as the BAI, BDI, STAI, MINI, average HR, resting HR, and deep sleep duration.
Conclusions
It is possible to predict PAs using a combination of data from questionnaires and physiological and environmental data.
Verbatim from the publisher's DOI record (opens in a new tab) · Abstract as deposited by the publisher with Crossref; © the authors/publisher
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Cite this paper
Tsai, C.-H., Chen, P.-C., Liu, D.-S., Kuo, Y.-Y., Hsieh, T.-T., Chiang, D.-L., Lai, F., & Wu, C.-T. (2022). Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study. JMIR Medical Informatics, 10(2), e33063. https://doi.org/10.2196/33063
Tsai, Chan-Hen, Pei-Chen Chen, Ding-Shan Liu, Ying-Ying Kuo, Tsung-Ting Hsieh, Dai-Lun Chiang, Feipei Lai, and Chia-Tung Wu. 2022. "Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study." JMIR Medical Informatics 10 (2): e33063. https://doi.org/10.2196/33063.
Tsai, C.-H., Chen, P.-C., Liu, D.-S., Kuo, Y.-Y., Hsieh, T.-T., Chiang, D.-L., Lai, F. and Wu, C.-T. (2022) 'Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study', JMIR Medical Informatics, 10(2), p. e33063. doi: 10.2196/33063.
Tsai, Chan-Hen, et al. "Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study." JMIR Medical Informatics, vol. 10, no. 2, 2022, p. e33063. https://doi.org/10.2196/33063.
C.-H. Tsai et al., "Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study," JMIR Medical Informatics, vol. 10, no. 2, p. e33063, 2022, doi: 10.2196/33063.
@article{tsai2022panic,
title = {{Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study}},
author = {Tsai, Chan-Hen and Chen, Pei-Chen and Liu, Ding-Shan and Kuo, Ying-Ying and Hsieh, Tsung-Ting and Chiang, Dai-Lun and Lai, Feipei and Wu, Chia-Tung},
year = {2022},
journal = {JMIR Medical Informatics},
volume = {10},
number = {2},
pages = {e33063},
doi = {10.2196/33063}
}Added to the ChatPPG research hub
