Expecting the Unexpected: Predicting Panic Attacks from Mood and Twitter

Abstract

AbstractBackgroundPanic attacks are an impairing mental health problem that affects about one in 10 US adults every year. Current DSM criteria describe panic attacks as unexpected, occurring without warning or triggering events. The unexpected nature of panic attacks not only leads to increased anxiety for the individual but has also made panic attacks particularly challenging to study. However, recent evidence suggests that individuals who experience such attacks could identify attack triggers.ObjectiveWe aimed to explore both retrospectively and prospectively, qualitative, and quantitative factors associated with the onset of panic attacks.MethodWe remotely recruited a diverse sample of 87 individuals who regularly experienced panic attacks from 30 states in the US. Participants responded to daily questions relating to their panic attacks and wellness behaviors each day for 28 days. We also considered daily community level factors captured by the Hedonometer, a metric which estimates population-level happiness daily using a random 10% of all public tweets.ResultsConsistent with our prior work, most participants (95%) were able to retrospectively identify a trigger for their attack. Worse individual mood was associated with greater likelihood of experiencing asame-daypanic attack over and above other individual wellness factors. Worse individually reported mood and state-based population level mood as indicated by the Hedonometer were associated with greater likelihood ofnext-daypanic attack.ConclusionsThese promising results suggest that individuals who experience panic attacks may be able to expect the unexpected. The importance of individual and state-based population level mood in panic attack risk could be used to ultimately inform future prevention and intervention efforts.