Article Text

Health challenges and acute sports injuries restrict weightlifting training of older athletes
  1. Marianne Huebner1,2,
  2. Wenjuan Ma3
  1. 1Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
  2. 2Department of Kinesiology, Michigan State University, East Lansing, Michigan, USA
  3. 3Center for Statistical Training and Consulting, Michigan State University, East Lansing, Michigan, USA
  1. Correspondence to Dr Marianne Huebner; huebner{at}msu.edu

Abstract

Objectives To quantify acute injuries sustained during weightlifting that result in training restrictions and identify potential risk factors or preventative factors in Master athletes and to evaluate potentially complex interactions of age, sex, health-related and training-related predictors of injuries with machine learning (ML) algorithms.

Methods A total of 976 Masters weightlifters from Australia, Canada, Europe and the USA, ages 35–88 (51.1% women), completed an online survey that included questions on weightlifting injuries, chronic diseases, sport history and training practices. Ensembles of ML algorithms were used to identify factors associated with acute weightlifting injuries and performance of the prediction models was evaluated. In addition, a subgroup of variables selected by six experts were entered into a logistic regression model to estimate the likelihood of an injury.

Results The accuracy of ML models predicting injuries ranged from 0.727 to 0.876 for back, hips, knees and wrists, but were less accurate (0.644) for shoulder injuries. Male Master athletes had a higher prevalence of weightlifting injuries than female Master athletes, ranging from 12% to 42%. Chronic inflammation or osteoarthritis were common among both men and women. This was associated with an increase in acute injuries.

Conclusions Training-specific variables, such as choices of training programmes or nutrition programmes, may aid in preventing acute injuries. ML models can identify potential risk factors or preventative measures for sport injuries.

  • aging
  • training
  • weight lifting
  • sporting injuries
  • gender

Data availability statement

Data are available in a public, open access repository. Reference: Huebner, Marianne (2022), Weightlifting Injuries in Master Athletes, Dryad Digital Repository, Dataset, https://doi.org/10.5061/dryad.51c59zwb3.

http://creativecommons.org/licenses/by-nc/4.0/

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Data availability statement

Data are available in a public, open access repository. Reference: Huebner, Marianne (2022), Weightlifting Injuries in Master Athletes, Dryad Digital Repository, Dataset, https://doi.org/10.5061/dryad.51c59zwb3.

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Footnotes

  • Twitter @MHuebnerPhD

  • Contributors MH conceptualised the study and reviewed the literature. MH is the guarantor of the study. Both authors performed statistical analyses and wrote the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient and public involvement Active weightlifters provided feedback on the survey questions.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.