In Journal of functional morphology and kinesiology
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: R2corr = 0.63; dominant hand: R2corr = 0.55). Deeper exploration using a machine learning model including all available data showed a predictive performance with R2 = 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.
Dindorf Carlo, Bartaguiz Eva, Dully Jonas, Sprenger Max, Merk Anna, Becker Stephan, Fröhlich Michael, Ludwig Oliver
2022-Oct-27
artificial intelligence, climbing, data mining, exhaustion, fatigue, gender, machine learning, sports, training
