The Effectiveness of Spinal Manipulation in Increasing Muscle Strength in Healthy Individuals: A Systematic Review and Meta-Analysis
Abstract
OBJECTIVE: The purpose of this study was to systematically review the effects of spinal manipulation on muscular strength in healthy individuals and conduct a meta-analysis to appraise the quality of evidence.
METHODS: Articles were searched and retrieved from MEDLINE, EMBASE, CINAHL, Cochrane Library, PubMed, Academic Search Premier, SPORTDiscus, and AMED. Searches were conducted in September 2017 without a limit on the starting period. The Physiotherapy Evidence Database scale was used to appraise the quality of the included studies. Data from eligible articles were pooled, and meta-analyses were conducted. The quality of evidence was appraised by the Grading of Recommendations, Assessment, Development and Evaluations approach. The registration number for the review on PROSPERO is CRD42017075215.
RESULTS: A total of 911 records were screened, and 3 randomized controlled trials were eligible to be included in this review. There was a significant pooled standardized mean difference in isometric strength (0.93, 95% confidence interval [CI], 0.17-1.68; P = .02) between the experimental and control groups, with a moderate level of heterogeneity.
CONCLUSION: This review suggests that spinal manipulative therapy augments the percentage of change in isometric strength gain among healthy participants when compared to no intervention or sham manipulation. However, the heterogeneity of pooled studies in this review suggests that the results should be interpreted with caution.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.