ArticleBioengineering (Basel, Switzerland)2026
Engineering Elite Swimming Start Performance: Key Kinetic and Kinematic Variables with Reference Values.
Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Biomechanics of backstroke start in elite swimmers: examining the relationship between posture, dynamic balance, and vertical jump performance.BMC sports science, medicine & rehabilitation · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
To provide deeper insights into the complex and multidimensional nature of swimming start performance, the present study aimed to determine its key performance indicators (KPIs) and provide percentile-based reference values for elite junior and adult swimmers. Hence, routine performance analysis data of Swiss junior and senior national team members were analyzed, including multiple European champions, World champions, Olympic medalists and a World record holder (n = 136, age: 18.3 ± 3.6 [13-32] years, World Aquatics swimming points: 761 ± 73 [609-1061]). All kinetic and kinematic variables measured by the instrumented starting block were analyzed, and variables with pairwise correlation > 0.80 were clustered using principal component analysis with orthogonal Varimax rotation, retaining components with Eigenvalue > 1.0 and factor loadings > 0.6. The highest loaded variables of each component were used as independent variables, alongside the variables with low co-variance, to determine KPIs with multiple linear regression analysis. As such, peak and average power (
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Registered trials
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