Literature DB >> 33589667

Effects of exercise on cervical muscle strength and cross-sectional area in patients with thoracic hyperkyphosis and chronic cervical pain.

Hyunghun Moon1, Sung-Ki Lee2, Won-Moon Kim3, Yong-Gon Seo4.   

Abstract

There is a lack of studies comparing the effects of different exercise types in patients with thoracic hyperkyphosis. Twenty-four subjects were divided into three groups: corrective exercise, resistance exercise, and physical therapy. The groups performed their respective interventions, two times per week for three months. Clinical outcomes, including the value of Cobb's angle, cervical muscle strength and endurance, and the cross-sectional area of the cervical deep muscles were measured pre- and post-intervention. There was a significant difference in the changes in the thoracic Cobb's angle between the groups (P < 0.001). The corrective exercise group revealed a significantly superior increase in muscle strength and endurance between pre- and post-intervention (P < 0.012). There was a significant difference in the cross-sectional area of the cervical deep muscles included longus capitis and multifidus between the groups (P < 0.036 and 0.007, respectively). The corrective exercise group showed the most significant increase in cross-sectional area between pre- and post-intervention (P < 0.012). A corrective exercise program is a more effective intervention than traditional resistance exercise and physical therapy for improving the thoracic Cobb's angle, cervical muscle strength and endurance, and the cross-sectional area of the deep muscles in patients with thoracic hyperkyphosis.Trial registration: KCT0005292.

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Year:  2021        PMID: 33589667      PMCID: PMC7884681          DOI: 10.1038/s41598-021-83344-4

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  20 in total

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9.  Relations between standing stability and body posture parameters in adolescent idiopathic scoliosis.

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10.  Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network.

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