Currently accepted at: Journal of Medical Internet Research
Date Submitted: May 14, 2026
Date Accepted: Sep 24, 2026
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/101329
The final accepted version (not copyedited yet) is in this tab.
Online Behavioral Digital Phenotyping for Depression: Scoping Review of Behavioral Features and Exploratory Assessment of RDoC Construct Relevance
ABSTRACT
Background:
Traditional methods for assessing depression are limited by recall bias and fail to capture dynamic symptom changes. The Research Domain Criteria (RDoC) framework supports multilevel analysis, and digital phenotyping using passive smartphone data facilitates objective multidimensional assessment. However, existing reviews often conflate online behavior with physiological sensor data and do not systematically examine digital phenotyping based exclusively on online behaviors within a unified theoretical framework.
Objective:
This study conducted a scoping review aiming to: (1) identify and summarize key online behavioral modalities and their specific features for depression monitoring and assessment; (2) explore the connection between behavioral features and psychopathology dimensions based on the RDoC framework to improve clinical interpretability.
Methods:
We conducted a systematic scoping review searching five databases (PubMed, Scopus, Web of Science, EMBASE, and PsycINFO) from inception through January 2026, supplemented by citation tracking and gray literature websites. Included studies focused on online behavioral digital phenotyping for depression and reported specific behavioral features. Two reviewers independently screened studies using predefined criteria. Data were extracted by one reviewer and verified by another. Findings were descriptively synthesized.
Results:
74 articles were included. Five primary behavioral modalities were identified: call behavior (n = 31), short message service (SMS, n = 21), screen usage (n = 41), app usage (n = 26), and keyboard dynamics (n = 14). These behaviors mapped to RDoC domains, including social processes, sensorimotor systems, cognitive systems, and arousal/regulatory systems.
Conclusions:
Online behavioral digital phenotyping offers an objective, continuous approach to dimensional depression assessment aligned with RDoC. Future work should standardize feature engineering, clarify mechanistic links to RDoC constructs, and develop tools for personalized clinical monitoring. Clinical Trial: This review was prospectively registered on the Open Science Framework (registration 10.17605/OSF.IO/6X8DR).
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.