Literature DB >> 17513914

Risk factors and risk statistics for sports injuries.

Will G Hopkins1, Stephen W Marshall, Kenneth L Quarrie, Patria A Hume.   

Abstract

BACKGROUND: Risk factors for sports injuries include characteristics and behaviors of athletes and characteristics of sports and the environment that are associated with some measure of risk of injury.
OBJECTIVE: To introduce risk statistics to clinicians evaluating studies of sports injuries.
METHODS: Plain-language review of risk statistics and their practical application to sports injuries.
RESULTS: The various measures of injury incidence are injury risk (proportion of athletes injured in a given period of training, playing, or other exposure time), injury rate (number of injuries per unit of exposure time), odds of injury (probability injury will happen divided by probability injury will not happen), injury hazard (instantaneous proportion injured per unit of time or mean injury count per unit of time), and mean time or mean number of playing exposures to injury. Effects of risk factors are estimated as values of effect statistics representing differences or ratios of one or more of these measures between groups defined by the risk factor. Values of some ratios and their sampling uncertainty (confidence limits) are estimated with specialized procedures: odds ratios with logistic regression, rate ratios with Poisson regression, and hazard ratios with proportional hazards (Cox) regression. Injury risks and mean time to injury in each group can also be estimated and can give a better sense of the effect of a risk factor. Risk factors identified in nonexperimental cohort and case-control studies are not always causes of injury; data from randomized controlled trials provide stronger evidence of causality.
CONCLUSION: Expressing risk statistics as meaningful numbers should help clinicians make better use of sports injury studies.

Entities:  

Mesh:

Year:  2007        PMID: 17513914     DOI: 10.1097/JSM.0b013e3180592a68

Source DB:  PubMed          Journal:  Clin J Sport Med        ISSN: 1050-642X            Impact factor:   3.638


  13 in total

Review 1.  Is High-Intensity Functional Training (HIFT)/CrossFit Safe for Military Fitness Training?

Authors:  Walker S C Poston; Christopher K Haddock; Katie M Heinrich; Sara A Jahnke; Nattinee Jitnarin; David B Batchelor
Journal:  Mil Med       Date:  2016-07       Impact factor: 1.437

2.  The influence of playing surface on injury risk in italian elite rugby players.

Authors:  Riccardo Maria Lanzetti; Domenico Lupariello; Teresa Venditto; Pierpaolo Rota; Matteo Guzzini; Antonio Vadalà; Attilio Rota; Andrea Ferretti
Journal:  Muscles Ligaments Tendons J       Date:  2017-05-10

3.  Heart Rate Variability is a Moderating Factor in the Workload-Injury Relationship of Competitive CrossFit™ Athletes.

Authors:  Sean Williams; Thomas Booton; Matthew Watson; Daniel Rowland; Marco Altini
Journal:  J Sports Sci Med       Date:  2017-12-01       Impact factor: 2.988

4.  Incidence of injuries in high school softball and baseball players.

Authors:  Ellen Shanley; Mitchell J Rauh; Lori A Michener; Todd S Ellenbecker
Journal:  J Athl Train       Date:  2011 Nov-Dec       Impact factor: 2.860

5.  Can pre-season fitness measures predict time to injury in varsity athletes?: a retrospective case control study.

Authors:  Michael D Kennedy; Robyn Fischer; Kristine Fairbanks; Lauren Lefaivre; Lauren Vickery; Janelle Molzan; Eric Parent
Journal:  Sports Med Arthrosc Rehabil Ther Technol       Date:  2012-07-23

Review 6.  What are the main running-related musculoskeletal injuries? A Systematic Review.

Authors:  Alexandre Dias Lopes; Luiz Carlos Hespanhol Júnior; Simon S Yeung; Leonardo Oliveira Pena Costa
Journal:  Sports Med       Date:  2012-10-01       Impact factor: 11.136

7.  Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables.

Authors:  Mohamed Elshafei; Emad Shihab
Journal:  Sensors (Basel)       Date:  2021-01-23       Impact factor: 3.576

8.  Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables' Data from the Crowd.

Authors:  Mohamed Elshafei; Diego Elias Costa; Emad Shihab
Journal:  Sensors (Basel)       Date:  2022-02-14       Impact factor: 3.576

9.  Discussion about different cut-off values of conventional hamstring-to-quadriceps ratio used in hamstring injury prediction among professional male football players.

Authors:  Monika Grygorowicz; Martyna Michałowska; Tomasz Walczak; Adam Owen; Jakub Krzysztof Grabski; Andrzej Pyda; Tomasz Piontek; Tomasz Kotwicki
Journal:  PLoS One       Date:  2017-12-07       Impact factor: 3.240

10.  The Individual and Combined Effects of Multiple Factors on the Risk of Soft Tissue Non-contact Injuries in Elite Team Sport Athletes.

Authors:  Alireza Esmaeili; William G Hopkins; Andrew M Stewart; George P Elias; Brendan H Lazarus; Robert J Aughey
Journal:  Front Physiol       Date:  2018-09-21       Impact factor: 4.566

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.