Predictive Accuracy of Digital Biomarker Technologies for Detection of Mild Cognitive Impairment and Pre-Frailty Amongst Older Adults: A Systematic Review and Meta-Analysis

Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted for reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48–1.00 and 0.55–1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53–1.00 and specificity of 0.61–1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79–0.88) and 0.82 (95% CI: 0.74–0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80–0.89) and 0.82 (95% CI: 0.75–0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.