English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This study examined whether analysing kinetic features of drop jumps (DJ) as one-dimensional biomechanical curves can reveal specific patterns that are consistent and can cluster DJ performance. Hierarchical clustering analysis on DJ from 40 cm data performed by 128 physically active male participants (23.0 ± 4.5 yrs, 1.84 ± 0.07 m, 79.1 ± 10.8 kg) was performed on the derived time-normalised force, power and vertical stiffness curves to unmask the underlying patterns and to explore the dissimilarities identified from the subgroup (cluster) analysis. Results revealed poor, average and top DJ performers. Top performers exhibited larger peak force, power and vertical stiffness compared to the other two groups, and the poor performers had lower values compared to the average performers ( p  &lt; .05). The time curves of force, power and vertical stiffness exhibited between cluster dissimilarities from ~25% to ~70%, and ~20% to 40% plus ~55% to 70% from the beginning of the ground contact, respectively. The force and power time-curves distinguished DJ ability similarly since they shared 69% of the cases in the top performers’ cluster. The content of cases (membership) for vertical stiffness was different from the membership for the force and power time-curve clusters. In conclusion, stiffness should be considered during plyometric training, but does not distinctly define DJ performance.

Kinetic time-curves can classify individuals in distinct levels of drop jump performance