Literature DB >> 33345411

Modelling the age-related trajectory of performance in Para swimmers with physical, vision and intellectual impairment.

Luke Hogarth1, Vaughan Nicholson2, Carl Payton3, Brendan Burkett1,4.   

Abstract

This study is the first to provide information on the age-related trajectories of performance in Para swimmers with physical, vision and intellectual impairment. Race times from long-course swim meets between 2009 and 2019 were obtained for Para swimmers with an eligible impairment. A subset of 10 661 times from 411 Para swimmers were included in linear mixed effects modelling to establish the relationship between age and performance expressed relative to personal best time and world record time. The main findings were: (a) age has the most noticeable influence on performance between the ages of 12-20 years before performances stabilize and peak in the early to late twenties, (b) women have faster times relative to personal best and world record time than men during early adolescence and their performances stabilize, peak and decline at younger ages, and (c) Para swimmers from different sport classes show varying age-related trajectories in performance after maturation and when training-related factors are more likely to explain competitive swim performance. The results of this study can guide talent identification and development of Para swimmers at various stages of their career and help to inform decision-making on the allocation of sport class and sport class status in Para swimming classification.
© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  Para sport; Paralympic; athlete development; evidence-based classification; swimming; talent identification

Mesh:

Year:  2021        PMID: 33345411     DOI: 10.1111/sms.13910

Source DB:  PubMed          Journal:  Scand J Med Sci Sports        ISSN: 0905-7188            Impact factor:   4.221


  3 in total

1.  Factor Analysis and Regression Prediction Model of Swimmers' Performance Structure Based on Mixed Genetic Neural Network.

Authors:  Rui Yuan; Yuexing Han
Journal:  Comput Intell Neurosci       Date:  2022-05-31

2.  Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model.

Authors:  Wei Wen; Tingyu Yang; Yanhao Fu; Siwen Liu
Journal:  Comput Intell Neurosci       Date:  2022-04-11

3.  Physical giftedness/talent: A systematic review of the literature on identification and development.

Authors:  Jae Yup Jung
Journal:  Front Psychol       Date:  2022-08-26
  3 in total

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