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Establishing the V<scp>o</scp><sub>2</sub> versus constant-work-rate relationship from ramp-incremental exercise: simple strategies for an unsolved problem

2019· article· en· W2978959867 on OpenAlexafffund
Danilo Iannetta, Rafael de Almeida Azevedo, Daniel A. Keir, Juan M. Murias

Bibliographic record

VenueJournal of Applied Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity Health NetworkUniversity of Calgary
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsWork rateRespiratory compensationWork (physics)Constant (computer programming)ChemistryReaction rate constantAnimal scienceMathematicsSteady state (chemistry)Time constantAnalytical Chemistry (journal)Heart rateAnaerobic exerciseThermodynamicsPhysicsKineticsInternal medicineMedicineBiologyPhysical therapyChromatographyPhysical chemistryComputer science

Abstract

fetched live from OpenAlex

The dissociation between constant work rate of O2 uptake (V̇o2) and ramp V̇o2 at a given work rate might be mitigated during slowly increasing ramp protocols. This study characterized the V̇o2 dynamics in response to five different ramp protocols and constant-work-rate trials at the maximal metabolic steady state (MMSS) to characterize 1) the V̇o2 gain (G) in the moderate, heavy, and severe domains, 2) the mean response time of V̇o2 (MRT), and 3) the work rates at lactate threshold (LT) and respiratory compensation point (RCP). Eleven young individuals performed five ramp tests (5, 10, 15, 25, and 30 W/min), four to five time-to-exhaustions for critical power estimation, and two to three constant-work-rate trials for confirmation of the work rate at MMSS. G was greatest during the slowest ramp and progressively decreased with increasing ramp slopes (from ~12 to ~8 ml·min−1·W−1, P < 0.05). The MRT was smallest during the slowest ramp slopes and progressively increased with faster ramp slopes (1 ± 1, 2 ± 1, 5 ± 3, and 10 ± 4, 15 ± 6 W, P < 0.05). After “left shifting” the ramp V̇o2 by the MRT, the work rate at LT was constant regardless of the ramp slope (~150 W, P > 0.05). The work rate at MMSS was 215 ± 55 W and was similar and highly correlated with the work rate at RCP during the 5 W/min ramp ( P > 0.05, r = 0.99; Lin’s concordance coefficient = 0.99; bias = −3 W; root mean square error = 6 W). Findings showed that the dynamics of V̇o2 (i.e., G) during ramp exercise explain the apparent dichotomy existing with constant-work-rate exercise. When these dynamics are appropriately “resolved”, LT is constant regardless of the ramp slope of choice, and RCP and MMSS display minimal variations between each other. NEW & NOTEWORTHY This study demonstrates that the dynamics of V̇o2 during ramp-incremental exercise are dependent on the characteristics of the increments in work rate, such that during slow-incrementing ramp protocols the magnitude of the dissociation between ramp V̇o2 and constant V̇o2 at a given work rate is reduced. Accurately accounting for these dynamics ensures correct characterizations of the V̇o2 kinetics at ramp onset and allows appropriate comparisons between ramp and constant-work-rate exercise-derived indexes of exercise intensity.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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

Citations107
Published2019
Admission routes2
Has abstractyes

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