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Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers
To provide percentile curves for short-course swimming events, including 5 swimming strokes, 6 race distances, and both sexes, as well as to compare differences in race times between cross-sectional analysis and longitudinal tracking, a total of 31,645,621 race times of male and female swimmers were...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9206680/ https://www.ncbi.nlm.nih.gov/pubmed/35717501 http://dx.doi.org/10.1038/s41598-022-13837-3 |
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author | Born, Dennis-Peter Rüeger, Eva Beaven, C. Martyn Romann, Michael |
author_facet | Born, Dennis-Peter Rüeger, Eva Beaven, C. Martyn Romann, Michael |
author_sort | Born, Dennis-Peter |
collection | PubMed |
description | To provide percentile curves for short-course swimming events, including 5 swimming strokes, 6 race distances, and both sexes, as well as to compare differences in race times between cross-sectional analysis and longitudinal tracking, a total of 31,645,621 race times of male and female swimmers were analyzed. Two percentile datasets were established from individual swimmers’ annual best times and a two-way analysis of variance (ANOVA) was used to determine differences between cross-sectional analysis and longitudinal tracking. A software-based percentile calculator was provided to extract the exact percentile for a given race time. Longitudinal tracking reduced the number of annual best times that were included in the percentiles by 98.35% to 262,071 and showed faster mean race times (P < 0.05) compared to the cross-sectional analysis. This difference was found in the lower percentiles (1st to 20th) across all age categories (P < 0.05); however, in the upper percentiles (80th to 99th), longitudinal tracking showed faster race times during early and late junior age only (P < 0.05), after which race times approximated cross-sectional tracking. The percentile calculator provides quick and easy data access to facilitate practical application of percentiles in training or competition. Longitudinal tracking that accounts for drop-out may predict performance progression towards elite age, particularly for high-performance swimmers. |
format | Online Article Text |
id | pubmed-9206680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92066802022-06-20 Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers Born, Dennis-Peter Rüeger, Eva Beaven, C. Martyn Romann, Michael Sci Rep Article To provide percentile curves for short-course swimming events, including 5 swimming strokes, 6 race distances, and both sexes, as well as to compare differences in race times between cross-sectional analysis and longitudinal tracking, a total of 31,645,621 race times of male and female swimmers were analyzed. Two percentile datasets were established from individual swimmers’ annual best times and a two-way analysis of variance (ANOVA) was used to determine differences between cross-sectional analysis and longitudinal tracking. A software-based percentile calculator was provided to extract the exact percentile for a given race time. Longitudinal tracking reduced the number of annual best times that were included in the percentiles by 98.35% to 262,071 and showed faster mean race times (P < 0.05) compared to the cross-sectional analysis. This difference was found in the lower percentiles (1st to 20th) across all age categories (P < 0.05); however, in the upper percentiles (80th to 99th), longitudinal tracking showed faster race times during early and late junior age only (P < 0.05), after which race times approximated cross-sectional tracking. The percentile calculator provides quick and easy data access to facilitate practical application of percentiles in training or competition. Longitudinal tracking that accounts for drop-out may predict performance progression towards elite age, particularly for high-performance swimmers. Nature Publishing Group UK 2022-06-18 /pmc/articles/PMC9206680/ /pubmed/35717501 http://dx.doi.org/10.1038/s41598-022-13837-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Born, Dennis-Peter Rüeger, Eva Beaven, C. Martyn Romann, Michael Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers |
title | Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers |
title_full | Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers |
title_fullStr | Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers |
title_full_unstemmed | Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers |
title_short | Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers |
title_sort | comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9206680/ https://www.ncbi.nlm.nih.gov/pubmed/35717501 http://dx.doi.org/10.1038/s41598-022-13837-3 |
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