Active and passive physical therapy in patients with chronic low-back pain: a level I Bayesian network meta-analysis
Alice Baroncini, Nicola Maffulli, Nicola Manocchio, M. Bossa, Calogero Foti, Luise Schäfer, Alexandra Klimuch, Filippo Migliorini
Abstract
BACKGROUND: Chronic low back pain (cLBP) is common. Physiotherapy is frequently indicated as a non-pharmacological management of these patients. This Bayesian network meta-analysis compared active versus passive physiotherapy versus their combination in terms of pain and disability in patients with mechanical and/or aspecific cLBP. METHODS: In June 2025, the following databases were accessed: PubMed, Web of Science, Google Scholar and Embase. All the randomised controlled trials (RCTs) which evaluated the efficacy of a physiotherapy program in patients with LBP were accessed. Data regarding pain scores, the Roland-Morris Disability Questionnaire (RMQ) and the Oswestry Disability Index (ODI) were collected. The network meta-analyses were performed using the STATA (version 14; StataCorp, College Station, TX, USA) routine for Bayesian hierarchical random-effects model analysis, employing the inverse variance method. The standardised mean difference (STD) was used for continuous data. RESULTS: ) were collected. The mean length of follow-up was 6.2 ± 6.1 months. Between groups, comparability was found at baseline in terms of mean age, proportion of women, mean BMI, symptom duration and patient-reported outcome measures (PROMs). By the end of the follow-up period, the active group evidenced the lowest pain scores (SMD 1.00; 95% CI -3.28 to 5.28). The active group evidenced the lowest RMQ score (SMD 0.94; 95% CI -4.96 to 3.09). The active group evidenced the lowest ODI score (SMD -1.23; 95% CI -9.83 to 7.36). CONCLUSION: Active physiotherapy showed better results than passive physiotherapy and a combination of both for the management of mechanical and/or non-specific cLBP. LEVEL OF EVIDENCE: Level I, Bayesian network meta-analysis of RCTs.