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Record W1652080261 · doi:10.1055/s-0031-1279768

Accuracy of the SenseWear Armband™ during Ergocycling

2011· article· en· W1652080261 on OpenAlexafffund
Anne‐Sophie Brazeau, Antony D. Karelis, Diane Mignault, M.-J. Lacroix, Denis Prud’homme, R. Rabasa-Lhoret

Bibliographic record

VenueInternational Journal of Sports Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of OttawaUniversité du Québec à MontréalMontreal Clinical Research InstituteUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsEnergy expenditureMedicineMotion sensorsEnergy metabolismPhysical activityTotal energy expenditureBody mass indexPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

The present study aims to show the accuracy of a portable motion sensor, the SenseWear Armband, for the estimation of energy expenditure vs. energy expenditure measured by indirect calorimetry during ergocycling. 31 healthy adults (52% women; age: 26.7±6.3 years; Body Mass Index: 23.9±3.3 kg/m2) completed a 45-min ergocycling session at 50% of their VO2(peak). Despite a significant underestimation of 18.7±13.2 kcal during the first 10 min of the activity (T=5.06; p<0.001), we observed an overall good agreement between energy expenditure estimated by the SenseWear Armband during ergocycling and indirect calorimetry (260.3±80.1 vs. 287.8±97.1 kcal, respectively) (T=-2.148; p=0.04) and a significant intra-class correlation (r=0.81; p<0.001). The results of the present study indicate that the SenseWear Armband underestimated energy expenditure during a 45-min ergocycling session at a 50% VO2(peak) intensity, mainly during the first 10 min. Underestimation at the onset of the activity warrants further research.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.050
GPT teacher head0.335
Teacher spread0.285 · 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 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

Citations24
Published2011
Admission routes2
Has abstractyes

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