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Record W1512712519 · doi:10.3233/wor-2010-1034

A sub-maximal occupational aerobic fitness test alternative, when the use of heart rate is not appropriate

2010· article· en· W1512712519 on OpenAlexaff
Tara Reilly, Mike Tipton

Bibliographic record

VenueWork · 2010
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsCanadian Armed Forces
FundersRoyal National Lifeboat InstitutionUniversity of Portsmouth
KeywordsAerobic exerciseTest (biology)Heart rateStep testAerobic capacityPhysical fitnessCardiovascular fitnessVO2 maxFitness testPhysical therapyMedicinePsychologyBlood pressureInternal medicineSignificant difference

Abstract

fetched live from OpenAlex

UNLABELLED: Several emergency response organisations have introduced a minimum aerobic fitness test to predict performance on critical tasks, as well as to help ensure some protection against the cardiovascular stress associated with emergency situations. A popular indirect sub-maximal test of aerobic fitness is the step test; this test relies on the relationship between exercise intensity, heart rate and aerobic capacity. This relationship, and the tests that rely on it, are not valid for individuals who are on prescribed medication (often for high blood pressure) that alter the heart rate response to exercise. OBJECTIVE: The purpose of the work described in this paper was to develop a sub-maximal test of aerobic fitness that did not rely on heart rate. PARTICIPANTS AND METHODS: Eighty-four subjects undertook the Tecumseh step test and a six-minute maximal walk test. RESULTS: A Pearson Product correlation of r=- 0.81, P< 0.01 was identified between the distance that an individual could walk in six minutes and their heart rate response to the step test. CONCLUSIONS: It is concluded that the walk test offers a valid alternative to the step test for the indirect sub-maximal assessment of aerobic fitness.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.298

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 head0.037
GPT teacher head0.267
Teacher spread0.230 · 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 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

Citations6
Published2010
Admission routes1
Has abstractyes

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