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Modelling the Influence of Age, Body Size and Sex on Maximum Oxygen Uptake in Older Humans

2000· article· en· W2081582053 on OpenAlexaff
Patrick Johnson, Edward Winter, D. H. Paterson, John J. Koval, Alan Nevill, David A. Cunningham

Bibliographic record

VenueExperimental Physiology · 2000
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsWestern University
Fundersnot available
KeywordsVO2 maxOxygenPhysiologyDemographyBiologyChemistryEndocrinologyHeart rate

Abstract

fetched live from OpenAlex

The purpose of this study was to describe the influence of body size and sex on the decline in maximum oxygen uptake (O2,max) in older men and women. A stratified random sample of 152 men and 146 women, aged 55-86 years, was drawn from the study population. Influence of age on O2,max, independent of differences in body mass (BM) or fat-free mass (FFM), was investigated using the following allometric model: O2,max = BMb (or FFMb) exp(a + (c ' age) + (d ' sex)) [epsilon]. The model was linearised and parameters identified using standard multiple regression. The BM model explained 68.8 % of the variance in O2,max. The parameters (+/- s.e.e., standard error of the estimate) for lnBM (0.563 +/- 0.070), age (-0.0154 +/- 0.0012), sex (0.242 +/- 0.024) and the intercept (-1.09 +/- 0.32) were all significant (P < 0.001). The FFM model explained 69.3 % of the variance in O2,max, and the parameters (+/- s.e.e) lnFFM (0.772 +/- 0.090), age (-0.0159 +/- 0.0012) and the intercept (-1.57 +/- 0.36) were significant (P < 0.001), while sex (0.077 +/- 0.038) was significant at P = 0.0497. Regardless of the model used, the age-associated decline was similar, with a relative decline of 15 % per decade (0.984 exp(age)) in O2,max in older humans being estimated. The study has demonstrated that, for a randomly drawn sample, the age-related loss in O2,max is determined, in part, by the loss of fat-free body mass. When this factor is accounted for, the loss of O2,max across age is similar in older men and women.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.270
Teacher spread0.257 · 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 teacher head, 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".

Quick stats

Citations19
Published2000
Admission routes1
Has abstractyes

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