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

Clinical Snapshot

Key Findings

• P Value: Significant differences found for sleep onset latency (P=0.005), time in bed (P=0.01), and physical activity (P=0.03)

• Effect Size: Physical activity standardized mean difference -0.71 (95% CI -1.33 to -0.09)

• Primary Outcome: Sleep onset latency significantly longer in depression group by 4.75 minutes (95% CI 2.46-7.04, P=0.005)

• Nnt Or Sensitivity: Diagnostic accuracy metrics not reported; wide prediction intervals (sleep onset latency PI 0.01-10.27 minutes) indicate poor clinical utility for individual patient assessment

• Confidence Interval: Time in bed difference +31.81 minutes (95% CI 18.22-45.39)

Clinical Application

Current evidence insufficient for routine clinical implementation due to wide prediction intervals and inconsistent results across settings No specific Australian studies identified. Would require local validation studies and integration with existing mental health frameworks before TGA approval or PBS consideration for digital therapeutics Adults with depression in research settings, but clinical applicability limited by high between-study variability and lack of standardized protocols

Abstract

BACKGROUND: Digital biomarkers are increasingly being 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. OBJECTIVE: This systematic review evaluated digital biomarkers for depression and synthesized evidence on differences between individuals with depression and controls. METHODS: Eligible studies included observational or interventional studies examining digital biomarkers for depression with validated outcome measures. We searched major international and Korean databases, including MEDLINE, PsycINFO, CINAHL, IEEE Xplore, Web of Science, Cochrane Library, KISS, RISS, KMbase, and KoreaMed, from inception to December 28, 2025. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool and the Scottish Intercollegiate Guidelines Network checklist. Meta-analyses were conducted using random-effects models with the Hartung-Knapp-Sidik-Jonkman method, and other outcomes were narratively summarized. RESULTS: 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, GPS data, and circadian rhythms. A meta-analysis of 22 studies (6947 participants) revealed that individuals with depression had significantly longer sleep onset latency (5 studies; n=292; +4.75 min, 95% CI 2.46-7.04; P=.005; 95% prediction interval [PI] 0.01-10.27) and time in bed (3 studies; n=236; +31.81 min, 95% CI 18.22-45.39; P=.01; 95% PI 2.28-55.16). Physical activity counts were also significantly lower (5 studies; n=462; standardized mean difference -0.71, 95% CI -1.33 to -0.09; P=.03; 95% PI -2.18 to 0.71). Although individuals with depression showed a lower sleep efficiency, higher mean heart rate, and lower SD 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 PIs. CONCLUSIONS: Certain digital biomarkers, particularly sleep onset latency and physical activity counts, showed consistent average differences between the depression and control groups. However, wide PIs indicate substantial variability across settings, suggesting that no single marker is sufficient for reliable detection. This study advances the field by providing a comprehensive meta-analysis of multidimensional digital biomarkers, establishing a quantitative foundation for objective depression screening and monitoring. 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.

References

1. 1.Lee, H., Kang, S. G., & Lee, S. (2026). The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis. Journal of Medical Internet Research. https://doi.org/10.3390/ijms24010835