Evaluation of Influencing Factors on the Maximum Climbing Specific Holding Time: An Inferential Statistics and Machine Learning Approach

Abstract

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.

In Vivo Monitoring of Acute and Intermittent Fatigue in Sport Climbing Using Near-Infrared Spectroscopy Wearable Biosensors

“…At this point, it should be stated that the training status certainly determines the CSHT significantly, but a person also achieves a long holding time simply because of low body weight [ 4 ]. Accordingly, it cannot be said in general that people with longer CSHT are better trained.…”

“…Therefore, the objectification of acute fatigue (caused during isometric muscle contraction) and cumulative fatigue (as a result of multiple intermittent isometric muscle contractions) of the finger flexor muscles might play an important role in training planning, monitoring, and competition, which highlights the research necessity. Measuring handgrip strength (HGS) by means of a hand dynamometer and the maximal climbing-specific holding time (CSHT) is often associated with climbing-specific performance [ 2 , 3 , 4 ]. As these parameters are easy to measure objectively, they might be suitable for monitoring fatigue.…”

3D Analysis of Human Movement, Sport, and Health Promotion

“…Deep learning could be adapted to analyze behaviors with standardized methods proposed in the literature [22]. In one of the studies included in this Special Issue, a machine learning approach was adopted in the evaluation of climbing holding time [23]. The concept of the study could also be adopted in other studies using 3D data.…”