{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T20:30:51Z","timestamp":1792269051442,"version":"4.4.0"},"reference-count":49,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Digital health interventions incorporating self-management strategies are increasingly used to support individuals in managing depression. These interventions often leverage self-monitoring and passive sensor-based data collection to provide personalized feedback, guiding 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This scoping review aims to identify and synthesize common digital phenotyping and self-report parameters for monitoring and predicting depression. Specifically, it addresses the methodological and knowledge gap concerning which types of sensor-based and self-reported data are most frequently used and which demonstrate predictive value in tracking changes in depressive symptoms across digital platforms.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A literature search was conducted across 4 databases, including PubMed, Embase, Cochrane Library, and the Web of Science Core Collection. Articles published between January 1, 2021, and November 26, 2025, were included. Eligible studies included adults (\u226518 years) with depression confirmed through validated clinical measures and using digital approaches that collected passive sensor data, self-reports, or both. Studies focusing on comorbid disorders, nondigital interventions, or not reporting depression-related outcomes were excluded. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines were followed, and a 5-stage methodological framework for scoping reviews was implemented. Quality assessment was performed using the Downs and Black Instrument and the Mixed Methods Appraisal Tool (MMAT).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Nineteen studies were included, comprising a total of 85,193 participants. Most studies used smartphone- or wearable-based tools, with passive sensing as the predominant data source and Patient Health Questionnaire-9 (PHQ-9) as the most commonly used depression measure. 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 categories, 11 metrics, including step count, heart rate variability, sleep duration and mood self-ratings, were most frequently reported. Most studies used a multimodal digital phenotyping approach, integrating passive sensor-derived data with active user-reported input, enabling more individualized symptom monitoring over time.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/70840","type":"journal-article","created":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:55:07Z","timestamp":1766300107000},"page":"e70840-e70840","source":"Crossref","is-referenced-by-count":4,"title":["Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3013-911X","authenticated-orcid":false,"given":"Lisa","family":"Busshart","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9577-0811","authenticated-orcid":false,"given":"Milica","family":"Petrovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2269-0447","authenticated-orcid":false,"given":"Rebeka","family":"Amin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3039-7470","authenticated-orcid":false,"given":"Ulrich","family":"Hegerl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"issue":"10-11","key":"R1","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1080\/10810730.2018.1544676","article-title":"Theoretical advancements in mHealth: a systematic review of mobile apps","volume":"23","author":"Chib","journal-title":"J Health Commun"},{"issue":"2","key":"R2","doi-asserted-by":"publisher","DOI":"10.2196\/mental.5165","article-title":"New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research","volume":"3","author":"Torous","journal-title":"JMIR Ment Health"},{"key":"R3","unstructured":"What kinds of sensors are embedded in smartphones? 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