Does pain influence control of muscle force? A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: In the presence of pain, whether clinical or experimentally induced, individuals commonly show impairments in the control of muscle force (commonly known as force steadiness). In this systematic review and meta-analysis, we synthesized the available evidence on the influence of clinical and experimental pain on force steadiness. DATABASES AND DATA TREATMENT: MEDLINE, EMBASE, PubMed, CINAHL Plus and Web of Science databases were searched from their inception to 19 December 2023, using MeSH terms and pre-selected keywords related to pain and force steadiness. Two independent reviewers screened studies for inclusion and assessed their methodological quality using a modified Newcastle-Ottawa risk of bias tool. RESULTS: In total, 32 studies (19 clinical pain and 13 experimental pain) were included. Meta-analyses revealed reduced force steadiness in the presence of clinical pain as measured by the coefficient of variation (CoV) and standard deviation (SD) of force (standardized mean difference; SMD = 0.80, 95% CI = 0.31-1.28 and SMD = 0.61, 95% CI = 0.11-1.11). These findings were supported by moderate and low strength of evidence respectively. In the presence of experimental pain, meta-analyses revealed reductions in force steadiness when measured by the CoV of force but not by the SD of force (SMD = 0.50, 95% CI = 0.01-0.99; and SMD = 0.44, 95% CI = -0.04 to 0.92), each supported by very low strength of evidence. CONCLUSIONS: This work demonstrates that pain, particularly clinical pain, impairs force steadiness. Such impairments likely have clinical relevance and could become targets for treatment when managing people experiencing musculoskeletal pain. SIGNIFICANCE STATEMENT: This systematic review and meta-analyses enhances our understanding of motor impairments observed in people experiencing musculoskeletal pain. It underscores the significance of incorporating force steadiness assessment when managing individuals experiencing musculoskeletal pain. Additionally, it suggests that future research should explore the potential benefits of force steadiness training in alleviating patients' symptoms and enhancing their functional performance. This could potentially lead to the development of innovative therapeutic approaches for individuals suffering from musculoskeletal pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".