Machine Learning-Based Risk Identification and Precision Exercise for Common Running Injuries
CompletedNot applicableNCT07689916
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This study has no listed site in Canada in this snapshot. It is included as part of the worldwide chronic pain corpus.
Brief summary (from the registry)
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.
Study details
- Official title
- Risk Factor Identification and Precision Exercise Intervention for Common Injuries in Recreational Runners Based on Machine Learning
- Conditions
- Running-related Injuries
- Medial Tibial Stress Syndrome
- Patellofemoral Pain, PFP
- Chronic Achilles Tendinopathy
- Interventions
- Behavioral: Multidimensional Precision Intervention
- Behavioral: Precision Exercise Intervention
- Sponsor
- Zeyi Zhang
- Organization
- East China Normal University
- Start date
- 2025-08-01
- Primary completion
- 2026-01-01
- Sex
- Male
- Age range
- from 18 Years to 30 Years
- Results posted
- No results posted in this snapshot
- Countries
- China
- Total sites listed
- 1
Eligibility criteria
Show eligibility criteria (verbatim from the registry)
Inclusion Criteria: * Male recreational runners (without formal professional training) * Weekly running distance greater than 10 km * No history of lower-limb injury within the past 3 months Exclusion Criteria: * Other musculoskeletal or soft-tissue injuries, including arthritis, muscle injury, or meniscal injury * History of lower-limb surgery * Regular participation in contact or high-impact sports
Source: ClinicalTrials.gov (US National Library of Medicine), snapshot October 2026. Check the registry for current recruiting status. Record: https://clinicaltrials.gov/study/NCT07689916