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Record W4413955884 · doi:10.3389/fspor.2025.1582387

Tourniquet cuff pressure during blood flow restriction exercise

2025· article· en· W4413955884 on OpenAlexaff
Patrick Swain, James A. McEwen, Luke Hughes

Bibliographic record

VenueFrontiers in Sports and Active Living · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBlood flow restrictionTourniquetCuffBlood flowMedicineBlood pressureAnesthesiaCardiologyInternal medicineSurgeryResistance training

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.003
GPT teacher head0.207
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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