Aged Lumbar Extension Strength of Chronic Low Back Pain in Korean Population of 10-80 Years

Ji-Hoon Cho, Ki-Hyuk Lee, Seung-Taek Lim, Ji-Hoon Cho, Ki-Hyuk Lee, Seung-Taek Lim

Abstract

Background: The purpose of this study was to find the basic data of medical and exercise therapy by indexing lumbar extension muscle strength of low back pain (LBP) patients.

Methods: In this cross-sectional study, 3078 chronic LBP participants from The J hospital, Seoul, Republic of Korea, from 2003 to 2010 were enrolled. Maximum muscle strength was measured at maximum flexion angle and maximum extension angle according to range of motion (ROM) results. For each isometric test, participants were seated and secured in the MEDX (medx lumbar extension machine, Ocala, FL, USA) machine.

Results: The relative ROM (P=0.012) differed significantly among the aged groups in all participants. In addition, mean of strength (P<0.001), maximal of strength (P<0.001), mean of strength %BW (P<0.001) and maximal of strength %BW (P<0.001) are significant differences in all participants. The results of multiple regression analysis was the 'model A', maximal of strength for 32.1% of the variance in weigh, body mass index and range of motion. In addition, 'model B' was 30.4%, 'model C' was 28.8%, 'model D' was 28.5%, 'model E' was 21.7%, and 'model F' was 23.5% of the variance in weigh, body mass index and range of motion.

Conclusion: We found the three predictor (weight, BMI, and ROM) variables accounted for 32.1% of the variance in maximal of strength %BW, the highest in < 29 yr groups. Our data indicate the basic data of medical and exercise therapy by indexing lumbar extension muscle strength of LBP patients.

Keywords: Age; Chronic low back pain; Extension strength; Lumbar.

Conflict of interest statement

Conflict of interest The authors declare that there is no conflict of interest.

Copyright© Iranian Public Health Association & Tehran University of Medical Sciences.

Figures

Fig. 1:
Fig. 1:
Scatter plot of the multiple regression analysis in enter model Predictors: (Constant), a = Weight, b = Body Mass Index (BMI), and c = Range of Motion (ROM). A. 2 = 0.321, (df = 3, F = 57.594, P< .001), a, β = .919 (P<.001), b, β = −.674 (P<.001), c, β = .422 (P<.001) B. 30~39 yr (n=539). Adjusted R2 = 0.304, (df = 3, F = 77.516, P < .001), a, β = .777 (P<.001), b, β = −.472 (P<.001), c, β = .363 (P<.001) C. 40~49 yr (n=571). Adjusted R2 = 0.288 (df = 3, F = 76.415, P < .001), a, β = .727 (P<.001), b, β = −.389 (P<.001), c, β = .350 (P<.001) D. 50~59 yr (n=676). Adjusted R2 = 0.285 (df = 3, F = 89.051, P < .001), a, β = .742 (P<.001), b, β = −.508 (P<.001), c, β = .271 (P<.001) E. 60~69 yr (n=595). Adjusted R2 = 0.217 (df = 3, F = 54.636, P< .001), a, β = .536 (P<.001), b, β = −.328 (P<.001), c, β = .264 (P<.001) F. > 70 yr (n=328). Adjusted R2 = 0.235 (df = 3, F = 33.095, P < .001), a, β = .488 (P<.001), b, β = −.210 (P = .002), c, β = .348 (P<.001)

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