JMIR Publications

ABSTRACT

Background:

Background:

Adolescence is marked by an increasing risk for developing depression, but also presents an optimal window for high-impact interventions. Unfortunately, meta-analyses have revealed smaller effect sizes for youth psychotherapy for depression compared to adults and compared to other disorders in youth. Personalizing intervention sequencing may maximize therapeutic benefit for youth with depression, a markedly heterogeneous disorder. However, there is a lack of empirical evidence for how to personalize psychotherapy for youth. Identifying person-specific drivers during adolescence could inform treatments that account for both developmental and individual differences to shift the trajectory of depression onset and maintenance.

Objective:

Objectives: We aim to test the feasibility of using adolescents’ everyday smartphone use to identify idiographic drivers of depression. We will also test the validity of passive sensing of everyday smartphone use against established ambulatory methods (i.e., ecological momentary assessment [EMA] and actigraphy) to identify person-specific drivers of adolescent depression.

Methods:

Methods:

Fifty adolescents with elevated symptoms of depression will participate in 28 days of a) smartphone-based EMA of depressive symptoms, processes, affect, and sleep; (b) mobile sensing of mobility, physical activity, sleep, natural language use in typed interpersonal communication, screen-on time and call frequency/duration using the Effortless Assessment of Risk States (EARS) smartphone application; and (c) wrist actigraphy of physical activity and sleep. Adolescents and caregivers will complete developmental and clinical measures at baseline, as well as user feedback interviews at follow-up. Idiographic, within-subject networks of EMA symptoms will be modeled to identify each adolescent’s person-specific drivers of depression. Correlations among EMA, mobile sensor, and actigraph measures of sleep, physical, and social activity will be used to assess the validity of mobile sensing for identifying person-specific drivers. Machine learning prediction of core depressive symptoms (self-reported mood and anhedonia) will also be used to assess the validity of mobile sensing for identifying drivers. Finally, between-subject baseline characteristics will be explored as predictors of person-specific drivers.

Results:

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Conclusions:

Conclusions:

We hope to leverage depressed adolescents’ everyday smartphone use to assess the validity of mobile sensing against established ambulatory methods to identify person-specific drivers of adolescent depression. These results will inform future development of a scalable, low-burden smartphone-based tool that can guide personalized treatment decisions for depressed adolescents.