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Inertial sensor real-time feedback enhances the learning of cervical spine manipulation: a prospective study.

Cuesta-Vargas, A.I. and Williams, J. M., 2014. Inertial sensor real-time feedback enhances the learning of cervical spine manipulation: a prospective study. BMC Medical Education, 14, 120.

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DOI: 10.1186/1472-6920-14-120

Abstract

BACKGROUND: Cervical Spinal Manipulation (CSM) is considered a high-level skill of the central nervous system because it requires bimanual coordinated rhythmical movements therefore necessitating training to achieve proficiency. The objective of the present study was to investigate the effect of real-time feedback on the performance of CSM. METHODS: Six postgraduate physiotherapy students attending a training workshop on Cervical Spine Manipulation Technique (CSMT) using inertial sensor derived real-time feedback participated in this study. The key variables were pre-manipulative position, angular displacement of the thrust and angular velocity of the thrust. Differences between variables before and after training were investigated using t-tests. RESULTS: There were no significant differences after training for the pre-manipulative position (rotation p = 0.549; side bending p = 0.312) or for thrust displacement (rotation p = 0.247; side bending p = 0.314). Thrust angular velocity demonstrated a significant difference following training for rotation (pre-training mean (sd) 48.9°/s (35.1); post-training mean (sd) 96.9°/s (53.9); p = 0.027) but not for side bending (p = 0.521). CONCLUSION: Real-time feedback using an inertial sensor may be valuable in the development of specific manipulative skill. Future studies investigating manipulation could consider a randomized controlled trial using inertial sensor real time feedback compared to traditional training.

Item Type:Article
ISSN:1472-6920
Uncontrolled Keywords:Adult; Feedback; Female; Humans; Male; Manipulation, Spinal; Physical Therapy Specialty; Prospective Studies; Students
Group:Faculty of Health & Social Sciences
ID Code:23351
Deposited By: Symplectic RT2
Deposited On:24 Mar 2016 14:24
Last Modified:14 Mar 2022 13:55

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