Estimate Body Fat Research Articles

Which Body Density Equations Calculate Body Fat Percentage Better in Olympic Wrestlers?—Comparison Study with Air Displacement Plethysmography

Although skinfold-derived equations seem to be practical for field application in estimating body fat percentage (BF%) and minimum body mass in Olympic wrestlers, prediction equations applied first need to be cross-validated in Olympic wrestlers to define the best prediction equation. This study aimed to evaluate the most accurate field method to predict BF% in Olympic wrestlers compared to BF% estimated by air displacement plethysmography (ADP). Sixty-one male (body mass 72.4 ± 13.5 kg; height 170.3 ± 7.0 cm; body mass index (BMI) 24.9 ± 3.5 kg.m−2; BF% 8.5 ± 4.9%) and twenty-five female wrestlers (body mass 60.3 ± 9.9 kg; height 161.3 ± 7.1 cm; BMI 23.1 ± 2.5 kg.m−2; BF% 18.7 ± 4.7%) undertook body composition assessments including ADP and nine-site skinfold measurements. Correlations, bias, limits of agreement, and standardized differences between alterations in BF% measured by ADP and other prediction equations were evaluated to validate measures, and multiple regression analyses to develop an Olympic wrestlers-specific prediction formula. The Stewart and Hannan equation for male wrestlers and the Durnin and Womersley equation for female wrestlers provided the most accurate BF% compared to the measured BF% by ADP, with the lowest bias and presented no significant differences between the measured and predicted BF%. A new prediction equation was developed using only abdominal skinfold and sex as variables, predicting 83.2% of the variance. The findings suggest the use of the new wrestler-specific prediction equation proposed in the study as a valid and accurate alternative to ADP to quantify BF% among Olympic wrestlers.

Relationship between Body Mass Index and Skin Fold Thickness in Young Females

Background: Anthropometric indicators are used to evaluate the prognosis of chronic and acute diseases, and to guide medical intervention. In anthropometry, body mass index (BMI) is widely accepted in determining obesity and skin fold thickness measurements provide good estimates of body fat.
 Aim: To see the relationship between BMI and skin fold thickness in young females.
 Study Design: Observational study
 Methodology: This was a cross-sectional study. Measurements of height, weight, BMI and skin fold thickness were obtained for young adult females. Triceps, suprailiac and abdomen skin fold thickness were measured by using a skin fold calliper. For skin fold thickness, all measurements were taken three times and an average value was recorded.
 Results: Total 251 participants took part in the study. The mean skin fold thickness in the triceps was 22±6.02 mm, abdominal was 24.41±5.84 mm, and suprailiac was 21.23±5.74 mm. There was a significant correlation seen between BMI and triceps SFT (ρ=0.816,P=0.000), BMI and Abdominal SFT (ρ=0.854,P=0.000) and BMI and suprailiac SFT (ρ=0.850,P=0.000). There was a significant association between BMI and physical activities (P=0.000).
 Conclusion: There was a significant correlation seen between BMI and SFT at triceps, abdominal and suprailiac region. There was a significant association between BMI and physical activities.

Evaluating indices of nutritional condition for caribou (Rangifer tarandus): which are the most valuable and why?

Body composition studies are critical for evaluating the accuracy of nutritional condition indices for predicting body components. We evaluated >40 indices of nutritional condition for caribou (Rangifer tarandus (Linnaeus, 1758)) using 29 female caribou captured from three populations in Alaska (USA) that ranged in condition from 2.3% to 11.2% ingesta-free body fat (IFBF) and 6 captive female caribou that ranged in condition from 8.1% to 26.0% IFBF. Estimates of body fat, protein, and gross energy were regressed against each index of nutritional condition. Generally, indices with linear or slightly curvilinear relations to body fat and those based on multiple fat depots were the most accurate in predicting nutritional condition and the most useful over the full range of nutritional condition. A scaledLIVINDEX (a combination of subcutaneous fat thickness and a condition score), CONINDEX (a combination of kidney fat and marrow fat), and a subset of the Kistner score (pericardium and kidneys only) had the strongest relationship with body fat (r2 > 0.86) and were useful over the entire range of nutritional condition. If used properly and with adequate training, indices of nutritional condition can be a critical tool for understanding the severity and seasonality of nutritional limitations in wild caribou populations.

Benchmarking the Discriminatory Performance of Body Mass Index and Body Fat for Obesity Detection in Patients Treated by Intragastric Balloon.

The accuracy of body mass index (BMI) in detecting obesity in patients treated by intragastric balloon (IGB) remains still speculative. We aimed to determine the discriminatory performance of BMI as an estimate of excess body fat (%BF) in an IGB-treated population. Retrospective analysis of prospectively collected data of 476 patients who completed the 6-month IGB treatment period and were subjected to body composition analysis. We evaluated the relationship between BMI and %BF or lean mass and the diagnostic performance of BMI ≥ 30 kg/m2 for adipose tissue detection, stratified by age (< 40 and ≥ 40 years) and gender. Moreover, we identified anthropometric and body composition parameters serving as predictors of obesity according to %BF-based criteria (> 25% in men or > 35% in women). Gender emerged as an effect modifier in the quadratic polynomial relationship between BMI and %BF (R2 = 0.849 for men, R2 = 0.715 for women), while BMI was linearly associated with %BF in both age groups (R2 = 0.435 for men, R2 = 0.474 for women). BMI was strongly correlated with both %BF (r = 0.67) and lean mass (r = - 0.65). The overall area under the ROC curve for BMI ≥ 30 kg/m2 to detect %BF was 0.87 (95%CI 0.85-0.90). A regression model including lean mass, total body water, age, BMI, and female gender explained 0.970 of the variance in %BF. The discriminatory performance of BMI as an estimate of excess body fat is enhanced by the implementation of gender- and age-specific BMI thresholds for defining obesity, in IGB-treated patients.

Estimation of Body Fat Percentage Using Support Vector Machine and Random Forest Methods

Obezite, önemli bir sağlık problemidir ve yaygınlığı giderek artmaktadır. Bu hastalığın, diğer birçok hastalığın tetikleyicisi ve habercisi olduğu bilinmektedir. Obezite hastalığının tedavi sürecinden önce, vücut yağ yüzdesinin doğru bir şekilde tespit edilmesi önemlidir. Yüksek maliyetli yöntemler ile vücut yağ yüzdesi kesin olarak ölçülmektedir. Bu çalışmada, kişilerden alınan antropometrik veri seti ile vücut yağı yüzdesi tespitinin doğru ve maliyetsiz bir şekilde tahmin edilebilmesi için destek vektör regresyonu ile rastgele orman ağaçları regresyon yöntemleri uygulanmıştır. Regresyon yöntemlerinde, model parametre değerleri, veri sayısı, özellik sayısı ve özellik seçimi tahmin başarımında önemlidir. 13 özellikli vücut yağ yüzdesi veri seti baz olarak alınan çalışmada, literatürde sıklıkla kullanılan 25 istatiski yöntem (çarpıklık, merkezi moment, basıklık vb.) ile yeni bir veri seti oluşturulmuş ve elde edilen yeni veri setinin başarım performansının literatürde yer alan diğer çalışmalardan yüksek olduğu görülmüştür. Regresyon parametrelerinin ızgara tarama yöntemleriyle belirlenmesi sayesinde tahmin doğrulukları arttırılmıştır. Ayrıca özellik azaltma yöntemleri ile vücut yağ yüzdesi ile yüksek ilintili özellikler belirlenmiştir. Seçilen özellikler ile gerçekleştirilen regresyon yöntemlerinin tahmin başarı performansının da benzer diğer çalışmalardan yüksek olduğu gözlenmiştir. En iyi ortalama karesel hata değerleri olarak, Rasgele Orman Ağaçları Yöntemi ve istatistiki yöntemle oluşturulan yeni veri seti ile gerçekleştirilen deneyde 2,2519 değeri elde edilirken, Karar Destek Makinaları ve en iyi 6 F-skor değerine sahip özellikler ile yapılan regresyon deneyinde 3,174 değerine ulaşılmıştır.

Effect of Predicted Versus Measured Thoracic Gas Volume on Body Fat Percentage in Young Adults.

The BodPod® (COSMED, Concord, CA) uses predicted (pTGV) or measured thoracic gas volume (mTGV) during estimations of percentage body fat (%BF). In young adults, there is inconsistent evidence on the variation between pTGV and mTGV, and the effect of sex as a potential covariate on this relationship is unknown. This study examined the difference between TGV assessments and its effect on %BF and potential sex differences that may impact this relationship. A retrospective analysis of BodPod® pTGV and mTGV for 95 men and 86 women ages 18-30years was performed. Predicted TGV was lower than mTGV for men (-0.49 ± 0.7L; p < .0001). For men, %BF derived by pTGV was lower than that by mTGV (-1.3 ± 1.8%; p < .0001). For women, no differences were found between pTGV and mTGV (-0.08 ± 0.6L; p > .05) or %BF (-0.03 ± 0.2%; p > .05). The two-predictor model of sex and height was able to account for 57.9% of the variance in mTGV, F(2, 178) = 122.5, p < .0001. Sex corrected for the effect of height was a significant predictor of mTGV (β = 0.483L, p < .0001). There is bias for pTGV to underestimate mTGV in individuals with a large mTGV, which can lead to significant underestimations of %BF in young adults; this was especially evident for men in this study. Sex is an important covariate that should be considered when deciding to use pTGV. The results indicate that TGV should be measured whenever possible for both men and women ages 18-30years.

Pre- and Post-Slaughter Methodologies to Estimate Body Fat Reserves in Lactating Saanen Goats.

Simple SummaryIn this study, we present the results of a trial on which we compared pre- and post-slaughter methodologies to estimate body fat reserves in dairy goats. Our results evidenced that fat thickness measured with ultrasound in the perirenal region was the best pre-slaughter measurement for estimating fat reserves in lactating Saanen goats, whereas empty body weight and hot carcass weight were the best post-slaughter predictors for estimating fat reserves. Body condition score could be a useful tool, but it seems that it needs to be re-evaluated to predict adequately fat depots in lactating Saanen goats.This work aimed to compare pre- and post-slaughter methodologies to estimate body fat reserves in dairy goats. Twenty-six lactating Saanen goats ranging from 43.6 to 69.4 kg of body weight (BW) and from 1.84 to 2.96 of body condition score (BCS; 0–5 range) were used. Fifteen pre-slaughter and four post-slaughter measurement values were used to estimate the weight of fat in the omental (OM), mesenteric (MES), perirenal (PR), organ (ORG), carcass (CARC), and non-carcass components (NC) and total (TOT, calculated as the sum of CARC and NC) depots in goats. The pre-slaughter measurements were withers height; rump height; rump length; pelvis width; chest depth; shoulder width; heart girth; body length; sternum height; BW; BCS assessed in the lumbar (BCSl) and sternal (BCSs) regions; and fat thickness measured by ultrasound in the lumbar (FTUSl), sternal (FTUSs), and perirenal (FTUSpr) regions. The post-slaughter measurements were hot carcass weight (HCW), empty body weight (EBW), and fat thickness measured by digital caliper in the lumbar (FTDCl) and sternal (FTDCs) regions. Linear and multiple regressions were fit to data collected. BW, BCS (from lumbar and sternal regions), all somatic measurements, and fat thickness measured by ultrasound in the lumbar and sternal regions were not adequate to estimate the weight of total fat in lactating Saanen goats (R2 ≤ 0.55). The best pre-slaughter and post-slaughter estimators of OM, MES, PR, ORG, NC, and TOT fat were FTUSpr and EBW, respectively. Among pre- and post-slaughter measurements, BCSl (R2 = 0.63) and HCW (R2 = 0.82) provided the most accurate predictions of CARC fat, respectively. Multiple regression using the pre-slaughter variables FTUSpr, BW, and BCSl yielded estimates of TOT fat with an R2 = 0.92 (RSD = 1.14 kg). On the other hand, TOT fat predicted using the post-slaughter variables HCW and FTDCs had an R2 = 0.83 (RSD = 1.41 kg). These results confirm that fat reserves can be predicted in lactating Saanen goats with high precision using multiple regression equations combining in vivo measurements.

High‐fat diet alters body composition and serum cytokines before the overt onset of obesity

Rising trends in childhood obesity are related, in part, to increased dietary fat intake. Accelerated bone growth, a hallmark of juvenile obesity but one of its most overlooked complications, can lead to irreversible skeletal damage and chronic adult disability. Paradoxically, obese children typically have low to normal levels of the growth promoting hormone insulin-like growth factor-1 (IGF-1), despite their accelerated growth rate. Obesity is characterized by chronic inflammation driven by cytokines such as interleukin-6 (IL-6) which can suppress bone elongation by inhibiting IGF-1. We previously found that a high-fat diet increases bone growth and alters serum cytokines in young male and female mice before significant changes in body mass were evident. Our goal here was to determine whether excess body fat could explain the increase in bone elongation rate when overall body mass remains similar. We tested the hypothesis that mice on a high-fat diet exhibit changes in body composition along with higher levels of inflammatory cytokines before they develop overt obesity. Methods Male 3-week-old C57BL/6 mice (N=6/diet) were put on high-fat (60% kCal fat) or control (10% kCal fat) diet for 2 weeks after weaning. Serum cytokines (Leptin, TNF-α, IGF-1, IL-6, VEGF, IL-1α, IL-1β, MCP-1) were measured by ELISA. Skinfold thickness in the rump region was measured in a separate group of mice using a dial micrometer to estimate body fat. Statistical significance (p<0.05) was determined in SPSS using Mann-Whitney and t-tests. Results After only 2 weeks, tibial elongation rate was over 10% higher in mice on a high-fat diet (t=2.1, p<0.05), but had no difference in body mass (t=0.5, p=0.62). Among the cytokines measured, TNF-α (z=2.9, p<0.01) and IL-6 (z=2.7, p<0.01) were both decreased in the high-fat diet group, while VEGF (z=2.7, p<0.01) was increased. Rump skinfold was nearly 7% greater (t=2.6, p<0.05) in the high-fat diet group, indicating a higher proportion of body fat. Discussion Our results support the hypothesis that a high-fat diet alters body composition and inflammatory cytokines before overt signs of obesity. Unexpectedly, the most robust change was a 1.5-fold decrease in IL-6, rather than the increase that we had predicted. Since elevated levels of IL-6 suppress growth, our findings of decreased IL-6 are consistent with accelerated bone lengthening. IL-6 also has a role in energy metabolism, and so the changes in body composition that we observed (increased subcutaneous fat) might play a role in altering serum cytokines. Significance These results are relevant for understanding the development of obesity-related skeletal complications. Reduced inflammatory cytokines such as IL-6 could serve as a useful biomarker of potential adverse effects, providing the opportunity to implement interventions to mitigate long-term skeletal damage before it might otherwise be recognized.

English

BACKGROUND With the availability of more sophisticated methods, the utility of body mass index (BMI) as a measure of obesity and body fat estimation is being questioned. The current study aims to compare the estimation of body fat derived from BMI to that of directly measured bio impedance analysis (BIA). METHODS This is a cross sectional study conducted among IT employees of Bangalore City. Body fat % estimated from BMI using formula (adult BF = (1.20 x BMI) + (0.23 x age) - (10.8 x sex) - 5.4 where value for male = 1 &amp; female = 0) and bioimpedance analysis (BIA) was compared using Bland-Altman analysis. RESULTS A total of 6901 participants were included and out of 6901 participants, 67.63 % were women. The mean age was 29.6 ± 6.68 years. The mean of the difference in body fat % calculated from BMI and estimated by BIA was 3.29, (- 9.99 to + 16.58 %). The mean difference was 3.43, (- 9.58 to + 16.45 %) and 2.89, (- 10.90 to + 16.68 %) among men and women respectively. CONCLUSIONS The results showed that the body fat estimated by BMI varies significantly from body fat estimated from BIA. KEYWORDS Obesity, Body Mass Index, Body Fat Percentage, Bioelectrical Impedance

Ethnic Specific\xa0body fat percent prediction equation as surrogate marker of obesity in Ethiopian adults

BackgroundApplication of advanced body composition measurement methods is not practical in developing countries context due to cost and unavailability of facilities. This study generated ethnic specific body fat percent prediction equation for Ethiopian adults using appropriate data.MethodsA cross-sectional study was carried ifrom February to April 2015 among 704 randomly selected adult employees of Jimma University. Ethnic specific Ethiopian body fat percent (BF%) prediction equation was developed using a multivariable linear regression model with measured BF% as dependent variable and age, sex, and body mass index as predictor variables. Agreement between fat percent measured using air displacement plethysmography and body fat percent estimated using Caucasian prediction equations was determined using Bland Altman plot.ResultsComparison of ADP measured and predicted BF% showed that Caucasian prediction equation underestimated body fat percent among Ethiopian adults by 6.78% (P < 0.0001). This finding is consistent across all age groups and ethnicities in both sexes. Bland Altman plot did not show agreement between ADP and Caucasian prediction equation (mean difference = 6.7825) and some of the points are outside 95% confidence interval. The caucasian prediction equation significantly underestimates body fat percent in Ethiopian adults, which is consistent across all ethnic groups in the sample. The study developed Ethnic specific BF% prediction equations for Ethiopian adults.ConclusionThe Caucasian prediction equation significantly underestimates body fat percent among Ethiopian adults regardless of ethnicity. Ethiopian ethnic-specific prediction equation can be used as a very simple, cheap, and cost-effective alternative for estimating body fat percent among Ethiopian adults for health care provision in the prevention of obesity and related morbidities and for research purposes.

Evaluation of Body Fat Prediction Equations from a Portable A-Mode Ultrasound Compared to Air Displacement Plethysmography

ABSTRACT BodyMetrix BX2000 A-mode ultrasound software offers nine options for estimating body fat percentage (%BF) from fat thickness measurements. These formulas were evaluated against %BF estimated from air displacement plethysmography in a sample of 42 men. Compared to the Bod Pod, three of the nine equations – Jackson and Pollock 3 site (JP3), Jackson and Pollock 7 site (JP7), and the National Health Center of America (NHCA) 4 site – had acceptable correlation coefficients (> 0.80), nonsignificant (p > .05) mean differences, and insignificant bias. Standard errors of estimate (SEE) ranged from 3.55 to 4.03%BF and total errors (TE) of 3.69 to 4.16%BF for these three formulas. Caution should be used when estimating %BF from the compilation of individual ultrasound sites due to estimation errors > 3.5%BF. The JP3, JP7, and NHCA 4-site formulas in the BX2000 A-mode ultrasound are recommended over other formula options for estimating the %BF of men.

Is body mass index (BMI) or body adiposity index (BAI) a better indicator to estimate body fat and selected cardiometabolic risk factors in adults with intellectual disabilities?

BackgroundThe BMI index cannot always be used in people with intellectual disabilities due to neuromuscular coordination disorders and psychological barriers that may hinder conventional body weight measurement. The study aimed to assess the usefulness of BMI and BAI in estimating obesity and body fat in people with intellectual disabilities.MethodsThe first stage of the research involved 161 people with profound intellectual disabilities. Somatic parameters (BM, BH, WC, HC) were measured and BMI, BAI, WHR were calculated. Fifty seven persons with above-normal BMI and BAI were included in the second stage of the study and biochemical parameters were determined (TC, LDL-cholesterol, HDL-cholesterol, TG, GL).ResultsAccording to both BMI and BAI classifications, most people were overweight or obese. A high correlation of %BF with BMI and BAI indices was observed (r = 0.78). The sensitivity of both indices was 95.65%. In groups with above-normal BMI and BAI, an upward trend was found for mean values of TC, LDL, TG, and GL, with a simultaneous downward trend for HDL. Statistically significant intergroup differences were recorded for TG and GL (p < 0.05) for both indices (BMI and BAI).ConclusionsOur research demonstrated that BAI is complementary to BMI and can be recommended for the estimation of body fat and cardiometabolic risks in people with intellectual disabilities. Due to the ease of measurement, BAI has high utility value.

Discordance between Body-Mass Index and Body Adiposity Index in the Classification of Weight Status of Elderly Patients with Stable Coronary Artery Disease.

Background and Aims: Body-mass index (BMI) is a popular method implemented to define weight status. However, describing obesity by BMI may result in inaccurate assessment of adiposity. The Body Adiposity Index (BAI) is intended to be a directly validated method of estimating body fat percentage. We set out to compare body weight status assessment by BMI and BAI in a cohort of elderly patients with stable coronary artery disease (CAD). Methods: A total of 169 patients with stable CAD were enrolled in an out-patient cardiology clinic. The National Research Council (US) Committee on Diet and Health classification was used for individuals older than 65 years as underweight BMI < 24 kg/m2, normal weight BMI 24–29 kg/m2, overweight BMI 29–35 kg/m2, and obesity BMI > 35 kg/m2. In case of BAI, we used sex- and age-specific classification of weight status. In addition, body fat was estimated by bioelectrical impedance analysis (BImpA). Results: Only 72 out of 169 patients (42.6%) had concordant classification of weight status by both BMI and BAI. The majority of the patients had their weight status either underestimated or overestimated. There were strong positive correlations between BMI and BImpA (FAT%) (R = 0.78 p < 0.001); BAI and BImpA (FAT%) (R = 0.79 p < 0.001); and BMI and BAI (R = 0.67 p < 0.001). BMI tended to overestimate the rate of underweight, normal weight or overweight, meanwhile underestimating the rate of obesity. Third, BMI exhibited an average positive bias of 14.4% compared to the reference method (BImpA), whereas BAI exhibited an average negative bias of −8.3% compared to the reference method (BImpA). Multivariate logistic regression identified independent predictors of discordance in assessing weight status by BMI and BAI: BImpA (FAT%) odds ratio (OR) 1.29, total body water (%) OR 1.61, fat mass index OR 2.62, and Controlling Nutritional Status (CONUT) score OR 1.25. Conclusions: There is substantial rate of misclassification of weight status between BMI and BAI. These findings have significant implications for clinical practice as the boundary between health and disease in malnutrition is crucial to accurately define criteria for intervention. Perhaps BMI cut-offs for classifying weight status in the elderly should be revisited.

Obesity among postmenopausal women: what is the best anthropometric index to assess adiposity and success of weight-loss intervention?

First, to establish the respective ability of body mass index (BMI), waist circumference (WC), and relative fat mass index (RFM), to estimate body fat (BF%) measured by DXA (DXA-BF%) and correctly identify postmenopausal women living with obesity (BF% > 35). Second, to identify the best indicator of successful weight-loss intervention in postmenopausal women living with obesity. A total of 277 women (age: 59.8 ± 5.3 y; BF%: 43.4 ± 5.3) from five weight-loss studies with complete data for anthropometric measurements [BMI = weight/height (kg/m2); WC (cm)] and BF% were pooled together. Statistical performance indicators were determined to assess ability of RFM [64-(20 × height/waist circumference) + (12 × sex)], BMI and WC to estimate BF% before and after weight-loss intervention and to correctly identify postmenopausal women living with obesity. Compared with RFM (r = 0.51; r2 = 0.27; RMSE = 4.4%; Lin's CCC = 0.46) and WC (r = 0.49; r2 = 0.25; RMSE = 4.8%; Lin's CCC = 0.41), BMI (r = 0.73; r2 = 0.52; RMSE = 3.7%; Lin's CCC = 0.71) was the best anthropometric index to estimate DXA-BF% and correctly identify postmenopausal women living with obesity (sensitivity + specificity: BMI = 193; RFM = 152; WC = 158), with lower misclassification error, before weight-loss intervention. After weight-loss, the change in BMI was strongly correlated with change in DXA-BF%, indicating that the BMI is the best indicator of success weight-loss intervention. In the absence of more objective measures of adiposity, BMI is a suitable proxy measure for BF% in postmenopausal women, for whom a lifestyle intervention is relevant. Furthermore, BMI can be used as an indicator to assess success of weight-loss intervention in this subpopulation.

Associations between relative body fat and areal body surface roughness characteristics in 3D photonic body scans\u2014a proof of feasibility

IntroductionA reliable and accurate estimate of the percentage and distribution of adipose tissue in the human body is essential for evaluating the risk of developing chronic and noncommunicable diseases. A precise and differentiated method, which at the same time is fast, noninvasive, and straightforward to perform, would, therefore, be desirable. We sought a new approach to this research area by linking a person’s relative body fat with their body surface’s areal roughness characteristics.Materials and methodsFor this feasibility study, we compared areal surface roughness characteristics, assessed from 3D photonic full-body scans of 76 Swiss young men, and compared the results with body impedance-based estimates of relative body fat. We developed an innovative method for characterizing the areal surface roughness distribution of a person’s entire body, in a similar approach as it is currently used in geoscience or material science applications. We then performed a statistical analysis using different linear and stepwise regression models.ResultsIn a stepwise regression analysis of areal surface roughness frequency tables, a combination of standard deviation, interquartile range, and mode showed the best association with relative body fat (R2 = 0.55, p < 0.0001). The best results were achieved by calculating the arithmetic mean height, capable of explaining up to three-quarters of the variance in relative body fat (R2 = 0.74, p < 0.001).Discussion and conclusionThis study shows that areal surface roughness characteristics assessed from 3D photonic whole-body scans associate well with relative body fat, therefore representing a viable new approach to improve current 3D scanner-based methods for determining body composition and obesity-associated health risks. Further investigations may validate our method with other data or provide a more detailed understanding of the relation between the body’s areal surface characteristics and adipose tissue distribution by including larger and more diverse populations or focusing on particular body segments.

Potential for body mass index as a tool to estimate body fat in young people

ObjectiveWe aim to determine the anthropometric indicator that is most strongly associated with the percentage of body fat and self-regulation of eating behaviour and physical activity among young university students. MethodsA cross-sectional study was conducted on 3869 Mexican university students, of which 53.9% were women. Standard procedures for anthropometry were followed, including weight, height and waist circumference (WC). This data was used for calculating Body Mass Index (BMI), A Body Shape Index (ABSI), waist-to-height ratio (W/Hr), Body Roundness Index (BRI) and Conicity index (CI). The self-regulation of eating habits scale and the self-regulation of physical activity scale were used. Mean with standard deviation, percentages and Pearson correlation coefficient were estimated. ResultsThe group of men shown a higher prevalence of excess weight compared to the women. Inverse correlations between most anthropometric indices and self-regulation of eating behaviour and physical activity were found. However, in all cases the correlations were weak. The percentage of fat had a medium frequency of association with CI and high with BMI, WC, W/Hr and BRI, where the BMI showed the highest correlation coefficient ConclusionsThe BMI shows the highest magnitude of association with percentage of body fat in university students among the indicators analysed. Therefore it is suggested that nurses use BMI to determine obesity because it is easy to calculate.

Associations Between Adiposity and Muscular Strength in Healthy Adults

Excessive adipose tissue has deleterious effects on the physiology of humans, including consequences related to skeletal muscle performance. Since adiposity is assessed using various tools, the relationship between adiposity and muscular strength is not well understood. The purpose of this study was to examine the relationship between adiposity and normalized strength (NS) in healthy United States adults and determine if sex differences existed. One hundred forty (140) healthy males and females aged 18-40 years participated in this study. Data were collected during two sessions separated by at least 48 hours. During session one, measurements performed included skinfold analysis (3-site test; Jackson Pollock nomogram was used to estimate body fat percentage), waist circumference (WC), height, and body mass. During session two, participants performed a one-repetition maximum (1RM) test in the barbell bench press exercise. Muscular strength was normalized by dividing 1RM values by body mass. Linear regression was used to explore the participant’s NS values in relation to their body fat percentage (BF%), body mass index (BMI), WC, and waist-to-height ratio (WHtR) values. Inverse associations were found between BF% and NS in both males (p &lt; 0.0001, parameter estimate: -0.0354) and females (p = 0.0147, parameter estimate: -0.0084); positive associations were found between BMI and NS for both males (p = 0.0031, parameter estimate: 0.0509) and females (p = 0.0098, parameter estimate: 0.0211); an inverse association between WC and NS was exhibited only amongst females (p = 0.0370, parameter estimate: -0.0111), and no significant associations were observed between WHtR and NS for either sex. Higher BF% is associated with lower NS in both sexes, and higher abdominal adiposity is associated with decreased NS only in females. These data highlight the importance of including measures of adiposity and abdominal adiposity in routine clinical practice and in fitness settings.

Muscle Damage and Overreaching During Wildland Firefighter Critical Training.

To document the effects of wildland firefighter (WLFF) critical training (CT) on physiologic markers of muscle damage and acute overreaching. Eighteen male and three female WLFFs were studied during an 11-day CT. Upper-body (US), lower-body (LS) muscle soreness and body weight (BW) were collected daily. Blood was collected on days 1, 4, 8, and 11 to measure creatine kinase (CK), lactate dehydrogenase (LDH), cortisol, and testosterone. Skinfolds were taken on days 1 and 11 to estimate body fat (BF) and lean body weight (LBW). CT resulted in a significant depression in BF and elevation in LBW, while main effects of time were seen in US, LS, CK, LDH, cortisol, and testosterone/cortisol ratio (P < 0.05). These data suggest WLFFs undergo significant physiological stressors resulting in muscle soreness, damage, and possible overreaching during CT.

Comparison of Body Composition Monitor and InBody 720 Bioimpedance Devices for Body Composition Estimation in Hemodialysis Patients and Healthy Controls

Bioelectric impedance devices have become a standard of care not only for peritoneal dialysis but also for hemodialysis patients. We compared the most important body composition variables (extracellular water, intracellular water, total body water and fat mass) measured with the multifrequency bioelectric impedance device InBody 720 (MF-BIA) and bioimpedance spectroscopy body composition monitor Fresenius (BIS BCM) in hemodialysis patients (n = 51, 175.1 + 7.8 cm, 82.2 + 15.2 kg) and healthy controls (n = 51, 175.1 + 7.6 cm, 82.3 + 15.3 kg). The MF-BIA InBody 720 device compared to the BIS BCM device showed significantly larger total body water and intracellular water estimates and significantly smaller extracellular water and body fat estimates in hemodialysis patients (p &lt; 0.001). These differences (p &lt; 0.001) were similar in the cohort of healthy controls; moreover, we observed high correlations in all variables between the hemodialysis patients and the healthy controls (0.80–0.95, p &lt; 0.001). The mean relative differences in the order of 8% were lower for extracellular water and total body fat, but the limits of agreement were still wide enough to be clinically significant. We conclude that the results of the measurements with InBody 720 and BCM Fresenius cannot be used interchangeably. Physicians and nutritionists involved in the care of hemodialysis patients should be aware of this discrepancy between the two devices and should try to use the same device to track the body in their hemodialysis population in a longitudinal direction.

Wrist-wearable bioelectrical impedance analyzer with miniature electrodes for daily obesity management

Bioelectrical impedance analysis (BIA) is used to analyze human body composition by applying a small alternating current through the body and measuring the impedance. The smaller the electrode of a BIA device, the larger the impedance measurement error due to the contact resistance between the electrode and human skin. Therefore, most commercial BIA devices utilize electrodes that are large enough (i.e., 4 × 1400 mm2) to counteract the contact resistance effect. We propose a novel method of compensating for contact resistance by performing 4-point and 2-point measurements alternately such that body impedance can be accurately estimated even with considerably smaller electrodes (outer electrodes: 68 mm2; inner electrodes: 128 mm2). Additionally, we report the use of a wrist-wearable BIA device with single-finger contact measurement and clinical test results from 203 participants at Seoul St. Mary’s Hospital. The correlation coefficient and standard error of estimate of percentage body fat were 0.899 and 3.76%, respectively, in comparison with dual-energy X-ray absorptiometry. This result exceeds the performance level of the commercial upper-body portable body fat analyzer (Omron HBF-306). With a measurement time of 7 s, this sensor technology is expected to provide a new possibility of a wearable bioelectrical impedance analyzer, toward obesity management.