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Record W2889546067 · doi:10.2196/resprot.9708

A Tailored Advice Tool to Prevent Injuries Among Novice Runners: Protocol for a Randomized Controlled Trial

2018· article· en· W2889546067 on OpenAlexvenueno aff
Ellen Kemler, Vincent Gouttebarge

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsnot available
FundersZonMw
KeywordsRandomized controlled trialIntervention (counseling)MedicinePhysical therapyProtocol (science)Descriptive statisticsNursingAlternative medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.087
GPT teacher head0.468
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreProtocol

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".

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

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