Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

There is a significant need to develop objective measures for identifying children under the age of 8 who have anxiety and depression. If left untreated, early internalizing symptoms can lead to adolescent and adult internalizing disorders as well as comorbidity which can yield significant health problems later in life including increased risk for suicide. To this end, we propose the use of an instrumented fear induction task for identifying children with internalizing disorders, and demonstrate its efficacy in a sample of 63 children between the ages of 3 and 7. In so doing, we extract objective measures that capture the full six degree-of-freedom movement of a child using data from a belt-worn inertial measurement unit (IMU) and relate them to behavioral fear codes, parent-reported child symptoms and clinician-rated child internalizing diagnoses. We find that IMU motion data, but not behavioral codes, are associated with parent-reported child symptoms and clinician-reported child internalizing diagnosis in this sample. These results demonstrate that IMU motion data are sensitive to behaviors indicative of child psychopathology. Moreover, the proposed IMU-based approach has increased feasibility of collection and processing compared to behavioral codes, and therefore should be explored further in future studies.

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.