Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality of life. Individuals who experience panic attacks would benefit from advance warning of when an attack is likely to occur so that appropriate steps could be taken to manage or prevent it. Our recent work suggests that an individual's likelihood of experiencing a panic attack can be predicted by self-reported mood and community-level Twitter-derived mood the previous day. Prior work also suggests that physiological markers may indicate a pending panic attack. However, the ability of objective physiological, behavioral, and environmental measures to predict next-day panic attacks has not yet been explored. To address this question, we consider data from 38 individuals who regularly experienced panic attacks recruited from across the US. Participants responded to daily questions about their panic attacks for 28 days and provided access to data from their Apple Watches. Results indicate that objective measures of ambient noise (louder) and resting heart rate (higher) are related to the likelihood of experiencing a panic attack the next day. These preliminary results suggest, for the first time, that panic attacks may be predictable from data passively collected by consumer wearable devices, opening the door to improvements in how panic attacks are managed and to the development of new preventative interventions.
Clinical relevance: Objective data from consumer wearables may predict when an individual is at high risk for experiencing a next-day panic attack. This information could guide treatment decisions, help individuals manage their panic, and inform the development of new preventative interventions.
Keywords: digital biomarkers; digital medicine; mHealth; mental health; panic attacks.