Effect of Massage on Clinical and Physiological Variables During Muscle Fatigue Task in Participants With Chronic Low Back Pain: A Crossover Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine if a reduction of short-term physiological and clinical effects of muscle fatigue can be seen after a session of massage in nonspecific chronic low back pain (cLBP) individuals and to study the possible association between physiological and clinical changes induced by massage. METHODS: Thirty-six cLBP individuals participated in 2 experimental sessions. In one session, the Sorenson protocol was preceded by a 30-minute massage, but in the other session, only the Sorenson test was performed by participants. Lumbar paraspinal muscle activity was recorded using surface electromyography, and maximal voluntary contraction force was measured using a load cell. Participants rated their lumbar pain intensity before and after massage and after the Sorensen protocol. A 2-way repeated-measures analysis of variance was conducted to test the effect of massage on both variables for both conditions. Pearson correlation analyses were conducted to determine the linear association between physiological and clinical responses to massage. RESULTS: Results showed that pain perception was significantly reduced after massage (P = .004) but did not seem to influence pain score increases occurring after the Sorensen protocol. Individuals with a high score of low back pain-related disability showed lower back muscle endurance time (r = -.35). Massage yielded no significant effect on fatigue-related physiological variables. CONCLUSION: The perception of pain in cLBP individuals was reduced after massage. Although massage yielded some positives clinical effects, they were not explained by a reduction in physiological effect of muscle fatigue.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".