Utility of wearable technology in predicting panic attacks: A scoping review

This scoping review reports on findings of recent studies which assess the methodologies, significant features, and machine learning (ML) models employed in wearable-based panic attack (PA) research.

PAs affect a significant percentage of people worldwide and can induce rapid heartbeat, sweating, and trembling in affected individuals. They are often unpredictable and can seriously affect day-to-day activities and functioning. The integration of wearable technology with advanced ML methods has been successfully used in identifying and managing several health conditions. Despite significant advancements, the predictive capabilities of these tools to detect PAs remain less understood.

A comprehensive search of databases including PubMed, PsycINFO, Embase, and Google Scholar identified seven studies focusing on PA prediction using wearable devices.

These studies employed a range of ML models, such as supervised anomaly detection, deep learning (e.g. LSTM, RNN), random forests, and mixed regression models. The studies analyzed physiological metrics like heart rate (HR) variability, respiratory rate, and activity levels. Accuracy rates varied, with models achieving between 67.4% and 94.8% predictive accuracy.

Findings show the utility of combining psychological, physiological, and environmental data for improved predictions, and highlight the key data features, such as resting HR, heart rate variability, and certain sleep metrics that may help predict the onset of PAs. However, most of these studies have impractical prediction time frames of PAs with limited evidence of successful near-real-time prediction, highlighting the need for further research to predict the onset of PAs in real time.