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Record W3102164953 · doi:10.1080/1091367x.2020.1843042

Comparison of Body Composition Assessment Using Air-Displacement Plethysmography and A-Mode Ultrasound before and after a 12-Week Exercise Intervention in Normal Weight Adult Males

2020· article· en· W3102164953 on OpenAlexaff
Aaron D. Bridge, Joseph Alexander Brown, Hayden Snider, Shannon N. Rowan, Lauren E. Skelly, Andrea R. Josse

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2020
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsYork UniversityBrock University
Fundersnot available
KeywordsPlethysmographPhysical therapyModalitiesMedicinePhysical activityUltrasoundUltrasonographyBody weightInternal medicineSurgery

Abstract

fetched live from OpenAlex

Body composition (BC) is a valuable indicator of health and performance. Air-displacement plethysmography (ADP) is a popular, efficient, and reliable method for assessing BC. A-mode ultrasonography (AUS) is another method, and its affordability, mobility, and practicality make it a plausible device for industry and health professionals. This study assessed the cross-sectional and change relationships and agreements between AUS and ADP from a 12-week resistance exercise intervention designed to increase fat-free mass (FFM). BC was measured by both modalities pre/post-intervention. Twenty-nine normal weight males (age: 20.6 ± 2.2 years) participated. Both modalities tracked changes in FFM and % body fat (%BF) (∆ADP = 1.7 ± 1.5 kg, −0.4 ± 2.5%; ∆AUS = 2.4 ± 1.9 kg, −1.4 ± 2.1%; main effects for time p < .005, respectively), with no differences between modalities (interactions p > .05), and were moderately significantly correlated (r = 0.58 and r = 0.60, p < .01). Bland-Altman plots revealed no proportional biases. These devices tracked changes in FFM and %BF similarly over time. Future research should assess AUS at different levels of adiposity.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.350
Teacher spread0.320 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

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