A Tailored Advice Tool to Prevent Injuries Among Novice Runners: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Besides the beneficial health effects of being active, running is associated with a risk of sustaining injuries. Runners need to change their behavior to increase the use of effective measures and subsequently reduce the number of running-related injuries. OBJECTIVE: The RunFitCheck intervention was developed according to an evidence- and practice-based approach to stimulate injury-preventive behavior among novice runners. This paper describes the study design in detail. METHODS: A randomized controlled trial with a follow-up period of 5 months will be conducted. The participants will be novice runners. At enrollment, participants will be asked to report injury-preventive measures they usually take during their running activities. After completing the enrollment questionnaire, participants will be randomized to intervention and control groups. The intervention group will have access to the RunFitCheck intervention; the control group will perform their running activities as usual. Participants will be asked to report retrospectively in detail what they have done regarding injury prevention during their running activities at 1, 3, and 5 months after enrollment. Descriptive analyses will be conducted for different baseline variables in the intervention and control group. Relative risks and 95% CIs will be used to analyze behavioral changes according to the intention-to-treat principle. RESULTS: The project was funded in 2016 and enrollment was completed in 2017. Data analysis is currently under way and the results are expected to be submitted for publication in 2019. CONCLUSIONS: To nullify the negative side effects of running, prevention of training errors is desirable. As the use of injury prevention measures is not compulsory in running, a behavioral change is necessary to increase the use of effective injury-preventive measures and to prevent running-related injuries. TRIAL REGISTRATION: Netherlands Trial Register NTR6381; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=6381 (Archived by WebCite at http://www.webcitation.org/736Xjm5jv). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/9708.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".