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AI-Guided Sarcopenia Risk Assessment and Detection
Sponsor: Tel Aviv University
Summary
Sarcopenia, the age-related decline in muscle mass and function, is a major contributor to frailty, disability, and mortality in older adults. Current diagnostic tools assess muscle quantity or function separately and lack predictive biomarkers, limiting early detection and personalized management. This study proposes an AI-driven framework that integrates multimodal physiological, metabolic, and functional data with wearable sensor monitoring to improve sarcopenia risk assessment and guide individualized interventions. In Phase 1, we will analyze a large retrospective dataset of 3,500 adults to identify early predictors of sarcopenia and develop a machine learning-based risk stratification model. Phase 2 will test a 12-week personalized exercise and nutrition intervention in 120 participants, using real-time sensor data and AI-guided adjustments to optimize outcomes. This integrative approach aims to advance early detection, precision intervention, and long-term muscle health in aging populations.
Official title: AI-Driven Integration of Muscle Mass and Muscle Function: A Novel Approach to Sarcopenia Risk Assessment and Intervention
Key Details
Gender
All
Age Range
50 Years - 70 Years
Study Type
INTERVENTIONAL
Enrollment
120
Start Date
2026-02-01
Completion Date
2027-12-31
Last Updated
2026-02-23
Healthy Volunteers
Yes
Conditions
Interventions
Personalized AI-Guided Exercise and Nutrition
Participants complete 12 weeks of supervised resistance and aerobic training combined with personalized nutrition support. Exercise prescriptions (3 resistance sessions/week; 2-3 aerobic sessions/week) and dietary guidance (including protein targets) are individualized using AI models and wearable data. A mobile app provides real-time feedback and monitoring, with biweekly safety check-ins.
Locations (1)
Sylvan Adams Sport Institute
Tel Aviv, Israel