학술논문

Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data
Document Type
Periodical
Source
IEEE Open Journal of Engineering in Medicine and Biology IEEE Open J. Eng. Med. Biol. Engineering in Medicine and Biology, IEEE Open Journal of. 5:14-20 2024
Subject
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Mood
Wearable computers
Behavioral sciences
Data models
Social networking (online)
Mental health
Heart rate variability
Panic attacks
wearables
apple watch
mental health
twitter
Language
ISSN
2644-1276
Abstract
Objective: Panic attacks are an impairing mental health problem that affects 11% of adults every year. Current criteria describe them as occurring without warning, despite evidence suggesting individuals can often identify attack triggers. We aimed to prospectively explore qualitative and quantitative factors associated with the onset of panic attacks. Results: Of 87 participants, 95% retrospectively identified a trigger for their panic attacks. Worse individually reported mood and state-level mood, as indicated by Twitter ratings, were related to greater likelihood of next-day panic attack. In a subsample of participants who uploaded their wearable sensor data (n = 32), louder ambient noise and higher resting heart rate were related to greater likelihood of next-day panic attack. Conclusions: These promising results suggest that individuals who experience panic attacks may be able to anticipate their next attack which could be used to inform future prevention and intervention efforts.