A Machine-Learning Approach to Identify Risk Factors for Running-Related Injuries: Protocol for a Prospective Longitudinal Cohort Study
- Conditions
- Running injuries
- Registration Number
- DRKS00026904
- Lead Sponsor
- Institut für Bewegungswissenschaft Universität Hamburg
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Pending
- Sex
- All
- Target Recruitment
- 100
Inclusion Criteria
Running over 20 km a week. Free of injuries for three months.
Exclusion Criteria
Running under 20 km per week. Injuries within the last three months.
Study & Design
- Study Type
- observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method
- Secondary Outcome Measures
Name Time Method
Related Research Topics
Explore scientific publications, clinical data analysis, treatment approaches, and expert-compiled information related to the mechanisms and outcomes of this trial. Click any topic for comprehensive research insights.
What machine learning models are used in DRKS00026904 to predict biomechanical risk factors for running injuries?
How does the DRKS00026904 study compare ML-based injury prediction to traditional statistical methods in sports medicine?
Which biomechanical or physiological biomarkers are prioritized in DRKS00026904 for identifying high-risk runners?
What strategies does DRKS00026904 employ to minimize false positives in ML-driven running injury risk assessment?
How does the DRKS00026904 protocol align with other longitudinal cohort studies on exercise-induced musculoskeletal injuries?