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Effect Of Handgrip Training With Blood Flow Restriction On Resting Blood Flow And Vascular Resistance

2023· article· en· W4387062887 on OpenAlexaff
Anna Kang, Vickie Wong, Robert W. Spitz, Ryo Kataoka, Jun Seob Song, Yujiro Yamada, William B. Hammert, Aldo Seffrin, Zachary W. Bell, Jeremy P. Loenneke

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMcGill University
Fundersnot available
KeywordsBlood flow restrictionForearmMedicineBlood flowCardiologyIntensity (physics)HemodynamicsResistance trainingBlood pressureInternal medicinePhysical therapyPhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

Muscle adaptations (changes in muscle size and strength) with low-intensity exercise paired with blood flow restriction are known to increase to a greater extent than the same exercise without the restriction. However, changes in vasculature associated with the exercise protocol is not fully understood. In addition, some have hypothesized that repeated bouts of blood flow restriction might be detrimental to vascular health and function. PURPOSE: To evaluate changes in resting forearm blood flow following low intensity handgrip training with and without blood flow restriction. We also compared this to high intensity handgrip training. METHODS: 179 participants (18-35 years) completed this 6-week study. Participants were randomly assigned to one of the four conditions: high-intensity (HI; n = 47; maximum voluntary contraction (100% MVC); 5 sec/set; 4 sets/session), low-intensity (LI; n = 47; 30% MVC; 2 min/set, 4 sets/session), low-intensity with blood flow restriction (LI-BFR; n = 41; 30% MVC, 50% arterial occlusion pressure [AOP]; 2 min/set, 4 sets/session), or non-exercise control (CON; n = 44). Participants came in for 3 visits/week (excluding CON) to perform the exercise with their dominant arm under their assigned condition. Before and after the training period, each participant had their resting limb blood flow measured in their dominant forearm. Changes in resting limb blood flow were compared using a Bayesian ANCOVA. The pre-value served as the covariate. Changes are noted as means (SD). RESULTS: The ANCOVA found no evidence for changes in resting forearm blood flow (BF10: 0.06). Pre-post changes in blood flow were 0.08 (0.98), 0.26 (0.67), 0.34 (0.96), and 0.03 (0.76) ml·min-1·100 ml-1 for the CON, LI, LI-BFR, and HI groups, respectively. There were also no changes when mean arterial pressure was divided by blood flow and represented as forearm vascular resistance (BF10: 0.08). Pre-post changes in forearm vascular resistance were 0.04 (14), -2.9 (12.9), -5.4 (18) and -2.7 (14) mmHg per flow for the CON, LI, LI-BFR, and HI groups, respectively. CONCLUSION: Low intensity exercise with BFR did not impede or improve blood flow. The beneficial effects to vascular health may only be observed with higher pressures not used in this study (e.g. 80% AOP). This requires further study.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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 designNon-randomized trial
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

Citations0
Published2023
Admission routes1
Has abstractyes

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