Evidence map›Paper›PMID 41007202›Full record

ArticleBioengineering (Basel, Switzerland)2025

Performance Insights in Speed Climbing: Quantitative and Qualitative Analysis of Key Movement Metrics.

Dominik Pandurević, Paweł Draga, Alexander Sutor, Klaus Hochradel

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Dominik PandurevićInstitute of Measurement and Sensor Technology, UMIT TIROL-Private University for Health Sciences and Health Technology, 6060 Hall in Tirol, Austria.ORCID 0000-0002-7801-2090
Paweł DragaInstitute of Measurement and Sensor Technology, UMIT TIROL-Private University for Health Sciences and Health Technology, 6060 Hall in Tirol, Austria.ORCID 0000-0002-1359-6806
Alexander SutorInstitute of Measurement and Sensor Technology, UMIT TIROL-Private University for Health Sciences and Health Technology, 6060 Hall in Tirol, Austria.ORCID 0000-0003-0064-5493
Klaus HochradelInstitute of Measurement and Sensor Technology, UMIT TIROL-Private University for Health Sciences and Health Technology, 6060 Hall in Tirol, Austria.ORCID 0000-0003-4695-5809

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a comprehensive analysis of Speed Climbing athletes by examining motion parameters critical to elite performance. As such, several key values are extracted from about 900 competition recordings in order to generate a dataset for the identification of patterns in athletes' technique and efficiency. A CNN-based framework is used to automate the detection of human keypoints and features, enabling a large-scale evaluation of climbing dynamics. The results revealed significant variations in performance for single sections of the wall, particularly in relation to start reaction times (with differences of up to 0.27 s) and increased split times the closer the athletes are to the end of the Speed Climbing wall (from 0.39 s to 0.45 s). In addition, a more detailed examination of the movement sequences was carried out by analyzing the velocity trajectories of hands and feet. The results showed that coordinated and harmonic movements, especially of the lower limbs, correlate strongly with the performance outcome. To ensure an individualized view of the data points, a comparison was made between multiple athletes, revealing insights into the influence of individual biomechanics on the efficiency of movements. The findings provide both trainers and athletes with interesting insights in relation to tailoring training methods by including split time benchmarks and limb coordination.

Indexed as

computer visionconvolutional neural networksdeep learningkinematic and performance analysismachine learningSpeed Climbingsport biomechanicssports biomechanicssports science in sportstraining optimization

Identifiers

PMID41007202
PMCPMC12467755

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.