Predictors of disability in patients with chronic low back pain
Introduction Chronic low back pain (CLBP) is a common and disabling health problem. In this study, we aimed to assess the relationship between pain intensity, the components of catastrophizing, depression and disability in patients with chronic low back pain. Material and methods Seventy-six patien...
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Language: | English |
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Termedia Publishing House
2020-07-01
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Series: | Archives of Medical Science |
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Online Access: | https://www.archivesofmedicalscience.com/Predictors-of-disability-in-patients-with-chronic-low-back-pain,115236,0,2.html |
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author | Elena Sirbu Roxana Ramona Onofrei Simona Szasz Monica Susan |
author_facet | Elena Sirbu Roxana Ramona Onofrei Simona Szasz Monica Susan |
author_sort | Elena Sirbu |
collection | DOAJ |
description | Introduction
Chronic low back pain (CLBP) is a common and disabling health problem. In this study, we aimed to assess the relationship between pain intensity, the components of catastrophizing, depression and disability in patients with chronic low back pain.
Material and methods
Seventy-six patients diagnosed with CLBP (age range 25–77 years; 73.7% female) participated in the study. Participants’ socio-demographic data were collected: age, gender, height, weight, and work status (employed or retired). All participants were asked to complete the Pain Catastrophizing Scale (PCS), the visual analogue scale (VAS), the Oswestry Disability Questionnaire (ODQ), and the Beck Depression Inventory (BDI).
Results
The mean group scores revealed moderate CLBP complaints (VAS – 4 [3–6]), mild depression (BDI – 10 [5–16]), a moderate level of catastrophizing (PCS total score 20.5 [10–34]) and moderate disability (Oswestry Disability Index [ODI] – 31 [14–38]). Positive significant correlations were found between ODI and age, residence, work status, VAS, PCS-rumination, PCS-magnification, PCS-helplessness and BDI, and also between PCS subscales and VAS. Our multivariate linear regression analysis showed that age, pain intensity, PCS-helplessness and depression can predict disability in patients with CLBP, explaining 84% of the variance of disability (R2 = 0.851, adjusted R2 = 0.843).
Conclusions
A multidisciplinary approach is needed for patients with CLBP and should include physical, mental and social evaluation in order to offer an optimal treatment. |
first_indexed | 2024-03-09T10:46:07Z |
format | Article |
id | doaj.art-1046b552b80349e980c0c1fb079b16b8 |
institution | Directory Open Access Journal |
issn | 1734-1922 1896-9151 |
language | English |
last_indexed | 2024-03-09T10:46:07Z |
publishDate | 2020-07-01 |
publisher | Termedia Publishing House |
record_format | Article |
series | Archives of Medical Science |
spelling | doaj.art-1046b552b80349e980c0c1fb079b16b82023-12-01T09:55:34ZengTermedia Publishing HouseArchives of Medical Science1734-19221896-91512020-07-011919410010.5114/aoms.2020.97057115236Predictors of disability in patients with chronic low back painElena Sirbu0Roxana Ramona Onofrei1Simona Szasz2Monica Susan3Department of Physical Therapy and Special Motility, West University of Timişoara, Timişoara, RomaniaDepartment of Rehabilitation, Physical Medicine and Rheumatology, “Victor Babes” University of Medicine and Pharmacy Timisoara, RomaniaDepartment of Rheumatology, The University of Medicine and Pharmacy Tg Mures, RomaniaDepartment of Internal Medicine I, “Victor Babes” University of Medicine and Pharmacy Timisoara, RomaniaIntroduction Chronic low back pain (CLBP) is a common and disabling health problem. In this study, we aimed to assess the relationship between pain intensity, the components of catastrophizing, depression and disability in patients with chronic low back pain. Material and methods Seventy-six patients diagnosed with CLBP (age range 25–77 years; 73.7% female) participated in the study. Participants’ socio-demographic data were collected: age, gender, height, weight, and work status (employed or retired). All participants were asked to complete the Pain Catastrophizing Scale (PCS), the visual analogue scale (VAS), the Oswestry Disability Questionnaire (ODQ), and the Beck Depression Inventory (BDI). Results The mean group scores revealed moderate CLBP complaints (VAS – 4 [3–6]), mild depression (BDI – 10 [5–16]), a moderate level of catastrophizing (PCS total score 20.5 [10–34]) and moderate disability (Oswestry Disability Index [ODI] – 31 [14–38]). Positive significant correlations were found between ODI and age, residence, work status, VAS, PCS-rumination, PCS-magnification, PCS-helplessness and BDI, and also between PCS subscales and VAS. Our multivariate linear regression analysis showed that age, pain intensity, PCS-helplessness and depression can predict disability in patients with CLBP, explaining 84% of the variance of disability (R2 = 0.851, adjusted R2 = 0.843). Conclusions A multidisciplinary approach is needed for patients with CLBP and should include physical, mental and social evaluation in order to offer an optimal treatment.https://www.archivesofmedicalscience.com/Predictors-of-disability-in-patients-with-chronic-low-back-pain,115236,0,2.htmlchronic low back paincatastrophizingdepressiondisability |
spellingShingle | Elena Sirbu Roxana Ramona Onofrei Simona Szasz Monica Susan Predictors of disability in patients with chronic low back pain Archives of Medical Science chronic low back pain catastrophizing depression disability |
title | Predictors of disability in patients with chronic low back pain |
title_full | Predictors of disability in patients with chronic low back pain |
title_fullStr | Predictors of disability in patients with chronic low back pain |
title_full_unstemmed | Predictors of disability in patients with chronic low back pain |
title_short | Predictors of disability in patients with chronic low back pain |
title_sort | predictors of disability in patients with chronic low back pain |
topic | chronic low back pain catastrophizing depression disability |
url | https://www.archivesofmedicalscience.com/Predictors-of-disability-in-patients-with-chronic-low-back-pain,115236,0,2.html |
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