Tourniquet cuff pressure during blood flow restriction exercise
Bibliographic record
Abstract
Background The present study examined how well different blood flow restriction (BFR) devices deliver the prescribed tourniquet cuff pressure. Methods Fifteen participants completed four BFR exercise sessions, each with a different BFR device [Delfi Personalized Tourniquet System (PTS) for BFR, Saga, SmartCuffs, and Suji], comprising four sets of unilateral leg press (30-15-15-15 repetitions) against resistance bands with 30-second rest periods. The tourniquet cuff was secured proximally on the exercising leg, and the target pressure was set to 80% limb occlusion pressure (LOP), as measured by the device, applied continuously throughout the exercise/rest periods. Tourniquet cuff pressure was sampled at 100 Hz via a pressure transducer. Results Despite prescribing tourniquet cuff pressure at 80% LOP, the actual pressure can vary substantially and be inconsistent between individuals depending on the BFR device used. During the exercise periods, the median percentage of time pressure was within ±10% the target pressure was 95% (Delfi PTS for BFR), 25% (Saga), 26% (SmartCuffs), and 34% (Suji). During the rest periods, the median percentage of time pressure was within ±5% the target pressure was 99% (Delfi PTS for BFR), and 0% for the Saga, SmartCuffs, and Suji BFR devices. Tourniquet cuff pressure during BFR exercise behaves in a wave-like manner characterised by cyclical pressure peaks and valleys. The magnitude of pressure peaks and valleys was: Delfi PTS for BFR (89 ± 2% and 72 ± 3% LOP), Saga (79 ± 9 and 58 ± 7% LOP), SmartCuffs (79 ± 9% and 61 ± 7% LOP), and Suji (90 ± 15 and 65 ± 10% LOP). In several cases, participants experienced tourniquet pressures >100% LOP using the Saga, SmartCuffs, and Suji BFR devices, for up to ∼30%–55% of the exercise set duration. A progressive loss of pressure occurred throughout the BFR application period by an average of 2–4 mmHg·min−1 (∼1%–2% LOP·min−1) in the Saga, SmartCuffs, and Suji BFR devices, whilst the Delfi PTS for BFR exhibited trivial pressure drifts. Conclusion Differences between the actual and prescribed tourniquet cuff pressure can highly depend on the BFR device employed. The selection of the BFR apparatus is thus important to delivering the prescribed tourniquet cuff pressure to allow for standardisation of the relative occlusion pressure between users.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".