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Understanding The Difference In Body Composition Assessment Methods In University Aged Canadian Students

2023· article· en· W4387062710 on OpenAlexaffabout
N. Adam, Chad A. Sutherland, Andrew S. Perrotta, Paula M. van Wyk, Sarah J. Woodruff, Adriana M. Duquette

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2023
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBioelectrical impedance analysisMedicineAnthropometryBody fat percentageBody weightPhysical therapyFat massBody mass indexInternal medicine

Abstract

fetched live from OpenAlex

Body composition is expressed as relative proportions of muscle, fat, bone, and other vital components of the body and is utilized as an overall health indicator. Body composition can be measured in several ways, including air displacement plethysmography (e.g., BOD POD) and Bioelectrical Impedance Analysis (BIA). To calculate body composition, the BOD POD measures body volume by changes in pressure in a closed chamber while the BIA measures the rate at which an electrical current travels through the body. The reasons for using different methods can depend on factors including time, cost, comfort, accessibility, and accuracy requirements. PURPOSE: The purpose of this study is to assess the accuracy of three different BIA devices in comparison to the BOD POD in healthy university aged Canadian students. METHODS: 617 undergraduate students (342 females and 275 males) with an age of (mean ± SD) 21.2 ± 2.0 years old, a height of 171.9 ± 9.9 cm, and a weight of 71.6 ± 15.2 kg completed the BOD POD, BIA Tanita BC-568, and BIA Tanita BF-683 body composition assessments. A 319 participant (187 females and 132 males) subsample with an age of 21.2 ± 1.8 years old, a height of 171.3 ± 9.4 cm, and a weight of 71.4 ± 15.0 kg also completed a BIA InBody test. Before all assessment measures, students were required to fast for two hours, refrain from exercise for six hours, not use diuretics for six hours, and void their bladder. Subjects completed their required assessments consecutively on the same day. RESULTS: Significant differences in body fat % (mean ± SD) were observed between the BOD POD (22.9 ± 9.0 %) and the Tanita-568 (21.6 ± 8.4 %, p < 0.0001), the Tanita-683 (22.4 ± 8.9%, p < 0.0001) and the InBody (23.1 ± 9.5, p < 0.05). The mean absolute error for each secondary measure was the following: Tanita-568 = 4.8%, Tanita-683 = 5.8% and the InBody = 4.6%. Bland-Altman plots demonstrated each secondary measure provided a positive bias whose 95% CI failed to overlap the line of equality. CONCLUSION: All secondary measures demonstrated significantly different results in comparison to the BOD POD. The InBody was the most accurate BIA device. Future research could investigate if the assessment from the various devices affects the body composition classifications based on body fat percentage.

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)0.007
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 score0.010
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.390
Teacher spread0.296 · 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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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