A systematic review and meta-analysis of the effectiveness of high-intensity laser therapy in patients with carpal tunnel syndrome

Objectives This systematic review aimed to evaluate the effects of high-intensity laser therapy (HILT) on pain intensity, disability, and electrophysiological parameters in individuals diagnosed with carpal tunnel syndrome (CTS). Data sources and synthesis A search of electronic databases, including PubMed, Web of Science, Scopus, CINAHL, Science Direct, Cochrane Library, the PEDro database, and Google Scholar (updated until February 18, 2025), was conducted for randomized controlled trials (RCTs). Data extraction and quality assessment Data were extracted independently by three researchers. The quality of RCTs was assessed using the Cochrane Risk of Bias (RoB) 2.0 tool, while evidence certainty was evaluated with the GRADE approach. Primary outcomes included pain intensity, with secondary outcomes comprising electrophysiological parameters and disability. Results Nine RCTs met inclusion criteria. Most studies exhibited a low risk of bias across RoB2 domains, except for outcome measurement, resulting in an overall RoB of 44%. Meta-analyses demonstrated that HILT, alone or combined with interventions such as splinting, exercise, TENS, vitamin B supplements, or ergonomic modifications, significantly reduced pain (SMD = 0.7 to 1.6) and disability (SMD = 0.68). Electrophysiological improvements included enhanced distal motor latency (SMD = 0.98) and sensory conduction velocity (SMD = 0.8), particularly when combined with median nerve gliding or splinting. Conclusion This review confirms HILT's effectiveness in reducing pain and disability and improving electrophysiological parameters in CTS. However, evidence certainty remains moderate to low. Future RCTs should standardize outcome measures to evaluate HILT's clinical impact beyond statistical significance, and comparative studies with LLLT are needed to refine laser therapy protocols. (c) 2025 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.