Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review

This scoping review provides a novel synthesis of common digital parameters used across diverse tools for monitoring and predicting depression, moving beyond tool- or modality-specific perspectives adopted in prior reviews. Unlike existing reviews focusing on individual sensing modalities, prediction methods, or intervention effectiveness, this review maps shared parameters across observational, predictive, and interventional studies. By identifying convergent digital markers, the review supports comparability across studies and informs future model development. These findings have practical implications for the design of scalable digital mental health tools and for translating digital phenotyping into real-world clinical and self-management contexts.

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Publication Details

Journal
JMIR mhealth and uhealth
Published
2025-12-21
DOI
https://doi.org/10.2196/70840
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
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article

Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review

JMIR mhealth and uhealth
Digital Mental Health Interventions
article

Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review

article en

Abstract

This scoping review provides a novel synthesis of common digital parameters used across diverse tools for monitoring and predicting depression, moving beyond tool- or modality-specific perspectives adopted in prior reviews. Unlike existing reviews focusing on individual sensing modalities, prediction methods, or intervention effectiveness, this review maps shared parameters across observational, predictive, and interventional studies. By identifying convergent digital markers, the review supports comparability across studies and informs future model development. These findings have practical implications for the design of scalable digital mental health tools and for translating digital phenotyping into real-world clinical and self-management contexts.

JMIR mhealth and uhealthVol. 14
Goethe University Frankfurt (DE), Goethe-Institute United Kingdom (GB), University Hospital Frankfurt (DE)
Openalex Percentile: Top 54%
Digital Mental Health Interventions
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Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review · JMIR mhealth and uhealth (2025) | TGRS Research Map | TGRS