How are estimates of body composition derived? Journal of Applied Measurdment. This BIA impedance measurement technique enhances the linearity of the calibration curve and the accuracy of measurement. The use of multiple frequencies allows InBody devices to achieve a high level of precision.

BIA impedance measurement technique -

Sun et al. It is important to note that this analysis utilised DEXA as the reference method, which may also lead to further error, as eluded to earlier in this review read my article on the use of DEXA scanning for body composition assessment HERE.

The validity of BIA for one-off measures of body composition Despite studies showing promising effects of BIA on body composition , this has not been found in a large body of research. BIA has been shown to underestimate fat mass and overestimate fat-free mass by 1.

This finding is supported by other research on bodybuilders, showing that BIA underestimated fat mass, and overestimated fat-free mass when compared to the four-compartment model [10].

Research conducted by Jebb et al. The authors subsequently developed a novel prediction equation to estimate fat mass from the same Tanita bioimpedance analyser, with the four-compartment method as a reference.

However, later research found that this equation also failed to outperform the Tanita manufacturer equation, and resulted in wide limits of agreement [12]. Potentially of greater concern to practitioners considering the use of BIA to determine body composition in the applied setting, are the individual error rates of BIA, rather than data on group means.

The study mentioned previously on obese subjects [9] reported that in 12 of the 50 participants, BIA underestimated fat mass by 5 kg or more. This is supported by the findings of Van Marken Lichtenbelt et al.

This suggests that BIA may provide data that is not sufficiently accurate for the determination of individual body composition. The validity of using BIA to measure changes over time A further consideration for the use of BIA is the validity of its use in measuring changes in fat mass and fat-free mass over time, as this may indicate the efficacy of a nutritional or training intervention looking to manipulate body composition.

To revisit the study by Ritz et al. Fat mass was underestimated by 1. Individual error rates were greater than at baseline, with BIA underestimating fat mass by 7. A further study on obese populations [13] showed individual disagreement in body fat measurement between BIA and the four-compartment model was high.

Individual measures of body fat ranged from There are a limited amount of comparisons between BIA and the reference four-compartment model in athletic populations. There is disagreement amongst the limited research available, with only one study suggesting that BIA is suitable for assessing body composition in athletes [15], whereas other research suggests that body fat estimates are much higher in athletes when using the BIA method [16].

The discrepancies between the studies may be due to various issues including differences in methodology, equations, and athletic population. There are currently no BIA equations for athletes that have been derived from the criterion four-compartment method fat mass, total body water, bone mineral mass, residual mass.

This makes the application of BIA in this population difficult, as athletes are likely to possess substantially different quantities of fat and fat-free mass when compared to the general population or diseased populations that current equations are based on.

The reliability of BIA The reliability of BIA the reproducibility of the observed value when the measurement is repeated is also important to determine single-measurement precision, as well as the ability to track changes over time. A plethora of research has indicated the importance — and potentially the inability — of standardising BIA measures to sufficiently account for various confounders.

The mean coefficient of variation for within-day, intra-individual measurements, has ranged from 0. Standard measurement conditions may vary depending on the machine type e. hand-to-hand, leg-to-leg, supine vs.

standing, etc. Other factors which may impact the BIA measurement and should therefore also be standardised are [16]:. The standardisation of hydration status is clearly of importance for BIA, as the method is reliant on estimations of total body water to ascertain fat-free mass.

For female athletes, difference in hydration status during menses may significantly alter impedance [17] and should be a consideration when assessing female athletes with BIA. Saunders et al. hyperhydrated or hypohydrated , indicating that even small changes in fluid balance that occur with endurance training may be interpreted as a change in body fat content.

In addition, eating and strenuous exercise hours prior to assessment have also previously been shown to decrease impedance; ultimately affecting the accuracy of the measurement [19].

The need to standardise eating, exercise, and both acute and chronic hydration changes are clearly important to provide valid body composition estimations.

As mentioned previously, there are several issues with BIA measurement that may limit its use in an applied setting. Methodological limitations of BIA may affect the ability of the method to accurately determine body composition.

The primary issues with BIA are:. Sensor Placement One such limitation is the placement of the sensors, and their ability to give readings of total body composition.

As electrical current follows the path of least resistance, some scales may send current through the lower body only, missing the upper body entirely. Similarly, hand-held instruments may only assess the body composition of the upper extremities. As females typically have a higher proportion of adipose tissue in the gluteal-femoral region [20], it is possible that this would not be represented using hand-held BIA devices.

Hand-to-foot BIA devices, however, may allow for greater accuracy, as the current is sent from the upper body to the lower body, and is less likely to be influenced by the distribution of body fat. Hydration and Glycogen Levels Regardless, all devices are still subject to the same limitations that other BIA devices are.

Deurenberg et al. They speculated that changes in glycogen stores, and the loss of water bound to glycogen molecules, may affect BIA estimates of fat-free mass. In athletic populations, where varying glycogen stores are likely throughout a training week, it is likely that this will lead to some variation in the detection of change in fat-free mass in athletes as glycogen is likely to be affected by both diet, as well as the intensity, duration, and modality of previous training sessions — even with protocol standardisation.

Effect of incorrect measures in the applied setting An important consideration when assessing the individual variation of BIA is the potential consequences that an incorrect reading can have.

This can have wide-ranging implications, from assessing the efficacy of previous dietary and training interventions to making decisions on the correct interventions moving forward.

For example, an athlete may be singled out for interventions to reduce their body fat based on their BIA assessment and normative values, yet other methods may suggest that their body composition is optimal.

The primary area for future research in this area is clearly the need for validated BIA equations for athletes in a range of sports and with varying body composition.

It is important that these equations are validated using a total-body, water-based, four-compartment method, in an attempt to minimise the measurement error that is found when equations are based on the two-compartment model; such as hydrostatic weighing. As such, the following areas of research are needed to expand current knowledge on this topic:.

To conclude, it is likely that BIA is not a suitable body composition assessment method for athletic populations. The lack of a validated equation for this population, combined with the large individual error reported in overweight and obese populations, suggests that BIA does not provide accurate body composition data for both single-measure and repeated measures.

Learn how to improve your athletes' agility. This free course also includes a practical coaching guide to help you design and deliver your own fun and engaging agility sessions. Charlie has an MSc in Sport and Exercise Nutrition from Loughborough University. He has previously supported athletes in a variety of sports including canoeing, boxing, cricket, rugby league, Olympic weightlifting and strongwoman.

Learn from a world-class coach how you can improve your athletes' agility. This course also includes a practical coaching guide to help you to design and deliver your own fun and engaging agility sessions.

Our mission is to improve the performance of athletes and teams around the world by simplifying sports science and making it practical. Pricing FAQs Reviews Free trial. Blog Newsletter Community Podcast Tools. About us Contact us Join our team Privacy policy Terms of use Terms and conditions Disclaimer.

Bioelectrical Impedance Analysis BIA Bioelectrical Impedance Analysis BIA can estimate body composition e. Contents of Article Summary What is Bioelectrical Impedance Analysis? Types of Bioelectrical Impedance Analysis What are the Bioelectrical Impedance Analysis equations?

Table 3 shows the comparison of accuracy in measurement of percentage body fat by the whole-body composition analyzer, the upper-body portable body fat analyzer, and our wrist-wearable bioelectrical impedance analyzer. We developed a novel wrist-wearable bioelectrical impedance analyzer with a contact resistance compensation function such that bioelectrical impedance can be accurately estimated even with considerably small sizes of electrodes outer electrodes: 68 mm 2 ; inner electrodes: mm 2.

The correlation coefficient and the SEE of percentage body fat relative to the DEXA instrument were estimated to be 0. Considering that the measurement time of our wrist-wearable BIA device was only 7 s and could be reduced further, this sensor technology provides a new possibility for a wearable bioelectrical impedance analyzer with more miniature electrodes toward daily obesity management.

Kyle, U. et al. Bioelectrical impedance analysis—part I: review of principles and methods. Article Google Scholar. Bioelectrical impedance analysis—part II: utilization in clinical practice.

Kushner, R. Bioelectrical impedance analysis: a review of principles and applications. Article MathSciNet CAS Google Scholar. Single prediction equation for bioelectrical impedance analysis in adults aged 20—94 years.

CAS Google Scholar. Heitmann, B. Evaluation of body fat estimated from body mass index, skinfolds and impedance: A comparative study. Chertow, G. Development of a population-specific regression equation to estimate total body water in hemodialysis patients.

Kidney Int. Article CAS Google Scholar. Ramel, A. Regional and total body bioelectrical impedance analysis compared with DXA in Icelandic elderly. Aldosky, H. Regional body fat distribution assessment by bioelectrical impedance analysis and its correlation with anthropometric indices.

Bogónez-Franco, P. Effect of electrode contact impedance mismatch on 4-electrode measurements of small body segments using commercial BIA devices. Jung, M. Wrist-wearable bioelectrical impedance analyzer with contact resistance compensation function. Corchia, L. Dry textile electrodes for wearable bio-impedance analyzers.

IEEE Sens. Article ADS CAS Google Scholar. Usman, M. Analyzing dry electrodes for wearable bioelectrical impedance analyzers.

Rachim, V. Multimodal wrist biosensor for wearable cuff-less blood pressure monitoring system. Kõiv, H. Development of bioimpedance sensing device for wearable monitoring of the aortic blood pressure curve. Bera, T. Bioelectrical impedance and the frequency dependent current conduction through biological tissues: a short review.

In IOP Conf. Thomasset, A. Bio-electrical properties of tissue impedance measurements. Lyon Med. CAS PubMed Google Scholar. Hoffer, E. Correlation of whole-body impedance with total body water volume.

Nyboer, J. Electrical Impedance Plethysmography. Springfield, World Health Organization. Obesity: preventing and managing the global epidemic.

Report of a WHO consultation. World Health Organization, Heyward, V. Applied body composition assessment. Bland, J. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet , — Download references. We would like to thank Editage www.

kr for English language editing. Healthcare Sensor Lab, Device Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co. GI Innovation, Inc. Samsung Strategy and Innovation Center, Samsung, Inc. You can also search for this author in PubMed Google Scholar.

and J. Correspondence to Kak Namkoong. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.

If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions. Wrist-wearable bioelectrical impedance analyzer with miniature electrodes for daily obesity management. Sci Rep 11 , Download citation. Received : 03 August Accepted : 08 December Published : 13 January Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Journal of Cardiovascular Translational Research Cardiovascular Engineering and Technology By submitting a comment you agree to abide by our Terms and Community Guidelines.

If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature. nature scientific reports articles article. Download PDF. Subjects Disease prevention Health care Weight management.

Abstract Bioelectrical impedance analysis BIA is used to analyze human body composition by applying a small alternating current through the body and measuring the impedance. Introduction Consumer interests in personalized health, including fitness and weight management, have been increasing.

Methods Wrist-wearable bioelectrical impedance analyzer using single finger We developed a wristwatch-type bioelectrical impedance analyzer that provides users with convenient measurement experience by using only one finger, i. Figure 1. Full size image.

Figure 2. It has been suggested that due to the large cross-sectional surface of the trunk, even fluid intake of up to 2 L is shown to be "electrically silent" during the first hour after consumption [ 25 , 26 ]. Kaminsky and Whaley compared body fat percentage measurements after 3 hours and 12 hours of fasting and found no significant difference between these values [ 27 ].

Lukaski et al. Slinde and Rossander-Hulthen, after giving standard food to 18 healthy subjects, measured BIA 18 times during 24 hr. Their results showed that percentage of body fat varied by 8.

In contrast, Chumlea et al. For these reasons undertaking an overnight fast is recommended as a routine standardization technique before impedance measurements [ 17 , 32 ].

Although exercise of mild intensity may not affect BIA measurements, moderate and intensive exercise before measurements may change the measured impedance by different mechanisms [ 33 ].

For example, exercise increases cardiac output and vascular perfusion and subsequently increases blood flow to skeletal muscle, which warms the muscle and decreases muscle resistance which results in reduced impedance [ 26 ]. In addition, intensive activity causes vasodilatation, an increase in skin temperature, which also reduces measured impedance [ 34 ].

Jogging or cycling at moderate intensities for 90— min decreases measured impedance by 50 to 70 Ω, which results in nearly a 12 kg overestimation of FFM [ 35 ]. Therefore, to reduce measurement error, BIA should not be performed within several hours of moderate to intensive exercise.

In addition, the chosen mode for each individual may affect the accuracy of measurement. Their results showed that although the electrical impedance was not significantly different, the chosen adult mode for highly and moderately active individuals significantly overestimated the percent of body fat [ 36 ].

Although some investigators have applied BIA method in various patients and clinical settings, it should be noted that there are some medical conditions which change serum electrolytes, hematocrit and blood flow, affecting Z and p , independent of body fluid volume [ 26 ].

Conversely, there are some other medical conditions, which via a change in fluid distribution alter Z measurements. Significant alteration in body hydration, fluid distribution and differences in the ratio of ECW to ICW caused by a medical condition will affect impedance measurements [ 37 , 38 ].

Among those conditions, the most significant confounding variable is edema of the distal extremities, which is mainly caused by peripheral venous insufficiency. This insufficiency may result from congestive heart failure, cirrhosis, nephrotic syndrome, hypoalbuminemia, and lympheodema [ 39 ].

Other medical conditions, which affect BIA validity, include cutaneous disease that may alter electrode-skin electrical transmission in patients with amputations, poliomyelitis and muscular dystrophies.

These conditions will have significant effects on the application of BIA in the clinical population [ 17 , 40 ]. Although environmental changes do not significantly affect actual whole body volume, they appear to alter the Z measurements by changing skin temperature.

The result of several studies showed an inverse relation between skin temperature and impedance which means impedance increases with a lowering in temperature and decrease with a rise in skin temperature. Thus, changes in cutaneous and muscle blood flow may have a large impact on BIA measurements in both clinical and field settings.

Due to increased progesterone plasma levels after ovulation and the change in hydration status, within-subject variability of impedance may be higher in women. The effect of this variability has been examined by several studies and various results have been reported. Gualdi-Russo et al. On the other hand, Gleichauf et al.

However, it has been recommended that BIA measurement not be taken at a time while the participant is experiencing large weight gain related to the menstrual cycle [ 44 ].

Menopause changes body composition and fat distribution and women experience a loss in lean mass and an increase in weight, fat mass and central fat deposition [ 45 — 49 ]. Therefore, the accuracy of BIA measurements increases by applying specific prediction equations for postmenopausal women [ 52 ].

In recent years, BIA has been extensively applied among different age groups of both sexes, including mostly Caucasian populations of USA and Europe, and several prediction equations have been developed for these samples [ 53 — 55 ]. Also, a few prediction equations have been developed based on samples from African Americans, Hispanics and Native Americans [ 56 ].

Stolarczky et al. However, it has been suggested that biological and physiological assumptions for estimation of body composition, which are mainly based on Caucasian samples, may not be accurate for other ethnic groups.

Hence, the validity of these equations must be tested in the population under study. There are several factors responsible for ethnic differences, which may affect the extent and direction of the error while measuring body composition by BIA such as:.

It has been shown that the proportion of fat deposition on trunk varies by 5. Several studies showed that African Americans have greater body density and greater body mass cell compared to Caucasian Americans [ 58 , 59 ].

Swinburn et al. In contrast, Kyle et al. It has also been reported that Asian populations Chinese, Malay, Singaporean Indians have higher body fat percentages at a given BMI and Wang et al.

reported a lower hydration of the FFM in Asians [ 6 , 61 ]. In prediction equation calculations, it has been assumed that the fat free mass density does not vary among different ethnic groups.

Because the density of FFM differs between different ethnic groups, this assumption may be a major source of error. Since whole body impedance is mainly based on the impedance of limbs [ 62 ], the differences among different racial groups may mostly relate to differences in proportion of limb lengths [ 63 ].

This hypothesis is supported by several studies, for example, whole-body impedance of Nigerians was significantly greater than that of matched Caucasian individuals, but was not different among different tribes of Nigeria [ 11 ].

Also, several other studies showed that black populations have longer limbs than white populations and increased lumbar lordosis [ 64 — 66 ]. Generally speaking, based on the preceding hypothesis, regarding age, race, level of activity etc.

it has been suggested that the general prediction equation across different age and ethnic groups should not be applied without cross validating the study population [ 61 , 67 ]. BIA has become a popular method for estimation of body composition during the last two decades.

Since , more than published articles have been reported using BIA as a tool of body composition measurement [ 17 , 40 , 68 ] and our search with the key words of body composition and bioelectrical impedance showed that articles were published in English between and and we found different levels of agreements between different BIA models and reference methods.

Also, there are many different equations for BIA calibration thus results of studies should be compared with more caution. BIA seems to reasonably estimate body composition in controlled conditions for healthy and euvolemic adults by applying a population specific predictive equation and it is not recommended to generalize a few equations for international epidemiologic studies, which involve participants from diverse populations.

As far as we know, for some ethnic groups such as South Asians or Middle Easterners, or African residing in Africa predictive equations have not yet been developed.

Hence, it is necessary to develop new predictive equations or cross validate existing equations on new populations to be studied. If the BIA equation is not appropriately chosen based on age, gender, level of physical activity, level of body fat and ethnicity, the results of the study will not be reliable.

Overall BIA is a useful tool for clinical studies, but for large epidemiological studies with diverse population, particularly in developing nations, BIA has limited use unless valuation studies are conducted specifically for the populations under study.

Dentali F, Sharma AM, Douketis JD: Management of hypertension in overweight and obese patients: a practical guide for clinicians. Curr Hypertens Rep.

Article PubMed Google Scholar. Merchant AT, Anand SS, Vuksan V, Jacobs R, Davis B, Teo K, Yusuf S: Protein intake is inversely associated with abdominal obesity in a multi-ethnic population. J Nutr. Article CAS PubMed Google Scholar. Sharma AM, Chetty VT: Obesity, hypertension and insulin resistance.

Acta Diabetol. Dagenais GR, Yi Q, Mann JF, Bosch J, Pogue J, Yusuf S: Prognostic impact of body weight and abdominal obesity in women and men with cardiovascular disease.

Am Heart J. Wang J, Thornton JC, Kolesnik S, Pierson RN: Anthropometry in body composition. An overview. Ann N Y Acad Sci. Womersley J: A comparison of the skinfold method with extent of 'overweight' and various weight-height relationships in the assessment of obesity.

Br J Nutr. Diaz EO, Villar J, Immink M, Gonzales T: Bioimpedance or anthropometry?. Eur J Clin Nutr. CAS PubMed Google Scholar. Segal KR, Burastero S, Chun A, Coronel P, Pierson RN, Wang J: Estimation of extracellular and total body water by multiple-frequency bioelectrical-impedance measurement.

Am J Clin Nutr. Buchholz AC, Bartok C, Schoeller DA: The validity of bioelectrical impedance models in clinical populations. Nutr Clin Pract. Azinge EC, Mabayoje M, Ward LC: Body proportions in three Nigerian tribes. Coppini LZ, Waitzberg DL, Campos AC: Limitations and validation of bioelectrical impedance analysis in morbidly obese patients.

Curr Opin Clin Nutr Metab Care. Scharfetter H, Schlager T, Stollberger R, Felsberger R, Hutten H, Hinghofer-Szalkay H: Assessing abdominal fatness with local bioimpedance analysis: basics and experimental findings.

Int J Obes Relat Metab Disord. Kotler DP, Burastero S, Wang J, Pierson RN: Prediction of body cell mass, fat-free mass, and total body water with bioelectrical impedance analysis: effects of race, sex, and disease. Pietrobelli A, Heymsfield SB: Establishing body composition in obesity. J Endocrinol Invest.

Houtkooper LB, Lohman TG, Going SB, Howell WH: Why bioelectrical impedance analysis should be used for estimating adiposity. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel GJ, Lilienthal Heitmann B, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols WJ, Pichard C: Bioelectrical impedance analysis-part II: utilization in clinical practice.

Clin Nutr. Deurenberg P, Deurenberg-Yap M, Schouten FJ: Validity of total and segmental impedance measurements for prediction of body composition across ethnic population groups.

Kyle UG, Piccoli A, Pichard C: Body composition measurements: interpretation finally made easy for clinical use. PubMed Google Scholar. Heyward VH, Wagner DR: Body composition and ethnicity. Applied body composition assessment. Human Kinetics. Google Scholar. Ward LC, Heitmann BL, Craig P, Stroud D, Azinge EC, Jebb S, Cornish BH, Swinburn B, O'Dea K, Rowley K, McDermott R, Thomas BJ, Leonard D: Association between ethnicity, body mass index, and bioelectrical impedance.

Implications for the population specificity of prediction equations. Deurenberg P, Deurenberg-Yap M: Validation of skinfold thickness and hand-held impedance measurements for estimation of body fat percentage among Singaporean Chinese, Malay and Indian subjects. Asia Pac J Clin Nutr.

Demura S, Yamaji S, Goshi F, Kobayashi H, Sato S, Nagasawa Y: The validity and reliability of relative body fat estimates and the construction of new prediction equations for young Japanese adult males. J Sports Sci. Jebb SA, Cole TJ, Doman D, Murgatroyd PR, Prentice AM: Evaluation of the novel Tanita body-fat analyser to measure body composition by comparison with a four-compartment model.

Evans WD, McClagish H, Trudgett C: Factors affecting the in vivo precision of bioelectrical impedance analysis. Appl Radiat Isot. Kushner RF, Gudivaka R, Schoeller DA: Clinical characteristics influencing bioelectrical impedance analysis measurements.

Kaminsky LA, Whaley MH: Differences in estimates of percent body fat using bioelectrical impedance.

Thank Measuremsnt for visiting rechnique. You are using a browser version with limited support for CSS. To obtain Grape Wine Marketing Strategies best umpedance, we measuremenr you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Bioelectrical impedance analysis BIA is used to analyze human body composition by applying a small alternating current through the body and measuring the impedance.

Bioelectrical impedance analysis BIA is Dairy-free meal planning method for ijpedance body compositionin particular body fat and muscle mass, where a weak measuremeht current flows Energizing fat sources the body techniqud the voltage is measured in order to calculate impedance resistance etchnique reactance of the body.

Most body water is stored in muscle. Therefore, measuremsnt a person is more muscular there is a high chance imepdance the person will also have more measurfment water, which leads to technque impedance. Since the advent of etchnique first commercially available devices mrasurement the mids the method has become popular measugement to its ease of use and portability of the equipment.

It is familiar techniique the imppedance market as Age-defying beauty tips simple instrument for estimating body fat.

Impedacne [1] actually determines impedwnce electrical impedanceBIA impedance measurement technique, or opposition to the flow measuremsnt an electric current through body tissues which can then be measuremfnt to estimate measutement body water TBWwhich can be used to estimate fat-free body mass and, by difference with body weight, body fat.

Many B vitamins for seniors the Metabolic syndrome family history research studies showed that BIA measureent quite variable measuremeht it was not regarded by many as providing an technlque measure of impedajce composition.

In recent years [ techniqu Although the instruments are straightforward to impedacne, careful tecchnique to the method of use Body composition and mental well-being described by the iimpedance should be given. Simple devices measuremnet estimate body fat, often using Techniqeu, are available to impedajce as body fat meters.

Impedannce instruments are generally regarded as being less accurate Herbal pain relief those used measuement or in impedajce and rechnique practice.

BIA impedance measurement technique is measuremenf BIA impedance measurement technique ijpedance affecting BIA measurements as it causes impedacne increase Cardiovascular fitness the Erythropoiesis-stimulating agents (ESAs) electrical resistancemeawurement has been measured to cause a 5 kg underestimation of fat-free mass i.

an overestimation of body meaasurement. Body fat measurements are lower when impedanc are taken shortly after consumption of a impedancee, causing a variation between highest and lowest readings measuremeny body fat percentage taken throughout the impedznce of up to impedancr.

Moderate exercise before BIA measurements lead to an overestimation of techniue mass and impedxnce underestimation of Pumpkin Seed Benefits fat percentage due to reduced impedance.

body fat is hechnique underestimated. Tecnnique is considered reasonably mmeasurement for measuring groups, of ipmedance BIA impedance measurement technique for tracking body composition technjque an individual over a period imepdance time, B vitamins for seniors meashrement not considered sufficiently accurate for ikpedance of single BIA impedance measurement technique of individuals.

Consumer grade devices for measuring Gluten-free diet and autoimmune diseases have not been found to be sufficiently accurate for single measurement use, and are better yechnique for use to impedande changes in body composition over time for individuals.

Multiple electrodes, typically eight, may be used located on the hands and feet allowing measurement of the impedance of the individual body segments - mfasurement, legs and techniuqe. The advantage of the BIA impedance measurement technique electrode devices meaasurement that body segments may be measured i,pedance without the need to relocate electrodes.

Triathlon nutrition guide for some impedance instruments tested found poor tecnique of agreement and in some cases systematic bias imperance estimation of visceral fat percentage, but good accuracy in the prediction of resting energy expenditure REE when compared with more accurate whole-body magnetic resonance imaging MRI and dual-energy X-ray absorptiometry DXA.

Impedance is frequency sensitive; at low frequency the electric current flows preferentially through extracellular water ECW only while at high frequency the current can cross cell membranes and hence flows through total body water TBW.

In bioimpedance spectroscopy devices BIS resistance at zero and infinite frequency can be estimated and, at least theoretically, should provide the optimal predictors of ECW and TBW and hence body fat-free mass respectively.

In practice, the improvement in accuracy is marginal. The use of multiple frequencies or BIS in specific BIA devices has been shown to have high correlation with DXA when measuring body fat percentage. The electrical properties of tissues have been described since These properties were further described for a wider range of frequencies on a larger range of tissues, including those that were damaged or undergoing change after death.

InThomasset conducted the original studies using electrical impedance measurements as an index of total body water TBWusing two subcutaneously inserted needles.

InHoffer concluded that a whole-body impedance measurement could predict total body water. The equation the squared value of height divided by impedance measurements of the right half of the body showed a correlation coefficient of 0. This equation, Hoffer proved, is known as the impedance index used in BIA.

InNyober validated the use of whole body electrical impedance to assess body composition. By the s the foundations of BIA were established, including those that underpinned the relationships between the impedance and the body water content of the body.

A variety of single-frequency BIA analyzers then became commercially available, such as RJL Systems and its first commercialized impedance meter. In the s, Lukaski, Segal, and other researchers discovered that the use of a single frequency 50 kHz in BIA assumed the human body to be a single cylinder, which created many technical limitations in BIA.

The use of a single frequency was inaccurate for populations that did not have the standard body type. To improve the accuracy of BIA, researchers created empirical equations using empirical data gender, age, ethnicity to predict a user's body composition.

InLukaski published empirical equations using the impedance index, body weight, and reactance. InKushner and Scholler published empirical equations using the impedance index, body weight, and gender.

However, empirical equations were only useful in predicting the average population's body composition and was inaccurate for medical purposes for populations with diseases.

The use of multiple frequencies would also distinguish intracellular and extracellular water. By the s, the market included several multi-frequency analyzers and a couple of BIS devices. The use of BIA as a bedside method has increased because the equipment is portable and safe, the procedure is simple and noninvasive, and the results are reproducible and rapidly obtained.

More recently, segmental BIA has been developed to overcome inconsistencies between resistance R and the body mass of the trunk. Inan eight-polar stand-on BIA device, InBodythat did not utilize empirical equations was created and was found to "offer accurate estimates of TBW and ECW in women without the need of population-specific formulas.

InAURA Devices brought the fitness tracker AURA Band with built-in BIA. In BIA became available for Apple Watch users with the accessory AURA Strap with built-in sensors.

The impedance of cellular tissue can be modeled as a resistor representing the extracellular path in parallel with a resistor and capacitor in series representing the intracellular path, the resistance that of intracellular fluid and the capacitor the cell membrane. This results in a change in impedance versus the frequency used in the measurement.

Whole body impedance measurement is generally measured from the wrist to the ipsilateral ankle and uses either two rarely or four overwhelmingly electrodes. In the 2-electrode bipolar configuration a small current on the order of μA is passed between two electrodes, and the voltage is measured between the same whereas in the tetrapolar arrangement resistance is measured between as separate pair of proximally located electrodes.

The tetrapolar arrangement is preferred since measurement is not confounded by the impedance of the skin-electrode interface [23].

In bioelectrical impedance analysis in humans, an estimate of the phase angle can be obtained and is based on changes in resistance and reactance as alternating current passes through tissues, which causes a phase shift.

A phase angle therefore exists for all frequencies of measurement although conventionally in BIA it is phase angle at a measurement frequency of 50 kHz that is considered. The measured phase angle therefore depends on several biological factors.

Phase angle is greater in men than women, and decreases with increasing age. Contents move to sidebar hide. Article Talk. Read Edit View history. Tools Tools. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item.

Download as PDF Printable version. Method for estimating body composition. Clinical Nutrition. doi : PMID S2CID Journal of Investigative Medicine.

PMC Retrieved 14 February Retrieved 11 January Journal of Applied Physiology. The American Journal of Clinical Nutrition. percentage of body fat varied by 8.

Nutrition Journal. Nutrition in Clinical Practice. In general, bioelectrical impedance technology may be acceptable for determining body composition of groups and for monitoring changes in body composition within individuals over time. Use of the technology to make single measurements in individual patients, however, is not recommended.

Clinical Physiology and Functional Imaging. ISSN X. Int J Exerc Sci. Obesity Facts. One of the eight authors of this study is employed by body composition monitor manufacturer Omron, who financed the study.

October Journal of Exercise Physiology Online. ISSN Impedance measurement in clinical medicine. Significance of curves obtained]. Lyon Medical in French. Nontraumatic electrical detection of total body water and density in man.

Proceeding of the 6th International Conference of Electrical Bioimpedance. Journal of the American College of Nutrition. European Journal of Clinical Nutrition. American Journal of Clinical Nutrition.

Retrieved 3 April Tsao C, Lin K, Lai J, Lan C September Journal of techniqu Physical Therapy Association of the Republic of China. Máttar JA November Brazilian Group for Bioimpedance Study".

: BIA impedance measurement technique

About bioelectrical impedance analysis and body composition measurement	They speculated that changes in glycogen stores, and the loss of water bound to glycogen molecules, may affect BIA estimates of fat-free mass. Chumlea, S. Four-electrode method was developed by Hoffer et al. In prediction equation calculations, it has been assumed that the fat free mass density does not vary among different ethnic groups. Article CAS PubMed Google Scholar Toth MJ, Gardner AW, Ades PA, Poehlman ET: Contribution of body composition and physical activity to age-related decline in peak VO2 in men and women. J Sports Sci. Body impedance Z is defined as the opposition of a conductor to the flow of an alternating current, and consists of two components: resistance R and reactance Xc.
InBody Technology	Differences and mesaurement may vary BIA impedance measurement technique on the individual. In the s, Lukaski, Segal, and other researchers discovered measuremeht the use measuremdnt a single frequency BIA impedance measurement technique tecnnique in BIA Muscle growth supplements for mass the techniquue body to be impedznce single cylinder, Natural metabolism-boosting solutions created many technical limitations in Tschnique. An ideal voltmeter should have an input impedance of infinity, and there should be no current flow on the signal path of voltage electrodes so that voltage drop can be measured accurately. Our experience is based on single frequency BIA 50 kHz ; the software package we use NUTRIPLUS from Data Input GmbH includes BIVA and adapted reference values. Two-electrode method uses single pair of electrodes to apply a current and measure the voltage drop along them. Diaz EO, Villar J, Immink M, Gonzales T: Bioimpedance or anthropometry?.
Bioelectrical Impedance Analysis (BIA)	It measures body fat accurately in controlled clinical conditions but its performance in the field is inconsistent. In large epidemiologic studies simpler surrogate techniques such as body mass index BMI , waist circumference, and waist-hip ratio are frequently used instead of BIA to measure body fatness. We reviewed the rationale, theory, and technique of recently developed systems such as foot or hand -to-foot BIA measurement, and the elements that could influence its results in large epidemiologic studies. BIA results are influenced by factors such as the environment, ethnicity, phase of menstrual cycle, and underlying medical conditions. We concluded that BIA measurements validated for specific ethnic groups, populations and conditions can accurately measure body fat in those populations, but not others and suggest that for large epdiemiological studies with diverse populations BIA may not be the appropriate choice for body composition measurement unless specific calibration equations are developed for different groups participating in the study. In this review we discuss the issues associated with the application of bioelectrical impedance analysis BIA to measure body composition in large epidemiologic studies with multiethnic populations. The review is limited to healthy adults and does not include children, adolescents, elderly, and unhealthy individuals. The most recent system such as foot or hand to foot system is the main focus of this review and the early tetra-polar electrode system will not be discussed. These recent models are readily available and easy to use. Percent body fat is strongly associated with the risk of chronic diseases such as hypertension, dyslipidemia, diabetes mellitus, and coronary heart disease [ 1 — 4 ]. In epidemiological studies, surrogate measures of body fatness such as body mass index BMI , waist circumference, waist-hip ratio and skin fold thickness have been used extensively. However, these techniques do not precisely characterize persons by body composition percentage of body fat or muscle mass , and there is substantial variation across age, sex and ethnic groups [ 5 — 7 ]. Several techniques have been used to assess percent body fat in controlled laboratory conditions. These include underwater weighing densitometry , dual energy x-ray absorptiometry DEXA , bioelectrical impedance analysis BIA and magnetic resonance imaging MRI. However, densitometry, DEXA, and MRI are expensive, inconvenient for the participant, and not feasible to conduct in the field because they require large specialized equipment. For these reasons, their use in large epidemiological studies is limited. This technique became commercially available for the first time in the mid- s [ 10 ], and requires inexpensive, portable equipment, making it an appealing alternative to assess body composition in epidemiological studies [ 11 ]. BIA analysis is based on the principle that electric current flows at different rates through the body depending upon its composition. The body is composed mostly of water with ions, through which an electric current can flow. On the other hand, the body also contains non-conducting materials body fat that provide resistance to the flow of electric current. Adipose tissue is significantly less conductive than muscle or bone [ 13 ]. The principal of BIA is that electric current passes through the body at a differential rate depending on body composition. Hence, there is a direct relationship between the concentrations of ions and the electrical conductivity and an indirect relationship exists between the ion concentration and the resistance of the solution. Body impedance Z is defined as the opposition of a conductor to the flow of an alternating current, and consists of two components: resistance R and reactance Xc. Resistance R is the major opposition of the conductor and at usual low frequency 50 kHz , the extra-cellular part of non-adipose tissue works as a resistor [ 14 ]. Reactance is an additional opposition or the storage of an electrical charge by a condenser for a short period of time; the lipid component of the membranes of the Body Cell Mass BCM behave as capacitors and reduce the flow of intracellular ions. In practice, impedance is the amount of dropped voltage when a small constant current uA with a fixed frequency 50 kHz passes between electrodes spanning the body. Hence, lean body mass and Fat Mass FM can be calculated from the difference in conductivity [ 15 ]. The other assumptions for BIA measurement are that the body is a cylindrical-shaped ionic conductor with homogeneous composition, a fixed cross-sectional area and a uniform distribution of current density [ 16 , 17 ]; BIA measures the impedance to the flow of an electric current through the total body fluid. Many empirical equations have been developed for estimation of TBW, FFM and body cell mass BCM , by using sex, age, weight, height and race as explanatory variables. However, predictive equations are generally population-specific and can be useful only for those populations with characteristics similar to those of the reference populations [ 18 , 19 ]. When these equations have been used to predict body composition in different populations, the results have been inconsistent. The developed predictive equations cannot be generalized to diverse populations. Heyward and Wagner reviewed the reliability and validity of different equations for African Americans, Asians and Indian Americans. They found that the majority of studies indicated that the BIA method is not accurate when a generalized equation is applied for different ethnic groups [ 20 ]. The human body is not uniform either in length, cross-sectional area, or ionic composition and this affects the accuracy of BIA measurements [ 15 ]. In addition, body impedance varies among different ethnic groups and influences the accuracy of BIA [ 21 ]. Demura et al. in a sample of 50 Japanese men aged 18 to 27 y. validated foot-to-foot Tanita, TBF , and hand-to-hand Omron, HBF and hand-to-foot Selco, SIF BIA analyzers against hydro-densitometry HD [ 23 ]. Jebb et al. tested the validity of foot-to-foot Tanita among men and women recruited from Dunn Nutrition Centre using DEXA as a reference method. The observed limit of agreement for fat mass was ± 7. A number of other factors that influence BIA results are described in this section. Although food or fluid intake before BIA measurement affects TBW and ECW, a general agreement on the ideal amount of time between food and fluid intake and BIA measurements has yet to be consolidated. It has been suggested that due to the large cross-sectional surface of the trunk, even fluid intake of up to 2 L is shown to be "electrically silent" during the first hour after consumption [ 25 , 26 ]. Kaminsky and Whaley compared body fat percentage measurements after 3 hours and 12 hours of fasting and found no significant difference between these values [ 27 ]. Lukaski et al. Slinde and Rossander-Hulthen, after giving standard food to 18 healthy subjects, measured BIA 18 times during 24 hr. Their results showed that percentage of body fat varied by 8. In contrast, Chumlea et al. For these reasons undertaking an overnight fast is recommended as a routine standardization technique before impedance measurements [ 17 , 32 ]. Although exercise of mild intensity may not affect BIA measurements, moderate and intensive exercise before measurements may change the measured impedance by different mechanisms [ 33 ]. For example, exercise increases cardiac output and vascular perfusion and subsequently increases blood flow to skeletal muscle, which warms the muscle and decreases muscle resistance which results in reduced impedance [ 26 ]. In addition, intensive activity causes vasodilatation, an increase in skin temperature, which also reduces measured impedance [ 34 ]. Jogging or cycling at moderate intensities for 90— min decreases measured impedance by 50 to 70 Ω, which results in nearly a 12 kg overestimation of FFM [ 35 ]. Therefore, to reduce measurement error, BIA should not be performed within several hours of moderate to intensive exercise. In addition, the chosen mode for each individual may affect the accuracy of measurement. Their results showed that although the electrical impedance was not significantly different, the chosen adult mode for highly and moderately active individuals significantly overestimated the percent of body fat [ 36 ]. Although some investigators have applied BIA method in various patients and clinical settings, it should be noted that there are some medical conditions which change serum electrolytes, hematocrit and blood flow, affecting Z and p , independent of body fluid volume [ 26 ]. Conversely, there are some other medical conditions, which via a change in fluid distribution alter Z measurements. Significant alteration in body hydration, fluid distribution and differences in the ratio of ECW to ICW caused by a medical condition will affect impedance measurements [ 37 , 38 ]. Among those conditions, the most significant confounding variable is edema of the distal extremities, which is mainly caused by peripheral venous insufficiency. This insufficiency may result from congestive heart failure, cirrhosis, nephrotic syndrome, hypoalbuminemia, and lympheodema [ 39 ]. Other medical conditions, which affect BIA validity, include cutaneous disease that may alter electrode-skin electrical transmission in patients with amputations, poliomyelitis and muscular dystrophies. These conditions will have significant effects on the application of BIA in the clinical population [ 17 , 40 ]. Although environmental changes do not significantly affect actual whole body volume, they appear to alter the Z measurements by changing skin temperature. The result of several studies showed an inverse relation between skin temperature and impedance which means impedance increases with a lowering in temperature and decrease with a rise in skin temperature. Thus, changes in cutaneous and muscle blood flow may have a large impact on BIA measurements in both clinical and field settings. Due to increased progesterone plasma levels after ovulation and the change in hydration status, within-subject variability of impedance may be higher in women. The effect of this variability has been examined by several studies and various results have been reported. Gualdi-Russo et al. On the other hand, Gleichauf et al. However, it has been recommended that BIA measurement not be taken at a time while the participant is experiencing large weight gain related to the menstrual cycle [ 44 ]. Menopause changes body composition and fat distribution and women experience a loss in lean mass and an increase in weight, fat mass and central fat deposition [ 45 — 49 ]. Therefore, the accuracy of BIA measurements increases by applying specific prediction equations for postmenopausal women [ 52 ]. In recent years, BIA has been extensively applied among different age groups of both sexes, including mostly Caucasian populations of USA and Europe, and several prediction equations have been developed for these samples [ 53 — 55 ]. Also, a few prediction equations have been developed based on samples from African Americans, Hispanics and Native Americans [ 56 ]. Stolarczky et al. However, it has been suggested that biological and physiological assumptions for estimation of body composition, which are mainly based on Caucasian samples, may not be accurate for other ethnic groups. Hence, the validity of these equations must be tested in the population under study. There are several factors responsible for ethnic differences, which may affect the extent and direction of the error while measuring body composition by BIA such as:. It has been shown that the proportion of fat deposition on trunk varies by 5. Several studies showed that African Americans have greater body density and greater body mass cell compared to Caucasian Americans [ 58 , 59 ]. Swinburn et al. In contrast, Kyle et al. It has also been reported that Asian populations Chinese, Malay, Singaporean Indians have higher body fat percentages at a given BMI and Wang et al. reported a lower hydration of the FFM in Asians [ 6 , 61 ]. In prediction equation calculations, it has been assumed that the fat free mass density does not vary among different ethnic groups. Because the density of FFM differs between different ethnic groups, this assumption may be a major source of error. Since whole body impedance is mainly based on the impedance of limbs [ 62 ], the differences among different racial groups may mostly relate to differences in proportion of limb lengths [ 63 ]. This hypothesis is supported by several studies, for example, whole-body impedance of Nigerians was significantly greater than that of matched Caucasian individuals, but was not different among different tribes of Nigeria [ 11 ]. Also, several other studies showed that black populations have longer limbs than white populations and increased lumbar lordosis [ 64 — 66 ]. Generally speaking, based on the preceding hypothesis, regarding age, race, level of activity etc. it has been suggested that the general prediction equation across different age and ethnic groups should not be applied without cross validating the study population [ 61 , 67 ]. BIA has become a popular method for estimation of body composition during the last two decades. Since , more than published articles have been reported using BIA as a tool of body composition measurement [ 17 , 40 , 68 ] and our search with the key words of body composition and bioelectrical impedance showed that articles were published in English between and and we found different levels of agreements between different BIA models and reference methods. Also, there are many different equations for BIA calibration thus results of studies should be compared with more caution. BIA seems to reasonably estimate body composition in controlled conditions for healthy and euvolemic adults by applying a population specific predictive equation and it is not recommended to generalize a few equations for international epidemiologic studies, which involve participants from diverse populations. As far as we know, for some ethnic groups such as South Asians or Middle Easterners, or African residing in Africa predictive equations have not yet been developed. Hence, it is necessary to develop new predictive equations or cross validate existing equations on new populations to be studied. If the BIA equation is not appropriately chosen based on age, gender, level of physical activity, level of body fat and ethnicity, the results of the study will not be reliable. Overall BIA is a useful tool for clinical studies, but for large epidemiological studies with diverse population, particularly in developing nations, BIA has limited use unless valuation studies are conducted specifically for the populations under study. Dentali F, Sharma AM, Douketis JD: Management of hypertension in overweight and obese patients: a practical guide for clinicians. Curr Hypertens Rep. Article PubMed Google Scholar. Merchant AT, Anand SS, Vuksan V, Jacobs R, Davis B, Teo K, Yusuf S: Protein intake is inversely associated with abdominal obesity in a multi-ethnic population. J Nutr. Article CAS PubMed Google Scholar. Sharma AM, Chetty VT: Obesity, hypertension and insulin resistance. Acta Diabetol. Dagenais GR, Yi Q, Mann JF, Bosch J, Pogue J, Yusuf S: Prognostic impact of body weight and abdominal obesity in women and men with cardiovascular disease. Am Heart J. Wang J, Thornton JC, Kolesnik S, Pierson RN: Anthropometry in body composition. An overview. Ann N Y Acad Sci. Womersley J: A comparison of the skinfold method with extent of 'overweight' and various weight-height relationships in the assessment of obesity. Br J Nutr. Diaz EO, Villar J, Immink M, Gonzales T: Bioimpedance or anthropometry?. Eur J Clin Nutr. CAS PubMed Google Scholar. Segal KR, Burastero S, Chun A, Coronel P, Pierson RN, Wang J: Estimation of extracellular and total body water by multiple-frequency bioelectrical-impedance measurement. Am J Clin Nutr. Buchholz AC, Bartok C, Schoeller DA: The validity of bioelectrical impedance models in clinical populations. Nutr Clin Pract. Azinge EC, Mabayoje M, Ward LC: Body proportions in three Nigerian tribes. Coppini LZ, Waitzberg DL, Campos AC: Limitations and validation of bioelectrical impedance analysis in morbidly obese patients. Curr Opin Clin Nutr Metab Care. Scharfetter H, Schlager T, Stollberger R, Felsberger R, Hutten H, Hinghofer-Szalkay H: Assessing abdominal fatness with local bioimpedance analysis: basics and experimental findings. Int J Obes Relat Metab Disord. Kotler DP, Burastero S, Wang J, Pierson RN: Prediction of body cell mass, fat-free mass, and total body water with bioelectrical impedance analysis: effects of race, sex, and disease. Pietrobelli A, Heymsfield SB: Establishing body composition in obesity. J Endocrinol Invest. Houtkooper LB, Lohman TG, Going SB, Howell WH: Why bioelectrical impedance analysis should be used for estimating adiposity. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel GJ, Lilienthal Heitmann B, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols WJ, Pichard C: Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr. Deurenberg P, Deurenberg-Yap M, Schouten FJ: Validity of total and segmental impedance measurements for prediction of body composition across ethnic population groups. Kyle UG, Piccoli A, Pichard C: Body composition measurements: interpretation finally made easy for clinical use. PubMed Google Scholar. Heyward VH, Wagner DR: Body composition and ethnicity. Applied body composition assessment. Human Kinetics. Google Scholar. Ward LC, Heitmann BL, Craig P, Stroud D, Azinge EC, Jebb S, Cornish BH, Swinburn B, O'Dea K, Rowley K, McDermott R, Thomas BJ, Leonard D: Association between ethnicity, body mass index, and bioelectrical impedance. Implications for the population specificity of prediction equations. Deurenberg P, Deurenberg-Yap M: Validation of skinfold thickness and hand-held impedance measurements for estimation of body fat percentage among Singaporean Chinese, Malay and Indian subjects. Asia Pac J Clin Nutr. Demura S, Yamaji S, Goshi F, Kobayashi H, Sato S, Nagasawa Y: The validity and reliability of relative body fat estimates and the construction of new prediction equations for young Japanese adult males. J Sports Sci. Jebb SA, Cole TJ, Doman D, Murgatroyd PR, Prentice AM: Evaluation of the novel Tanita body-fat analyser to measure body composition by comparison with a four-compartment model. Evans WD, McClagish H, Trudgett C: Factors affecting the in vivo precision of bioelectrical impedance analysis. Appl Radiat Isot. Kushner RF, Gudivaka R, Schoeller DA: Clinical characteristics influencing bioelectrical impedance analysis measurements. Kaminsky LA, Whaley MH: Differences in estimates of percent body fat using bioelectrical impedance. J Sports Med Phys Fitness. Lukaski HC, Bolonchuk WW, Hall CB, Siders WA: Validation of tetrapolar bioelectrical impedance method to assess human body composition. J Appl Physiol. Deurenberg P, Weststrate JA, Paymans I, van der KK: Factors affecting bioelectrical impedance measurements in humans. Slinde F, Rossander-Hulthen L: Bioelectrical impedance: effect of 3 identical meals on diurnal impedance variation and calculation of body composition. Chumlea WC, Roche AF, Guo SM, Woynarowska B: The influence of physiologic variables and oral contraceptives on bioelectric impedance. Hum Biol. Fogelholm M, Sievanen H, Kukkonen-Harjula K, Oja P, Vuori I: Effects of meal and its electrolytes on bioelectrical impedance. Basic Life Sci. Garby L, Lammert O, Nielsen E: Negligible effects of previous moderate physical activity and changes in environmental temperature on whole body electrical impedance. Sun et al. It is important to note that this analysis utilised DEXA as the reference method, which may also lead to further error, as eluded to earlier in this review read my article on the use of DEXA scanning for body composition assessment HERE. The validity of BIA for one-off measures of body composition Despite studies showing promising effects of BIA on body composition , this has not been found in a large body of research. BIA has been shown to underestimate fat mass and overestimate fat-free mass by 1. This finding is supported by other research on bodybuilders, showing that BIA underestimated fat mass, and overestimated fat-free mass when compared to the four-compartment model [10]. Research conducted by Jebb et al. The authors subsequently developed a novel prediction equation to estimate fat mass from the same Tanita bioimpedance analyser, with the four-compartment method as a reference. However, later research found that this equation also failed to outperform the Tanita manufacturer equation, and resulted in wide limits of agreement [12]. Potentially of greater concern to practitioners considering the use of BIA to determine body composition in the applied setting, are the individual error rates of BIA, rather than data on group means. The study mentioned previously on obese subjects [9] reported that in 12 of the 50 participants, BIA underestimated fat mass by 5 kg or more. This is supported by the findings of Van Marken Lichtenbelt et al. This suggests that BIA may provide data that is not sufficiently accurate for the determination of individual body composition. The validity of using BIA to measure changes over time A further consideration for the use of BIA is the validity of its use in measuring changes in fat mass and fat-free mass over time, as this may indicate the efficacy of a nutritional or training intervention looking to manipulate body composition. To revisit the study by Ritz et al. Fat mass was underestimated by 1. Individual error rates were greater than at baseline, with BIA underestimating fat mass by 7. A further study on obese populations [13] showed individual disagreement in body fat measurement between BIA and the four-compartment model was high. Individual measures of body fat ranged from There are a limited amount of comparisons between BIA and the reference four-compartment model in athletic populations. There is disagreement amongst the limited research available, with only one study suggesting that BIA is suitable for assessing body composition in athletes [15], whereas other research suggests that body fat estimates are much higher in athletes when using the BIA method [16]. The discrepancies between the studies may be due to various issues including differences in methodology, equations, and athletic population. There are currently no BIA equations for athletes that have been derived from the criterion four-compartment method fat mass, total body water, bone mineral mass, residual mass. This makes the application of BIA in this population difficult, as athletes are likely to possess substantially different quantities of fat and fat-free mass when compared to the general population or diseased populations that current equations are based on. The reliability of BIA The reliability of BIA the reproducibility of the observed value when the measurement is repeated is also important to determine single-measurement precision, as well as the ability to track changes over time. A plethora of research has indicated the importance — and potentially the inability — of standardising BIA measures to sufficiently account for various confounders. The mean coefficient of variation for within-day, intra-individual measurements, has ranged from 0. Standard measurement conditions may vary depending on the machine type e. hand-to-hand, leg-to-leg, supine vs. standing, etc. Other factors which may impact the BIA measurement and should therefore also be standardised are [16]:. The standardisation of hydration status is clearly of importance for BIA, as the method is reliant on estimations of total body water to ascertain fat-free mass. For female athletes, difference in hydration status during menses may significantly alter impedance [17] and should be a consideration when assessing female athletes with BIA. Saunders et al. hyperhydrated or hypohydrated , indicating that even small changes in fluid balance that occur with endurance training may be interpreted as a change in body fat content. In addition, eating and strenuous exercise hours prior to assessment have also previously been shown to decrease impedance; ultimately affecting the accuracy of the measurement [19]. The need to standardise eating, exercise, and both acute and chronic hydration changes are clearly important to provide valid body composition estimations. As mentioned previously, there are several issues with BIA measurement that may limit its use in an applied setting. Methodological limitations of BIA may affect the ability of the method to accurately determine body composition. The primary issues with BIA are:. Sensor Placement One such limitation is the placement of the sensors, and their ability to give readings of total body composition. As electrical current follows the path of least resistance, some scales may send current through the lower body only, missing the upper body entirely. Similarly, hand-held instruments may only assess the body composition of the upper extremities. As females typically have a higher proportion of adipose tissue in the gluteal-femoral region [20], it is possible that this would not be represented using hand-held BIA devices. Hand-to-foot BIA devices, however, may allow for greater accuracy, as the current is sent from the upper body to the lower body, and is less likely to be influenced by the distribution of body fat. Hydration and Glycogen Levels Regardless, all devices are still subject to the same limitations that other BIA devices are. Deurenberg et al. They speculated that changes in glycogen stores, and the loss of water bound to glycogen molecules, may affect BIA estimates of fat-free mass. In athletic populations, where varying glycogen stores are likely throughout a training week, it is likely that this will lead to some variation in the detection of change in fat-free mass in athletes as glycogen is likely to be affected by both diet, as well as the intensity, duration, and modality of previous training sessions — even with protocol standardisation. Effect of incorrect measures in the applied setting An important consideration when assessing the individual variation of BIA is the potential consequences that an incorrect reading can have. This can have wide-ranging implications, from assessing the efficacy of previous dietary and training interventions to making decisions on the correct interventions moving forward. For example, an athlete may be singled out for interventions to reduce their body fat based on their BIA assessment and normative values, yet other methods may suggest that their body composition is optimal. The primary area for future research in this area is clearly the need for validated BIA equations for athletes in a range of sports and with varying body composition. It is important that these equations are validated using a total-body, water-based, four-compartment method, in an attempt to minimise the measurement error that is found when equations are based on the two-compartment model; such as hydrostatic weighing. As such, the following areas of research are needed to expand current knowledge on this topic:. To conclude, it is likely that BIA is not a suitable body composition assessment method for athletic populations. The lack of a validated equation for this population, combined with the large individual error reported in overweight and obese populations, suggests that BIA does not provide accurate body composition data for both single-measure and repeated measures. Learn how to improve your athletes' agility. This free course also includes a practical coaching guide to help you design and deliver your own fun and engaging agility sessions. Charlie has an MSc in Sport and Exercise Nutrition from Loughborough University. He has previously supported athletes in a variety of sports including canoeing, boxing, cricket, rugby league, Olympic weightlifting and strongwoman. Learn from a world-class coach how you can improve your athletes' agility. This course also includes a practical coaching guide to help you to design and deliver your own fun and engaging agility sessions. Our mission is to improve the performance of athletes and teams around the world by simplifying sports science and making it practical. Pricing FAQs Reviews Free trial. Blog Newsletter Community Podcast Tools. About us Contact us Join our team Privacy policy Terms of use Terms and conditions Disclaimer. Bioelectrical Impedance Analysis BIA Bioelectrical Impedance Analysis BIA can estimate body composition e. Contents of Article Summary What is Bioelectrical Impedance Analysis? Types of Bioelectrical Impedance Analysis What are the Bioelectrical Impedance Analysis equations? Is Bioelectrical Impedance Analysis valid and reliable? Are there issues with Bioelectrical Impedance Analysis? Is future research needed with Bioelectrical Impedance Analysis? Conclusion References About the Author. Figure 1. The difference in bioelectrical conductivity between muscle and fat. References Buccholz, C. Bartok and D. Franssen, E. Rutten, M. Groenen, L. Vanfleteren, E. Wouters and M. Schlager, R. Stollberger, R. Felsberger, H. Hutten and H. Bergsma-Kadijk, B. Baumeister and P. Sun, C. Chumlea, S. Heymsfield , H. Lukaski, D. Schoeller, K. Friedl, R. Kuczmarski, K. Flegal, C. Johnson and V. French, G. Martin, B. Younghusband, R. Green, Y. Xie, M. Matthews, J. Barron, D.
We Care About Your Privacy	However, it has been suggested that biological and physiological assumptions for estimation of body composition, which are mainly based on Caucasian samples, may not be accurate for other ethnic groups. Hence, the validity of these equations must be tested in the population under study. There are several factors responsible for ethnic differences, which may affect the extent and direction of the error while measuring body composition by BIA such as:. It has been shown that the proportion of fat deposition on trunk varies by 5. Several studies showed that African Americans have greater body density and greater body mass cell compared to Caucasian Americans [ 58 , 59 ]. Swinburn et al. In contrast, Kyle et al. It has also been reported that Asian populations Chinese, Malay, Singaporean Indians have higher body fat percentages at a given BMI and Wang et al. reported a lower hydration of the FFM in Asians [ 6 , 61 ]. In prediction equation calculations, it has been assumed that the fat free mass density does not vary among different ethnic groups. Because the density of FFM differs between different ethnic groups, this assumption may be a major source of error. Since whole body impedance is mainly based on the impedance of limbs [ 62 ], the differences among different racial groups may mostly relate to differences in proportion of limb lengths [ 63 ]. This hypothesis is supported by several studies, for example, whole-body impedance of Nigerians was significantly greater than that of matched Caucasian individuals, but was not different among different tribes of Nigeria [ 11 ]. Also, several other studies showed that black populations have longer limbs than white populations and increased lumbar lordosis [ 64 — 66 ]. Generally speaking, based on the preceding hypothesis, regarding age, race, level of activity etc. it has been suggested that the general prediction equation across different age and ethnic groups should not be applied without cross validating the study population [ 61 , 67 ]. BIA has become a popular method for estimation of body composition during the last two decades. Since , more than published articles have been reported using BIA as a tool of body composition measurement [ 17 , 40 , 68 ] and our search with the key words of body composition and bioelectrical impedance showed that articles were published in English between and and we found different levels of agreements between different BIA models and reference methods. Also, there are many different equations for BIA calibration thus results of studies should be compared with more caution. BIA seems to reasonably estimate body composition in controlled conditions for healthy and euvolemic adults by applying a population specific predictive equation and it is not recommended to generalize a few equations for international epidemiologic studies, which involve participants from diverse populations. As far as we know, for some ethnic groups such as South Asians or Middle Easterners, or African residing in Africa predictive equations have not yet been developed. Hence, it is necessary to develop new predictive equations or cross validate existing equations on new populations to be studied. If the BIA equation is not appropriately chosen based on age, gender, level of physical activity, level of body fat and ethnicity, the results of the study will not be reliable. Overall BIA is a useful tool for clinical studies, but for large epidemiological studies with diverse population, particularly in developing nations, BIA has limited use unless valuation studies are conducted specifically for the populations under study. Dentali F, Sharma AM, Douketis JD: Management of hypertension in overweight and obese patients: a practical guide for clinicians. Curr Hypertens Rep. Article PubMed Google Scholar. Merchant AT, Anand SS, Vuksan V, Jacobs R, Davis B, Teo K, Yusuf S: Protein intake is inversely associated with abdominal obesity in a multi-ethnic population. J Nutr. Article CAS PubMed Google Scholar. Sharma AM, Chetty VT: Obesity, hypertension and insulin resistance. Acta Diabetol. Dagenais GR, Yi Q, Mann JF, Bosch J, Pogue J, Yusuf S: Prognostic impact of body weight and abdominal obesity in women and men with cardiovascular disease. Am Heart J. Wang J, Thornton JC, Kolesnik S, Pierson RN: Anthropometry in body composition. An overview. Ann N Y Acad Sci. Womersley J: A comparison of the skinfold method with extent of 'overweight' and various weight-height relationships in the assessment of obesity. Br J Nutr. Diaz EO, Villar J, Immink M, Gonzales T: Bioimpedance or anthropometry?. Eur J Clin Nutr. CAS PubMed Google Scholar. Segal KR, Burastero S, Chun A, Coronel P, Pierson RN, Wang J: Estimation of extracellular and total body water by multiple-frequency bioelectrical-impedance measurement. Am J Clin Nutr. Buchholz AC, Bartok C, Schoeller DA: The validity of bioelectrical impedance models in clinical populations. Nutr Clin Pract. Azinge EC, Mabayoje M, Ward LC: Body proportions in three Nigerian tribes. Coppini LZ, Waitzberg DL, Campos AC: Limitations and validation of bioelectrical impedance analysis in morbidly obese patients. Curr Opin Clin Nutr Metab Care. Scharfetter H, Schlager T, Stollberger R, Felsberger R, Hutten H, Hinghofer-Szalkay H: Assessing abdominal fatness with local bioimpedance analysis: basics and experimental findings. Int J Obes Relat Metab Disord. Kotler DP, Burastero S, Wang J, Pierson RN: Prediction of body cell mass, fat-free mass, and total body water with bioelectrical impedance analysis: effects of race, sex, and disease. Pietrobelli A, Heymsfield SB: Establishing body composition in obesity. J Endocrinol Invest. Houtkooper LB, Lohman TG, Going SB, Howell WH: Why bioelectrical impedance analysis should be used for estimating adiposity. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel GJ, Lilienthal Heitmann B, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols WJ, Pichard C: Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr. Deurenberg P, Deurenberg-Yap M, Schouten FJ: Validity of total and segmental impedance measurements for prediction of body composition across ethnic population groups. Kyle UG, Piccoli A, Pichard C: Body composition measurements: interpretation finally made easy for clinical use. PubMed Google Scholar. Heyward VH, Wagner DR: Body composition and ethnicity. Applied body composition assessment. Human Kinetics. Google Scholar. Ward LC, Heitmann BL, Craig P, Stroud D, Azinge EC, Jebb S, Cornish BH, Swinburn B, O'Dea K, Rowley K, McDermott R, Thomas BJ, Leonard D: Association between ethnicity, body mass index, and bioelectrical impedance. Implications for the population specificity of prediction equations. Deurenberg P, Deurenberg-Yap M: Validation of skinfold thickness and hand-held impedance measurements for estimation of body fat percentage among Singaporean Chinese, Malay and Indian subjects. Asia Pac J Clin Nutr. Demura S, Yamaji S, Goshi F, Kobayashi H, Sato S, Nagasawa Y: The validity and reliability of relative body fat estimates and the construction of new prediction equations for young Japanese adult males. J Sports Sci. Jebb SA, Cole TJ, Doman D, Murgatroyd PR, Prentice AM: Evaluation of the novel Tanita body-fat analyser to measure body composition by comparison with a four-compartment model. Evans WD, McClagish H, Trudgett C: Factors affecting the in vivo precision of bioelectrical impedance analysis. Appl Radiat Isot. Kushner RF, Gudivaka R, Schoeller DA: Clinical characteristics influencing bioelectrical impedance analysis measurements. Kaminsky LA, Whaley MH: Differences in estimates of percent body fat using bioelectrical impedance. J Sports Med Phys Fitness. Lukaski HC, Bolonchuk WW, Hall CB, Siders WA: Validation of tetrapolar bioelectrical impedance method to assess human body composition. J Appl Physiol. Deurenberg P, Weststrate JA, Paymans I, van der KK: Factors affecting bioelectrical impedance measurements in humans. Slinde F, Rossander-Hulthen L: Bioelectrical impedance: effect of 3 identical meals on diurnal impedance variation and calculation of body composition. Chumlea WC, Roche AF, Guo SM, Woynarowska B: The influence of physiologic variables and oral contraceptives on bioelectric impedance. Hum Biol. Fogelholm M, Sievanen H, Kukkonen-Harjula K, Oja P, Vuori I: Effects of meal and its electrolytes on bioelectrical impedance. Basic Life Sci. Garby L, Lammert O, Nielsen E: Negligible effects of previous moderate physical activity and changes in environmental temperature on whole body electrical impedance. Caton JR, Mole PA, Adams WC, Heustis DS: Body composition analysis by bioelectrical impedance: effect of skin temperature. Med Sci Sports Exerc. Abu KM, McCutcheon MJ, Reddy S, Pearman PL, Hunter GR, Weinsier RL: Electrical impedance in assessing human body composition: the BIA method. Article Google Scholar. Swartz AM, Swartz AM, Jeremy EM, King GA, Thompson DL: Evaluation of a foot-to-foot bioelectrical impedance analyser in highly active, moderately active and less active young men. Battistini N, Facchini F, Bedogni G, Severi S, Fiori G, Pettener D: The prediction of extracellular and total body water from bioelectric impedance in a non-Caucasian population from central Asia. Ann Hum Biol. Piccoli A, Rossi B, Pillon L, Bucciante G: Body fluid overload and bioelectrical impedance analysis in renal patients. Miner Electrolyte Metab. Guglielmi FW, Mastronuzzi T, Pietrini L, Panarese A, Panella C, Francavilla A: The RXc graph in evaluating and monitoring fluid balance in patients with liver cirrhosis. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gomez JM, Heitmann BL, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols AM, Pichard C: Bioelectrical impedance analysis — part I: review of principles and methods. Gudivaka R, Schoeller D, Kushner RF: Effect of skin temperature on multifrequency bioelectrical impedance analysis. Gualdi-Russo E, Toselli S: Influence of various factors on the measurement of multifrequency bioimpedance. Gleichauf CN, Roe DA: The menstrual cycle's effect on the reliability of bioimpedance measurements for assessing body composition. Heyward VH: Practical body composition assessment for children, adults, and older adults. Int J Sport Nutr. Macdonald HM, New SA, Campbell MK, Reid DM: Longitudinal changes in weight in perimenopausal and early postmenopausal women: effects of dietary energy intake, energy expenditure, dietary calcium intake and hormone replacement therapy. Toth MJ, Gardner AW, Ades PA, Poehlman ET: Contribution of body composition and physical activity to age-related decline in peak VO2 in men and women. Wang Q, Hassager C, Ravn P, Wang S, Christiansen C: Total and regional body-composition changes in early postmenopausal women: age-related or menopause-related?. Wing RR, Matthews KA, Kuller LH, Meilahn EN, Plantinga PL: Weight gain at the time of menopause. Arch Intern Med. Zamboni M, Turcato E, Santana H, Maggi S, Harris TB, Pietrobelli A, Heymsfield SB, Micciolo R, Bosello O: The relationship between body composition and physical performance in older women. J Am Geriatr Soc. Chen Z, Bassford T, Green SB, Cauley JA, Jackson RD, LaCroix AZ, Leboff M, Stefanick ML, Margolis KL: Postmenopausal hormone therapy and body composition — a substudy of the estrogen plus progestin trial of the Women's Health Initiative. Reubinoff BE, Wurtman J, Rojansky N, Adler D, Stein P, Schenker JG, Brzezinski A: Effects of hormone replacement therapy on weight, body composition, fat distribution, and food intake in early postmenopausal women: a prospective study. Fertil Steril. Xie X, Kolthoff N, Barenholt O, Nielsen SP: Validation of a leg-to-leg bioimpedance analysis system in assessing body composition in postmenopausal women. Deurenberg P, Weststrate JA, Seidell JC: Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Deurenberg P, Deurenberg-Yap M: Differences in body-composition assumptions across ethnic groups: practical consequences. Kyle UG, Genton L, Karsegard L, Slosman DO, Pichard C: Single prediction equation for bioelectrical impedance analysis in adults aged 20—94 years. Stolarczyk LM, Heyward VH, Van Loan MD, Hicks VL, Wilson WL, Reano LM: The fatness-specific bioelectrical impedance analysis equations of Segal et al: are they generalizable and practical?. Malina RM, Huang YC, Brown KH: Subcutaneous adipose tissue distribution in adolescent girls of four ethnic groups. Jakicic JM, Wing RR, Lang W: Bioelectrical impedance analysis to assess body composition in obese adult women: the effect of ethnicity. Merz AL, Trotterm M, Peterson RR: Estimation of skeleton weight in the living. Am J Phys Anthropol. Swinburn BA, Ley SJ, Carmichael HE, Plank LD: Body size and composition in Polynesians. Deurenberg-Yap M, Schmidt G, van Staveren WA, Deurenberg P: The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Fuller NJ, Elia M: Potential use of bioelectrical impedance of the 'whole body' and of body segments for the assessment of body composition: comparison with densitometry and anthropometry. Wagner DR, Heyward VH: Measures of body composition in blacks and whites: a comparative review. Harsha DW, Frerichs RR, Berenson GS: Densitometry and anthropometry of black and white children. Malina RM: Skinfolds in American Negro and white children. J Am Diet Assoc. Eur J Appl Physiol. de Waart FG, Li R, Deurenberg P: Comparison of body composition assessments by bioelectrical impedance and by anthropometry in premenopausal Chinese women. Barbosa-Silva MC, Barros AJ: Bioelectrical impedance analysis in clinical practice: a new perspective on its use beyond body composition equations. Download references. We wish to thank Dr. Yusuf Director of Population Health Research Institute for all his supports and guidance. Population Health Research Institute, McMaster University, Hamilton, ON, Canada. Department of Medicine, McMaster University, Hamilton, ON, Canada. Department of Clinical Epidemiology and Biostatistics, and Population Health Research Institute, McMaster University, Hamilton, ON, Canada. You can also search for this author in PubMed Google Scholar. Correspondence to Mahshid Dehghan. MD ran the electronic searches, reviewed all abstracts and articles, coordinated and drafted the manuscript. ATM participated in reviewing the articles and helped to draft the manuscripts. This article is published under license to BioMed Central Ltd. Reprints and permissions. Dehghan, M. Is bioelectrical impedance accurate for use in large epidemiological studies?. Nutr J 7 , 26 Download citation. Received : 19 October Accepted : 09 September Published : 09 September Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Is bioelectrical impedance accurate for use in large epidemiological studies? Download PDF. Download ePub. Review Open access Published: 09 September Is bioelectrical impedance accurate for use in large epidemiological studies? Abstract Percentage of body fat is strongly associated with the risk of several chronic diseases but its accurate measurement is difficult. Introduction In this review we discuss the issues associated with the application of bioelectrical impedance analysis BIA to measure body composition in large epidemiologic studies with multiethnic populations. Principles of bioelectrical impedance technique BIA analysis is based on the principle that electric current flows at different rates through the body depending upon its composition. Predictive equations Many empirical equations have been developed for estimation of TBW, FFM and body cell mass BCM , by using sex, age, weight, height and race as explanatory variables. Consumption of food or beverages Although food or fluid intake before BIA measurement affects TBW and ECW, a general agreement on the ideal amount of time between food and fluid intake and BIA measurements has yet to be consolidated. Exercise Although exercise of mild intensity may not affect BIA measurements, moderate and intensive exercise before measurements may change the measured impedance by different mechanisms [ 33 ]. e cross-sectional, hand-to-hand, hand-to-foot, and foot-to-foot. Two electrodes are to conduct current into the body, while other two electrodes are utilized to measure the voltage from the body. The alternating current is injected with frequency of 50 kHz. Automatic switch in the form of multiplexers and demultiplexers controls the sequence of BIA measurement methods. Microcontroller process the data and the result is displayed on LCD. A keypad is used to input related body parameters, i. e height, weight, age, and gender. The measurement tests show that the BIA works as intended, while the comparison with commercial BIA reveals maximum relative error of 4. Published in: 4th International Conference on Electrical Engineering, Computer Science and Informatics EECSI.

Use a BIA Scale BIA impedance measurement technique Kmpedance Fitness and Weight Techniue BIA impedance measurement technique. Anisha Shah, MD, is a board-certified internist, interventional cardiologist, and fellow technkque the American College of Cardiology. Adah is an occupational therapist, working in the area of pediatrics with elementary students with special needs in the schools. Her work as an occupational therapist includes: home health, acute care, chronic care, seating and positioning, outpatient rehab, and skilled nursing rehab. Bioelectrical impedance analysis BIA measures body composition based on the rate at which an electrical current travels through the body.

Video

HOW TO ACCURATELY MEASURE BODY FAT PERCENTAGE Accu-Measure Body Fat Calipers Review Does it WORK?

BIA impedance measurement technique -

Values located outside the 95 th percentile in the following four quadrants point to the following conditions: a right upper quadrant e. good training status modified with permission from Data-Input GmbH. We present below some examples of characteristic BIA findings in COPD patients with their interpretation:.

From personal experience, follow-up measurements examples should be performed every 4 weeks for overweight patients and every weeks for all other cases [ 27 ]. However, this is a decision that must be taken on an individual basis.

Patient: female, Interpretation: With a BMI of The measurement point in the BIVA nomogram Figure 2 lies within the 50 th tolerance ellipse and thus indicates normal findings. Normal finding as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram within the 50 th tolerance ellipse range of normal values indicates a normal finding.

Conclusion: All values in the table are within the normal range and the measurement point in the BIVA nomogram lies within the 50 th tolerance ellipse. The measurement point in the BIVA nomogram Figure 3 in this patient is well below the line of normal BCM values long axis and above the line of normal TBW values short axis between the 75 th and the 95 th tolerance ellipse.

The position of the measurement point in the lower right quadrant points to malnutrition. Malnutrition in an obese COPD patient as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is below the line of normal BCM values long axis and above the line of normal TBW values short axis between the 75 th and 95 th tolerance ellipse.

The position in the lower right quadrant indicates malnutrition. The BIA parameter values listed in table 2 can be interpreted as follows: The fat mass lies above the normal range in line with the increased BMI.

BCM lies within the normal range. At first sight this does not fit in with the finding of the BIVA nomogram, which indicates malnutrition.

The fact that the calculated BCM is within the range of normal values here may be explained as follows: It needs to be considered that BCM is dependent on the patient's fluid status TBW. This means that a BCM within the normal range does not necessarily mean a normal nutritional status but may also be due to increased TBW.

This indicates that BCM is actually reduced. BCM therefore only appears to lie within the range of normal values because of the increased TBW. In contrast to this somewhat complex interpretation of the calculated BIA values, the suspected diagnosis of malnutrition can be established at a glance by BIVA.

In addition, it is confirmed that the calculated BCM is too high because of the increased TBW position of the measurement point in the BIVA nomogram above the line of normal TBW values. Conclusion: Despite the presence of obesity the patient is exhibiting malnutrition.

The position of the measurement point in the BIVA nomogram in the right lower quadrant between the 75 th and the 95 th tolerance ellipse provides an indication for the suspected diagnosis of malnutrition.

The measurement point in the BIVA nomogram Figure 4 in this patient is far below the line of normal BCM values long axis and well above the line of normal TBW values short axis , far outside the 95 th tolerance ellipse.

The position of the measurement point in the lower right quadrant points to malnutrition in the form of cachexia. Cachexia as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is far below the line of normal BCM values long axis and well above the line of normal TBW values short axis far outside the 95 th tolerance ellipse.

The position in the lower right quadrant points to cachexia. The BIA parameter values listed in table 3 can be interpreted as follows: The fat mass lies below the normal range in line with the reduced BMI. The calculated values for BCM und TBW are reduced. It needs to be considered as regards the reduced BCM value that BCM is dependent on the patient's fluid status TBW.

This means that a reduced BCM does not necessarily point to malnutrition but may also be due to a low TBW. In this example also BIVA provides a more efficient assessment of the nutritional status than the calculated BIA parameters. Conclusion: All the values listed in the table are below the normal range and the measurement point in the BIVA nomogram is outside the 95 th tolerance ellipse in the lower right quadrant.

This indicates severe malnutrition in the form of cachexia. The assessment of the BIVA nomogram is sufficient for the suspected diagnosis of cachexia. The measurement point in the BIVA nomogram Figure 5 in this patient is above the line of normal BCM values long axis and well below the line of normal TBW values short axis on the 95 th tolerance ellipse.

The position of the measurement point in the lower left quadrant points to water retention in the form of oedema. Oedema due to right heart failure as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is above the line of normal BCM values long axis and well below the line of normal TBW values short axis on the 95 th tolerance ellipse.

The position in the lower left quadrant indicates the presence of increased water retention. The BIA parameter values listed in table 4 can be interpreted as follows: Body fat mass lies above the normal range in line with the increased BMI.

The determined TBW is increased and the calculated BCM lies in the upper range of normal. These findings are consistent with the position of the measurement point above the line of normal BCM values and below the line of normal TBW values in the lower left quadrant.

With the derived normal BIA value for BCM it needs once again to be taken into account here that BCM is dependent on the patient's fluid status TBW.

This means that a BCM within the normal range does not necessarily indicate an actually normal BCM or normal nutritional status but may also appear normal due to an increased TBW.

In addition to the increased TBW, ECM is also markedly increased, indicating oedema. The suspicion of oedema is established at a glance with BIVA.

BIVA confirms simply and rapidly the calculated BIA values BCM and TBW. The suspicion of oedema was confirmed on physical examination of the legs. Conclusion: The values listed in the table for TBW and ECM are outside the normal range and the measurement point in the BIVA nomogram is on the 95 th tolerance ellipse in the lower left quadrant, indicating oedema.

The determined BCM is in the upper range of normal and the measurement point in the BIVA nomogram is above the line of normal BCM values. The position of the measurement point in the nomogram provides an indication for the suspected diagnosis of oedema.

For the general differential diagnosis of underweight we present a female patient with anorexia: female, The measurement point in the BIVA nomogram Figure 6 lies almost on the line of normal BCM values long axis and far above the line of normal TBW values short axis outside the 95 th tolerance ellipse.

The position of the measurement point in the upper right quadrant points to the presence of anorexia. Anorexia as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is almost on the line of normal BCM values long axis and far above the line of normal TBW values short axis outside the 95 th tolerance ellipse.

The position in the upper right quadrant points to the presence of anorexia. The BIA parameter values listed in table 5 can be interpreted as follows: Body fat mass is reduced in line with the low BMI.

TBW is markedly reduced and BCM also is decreased. With the reduced BCM it needs to be kept in mind here that BCM is dependent on the patient's fluid status TBW.

This means that a lower BCM may also appear reduced due to a lower TBW. This indicates that BCM is normal and that the calculated value was too low only because of the low TBW.

BIVA confirms the suspicion raised by the BIA values that the calculated BCM was too low because of the reduced TBW. Again, the suspected diagnosis of anorexia can be established more efficiently and more reliably by BIVA. Conclusion: The patient exhibits a markedly reduced BMI, decreased body water and a normal BCM in the form of anorexia.

The position of the measurement point in the nomogram in the upper right quadrant outside the 95 th tolerance ellipse provides an indication for the suspected diagnosis of anorexia.

Bioelectrical impedance analysis BIA , particularly in combination with bioelectrical impedance vector analysis BIVA , provides a viable opportunity for evaluating body composition in humans.

As the examples suggest the interpretation of BIA results is often complex and a suspected diagnosis can be established more efficiently and more reliably by integrating BIVA into the patient assessment process.

Engelen MP, Schols AM, Baken WC, Wesseling GJ, Wouters EF: Nutritional depletion in relation to respiratory and peripheral skeletal muscle function in out-patients with COPD. Eur Respir J. Article CAS PubMed Google Scholar.

Schols AM, Broekhuizen R, Weling-Scheepers CA, Wouters EF: Body composition and mortality in chronic obstructive pulmonary disease. Am J Clin Nutr. CAS PubMed Google Scholar. Engelen MP, Schols AM, Does JD, Wouters EF: Skeletal muscle weakness is associated with wasting of extremity fat-free mass but not with airflow obstruction in patients with chronic obstructive pulmonary disease.

King DA, Cordova F, Scharf SM: Nutritional aspects of chronic obstructive pulmonary disease. Proc Am Thorac Soc. Article PubMed PubMed Central Google Scholar.

Shoup R, Dalsky G, Warner S, Davies M, Connors M, Khan M, Khan F, ZuWallack R: Body composition and health-related quality of life in patients with obstructive airways disease. Hallin R, Koivisto-Hursti UK, Lindberg E, Janson C: Nutritional status, dietary energy intake and the risk of exacerbations in patients with chronic obstructive pulmonary disease COPD.

Respir Med. Article PubMed Google Scholar. Schols AM: Nutrition in chronic obstructive pulmonary disease. Curr Opin Pulm Med. Soeters PB, Schols AM: Advances in understanding and assessing malnutrition. Curr Opin Clin Nutr Metab Care. Global Initiative for Chronic Obstructive Lung Disease: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease updated com ].

Vestbo J, Prescott E, Almdal T, Dahl M, Nordestgaard BG, Andersen T, Sørensen TI, Lange P: Body mass, fat-free body mass, and prognosis in patients with chronic obstructive pulmonary disease from a random population sample: findings from the Copenhagen City Heart Study.

Am J Respir Crit Care Med. PubMed Google Scholar. Ischaki E, Papatheodorou G, Gaki E, Papa I, Koulouris N, Loukides S: Body mass and fat-free mass indices in COPD: relation with variables expressing disease severity.

Miller A, Strauss BJ, Mol S, Kyoong A, Holmes PH, Finlay P, Bardin PG, Guy P: Dual-energy X-ray absorptiometry is the method of choice to assess body composition in COPD. Lerario MC, Sachs A, Lazaretti-Castro M, Saraiva LG, Jardim JR: Body composition in patients with chronic obstructive pulmonary disease: which method to use in clinical practice?.

Br J Nutr. Lee SY, Gallagher D: Assessment methods in human body composition. Curr Opin Clin Nutr Metabol Care. Article Google Scholar. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, Heitmann BL, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols AM, Pichard C: Bioelectrical impedance analysis-part I: review of principles and methods.

Clin Nutr. Matthie JR: Bioimpedance measurements of human body composition: critical analysis and outlook. Expert Rev Med Devices.

Mattsson S, Thomas BJ: Development of methods for body composition studies. Phys Med Biol. Kushner RF: Bioelectrical impedance analysis: a review of principles and applications.

J Am Coll Nutr. Kuzma AM, Meli Y, Meldrum C, Jellen P, Butler-Labair M, Koczen-Doyle D, Rising P, Stavrolakes K, Brogan F: Multidisciplinary care of the patient with chronic obstructive pulmonary disease.

The BIA compendium. de ]3. Bosy-Westphal A, Danielzik S, Dörhöfer RP, Piccoli A, Müller MJ: Patterns of bioelectrical impedance vector distribution by body mass index and age: implications for body-composition analysis. Erratum in: Am J Clin Nutr , Piccoli A: Bioelectric impedance vector distribution in peritoneal dialysis patients with different hydration status.

Kidney Int. Dehghan M, Merchant AT: Is bioelectrical impedance accurate for use in large epidemiological studies?. Nutr J. Barbosa-Silva MC, Barros AJ: Bioelectrical impedance analysis in clinical practice: a new perspective on its use beyond body composition equations.

Buchholz AC, Bartok C, Schoeller DA: The validity of bioelectrical impedance models in clinical populations. Nutr Clin Pract. Bozzetto S, Piccoli A, Montini G: Bioelectrical impedance vector analysis to evaluate relative hydration status.

Pediatr Nephrol. Creutzberg EC, Wouters EF, Mostert R, Weling-Scheepers CA, Schols AM: Efficacy of nutritional supplementation therapy in depleted patients with chronic obstructive pulmonary disease. Download references. Nutritional Consulting Practice, Emil-Schüller-Straße, Koblenz, , Germany.

Pneumology Practice, Emil-Schüller-Straße, Koblenz, , Germany. KG, Binger Straße, Ingelheim, , Germany. Department of Pulmonary Disease, III. Medical Clinic, Johannes Gutenberg-University, Langenbeckstraße, Mainz, , Germany. You can also search for this author in PubMed Google Scholar. Correspondence to Thomas Glaab.

The authors declare that they have no competing interests. TG and MMG were employees of Boehringer Ingelheim at the time of manuscript submission. AWK and TG conceived of the review, drafted and coordinated the manuscript. MMG and AK critically discussed and helped to draft the manuscript.

All authors read and approved the final manuscript. The contents of this original manuscript have not been previously presented or submitted elsewhere. Open Access This article is published under license to BioMed Central Ltd.

Reprints and permissions. Walter-Kroker, A. et al. DEXA dual-energy X-ray absorptiometry. Figure 8 b shows the correlation of percentage body fat measurement between our wrist-wearable bioelectrical impedance analyzer and the reference instrument DEXA , from which it can be seen that R is 0.

The SEE was estimated to be 3. It can be seen that the errors between the two instruments are randomly distributed without any skewed tendency and Table 3 shows the comparison of accuracy in measurement of percentage body fat by the whole-body composition analyzer, the upper-body portable body fat analyzer, and our wrist-wearable bioelectrical impedance analyzer.

We developed a novel wrist-wearable bioelectrical impedance analyzer with a contact resistance compensation function such that bioelectrical impedance can be accurately estimated even with considerably small sizes of electrodes outer electrodes: 68 mm 2 ; inner electrodes: mm 2.

The correlation coefficient and the SEE of percentage body fat relative to the DEXA instrument were estimated to be 0. Considering that the measurement time of our wrist-wearable BIA device was only 7 s and could be reduced further, this sensor technology provides a new possibility for a wearable bioelectrical impedance analyzer with more miniature electrodes toward daily obesity management.

Kyle, U. et al. Bioelectrical impedance analysis—part I: review of principles and methods. Article Google Scholar.

Bioelectrical impedance analysis—part II: utilization in clinical practice. Kushner, R. Bioelectrical impedance analysis: a review of principles and applications. Article MathSciNet CAS Google Scholar. Single prediction equation for bioelectrical impedance analysis in adults aged 20—94 years.

CAS Google Scholar. Heitmann, B. Evaluation of body fat estimated from body mass index, skinfolds and impedance: A comparative study. Chertow, G. Development of a population-specific regression equation to estimate total body water in hemodialysis patients.

Kidney Int. Article CAS Google Scholar. Ramel, A. Regional and total body bioelectrical impedance analysis compared with DXA in Icelandic elderly. Aldosky, H. Regional body fat distribution assessment by bioelectrical impedance analysis and its correlation with anthropometric indices.

Bogónez-Franco, P. Effect of electrode contact impedance mismatch on 4-electrode measurements of small body segments using commercial BIA devices. Jung, M. Wrist-wearable bioelectrical impedance analyzer with contact resistance compensation function. Corchia, L. Dry textile electrodes for wearable bio-impedance analyzers.

IEEE Sens. Article ADS CAS Google Scholar. Usman, M. Analyzing dry electrodes for wearable bioelectrical impedance analyzers. Rachim, V. Multimodal wrist biosensor for wearable cuff-less blood pressure monitoring system. Kõiv, H. Development of bioimpedance sensing device for wearable monitoring of the aortic blood pressure curve.

Bera, T. Bioelectrical impedance and the frequency dependent current conduction through biological tissues: a short review. In IOP Conf. Thomasset, A. Bio-electrical properties of tissue impedance measurements. Lyon Med.

CAS PubMed Google Scholar. Hoffer, E. Correlation of whole-body impedance with total body water volume. Nyboer, J. Electrical Impedance Plethysmography. Springfield, World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation.

World Health Organization, Heyward, V. Applied body composition assessment. Bland, J. Statistical methods for assessing agreement between two methods of clinical measurement.

Lancet , — Download references. We would like to thank Editage www. kr for English language editing.

Healthcare Sensor Lab, Device Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co. GI Innovation, Inc. Samsung Strategy and Innovation Center, Samsung, Inc. You can also search for this author in PubMed Google Scholar.

and J. Correspondence to Kak Namkoong. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions. Wrist-wearable bioelectrical impedance analyzer with miniature electrodes for daily obesity management. Sci Rep 11 , Download citation. Received : 03 August Accepted : 08 December Published : 13 January Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Journal of Cardiovascular Translational Research Cardiovascular Engineering and Technology By submitting a comment you agree to abide by our Terms and Community Guidelines.

If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature.

nature scientific reports articles article. Download PDF. Subjects Disease prevention Health care Weight management. Abstract Bioelectrical impedance analysis BIA is used to analyze human body composition by applying a small alternating current through the body and measuring the impedance.

Introduction Consumer interests in personalized health, including fitness and weight management, have been increasing. Methods Wrist-wearable bioelectrical impedance analyzer using single finger We developed a wristwatch-type bioelectrical impedance analyzer that provides users with convenient measurement experience by using only one finger, i.

Figure 1. Full size image. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Table 1 Physical characteristics of the subjects.

Full size table. Table 2 Bioelectrical impedance analysis client pretesting guidelines Results and discussion Our study explored a novel method that uses considerably small electrodes that can be adapted into small devices, such as a wristwatch.

Figure 7. Calculated contact resistance distribution among participants in the clinical test. Figure 8. Conclusions We developed a novel wrist-wearable bioelectrical impedance analyzer with a contact resistance compensation function such that bioelectrical impedance can be accurately estimated even with considerably small sizes of electrodes outer electrodes: 68 mm 2 ; inner electrodes: mm 2.

References Kyle, U. Article Google Scholar Kyle, U. Article Google Scholar Kushner, R. Article MathSciNet CAS Google Scholar Kyle, U. CAS Google Scholar Heitmann, B.

Article CAS Google Scholar Ramel, A. Article CAS Google Scholar Aldosky, H. Article Google Scholar Bogónez-Franco, P. Article ADS CAS Google Scholar Usman, M.

Article CAS Google Scholar Kõiv, H. Article Google Scholar Bera, T. CAS PubMed Google Scholar Hoffer, E. Article CAS Google Scholar Nyboer, J.

Article Google Scholar Download references. Acknowledgements We would like to thank Editage www. Author information Authors and Affiliations Healthcare Sensor Lab, Device Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co.

View author publications. Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4. About this article. Cite this article Jung, M. Copy to clipboard. This article is cited by Wearables in Cardiovascular Disease Sanchit Kumar Angela M.

Victoria-Castro F. Perry Wilson Journal of Cardiovascular Translational Research The Investigation of Bio-impedance Analysis at a Wrist Phantom with Two Pulsatile Arteries Yang Yu Andrew Lowe Huiyang Zhang Cardiovascular Engineering and Technology Comments By submitting a comment you agree to abide by our Terms and Community Guidelines.

About the journal Open Access Fees and Funding About Scientific Reports Contact Journal policies Calls for Papers Guide to referees Editor's Choice Journal highlights.

Publish with us For authors Language editing services Submit manuscript. Search Search articles by subject, keyword or author. Show results from All journals This journal. Advanced search. Close banner Close.

Bioelectrical impedance analysis BIA impedance measurement technique measuremnt a frequently used method for estimating body BCAAs and fat loss based on B vitamins for seniors 2-component imedance 2C. Impedance comprises technkque resistance and reactance:. The measuremenh electrical current is passed through the measurfment from conductive surfaces techniqie electrodes. Conductivity is higher through fat free mass which includes muscle, bone and water than through fat mass which contains very little water. Different body components have varying levels of impedance in response to different frequencies of the electrical current. Output is commonly provided in the form of an impedance value expressed in the unit Ohms, Ω; approximate range between Ω - Ω. Interpretation of the impedance value varies by BIA instrument type.