scholarly journals Despite Low Obesity Rates, Body Mass Index Under-Estimated Obesity among Russian Police Officers When Compared to Body Fat Percentage

Author(s):  
Katie M. Heinrich ◽  
Konstantin G. Gurevich ◽  
Anna N. Arkhangelskaia ◽  
Oleg P. Karazhelyaskov ◽  
Walker S. C. Poston

In some countries, obesity rates among police officers are higher than the general public, despite physically demanding jobs. Obesity rates based on body mass index (BMI) may lack accuracy as BMI does not directly address body composition. Since data are lacking for obesity rates among Russian police officers, this study documented and compared officer obesity rates to the adult Russian population and compared the accuracy of body mass index (BMI) for obesity classification to two direct measures of body composition. Moscow region police officers (N = 182, 84% men) underwent height, weight, waist circumference (WC), and body fat percentage (BF%) bioelectrical impedance measurements during annual medical examinations. BMI-defined obesity rates were 4.6% for men and 17.2% for women, which were >3 and >1.8 times lower than Russian adults, respectively. WC-defined obesity rates were similar to BMI (3.3% for men and 10.3% for women), but BF%-defined obesity rates were much higher (22.2% for men and 55.2% for women). Although obesity rates were lower than those found among police officers in other countries, BMI alone was not a particularly accurate method for classifying weight status among Russian police officers.

2017 ◽  
Vol 22 (11) ◽  
pp. 3689-3698
Author(s):  
Gerson Luis de Moraes Ferrari ◽  
Timóteo Araújo ◽  
Luis Carlos Oliveira ◽  
Victor Keihan Rodrigues Matsudo ◽  
Emily Mire ◽  
...  

Abstract The purpose of this study was to determine the relationship between peak cadence indicators and body mass index (BMI) and body fat percentage (BF%)-defined weight status in children. The sample comprised 485 Brazilian children. Minute-by-minute step data from accelerometry were rank ordered for each day to identify the peak 1-minute, 30-minute and 60-minute cadence values. Data were described by BMI–defined and bioelectrical impedance-determined BF% weight status. BMI-defined normal weight children had higher peak 1-minute (115.5 versus 110.6 and 106.6 steps/min), 30-minute (81.0 versus 77.5 and 74.0 steps/min) and 60-minute cadence (67.1 versus 63.4 and 60.7 steps/min) than overweight and obese children (p<.0001), respectively. Defined using %BF, normal weight children had higher peak 1-minute (114.5 versus 106.1 steps/min), 30-minute (80.4 versus 73.1 steps/min) and 60-minute cadence (66.5 versus 59.9 steps/min) than obese children (p<.0001). Similar relationships were observed in boys; however, only peak 1- minute cadence differed significantly across BMI and %BF-defined weight status categories in girls. Peak cadence indicators were negatively associated with BMI and BF% in these schoolchildren and significantly higher among normal weight compared to overweight or obese children.


2021 ◽  
Author(s):  
Diana Vrabie ◽  
George-Sebastian Iacob

Bioelectrical impedance analysis (BIA) also called bioelectrical impedance (BEI) is a non-invasive method based on the electric conductibility properties of tissues and is a commonly used technique for estimating body composition.Percentage of body fat is strongly associated with the risk of several chronic diseases but its accurate measurement is difficult. Body Mass Index is a useful population-level measure of overweight and obesity. It is used for all categories of people, male or female.The main objective of this study was to determine if there is a relationship between BMI and body fat percentage (BF%) in a group of Romanian female students. This relationship has been studiedin various ethnic groups before. To conduct the study, we examined 29 young females (aged 20-36 years old) estimating BF% from bioelectrical impedance analysis using Tanita Body Fat Monitor Scale UM-076.In this research group, the BMI and BodyFat dependent variables have a moderate to strong correlation (r = .839; 0.75 < r < 1), the favorable score for the first measurement being a statistically relevant benchmark for the second (sig < 0.05).


Author(s):  
alexandru godescu

The classic Body Mass Index, (BMI), developed in the 19th century by the Belgian mathematician Adolphe Quetelet [1] is an important indicator of the risk of death, of obesity, of negative health consequences, body fat percentage and of the shape of the body. While he BMI is assumed to indicate obesity in sedentary people and in people who do not practice sports, it is undisputed and a consensus among researchers [2][3][4][5][9][25] that Body Mass Index (BMI) is not a good indicator for obesity in people who developed their body through heavy physical work or sport but also in other segments of population such as those who appear to have a normal weight but in fact have a high body fat percentage and obese methabolism. The BMI also does not include all the variables essential for a health predictor. The BMI is not always a good predictor of metabolic disease, people who appear of healthy weight according to BMI have in some cases an obese metabolic syndrome. The BMI was developed as a law of natural sciences and &ldquo;social physics&rdquo; [1], as it was called then, before the middle of the 19th century, and it had been used from the 70s for medical purposes, to detect obesity and the risk of mortality [6][7]. The BMI has a huge importance for modern society, affected by an obesity epidemic [8]. BMI has applications in medicine, sport medicine, sport, fitness, bodybuilding, insurance, nutrition, pharmacology. The main limitation of the BMI is that it does not account for body composition including non fat body mass such as muscles, joints, body frame and makes no difference between fat and non fat components of the body weight. The body composition and the proportion of fat and muscles make a difference in health outcomes [12][13][14][25][26][27][35][36][37] [38][39][40][41][42][43][44]&hellip;[100]. Body composition makes a difference also in the level of sport performance for athletes of every level. In nearly two centuries since the Body Mass Index was developed, no formula had been successfully developed to account for body composition and make the difference between muscle and fat in a consistent way. This can be considered a longstanding open problem of major importance for society. The objective of this analysis is to develop new formulae taking into account the health implication of body composition measured through indirect, simple indicators and making the difference between muscles and fat, healthy and non healthy metabolism. The formulae developed in this article are the only formula to successfully generalize BMI and make this difference. I develop a direct generalization of BMI, in the mathematical and physiological sense to account for fat and fat free mass and muscles, small and large body frames. It is the first such generalization because the classic BMI can be determined as a particular case of my formulae in the strict mathematical and practical physiologic sense. No other formula generalized the BMI to make the difference between fat and a large frame and muscles has ever been published in nearly two centuries since the BMI formula had been developed. The formulae I developed explain and generalize the conclusions of a large number of highly cited empirical experiments cited in the reference section. [35][36][37][38][38][39] [40][42][43][44]..[100] Most of the experimental proof I bring in support of my formulae and bodyweight quantification theory comes from many highly cited experimental research publications in medicine, sports medicine, sport science and physiology. My formulae explain also performance in decades of competitive sports and athletics


2021 ◽  
pp. 1-27
Author(s):  
Masoome Piri Damaghi ◽  
Atieh Mirzababaei ◽  
Sajjad Moradi ◽  
Elnaz Daneshzad ◽  
Atefeh Tavakoli ◽  
...  

Abstract Background: Essential amino acids (EAAs) promote the process of regulating muscle synthesis. Thus, whey protein that contains higher amounts of EAA can have a considerable effect on modifying muscle synthesis. However, there is insufficient evidence regarding the effect of soy and whey protein supplementation on body composition. Thus, we sought to perform a meta-analysis of published Randomized Clinical Trials that examined the effect of whey protein supplementation and soy protein supplementation on body composition (lean body mass, fat mass, body mass and body fat percentage) in adults. Methods: We searched PubMed, Scopus, and Google Scholar, up to August 2020, for all relevant published articles assessing soy protein supplementation and whey protein supplementation on body composition parameters. We included all Randomized Clinical Trials that investigated the effect of whey protein supplementation and soy protein supplementation on body composition in adults. Pooled means and standard deviations (SD) were calculated using random-effects models. Subgroup analysis was applied to discern possible sources of heterogeneity. Results: After excluding non-relevant articles, 10 studies, with 596 participants, remained in this study. We found a significant increase in lean body mass after whey protein supplementation weighted mean difference (WMD: 0.91; 95% CI: 0.15, 1.67. P= 0.019). Subgroup analysis, for whey protein, indicated that there was a significant increase in lean body mass in individuals concomitant to exercise (WMD: 1.24; 95% CI: 0.47, 2.00; P= 0.001). There was a significant increase in lean body mass in individuals who received 12 or less weeks of whey protein (WMD: 1.91; 95% CI: 1.18, 2.63; P<0.0001). We observed no significant change between whey protein supplementation and body mass, fat mass, and body fat percentage. We found no significant change between soy protein supplementation and lean body mass, body mass, fat mass, and body fat percentage. Subgroup analysis for soy protein indicated there was a significant increase in lean body mass in individuals who supplemented for 12 or less weeks with soy protein (WMD: 1.48; 95% CI: 1.07, 1.89; P< 0.0001). Conclusion: Whey protein supplementation significantly improved body composition via increases in lean body mass, without influencing fat mass, body mass, and body fat percentage.


BMJ ◽  
2021 ◽  
pp. n365
Author(s):  
Buyun Liu ◽  
Yang Du ◽  
Yuxiao Wu ◽  
Linda G Snetselaar ◽  
Robert B Wallace ◽  
...  

AbstractObjectiveTo examine the trends in obesity and adiposity measures, including body mass index, waist circumference, body fat percentage, and lean mass, by race or ethnicity among adults in the United States from 2011 to 2018.DesignPopulation based study.SettingNational Health and Nutrition Examination Survey (NHANES), 2011-18.ParticipantsA nationally representative sample of US adults aged 20 years or older.Main outcome measuresWeight, height, and waist circumference among adults aged 20 years or older were measured by trained technicians using standardized protocols. Obesity was defined as body mass index of 30 or higher for non-Asians and 27.5 or higher for Asians. Abdominal obesity was defined as a waist circumference of 102 cm or larger for men and 88 cm or larger for women. Body fat percentage and lean mass were measured among adults aged 20-59 years by using dual energy x ray absorptiometry.ResultsThis study included 21 399 adults from NHANES 2011-18. Body mass index was measured for 21 093 adults, waist circumference for 20 080 adults, and body fat percentage for 10 864 adults. For the overall population, age adjusted prevalence of general obesity increased from 35.4% (95% confidence interval 32.5% to 38.3%) in 2011-12 to 43.4% (39.8% to 47.0%) in 2017-18 (P for trend<0.001), and age adjusted prevalence of abdominal obesity increased from 54.5% (51.2% to 57.8%) in 2011-12 to 59.1% (55.6% to 62.7%) in 2017-18 (P for trend=0.02). Age adjusted mean body mass index increased from 28.7 (28.2 to 29.1) in 2011-12 to 29.8 (29.2 to 30.4) in 2017-18 (P for trend=0.001), and age adjusted mean waist circumference increased from 98.4 cm (97.4 to 99.5 cm) in 2011-12 to 100.5 cm (98.9 to 102.1 cm) in 2017-18 (P for trend=0.01). Significant increases were observed in body mass index and waist circumference among the Hispanic, non-Hispanic white, and non-Hispanic Asian groups (all P for trend<0.05), but not for the non-Hispanic black group. For body fat percentage, a significant increase was observed among non-Hispanic Asians (30.6%, 29.8% to 31.4% in 2011-12; 32.7%, 32.0% to 33.4% in 2017-18; P for trend=0.001), but not among other racial or ethnic groups. The age adjusted mean lean mass decreased in the non-Hispanic black group and increased in the non-Hispanic Asian group, but no statistically significant changes were found in other racial or ethnic groups.ConclusionsAmong US adults, an increasing trend was found in obesity and adiposity measures from 2011 to 2018, although disparities exist among racial or ethnic groups.


Author(s):  
Clíodhna McHugh ◽  
Karen Hind ◽  
Aoife O'Halloran ◽  
Daniel Davey ◽  
Gareth Farrell ◽  
...  

AbstractThe purpose of this study was to investigate longitudinal body mass and body composition changes in one professional rugby union team (n=123), (i) according to position [forwards (n=58) versus backs (n=65)], analysis of players with 6 consecutive seasons of DXA scans (n=21) and, (iii) to examine differences by playing status [academy and international], over 7 years. Players [mean age: 26.8 y, body mass index: 28.9+kg.m2] received DXA scans at fourtime points within each year. A modest (but non-significant) increase in mean total mass (0.8 kg) for professional players was reflected by increased lean mass and reduced body fat mass. At all-time points, forwards had a significantly greater total mass, lean mass and body fat percentage compared to backs (p<0.05). Academy players demonstrated increased total and lean mass and decreased body fat percentage over the first 3 years of senior rugby, although this was not significant. Senior and academy international players had greater lean mass and lower body fat percentage (p<0.05) than non-international counterparts. Despite modest increases in total mass; reflected by increased lean mass and reduced fat mass, no significant changes in body mass or body composition, irrespective of playing position were apparent over 7 years.


Medicine ◽  
2017 ◽  
Vol 96 (39) ◽  
pp. e8126 ◽  
Author(s):  
Yiu-Hua Cheng ◽  
Yu-Chung Tsao ◽  
I-Shiang Tzeng ◽  
Hai-Hua Chuang ◽  
Wen-Cheng Li ◽  
...  

1991 ◽  
Vol 65 (2) ◽  
pp. 105-114 ◽  
Author(s):  
Paul Deurenberg ◽  
Jan A. Weststrate ◽  
Jaap C. Seidell

In 1229 subjects, 521 males and 708 females, with a wide range in body mass index (BMI; 13.9–40.9 kg/m2), and an age range of 7–83 years, body composition was determined by densitometry and anthropometry. The relationship between densitometrically-determined body fat percentage (BF%) and BMI, taking age and sex (males =1, females = 0) into account, was analysed. For children aged 15 years and younger, the relationship differed from that in adults, due to the height-related increase in BMI in children. In children the BF% could be predicted by the formula BF% = 1.51xBMI–0.70xage–3.6xsex+1.4 (R2 0.38, SE of estimate (see) 4.4% BF%). In adults the prediction formula was: BF% = 1.20xBMI+0.23xage−10.8xsex–5.4 (R2 0.79, see = 4.1% BF%). Internal and external cross-validation of the prediction formulas showed that they gave valid estimates of body fat in males and females at all ages. In obese subjects however, the prediction formulas slightly overestimated the BF%. The prediction error is comparable to the prediction error obtained with other methods of estimating BF%, such as skinfold thickness measurements or bioelectrical impedance.


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