Respiratory Effects on Experimental Heat Pain and Cardiac Activity
Bibliographic record
Abstract
OBJECTIVE: Slow deep breathing has been proposed as an effective method to decrease pain. However, experimental studies conducted to validate this claim have not been carried out. DESIGN: We measured thermal pain threshold and tolerance scores from 20 healthy adults during five different conditions, namely, during natural breathing (baseline), slow deep breathing (6 breaths/minute), rapid breathing (16 breaths/minute), distraction (video game), and heart rate (HR) biofeedback. We measured respiration (rate and depth) and HR variability from the electrocardiogram (ECG) output and analyzed the effects of respiration on pain and HR variability using time and frequency domain measures of the ECG. RESULTS: Compared with baseline, thermal pain threshold was significantly higher during slow deep breathing (P = 0.002), HR biofeedback (P < 0.001), and distraction (P = 0.006), whereas thermal pain tolerance was significantly higher during slow deep breathing (P = 0.003) and HR biofeedback (P < 0.001). Compared with baseline, only slow deep breathing and HR biofeedback conditions had an effect on cardiac activity. These conditions increased the amplitude of vagal cardiac markers (peak-to-valley, P < 0.001) as well as low frequency power (P < 0.001). CONCLUSION: Slow deep breathing and HR biofeedback had analgesic effects and increased vagal cardiac activity. Distraction also produced analgesia; however, these effects were not accompanied by concomitant changes in cardiac activity. This suggests that the neurobiology underlying respiratory-induced analgesia and distraction are different. Clinical implications are discussed, as are the possible cardiorespiratory processes responsible for mediating breathing-induced analgesia.
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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".