Running Every Time, Planking Sometimes: Youth Adherence to a Neuromuscular Training Program
Bibliographic record
Abstract
ABSTRACT Introduction/Purpose Neuromuscular training warm-up programs are effective in reducing sport and recreation injuries when adherence is adequate. To understand how to improve adherence, it is important to analyze different measures of adherence, such as the utilization of different exercises and components. Methods The intervention arm of a randomized controlled trial in junior high school hysical education (PE) was included in this study. After one school was excluded because of inadequate adherence data, five schools (32 classes) were analyzed. For 12 wk, the schools implemented the Implementing a School Prevention Program to Reduce Injuries Through Neuromuscular Training (iSPRINT) program comprising 15 exercises in four components (aerobic, agility, strength, balance). Utilization fidelity, cumulative utilization (program/component/exercise), and utilization frequency (program/component) were calculated. Results An iSPRINT session was conducted (i.e., ≥1 exercise out of 15 was performed) during 858 out of 1572 (54.6%) PE classes. Utilization fidelity was 13 (Q1, Q3: 7, 14) out of 15 exercises. Between the five schools, utilization fidelity ranged from 5 to 14 exercises. The most frequently utilized exercise was the forward run (96% of all iSPRINT sessions), and the least frequently utilized exercise was forward running with intermittent stops (36%). Utilization frequency of different exercises in individual schools varied from 9% to 100%. On average, the program was conducted twice a week. Out of all the iSPRINT sessions, 98% included aerobic, 89% included agility, 90% included strength, and 78% included balance exercises. Conclusions On average, the schools adhered well to the program, and all components were implemented to some extent. This indicates that there are no program components or exercises that are systematically underperformed. With adherence varying between the schools, it is important to take into account that the implementation context may differ across school environments and barriers to maximizing adherence require consideration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".