JMIR Publications

ABSTRACT

Background:

In recent years, advances in wearable sensor technology and artificial intelligence (AI) have opened up new possibilities for detecting and monitoring depression.

Objective:

This study systematically reviewed and meta-analyzed the diagnostic and predictive performance of wearable device–based AI models for detecting depression and predicting depressive episodes, and explored factors influencing outcomes.

Methods:

Following PRISMA-DTA guidelines, PubMed, Embase, Web of Science, and PsycINFO were searched to May 27, 2025. Eligible studies used AI algorithms on wearable device data for depression detection or episode prediction. Sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were pooled using a bivariate random-effects model. Risk of bias was assessed with PROBAST+AI, and certainty of evidence with GRADE.

Results:

Sixteen studies (32 datasets) with 1,189 patients and 13,593 samples were included. For depression detection, pooled sensitivity and specificity were 0.89 (95% CI, 0.83–0.93) and 0.93 (95% CI, 0.87–0.96), with a DOR of 110.47 (95% CI, 33.33–366.17) and AUC of 0.96 (95% CI, 0.94–0.98). Random forest models showed the best performance (sensitivity 0.89, specificity 0.91, AUC 0.97). Subgroup analyses indicated that study design, AI method, reference standard, and input type significantly affected diagnostic accuracy (P < 0.05). For depressive episode prediction (3 datasets), pooled sensitivity was 0.86 (95% CI, 0.80–0.91) and specificity 0.65 (95% CI, 0.59–0.71). Overall risk of bias was low to moderate, with no evidence of publication bias.

Conclusions:

Wearable device–based AI models achieve high accuracy in detecting depression and moderate utility in predicting episodes. Yet, heterogeneity, reliance on retrospective and public datasets, and lack of standardized methods limit generalizability.