JMIR Publications

ABSTRACT

Background:

Digital health interventions that incorporate self-management strategies are increasingly used to support individuals in managing their mental health. These interventions often leverage self-monitoring, ecological momentary assessments, and passive sensor-based data collection to provide personalized feedback and guide behavioral change. With the proliferation of smartphones and wearable devices, there is growing potential to continuously collect behavioral and physiological data. However, a major limitation in the field is the lack of consolidated evidence on which specific parameters are most useful for monitoring and predicting depression-related outcomes.

Objective:

This scoping review aims to identify and synthesize common passively and actively collected parameters used in digital health interventions for depression. Specifically, it addresses the methodological and knowledge gap concerning which types of sensor-based (passively collected) and self-reported data (actively collected) are most frequently employed and which demonstrate predictive value in tracking changes in depressive symptoms across different digital intervention platforms.

Methods:

A comprehensive literature search was conducted across four databases: PubMed, Embase, Cochrane Library, and Web of Science Core Collection. Articles published between January 2021 and November 2025 were included. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines and implemented Arksey and O’Malley’s five-stage methodological framework. Quality assessment of included studies was performed using the Downs and Black Instrument and the Mixed Methods Appraisal Tool.

Results:

From the reviewed studies, five overarching parameter categories were identified: (1) Physical activity and location, (2) Behavioral patterns, (3) Physiological signals, (4) Sleep indicators, and (5) Sociability and self-reported assessments. Within these, eleven distinct metrics such as step count, heart rate variability, sleep duration, phone usage patterns, and mood self-ratings emerged as the most commonly used indicators. Most interventions employed a multimodal digital phenotyping approach, integrating passive sensor-derived data with active user-reported input. This hybrid methodology allowed for more nuanced, individualized symptom tracking over time.

Conclusions:

Findings indicate that digital interventions combining multiple data modalities, especially sensor-based monitoring and self-reported assessments, demonstrate greater efficacy in capturing and predicting depressive symptom trajectories. The integration of diverse parameters across behavioral, physiological, and subjective domains enables a more comprehensive understanding of mood fluctuations and treatment needs. Establishing a standardized set of parameters for digital phenotyping in depression could improve clinical applicability, facilitate cross-study comparisons, and support the development of personalized, scalable mental health interventions.