Currently accepted at: JMIR mHealth and uHealth
Date Submitted: Jan 19, 2026
Date Accepted: Jun 4, 2026
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/91479
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Daily Actigraphy-based Passive Screening of Depression using Artificial Intelligence
ABSTRACT
Background:
Depressive symptoms are common yet often underrecognized in routine care, underscoring the need for scalable screening approaches beyond episodic self-report assessments. Wearable actigraphy can passively and continuously capture daily activity and 24-hour rest–activity rhythms associated with depressive symptom burden. However, the performance of artificial intelligence (AI) models that leverage actigraphy data for depression screening remains insufficiently established.
Objective:
This study aimed to develop and evaluate AI-based models for passive screening of depressive symptoms from daily wrist actigraphy data.
Methods:
We analyzed actigraphy recordings from 1,160 Hispanic/Latino adults in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) who completed the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), a self-reported depressive symptom screening scale. Multichannel actigraphy data, including activity counts, light exposure, and wake status, were used as inputs to five deep learning architectures to classify CESD-10–defined depressive symptom groups, comparing mild and higher symptoms with the normal group.
Results:
Actigraphy-derived behavioral markers differed across depressive symptom groups, showing lower daytime activity and altered circadian rest–activity organization with increasing symptom burden. In held-out testing, the best-performing models achieved AUROCs of 0.791 for mild symptoms and 0.832 for higher depressive symptoms.
Conclusions:
Our study suggests that actigraphy-derived data can support AI-based classification of depressive symptoms. An actigraphy-based AI model may serve as a scalable, passive, and noninvasive complementary signal to aid early screening alongside traditional depression assessments before clinical diagnosis.
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.