Multimodal sensor-based framework detects frailty in retired adults aged 65+ in primary care

IntroductionFrailty reflects age-related decline across multiple physiological systems, reducing resilience and increasing risks of falls, hospitalization, disability, and mortality. Scalable approaches are needed to identify pre-frailty earlier in community-dwelling older adults and enable timely prevention in primary care.ObjectiveTo develop and evaluate a multivariable sensor-based framework for early frailty detection using standardized gait and balance assessments in general practice.MethodsWe conducted a prospective cohort study (2021–2024) in southern Réunion Island among retired adults aged ≥65 years recruited in primary care. The protocol included: (1) baseline general practitioner (GP) assessment with expert frailty rating, Fried phenotype, and WHO ICOPE Step 1; (2) telephone assessment of mental health, self-rated health, and quality of life; (3) outpatient instrumented evaluation combining IMU-based gait analysis, force-platform posturography, grip strength, and ICOPE Step 2 measures; (4) monthly falls surveillance over 6 months; and (5) repeat instrumented gait and balance assessment at 6 months. Correlation analyses and machine-learning models examined relationships between frailty measures and the discriminative value of sensor-derived and multimodal predictors.ResultsAmong 145 participants (mean age 71 ± 5 years), 98.5% had impairment in at least one intrinsic capacity domain at baseline, most commonly vision (77.7%), locomotion (53.1%), hearing (52.5%), and psychological (27.3%). Sedentary behavior was frequent (77%). Expert frailty scores correlated with the Fried phenotype, whereas associations with self-rated health were weaker. Models based on sensor parameters alone showed limited ability to reproduce Fried-defined frailty, while multimodal models integrating clinical and questionnaire variables improved discrimination. Over 6 months, kinesiotherapy and regular physical activity were associated with improved postural control metrics (including center-of-pressure features and mediolateral sway), while changes in gait speed were modest.ConclusionAn IoT-supported platform combining quantitative gait, balance, and grip strength measures with targeted questionnaires is feasible in outpatient primary care and yields frailty estimates broadly consistent with GP assessment. However, subjective and clinical inputs remain essential to capture psychological aspects of frailty not fully reflected by sensor signals alone. These findings support scalable frailty screening and longitudinal monitoring, and warrant validation in larger samples, including deployment by trained non-medical personnel and integration into precision-prevention pathways.