Sport Science, New South Wales Institute of Sport, Sydney Olympic Park, New South Wales, Australia.
Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Queensland, Australia.
PeerJ. 2024 Oct 4;12:e17971. doi: 10.7717/peerj.17971. eCollection 2024.
Repeat power ability (RPA) assessments traditionally use discrete variables, such as peak power output, to quantify the change in performance across a series of jumps. Rather than using a discrete variable, the analysis of the entire force-time curve may provide additional insight into RPA performance. The aims of this study were to (1) analyse changes in the force-time curve recorded during an RPA assessment using statistical parametric mapping (SPM) and (2) compare the differences in the force-time curve between participants with low and high RPA scores, as quantified by traditional analysis.
Eleven well-trained field hockey players performed an RPA assessment consisting of 20 loaded countermovement jumps with a 30% one repetition maximum half squat load (LCMJ20). Mean force-time series data was normalized to 100% of the movement duration and analysed using SPM. Peak power output for each jump was also derived from the force-time data and a percent decrement score calculated for jumps 2 to 19 (RPA). An SPM one-way ANOVA with significance accepted at = 0.05, was used to identify the change in the force-time curve over three distinct series of jumps across the LCMJ20 (series 1 = jumps 2-5, series 2 = jumps 9-12 and series 3 = jumps 16-19). A secondary analysis, using an independent -test with significance accepted at < 0.001, was also used to identify differences in the force-time curve between participants with low and high RPA.
Propulsive forces were significantly lower ( < 0.001) between 74-98% of the movement compared to 0-73% for changes recorded during the LCMJ20. analysis identified the greatest differences to occur between jump series 1 and jump series 2 ( < 0.001) at 70-98% of the movement and between jump series 1 and jump series 3 ( < 0.001) at 86-99% of the movement. No significant differences were found between jump series 2 and jump series 3. Significant differences ( < 0.001) in both the braking phase at 44-48% of the jump and the propulsive phase at 74-94% of the jump were identified when participants were classified based on low or high RPA scores (with low scores representing an enhanced ability to maintain peak power output than high scores).
A reduction in force during the late propulsive phase is evident as the LCMJ20 progresses. SPM analysis provides refined insight into where changes in the force-time curve occur during performance of the LCMJ20. Participants with the lower RPA scores displayed both larger braking and propulsive forces across the LCMJ20 assessment.
重复功率能力（RPA）评估传统上使用离散变量，如峰值功率输出，来量化一系列跳跃中的性能变化。而不是使用离散变量，对整个力-时间曲线的分析可能会提供对 RPA 性能的更多见解。本研究的目的是：（1）使用统计参数映射（SPM）分析 RPA 评估中记录的力-时间曲线的变化；（2）比较传统分析中 RPA 得分较低和较高的参与者之间的力-时间曲线的差异。
11 名训练有素的曲棍球运动员进行了 RPA 评估，包括 20 次负载的反向运动跳跃，负载为 30%一次重复最大半蹲负载（LCMJ20）。平均力-时间序列数据被归一化为运动持续时间的 100%，并使用 SPM 进行分析。从力-时间数据中还得出了每个跳跃的峰值功率输出，并计算了跳跃 2 到 19 的百分比递减分数（RPA）。使用 SPM 单向方差分析，接受显著性水平为 0.05，用于识别 LCMJ20 三次跳跃系列（系列 1=跳跃 2-5，系列 2=跳跃 9-12，系列 3=跳跃 16-19）中力-时间曲线的变化。还使用独立 t 检验（显著性水平接受 0.001）进行了二次分析，以确定 RPA 得分较低和较高的参与者之间的力-时间曲线的差异。
与 0-73%的运动相比，在 74-98%的运动中，推进力显著降低（<0.001）。分析确定，在 LCMJ20 期间记录的变化中，最大的差异发生在跳跃系列 1 和跳跃系列 2 之间（<0.001），在 70-98%的运动中，以及跳跃系列 1 和跳跃系列 3 之间（<0.001），在 86-99%的运动中。在跳跃系列 2 和跳跃系列 3 之间未发现显著差异。当根据 RPA 得分的高低（低得分代表比高分更高的保持峰值功率输出的能力）对参与者进行分类时，在跳跃的 44-48%的制动阶段和跳跃的 74-94%的推进阶段都发现了显著差异（<0.001）。
随着 LCMJ20 的进行，在后期推进阶段力的下降是明显的。SPM 分析提供了对 LCMJ20 性能期间力-时间曲线变化的更深入了解。RPA 得分较低的参与者在整个 LCMJ20 评估中显示出更大的制动和推进力。
