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Record W2010288910 · doi:10.1038/oby.2001.87

Intraobserver and Interobserver Reliability of Waist Circumference and the Waist‐to‐Hip Ratio

2001· letter· en· W2010288910 on OpenAlexaff
Morie M. Chen, Scott A. Lear, Min Gao, Jiří Fröhlich, C. Laird Birmingham

Bibliographic record

VenueObesity Research · 2001
Typeletter
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsCentre for Advancing Health OutcomesSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineWaistIntraclass correlationCircumferenceWaist–hip ratioAbdominal obesityPhysical therapyReliability (semiconductor)ObesityInternal medicineMathematicsPsychometrics

Abstract

fetched live from OpenAlex

Abdominal obesity correlates with numerous risk factors for cardiovascular disease and type 2 diabetes (1). It has been suggested that waist circumference (WC) is a better predictor of metabolic risk factors than the waist-to-hip ratio (WHR) (2, 3). Due to the importance of abdominal obesity in clinical practice, we evaluated the intra- and interobserver reliability of the WC measurement and compared it with the WHR in men and women. Participants were recruited from hospital volunteers, staff members, and students, all of whom provided institution-approved informed consent. Those younger than 19 years of age or with increased abdominal volume unrelated to increased adiposity were excluded. Participants (n = 218,121 women) were given two assessments within 1 week of each other: WC, hip circumference (HC), weight, and height were measured by observer 1, and WC and HC were reassessed by observer 1 and a second observer (observer 2). Both observers were blinded to the previous measures. WC was measured over the skin at the point of maximal narrowing from the anterior view (or at the 12th lateral rib when this was not obvious). HC was measured at the maximal gluteal protuberance from the lateral view over undergarments. Both WC and HC were measured to the nearest 0.1 cm. WHR was calculated as WC divided by HC. Weight and height were measured using standard methods. Intraclass correlation coefficients (ICCs) were calculated to determine reliability. The intraobserver reliability was evaluated between the results of the two assessments by observer 1. The interobserver reliability was assessed in 75 participants and evaluated between the results of the first assessment of observer 1 and the second assessment of observer 2. WC ICCs were compared with the WHR ICCs using the extension of Feldt's approach as described by Kraemer (4). The mean age and body mass index of the participants were 39 ± 11.9 years and 24.6 ± 4.4 kg/m2, respectively. WC and WHR from assessment 1 were 80.4 ± 10.7 cm (range, 57.2 to 119.4 cm) and 0.82 ± 0.08 (range, 0.58 to 1.04), respectively. The intraobserver ICC for WC was 0.987 (95% confidence interval [CI]: 0.983 to 0.990) and for WHR was 0.970 (95% CI: 0.960 to 0.977). The interobserver ICC for WC was 0.988 (95% CI: 0.982 to 0.993) and for WHR was 0.969 (95% CI: 0.951 to 0.980). Although WC and WHR demonstrated high intra- and interobserver reliability, WC was significantly more reliable than the WHR for both analyses (p = 0.0001, WC ICCs compared with WHR ICCs). This is the first report to compare both intra- and interobserver reliability between WC and WHR in such a large cohort. Our results confirm those reported in a smaller cohort (n = 51) in which the intraobserver WC ICC was greater than that of the WHR; however, the authors did not report the interobserver reliability (5). We demonstrated that WC also has a greater interobserver reliability than does the WHR. Because WC is more reliable than the WHR and a better predictor of cardiovascular risk factors, it is the preferable anthropometric tool for assessing patients and is recommended for routine use in physician offices.

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)0.043
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.043
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.117
GPT teacher head0.366
Teacher spread0.249 · 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 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

Citations71
Published2001
Admission routes1
Has abstractyes

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