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Machine Learning-Based Risk Identification and Precision Exercise for Common Running Injuries
Sponsor: Zeyi Zhang
Summary
This study aims to identify biomechanical risk factors associated with common running-related injuries in recreational runners using machine learning analysis. It also aims to evaluate whether a precision exercise intervention based on these risk factors can improve injury-related biomechanical and kinematic outcomes. The main questions it aims to answer are: Which biomechanical features identified by machine learning are associated with the occurrence of common running-related injuries, including medial tibial stress syndrome (MTSS), patellofemoral pain (PFP), and chronic Achilles tendinopathy? Whether a precision exercise intervention based on these risk factors can improve injury-related biomechanical and kinematic characteristics? Participants will: Undergo baseline biomechanical assessment during running, including motion capture, ground reaction force, and surface electromyography Be prospectively followed for 6 months to record the occurrence of running-related injuries Be classified into injury groups based on diagnosis, including MTSS, PFP, and chronic Achilles tendinopathy Following the completion of the follow-up period, participants will be allocated to either a precision multidimensional intervention group, a precision exercise intervention group, or a control group. The precision multidimensional intervention group will receive patient education in addition to an 8-week intervention program, including stretching, strength training, and real-time movement feedback. The precision exercise intervention group will receive the same 8-week intervention program, consisting of stretching, strength training, and real-time movement feedback. The control group will maintain their habitual physical activity patterns without receiving any additional intervention. Participants will be reassessed after the intervention and at the 3-month follow-up using the same biomechanical testing protocol.
Official title: Risk Factor Identification and Precision Exercise Intervention for Common Injuries in Recreational Runners Based on Machine Learning
Key Details
Gender
MALE
Age Range
18 Years - 30 Years
Study Type
INTERVENTIONAL
Enrollment
204
Start Date
2025-08-01
Completion Date
2026-02-01
Last Updated
2026-07-08
Healthy Volunteers
No
Conditions
Interventions
Multidimensional Precision Intervention
Participants receive an 8-week precision exercise program including stretching, strength training, and real-time movement feedback based on machine learning-identified biomechanical risk factors. In addition, participants receive structured patient education in the multidimensional intervention group.
Precision Exercise Intervention
Participants receive an 8-week precision exercise program including stretching, strength training, and real-time movement feedback based on biomechanical risk factors identified by machine learning.
Locations (1)
Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, East China Normal University
Shanghai, Minhang District, China