Evaluation of Influencing Factors on the Maximum Climbing Specific Holding Time: An Inferential Statistics and Machine Learning Approach
Handgrip strength (HGS) appears to be an indicator of climbing performance. The transferability of HGS measurements obtained using a hand dynamometer and factors that influence the maximal climbing-specific holding time (CSHT) are largely unclear. Forty-eight healthy subjects (27 female, 21 male; ag...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Journal of Functional Morphology and Kinesiology |
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| Online Access: | https://www.mdpi.com/2411-5142/7/4/95 |
| _version_ | 1797457016064573440 |
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| author | Carlo Dindorf Eva Bartaguiz Jonas Dully Max Sprenger Anna Merk Stephan Becker Michael Fröhlich Oliver Ludwig |
| author_facet | Carlo Dindorf Eva Bartaguiz Jonas Dully Max Sprenger Anna Merk Stephan Becker Michael Fröhlich Oliver Ludwig |
| author_sort | Carlo Dindorf |
| collection | DOAJ |
| description | Handgrip strength (HGS) appears to be an indicator of climbing performance. The transferability of HGS measurements obtained using a hand dynamometer and factors that influence the maximal climbing-specific holding time (CSHT) are largely unclear. Forty-eight healthy subjects (27 female, 21 male; age: 22.46 ± 3.17 years; height: 172.76 ± 8.91 cm; weight: 69.07 ± 12.41 kg; body fat: 20.05% ± 7.95%) underwent a maximal pull-up test prior to the experiment and completed a self-assessment using a Likert scale questionnaire. HGS was measured using a hand dynamometer, whereas CSHT was measured using a fingerboard. Multiple linear regressions showed that weight, maximal number of pull-ups, HGS normalized by subject weight, and length of the middle finger had a significant effect on the maximal CSHT (non-dominant hand: R<sup>2</sup><sub>corr</sub> = 0.63; dominant hand: R<sup>2</sup><sub>corr</sub> = 0.55). Deeper exploration using a machine learning model including all available data showed a predictive performance with R<sup>2</sup> = 0.51 and identified another relevant parameter for the regression model. These results call into question the use of hand dynamometers and highlight the performance-related importance of body weight in climbing practice. The results provide initial indications that finger length may be used as a sub-factor in talent scouting. |
| first_indexed | 2024-03-09T16:15:11Z |
| format | Article |
| id | doaj.art-b3b778bd37db4193a1e48c3d311c9831 |
| institution | Directory Open Access Journal |
| issn | 2411-5142 |
| language | English |
| last_indexed | 2024-03-09T16:15:11Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Functional Morphology and Kinesiology |
| spelling | doaj.art-b3b778bd37db4193a1e48c3d311c98312023-11-24T15:51:37ZengMDPI AGJournal of Functional Morphology and Kinesiology2411-51422022-10-01749510.3390/jfmk7040095Evaluation of Influencing Factors on the Maximum Climbing Specific Holding Time: An Inferential Statistics and Machine Learning ApproachCarlo Dindorf0Eva Bartaguiz1Jonas Dully2Max Sprenger3Anna Merk4Stephan Becker5Michael Fröhlich6Oliver Ludwig7Department of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, GermanyDepartment of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, GermanyDepartment of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, GermanyDepartment of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, GermanyDepartment of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, GermanyDepartment of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, GermanyDepartment of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, GermanyDepartment of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, GermanyHandgrip strength (HGS) appears to be an indicator of climbing performance. The transferability of HGS measurements obtained using a hand dynamometer and factors that influence the maximal climbing-specific holding time (CSHT) are largely unclear. Forty-eight healthy subjects (27 female, 21 male; age: 22.46 ± 3.17 years; height: 172.76 ± 8.91 cm; weight: 69.07 ± 12.41 kg; body fat: 20.05% ± 7.95%) underwent a maximal pull-up test prior to the experiment and completed a self-assessment using a Likert scale questionnaire. HGS was measured using a hand dynamometer, whereas CSHT was measured using a fingerboard. Multiple linear regressions showed that weight, maximal number of pull-ups, HGS normalized by subject weight, and length of the middle finger had a significant effect on the maximal CSHT (non-dominant hand: R<sup>2</sup><sub>corr</sub> = 0.63; dominant hand: R<sup>2</sup><sub>corr</sub> = 0.55). Deeper exploration using a machine learning model including all available data showed a predictive performance with R<sup>2</sup> = 0.51 and identified another relevant parameter for the regression model. These results call into question the use of hand dynamometers and highlight the performance-related importance of body weight in climbing practice. The results provide initial indications that finger length may be used as a sub-factor in talent scouting.https://www.mdpi.com/2411-5142/7/4/95climbingexhaustionfatiguetrainingmachine learningsports |
| spellingShingle | Carlo Dindorf Eva Bartaguiz Jonas Dully Max Sprenger Anna Merk Stephan Becker Michael Fröhlich Oliver Ludwig Evaluation of Influencing Factors on the Maximum Climbing Specific Holding Time: An Inferential Statistics and Machine Learning Approach Journal of Functional Morphology and Kinesiology climbing exhaustion fatigue training machine learning sports |
| title | Evaluation of Influencing Factors on the Maximum Climbing Specific Holding Time: An Inferential Statistics and Machine Learning Approach |
| title_full | Evaluation of Influencing Factors on the Maximum Climbing Specific Holding Time: An Inferential Statistics and Machine Learning Approach |
| title_fullStr | Evaluation of Influencing Factors on the Maximum Climbing Specific Holding Time: An Inferential Statistics and Machine Learning Approach |
| title_full_unstemmed | Evaluation of Influencing Factors on the Maximum Climbing Specific Holding Time: An Inferential Statistics and Machine Learning Approach |
| title_short | Evaluation of Influencing Factors on the Maximum Climbing Specific Holding Time: An Inferential Statistics and Machine Learning Approach |
| title_sort | evaluation of influencing factors on the maximum climbing specific holding time an inferential statistics and machine learning approach |
| topic | climbing exhaustion fatigue training machine learning sports |
| url | https://www.mdpi.com/2411-5142/7/4/95 |
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