Trezise J, Blazevich A J
School of Medical and Health Sciences, Centre for Exercise and Sports Science Research (CESSR), Edith Cowan University, Joondalup, WA, Australia.
Front Physiol. 2019 Aug 6;10:1001. doi: 10.3389/fphys.2019.01001. eCollection 2019.
This study examined whether changes in strength following a moderate-duration strength training program were associated with changes in specific combinations of anatomical and neuromuscular variables. 36 men (18-40 y) completed 10 weeks of lower-limb heavy resistance (6-RM) strength training. Measurements included cross-sectional area (CSA), fascicle length (l) and fascicle angle (θ) from proximal, middle and distal regions of the four quadriceps components; agonist (EMG:M), antagonist (EMG) muscle activities and percent voluntary quadriceps activation (%VA; interpolated twitch technique); patellar tendon moment arm distance; and maximal isometric, concentric and eccentric (60° s) torque. Multiple regression models were developed to quantify the relationship between the change in maximum torque and the changes in combinations of anatomical and neuromuscular variables. The best model for each contraction mode was determined using Akaike's Information Criterion (AIC), an information-theoretic approach for model selection. Strength increased significantly following training (mean range = 12.5-17.2%), and moderate relationships were observed between modeled data (using best-fit prediction models) and the change in torque for each contraction mode. The change in isometric torque was best (although weakly) predicted by the linear combination of the change in proximal-region vastus lateralis (VL) CSA and fascicle angle ( = 0.27, < 0.05; AIC = 0.52, i.e., the probability the model would be selected as the "best model"). The models best predicting the change in concentric and eccentric torque both included the combination of the change in quadriceps (i.e., mean of all muscles) EMG:M and the change in vastus intermedius fascicle angle combined with either a change in proximal-region VL ( = 0.40, < 0.001; AIC = 0.15) or whole quadriceps ( = 0.41, < 0.001; AIC = 0.30) CSA (concentric and eccentric, respectively). Models incorporating the change in proximal CSA typically received substantial support (AIC < 2) for concentric torque prediction models, and the change in % VA and pre-training moment arm distance had substantial support for use in eccentric torque prediction models. In conclusion, adaptations varied between individuals, however strength training programs targeted to improve a group of variables that particularly includes agonist muscle activation might yield the greatest improvements in concentric and eccentric knee extension strength, whereas proximal muscle size and fascicle angle appear most important for isometric torque improvements.
本研究探讨了中等时长力量训练计划后力量的变化是否与解剖学和神经肌肉变量的特定组合变化相关。36名男性(18 - 40岁)完成了为期10周的下肢大重量抗阻(6次重复最大值)力量训练。测量指标包括股四头肌四个组成部分近端、中部和远端区域的横截面积(CSA)、肌束长度(l)和肌束角度(θ);主动肌(肌电图:最大值,EMG:M)、拮抗肌(肌电图,EMG)的肌肉活动以及股四头肌自主激活百分比(%VA;内插单收缩技术);髌腱力臂距离;以及最大等长、向心和离心(60°/秒)扭矩。建立了多元回归模型,以量化最大扭矩变化与解剖学和神经肌肉变量组合变化之间的关系。使用赤池信息准则(AIC)确定每种收缩模式的最佳模型,AIC是一种用于模型选择的信息论方法。训练后力量显著增加(平均增幅 = 12.5 - 17.2%),并且观察到建模数据(使用最佳拟合预测模型)与每种收缩模式扭矩变化之间存在中等程度的关系。等长扭矩变化最好(尽管较弱)由近端外侧股四头肌(VL)CSA变化和肌束角度的线性组合预测(r = 0.27,P < 0.05;AIC = 0.52,即该模型被选为“最佳模型”的概率)。预测向心和离心扭矩变化的最佳模型均包括股四头肌(即所有肌肉的平均值)EMG:M变化与股中间肌肌束角度变化的组合,再结合近端区域VL(r = 0.40,P < 0.001;AIC = 0.15)或整个股四头肌(r = 0.41,P < 0.001;AIC = 0.30)CSA的变化(分别对应向心和离心情况)。纳入近端CSA变化的模型通常在向心扭矩预测模型中获得大量支持(AIC < 2),%VA变化和训练前力臂距离在离心扭矩预测模型中有大量支持用于分析。总之,个体间的适应性各不相同,然而,针对改善一组特别包括主动肌肌肉激活的变量的力量训练计划,可能会在向心和离心膝关节伸展力量方面产生最大程度的提高,而近端肌肉大小和肌束角度似乎对改善等长扭矩最为重要。