Machine Learning-Based Risk Identification and Precision Exercise for Common Running Injuries

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.

Conditions

• Running-related Injuries
• Medial Tibial Stress Syndrome
• Patellofemoral Pain, PFP
• Chronic Achilles Tendinopathy

Interventions

BEHAVIORAL

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.

BEHAVIORAL

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.

Sponsors & Collaborators

• Zeyi Zhang

  lead OTHER

Principal Investigators

• Zeyi Zhang · East China Normal University

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Max Age

30 Years

Sex

MALE

Healthy Volunteers

No

Timeline & Regulatory

Start

2025-08-01

Primary Completion

2026-01-01

Completion

2026-02-01

Countries

• China

Study Locations