Validation of an Automated Brief Frailty Screening Tool Using Interactive Voice Response in Community‐Dwelling Older Adults

Background Frailty is a multidimensional condition linked to adverse outcomes in older adults, yet most screening tools rely on in‐person assessment, limiting scalability. Automated approaches are needed for large‐scale screening. Participants and Setting We studied community‐dwelling older adults enrolled in the Aging Study of PyeongChang Rural Area (ASPRA), South Korea. Cohort 1 included 154 participants (mean age 77.4 years, 77.3% women) with comprehensive geriatric assessments; Cohort 2 included 1532 participants (mean age 74.9 years, 53.8% women). An additional External Cohort of 86 adults aged ≥ 90 years (mean age 92.1 years, 59.3% women) was recruited across five urban and rural sites to evaluate generalizability. Methods We developed a rule‐based frailty classification algorithm implemented on an Interactive Voice Response (IVR) platform. Participants were contacted by automated telephone calls and categorized as robust, pre‐frail, frail, or severely frail. Validity was assessed against the 45‐item Frailty Index (FI) and other geriatric outcomes. Discriminative ability was compared with the Clinical Frailty Scale (CFS) using area under the curve (AUC) analyses. Results IVR‐based frailty classification showed strong, graded associations with FI, disability, gait speed, SPPB, and health‐related quality of life (all p for trend < 0.01). In Cohort 1, the AUC for detecting frailty (FI ≥ 0.25) was 0.77 (95% CI, 0.69–0.85), comparable to the CFS (AUC 0.76, 95% CI, 0.67–0.85; p = 0.87). In Cohort 2, CFS scores increased consistently across IVR frailty categories in all age and gender strata (p for trend < 0.001). In the External Cohort, the FI also increased progressively across IVR categories (p for trend = 0.006), demonstrating external validity. Conclusions A fully automated IVR system is a valid and feasible tool for frailty screening in community‐dwelling older adults. Requiring no internet or human intervention, it offers a scalable solution for population‐level frailty surveillance, especially in resource‐limited settings.