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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".