ClinicalTrials.gov
public full text
Public Domain
Running Online Injury Prevention Feasibility Study
The main purpose of this study is to test the feasibility of a larger scale interventional study. The investigators want to understand whether data can be reliably collected data various different streams while participants follow a standard 12-week half-marathon training program, and whether participants follow training recommendations.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- The dark side of streaking: Examining the backfire potential of run streaking in recreational runners who broke a long-term streak#278010
- Myocardial Injury is Not Related to Training Levels Among Recreational Participants in the Silicon Valley Marathon#827100
- Effect of an unsupervised multidomain intervention integrating education, exercises, psychological techniques and machine learning feedback, on injury risk reduction in athletics (track and field): protocol of a randomised controlled trial (I-ReductAI)#144970
- Exploring contextual factors for management and prevention of running-related injuries: runners and experts’ perspectives#672496
- The Effect of Breaking up Sitting in the Workplace on Cardiometabolic Risk and Worker Productivity#912389
Record · ID 887561
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive