Combining evidence and practice to optimise neck training aimed at reducing head acceleration events in sport: a systematic review and Delphi-consensus study
Bibliographic record
Abstract
Head acceleration events (HAEs) can potentially have adverse consequences for athlete brain health. In sports, in which head injuries have the highest incidence, identifying strategies to reduce HAE frequency and magnitude is a priority. Neck training is a potential strategy to mitigate against the magnitude of HAEs. This two-part study aimed to (1) systematically review the literature of neck training interventions in sport and (2) undertake an expert Delphi consensus on the best practices for neck training implementation to reduce HAEs in sport. Part I: a systematic search of four databases was undertaken from the earliest records to September 2024. The PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines were followed, and a quality assessment was completed using a modified Downs and Black assessment tool and the GRADE (Grading of Recommendations Assessment, Development and Evaluation). Papers were eligible if they both (1) implemented a reproducible exercise intervention targeting the neck within collision, combat or motor sport, and (2) assessed outcomes relating to either: the physical profile of the neck; head/neck injury incidence; and/or HAEs. Part II: 18 international experts, with experience in research and/or applied practice of neck exercise training, concussion and/or HAEs, reviewed the part I findings before completing a three-round Delphi consensus process. Part I included 21 papers, highlighting the heterogeneity of existing interventions. Part II resulted in 57 statements coded into five categories: contextual factors (n=17), neck training periodisation (n=12), training adaptations (n=10), neck training content (n=15) and athlete adherence (n=3). This study presents recommendations for neck exercise training aiming to reduce HAEs in sport, supporting both practice and future research.
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 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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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; both teacher heads agree on what is shown here.
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".