A Digital Therapeutic Intervention Delivering Biofeedback for Panic Attacks (PanicMechanic): Feasibility and Usability Study

Abstract

Background Panic attacks (PAs) are an impairing mental health problem that affects >11% of adults every year. PAs are episodic, and it is difficult to predict when or where they may occur; thus, they are challenging to study and treat. Objective The aim of this study is to present PanicMechanic, a novel mobile health app that captures heart rate–based data and delivers biofeedback during PAs. Methods In our first analysis, we leveraged this tool to capture profiles of real-world PAs in the largest sample to date (148 attacks from 50 users). In our second analysis, we present the results from a pilot study to assess the usefulness of PanicMechanic as a PA intervention (N=18). Results The results demonstrate that heart rate fluctuates by about 15 beats per minute during a PA and takes approximately 30 seconds to return to baseline from peak, cycling approximately 4 times during each attack despite the consistently decreasing anxiety ratings. Thoughts about health were the most common trigger and potential lifestyle contributors include slightly worse stress, sleep, and eating habits and slightly less exercise and drug or alcohol consumption than typical. Conclusions The pilot study revealed that PanicMechanic is largely feasible to use but would be made more so with modifications to the app and the integration of consumer wearables. Similarly, participants found PanicMechanic useful, with 94% (15/16) indicating that they would recommend PanicMechanic to others who have PAs. These results highlight the need for future development and a controlled trial to establish the effectiveness of this digital therapeutic for preventing PAs.

Expecting the Unexpected: Predicting Panic Attacks from Mood and Twitter

“…For instance, in our previous paper, health was listed as a trigger of panic attacks by 38% of respondents during the beginning of the 2020 pandemic lockdown in the United States [4]. Other studies have shown an increase of anxiety due to an increase of mortality-related thoughts due to the pandemic [15].…”

“…The daily survey (5 minutes) included items on whether they had experienced a panic attack and associated descriptive information including what they believed their trigger was (open text response), their mood, sleep quality, eating habits (each on a scale of 1-poor, 2-fine, 3-neutral, 4-good, 5-great), stress (on a scale of 1-low stress to 5-very high stress), conflict (yes or no), exercise minutes (open numeric response), and substance use inquiring separately about caffeine, alcohol, prescription drugs, recreational drugs (yes or no) (see Supplemental Table 1 for more detail). Participants were also asked to upload their Apple Watch data weekly after wearing it every day and to use an app, PanicMechanic TM to record their heart rates during any panic attack they experienced [4,22,23]. Only data from surveys are included in subsequent analyses.…”

“…Qualitative content analysis [4,26] was conducted on open text responses of panic attack triggers. Two coders read all responses and developed categories with respective definitions based on commonalities amongst responses.…”

“…The DSM-5 mentions some cognitive risk factors associated with panic attacks such as negative affect and anxiety sensitivity, but new evidence suggests anxiety sensitivity could have a causal role in panic [8]. Our previous work demonstrates nearly all (98%) of 85 participants suffering from panic attacks could identify a trigger of their latest attack [4]. Health was the most frequently identified trigger (38%), followed by conflict (19%), performance (17%), and workload (15%), with only 2% reporting they were unsure of the trigger [4].…”

Advancing Digital Medicine with Wearables in the Wild

“…While individuals with episodic conditions may be able to report their symptoms and even seek help during the episode, an accurate characterization of these symptoms is often difficult, and treatment is often necessary immediately or better prescribed as a preventative measure. For instance, conditions such as experiencing panic attacks [27,28], mania [29,30], suicidality [31,32], epileptic seizures [33], or atrial fibrillation [11,34] would all benefit from objective characterization of frequency, severity, and duration to inform appropriate care. Moreover, while these conditions may feel unpredictable, emerging data suggests digital markers may indicate elevated risk for an episode.…”