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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • McGinnis, EW; Scism, J; Hruschak, J; Muzik, M; Rosenblum, KL; Fitzgerald, K; Copeland, W; McGinnis, RS

Published Date

  • August 2021

Published In

Volume / Issue

  • 25 / 8

Start / End Page

  • 3176 - 3184

PubMed ID

  • 33481724

Pubmed Central ID

  • PMC8384142

Electronic International Standard Serial Number (EISSN)

  • 2168-2208

Digital Object Identifier (DOI)

  • 10.1109/JBHI.2021.3053846

Language

  • eng

Conference Location

  • United States