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Record W3003165045 · doi:10.1136/bjsports-2021-ioc.48

051 Implementing a school prevention program to reduce injuries through neuromuscular training (iSPRINT): a cluster-randomized controlled trial

2021· article· en· W3003165045 on OpenAlexaffabout
Carla van den Berg, Carolyn A. Emery, Sarah A. Richmond, Luz Palacios‐Derflingher, Carly McKay, Patricia K. Doyle–Baker, Megan McKinlay, Clodagh Toomey, Alberto Nettel‐Aguirre, Evert Verhagen, Kathy Belton, Alison Macpherson, Brent Hagel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of AlbertaYork UniversityPublic Health OntarioHotchkiss Brain InstituteAlberta Children's HospitalAlberta Bone and Joint Health InstituteUniversity of Calgary
Fundersnot available
KeywordsPoisson regressionPhysical therapyMedicineRandomized controlled trialRate ratioPsychological interventionPoison controlCluster randomised controlled trialInjury preventionEmergency medicinePopulationConfidence intervalSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

Background The effectiveness of neuromuscular training (NMT) programs in preventing injuries in youth sport is well documented, however there is little evidence on NMT programs delivered in school physical education (PE). Objective To assess the effectiveness of iSPRINT, a NMT warm-up implemented in PE classes in reducing injuries and improving performance. Design Cluster-randomized controlled trial. Setting Junior high schools in Calgary, Canada. Patients (or Participants) 1,067 students (12 schools; ages 11–16; 53.7% female). Interventions (or Assessment of Risk Factors) Following a workshop, teachers delivered a 12-week NMT (6 schools) or standard-of-practice (6 schools) warm-up at the beginning of PE classes. Main Outcome Measurements Validated injury surveillance included sport/recreational injuries resulting in time loss from activity or medical attention. Predicted-VO2max, vertical jump, and single-leg eyes-closed dynamic balance on foam pad were measured at baseline and 12-weeks. Multiple multilevel regression analyses (adjusting for previous injury and random effect by school and class) estimated injury incidence rate ratios (IRR) (Poisson regression considering interaction with sex) and mean changes in performance (linear regression). Results iSPRINT was protective against all injuries (IRR=0.543, 95%CI; 0.295–0.998), lower extremity injuries (IRR=0.357, 95%CI; 0.159–0.799) and medically-treated injuries (IRR=0.289, 95%CI; 0.135–0.619) in girls but not boys (IRR=0.866, 95%CI; 0.425–1.766, 1.055, 95%CI; 0.404–2.753, and 0.639, 95%CI; 0.266–1.532; respectively). Mean baseline balance times (seconds) were similar between iSPRINT (7.4, SD+/-2.6) and control participants (6.9, SD+/-2.2). At 12-weeks mean balance time was greater in iSPRINT group (9.1 SD+/-2.6) than control (7.9, SD+/-6.5). The difference in mean change over 12-weeks favoured the iSPRINT group (1.2 seconds, 95%CI; 0.2–2.1). No between group differences were observed for changes in predicted-VO2max or vertical jump. Conclusions An NMT program is effective in reducing injuries in girls and improving dynamic balance in all youth. This research informs the current standard-of-practice warm-up in youth PE. Future research should consider exercise fidelity differences between girls and boys.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.032
GPT teacher head0.374
Teacher spread0.342 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

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Citations1
Published2021
Admission routes2
Has abstractyes

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