Massage Impairs Postexercise Muscle Blood Flow and "Lactic Acid" Removal
Bibliographic record
Abstract
PURPOSE: This study tested the hypothesis that one of the ways sports massage aids muscle recovery from exercise is by increasing muscle blood flow to improve "lactic acid" removal. METHODS: Twelve subjects performed 2 min of strenuous isometric handgrip (IHG) exercise at 40% maximum voluntary contraction to elevate forearm muscle lactic acid. Forearm blood flow (FBF; Doppler and Echo ultrasound of the brachial artery) and deep venous forearm blood lactate and H+ concentration ([La-], [H+]) were measured every minute for 10 min post-IHG under three conditions: passive (passive rest), active (rhythmic exercise at 10% maximum voluntary contraction), and massage (effleurage and pétrissage). Arterialized [La-] and [H+] from a superficial heated hand vein was measured at baseline. RESULTS: Data are presented as mean +/- SE. Venoarterial [La-] difference ([La-]v-a) at 30 s of post-IHG was the same across conditions (passive = 6.1 +/- 0.6 mmol x L(-1), active = 5.7 +/- 0.6 mmol x L(-1), massage = 5.5 +/- 0.6 mmol x L(-1), NS), whereas FBF was greater in passive (766 +/- 101 mL x min(-1)) versus active (614 +/- 62 mL x min(-1), P = 0.003) versus massage (540 +/- 60 mL x min(-1), P < 0.0001). Total FBF area under the curve (AUC) for 10 min after handgrip was significantly higher in passive versus massage (4203 +/- 531 vs 3178 +/- 304 mL, P = 0.024) but not versus active (3584 +/- 284 mL, P = 0.217). La(-)- efflux (FBF x [La-]v-a) AUC mirrored FBF AUC (passive = 20.5 +/- 2.8 mmol vs massage = 14.7 +/- 1.6 mmol, P = 0.03, vs active = 15.4 +/- 1.9 mmol, P = 0.064). H+ efflux (FBF x [H+]v-a) was greater in passive versus massage at 30 s (2.2 +/- 0.4e(-5) vs 1.3 +/- 0.2e(-5) mmol, P < 0.001) and 1.5 min (1.0 +/- 0.2e(-5) vs 0.6 +/- 0.09e(-5) mmol, P = 0.003) after IHG. CONCLUSIONS: Massage impairs La(-) and H+ removal from muscle after strenuous exercise by mechanically impeding blood flow.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".