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Repeatability of Thoracoabdominal Wall Compartmental Contributions to Tidal Volume during Low‐, Moderate‐ and Heavy‐Intensity Exercise

2021· article· en· W3167067402 on OpenAlexafffund
Eric T. Hedge, Paolo B. Dominelli, Richard L. Hughson

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRepeatabilityTidal volumeIntensity (physics)MedicinePlethysmographRespiratory rateExercise intensityCardiologyRespiratory minute volumeVentilation (architecture)Volume (thermodynamics)Heart rateRespiratory systemAnimal scienceInternal medicineChemistryBlood pressurePhysicsBiologyMeteorology

Abstract

fetched live from OpenAlex

The patterns of breathing and the thoracoabdominal wall compartmental contributions to tidal volume at different exercise intensities are typically assessed during a single laboratory visit, where individuals complete progressively higher intensity stages of exercise. The relative contributions of the thoracic and abdominal compartments have been reported to be invariant across exercise intensities; however, inter‐individual differences have been observed. Therefore, the purpose of this study was to assess the repeatability of the compartmental contributions to tidal volume during low‐, moderate‐, and heavy‐intensity exercise. It was hypothesized that the inter‐individual differences in the compartmental contributions would be repeatable day‐to‐day for each intensity. Seven healthy adults (2 female, age: 28±7 yr, peak oxygen uptake: 45±5 ml·min ‐1 ·kg ‐1 ) completed two step exercise tests on separate days, consisting of low‐, moderate‐, and heavy‐intensity stages for each individual based on their ramp exercise responses. Tidal volume and breathing rate were assessed using a bi‐directional turbine, and thoracic and abdominal contributions to tidal volume were measured by respiratory inductance plethysmography. Day‐to‐day repeatability was evaluated using the intra‐class correlation coefficient (ICC). With increasing exercise intensity, the tidal volume (p<0.001) and breathing rate (p<0.001) increased, but no differences in tidal volume (p=0.97; low: 1.14±0.21 L vs. 1.22±0.48 L, moderate: 2.02±0.49 L vs. 1.92±0.54 L, heavy: 2.51±0.60 L vs. 2.54±0.59 L) or breathing rate (p=0.94; low: 18±4 breaths·min ‐1 vs. 17±6 breaths·min ‐1 , moderate: 22±8 breaths·min ‐1 vs. 23±7 breaths·min ‐1 , heavy: 37±9 breaths·min ‐1 vs. 37±10 breaths·min ‐1 ) were observed between testing days. The relative contribution of the thoracic (low: 64±12% vs. 63±15%, moderate: 66±12% vs. 64±14%, heavy: 67±11% vs. 67±12%) and abdominal (low: 36±12% vs. 37±15%, moderate: 34±12% vs. 36±14%, heavy: 33±11% vs. 33±12%) compartments did not differ significantly between test days (p=0.59), or exercise intensities (p=0.38). However, there were inter‐individual differences in how the compartmental contribution to tidal volume changed with exercise intensity, as the relative contribution of the thoracic compared to abdominal compartment increased with higher intensity exercise for 3 subjects, decreased for 1 subject, and remained relatively constant for 3 subjects. These inter‐individual differences to increasing exercise intensity do not appear random, as the test‐retest repeatability of the compartmental contribution to tidal volume was good to excellent for low (ICC=0.87, p=0.003), moderate (ICC=0.89, p=0.002) and heavy (ICC=0.93, p=0.001) exercise. Future work should seek to identify factors contributing to the observed inter‐individual differences in compartmental contribution to tidal volume with increasing exercise intensity and whether these impart functional consequences.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.016
GPT teacher head0.302
Teacher spread0.286 · 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
Published2021
Admission routes2
Has abstractyes

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