The influence of environmental factors on rugby football injuries

Lee, A. J.; Garraway, W. M.

doi:10.1080/026404100365153

Cited by 37 publications

(31 citation statements)

References 11 publications

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“…However, previous studies in New Zealand and Ireland reported that over half of all injuries in rugby union occurred on softer sports grounds 38 39. Conversely, a more recent and detailed epidemiological study demonstrated an increased injury incidence on harder grounds,2 although this study only classified ground hardness qualitatively.…”

Section: Discussionmentioning

confidence: 89%

Association of ground hardness with injuries in rugby union

et al. 2007

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Background: Ground hardness is considered one of the possible risk factors associated with rugby injuries. Objectives: To examine the contribution of ground hardness, rainfall and evapotranspiration to the incidence of injury, and to investigate seasonal injury bias throughout one full season of rugby union. Methods: A prospective epidemiological study of rugby injuries was performed on 271 players from rugby union teams involved in the premier grade rugby competition in Dunedin, New Zealand. Ground hardness was measured before each match over 20 rounds with an industrial penetrometer, and local weather information was collected through the National Institute of Weather and Atmospheric Research and the Otago Regional Council. Poisson mixed models were used to describe injury incidence as a function of ground hardness throughout the season. Results: The overall injury incidence during the season was 52 injuries per 1000 match player-hours (95% CI 42 to 65). Although injury incidence decreased gradually by round with a rate ratio of 0.98 (95% CI 0.96 to 0.99) (p = 0.036), and the hardness of match grounds decreased significantly over the season (0.16 MPa/ round, 95% CI 0.12 to 0.21, p,0.001), a non-significant association was demonstrated between injury incidence and ground hardness. Injury incidence was not associated with a combination of ground hardness, rainfall and evapotranspiration on the day of the match or cumulative rainfall and evapotranspiration before each match. Conclusions: Seasonal change in ground hardness and an early-season bias of injuries was demonstrated. Although the contribution of ground hardness to injury incidence was not statistically significant, match round and injury incidence were highly correlated, confirming a seasonal bias, which may confound the relationship of injury to ground condition.

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Section: Discussionmentioning

confidence: 89%

Association of ground hardness with injuries in rugby union

et al. 2007

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“…Another example of logistic regression can be found in Lee and Garraway (2000), who identi® ed the in¯uence of environmental factors on rugby union injuries. Using team possessions as the units of observation, Pollard and Reep (1997) recognized that adopting `goals scored' as their categorical [0,1] dependent variable would result in too few`g oals scored' compared with the many team possessions that failed to result in a goal.…”

Section: Discussionmentioning

confidence: 99%

Selected issues in the design and analysis of sport performance research

Atkinson¹,

Nevill²

2001

Journal of Sports Sciences

205

137

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The aim of this review is to discuss some issues in the design and statistical analysis of sport performance research, rather than to supply an authoritative `cookbook' of methods. In general, we try to communicate some possible solutions to the conundrum of how to maintain both internal and external validity, as well as optimize statistical power, in applied sport performance research. We start by arguing that some sport performance research has been overly concerned with physiological predictors of performance, at the expense of not providing a valid and reliable description of the exact nature of the task in question. We show how the in¯uence of certain factors on competitive performances can be described using linear or logistic regression. We discuss the choice of analysis for factorial repeated-measures designs, which is complicated by the assumption of `sphericity' in a univariate general linear model, and the relatively low statistical power of the multivariate approach when used with small samples. We consider a little-used and simpler technique known as `analysis of summary statistics' . In multi-group pre-and post-test designs, a useful technique can be to pair-match individuals on their performance scores in a counterbalanced fashion before the intervention or control has been introduced. Finally, we outline how con® dence intervals can help in making statements about the probability of the population diþ erence in performance exceeding the value designated as being worthwhile or not.

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“…While many putative risk factors are named in the literature, there have been few attempts to confirm these in analytical studies, with two notable exceptions undertaken in Scotland4–7 and New Zealand 8–10. The objective of this study was to investigate the independent effect on injury incidence of a number of commonly postulated risk factors (as listed in methods) 5–9 11…”

Section: Introductionmentioning

confidence: 99%