SynthesisScientific reports2025
Effectiveness of movement representation techniques in non-specific shoulder pain: a systematic review and meta-analysis.
Synthesis in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effectiveness of Movement Representation Techniques in Chronic Non-Specific Spinal Pain: A Systematic Review and Meta-Analysis.Medicina (Kaunas, Lithuania) · 2026Pooled it
- Effectiveness of AI-assisted rehabilitation for musculoskeletal disorders: a network meta-analysis of pain, range of motion, and functional outcomes.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Effectiveness of motor imagery techniques in the treatment of chronic musculoskeletal pain: A scoping review.Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia · 2026Review
- Frozen Shoulder Is More Than Local Pain: Widespread Sensory Hyperalgesia or Hypoesthesia?European journal of pain (London, England) · 2026Observational
- Classification of musculoskeletal pain using machine learning.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This systematic review and meta-analysis aims to assess the effects of movement representation techniques (MRT) on pain, range of motion, functional outcomes, and pain-related fear in patients with non-specific shoulder pain (NSSP). A literature search conducted in PubMed, PEDro, EBSCO, Scopus, Cochrane Library, ScienceDirect, and gray literature on April 31, 2023. We selected seven randomized controlled trials based on the PICOS framework. Incomplete data or non-NSSP excluded. Study quality was assessed using the PEDro scale (mean score = 6.43), and certainty of evidence was evaluated with the GRADE approach. MRT demonstrated a large effect size for pain reduction (high heterogeneity, I2 = 85.2%, Hedges'g = 1.324, 95% CI = 0.388-2.260, P = 0.006), functional improvement (moderate heterogeneity, I2 = 70.82%, Hedges'g = 1.263, 95% CI = 0.622-1.904, P < 0.001), and reduction of pain-related fear (moderate heterogeneity, I2 = 70.86%, Hedges'g = 0.968, 95% CI = 0.221-1.716, P < 0.001). MRT also showed significant benefits for range of motion, particularly in flexion (low heterogeneity, I2 = 26.38%, Hedges'g = 0.683), abduction (low heterogeneity, I2 = 33.27%, Hedges'g = 0.756), and external rotation (low heterogeneity, I2 = 48.33%, Hedges'g = 0.542) (P < 0.001 for all), while no significant effect was found for internal rotation (P > 0.05). No publication bias was detected. While limited evidence and methodological concerns necessitate further research, MRT appears to positively impact pain, range of motion, functional outcomes, and pain-related fear in NSSP patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.