Background context: 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.
Purpose: To identify if distinct spine spatiotemporal movement strategies are utilized in a homogenous sample of young healthy participants.
Study design: Kinematic data were captured from a single cohort of male participants (N=51) during a simple, self-controlled spine flexion-extension task.
Methods: 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.
Results: 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.
Conclusions: Spatiotemporal spine flexion-extension patterns are not uniform across a population of young healthy individuals.
Clinical significance: 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.
Keywords: Continuous relative phase; Kinematics; Machine learning; Movement; Principal component analysis; k-means clustering.
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