Predicting Body Fat to the Accuracy of Dexa Using Standard Anthropometric Measures

Oliver, JM; Lambert, Bradley S.; Martin, Steven E.; Tanguay, Jonathan; Bragg, Amy; Green, J S; Crouse, Stephen F.

doi:10.1097/01.jsc.0000395638.55606.50

Cited by 2 publications

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“…There are many options available for measuring body composition in research and clinical settings; however, dual-energy X-ray absorptiometry (DXA) is considered the gold standard method for analyzing body composition [12, 13], with excellent precision, reproducibility, and reliability between operators [12, 14]. Although its use is limited by cost and accessibility, requiring a certified radiology technician [15, 16], DXA can provide a rapid, non-invasive estimation of body composition with minimal radiation.…”

Section: Introductionmentioning

confidence: 99%

High baseline fat mass, but not lean tissue mass, is associated with high intensity low back pain and disability in community-based adults

et al. 2019

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Objectives Low back pain is the largest contributor to disability worldwide. The role of body composition as a risk factor for back pain remains unclear. Our aim was to examine the relationship between fat mass and fat distribution on back pain intensity and disability using validated tools over 3 years. Methods Participants (aged 25–60 years) were assessed at baseline using dual-energy X-ray absorptiometry (DXA) to measure body composition. All participants completed the Chronic Pain Grade Scale at baseline and 3-year follow-up. Of the 150 participants, 123 (82%) completed the follow-up. Results Higher baseline body mass index (BMI) and fat mass (total, trunk, upper limb, lower limb, android, and gynoid) were all associated with high intensity back pain at either baseline and/or follow-up (total fat mass: multivariable OR 1.05, 95% CI 1.01–1.09, p < 0.001). There were similar findings for all fat mass measures and high levels of back disability. A higher android to gynoid ratio was associated with high intensity back pain (multivariable OR 1.04, 95% CI 1.01–1.08, p = 0.009). There were no associations between lean mass and back pain. Conclusions This cohort study provides evidence for the important role of fat mass, specifically android fat relative to gynoid fat, on back pain and disability.

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Section: Introductionmentioning

confidence: 99%

High baseline fat mass, but not lean tissue mass, is associated with high intensity low back pain and disability in community-based adults

et al. 2019

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“…This is partly due to the fact that studies that reported BF% by both DXA and 2C HW HBW were typically validated against BF% by multi-component models incorporating HW HBW . DXA has also been embraced by some [43] as the reference method for BF% estimation in place of HW HBW . In any case, our purpose was not to address the absolute accuracy of BF% estimation, but rather to examine SEE of BF% prediction using BF% by 2C HW HBW as the reference method.…”

Section: Discussionmentioning

confidence: 99%

New Equations for Hydrostatic Weighing without Head Submersion

Tesch

Papadopoulos

Dolgener

et al. 2022

JFMK

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New equations were derived to predict the density of the body (DB) by hydrostatic weighing with the head above water (HWHAW). Hydrostatic weighing with the head below water (HWHBW) was the criterion for DB measurement in 90 subjects (44 M, 46 F). Head volume by immersion (HVIMM) was determined by subtracting the mass in water with the head below water (MWHBW) from the mass in water with the head above water (MWHAW), with subjects at residual lung volume. Equations were derived for head volume prediction (HVPRED) from head measurements and used to correct DB by HWHAW. Equations were also derived for HWHAW using direct regression of DB from uncorrected density (with MWHAW in place of MWHBW). Prediction equations were validated in 45 additional subjects (21 M, 24 F). Results were evaluated using equivalence testing, linear regression, Bland–Altman plots, and paired t-tests. Head girth, face girth, and body mass produced the smallest errors for HVPRED. In both M and F validation groups, equivalence (±2% fat by weight) was demonstrated between body fat percent (BF%) by HWHBW and BF% by HWHAW with HVPRED. Variance in computer-averaged samples of MWHAW was significantly less (p < 0.05) than MWHBW. Prediction error was smaller for BF% by HWHAW with HVPRED than for alternative methods. Conclusions: Equivalence between BF% by HWHBW and BF% by HWHAW with HVPRED was demonstrated and differences were not statistically significant. Weight fluctuations were smaller for HWHAW than HWHBW.

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Predicting Body Fat to the Accuracy of Dexa Using Standard Anthropometric Measures

Cited by 2 publications

References 0 publications

High baseline fat mass, but not lean tissue mass, is associated with high intensity low back pain and disability in community-based adults

High baseline fat mass, but not lean tissue mass, is associated with high intensity low back pain and disability in community-based adults

New Equations for Hydrostatic Weighing without Head Submersion

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