A Tailored Web-based Advice Tool for Skiers and Snowboarders: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Being active in sports has many positive health effects. The direct effects of engaging in regular physical activity are particularly apparent in the prevention of several chronic diseases, including cardiovascular disease, diabetes, cancer, hypertension, obesity, depression, and osteoporosis. Besides the beneficial health effects of being active, sports participation is unfortunately also associated with a risk of injuries. In the case of many sports injuries (eg, winter sports) preventive measures are not compulsory, which means that a behavioral change in sports participants is necessary to increase the use of effective measures, and subsequently prevent or reduce injuries in sports. OBJECTIVE: The evidence-based Wintersportklaar online intervention has been developed to stimulate injury preventive behavior among skiers and snowboarders. In this article, the design of the effectiveness study will be described. METHODS: A randomized controlled trial with a follow-up period of four months during the winter sport season will be conducted. The participants consist of inexperienced skiers and snowboarders. At baseline, skiers and snowboarders in the intervention and control groups are asked to report the injury preventive measures they usually take during their preparation for their winter sport holiday. One and three months after baseline, skiers and snowboarders are asked to report retrospectively in detail what measures they took regarding injury prevention during their current winter sport preparation and winter sport holiday. Descriptive analyses (mean, standard deviation, frequency, range) are conducted for the different baseline variables in both study groups. To evaluate the success of the randomization, baseline values are analyzed for differences between the intervention and control groups (chi square, independent T tests and/or Mann-Whitney test). Chi square tests and/or logistic regression analyses are used to analyze behavioral change according to the intention to treat principle (as initially assigned). RESULTS: The project was funded in 2016 and enrolment was completed in 2017. Data analysis is currently under way and the first results are expected to be submitted for publication in 2018. CONCLUSIONS: To combat the negative side effects of sports participation, the use of injury preventive measures is desirable. As the use of injury prevention is usually not compulsory in skiing and snowboarding, a behavioral change is necessary to increase the use of effective injury preventive measures in winter sports. TRIAL REGISTRATION: Dutch Trial Registry NTR6233; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=6233 (Archived by WebCite at http://www.webcitation.org/6wXZPzjUi).
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".