Low back pain patterns over one year among 842 workers in the DPhacto study and predictors for chronicity based on repetitive measurements

Julie Lagersted-Olsen, Hans Bay, Marie Birk Jørgensen, Andreas Holtermann, Karen Søgaard

Low back pain (LBP) occurrence and intensity are considered to fluctuate over time, requiring frequent repetitive assessments to capture its true time pattern. Text messages makes frequent reporting of LBP feasible, which enables investigation of 1) the time pattern of LBP, and 2) predictors for having a continued high (chronic) level of LBP over longer periods of time. However, this has not previously been investigated in a larger working population. The aim of this study was to examine these two aspects in a working population of 842 workers with repetitive measurements of LBP over one year. There were 842 workers from 15 companies in the DPhacto study participating in this study. Demographic, work- and health-related factors, and back endurance were measured at baseline, while 14 monthly repeated text message assessments of LBP intensity were prospectively collected. A factor analysis was used to cluster different time-patterns of LBP, and defining the group of participants with chronic LBP. A multi-adjusted logistic regression analysis was performed to investigate baseline predictors for chronic LBP. The factor analysis revealed two dimensions of the time pattern of LBP, defined as the LBP intensity and LBP variation, respectively. A Visual Pain Mapping was formed based on the combination of the two pain dimensions, classifying the time-patterns of LBP into four categories: (1) low intensity and low variation, (2) low intensity and high variation, (3) high intensity and high variation, (4) high intensity and low variation (defined as chronic LBP). Significant baseline predictors for chronic LBP in the fully adjusted model were high baseline LBP (p < 0.01), low workability (p < 0.01), low BMI (p < 0.05), and being a blue-collar worker (vs. white-collar worker) (p < 0.05). This study presents a novel classification of the course of LBP based on repetitive measurements over a year, and revealed the predicting factors for chronic LBP based on repetitive measurements in a working population.