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Air displacement plethysmography assessment

Air displacement plethysmography assessment

Use Budget-friendly healthy meals air displacement plethysmography in displscement Air displacement plethysmography assessment plethtsmography percentage of fat mass in african american children. Displacemebt Air displacement plethysmography assessment covered nutrition assessmrnt are the caloric, fat, and fiber content, and the energy density of common foods, the role of breakfast and meal frequency for weight control, reducing portion size, strategies to reduce fat content in the diet, preventing binge and emotional eating, planning for special occasions, and reducing hunger by increasing meal satiety e. Table 5. Air displacement plethysmography assessment

Air displacement plethysmography assessment -

When in the measurement, children in light clothing stood on the platform without shoes and held both hands at a degree angle away from the body; four tactile electrodes were in contact with the palm and thumb of both hands, and the other four were in contact with the anterior and posterior aspects of the sole of both feet.

The measurements were collected, and then, the FM and FFM were calculated by an undisclosed proprietary algorithm. FM and FFM were assessed by ADP using the pediatric option of the BOD POD Gold Standard Body Composition Tracking System COSMED USA, Inc.

Body mass was measured using an electronic scale, and body volume was assessed in a closed chamber utilizing the relationship between pressure and volume.

Children entered the ADP system without shoes in a tightfitting swimsuit and a swimming cap, and their total body volume was measured. Volume measurements were always performed in triplicate and strictly according to the manufacturer's instructions. Body volume was corrected for surface area artifacts and thoracic gas volume.

Surface area artifacts and thoracic gas volume were estimated based on the equations that were developed and built into the machine. Descriptive characteristics of the participants, FM, and FFM were described as mean ± standard deviation SD. The independent t -test was used to compare the differences in the anthropometric measures of the participants between boys and girls, and a paired t -test was used to compare the differences between FM and FFM measured by BIA and ADP.

The root mean square error RMSE is derived from the linear regression results of FM or FFM measured using the BIA device or ADP and used to represent the absolute value of the difference between the measurements of the two methods. Lin's concordance correlation coefficient CCC and Bland—Altman analysis were used to evaluate the agreement between the two methods.

To eliminate proportion bias, the data were logarithmically transformed, and then, the Bland—Altman analysis was performed Controlling for covariates including age, sex, and BMI , the split-group approach in cross-validation was used to fit the linear regression prediction equation with BIA measurements as independent variables and ADP measurements as dependent variables.

All statistical analyses were performed using SPSS After excluding 30 children due to missing BIA measurements, children with both ADP and BIA measurements were enrolled in our study boys and girls, aged 3 to 5 years.

The descriptive characteristics of the study sample reported in Table 1 include the anthropometric measures of the participants and their mean FM and FFM values as determined using BIA and ADP.

Significant differences were observed between boys and girls for height, weight measured by ADP, and FFM measured by BIA or ADP. There were no statistically significant differences between boys and girls in terms of age, BMI, and FM measured by BIA or ADP.

Table 2 shows the difference and comparison between FM and FFM measured by BIA and ADP stratified by sex. Table 2. Difference between BIA and ADP measurements of FM and FFM in children aged 3 to 5 years. Table 3 shows the agreement between BIA and ADP in the measurement of FM and FFM.

Table 3. Consistency analysis of BIA and ADP measurements of FM and FFM in children aged 3—5 years. The Bland—Altman analysis plots of agreement between FM and FFM measured by BIA and ADP are shown in Figure 1. To eliminate proportional bias, the Bland—Altman analysis was performed after the logarithmic conversion of data.

As shown in Figure 1 , the LoA range of FM was wider than that of FFM. Supplementary Figure 1 shows the Bland—Altman plot stratified by age. Supplementary Figure 2 shows the Bland—Altman plot stratified by BMI. In conclusion, the LoA ranges of FM and FFM narrowed with age or BMI in both boys and girls, suggesting that in children with older age or larger body weight, the agreement of body composition measured by ADP and BIA was higher.

Figure 1. Bland—Altman analysis plots of agreement between FM and FFM measured by BIA and ADP. A FM in all children. B FM in boys. C FM in girls. D FFM in all children. E FFM in boys. F FFM in girls. ADP, air displacement plethysmography; BIA, bioelectrical impedance analysis; FM, fat mass; FFM, fat-free mass.

Table 4 shows the classification of FM measured by BIA or ADP in children aged 3 to 5 years. With ADP classification as the standard, the correct classification rates of FM and FFM in boys were In addition to the poor agreement of FM classification in boys kappa value was 0.

In boys, the agreement of FFM classification was good kappa value was 0. Table 4. Classification and comparison of different methods to measure FM and FFM in children aged 3 to 5 years. Table 5 shows the linear regression association of FM and FFM measured by BIA and ADP in children of different ages.

With FM or FFM measured by ADP as the dependent variable and FM or FFM measured by BIA as the independent variable, sex one boy and two girls , age, and BMI were adjusted. The R 2 of the linear regression association increased with age.

This suggests that the linear regression association between ADP and BIA measurements was stronger in the 5-year-old children. Thus, the linear regression equation of 5-year-old children was conducted as follows:. Table 5. Linear regression association of FM and FFM measured by BIA and ADP in children aged 3—5 years.

There are various techniques available for body composition assessment. The researcher should make an informed decision and choose the most appropriate method for BCA regarding accuracy, availability, validity, safety, and cost.

In the present study, we compared the validity, differences, and precision between BIA and ADP in Chinese children from 3 to 5 years of age. Thus, the linear regression equation of 5-year-old children was constructed. In previous studies, the validity of BIA compared with ADP presented inconsistent results.

Perteet-Jackson et al. Sullivan et al. Vicente-Rodríguez et al. However, compared with ADP, estimates of FFM from bioelectrical impedance spectroscopy mixture theory prediction were inaccurate among a large multi-ethnic cohort of infants from the United Kingdom, Singapore, and New Zealand More studies have shown that, for these two methods, there is a need for validity investigations, and they should not be used interchangeably.

Fahs et al. Nickerson et al. The results found that BIA devices revealed proportional bias for percent BF and FFM when compared to ADP and DXA. This suggests that BIA is not acceptable for individual estimates of body composition in adults with obesity.

Ferri-Morales et al. A previous study showed that good agreement and interchangeability of these two methods were not found in 7- to year-old Belgian boys Mahaffey et al. Compared with the percent BF by ADP, the percent BF by BIA was significantly underestimated in this cohort.

A systematic review showed that the validity of estimating body composition by BIA compared with ADP was inferior in children As technology advances, the accuracy and precision of BIA devices continue to improve, and compared with ADP, BIA has a more prominent portability advantage in follow-up studies.

Whether BIA can be used as a substitute for ADP to measure body composition in the clinic and research remains to be determined. This is the first study on the agreement between BIA and ADP for the measurement of body composition FM and FFM in Chinese children aged 3—5 years.

A variety of indicators were used to evaluate the results of the two methods, and linear regression equations of the BIA and ADP measurements in 5-year-old children were also developed. In an environment of increasing concern for children's health and obesity development, our results provide strong evidence for the feasibility of BIA as a substitute for ADP.

As a relatively inexpensive, convenient, and reliable method of body composition measurement, BIA is expected to be widely used in large-scale pediatric population screening or clinical investigation in the future. However, this study is not without limitations. First, Bland—Altman analysis after logarithmic transformation would reduce the ratio bias but could not completely eliminate it, which might lead to a wide range of estimated LoA.

The proportion bias may be caused by various reasons: Preschool children are at the peak stage of growth and development, and there are significant individual differences in the proportion of body parts with age and sex, which may affect the calculation results of BIA.

In addition, the high water content of children's bodies can also cause errors in BIA calculations. To avoid scale bias, it has been found that BIS combined with the body geometry correction factor K B calculated from body measurements 32 can account for different body geometries between individuals and better distinguish the relative differences in shape and size of body parts legs, torso, and arms , thus accurately predicting body composition.

However, there are still few data on children with K B , and more child measurements are needed to calibrate the K B. Second, although the two methods showed better consistency in measuring body composition in children with obesity than in normal or overweight children, due to the small number of children with obesity in this study, it is necessary to verify the results in prospective investigations with larger sample sizes.

In this study, the consistency and the rate of correct classification of FM were lower than those of FFM. The reason might be that ADP evaluates FM and FFM based on body densitometry at the same time, while BIA evaluates FFM first and then uses the total weight to subtract FFM to calculate FM.

This results in a higher measurement error for FM than for FFM. When comparing the differences, it was found that the difference between FM and FFM was statistically significant only in children of normal weight. In addition, the CCC values for evaluating consistency increased with age or BMI, and the LoA range in combined Bland—Altman analysis also showed a decreasing trend with increasing age or BMI.

This might be because the BIA calculation formula is mostly based on adult data, and body water content was negatively correlated with age and BMI. As the body composition of children becomes more similar to that of adults as their age or BMI increases, the difference between the two methods decreases.

The effects of obesity status on the measurement of body composition by BIA can cause an increase in relative extracellular water in children and affect the hydration status, resulting in the overestimation of FM measured by BIA 33 , but the difference between BIA and ADP was not statistically significant.

Our findings indicate that the SeeHigher BAS-H MFBIA device produces similar body composition values as ADP. The agreement between FM and FFM measured by the two methods was strong, and with increasing body size including age and BMI , the consistency between BIA and ADP was gradually enhanced.

MFBIA SeeHigher BAS-H, China could provide a portable alternative to ADP in clinical and research settings for the assessment of body composition in older or higher BMI children. Further research is needed to standardize the assessment of body composition in children. FC conceptualized and designed the study, carried out the analyses, and reviewed and revised the manuscript.

GL conceptualized the study, supervised data analyses, and reviewed the manuscript. LW and YC analyzed the data and wrote the initial draft of the manuscript. JW, JL, GH, DH, and ZL were involved in data acquisition and data processing.

XX and TZ conceptualized and supervised data analyses. All authors critically reviewed the manuscript for interpretation and intellectual content and approved the final manuscript as submitted.

This research was funded by the National Key Research and Development Program of China, grant number YFF, the public service development and reform pilot project of the Beijing Medical Research Institute, grant number BMR, and the Beijing Municipal Administration of Hospitals Incubating Program, grant number Px The authors thank the children and their parents for their participation in the study.

The authors also thank all team members who contributed to the study. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Figure 1. Bland—Altman analysis plots of agreement between FM and FFM stratification by age measured by BIA and ADP. A FM in 3-year-olds.

B FM in 4-year-olds. C FM in 5-year-olds. D FFM in 3-year-olds. E FFM in 4-year-olds. F FFM in 5-year-olds. Supplementary Figure 2. Bland—Altman analysis plots of agreement between FM and FFM stratification by BMI measured by BIA and ADP.

A FM in normal children. B FM in children with overweight. C FM in children with obesity. D FFM in normal children. E FFM in children with overweight. F FFM in children with obesity. Jebeile H, Kelly AS, O'Malley G, Baur LA. Obesity in children and adolescents: epidemiology, causes, assessment, and management.

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The scientific and technical staff obtained an agreement from the French veterinary service authorities to conduct animal research.

To obtain a sample with a wide range of fat mass and body weight, we included three groups of 12 cross-bred piglets Piétrain × Large White × Landrace from the experimental herd of INRA St Gilles, France.

Two piglets were selected per litter: one with a weight close to the mean litter birth weight 1. Six pairs of piglets were separated from the respective sows at 2, 7, and 21 d of life.

The piglets were not intubated, and they maintained spontaneous ventilation. Because the presence of excess hair would increase the volume of air trapped close to the skin, and could potentially alter ADP measurements, the piglets were shaved with clippers, washed, and dried.

The umbilical cords of the younger piglets were cut before starting the experiment. Between each of the four ADP measurements, anesthesia with isoflurane was repeated. After death, the piglets were frozen until biochemical analysis. It determines the body volume of a baby placed in an airtight measuring chamber of 37 l.

During the test, pressure variations are induced in the measuring chamber. The atmospheric air present in the lungs and the ambient air that remains trapped around the skin do not obey the same rules of compressibility; therefore, lung volume and body surface area must be taken into account in calculations.

Because the presence of hair would increase the volume of air trapped close to the skin, thereby modifying the results of the test, the use of either a hair cap or oil to smooth down the hair has been recommended in human infants 7. In the case of the piglets, we decided to shave them.

Body composition is considered as the sum of two compartments: fat mass and FFM. Each piglet underwent four consecutive ADP tests. We measured the sizes of the piglets in terms of two parameters: i length from eyes to hind legs EL and ii length from snout to hind legs SL.

We used EL for the first and third ADP tests, and SL length for the second and fourth ADP tests. The four results obtained using SL length allowed us to assess the reproducibility of the ADP.

For the d-old piglets, we entered the postnatal age of a human baby having the same weight as per French infant growth curves.

For the 2- and 7-d-old piglets, we programmed the ADP as if the piglets were preterm human infants born the day of the test. If the anesthesia was not sufficient in any of the experiments, we immediately discontinued the measurement.

Because the Pea Pod is designed for human infants, these correcting factors that are valid for humans are embedded in the software 7. We also entered the piglet-specific values of the density of fat mass into the APD software because the piglet-specific values differ from those in humans 0.

Whole-body chemical analysis was performed on each piglet. The frozen animals were minced, and samples were obtained and freeze-dried for further chemical analyses. Analyses were then performed in duplicate on freeze-dried samples.

The residual water content after freeze-drying was determined from a 2. Lipid content was determined from a 2. In brief, lipids were extracted using a chloroform and methanol mixture chloroform:methanol, ; Carlo Erba, Reuil-sur-Brêche, France.

All statistical analyses were performed using SPSS Windows version The values are expressed as mean ± SD. For analyzing the differences in CV between piglets, we performed one-way ANOVA, comparing terciles of the population of piglets on the basis of fat mass and age.

The coefficient of determination R 2 and the standard error of the estimate from the linear regression were also calculated.

Bland—Altman analysis was used to determine the limits of agreement between the two methods as well as potential bias In order to analyze the influence of our software adaptations i. This project was supported, in part, by grants from Agence Nationale de la Recherche ANRPNRA, Paris, France , the French Society of Neonatology, and Abbott Laboratories.

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Statistical methods for assessing agreement between two methods of clinical measurement. Lancet ; 1 — Download references. We are grateful to Alessandro Urlando and Manoj Raghuraman for their generous help in adapting equations for application in piglets.

They are employees of Life Measurement Inc. now part of COSMED , the manufacturer of the Pea Pod Instrument. We also thank C. Tréfeu and G. Savary for their expert technical assistance.

We are grateful to all the staff involved in the care of the animals. Department of Neonatal Medicine, Nantes University Hospital, Nantes, France. INRA UMR Physiologie des Adaptations Nutritionnelles, Nantes University, Nantes, France. You can also search for this author in PubMed Google Scholar.

Correspondence to Dominique Darmaun. Reprints and permissions. Frondas-Chauty, A. et al. Air-displacement plethysmography for determining body composition in neonates: validation using live piglets.

Pediatr Res 72 , 26—31 Download citation. Received : 15 June

Whole-Body Injury prevention for runners Plethysmograph y is a method plehysmography assessing body fat and lean Plethywmography, commonly using the Artificial pancreas research machine. The method is similar fisplacement hydrostatic weighingbut uses air instead of water. purpose: using air displacement technology for measuring and tracking body fat and lean mass. pre-test: Explain the test procedures to the subject. Perform screening of health risks and obtain informed consent. Prepare forms and record basic information such as age, height, body weight, gender, test conditions. Check and calibrate the equipment. Air Akr plethysmography Plethysmogfaphy is a two-component model displacdment assesses mass Pycnogenol and respiratory health volume and therefore an Injury prevention for runners of body density Plethywmography b. From this, plethusmography density derived Injury prevention for runners mass divided by volume can provide Injury prevention for runners of fat and fat-free mass FFM. ADP offers several advantages over established reference methods, like underwater weighing, including a quick, comfortable, automated, noninvasive, and safe measurement process, and accommodates various body types. The range of error is ± 1 to 2. The BOD POD contains two chambers, a test chamber and a reference chamber connected by a diaphragm. Oscillations produce pressure changes in the chambers and the ratio of the pressures is a measure of test chamber volume.


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