Low back pain (LBP) is a leading cause of work-related disability. Although early tailored self-management is advocated, current approaches remain generic. Classifying LBP by predominant pain phenotype (nociceptive, neuropathic, nociplastic) may enable targeted self-management, yet practical tools for early or workplace use are limited. This study evaluated the reliability and validity of BACK-on-LINE, a self-administered digital tool differentiating nociceptive and nociplastic pain to support early mechanism-informed self-management in working populations, benchmarked against established patient-reported outcome measures (PROMs).
Employed adults with LBP (n = 211) recruited from occupational settings completed BACK-on-LINE and PROMs assessing pain (Numeric Pain Rating Scale; NPRS), disability (Roland–Morris Disability Questionnaire; RMDQ), and psychosocial risk (STarT Back Screening Tool; SBST). Reliability was assessed using internal consistency (Cronbach's α) and test–retest reliability (intraclass correlation coefficient; ICC). Validity was examined through convergent validity with PROMs, known-groups validity comparing nociceptive and nociplastic subgroups, and criterion validity using receiver operating characteristic (ROC) analyses against reference standards (pain intensity, disability, chronicity, sickness absence).
Data from 136 participants (64% completion) were analysed. BACK-on-LINE classified 68.4% as nociceptive and 31.6% as nociplastic. Reliability was strong (α = 0.83; ICC = 0.88). Convergent validity was moderate, strongest with SBST (r = 0.67). Known-groups validity was robust, with nociplastic participants reporting higher pain, disability and psychosocial risk (all p < 0.001). Criterion validity was moderate (AUC = 0.67–0.77), with BACK-on-LINE demonstrating comparable or superior discrimination to SBST.
BACK-on-LINE shows good reliability and multi-dimensional validity for self-classifying pain phenotypes in working adults with LBP, offering a scalable approach to support early mechanism-informed self-management in occupational health pathways.
This study provides robust initial evidence for a self-administered digital tool BACK-on-LINE that enables working adults to self-classify their low back pain into dominant pain mechanism phenotype. By supporting earlier, pain mechanism-informed self-management, it addresses a key gap in workplace back pain support, which is often generic advice poorly tailored. The tool's strength lies in scalability, early application and workplace relevance, outperforming a widely used prognostic tool in this context.



