Effect of Cross-legged Sitting Posture on Joint Range of Motion: Correlation with Musculoskeletal Symptoms and Facial Asymmetry

Abstract

Purpose: This study sought to study the effects of cross-legged sitting posture on joint motion. It also examined the correlation between the changes in the joint range of motion, musculoskeletal symptoms, and facial asymmetry. Methods: The Acumar Digital Inclinometer (Lafayette Instrument Company, USA) was used to measure the range of motion (ROM). We measured the flexion and extension of the cervical, thoracic, and lumbar spine using a dual inclinometer, and measured the ROM of the shoulder and hip joint with a single inclinometer. The Likert scale questionnaire was used to investigate musculoskeletal symptoms and facial asymmetry. Results: The data analysis was performed using the Jamovi version 1.6.23 statistical software. After confirming the normality of the ROM with descriptive statistics, it was compared with the normal ROM through a one-sample t-test. Correlation matrix analysis was performed to confirm the association between facial asymmetry and musculoskeletal symptoms. The result of the one-sample t-test showed a significant increase in the thoracic spine extension and right and left hip external rotation (p< 0.001***), while most other joints were restricted. As per the frequency analysis, facial asymmetry was found to be 81.70%. Conclusion:The independent variable, namely cross-legged sitting posture led to an increase in ROM. The study also suggests that facial asymmetry and musculoskeletal symptoms could occur. Therefore, to prevent the increase and limitation of ROM and to prevent the occurrence of facial asymmetry and musculoskeletal symptoms, it is suggested that the usual cross-legged sitting posture should be avoided.

Analysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training

“…The goal of human posture detection technology applied in the field of sports is to realise the indexing of sports norms, to provide scientific guidance for training and teaching, and to improve the quality of sports is of great significance [6]. Therefore, it is a very meaningful task to study sports teaching and training methods based on sports posture detection technology [7].…”

“…Sports posture recognition refers to the use of threedimensional or two-dimensional position of body joints, hand department joints and finger movement positioning, face and facial features position orientation to detect the movement state and performance [8]. At present, the application of sports posture detection technology in sports teaching and training, the construction of the playground sports posture detection effect analysis system, and the development of intelligent sports posture detection methods [7], are increasingly receiving the attention and research of experts in the field [8]. Sports posture detection effect analysis methods mainly include random forest, support vector machine, neural network, deep learning, heuristic optimisation algorithm and other methods [9].…”