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Record W4414084702 · doi:10.1186/s40798-025-00888-9

Player-Level Tackle Training Interventions in Tackle-Collision Sports: A Systematic Scoping Review

2025· review· en· W4414084702 on OpenAlexaff
Demi Davidow, Lara Paul, Ben Jones, Ameer Hohlfeld, Seipati Rasenyalo, Kathryn Dane, Isla Shill, Sharief Hendricks

Bibliographic record

VenueSports Medicine - Open · 2025
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsPsychological interventionTraining (meteorology)Systematic reviewMEDLINEIntervention (counseling)Health care

Abstract

fetched live from OpenAlex

BACKGROUND: In tackle-collision sports, the tackle has the highest incidence, severity, and burden of injury. Head injuries and concussions during the tackle are a major concern within tackle-collision sports. To reduce concussion and head impact risk, evaluating optimal tackle techniques to inform tackle-related prevention strategies has been recommended. The purpose of this study was to perform a systematic scoping review of player-level tackle training intervention studies in all tackle-collision sports. METHODS: The Arksey and O'Malley's five-stage scoping review process and Levac et al.'s framework were used, along with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) checklist. The main inclusion criteria were that the study included an intervention aimed at improving a player's tackle abilities, and the intervention had to be delivered/implemented at the player-level in a training setting. RESULTS: Thirteen studies were included in this review, seven studies in American Football (54%), followed by a combined cohort of rugby union and rugby league players (three studies; 23%), rugby union (two studies; 15%), and one study reported on a rugby league cohort (8%). Studies focused primarily on the tackler, with the intervention incorporating a form of instruction or feedback, delivered through video or an expert coach. Other interventions included an 8-week strength and power training programme, designing practice sessions based on baseline data, and helmetless training in American Football. All interventions demonstrated a favourable change in the outcome measured-which included tackler and ball-carrier kinematics based on motion capture video, tackler proficiency scoring, tackling task analysis, head impact frequencies by xPatch head-impact sensor technology, head impact kinematics using head-impact sensors (helmet or skin patches) and football tackle kinematics with motion capture systems or video. CONCLUSION: This review shows that a range of studies have been undertaken focusing on player-level training interventions. The quality of studies were rated as 'good', and all studies showed improvements in outcome measures. Coaches and policy makers should ensure tackle technique is profiled alongside other player characteristics, and an evidence-based approach to improving player tackling is adopted, improving both performance and reducing injury risk. KEY POINTS: Only 13 studies tested or implemented interventions at the player level in tackle-collision sports. The focus of the studies was primarily on the tackler, with the interventions incorporating a form of instruction or feedback, which was delivered through video or an expert coach. Other interventions included an 8-week strength and power training programme, designing practice sessions based on baseline data, and helmetless training in American Football. All interventions demonstrated a favourable change in the outcome measure and provide coaches and policymakers with tackle training insights. REGISTRATION: The systematic scoping review was prospectively registered with OSF (registration number: https://doi.org/10.17605/OSF.IO/V3KZC ).

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)0.013
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.248
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0130.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.442
GPT teacher head0.533
Teacher spread0.091 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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