Acupuncture and Mindfulness-Based Stress Reduction for Chronic Nonspecific Low Back Pain A 2 x 2 Factorial Randomized Clinical Trial

The primary outcome was the mean low back pain score over the past 7 days at week 7, measured on a visual analogue scale (VAS). Other outcomes included measures derived from the Short-Form McGill Pain Questionnaire (SF-MPQ), the Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI), the Fear-Avoidance Beliefs Questionnaire (FABQ), the Pain Sensitivity Questionnaire (PSQ), the Pain Catastrophizing Scale (PCS), the Pittsburgh Sleep Quality Index (PSQI), the modified 9-item Oswestry Disability Index (ODI), the Roland-Morris Disability Questionnaire (RMDQ), the Five Facet Mindfulness Questionnaire (FFMQ), the Depression Anxiety Stress Scales (DASS), and an emotion regulation self-efficacy scale.

Tactile measures included lumbar two-point discrimination, point localization, and two-point estimation error.The data are organized in both wide and long format. The wide-format analysis dataset contains all 120 randomized participants together with clinical outcomes at baseline, week 7, and month 4; the long-format analysis dataset contains 360 participant–timepoint records. The statistical code covers data import, scale scoring, variable derivation, data quality checks, primary analyses, longitudinal analyses, and sensitivity analyses.

The week 7 intention-to-treat analysis used fully conditional specification multiple imputation with predictive mean matching to generate 50 imputed datasets. Analysis of covariance included the baseline outcome value, centered age, the acupuncture factor, the MBSR factor, and their interaction, and estimates were combined using Rubin's rules. Longitudinal supportive analyses used repeated-measures models across baseline, week 7, and month 4 to estimate factorial effects at each time point.

Additional analyses included observed-case analysis, heteroscedasticity-robust standard error analysis, per-protocol analysis, sensitivity analyses under missing-not-at-random scenarios, sensory outcome analyses, and a 30% pain improvement responder analysis. The primary analyses were implemented in IBM SPSS Statistics 27.0.1, R 4.3.1 with mmrm 0.3.17, MATLAB R2024a, and Python.This data package supports verification of variable construction and scale scoring, reproduction of the factorial analysis of covariance and the longitudinal repeated-measures analyses, and evaluation of the missing-data handling and sensitivity-analysis procedures.

It also supports methodological research on non-pharmacological interventions for chronic nonspecific low back pain. Users should interpret the variables and analysis results in conjunction with the study protocol, the statistical analysis plan, and the data dictionary, and should not treat exploratory or sensitivity analysis results as prespecified confirmatory conclusions.