期刊
MUSCULOSKELETAL SCIENCE AND PRACTICE
卷 28, 期 -, 页码 39-45出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.msksp.2017.01.009
关键词
Left/right judgement; Motor imagery; Laterality; Shoulder pain
Background: Disruption of cortically-held working body schema has been associated with a variety of pain conditions. A motor imagery technique -the left right judgement task (LRJT) -has been used as an indirect assessment of the integrity of the working body schema. To date there is no LRJT specifically designed to investigate the body schema of persons with shoulder pain. Objectives: To develop a shoulder specific LRJT and assess its validity and reliability. Design: Cross-sectional repeated measures. Methods: Shoulder images were developed representing the shoulder in a variety of postures of graded complexity/awkwardness and degree of rotation. These images were digitally mirrored to represent both left and right shoulders. Participants viewed the images on a computer and determined whether images were of a left or right shoulder. Results: 1413 participants were recruited worldwide and performed the shoulder LRJT (laterality judgement). Mean response time (SD) for the task was 1738(741) ms. Mean accuracy (SD) was 93.5(9.2)%. Chronbach's Alpha for shoulder image response times was 0.95. Participants were fastest responding to images of simple postures and slowest to images corresponding to the more awkward postures (mean difference 520 ms, 95% CI 469-570 ms). Participants were fastest responding to the least rotated images and slowest responding to inverted images, (mean difference 981 ms, 95% CI 919-1043 ms). Conclusions: The shoulder specific LRJT proved to be highly reliable. Response times increased with complexity and rotation of images, implying a motor imagery strategy was used to complete the task, validating the task as a measure of shoulder joint implicit motor imagery. Abnormal performance cutoffs for age were reported. This result will enable further research examining the relationship between shoulder pain and body schema. (C) 2017 Elsevier Ltd. All rights reserved.
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