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Record W4392282038 · doi:10.1007/s40279-024-02004-5

Associations Between Physical Characteristics and Golf Clubhead Speed: A Systematic Review with Meta-Analysis

2024· review· en· W4392282038 on OpenAlexaff
Alex Brennan, Andrew Murray, Margo Mountjoy, John Hellström, Dan Coughlan, Jack Wells, Simon Brearley, Alex Ehlert, Paul Jarvis, Anthony N. Turner, Chris Bishop

Bibliographic record

VenueSports Medicine · 2024
Typereview
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMeta-analysisSports medicinePhysical medicine and rehabilitationMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Abstract Background Historically, golf does not have a strong tradition of fitness testing and physical training. However, in recent years, both players and practitioners have started to recognise the value of a fitter and healthier body, owing to its potential positive impacts on performance, namely clubhead speed (CHS). Objective The aim of this meta-analysis was to examine the associations between CHS (as measured using a driver) and a variety of physical characteristics. Methods A systematic literature search with meta-analysis was conducted using Medline, SPORTDiscus, CINAHL and PubMed databases. Inclusion criteria required studies to have (1) determined the association between physical characteristics assessed in at least one physical test and CHS, (2) included golfers of any skill level but they had to be free from injury and (3) been peer-reviewed and published in the English language. Methodological quality was assessed using a modified version of the Downs and Black Quality Index tool and heterogeneity assessed via the Q statistic and I 2 . To provide summary effects for each of the physical characteristics and their associations with CHS, a random effects model was used where z -transformed r values (i.e. z r ) were computed to enable effect size pooling within the meta-analysis. Results Of the 3039 studies initially identified, 20 were included in the final analysis. CHS was significantly associated with lower body strength ( z r = 0.47 [95% confidence intervals {CI} 0.24–0.69]), upper body strength ( z r = 0.48 [95% CI 0.28–0.68]), jump displacement ( z r = 0.53 [95% CI 0.28–0.78]), jump impulse ( z r = 0.82 [95% CI 0.63–1.02]), jumping peak power ( z r = 0.66 [95% CI 0.53–0.79]), upper body explosive strength ( z r = 0.67 [95% CI 0.53–0.80]), anthropometry ( z r = 0.43 [95% CI 0.29–0.58]) and muscle capacity ( z r = 0.17 [95% CI 0.04–0.31]), but not flexibility ( z r = − 0.04 [95% CI − 0.33 to 0.26]) or balance ( z r = − 0.06 [95% CI − 0.46 to 0.34]). Conclusions The findings from this meta-analysis highlight a range of physical characteristics are associated with CHS. Whilst significant associations ranged from trivial to large, noteworthy information is that jump impulse produced the strongest association, upper body explosive strength showed noticeably larger associations than upper body strength, and flexibility was not significant. These findings can be used to ensure practitioners prioritise appropriate fitness testing protocols for golfers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.726
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0120.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.318
Teacher spread0.268 · 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

Citations17
Published2024
Admission routes1
Has abstractyes

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