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. 2021 Feb:82:105271.
doi: 10.1016/j.clinbiomech.2021.105271. Epub 2021 Jan 11.

Multi-segmental postural control patterns in down syndrome

Affiliations

Multi-segmental postural control patterns in down syndrome

Matteo Zago et al. Clin Biomech (Bristol). 2021 Feb.

Abstract

Background: Patients with Down Syndrome (DS) exhibit less efficient and unstable standing postural control. The specificities of somatosensorial deficits might result in a different utilization of resources and in distinct whole-body kinematic patterns, to date still unexplored. In this paper we aim at addressing multi-segmental coordination patterns in people with DS while maintaining standing balance under different visual conditions (open and closed eyes).

Methods: This cross-sectional observational cohort study involved two groups of 23 patients with DS and 12 healthy controls. A 30-s standing balance test allowed to extract (i) the length of the trajectory of the center-of-pressure sway and 95% confidence ellipse area from Ground Reaction forces, and (ii) Principal Movement (PM) components from full-body motion kinematics; the latter were obtained exploiting a Principal Component Analysis-based approach, also embracing a motor-control perspective through the evaluation of the number of modifications applied by the neuromuscular controller on segments' acceleration.

Findings: Trajectory length was significantly higher in patients; 95% ellipse confidence area did not differ between groups/condition. Postural movement components differed in people with DS from healthy controls not only in the "observable", behavioural phenotype (PM3 and PM8), but also in the amount of activation of the associated control (PM1 to PM8, over-activated in DS) in all spatial directions.

Interpretation: Results reinforced the prevalence of a medio-lateral hip strategy (instead of an ankle strategy) in maintaining postural stability. Most important, they revealed a less frequent activation of postural patterns in all spatial directions.

Keywords: Balance; Neurological disorders; PCA; Stability; Unsupervised learning.

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