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The twofold goal of this study is to understand the link between muscle power, muscle strength, and muscle control degradation with the risk of falling, and to develop a framework for the comprehensive and quantitative assessment of muscle power (and strength) in an elderly population of patients with knee osteoarthritis, who are at higher risk of falling. The main question it aims to answer is:
● Are muscle power and motor control degradation better predictors of falls than muscle strength in the aging population?
Participants will undergo:
Full description
Falls are a critical yet common event among the elderly, with huge societal and economic impact (reduced quality of life and high costs for the healthcare system). Experimental measures to quantify the residual muscle strength and power may provide useful information to predict the risk of falls in the elderly. Isometric and isokinetic muscle contractions, performed on a dynamometer or during functional tasks can be collected, together with electromyography (to assess muscle activity) and imaging data (to quantify and characterize muscular tissue).
Such data can be collected at different time points to monitor subjects over time, and to inform virtual representation of the human musculoskeletal system (digital twins) to identify possible motor control deficits. Moreover, this same information can be used to better characterize/assess elder individuals at risk of falling (e.g., subjects with knee osteoarthritis), to prevent future falls.
All subjects enrolled in the PowerAGING study will be followed up for 24 months (5 visits in total: M0, M6, M12, M18 and M24 follow-up). At each visit, a series of experimental tests to quantify muscle power and muscle force, as well as a home-based mobility assessment (via single inertial sensor worn for 5 consecutive days), will be performed. In addition, only at start and end, the subjects will undergo a full lower limb MRI and a gait assessment.
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50 participants in 1 patient group
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Central trial contact
Lisa Berti, Professor; Massimiliano Baleani, Engineer
Data sourced from clinicaltrials.gov
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