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.