Figure 3: BIA body fat distribution analysis Plot for Comparison of BIA disteibution SFT Techniques for Distrribution of Body Fat Percentage. Standing height was measured with a Holtain stadiometer to the nearest millimeter ± 0. Zhou Y, Chi J, Lv W, Wang Y.

BIA body fat distribution analysis -

The impact of obesity on older adults goes beyond their inability to remain independent but also increases the burden on their families, their care givers, and their communities in general Obesity prevention and management programs at the clinical and public health levels for older Mexican people are required.

Low weight is associated with a precarious socioeconomic status, other factors that may favor low weight are a pro-inflammatory state, depressive symptoms, or cognitive disorders 61 , Unintentional weight loss or low body-mass index may be an indicator of malnutrition in the elderly because it may reflect energy and nutrient deficiencies, which are difficult to detect in the older adults As far as it was possible to investigate, this is the first study that assessed the concordance of BIA InBody using DXA as a reference method in older Mexican women.

Older Mexican women share anthropometric characteristics with women of Latin American and other countries in the world It is important to notice that no significant differences were observed in women taller than cm between BIA and DXA, with the SEE being 2.

BIA is a simple technique and available in many settings; thus, the finding of a satisfactory agreement between BIA and DXA supports the use of these devices in the nutritional assessment of older adults; however, improving its precision is desirable considering the large LoA observed.

Utilizing anthropometric measures in order to obtain body composition is useful when resources are limited and DXA is unavailable. The study included women 60 years old and older, active, and living in the community; therefore, the results may not be extrapolated to populations with severe illness and disabilities or those that are institutionalized.

There are limitations when using BIA; this method is affected by the hydration status and dehydration is difficult to diagnose in older adults. Additionally, DXA was used as the gold standard, yet there may be errors in estimations of body composition using this technique, regarding body thickness and adiposity, and limitations in the assessment of lean and fat tissue overlying bone structures.

Additionally, DXA results may change when using different software or equipment. However, it is frequently used as a reference method in body composition studies and has advantages such as the facts that it is not invasive and that it has good concordance with more advanced techniques in the evaluation of body composition In older women who were cm-tall or taller, BIA estimates were closer to the DXA results, and the concordance was good.

Thus, excluding the women with the lowest height decreased the mean difference between methods. Nevertheless, the concordance of the Wolcott prediction equation was only moderate. MV-A, MI-C, and MZ-Z: conceptualization and formal analysis. MV-A, IR-C, and MI-C: data curation, supervision, and investigation.

MV-A and MI-C: funding acquisition. MV-A, IR-C, AC-S, JF-F, and MI-C: methodology. MV-A, IA-C, IR-C, LM-G, and MI-C: resources.

MZ-Z and MI-C: formal analysis. MV-A, MZ-Z, IA-C, IR-C, LM-G, AC-S, JF-F, RG-J, and MI-C: writing—original draft and review and editing.

All authors contributed to the article and approved the submitted version. 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.

World Health Organization. Decade of healthy ageing. World Heal Organ. Google Scholar. GBD Compare. Wang H, Naghavi M, Allen C, Barber RM, Carter A, Casey DC, et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for causes of death, — a systematic analysis for the Global Burden of Disease Study doi: CrossRef Full Text Google Scholar.

Peralta M, Ramos M, Lipert A, Martins J, Marques A. Prevalence and trends of overweight and obesity in older adults from 10 European countries from to Scand J Public Health. PubMed Abstract CrossRef Full Text Google Scholar.

Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, — a systematic analysis for the Global Burden of Disease Study Wang HHX, Wang JJ, Wong SYS, Wong MCS, Li FJ, Wang PX, et al. Epidemiology of multimorbidity in China and implications for the healthcare system: cross-sectional survey among , community household residents in southern China.

BMC Med. Hu F, Xu L, Zhou J, Zhang J, Gao Z, Hong Z. Association between overweight, obesity and the prevalence of multimorbidity among the elderly: evidence from a cross-sectional analysis in Shandong, China.

Int J Environ Res Public Health. Hopstock LA, Deraas TS, Henriksen A, Martiny-Huenger T, Grimsgaard S. Changes in adiposity, physical activity, cardiometabolic risk factors, diet, physical capacity and well-being in inactive women and men aged years with obesity and cardiovascular risk — A 6-month complex lifestyle intervention with 6-month follow-up.

PLoS One. Shamah-Levy T, Campos-Nonato I, Cuevas-Nasu L, Hernández-Barrera L, Morales-Ruán MC, Rivera-Dommarco J, et al. Sobrepeso y obesidad en población mexicana en condición de vulnerabilidad.

Resultados de la Ensanut k. Salud Publica Mex. Obesity: Preventing and Managing the Global Epidemic: Report of a WHO Consultation. Geneva: World Health Organization Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index.

Am J Clin Nutr. Lipschitz DA. Screening for nutritional status in the elderly. Prim Care. Frisancho AR. New standards of weight and body composition by frame size and height for assessment of nutritional status of adults and the elderly.

Fernihough A, McGovern ME. Physical stature decline and the health status of the elderly population in England. Econ Hum Biol. Dey DK, Rothenberg E, Sundh V, Bosaeus I, Steen B.

Height and body weight in the elderly. A year longitudinal study of a population aged 70 to 95 years. Eur J Clin Nutr. Jain U, Ma M. Height shrinkage, health and mortality among older adults: evidence from Indonesia.

Kim YH, Ahn KS, Cho KH, Kang CH, Cho SB, Han K, et al. Gender differences in the relationship between socioeconomic status and height loss among the elderly in South Korea. Ramos-Jiménez A, Hernández-Torres RP, Chávez-Guevara IA, Alvarez-Sanchez JA, García-Villalvazo MA, Murguía-Romero M.

Barquera S, Hernández-Barrera L, Trejo-Valdivia B, Shamah T, Campos-Nonato I, Rivera-Dommarco J. Obesidad en México, prevalencia y tendencias en adultos. Ensanut [Obesity in Mexico, prevalence andtrends in adults. Ensanut ]. Guigoz Y, Vellas B, Garry PJ. Assesing the nutritional status of the elderly: the mini nutritional assessment as part of the geriatric evaluation.

Nutr Surv Elder. Vellas B, Villars H, Abellan G, Soto ME, Rolland Y, Guigoz Y, et al. Overview of the MNA ® - Its history and challenges. J Nutr Heal Aging. Ofenheimer A, Breyer-Kohansal R, Hartl S, Burghuber OC, Krach F, Schrott A, et al.

Reference values of body composition parameters and visceral adipose tissue VAT by DXA in adults aged 18—81 years—results from the LEAD cohort. Bauer JM, Morley JE.

Editorial: body composition measurements in older adults. Curr Opin Clin Nutr Metab Care. Solimeo SL, Nguyen VTT, Edmonds SW, Lou Y, Roblin DW, Saag KG, et al. Sex differences in osteoporosis self-efficacy among community-residing older adults presenting for DXA.

Osteoporos Int. Ponti F, Santoro A, Mercatelli D, Gasperini C, Conte M, Martucci M, et al. Aging and imaging assessment of body composition: from fat to facts.

Front Endocrinol. Borga M, West J, Bell JD, Harvey NC, Romu T, Heymsfield SB, et al. Advanced body composition assessment: from body mass index to body composition profiling.

J Investig Med. Siri WE, Lukaski HC. Body composition from fluid spaces and density: analysis of methods. Brožek J, Grande F, Anderson JT, Keys A.

Densitometric analysis of body composition: revision of some quantitative assumptions. Ann N Y Acad Sci. Khalil SF, Mohktar MS, Ibrahim F.

The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases. Meier NF, Bai Y, Wang C, Lee DC. Validation of a multielectrode bioelectrical impedance analyzer with a dual-energy x-ray absorptiometer for the assessment of body composition in older adults.

J Aging Phys Act. Puri T, Blake GM. Comparison of ten predictive equations for estimating lean body mass with dual-energy X-ray absorptiometry in older patients. Br J Radiol. Achamrah N, Colange G, Delay J, Rimbert A, Folope V, Petit A, et al.

Comparison of body composition assessment by DXA and BIA according to the body mass index: a retrospective study on measures. Pineau JC, Ramirez Rozzi FV. Measuring body fat—How accurate is the extrapolation of predictive models in epidemiology?

Eveleth PB, Andres R, Chumlea WC, Eiben O, Ge K, Harris T, et al. Uses and interpretation of anthropometry in the elderly for the assessment of physical status.

Report to the nutrition unit of the World Health Organization: the expert subcommittee on the use and interpretation of anthropometry in the elderly. Lohman TG, Roche AF, Martorell R.

Anthropometric Standardization Refence Manual. Illinois: Human Kinetics Publishers Physical Status? Silveira EA, Barbosa LS, Noll M, Pinheiro HA, de Oliveira C. Body fat percentage prediction in older adults: agreement between anthropometric equations and DXA. Clin Nutr.

Harper V. Reduced Major Axis Regression. stat accessed November 15, Lin L. A note on the concordance correlation coefficient. Descriptive statistics was used to represent the data. The bias and limits of agreement mean difference ±3SD were calculated by using mean and standard deviations of the difference between body fat percentages obtained from SFT and BIA Linear regression was used to study the association between body fat percentages obtained and its contributing factor anthropometric measurements and impedance values.

In total female students participated in the study. Mean height and weight of the participants was Their mean BMI was Table 1 describes details of all parameters measured for assessment of body composition. Mean BMI Visceral fat percentage was found to be within the healthy range. Figure 1: Distribution of Participants Across BMI Categories.

Click here to view Figure. It is illustrated in the figure 1. However, the body fat percentage obtained by both these methods differ. Figure 2: Correlation of BIA and SFT Techniques for Assessment of Body Fat Percentage. BIA overestimates the body fat percentage with limits of agreement Figure 3: Bland-Altman Plot for Comparison of BIA and SFT Techniques for Assessment of Body Fat Percentage.

Currently adiposity is used as a marker to define the obesity rather than relation of body weight to body height which is BMI. Our study has attempted to explore the comparison between two most commonly used methods in clinical practice and research for assessment of adiposity.

In our study, mean BMI was observed to be within the normal category but WHR is at borderline of cut-offs recommended by WHO 6 which describes the trend of central obesity.

Similarly, body fat percentage is also higher than the cut-offs for women Alvero-Cruz et al found that body fat percentage measured by BIA strongly correlated with readings by different anthropometric methods Results from our study also have depicted a linear correlation between both methods studied.

However, it is important to note that the results depend on number and sites of skinfolds as well as variations in the distribution of subcutaneous fat 14, In most settings, SFT, BIA and other 2 compartment models are the only techniques available for body composition measurements.

The Bland-Altman analysis was done to test the proportional bias and limits of agreement. Limits of agreement estimates likely differences between individual results measured by two methods.

Our observations have showed that BIA overestimates the body fat percentage compared with body fat percentage derived by SFT method. Similar findings have been reported by studies done in United States of America, Colombia and Indonesia 16,17, Study done in Indonesian girls have reported that SFT method is one of the practical approaches to assess body composition They further also have discussed that change in body water and electrolyte influences BIA measurements and this may lead to errors in body fay percentage evaluation.

According to study done on Indian population by Bhat et al a commercial BIA machine overestimated body fat percentage compared with multiple skinfolds and Durnin-Wormesley equation method 5. They have also suggested that SFT measurements by Durnin and Wormesley equation may be more appropriate for Indian population.

Findings from our study have shown contradictory results with the studies done on Indian population by Chahar et al and Devi et al 4, Both researchers have independently suggested that BIA underestimates body fat percentage when compared with SFT method.

Kuriyan R et al have stated that SFT and BIA both underestimate the body fat percentage when compared to the 4-compartment model to validate Both methods cannot be alternative to each other. Each method has its own limitations and applicability, but both are uncomplicated, practical, inexpensive and easy to administer particularly in epidemiological studies.

The paper was presented at 53 rd IDACON — Virtual International Conference of Indian Dietetic Association. HT was supported by a Junior Research Fellowship from the University Grants Commission, Government of India. Web of Science Coverage Emerging Sources Citation Index ESCI Journal Impact Factor: 0.

Scopus Journal Metrics CiteScore 1. This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics COPE. Journal is Indexed in: Cabells Whitelist.

Your Name required. Your Email required. Your Message. Type the above text in box below Case sensitive. Copyright © - This Site - All rights reserved.

Close Current Research in Nutrition and Food Science - An open access, peer reviewed international journal covering all aspects of Nutrition and Food Science Register for an account. Sayyad 3 1 Symbiosis School of Biological Sciences, Symbiosis International Deemed University , Pune, India.

Article Metrics PDF Downloads: Introduction Body composition measurements are quantitative methods of nutritional assessment in humans.

Materials and Methods This report is a part of the PMS study which investigated association of premenstrual syndrome with various lifestyle factors among young women.

Statistical Analysis Statistical analysis was carried out in SPSS software v. Results In total female students participated in the study. Table 1: Anthropometric Measurements and Body Composition Parameters. Measurement Mean SD Range Anthropometric measurements Height cm Click here to view Figure Discussion Currently adiposity is used as a marker to define the obesity rather than relation of body weight to body height which is BMI.

Acknowledgment The paper was presented at 53 rd IDACON — Virtual International Conference of Indian Dietetic Association.

Funding Source HT was supported by a Junior Research Fellowship from the University Grants Commission, Government of India. Conflict of Interest The authors declare no conflict of interest.

References Kuriyan R. Body composition techniques. Indian Journal of Medical Research. CrossRef Thibault R, Genton L, Pichard C. Body composition: why, when and for who?. Even though many factors can affect your reading accuracy, a regular BIA scale can show you changes in your body fat over time.

The actual number may not be perfect, but you can still track changes to your body composition. Because many BIA scales offer several features for a reasonable cost and are a quick and easy way to estimate body fat percent, body fat scales that use bioelectrical impedance analysis are a worthwhile investment for consumers who are curious about their body composition.

Keep in mind that they are not likely to be very accurate but you can use them to track changes over time. Using another method of tracking your body composition can help you get a better picture of your actual measurements.

It's also wise to understand that there is more to health than your body fat percentage or weight, and these scales are only a tool, not a reflection of your general wellness.

Gagnon C, Ménard J, Bourbonnais A, et al. Comparison of Foot-to-Foot and Hand-to-Foot Bioelectrical Impedance Methods in a Population with a Wide Range of Body Mass Indices. Metab Syndr Relat Disord. Demura S, Sato S. Comparisons of accuracy of estimating percent body fat by four bioelectrical impedance devices with different frequency and induction system of electrical current.

J Sports Med Phys Fitness. Bioelectrical impedance analysis BIA : A proposal for standardization of the classical method in adults. Journal of Physics Conference Series. Androutsos O, Gerasimidis K, Karanikolou A, Reilly JJ, Edwards CA. Impact of eating and drinking on body composition measurements by bioelectrical impedance.

J Hum Nutr Diet. Blue MNM, Tinsley GM, Ryan ED, Smith-Ryan AE. Validity of body-composition methods across racial and ethnic populations. Advances in Nutrition. By Malia Frey, M. Use limited data to select advertising. Create profiles for personalised advertising. Use profiles to select personalised advertising.

Create profiles to personalise content. Use profiles to select personalised content. Measure advertising performance. Measure content performance. Understand audiences through statistics or combinations of data from different sources.

Develop and improve services. Use limited data to select content. List of Partners vendors. Weight Management. Malia Frey, M. Learn about our editorial process.

Bioelectrical Aalysis Analysis BIA Performance-enhancing nutrition Body BIA body fat distribution analysis Analyse As a distributino, you know that the Nutrition myths busted Mass Index BMI by itself Electrolytes supplementation no t sufficient to bbody a patient's fatt status and distributoin composition thoroughly. Fat, muscle, water and other important indicators distributio underlying medical conditions are not considered in the BMI. Reason enough for Medeia to develop exactly that - a new device that measures patients' body compositions - the "BCA" Body Composition Analyzer. As a component of the QBioscan, it produces all these measurements and values at medical science highest standard levels. As a result, now a tool exists that, in less than 20 seconds, can determine fat mass, extracellular and intracellular water, and skeletal muscle mass, all fundamental assessment components to aid an accurate patient evaluation. Bioelectrical Impedance Bodh BIA can anapysis body composition e. fat Chronic wound healing and fat-free Nutrition myths busted via a small electrical Nutrition myths busted. Fag Charlie Beestone Last updated: September 25th, 16 min read. Bioelectrical Impedance Analysis BIA is able to make an estimation of body composition e. quantities of fat mass and fat-free mass by running a small electrical current through the body. This is possible simply because different bodily tissues e. muscle, fat, bone, etc.