Ohio University, Athens, OH, USA.
Ohio University, Athens, OH, USA.
Phys Ther Sport. 2020 Jan;41:97-102. doi: 10.1016/j.ptsp.2019.12.002. Epub 2019 Dec 6.
Determine the relationship between four foundational single-leg hop tests and respective neurocognitive single-leg hop tests.
Cross-sectional; SETTING: University gymnasium.
Twenty-two participants (9 Male, 13 Female, 20.9 ± 2.5 years, 171.2 ± 11.7 cm, 70.3 ± 11.0 kg) were recruited. Maximum distance was measured for three hop tests (single-leg hop, single-leg crossover hop, single-leg triple hop) and fastest time was measured for the fourth (single-leg 6-m hop) for traditional and neurocognitive conditions.
Pearson correlations were conducted to assess the relationship between the new neurocognitive hop and the analogous traditional hop. One repeated measures MANOVA was conducted for each leg to determine the difference in hop performance between hop conditions (traditional and neurocognitive) for the dependent variables. Alpha level was set at α < 0.05.
Correlations ranged from 0.86 to 0.92 between traditional and neurocognitive hop tests. The repeated measures MANOVA was significant for condition for both legs (p < 0.05). Specifically, the crossover hop (average percent decrease 10.37%), triple hop (average percent decrease 7.13%), and 6-m hop (average percent decrease 81.67%) were statistically different between traditional and neurocognitive conditions (p < 0.05).
The addition of neurocognitive reactive and anticipatory components to simulate more sport specific scenarios may improve functional testing for return to sport.
确定四项基础单腿跳跃测试与各自神经认知单腿跳跃测试之间的关系。
横断面研究;地点:大学体育馆。
招募了 22 名参与者(9 名男性,13 名女性,20.9±2.5 岁,171.2±11.7cm,70.3±11.0kg)。在传统和神经认知条件下,分别测量了三个跳跃测试(单腿跳跃、单腿交叉跳跃、单腿三连跳)的最大距离和第四个测试(单腿 6 米跳跃)的最快时间。
采用 Pearson 相关分析评估新的神经认知跳跃与类似传统跳跃之间的关系。对每条腿进行了一次重复测量 MANOVA,以确定跳跃条件(传统和神经认知)对依赖变量的跳跃表现差异。α 水平设为α<0.05。
传统和神经认知跳跃测试之间的相关性在 0.86 到 0.92 之间。双腿的重复测量 MANOVA 具有统计学意义(p<0.05)。具体而言,交叉跳跃(平均百分比下降 10.37%)、三连跳(平均百分比下降 7.13%)和 6 米跳跃(平均百分比下降 81.67%)在传统和神经认知条件之间存在统计学差异(p<0.05)。
在模拟更具运动特异性场景的过程中增加神经认知反应和预期成分可能会提高重返运动的功能测试。