Influence of acute fasting on pain tolerance in healthy subjects: a randomised crossover study
Bibliographic record
Abstract
Background: Although chronic pain and obesity are global health crises with substantial healthcare costs, little is known about the relationship between pain perception and eating behaviours. Food consumption has been reported to provide an analgesic effect by the release of neurotransmitters modulating the pain network. However, whether short-term (acute) fasting affects pain perception remains unclear. Purpose: This study aimed to investigate the effect of acute fasting on pain perception and whether attention and mood changes drove the observed changes. Patients and methods: The cold pressor test (CPT) was used to investigate the pain tolerance of 25 healthy participants in both non-fasting and 12-h fasting sessions. They were randomised to either session with a crossover to the other after at least 24 h, with the experimenter blinded to the sessions. The pain tolerance was measured using a Stroop task in both attentive and distracted states. The Profile of Mood States (POMS) questionnaire was used to capture the mood, and a 10-point hunger scale was used to measure hunger. Mixed-effects models were used to investigate the influence of fasting and distraction on pain perception, accounting for the repeated measures. Results: = 0.9). In addition, no evidence supporting that fasting affected attention or mood was found. Nonetheless, it increased hunger scores by 2.7 points on a 10-point scale (95% CI: 1.2-4.2) and decreased blood glucose concentration levels by 0.51 mmol/L (95% CI: 0.19-0.84). Conclusion: Acute fasting reduces pain tolerance in the healthy participants, and this effect is independent of gender and attention or mood changes.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".