Daily activity patterns from wearable accelerometry predict physical frailty and concern about falling.

Physical frailty and concern about falling are interrelated geriatric conditions that significantly impair mobility, limit daily activity, and reduce life quality among older adults. Traditional methods for assessing frailty and fall concern rely heavily on questionnaires such as the Fried Frailty Phenotype (FFP) and the Falls Efficacy Scale-International (FES-I). These tools, although clinically established, are subjective, require professional administration, and cannot capture the mutual influence between the two conditions. In this study, we propose a wearable-sensor-based, objective framework that simultaneously predicts physical frailty status and level of concern about falling by analyzing real-world activity patterns. We collected continuous chest acceleration data from 146 participants over a 48-hour period using a pendant sensor. These activity sequences were transformed into barcodes and analyzed using global complexity metrics (e.g., single- and multi-scale entropy) and local sequential dynamics via bidirectional LSTM networks. A multi-task deep learning model with attention mechanisms was proposed to jointly predict frailty and fall concern. Our model achieved high predictive performance for both tasks, achieving F1 scores of 93.12% for physical frailty and 86.27% for concern about falling. This study provides the first joint modeling of physical frailty and concern about falling using long-term real-world accelerometry data.