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Record W2952076464 · doi:10.3389/fphys.2019.00730

Influence of Hyperoxic-Supplemented High-Intensity Interval Training on Hemotological and Muscle Mitochondrial Adaptations in Trained Cyclists

2019· article· en· W2952076464 on OpenAlexaff
Daniele A. Cardinale, Filip J. Larsen, Johan Lännerström, Tom Manselin, Olof Södergård, Sara Mijwel, Peter Lindholm, Björn Ekblom, Robert Boushel

Bibliographic record

VenueFrontiers in Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCyclingHigh-intensity interval trainingInterval trainingEndurance trainingHyperoxiaMedicineLactate thresholdVO2 maxAnimal sciencePhysical therapyExercise physiologyCardiologyInternal medicineHeart rateBiologyBlood lactateBlood pressureLung

Abstract

fetched live from OpenAlex

Background: Hyperoxia (HYPER) increases the O2 carrying capacity resulting in a higher O2 delivery to the working muscles during exercise. Several lines of evidence indicate that lactate metabolism, power output and endurance are improved by HYPER compared to normoxia (NORM). Since HYPER enables a higher exercise power output compared to NORM and considering the O2 delivery limitation at exercise intensities near to maximum, we hypothesized that hyperoxic-supplemented high-intensity interval training (HIIT) would upregulate muscle mitochondrial respiratory function and enhance endurance performance compared to training in normoxia. Methods: 23 trained cyclists, age 35.3±6.4 years, body mass 75.2±9.6 kg, height 179.8± 7.9 m, and VO2max 4.5±0.7 L·min-1 performed 6 wks polarized and periodized endurance training on a cycle ergometer consisting of supervised HIIT sessions 3 days/wk and additional low-intensity training 2 days/wk. Participants were randomly assigned to either HYPER (FIO2 0.30) or NORM (FIO2 0.21) breathing condition during HIIT. Mitochondrial respiration in permeabilized fibers and isolated mitochondria together with maximal and submaximal VO2, hematological parameters and endurance cycle performance were tested pre and post training intervention. Results: Hyperoxic training led to a small, non-significant change in performance compared to normoxic training (HYPER 6.0±3.7%, NORM 2.4±5.0%; p value = 0.073, ES= 0.32). This small, beneficial effect on performance was not explained by the change in VO2max (HYPER 1.1±3.8%, NORM 0.0±3.7%; p value = 0.55, ES= 0.08), blood volume and haemoglobin mass, mitochondrial oxidative phosphorylation capacity (permeabilized fibers: HYPER 27.3±46.0%, NORM 16.5±49.1%; p value = 0.37, ES= -3.24 and in isolated mitochondria: HYPER 26.1±80.1%, NORM 15.9±73.3%; p = 0.66, ES= -0.51), or markers of mitochondrial content which were similar between groups post intervention. Conclusions: This study showed that 6 wks hyperoxic-supplemented HIIT led to marginal gain in cycle performance in already trained cyclists but was not superior to conventional training at sea level in improving VO2max, blood volume, haemoglobin mass, nor mitochondrial oxidative phosphorylation capacity. Therefore, despite the small effect on cycling performance that might be meaningful in elite sport, considering the cost/benefit, health and ethical issues of performing hyperoxic-supplemented HIIT, this strategy to maximize endurance performance in already trained cyclists is controvertible.

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: 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
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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