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.

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.

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Objective: Internalizing disorders, such as anxiety and depression, in pre-school aged children are common and often go undiagnosed into adulthood. To augment traditional parental-reports, we have previously presented an objective assessment for early childhood anxiety and depression which leverages movement and vocal biomarkers measured via wearable sensors during brief mood induction tasks that achieves good accuracy (75%-81%). However, these methods required specialized equipment and expertise in data and sensor engineering to administer and analyze. Method: To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes an Android mobile application for collecting movement and audio data during mood induction tasks and an open-source data analysis platform for extracting digital biomarkers and discovering digital phenotypes. As proof of principle, we present data collected using the ChAMP System from 50 children ages 5-8, with and without anxiety or depressive disorders. Results: Movement and vocal features derived from the ChAMP System support the consideration of theory-driven temporal phases within mood induction tasks, and the use of an assessment battery for characterizing childhood internalizing disorders. Results also demonstrate that features significantly differ between diagnostic groups and correlate with symptom severity implying their potential use as digital biomarkers. Conclusions: The ChAMP System provides clinically relevant digital biomarkers of childhood anxiety and depression. This new open-source tool lowers the barrier to entry for those interested in exploring digital phenotyping of childhood mental health.

UNSTRUCTURED Panic attacks are an impairing mental health problem that affects more than 11% of adults every year. Panic attacks are episodic, and it is difficult to predict when or where they may occur, thus they are challenging to study and treat. To this end, we present PanicMechanic, a novel mobile health (mHealth) application that captures heartrate-based data and delivers biofeedback during panic attacks. We leverage this tool to capture profiles of real-world panic attacks in a largest sample to date and present results from a pilot study to assess the feasibility and usefulness of PanicMechanic as a panic attack intervention. Results demonstrate that heart rate fluctuates by about 15 beats per minute during a panic attack and takes about 30 seconds to return to baseline from peak, cycling 4 to 5 times during each attack and that anxiety ratings consistently decrease throughout the attack. Thoughts about health were the most common trigger during the observed panic attacks, and potential lifestyle contributors include slightly worse stress, sleep, and eating habits, slightly less exercise, and slightly less drug/alcohol consumption than typical. The pilot study revealed that PanicMechanic is largely feasible to use, but would be made more so with simple modifications to the app and particularly the integration of consumer wearables. Similarly, participants found PanicMechanic useful, with 94% indicating that they would recommend PanicMechanic to a friend. These results point toward the need for future development and a controlled trial to establish effectiveness of this digital therapeutic for preventing panic attacks.