Sensor-Based Digital Biomarkers for Early Identification of Cognitive Frailty: A Systematic Review

Cognitive Frailty (CF), characterized by the co-occurrence of Physical Frailty (PF) and Mild Cognitive Impairment (MCI), is increasingly recognized as a critical predictor of adverse health outcomes in aging populations. Despite its clinical significance, early identification of CF remains challenging due to heterogeneous diagnostic criteria and reliance on resource-intensive assessments. This systematic review synthesizes current evidence on sensor-based digital biomarkers for CF detection across eight databases following PRISMA 2020 guidelines. A combined qualitative and quantitative synthesis is conducted based on 20 eligible studies. Key findings reveal that Inertial Measurement Units (IMUs) are the most frequently utilized sensors (n = 6 studies), primarily capturing gait and motor function parameters under various functional tasks. Among digital biomarkers, gait features, particularly Stride Length under single-task conditions and Velocity under dual-task or single-task conditions, show significant associations with CF. Body composition metrics, particularly Appendicular Skeletal Muscle Mass Index (ASM), and physical activity parameters, such as Moderate-to-Vigorous-intensity Physical Activity (MVPA), show consistent negative associations with CF, while cardiovascular indices like Cardio-Ankle Vascular Index (CAVI) reveal positive correlations. Eighteen studies develop a total of 23 CF-related models, of which only four focus on predictive classification. These four studies report ten distinct models, with AUCs ranging from 0.696 to 0.911 and accuracy from 0.480 to 0.863. A quantitative synthesis of six models from three studies yields pooled estimates of sensitivity at 0.81 (95% CI: 0.55-0.94) and specificity at 0.80 (95% CI: 0.49-0.95), suggesting moderate-to-good discriminative performance despite notable heterogeneity in diagnostic definitions, sample sizes, sensor types, and modeling approaches. These results systematically map the evolving landscape of digital biomarkers for CF, emphasizing the need for standardized sensor protocols and rigorous validation to advance clinical translation.