Data-informed Intervention Improves Football Technique and Reduces Head Impacts
Bibliographic record
Abstract
INTRODUCTION: Although sport participation is a key contributor to the physical and mental health of children and youth, exposure to subconcussive head impacts in football has raised concerns about safety for athletes. PURPOSE: To demonstrate the efficacy of incorporating targeted football drills into a team's practice routine with the goal of improving players' technique and reduce exposure to subconcussive head impacts. METHODS: Seventy high school football players (age, 16.4 ± 1.1 yr) were tested PRE season using a sport-specific functional assessment. Results from the testing were used to inform the design of a prepractice intervention aimed at improving tackling and blocking techniques while reducing exposure to head impacts. The assessment included drills which evaluated the players' ability to safely tackle, and block while simulating game-like situations. Testing was repeated at MID season (internal control) without an intervention, and again at POST season (experimental), after introduction of the prepractice intervention between these timepoints, administered twice weekly. All testing sessions were recorded, and subsequently reviewed by trained graders based on selected criteria defined by football coaches. A subset of 19 participants wore in-helmet accelerometers to assess the effectiveness of the intervention in decreasing head impacts during practice. RESULTS: Significant improvements in blocking and tackling techniques were observed after the introduction of the intervention (P < 0.0001). Participating athletes also showed better techniques when evaluated in new game-like situations, postseason, providing evidence for proper acquisition and generalizability of these safer habits. Finally, frequency of head impacts (>15g) per practice was significantly reduced by ~30% after 1 month of training. CONCLUSION: Our results suggest that data-informed methods can be used to improve coaching practices and promote safer play, which can have a positive public health impact moving forward.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".