Blood flow occlusion superimposed on submaximal knee extensions does not evoke hypoalgesia: A pilot study
Bibliographic record
Abstract
ABSTRACT Exercise-induced hypoalgesia (EIH) is a transient decrease in pain perception that can be observed following various tasks, including non-painful low-intensity and painful high-intensity exercise. The application of blood flow occlusion (BFO) can help enhance exercise adaptations while being able to exercise at a low intensity, which has important implications for clinical and rehabilitative settings. Through descending inhibitory pathways, BFO-induced pain can potentially alleviate exercise-induced pain. This study aimed to assess whether the superimposition of BFO – and its associated augmented perceived responses – during low-intensity, low-volume resistance exercise could induce hypoalgesia. Nineteen healthy adults (10 females) attended three sessions: i) no exercise (CTRL), ii) two minutes of dynamic single-leg knee extension at 10% body weight (EXER), and iii) EXER with complete occlusion applied to the upper exercising leg (OCCL). Handheld algometry-derived pain pressure threshold (PPT) of the trapezius and contralateral and ipsilateral rectus femoris muscles were measured pre- and post-exercise, and after 5 and 10 min of recovery. Perceived pain (0-10) and effort (6-20) were also rated after exercise. Although pain and effort were augmented in the OCCL condition (Pain: 6±2; Effort: 14±3) compared to CTRL (Pain: 2±2, p<0 . 001 ; Effort 9±2, p=0 . 017 ), PPT of all muscles did not change across time nor between any conditions. Therefore, the low-intensity, low-volume resistance exercise prescribed in the present study was insufficient to evoke EIH even with the application of BFO-induced pain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".