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High‐intensity Interval Training Combined With Blood‐flow Restriction Predominantly Alters Anaerobic Capacity in Endurance‐trained Athletes

2022· article· en· W4225408442 on OpenAlexaff
François Billaut, Pénélope Paradis‐Deschênes

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHigh-intensity interval trainingBlood flow restrictionAnaerobic exerciseWingate testInterval trainingMedicineTime trialAthletesPhysical therapyAerobic capacityVO2 maxCyclingIncremental exerciseEndurance trainingInternal medicineCardiologyHeart rateBlood pressureResistance training

Abstract

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Blood‐flow restriction (BFR) training has gained popularity amongst athletes and sport practitioners to enhance training adaptations and performance. However, BFR has typically been associated with low‐intensity exercise, and there is still limited evidence of its impact when combined with high‐intensity training. This study investigated the impact of high‐intensity interval training (HIIT) with concurrent BFR on anaerobic and aerobic physical capacities and key physiological responses. In a pre‐post, parallel‐groups design, fifteen endurance‐trained males (VO 2 max 65.0±4.8 mL/min/kg) included three sessions of HIIT per week (sets of 15 s ON/15 s OFF at 100% maximal aerobic power) into their usual training for three weeks either with (BFR group, n=9) or without restriction (CTL group, n=6). In BFR, cuffs were worn proximal on both quadriceps and inflated progressively from 50 to 70% of arterial occlusion pressure across training weeks. Before and after training, athletes completed a maximal incremental step cycling test, a Wingate anaerobic test (WAnT) and a 5‐km cycling time trial. Blood samples were drawn during the time trials. Maximal aerobic power increased in BFR (364.8±60.7 vs 383.4±61.4 watts, p=0.003, Cohen’s effect size ES 0.27) but not in CTL (381.2±62.1 vs 385.5±64.4 watts, p=0.45, ES 0.05). Concomitantly, mean power output achieved during the WAnt also increased with BFR (23.8±4.0 vs 24.6±4.1 kJ, p=0.08, ES 0.28) but not in CTL (23.4±2.5 vs 23.4±2.4 kJ, p=0.95, ES 0.01). However, there was no change between groups in VO 2 max (BFR: 0.86% vs CTL: 1.77%, ES ‐0.15) and in both the mean power output (BFR: 1.81% vs CTL: 6.51%, ES ‐0.21) and completion time (BFR: ‐0.69% vs CTL: ‐2.55%, ES ‐0.21) of the 5‐km time trial. During the time trial, BFR also induced greater changes in pH (BFR: ‐0.05 vs CTL: ‐0.03 units, ES ‐0.43) and base excess (BFR: ‐1.55 vs CTL: ‐0.70 units, ES ‐0.37), and lowered the potassium ion concentration (BFR: ‐0.55 vs CTL: 0.44 mmol/L, ES ‐0.63). There was no change between groups in lactate production from pre‐ to post‐training. These findings suggest that short‐term HIIT combined with BFR improved anaerobic capacity in endurance‐trained athletes without meaningful effect on endurance exercise performance.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.028
GPT teacher head0.220
Teacher spread0.192 · 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 designBench or experimental
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".

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Citations3
Published2022
Admission routes1
Has abstractyes

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