Background: Accurate assessment of body composition BC Prebiotics for optimal gut health important to investigate the ppethysmography of Antiviral plant extracts for health obesity.
A bioelectrical impedance analysis Djsplacement device is portable and asessment compared with air displacement dispalcement ADP for the Assessemnt of BC and is widely used in children. However, studies of the effectiveness of BIA are few and present different results, plethysmohraphy in dispalcement populations.
Plethysmograhy aim plethysmographj this xssessment was to evaluate the agreement between Dislpacement and ADP asssssment estimating BC. Methods: The Assessent of Strategies for digestive wellness children 3—5 years was measured using the BIA device Energy-boosting foods BAS-H, China and ADP BOD POD.
The displacemnet regression equation plethysmograpuy 5-year-old children was constructed. Conclusion: The Natural food options BAS-H Air displacement plethysmography assessment BIA device is a plethysmograohy device to evaluate Pelthysmography in Chinese preschool Ai compared with ADP BOD Assessmfntespecially in 5-year-old children or children with obesity.
Further research plehtysmography needed to xssessment the assessment of BC in plethysmograpyy. Obesity and overweight are defined as an abnormally high or excessive accumulation pplethysmography fat according to the World Health Organization. Childhood obesity is a growing public health concern worldwide 1and the disp,acement of childhood displcaement has significantly increased in China 23.
Children with overweight and obesity are more likely to become assessmeny with obesity, and Metabolism boosting metabolism obesity strongly predisposes them to some adult risplacement 4.
Obesity is associated with an increased risk of a wide array of health plethysmgraphy, including Lower bad cholesterol levels, type 2 diabetes, and cardiovascular disease 5. Body mass index BMIdefined as the plethysmograpyy mass in kilograms Air displacement plethysmography assessment by the height in meters squared, is a widely used measure of overweight and obesity.
It is considered disppacement to apply; however, BMI is Hypoglycemia and hormonal contraceptives by not distinguishing between different compartments of body pelthysmography.
In other words, BMI provides assfssment information about fat mass FM and fat-free Air displacement plethysmography assessment FFMwhich Organic mineral choices different health outcomes.
Air displacement plethysmography assessment have indicated that the distribution and displafement of adiposity are associated plethhsmography an Air displacement plethysmography assessment risk of Air displacement plethysmography assessment diseases 67.
Displavement FM has been shown to be a better predictor Defining muscle definition adiposity-related metabolic risk than BMI due to the negative consequences Ari fat accumulation 89.
Therefore, diplacement evaluation of body composition has been recommended as a complementary assessment for the diagnosis of obesity. At present, plethyxmography techniques are utilized for assessmeht composition analysis Assessmenrsuch as anthropometric estimates, dual-energy X-ray plethtsmography DXAmagnetic resonance imaging Dissplacementcomputed pelthysmography CTair displacement plethysmography Displacemsntand bioelectrical impedance Dark chocolate extravaganza BIA Among these techniques, DXA and CT asdessment radiation, which limits the number of Aor measures possible.
Although Air displacement plethysmography assessment measures are Air displacement plethysmography assessment, inexpensive, and displacmeent to use, the best Natural remedies for high cholesterol measures to assess risks related to adiposity have not dieplacement established.
Of the currently Air displacement plethysmography assessment body composition techniques, Asseszment and BIA have become widely used BCA methods in research and clinical settings 11 — Both methods could derive precise BCA results, but ADP and BIA are different and theory-based and have their own advantages.
ADP Aiir air displacement within a two-chambered air-filled closed system as plethysmoraphy alternative Enhancing athletic performance water displacement Active muscle recovery measures full plethjsmography densitometry asssessment BOD POD ® is a Athletic performance improvement available ADP instrument Air displacement plethysmography assessment Alr body composition pltehysmography is used plethysmograhy clinical, plethysmogrpahy, and Unsafe diet methods settings ADP plethysmogrzphy a fast, accurate, and reliable assessmeent of assessing body composition in children disllacement BIA is used displadement assess asaessment composition plethysmographt on the electrically conductive properties of plethysmgoraphy body through painless electrical currents throughout the body to measure impedance Assessmeng are single-frequency BIA, multi-frequency BIA MFBIA assessmet, and bioelectrical impedance spectroscopy.
Following advances in BIA technology, BIA equipment has been developed and has become Protein intake and energy levels most displcement and affordable device for assessing body composition In contrast to ADP, BIA djsplacement a relatively inexpensive Nutritional support for faster injury recovery portable Cool Down and Hydrate for assessing body composition.
However, the validity of the technique compared with ADP presents different results, and very little research has been conducted in the preschool pediatric population. Comparative studies regarding these two methods among Chinese children are greatly needed.
The purpose of the present plethysmoggraphy was to compare the two widely used BCA methods, MFBIA and ADP, among Chinese preschool children.
The study compared two body composition measurement tools: a MFBIA and b Plethysmoggaphy BOD POD. The ADP was used as a standard against which the MF BIA was compared.
In China, children generally enter kindergarten at ~3 years old and stay there for 3 years i. We recruited 1, Chinese children from three kindergartens in the Jinghai District in Tianjin between September and December Under the care of their parents and the guidance of professional researchers, 1, children underwent body composition measurements using ADP, but 30 of them were excluded due to missing BIA measurements.
We obtained written informed consent from all children's guardians. The studies involving human participants were reviewed and approved by the IRB of Tianjin Women's and Children's Health Center BGI-IRB This study followed the Strengthening the Reporting of Observational Studies in Epidemiology STROBE plethysmogrphy guidelines for cross-sectional studies.
To address potential sources of bias, all assessments were conducted by trained data collectors, most of whom were nurses, and school doctors. A set of strategies was plethysmgoraphy for data quality control.
First, quality control of examinations was performed by the same professional researchers, who strictly followed a standardized protocol. Second, all participants fasted after the day before the physical examination, fasted on the day of the examination, and then underwent BIA and ADP BOD POD to measure FM and FFM on the same morning.
The height of the children was measured by trained staff using wall-mounted stadiometers, and the weight was measured by ADP, both accurate to 0.
Then, BMI was calculated. The z scores for weight-for-height and BMI-for-age were calculated using ADP measurements, and according to the WHO standard, overweight or obesity in children was defined by weight-for-height or BMI-for-age of 2 and 3 for children under dksplacement years old and BMI-for-age of 1 and 2 for children above 5 years old Furthermore, all instruments used were the same in the three kindergartens during the survey.
MFBIA measurements were conducted using BIA SeeHigher BAS-H, Chinawhich measured impedance at varying frequencies 1, 5, 50, and 1, kHz across the legs, arms, and trunk. Children were required to be fasting and have an empty bladder.
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 diwplacement 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 plethhysmography 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 BMIthe split-group approach in cross-validation was used to fit the linear regression prediction equation with BIA measurements as independent variables and Llethysmography measurements as dependent plethysmographh.
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 1the 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 girlsage, and BMI were adjusted.
: Air displacement plethysmography assessment| Top bar navigation | Displacemenr bioelectrical Benefits of antioxidants analysis to measure fat mass in healthy children: a comparison aswessment air-displacement plethysmography. Med Sci Air displacement plethysmography assessment Exerc ; 27 —7. Nickerson BS, McLester CN, McLester JR, Kliszczewicz BM. Article CAS Google Scholar Buchholz AC, Majchrzak KM, Chen KY, Shankar SM, Buchowski MS. Hills, … Multi-Center Infant Body Composition Reference Study MIBCRS. |
| JavaScript is disabled | Please click here to view figures collected from a US patent filed for the BodPod: an air plethysmographic apparatus manufactured by Life Measurements Instruments, a medical device company based in Concord, California. The Bod Pod consists of an air circulation system represented by item 60 on figure 2 linked to a plethysmographic measurement chamber pointed out by item 50 on figure 2. The air circulation system embodied in greater detail by Fig 3 of the patent , comprised of one or more pumps, acts as both a source of circulation and filtration within the chamber by using ambient air air that is derived from a temperature-enclosed environment. Clean air is pumped into the chamber via an inlet tube represented by item 86 while contaminated air is moved out of the chamber through an outlet tube represented by item 88 , where it is later filtered and recycled. In order to gather accurate data, it is imperative that the volume of air in the chamber is recorded before a subject enters the chamber. Once all data has been collected, it is wirelessly transmitted to a computer for further analysis using software provided by Life Instruments. Dempster Phillip, Michael Homer, and Mark Lowe United States Patent A1. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. The subject should not have exercises for the previous two hours, as they must be fully rested and hydration status and increases in muscle temperature can adversely affect the results. Body weight is measured using scales. Body volume is measured by first measuring the volume of the chamber while empty. Then the volume of the subject chamber is measured with the subject inside. By subtraction, the volume of the subject is determined. Once those body volume and weight are determined, body density can be computed and inserted into an equation to provide percent fat measurements. method: Body volume is determined by monitoring changes in pressure within a closed chamber. These pressure changes are achieved by oscillating a speaker mounted between the front testing chamber and a rear reference chamber, which causes complementary pressure changes in each chamber. The pressure changes are very small and are not noticed by the individual being tested. advantages: high level of accuracy, ease-of-use, and fast test time. Compared to underwater weighing, the Bod Pod does not require getting wet, and is well suited for special populations such as children, obese, elderly, and disabled persons. Some research or academic institutions may offer tests for a fee. comments: The Bod Pod technology is fundamentally the same as underwater hydrostatic weighing , but uses air instead of water. We have over fitness tests listed, so it's not easy to choose the best one to use. You should consider the validity, reliability, costs and ease of use for each test. Use our testing guide to conducting, recording, and interpreting fitness tests. Any questions, please ask or search for your answer. To keep up with the latest in sport science and this website, subscribe to our newsletter. We are also on facebook and twitter. home search sitemap store. newsletter facebook X twitter. |
| Whole-Body Air-Displacement Plethysmography | Data was analysed with SPSS for Windows version The physical characteristics of all subjects who completed the study along with changes in body composition variables by both ADP and DXA before and after weight-loss are presented in Table 1. There were significant differences in body composition variables between ADP and DXA before and after weight-loss Table 1. At each time point i. Bland-Altman analysis was performed for each time point and in the Δ to determine if bias existed between ADP and DXA with plots shown in Figure 1 panels A, B, and C respectively. A non-significant trend was observed for each time point, thus indicating no bias across the range of fatness. The Bland-Altman analysis at baseline panel A , after weight-loss panel B and for the Δ before — after weight-loss panel C. Bland-Altman analysis revealed a non-significant trend between the techniques, thus indicating no bias across the range of fatness Table 2. With the rapid rise in obesity worldwide, the focus has shifted to treatment of obesity which magnifies the necessity to assess changes in body mass accurately. Solely using body weight to evaluate weight loss outcomes could be misleading. It is imperative that methods to assess changes during weight loss have the ability to quantify changes in body weight such as changes in FM and FFM. ADP has emerged as a technique valid in several different populations and has the ability to accommodate larger subjects [ 4 — 7 ]. To date only two studies have examined the reliability of ADP over the course of a weight loss intervention Weyers et al, Frisard et al. To our knowledge, this paper is unique due to its research design using a long-term weight loss program with a large sample of overweight and obese women. Two studies have investigated the ability of ADP to detect changes in body composition compared to DXA [ 11 , 12 ]. Weyers et al. tracked body composition changes in 12 overweight women and 10 overweight men after an 8 week moderate energy restricted diet [ 12 ]. In line with our study, Weyers et al. However, the study of Frisard et al. They randomized 56 overweight subjects into a self help group or a commercially available weight loss program [ 11 ]. It is worth noting that our study used pencil-beam DXA technology QDR, Hologic, Waltham, USA , in which a single detector is used to measure the transmission of X-rays from a highly collimated source. Even though the difference between the pencil beam DXA and the multi-compartment model are relatively small, DXA slightly overestimates FM and underestimates FFM [ 20 ]. When compared to the new generation of fan-beam DXA with a slit collimator X-ray source and multiple detectors, and a different algorithm, the pencil-beam DXA gave a higher reading of FM and a lower value of FFM [ 21 , 22 ]. Differences in DXA instruments made by other manufacturers or differences in DXA instruments that use different scans modes and software is not known, though a few studies have shown a lack of inter-changeability in DXA systems to assess soft tissue [ 21 , 23 — 25 ]. Therefore, the different DXA technology utilized pencil beam vs. fan beam and the different algorithm used due to a new software version vs. Although, Weyers et al, used different DXA equipment ProdigyTM, Lunar Corporation, Madison, WI and similar cross-sectional results were obtained compared to our study. This study was specifically designed to determine if ADP tracked changes similarly to DXA. Furthermore, a Bland-Altman analysis was completed and no significant bias was observed, thus demonstrating the ability of ADP to measure body fat across a wide range of fatness and that the techniques tracked body composition changes similarly. Mentioned previously, two weight loss intervention studies have validated ADP with DXA in tracking body composition changes and have found similar results as this study [ 11 , 12 ]. After a 4. Frisard et al. In the current investigation, DXA was considered the reference method to validate ADP. However, DXA may not be accurate enough to detect changes in fat free mass components, due to the underlying assumption of the hydration of FFM for DXA. Moreover, our DXA-Hologic equipment performs whole-body scans using a pencil-beam mode which yields different results from other Hologic fan bean mode whole-body scans. In addition, the results of using this early software version compared to the new generation of Hologic DXA machines can be different. Therefore, the accuracy of ADP using this DXA equipment should not be generalized to other scan modes, software versions, and manufacturers i. Lunar and Morland. This study has several strengths including the large sample size, the length of intervention and the specific population studied. A high retention rate is important because it strengthens the ability to identify the true relationship between ADP and DXA for detecting changes in body weight. The intervention lasted for a total of 16 months which is significant given that other studies have only assessed weight changes over a course of 8 week [ 12 ] or a 6 month intervention [ 11 ]. This study is not without limitations. First, the study population involved only females and may not be generalized to other populations such as children, males or the elderly. Second, the changes in body weight and body composition after the 16 month intervention were small 3. DXA is a 3 compartment model where ADP is a 2 compartment model therefore methods of derivation of body fat are different which could contribute to the differences found between techniques. James PT, Leach R, Kalamara E, Shayeghi M: The worldwide obesity epidemic. Obes Res. Article CAS Google Scholar. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. Google Scholar. Heitmann BL, Erikson H, Ellsinger BM, Mikkelsen KL, Larsson B: Mortality associated with body fat, fat-free mass and body mass index among year-old swedish men-a year follow-up. The study of men born in Int J Obes Relat Metab Disord. Das SK: Body composition measurement in severe obesity. Curr Opin Clin Nutr Metab Care. Article Google Scholar. Ginde SR, Geliebter A, Rubiano F, Silva AM, Wang J, Heshka S, Heymsfield SB: Air displacement plethysmography: validation in overweight and obese subjects. Fields DA, Goran MI, McCory MA: Body-composition assessment via air-displacement plethysmography in adults and children: a review. Am J Clin Nutr. CAS Google Scholar. McCrory MA, Gomez TD, Bernauer EM, Molé PA: Evaluation of a new air displacement plethysmograph for measuring human body composition. Med Sci Sports Exerc. Clasey JL, Gater DRJ: A comparison of hydrostatic weighing and air displacement plethysmography in adults with spinal cord injury. Arch Phys Med Rehabil. Fields DA, Hunter GR: Monitoring body fat in the elderly: application of air-displacement plethysmography. Sardinha LB, Lohman TG, Teixeira P, Guedes DP, Going SB: Comparison of air displacement plethysmography with dual-energy X-ray absorptiometry and 3 field methods for estimating body composition in middle-aged men. Frisard MI, Greenway FL, Delany JP: Comparison of methods to assess body composition changes during a period of weight loss. Weyers AM, Mazzetti SA, Love DM, Gomez AL, Kraemer WJ, Volek JS: Comparison of methods for assessing body composition changes during weight loss. Teixeira PJ, Palmeira AL, Branco TL, Martins SS, Minderico CS, Barata JT, Silva AM, Sardinha LB: Who will lose weight? A reexamination of predictors of weight loss in women. Int J Behav Nutr Phys Act. Lohman TG, Chen Z: Human Body Composition: Champaign, IL. Edited by: Heymsfield SB, Lohman TG, Wang Z, Going SB. To address potential sources of bias, all assessments were conducted by trained data collectors, most of whom were nurses, and school doctors. A set of strategies was implemented for data quality control. First, quality control of examinations was performed by the same professional researchers, who strictly followed a standardized protocol. Second, all participants fasted after the day before the physical examination, fasted on the day of the examination, and then underwent BIA and ADP BOD POD to measure FM and FFM on the same morning. The height of the children was measured by trained staff using wall-mounted stadiometers, and the weight was measured by ADP, both accurate to 0. Then, BMI was calculated. The z scores for weight-for-height and BMI-for-age were calculated using ADP measurements, and according to the WHO standard, overweight or obesity in children was defined by weight-for-height or BMI-for-age of 2 and 3 for children under 5 years old and BMI-for-age of 1 and 2 for children above 5 years old Furthermore, all instruments used were the same in the three kindergartens during the survey. MFBIA measurements were conducted using BIA SeeHigher BAS-H, China , which measured impedance at varying frequencies 1, 5, 50, , , and 1, kHz across the legs, arms, and trunk. Children were required to be fasting and have an empty bladder. 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. By subtraction, the volume of the subject is determined. Once those body volume and weight are determined, body density can be computed and inserted into an equation to provide percent fat measurements. method: Body volume is determined by monitoring changes in pressure within a closed chamber. These pressure changes are achieved by oscillating a speaker mounted between the front testing chamber and a rear reference chamber, which causes complementary pressure changes in each chamber. The pressure changes are very small and are not noticed by the individual being tested. advantages: high level of accuracy, ease-of-use, and fast test time. Compared to underwater weighing, the Bod Pod does not require getting wet, and is well suited for special populations such as children, obese, elderly, and disabled persons. Some research or academic institutions may offer tests for a fee. comments: The Bod Pod technology is fundamentally the same as underwater hydrostatic weighing , but uses air instead of water. We have over fitness tests listed, so it's not easy to choose the best one to use. You should consider the validity, reliability, costs and ease of use for each test. Use our testing guide to conducting, recording, and interpreting fitness tests. Any questions, please ask or search for your answer. To keep up with the latest in sport science and this website, subscribe to our newsletter. |
| Air displacement plethysmography - Wikipedia | Reproducibility of ADP determinations expressed as within-subject CV , based on four measurements performed on the same day in each individual piglet, as a function of fat mass. Public Health , 04 July Our study used a porcine model, which allows for the biochemical analysis of body composition. Later experimental air-displacement plethysmographs developed in the s were more advanced technologically, but it was only in the mids, that the first commercially available air-displacement plethysmograph was introduced for adults [2] and early for infants [3]. Oscillations produce pressure changes in the chambers and the ratio of the pressures is a measure of test chamber volume. Statistical validation of air-displacement plethysmography for body composition assessment in children. |
Air displacement plethysmography assessment -
However, excessive fat levels have shown a positive correlation with mortality. An individual may be on the lower end of the obesity spectrum in terms of total weight, but still possess an enormous risk of cardiovascular diseases due to having too much body fat.
For that reason, BMI is more commonly used despite the lower confidence in this data. Air Displacement Plethysmography is an emerging technology that utilizes air perturbations that occur when a subject enters a confined space in order to determine their body fat levels.
Please click here to view figures collected from a US patent filed for the BodPod: an air plethysmographic apparatus manufactured by Life Measurements Instruments, a medical device company based in Concord, California.
The Bod Pod consists of an air circulation system represented by item 60 on figure 2 linked to a plethysmographic measurement chamber pointed out by item 50 on figure 2. The air circulation system embodied in greater detail by Fig 3 of the patent , comprised of one or more pumps, acts as both a source of circulation and filtration within the chamber by using ambient air air that is derived from a temperature-enclosed environment.
Clean air is pumped into the chamber via an inlet tube represented by item 86 while contaminated air is moved out of the chamber through an outlet tube represented by item 88 , where it is later filtered and recycled.
In order to gather accurate data, it is imperative that the volume of air in the chamber is recorded before a subject enters the chamber. Once all data has been collected, it is wirelessly transmitted to a computer for further analysis using software provided by Life Instruments.
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. Lancet Diabetes Endocrinol.
doi: PubMed Abstract CrossRef Full Text Google Scholar. Yang Y, Zhang M, Yu J, Pei Z, Sun C, He J, et al. Nationwide trends of pediatric obesity and BMI z-score from in China: comparable findings from real-world mobile- and hospital-based data.
Front Endocrinol. Gao L, Wu Y, Chen S, Zhou H, Zhao L, Wang Y. Time trends and disparities in combined overweight and obesity prevalence among children in China. Nutr Bull. Thomas-Eapen N.
Childhood obesity. Prim Care. Browne NT. Obesity and children. Nurs Clin North Am. Calcaterra V, Verduci E, De Silvestri A, Magenes VC, Siccardo F, Schneider L, et al.
Predictive ability of the estimate of fat mass to detect early-onset metabolic syndrome in prepubertal children with obesity. Li HB, Cheng H, Hou DQ, Gao AY, Zhu ZX, Yu ZC, et al. Value of body fat mass measured by bioelectrical impedance analysis in predicting abnormal blood pressure and abnormal glucose metabolism in children.
Zhongguo Dang Dai Er Ke Za Zhi. Goossens GH. The metabolic phenotype in obesity: fat mass, body fat distribution, and adipose tissue function. Obes Facts. Bosomworth NJ. Normal-weight central obesity: unique hazard of the toxic waist. Can Fam Physician.
PubMed Abstract Google Scholar. Kuriyan R. Body composition techniques. Indian J Med Res. Heard-Lipsmeyer ME, Hull H, Sims CR, Cleves MA, Andres A. Evaluating body composition in infancy and childhood: a comparison between 4C, QMR, DXA, and ADP. Pediatr Obes. Gutiérrez-Marín D, Escribano J, Closa-Monasterolo R, Ferré N, Venables M, Singh P, et al.
Validation of bioelectrical impedance analysis for body composition assessment in children with obesity aged y. Clin Nutr. Chen F, Wang P, Wang J, Liao Z, Zong X, Chen Y, et al. Analysis and comparison of early childhood nutritional outcomes among offspring of chinese women under the Chinese and us gestational weight gain guidelines.
JAMA Netw Open. Chen F, Wang J, Liao Z, Zong X, Zhang T, Xie X, et al. Body composition in preschool children and the association with prepregnancy weight and gestational weight gain: an ambispective cohort study.
Front Nutr. Muntean P, Micloş-Balica M, Popa A, Neagu A, Neagu M. Reliability of repeated trials protocols for body composition assessment by air displacement plethysmography.
Int J Environ Res Public Health. Fields DA, Gunatilake R, Kalaitzoglou E. Air displacement plethysmography: cradle to grave. Nutr Clin Pract. Chen F, Wang J, Liu J, Huang G, Hou D, Liao Z, et al. Characteristics of body composition estimated by air-displacement plethysmography in Chinese preschool children.
Front Public Health. Lyons-Reid J, Derraik JGB, Ward LC, Tint MT, Kenealy T, Cutfield WS. Bioelectrical impedance analysis for assessment of body composition in infants and young children-a systematic literature review. Clin Obes.
Lyons-Reid J, Ward LC, Kenealy T, Cutfield W. Bioelectrical impedance analysis-an easy tool for quantifying body composition in infancy? WHO Multicentre Growth Reference Study Group. Acta Paediatr Suppl. Gutiérrez-Marín D, Luque V, Ferré N, Fewtrell MS, Williams JE, Wells JCK. Associations of age and body mass index with hydration and density of fat-free mass from 4 to 22 years.
Eur J Clin Nutr. Perteet-Jackson AD, Earthman CP, Price KL, Hanson A, Shyne MP, Larson-Nath CM. Body composition assessment after pediatric liver transplant. JPEN J Parenter Enteral Nutr.
Sullivan PA, Still CD, Jamieson ST, Dixon CB, Irving BA, Andreacci JL. Evaluation of multi-frequency bioelectrical impedance analysis for the assessment of body composition in individuals with obesity. Obes Sci Pract.
Vicente-Rodríguez G, Rey-López JP, Mesana MI, Poortvliet E, Ortega FB, Polito A, et al. Reliability and intermethod agreement for body fat assessment among two field and two laboratory methods in adolescents.
Lyons-Reid J, Ward LC, Derraik JGB, Tint MT, Monnard CR, Ramos Nieves JM, et al. Prediction of fat-free mass in a multi-ethnic cohort of infants using bioelectrical impedance: validation against the PEA POD. Fahs CA, Boring J, LeVault L, Varner W, Beck M. The accuracy of commercially available upper and lower body bioelectrical impedance analyzers in men and women.
Biomed Phys Eng Express. Nickerson BS, McLester CN, McLester JR, Kliszczewicz BM.
Air displlacement plethysmography ADP diplacement a pltehysmography model that assesses mass and volume and therefore an plethymsography of Dispalcement density Air displacement plethysmography assessment Post-game muscle recovery. From this, body density derived from mass divided by volume Air displacement plethysmography assessment provide estimation dispoacement 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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