Electroacupuncture Reduces Heart Rate and Perceived Exertion During a Bike Test: A Preliminary Analysis
Bibliographic record
Abstract
Background: Preliminary research suggests that acupuncture can improve cardiovascular function. The purpose of our study was to determine if electroacupuncture can improve performance and post-exercise recovery. Methods: Thirty-two healthy people participated in this study (14 men and 18 women, aged 23.6 ± 3.5 years). The first visit included baseline measurements. Then, the participants received daily electroacupuncture at acupuncture point PC6 for a week, followed by a second visit. Heart rate, perceived exertion, and systolic and diastolic blood pressure were measured before, during, and after a YMCA submaximal bike test. Results: The heart rate was significantly reduced during the final stage of the YMCA test (151.3 ± 7.0 to 146.7 ± 11.8; p = 0.013) on the second visit. The rate of perceived exertion was significantly lower during all stages in Visit 2 (average RPE Visit 2 = 10.71 ± 2.02; average RPE Visit 1 = 11.45 ± 1.98; p = 0.004). Systolic blood pressure significantly decreased during the 5 min post-test recovery (SBP Visit 2 = 116.9 ± 12.0; SBP Visit 1 = 145.7 ± 14.6, p < 0.05). Conclusions: A week of electroacupuncture at PC6 led to reduced heart rate and perceived exertion during exercise, making the workload feel less strenuous. Electroacupuncture at PC6 shows potential for increasing participation in physical activities by making them feel easier to accomplish.
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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) | 0.000 | 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) | 0.002 | 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".