Predictors of Self-management in Patients With Chronic Low Back Pain

Low Back Pain

low back pain, chronic pain, self-management, longitudinal study, predictive modelling

Supported self-management (SM) is one of the key recommendations in management of chronic low back pain (CLBP). SM programmes for patients with CLBP have failed to show clinically meaningful improvement in pain and disability markers, which potentially reflect the lack of treatment matching of SM programmes. Patient selection for a SM programme for patients with CLBP is particularly difficult due to lack of extensive research on what predicts SM and its change. The overarching purpose of this study is to identify predictors of SM and its change over time in patients with CLBP. This study is a prospective non-experimental longitudinal study.

Materials and methods: Patients will be recruited at baseline from the physiotherapy outpatient clinics. Three different methods have been planned to identify suitable patients in this study: from the outpatient clinics, through advertisements and therapist referrals to the study. All willing participants will be screened against the study selection criteria. Eligible participants will be provided with a pre-approved information sheet and verbal explanation about the study before obtaining written consent. The consenting patients will be requested to fill in the baseline questionnaire. The patients will be requested to fill the follow up questionnaire after six months from their baseline data collection via an online, telephone or paper-based questionnaire depending on their preference. Data required for the necessary agreement between paper-based and online or telephone survey, will be collected from up to 60 participants within two weeks from their baseline data collection. All patients will receive and continue their normal treatment as recommended by their primary care team. A participant may be withdrawn by the investigators, involved in major trauma or undergoes surgery, or a female participant becomes pregnant during the course of the study. Sample size was estimated using G\*Power (version 3.1.5). The statistical analyses will be performed using a statistical software (IBM SPSS 22) with statistical significance set at p\<0.05 for the entire analysis, unless mentioned. Descriptive statistics, interclass correlation coefficients, Bland-Altman limits of agreement plots, significance testing, correlations and regression analyses will be employed.

United Kingdom