Chronic low back pain (cLBP) is common. Physiotherapy is frequently indicated as a non-pharmacological management of these patients. Baroncini et al published an article in 'Journal of Orthopaedics and Traumatology'. 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 non-specific cLBP.
In June 2025, the Baroncini et al 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.
The key findings of the study were:
• Data from 2768 patients (mean age 46.9 ± 10.9 years, mean BMI 25.8 ± 2.9 kg/m2) 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).
The authors concluded – “Active physiotherapy showed better results than passive physiotherapy and a combination of both for the management of mechanical and/or non-specific, chronic LBP.”
Level of evidence: Level I, Bayesian network meta-analysis of RCTs.
Further reading:
Active and passive physical therapy in patients with chronic low‑back pain: a level I Bayesian network meta‑analysis
Baroncini et al.
Journal of Orthopaedics and Traumatology (2025) 26:66
https://doi.org/10.1186/s10195-025-00885-4
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