Back

Efficacy of physiotherapy in reducing pain in subjects with temporomandibular disorders: a systematic review with network meta-analysis

Alvisi, A.; Casalini, A.; Mammana, L.; Ugolini, A.

2026-10-13 rehabilitation medicine and physical therapy
10.64898/2026.10.11.26365246 medRxiv
Show abstract

Background: Temporomandibular disorders (TMDs) are common conditions in the general population. Several therapeutic approaches are available, but there is no consensus on the most effective, particularly in physiotherapy. Objectives: To assess the efficacy of manual therapy (MT) and therapeutic exercise (TE) in reducing pain in patients with TMDs. Methods: Four databases (MEDLINE, PEDro, EMBASE, CINAHL) were searched up to March 2025. We included randomized controlled trials (RCTs) evaluating the effects of MT and/or TE in individuals with TMDs, compared to any other rehabilitation treatment, with pain intensity as the primary outcome. The methodological quality was assessed using the PEDro scale. A network meta-analysis was performed to compare the effectiveness of different rehabilitative approaches. Results: 25 RCTs for a total of 1,540 people were included. The main comparisons were: MT and/or TE vs no treatment or placebo, occlusal splint, and instrumental physiotherapy. In most studies, MT and TE were more effective than controls in reducing pain. However, the network meta-analysis indicated that multimodal treatments were the most effective. The methodological quality of the included studies ranged from 3 to 9 points. Conclusion: MT and TE are effective strategies for reducing pain in subjects with TMDs and should be used as first-line interventions before more invasive treatments; the combination of dry needling and manual therapy appears to be the most effective approach. Nonetheless, the variability in study quality suggests that further high-quality research is needed to confirm these findings.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.