Mobile-assisted Case Management for Panic Disorder

A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and impairing quality of life. A prediction model for PAs could help clinicians and patients monitor, control, and do early intervention for recurrent panic attacks, enabling more personalized treatment for panic disorder. This study aimed to provide a seven-day PA prediction model and determine the relationship between a future PA and various features, including physiological factors, anxiety and depressive factors, and air quality index. We will enroll 200 participants (150 participants join case management with wearables study, 50 participants join TAU group) with PD (DMS-5 and MINI interview). Participants used smartwatches (Garmin vivosmart 4) and mobile applications to collect their sleep, heart rate, activity level, anxiety, and depression scores (BDI, BAI, STAI-S, STAI-T, and PDSS-SR) in their real-life for a duration of one year. We also included air quality indexes from open data. To analyze these data, our team used six machine learning methods: random forests, decision trees, LDA, AdaBoost, XGBoost, and regularized greedy forests, or other deep learning methods.