Predictors of self-management in patients with chronic low back pain: a longitudinal study

被引:8
|
作者
Banerjee, A. [1 ,2 ]
Hendrick, P. [3 ]
Blake, H. [3 ,4 ]
机构
[1] Keele Univ, Sch Allied Hlth Profess, Keele ST5 5BG, Staffs, England
[2] Nottingham CityCare Partnership C, Nottingham, England
[3] Univ Nottingham, Sch Hlth Sci, Nottingham, England
[4] NIHR Nottingham Biomed Res Ctr, Nottingham, England
关键词
Low back pain; Chronic low back pain; Self-management; Longitudinal study; Regression analysis; Predictors; Health education impact questionnaire; QUALITY-OF-LIFE; DEPRESSIVE SYMPTOMS; HEALTH-PROMOTION; INTERVENTIONS; EDUCATION; OUTCOMES; PEOPLE; DISABILITY; PROGRAM; HISTORY;
D O I
10.1186/s12891-022-05933-2
中图分类号
R826.8 [整形外科学]; R782.2 [口腔颌面部整形外科学]; R726.2 [小儿整形外科学]; R62 [整形外科学(修复外科学)];
学科分类号
摘要
Background: Self-management (SM) is a key recommended strategy for managing chronic low back pain (CLBP). However, SM programmes generate small to moderate benefits for reducing pain and disability in patients with CLBP. The benefits of the SM programme can potentially be optimised by identifying specific subgroups of patients who are the best responders. To date, no longitudinal study has examined the predictive relationships between SM and biopsychosocial factors in patients with CLBP. The aim was to determine whether biopsychosocial factors predict SM and its change over time in patients with CLBP. Methods: In this multi-centre longitudinal cohort study, we recruited 270 working-age patients with CLBP (mean age 43.74, 61% female) who consulted outpatient physiotherapy for their CLBP. Participants completed self-reported validated measures of pain intensity, disability, physical activity, kinesiophobia, catastrophising, depression and SM at baseline and six months. SM constructs were measured using eight subscales of the Health Education Impact Questionnaire (heiQ), including Health Directed Activity (HDA), Positive and Active Engagement in Life (PAEL), Emotional Distress (ED), Self-Monitoring and Insight (SMI), Constructive Attitudes and Approaches (CAA), Skill and Technique Acquisition (STA), Social Integration and Support (SIS) and Health Service Navigation (HSN). Data were analysed using General Linear Model (GLM) regression. Results: Physical activity and healthcare use (positively) and disability, depression, kinesiophobia, catastrophising (negatively) predicted (p < 0.05, R-2 0.07-0.55) SM constructs at baseline in patients with CLBP. Baseline depression (constructs: PAEL, ED, SMI, CAA and STA), kinesiophobia (constructs: CAA and HSN), catastrophising (construct: ED), and physical disability (constructs: PAEL, CAA and SIS) negatively predicted a range of SM constructs. Changes over six months in SM constructs were predicted by changes in depression, kinesiophobia, catastrophising, and physical activity (p < 0.05, R-2 0.13-0.32). Conclusions: Self-reported disability, physical activity, depression, catastrophising and kinesiophobia predicted multiple constructs of SM measured using the heiQ subscales in working-age patients with CLBP. Knowledge of biopsychosocial predictors of SM may help triage patients with CLBP into targeted pain management programmes.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] painACTION-Back Pain: A Self-Management Website for People with Chronic Back Pain
    Chiauzzi, Emil
    Pujol, Lynette A.
    Wood, Mollie
    Bond, Kathleen
    Black, Ryan
    Yiu, Elizabeth
    Zacharoff, Kevin
    PAIN MEDICINE, 2010, 11 (07) : 1044 - 1058
  • [22] Smartphone app in self-management of chronic low back pain: a randomized controlled trial
    H. S. Chhabra
    Sunil Sharma
    Shalini Verma
    European Spine Journal, 2018, 27 : 2862 - 2874
  • [23] Smartphone app in self-management of chronic low back pain: a randomized controlled trial
    Chhabra, H. S.
    Sharma, Sunil
    Verma, Shalini
    EUROPEAN SPINE JOURNAL, 2018, 27 (11) : 2862 - 2874
  • [24] Acute low back pain self-management intervention for urban primary care patients: Rationale, design, and predictors of participation
    Damush, TM
    Weinberger, M
    Clark, DO
    Tierney, WM
    Rao, JK
    Perkins, SM
    Verel, K
    ARTHRITIS & RHEUMATISM-ARTHRITIS CARE & RESEARCH, 2002, 47 (04): : 372 - 379
  • [25] Improving Precision in Self-Management of Acute Low Back Pain
    Starkweather, Angela
    Ramesh, Divya
    Acabchuck, Rebecca
    Park, Crystal
    Walker, Joseph
    NURSING RESEARCH, 2018, 67 (02) : E47 - E48
  • [26] Predictors of disability in patients with chronic low back pain
    Sirbu, Elena
    Onofrei, Roxana Ramona
    Szasz, Simona
    Susan, Monica
    ARCHIVES OF MEDICAL SCIENCE, 2023, 19 (01) : 94 - 100
  • [27] Self-Management of Low Back Pain Using Neural Network
    Sharma, Purushottam
    Alshheri, Mohammed
    Sharma, Richa
    Alfarraj, Osama
    CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 66 (01): : 885 - 901
  • [28] Evaluating Auricular Point Acupressure for Chronic Low Back Pain Self-Management Using Technology: A Feasibility Study
    Yeh, Chao Hsing
    Kawi, Jennifer
    Ni, Aiguo
    Christo, Paul
    PAIN MANAGEMENT NURSING, 2022, 23 (03) : 301 - 310
  • [29] Outcomes of Participatory Ergonomics and Self-management in Commercial Clam Farmers with Chronic Low Back Pain: A Feasibility Study
    Dunleavy, Kim
    Kane, Andrew
    Coffman, Ashleigh
    Reidy, Jacob
    Bishop, Mark D.
    JOURNAL OF AGROMEDICINE, 2022, 27 (02) : 217 - 231
  • [30] An mHealth App for Self-Management of Chronic Lower Back Pain (Limbr): Pilot Study
    Selter, Aliza
    Tsangouri, Christina
    Ali, Sana B.
    Freed, Diana
    Vatchinsky, Adrian
    Kizer, James
    Sahuguet, Arnaud
    Vojta, Deneen
    Vad, Vijay
    Pollak, J. P.
    Estrin, Deborah
    JMIR MHEALTH AND UHEALTH, 2018, 6 (09):