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The Effects of Recovery Frequency On V[Combining Dot Above]O2, Muscle Deoxygenation, And Energy System Contribution During Intermittent Work

2016· article· en· W2471943440 on OpenAlexaff
Jae Kim, Michael McCrudden, Daniel A. Keir, Glen R. Belfry

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsWestern University
Fundersnot available
KeywordsDeoxygenationBlood lactateEnergy expenditureChemistryHeart rateIntensity (physics)MedicineBiomedical engineeringInternal medicinePhysicsBiochemistryBlood pressure

Abstract

fetched live from OpenAlex

PURPOSE: To compare the effects of inserting 3 s recovery periods during high-intensity continuous exercise at 25 s and 10 s intervals on: 1) energy system contribution, 2) the rates of adjustment of pulmonary oxygen uptake (V[Combining Dot Above]O2p) and muscle deoxygenation (HHb), and 3) the overall changes of V[Combining Dot Above]O2p and HHb. METHODS: Eleven recreationally active men (age: 24 ± 3 yrs; V[Combining Dot Above]O2max: 47.9 ± 4.7 mL·kg-1·min-1) reported to the laboratory on six separate occasions to complete two trials of three cycling exercise protocols: 1) a continuous protocol (CONT) consisting of 8 min of constant work rate exercise at an intensity corresponding to 60% of the difference between lactate threshold and V[Combining Dot Above]O2max (60); 2) an 8 min intermittent exercise protocol consisting of a series of 25 s work periods at 60 separated by 3 s recovery periods (25INT); and 3) an 8 min intermittent exercise protocol consisting of a series of 10 s work periods at 60 separated by 3 s recovery periods (10INT). All protocols began with a 4 min baseline of 20 W. During each trial, breath-by-breath gas-exchange measurements were collected using mass spectrometry and volume turbine and near-infrared spectroscopy was used to measure [HHb] of the vastus lateralis muscle. Arterialized-capillary blood samples (~5 μL) were taken from the index finger 6 min before and 2 min after all trials and analyzed for blood lactate concentration ([Lac-]). RESULTS: Post-exercise [Lac-] was greatest (p<0.05) in CONT (14.3 mM), followed by 25INT (10.2 mM, p<0.05) and 10INT (7.0 mM, p<0.05). More frequent recovery periods decreased (p<0.05) respiratory exchange ratio (CONT: 1.13; 25INT: 1.09; 10INT: 1.06), and end-exercise V[Combining Dot Above]O2p (CONT: 3.69 L·min-1; 25INT: 3.29 L·min-1; 10INT: 2.89 L·min-1). The mean V[Combining Dot Above]O2p from time 0 to 180 s, was reduced (p<0.05) as the frequency of recovery periods increased (CONT: 2.62 L·min-1; 25INT: 2.50 L·min-1; 10INT: 2.32 L·min-1). The rate of adjustment of [HHb] and [HHb] at end exercise did not differ amongst conditions (p>0.05). CONCLUSION: Increasing the frequency of 3 s recovery periods during continuous exercise reduced and increased the contribution of oxidative phosphorylation and substrate-level phosphorylation, respectively. This ultimately resulted in a slower rate of adjustment of V[Combining Dot Above]O2p and likely a reduction in microvascular blood flow to the working muscle.

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)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.004
GPT teacher head0.220
Teacher spread0.215 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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