The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: A Systematic Review and...

Digital biomarkers from wearable devices, smartphones, and sensors are increasingly used to support depression assessment by providing objective, continuous, and real-time physiological and behavioral data. However, most existing studies have focused on individual biomarkers, such as sleep or cardiac parameters, while integrative evaluations that capture the multidimensional nature of depression remain limited.

This systematic review evaluated digital biomarkers for depression and synthesized evidence on differences between individuals with depression and non-depressed controls.

Eligible studies included observational or interventional studies examining digital biomarkers for depression with validated outcome measures. Non-English or non-Korean studies, reviews, qualitative studies, duplicates, and studies without accessible full texts were excluded. We searched major international and Korean databases, including MEDLINE, PsycINFO, CINAHL, IEEE Xplore, Web of Science, the Cochrane Library, KISS, RISS, KMbase, and KoreaMed, from inception to December 28, 2025. Risk of bias was assessed using the QUADAS-2 tool for diagnostic accuracy studies and the Scottish Intercollegiate Guidelines Network checklist for observational studies. Meta-analyses were conducted using random-effects models with the Hartung-Knapp-Sidik-Jonkman method, and other outcomes were narratively summarized.

The search yielded 39,617 records, of which 132 studies involving 57,852 participants met the inclusion criteria. These studies encompassed various digital biomarkers, including sleep, physical activity, cardiac measures, smartphone-derived data, speech, global positioning system data, and circadian rhythms. A meta-analysis of 22 studies (6,947 participants) revealed that individuals with depression had significantly longer sleep onset latency (5 studies, n=316; +4.75 min, 95% CI: 2.46 to 7.04, P=.005) and time in bed (7 studies, n=236; +30.55 min, 95% CI: 14.22 to 46.89, P=.01). Physical activity counts were also significantly lower (5 studies, n=417; standardized mean difference -0.71, 95% CI: -1.33 to -0.09, P=.03). Although depressed individuals showed lower sleep efficiency, higher mean heart rate, and lower standard deviation of normal-to-normal intervals, these differences were not statistically significant. Other digital markers yielded inconsistent results. Overall, these findings indicate that no single digital biomarker sufficiently captures depression-related changes. Instead, the results support the superiority of personalized, multimodal approaches. However, the generalizability of these findings is limited by the lack of standardized data collection protocols and high clinical heterogeneity across studies, as reflected in wide prediction intervals. Future research should prioritize harmonizing digital phenotyping methods to enhance the precision of depression assessments.

Certain digital biomarkers, particularly sleep onset latency and physical activity counts, showed consistent average differences between depressed and non-depressed groups. However, wide prediction intervals indicate substantial variability across settings, suggesting that no single marker is sufficient for reliable detection. These findings support the use of personalized, multimodal digital phenotyping approaches and highlight the need for standardized, clinically interpretable frameworks for real-world depression monitoring.

The protocol for this review was prospectively registered in the PROSPERO systematic review database (CRD42024518136).