School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa Ontario, Canada.
Department of Kinesiology and Physical Education, Wilfrid Laurier University, Waterloo Ontario, Canada.
Spine J. 2019 Jul;19(7):1264-1275. doi: 10.1016/j.spinee.2019.02.002. Epub 2019 Feb 8.
The spine is an anatomically complex system with numerous degrees of freedom. Due to this anatomical complexity, it is likely that multiple motor control options exist to complete a given task.
To identify if distinct spine spatiotemporal movement strategies are utilized in a homogenous sample of young healthy participants.
Kinematic data were captured from a single cohort of male participants (N=51) during a simple, self-controlled spine flexion-extension task.
Thoracic and lumbar flexion-extension data were analyzed to extract the continuous relative phase between each spine subsection. Continuous relative phase data were evaluated using a principal component analysis to identify major sources of variation in spine movement coordination. Unsupervised machine learning (k-means clustering) was used to identify distinct clusters present within the healthy participants sampled. Once distinguished, intersegmental spine kinematics were compared amongst clusters.
The findings of the current work suggest that there are distinct timing strategies that are utilized, within the participants sampled, to control spine flexion-extension movement. These strategies differentiate the sequencing of intersegmental movement and are not discriminable on the basis of simple participant demographic characteristics (ie, age, height, and body mass index), total movement time or range of motion.
Spatiotemporal spine flexion-extension patterns are not uniform across a population of young healthy individuals.
Future work needs to identify whether the motor patterns characterized with this work are driven by distinct neuromuscular activation patterns, and if each given pattern has a varied risk for low back injury.
脊柱是一个具有众多自由度的解剖结构复杂的系统。由于这种解剖复杂性,很可能存在多种运动控制选项来完成给定的任务。
确定在同质的年轻健康参与者样本中是否使用了不同的脊柱时空运动策略。
在一项简单的自我控制脊柱屈伸任务中,从单一队列的男性参与者(N=51)中获取运动学数据。
分析胸腰段屈伸数据,以提取每个脊柱节段之间的连续相对相位。使用主成分分析对连续相对相位数据进行评估,以确定脊柱运动协调的主要变化来源。使用无监督机器学习(k-均值聚类)来识别健康参与者样本中存在的不同聚类。一旦区分开来,就比较聚类之间的脊柱运动学。
目前工作的结果表明,在所采样的参与者中,存在控制脊柱屈伸运动的不同时间策略。这些策略区分了节段间运动的顺序,并且不能基于简单的参与者人口统计学特征(即年龄、身高和体重指数)、总运动时间或运动范围来区分。
在年轻健康人群中,脊柱时空屈伸模式并不统一。
未来的工作需要确定这项工作所描述的运动模式是否是由不同的神经肌肉激活模式驱动的,以及每个给定模式是否具有不同的腰部受伤风险。