Digital Phenotype for Childhood Internalizing Disorders: Less Positive Play and Promise for a Brief Assessment Battery.

Childhood internalizing disorders, like anxiety and depression, are common, impairing, and difficult to detect. Universal childhood mental health screening has been recommended, but new technologies are needed to provide objective detection. Instrumented mood induction tasks, designed to press children for specific behavioral responses, have emerged as means for detecting childhood internalizing psychopathology. In our previous work, we leveraged machine learning to identify digital phenotypes of childhood internalizing psychopathology from movement and voice data collected during negative valence tasks (pressing for anxiety and fear). In this work, we develop a digital phenotype for childhood internalizing disorders based on wearable inertial sensor data recorded from a Positive Valence task during which a child plays with bubbles. We find that a phenotype derived from features that capture reward responsiveness is able to accurately detect children with underlying internalizing psychopathology (AUC = 0.81). In so doing, we explore the impact of a variety of feature sets computed from wearable sensors deployed to two body locations on phenotype performance across two phases of the task. We further consider this novel digital phenotype in the context of our previous Negative Valence digital phenotypes and find that each task brings unique information to the problem of detecting childhood internalizing psychopathology, capturing different problems and disorder subtypes. Collectively, these results provide preliminary evidence for a mood induction task battery to develop a novel diagnostic for childhood internalizing disorders.

Validation of Smartphone Based Heart Rate Tracking for Remote Treatment of Panic Attacks.

Panic attacks are an impairing mental health problem that affects 11% of adults every year [1]. Those who suffer from panic attacks often do not seek psychological treatment, citing the inability to receive care during their attacks as a contributing factor. A digital medicine solution which provides an accessible, real-time mobile health (mHealth) biofeedback intervention for panic attacks may address this problem. Critical to this approach are methods for capturing physiological arousal during an attack. Herein, we validate an algorithm for capturing physiological arousal using smartphone video of the fingertip. Results demonstrate that the algorithm is able to estimate heart rates that are highly correlated with ECG-derived values (r > 0.99), effectively reject low-quality data often captured outside of controlled laboratory environments (AUC > 0.90), and resolve the physiological arousal experienced during a panic attack. Moreover, patient reported measures indicate that this measurement modality is feasible during panic attacks, and the act of taking the measurement may stop the attack. These results point toward the need for future development and clinical evaluation of this mHealth intervention for preventing panic attacks.