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Abstract
<title>Abstract</title> <p>Background Falls are a major public health concern among older adults and result from multiple interacting factors. Current approaches to fall assessment often rely on isolated measures and may not fully capture the complex mechanisms underlying falls. The Polymodal Evaluation of Predictors of Falls (PREDIFALL) study aims to investigate multimodal predictors associated with falls by integrating clinical, functional, sensory, imaging, and blood-based assessments using machine learning approaches. Methods PREDIFALL is a prospective observational cohort study including 100 healthy older adults aged ≥ 65 years. At baseline, participants will be classified as fallers and non-fallers based on their fall history over the previous 12 months. Participants will undergo comprehensive baseline assessments, including questionnaires, physical activity and sleep monitoring, cognitive testing, mobility and gait evaluation, muscle strength testing, posturography, vestibular and auditory assessment, brain and muscle imaging (magnetic resonance imaging, proton magnetic resonance spectroscopy, ultrasound, and functional near-infrared spectroscopy), and blood-based biomarker and multi-omics analyses. Falls will subsequently be monitored during the 12-month follow-up using a smartphone-based application completed weekly. Machine learning approaches will be used to integrate multimodal data and identify profiles associated with retrospective and prospective falls. Discussion By combining multimodal baseline assessments with retrospective fall history and prospective fall monitoring, the study may improve understanding of the mechanisms associated with falls in healthy older adults. In addition, the smartphone-based follow-up will help evaluate the feasibility of longitudinal fall monitoring in community-dwelling older adults. Findings from this study will inform future fall prevention strategies in our aging society.</p>