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ObjectiveInternalizing 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.MethodTo 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.ResultsMovement 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.ConclusionsThe 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. |
