-component model for estimating body composition. While HW is an often used criterion measure for body composition assessment, ADP via the BodPod® has grown as an alternative ( Dempster & Aitkens, 1995 ). Both methods depend on measuring an individual’s body mass, body volume (Vb), and correcting Vb for gas within
Jeremy B. Ducharme, Ann L. Gibson, and Christine M. Mermier
Ryan P. Durk, Esperanza Castillo, Leticia Márquez-Magaña, Gregory J. Grosicki, Nicole D. Bolter, C. Matthew Lee, and James R. Bagley
were given to the researchers, and body composition was measured using air displacement plethysmography (BodPod; Life Measurement, Inc., Concord, CA). Participants’ cardiorespiratory fitness was measured using a symptom-limited maximal graded treadmill exercise test to determine VO 2 max (Quark CPET
Ann L. Gibson, Jenevieve L. Roper, and Christine M. Mermier
Air displacement plethysmography (ADP) is a popular method for estimating body density (Db). Most ADP tests are performed once, with test-retest investigations scarce. Therefore, we investigated test-retest reliability of ADP. Active men (n = 25) and women (n = 25) volunteered and followed standard pretest guidelines. Participants wore dry, form-fitting swimwear and manufacturer-supplied swim caps. In a single session, two ADP trials with measured thoracic gas volume (TGV) were performed without repositioning participants. Separate 2 (sex) × 2 (ADP trial) repeated-measures ANOVAs were performed to investigate within-between comparisons of Db, TGV, body volume (Vb), and relative fatness (%BF). Paired t tests were used to investigate significant differences as appropriate. The Bland and Altman technique was used to depict individual intertrial variations. For all analyses, α =.05. A significant main effect for sex was found; men were lower in %BF and higher in all other variables compared with women. Individual variability was notable (ADP1–ADP2). The range of individual intertrial differences were larger for women than men, respectively, for Db (-0.0096–0.0045 g/cc; -0.0019–0.0054 g/cc), TGV (-0.623–1.325 L; -0.584–0.378 L), Vb (-0.249–2.10 L; -0.234–0.397 L), and %BF (-2.1–4.4%; -0.2–0.9%). When assessing body composition of women via ADP or using Db from ADP in a multicomponent model, at least two trials with measured TGV should be performed and the average of the values recorded and reported.
Kerri L. Vasold, Andrew C. Parks, Deanna M.L. Phelan, Matthew B. Pontifex, and James M. Pivarnik
2 Mean Height (m) 1.71 ± 0.09 1.70 ± 0.09 1.70 ± 0.09 1.78 ± 0.07 1.78 ± 0.07 1.78 ± 0.07 1.65 ± 0.06 1.65 ± 0.06 1.65 ± 0.06 Weight (kg) 66.2 ± 11.3 66.2 ± 11.5 66.2 ± 11.4 75.6 ± 9.4 75.7 ± 9.5 75.6 ± 9.4 59.9 ± 7.5 59.8 ± 7.7 59.8 ± 7.6 Fat-free mass (kg) BodPod 52.4 ± 11.1 52.5 ± 11.1 52.4 ± 11
Ava Kerr, Gary Slater, Nuala Byrne, and Janet Chaseling
The three-compartment (3-C) model of physique assessment (fat mass, fat-free mass, water) incorporates total body water (TBW) whereas the two-compartment model (2-C) assumes a TBW of 73.72%. Deuterium dilution (D2O) is the reference method for measuring TBW but is expensive and time consuming. Multifrequency bioelectrical impedance spectroscopy (BIS SFB7) estimates TBW instantaneously and claims high precision. Our aim was to compare SFB7 with D2O for estimating TBW in resistance trained males (BMI >25kg/m2). We included TBWBIS estimates in a 3-C model and contrasted this and the 2-C model against the reference 3-C model using TBWD2O. TBW of 29 males (32.4 ± 8.5 years; 183.4 ± 7.2 cm; 92.5 ± 9.9 kg; 27.5 ± 2.6 kg/m2) was measured using SFB7 and D2O. Body density was measured by BODPOD, with body composition calculated using the Siri equation. TBWBIS values were consistent with TBWD2O (SEE = 2.65L; TE = 2.6L) as were %BF values from the 3-C model (BODPOD + TBWBIS) with the 3-C reference model (SEE = 2.20%; TE = 2.20%). For subjects with TBW more than 1% from the assumed 73.72% (n = 16), %BF from the 2-C model differed significantly from the reference 3-C model (Slope 0.6888; Intercept 5.093). The BIS SFB7 measured TBW accurately compared with D2O. The 2C model with an assumed TBW of 73.72% introduces error in the estimation of body composition. We recommend TBW should be measured, either via the traditional D2O method or when resources are limited, with BIS, so that body composition estimates are enhanced. The BIS can be accurately used in 3C equations to better predict TBW and BF% in resistance trained males compared with a 2C model.
Andrew Pardue, Eric T. Trexler, and Lisa K. Sprod
Extreme body composition demands of competitive bodybuilding have been associated with unfavorable physiological changes, including alterations in metabolic rate and endocrine profile. The current case study evaluated the effects of contest preparation (8 months), followed by recovery (5 months), on a competitive drug-free male bodybuilder over 13 months (M1-M13). Serum testosterone, triiodothyronine (T3), thyroxine (T4), cortisol, leptin, and ghrelin were measured throughout the study. Body composition (BodPod, dualenergy x-ray absorptiometry [DXA]), anaerobic power (Wingate test), and resting metabolic rate (RMR) were assessed monthly. Sleep was assessed monthly via the Pittsburgh Sleep Quality Index (PSQI) and actigraphy. From M1 to M8, testosterone (623–173 ng∙dL-1), T3 (123–40 ng∙dL-1), and T4 (5.8–4.1 mg∙dL-1) decreased, while cortisol (25.2–26.5 mg∙dL-1) and ghrelin (383–822 pg∙mL-1) increased. The participant lost 9.1 kg before competition as typical energy intake dropped from 3,860 to 1,724 kcal∙day-1; BodPod estimates of body fat percentage were 13.4% at M1, 9.6% at M8, and 14.9% at M13; DXA estimates were 13.8%, 5.1%, and 13.8%, respectively. Peak anaerobic power (753.0 to 536.5 Watts) and RMR (107.2% of predicted to 81.2% of predicted) also decreased throughout preparation. Subjective sleep quality decreased from M1 to M8, but objective measures indicated minimal change. By M13, physiological changes were largely, but not entirely, reversed. Contest preparation may yield transient, unfavorable changes in endocrine profile, power output, RMR, and subjective sleep outcomes. Research with larger samples must identify strategies that minimize unfavorable adaptations and facilitate recovery following competition.
Ya-Wen Hsu, Chih-Ping Chou, Britni R. Belcher, Selena T. Nguyen-Rodriguez, Marc J. Weigensberg, Arianna D. McClain, and Donna Spruijt-Metz
While most studies have focused on investigating the preventive effects of physical activity on metabolic risk, the longitudinal impacts of metabolic syndrome (MetS) on activity levels is poorly understood. This study aims to examine the influence of MetS on initial activity levels and the trajectory of activity levels in Latina and African American female children over 12 months (n = 55, 9 ± 1 years). Metabolic measures, including fat and lean tissue mass by BodPod, fasting glucose, lipids, blood pressure, and waist circumference, were collected at baseline. Moderate-to-vigorous physical activity and sedentary behavior by accelerometry were collected on a quarterly basis. There were no significant differences in either initial activity levels by MetS status (Moderate-to-vigorous physical activity: 33 ± 12 mins/day for MetS, 48 ± 28 mins/day for Non-MetS, p = .12; sedentary behavior: 408 ± 57 mins/day for MetS, 421 ± 72 mins/day for Non-MetS, p = .67). Longitudinal declines in moderate-to-vigorous physical activity (p = .038) and increases in sedentary behavior (p = .003) were found. Daily sedentary behavior increased by 82.64 more minutes in youth with MetS than in those without over one year (p = .015). This study yields the first evidence of the adverse effect of MetS on sedentary behavior. Targeted intervention strategies to reduce progressive sedentariness evident in minority youth with MetS are warranted.
Marcus Colon, Andrew Hodgson, Eimear Donlon, and James E.J. Murphy
. Height was measured in bare feet using a stadiometer. Body composition was measured using whole-body densitometry in a BodPod (Cosmed, Concord, CA) using manufacturer protocols. Weight was also measured using a calibrated scale that accompanies the BodPod. All instruments were calibrated on the day of
Marco Meucci, Vibhav Nandagiri, Venkata S. Kavirayuni, Alexander Whang, and Scott R. Collier
with a stadiometer to the nearest 0.1 cm and with a scale to the nearest 0.1 kg, respectively. BMI was calculated through dividing body mass in kilogram by the stature in meter squared (in kilogram per meter square). Body composition was assessed using air-displacement plethysmography (BodPod; COSMED
J. Luke Pryor, Brittany Christensen, Catherine G. R. Jackson, and Stephanie Moore-Reed
Laboratory wearing tight-fitting shorts or leggings and a t-shirt, 10 participants (male = 6; female = 4) completed all questionnaires to ensure inclusion and exclusion criteria. Body fat percentage was estimated by air displacement plethysmography (BODPOD; COSMED USA Inc, Concord, CA). 15 Following a 5