Assessment of Body Fat Percentage in Emirati Females: A Comparative Analysis of BIA vs DXA

Assessment of Body Fat Percentage in Emirati Females: A Comparative Analysis of BIA vs DXA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessment of Body Fat Percentage in Emirati Females: A Comparative Analysis of BIA vs DXA Dalia Haroun, Aseel Ehsanallah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4636500/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background/Objectives: Obesity is a significant health issue in the UAE. Accurate body composition assessment is crucial for managing obesity-related health risks. This study aimed to evaluate the agreement between Bioelectrical Impedance Analysis (BIA) and Dual-Energy X-ray Absorptiometry (DXA) in measuring body fat percentage (%BF) among Emirati females. Subjects/Methods: This cross-sectional study involved 95 healthy Emirati females aged 17–27 years. Paired samples t-tests, correlation analyses, and Bland-Altman plots were used to compare the two methods. Results BIA significantly underestimated %BF and fat mass (FM) while overestimating fat-free mass (FFM) compared to DXA. The mean difference in %BF was − 14.1% (p < 0.001), and the mean difference in FFM was + 8.2 kg (p < 0.001). Despite strong correlations between BIA and DXA measurements (r = 0.855 for %BF, r = 0.984 for FM, and r = 0.929 for FFM), Bland-Altman plots indicated poor agreement, with wide limits of agreement. Conclusions BIA remains valuable for obesity assessment in large-scale studies and clinical settings due to its non-invasive, easy-to-use, and cost-effective characteristics. The results show that the in-built prediction equations cannot adequately predict the %fat, FM, and FFM for this sample. Future research should focus on developing and validating BIA-specific equations tailored for Emiratis. Health sciences/Health care/Diagnosis/Body mass index Health sciences/Health care/Nutrition Figures Figure 1 Introduction Obesity has emerged as a pressing health concern for the United Arab Emirates (UAE) in recent years. Data extracted from the UAE National Health Survey Report of 2017–2018 reveals a high prevalence of obesity among adults, estimated at around 27.8% ( 1 ). This statistic indicates a broader health context dominated by non-communicable diseases (NCDs), which the World Health Organization (WHO) identifies as the primary cause of mortality in the region. In fact, NCDs account for 55% of all deaths in the UAE, with cardiovascular diseases (CVDs) emerging as the primary culprit, responsible for 34% of these fatalities ( 2 ). Therefore, addressing the increasing prevalence of obesity and its related health risks has become an urgent priority in the UAE. Assessing obesity requires methods that are not only accurate but also cost-effective and efficient for use in large-scale settings. Weight and height measurements, offer a simpler and quicker alternative, with Body Mass Index (BMI) being a widely employed metric ( 3 ). BMI's limitations include its inability to differentiate between muscle, fat, bone, or vital organs, leading to misclassification of individuals with high fat-free mass (FFM) relative to stature as overweight or obese, and its failure to account for variations in body composition among individuals ( 4 , 5 ). Moreover, body fat percentage (BF%) varies with age, sex, ethnicity, and individual differences, complicating the interpretation of BMI values ( 6 ). Although BMI has its limitations, it remains popular in epidemiological research. Alternative methods for assessing obesity, such as body fat-mass measurement techniques including Bioelectrical Impedance Analysis (BIA), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Dual-Energy X-ray Absorptiometry (DXA), are considered more precise for the assessment of body composition. Dual-energy X-ray Absorptiometry (DXA) is renowned as the gold standard in body composition assessment and is recognized for its precision and applicability in various settings ( 7 – 9 ). Unlike conventional X-ray systems, DXA requires special beam filtering and precise spatial registration to measure both whole-body bone mass and soft tissue composition ( 10 ). DXA utilizes a 3-compartment model, with compartments encompassing fat mass (FM), FFM, and bone mineral content. Increasing the number of compartments enhances accuracy, lowers the chances of measurement errors, and reduces the need for assumptions in determining body composition ( 11 ). DXA assessments are rapid, non-invasive, and entail minimal inconvenience for patients, further enhancing its appeal in clinical practice and research ( 12 ). However, despite its advantages, DXA does have drawbacks, including the requirement for expensive specialized radiology equipment, small radiation exposure, and the need for trained technicians, which may limit its feasibility in routine clinical practice ( 12 , 13 ). To address these challenges, BIA has been suggested to assess body composition. BIA serves as an indirect method for assessing body composition, relying on parameters such as impedance and phase angle to calculate various body compartments ( 14 ). It utilizes the body's electrical properties to gauge resistance to an electric current, factoring in weight, height, and age to estimate total body water (TBW). Then applies equations to accurately determine BF% ( 6 , 15 ). BIA, a two-compartment model, operates under the assumption of constant FFM density. However, significant water fluctuations during growth and development can lead to inaccuracies in body composition measurements ( 13 ). Other factors including device type, water distribution, hydration status, weight, and height, may increase the risk of inaccurately assessing FFM ( 6 , 16 ). BIA is safe, simple, non-invasive, and cost-effective, making it increasingly popular for evaluating body composition in clinical and research settings ( 12 , 13 , 17 , 18 ) It has emerged as a popular alternative to DXA for assessing body composition ( 9 ). BIA’s advantages, such as portability, affordability, minimal training requirements, and lack of radiation exposure, make it a practical option for assessing body composition in both clinical practice and large-scale epidemiological studies ( 15 , 16 , 19 – 21 ). However, the accuracy of predictive equations for estimating TBW depends on factors such as the type of BIA device used to record impedance data, the reference method employed to assess FFM, and the characteristics of the population in which the equation was developed ( 22 ). BIA devices incorporate predictive equations for body fat that were originally developed using data from specific demographic groups ( 4 , 12 ). Many of these equations were initially derived from studies involving predominantly white populations and subsequently applied to individuals from diverse ethnic backgrounds. However, research indicates that there are variations in body composition among different ethnic groups, potentially impacting the precision of BIA measurements ( 23 – 26 ). Hence, past research suggests the necessity for customized BIA equations tailored to specific ethnic groups ( 27 , 28 ). Currently, there is a lack of research comparing BIA and DXA in the UAE. Therefore, the aim of this study was to examine the agreement between BIA and DEXA measurements of BF% among females. Materials and Methods Study Subjects In a retrospective analytical cross-sectional study, data, including questionnaire responses and measurements, were collected initially between March 2016 and March 2017. This study involved analyzing a comprehensive dataset obtained from a sample of 170 females aged 17 to 27 years. The participants were healthy Emirati women living in the UAE, selected using a convenience snowball sampling method. Participants were selected based on specific inclusion criteria, including female Emiratis aged between 17 and 28, having fasted for 12 hours, and not currently menstruating. Exclusion criteria encompassed males, non-Emiratis, individuals with metabolic disorders (such as diabetes, kidney disease, and hypertension), those taking certain medications, pregnant or lactating females, and those reporting weight fluctuations exceeding 3 kg. Following the application of these criteria, 95 out of the initial 170 participants were included in the analysis. A minimum sample of 91 participants was determined (power = 0.8, alpha = 0.05) to achieve a medium effect size for the coefficient of determination (R 2 ) increases, in a regression equation with up to 5 predictors. Therefore, our sample of 95 participants was enough to ensure adequate power analysis in the equation development ( 29 ). Participation in the study was voluntary and informed written consent was obtained from all participants. Ethical approval for the study was obtained from the Zayed University Research Ethics Committee (ZU16_016_F). Participants who agreed to take part were provided with a consent form to review and sign before the assessment session. To maintain confidentiality, the names and contact details of the participants were kept separately in a secure cabinet, and each participant was assigned a unique code. Various communication channels, such as email, text messages, and campus word of mouth, were utilized to disseminate information about the study and recruit female university students. Interested females were provided with either an electronic or paper copy of the information sheet upon expressing their willingness to participate. Measurements were conducted in the body composition laboratory at Zayed University in Dubai, UAE. All participants needed to arrive at the laboratory after fasting for at least 8–12 hours and with an empty stomach. All the measurements were taken on the same day. Participants were instructed to wear light clothing, avoid any physical activity, refrain from consuming caffeinated beverages, and empty their bladders before measurements were taken. A checklist was used to ensure that the participants met the eligibility criteria for the examination. The session, which lasted between 60 and 90 minutes, involved the collection of data and measurements. Data Collection Questionnaire A trained research assistant administered a structured questionnaire to gather personal information, including age, sex, ethnicity, marital status, body weight, height, medical history, and any medications used, if relevant. Participants were also asked about weight fluctuations over the past 3 months, with the question: "Have you experienced any changes in your weight? If so, how many kilograms?" Anthropometric Measurements and Body Fat Analysis Body weight was measured to the nearest 0.01 kg using the BodPod System electronic scale (Body Composition System; Life Measurement, Incorporated, Concord, CA). Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer (TANITA HR-200, China). The waist circumference was measured at the narrowest point to the nearest 0.1 cm over light clothing using a non-elastic, flexible tape (SECA 201) ( 30 ). The hip circumference was measured at the widest portion of the hip ( 31 ). BMI was calculated by dividing the weight in kilograms by the square of the height in meters (kg/m 2 ). WHO defines overweight as a BMI between 25 and 29.9 kg/m 2 , and the classification of obesity is defined as a BMI greater than 30 kg/m 2 ( 31 ). The DXA scanner provides accurate data on bone and soft tissue composition, including bone mineral density (BMD), lean and fat tissue mass, and fat percentage. The DXA scanner was calibrated daily according to the manufacturer’s instructions. Participants removed any objects and clothing containing metal before undergoing the scan. Whole-body scans were performed on participants who were instructed to lie flat on the scanning bed with their hands at their sides and to refrain from moving throughout the measurement. BIA was measured using a TANITA BC-418 MA hand-foot impedance machine, which prints out FM, FFM, and TBW measurements based on manufacturer-specific equations and whole-body resistance (Ω). For this device, impedance is recorded with the subject standing and holding hand grips. The machine requires the entry of the subject's age, gender, height, and standard body type. Inaccuracies in these inputs affect the body composition estimates but not the resistance measurement. Subjects were then asked to stand barefoot on the metal footplates while holding the handles for approximately one minute. Statistical Analysis Descriptive statistics including means and standard deviations were calculated for participant characteristics. Paired samples t-tests were used to assess if measures of %fat, FM, and FFM were significantly different between BIA and DXA. The level of statistical significance was set at p < 0.05. Cohen’s d for paired samples was calculated as a measure of effect size for differences between BIA and DXA measures. An effect size of d = 0.2 was considered a small effect size, d = 0.5 was considered a moderate effect size, and d = 0.8 was considered a large effect size ( 32 ). Pearson correlation coefficients were used to assess the correlation between fat, FM, and FFM measured by BIA and DXA. Stepwise multiple regression analysis was used to develop a predictive equation for FFM, incorporating resistance index (RI), weight, and age as independent variables, with FFM from DXA as the dependent variable. A tolerance and variance inflation factor (VIF) analysis was conducted to ensure no multicollinearity. A Durbin-Watson statistic was calculated to assess the assumption that the residuals are independent. Residual normality and variance homogeneity were evaluated during the model development phase. Results The descriptive characteristics of all 95 participants included in the analysis are shown in Table 1. The average age of the subjects was 19.7 years, and the mean BMI was 22.9 kg/m². Out of the 95 participants, 19 (20%) were classified as underweight, 52 (54.7%) had a normal weight, 14 (14.7%) were overweight, and 10 (10.5%) were obese. A paired samples t-test was conducted to assess differences in % Fat, FM, and FFM between BIA and DXA (Table 2). Results indicate that BIA significantly underestimated % Fat (14.1%) and FM (5.8 kg) compared to DXA, with a large effect size and a strong correlation between the two methods. Conversely, BIA significantly overestimated FFM by 8.2 kg compared to DXA, also showing a large effect size and strong correlation. All three body composition parameters measured by DXA and BIA showed significant positive correlations (p < 0.01). The Bland–Altman (B&A) plots were used to determine the limits of agreement (LoA) between BIA and DXA (Fig 1). The B&A plot for %fat and FM indicates that BIA tends to underestimate %fat to a greater extent among those with greater %fat compared to those with lower %fat (LoA: -25.1 to -3.1). A similar observation was seen for FM, where the discrepancy between BIA and DXA increases with higher FM, indicating proportional bias for both %fat and FM. For FM (LoA: -10.0 to -1.6). For FFM, the non-zero slope of the trend line also indicates a proportional bias. The mean difference in FFM estimates was larger for individuals with lower FFM, with BIA overestimating FFM in participants with lower FFM compared to those with higher FFM (LoA: 3.3 to 13.2). Across all measures, the greatest overestimate was 13.37 for FFM, and the greatest underestimate was -27.5 for %fat. These results show that the in-built prediction equations cannot adequately predict the %fat, FM, and FFM for this sample. Table 3 displays the results of the stepwise linear regression analysis investigating the predictor variables influencing FFM (DXA). The optimal model includes the resistance index, weight, and age as predictors, resulting in an R² of 0.906, indicating that these variables collectively explain 90.6% of the variance in FFM. The resistance index (β = 0.494), weight (β = 0.485), and age (β = 0.171) all demonstrate statistically significant contributions (p < 0.001) to predicting FFM. The final model retained weight (kg), age (years), and RI as significant predictors of FFM. The stepwise regression analysis yielded the following FFM prediction equation for this Emirati female population: FFM = - 4.599 + 0.171 (age) + 0.485 (weight) + 0.494 (RI) Discussion The increasing prevalence of obesity in the UAE highlights the urgent need for precise methods to assess body composition. This study addresses this need by evaluating the agreement between BIA and DXA measurements of %BF in a sample of 95 Emirati females living in the UAE. Accurate assessment methods are vital for developing effective public health interventions and clinical strategies to combat obesity and its associated health risks. Despite strong correlations between BIA and DXA, this study revealed poor agreement in %BF measurements between the two methods. Specifically, BIA underestimated %BF by 14.1%, while overestimating FFM by 8.2 kg. At the individual level, differences in %fat ranged from -3.1% to -25.1%, potentially leading to misclassification based on BMI category. Although there was a strong correlation overall, the agreement at the individual level was not clinically acceptable. The consistent underestimation of %BF and FM by BIA aligns with findings from other studies comparing BIA with DXA (22,33–36). A study by Lopes et al. in a sample of 121 adults observed a strong positive correlation between BIA and DXA in measuring FFM and %BF yet emphasized that these methods are not interchangeable. BIA consistently underestimated %BF by 5.56% and overestimated FFM by 2.90 kg, which could potentially impact nutritional planning by clinical dietitians and patient outcomes (37). Leahy et al.'s study, conducted on a sample of 403 healthy young Irish adults, similarly found that BIA consistently underestimated both %BF and FM compared to DXA, with a more pronounced effect in women and individuals with higher total %BF (38). Another study conducted on Turkish students reported comparable findings that BIA consistently underestimated %BF compared to DXA, with the degree of underestimation remaining consistent across varying levels of body fat (39). With numerous studies suggesting that BIA tends to underestimate %BF, particularly in individuals with higher BMI, its utility remains viable as a practical option for body composition assessment in the absence of more precise methods (24). Variations in BIA results are influenced by differences in device parameters, including the path of the electrical current, electrode configuration, participant positioning, frequency settings, and specific equations used (13). Another factor contributing to variability in BIA measurements is its susceptibility to bias in populations with altered hydration levels, such as during fasting, pregnancy, medication intake, and obesity. These biases arise primarily due to assumptions of consistent properties in FFM. However, the use of age, gender, and population-specific equations can enhance the accuracy of BIA in estimating body composition (24). One study reported a strong agreement between BIA and other methods for measuring FFM and FM but noted that BIA equations are specific to manufacturers and populations, often derived from data of healthy individuals with normal weight (14). These assumptions are critical for accurate BIA outcomes, and deviations can impact measurement precision. Developing tailored prediction equations for specific ethnic populations may further improve agreement between BIA and DXA (20). The primary reason for these ethnic-specific equations in BIA is likely due to recognized differences in physique and body proportions between ethnic groups (26). These differences include variations in stature and lean mass. Moreover, there are notable differences in the amount and distribution of body fat across ethnicities (28). Because impedance measurements are influenced by the body's cross-sectional area and the length of conducting segments, ethnic differences in body size and proportions may contribute to the ethnic-specific relationship observed between bioelectrical data and body composition (26). The inaccuracies observed in assessing body composition in Emirati females using built-in equations stem from their development based on populations of white ethnicity. Although numerous studies have proposed new predictive equations to enhance the accuracy of body composition assessments using BIA, no studies have been conducted in the UAE. Therefore, this study proposes a new prediction equation for body composition using BIA. While our study provides valuable insights into the agreement between BIA and DXA measurements of BF% in Emirati females, several limitations should be acknowledged. The study's reliance on convenience sampling may introduce selection bias, potentially limiting the generalizability of the findings to the broader population of Emirati females. The small sample size restricted our ability to compare the agreement between BIA and DXA across different BMI categories and hindered the capacity to validate the proposed equation in an independent sample. In conclusion, this study identified substantial discrepancies between BIA and DXA in measuring %BF among Emirati females. BIA consistently underestimated %BF and FM while overestimating FFM. Despite these discrepancies, BIA remains a valuable tool for obesity assessment due to its ease of use, non-invasiveness, and cost-effectiveness. Future research should focus on validating new BIA-specific equations tailored for females in the UAE to enhance its accuracy and reliability. Declarations Data Availability Statement Data is available within the published article. Additional data can be available from the corresponding author upon request. Acknowledgements The authors would like to acknowledge all the participants who participated in this study. Author Contributions DH designed the study. DH and AE analyzed the data. AE wrote the first draft of the manuscript. DH reviewed the manuscript. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethical Approval Ethical approval for the study was obtained from the Zayed University Research Ethics Committee (ZU16_016_F). Competing Interests The authors declare no conflict of interest. References Ahmad Q, Shaima A, Haifa M, Sangameshwar MB. 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A comparison of dual energy X-ray absorptiometry and bioelectrical impedance analysis to measure total and segmental body composition in healthy young adults. Eur J Appl Physiol. 2012 Feb;112(2):589-95. doi: 10.1007/s00421-011-2010-4. Duz S, Kocak M, Korkusuz F. Evaluation of body composition using three different methods compared to dual-energy X-ray absorptiometry. Eur J Sport Sci. 2009;9(3):181–90. Tables Table 1. Descriptive characteristics of the study sample (n = 95) Minimum Maximum Mean ± SD Age (years) 17 28 19.7 ± 2 Height (cm) 139.5 180 158.4 ± 6.4 Weight (kg) 36.1 113.8 58 ± 15.4 BMI (kg/m 2 ) 14.8 38.9 22.9 ± 5.2 Waist Circumference (cm) 52 105 68.7 ± 10.8 Tanita % Fat 3 50.6 24.9 ± 10.2 Tanita Fat mass (kg) 1.1 57.6 15.9 ± 10.8 Tanita Fat free mass (kg) 34.6 61.6 42.1 ± 4.9 Resistance (Ω) 432.7 920.8 622.9 ± 93.1 Resistance index (cm 2 /Ω)* 27.7 74.9 41.4 ± 7.9 DXA % Fat 23.9 56.2 39.1 ± 6.8 DXA Fat mass (kg) 8.9 57.6 22.1 ± 10.2 DXA Fat free mass (kg) 22.1 57.5 33.9 ± 6.3 *Resistance index was calculated as height in cm² divided by resistance (Ω) Table 2. Paired samples t-test results comparing % Fat, FM, and FFM between BIA and DXA Mean difference SD p - value Cohen’s d Correlation % Fat -14.1 5.6 < 0.001 -2.5 0.855 * Fat mass (kg) -5.8 2.2 < 0.001 -2.7 0.984 * Fat free mass (kg) 8.2 2.5 < 0.001 3.2 0.929 * Mean difference = BIA – DXA. *Correlation is significant at the 0.01 level Table 3. Stepwise linear regression analysis of predictors influencing FFM measured by DXA Model R 2 SEE β p - value Constant 1 Resistance index 0.803 2.81 0.896 <0.001 4.140 2 Resistance index 0.877 2.23 0.473 <0.001 6.210 Weight 0.503 <0.001 3 Resistance index 0.906 1.96 0.494 <0.001 - 4.599 Weight 0.485 <0.001 Age 0.171 <0.001 Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4636500","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":319226397,"identity":"2fae7fd1-2aa0-443e-aa2a-9d63195a9813","order_by":0,"name":"Dalia Haroun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIie2Rv2oCQRCHf8tBbBZsT070FfYI+AeVexUXizRXB8uVQGzuAbbwOVLvMaDNgu0VaSRgZWG6g1yR40TsloM0KfarZgY+5jcM4PH8QxIEygDm1l3bKLFiD4XpNoowTOGuBLyVcsg3psTncLzNRl/zitDdGnZ1JRRWqjzDOd5ZO35OOSG0y6DnSiiMVIaDmA7TUZSGBBRA5EoojieVV6CkUSaCMCwQ/FQupZCK6i2yUbCkeoKnyHV+rE+K+oJWmu9fe5l54bGV79PMoSTdFX1f1rTQnbePsKxmg8GBqChda27xHmV9ePMpj8fj8fyFXzxvUWAvRwznAAAAAElFTkSuQmCC","orcid":"","institution":"Zayed University","correspondingAuthor":true,"prefix":"","firstName":"Dalia","middleName":"","lastName":"Haroun","suffix":""},{"id":319226398,"identity":"600c1974-0497-41a4-a5b3-11705f65a5a3","order_by":1,"name":"Aseel Ehsanallah","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Aseel","middleName":"","lastName":"Ehsanallah","suffix":""}],"badges":[],"createdAt":"2024-06-25 12:45:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4636500/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4636500/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60945028,"identity":"2dad5a81-ffb9-4b7d-a02e-deb90ddcbc28","added_by":"auto","created_at":"2024-07-23 22:19:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":509131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBland and Altman plot analysis to evaluate the agreement between BIA and DXA. (a) % fat, (b) Fat mass, (c) Fat-free mass.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4636500/v1/5a5d1906ef61fc847834665d.jpg"},{"id":61944052,"identity":"619aeed9-63e6-4430-938d-dada9e387290","added_by":"auto","created_at":"2024-08-07 11:04:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1046980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4636500/v1/35846496-92c7-49d2-998f-b1a407135523.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Assessment of Body Fat Percentage in Emirati Females: A Comparative Analysis of BIA vs DXA","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity has emerged as a pressing health concern for the United Arab Emirates (UAE) in recent years. Data extracted from the UAE National Health Survey Report of 2017\u0026ndash;2018 reveals a high prevalence of obesity among adults, estimated at around 27.8% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This statistic indicates a broader health context dominated by non-communicable diseases (NCDs), which the World Health Organization (WHO) identifies as the primary cause of mortality in the region. In fact, NCDs account for 55% of all deaths in the UAE, with cardiovascular diseases (CVDs) emerging as the primary culprit, responsible for 34% of these fatalities (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Therefore, addressing the increasing prevalence of obesity and its related health risks has become an urgent priority in the UAE.\u003c/p\u003e \u003cp\u003eAssessing obesity requires methods that are not only accurate but also cost-effective and efficient for use in large-scale settings. Weight and height measurements, offer a simpler and quicker alternative, with Body Mass Index (BMI) being a widely employed metric (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). BMI's limitations include its inability to differentiate between muscle, fat, bone, or vital organs, leading to misclassification of individuals with high fat-free mass (FFM) relative to stature as overweight or obese, and its failure to account for variations in body composition among individuals (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Moreover, body fat percentage (BF%) varies with age, sex, ethnicity, and individual differences, complicating the interpretation of BMI values (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Although BMI has its limitations, it remains popular in epidemiological research. Alternative methods for assessing obesity, such as body fat-mass measurement techniques including Bioelectrical Impedance Analysis (BIA), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Dual-Energy X-ray Absorptiometry (DXA), are considered more precise for the assessment of body composition.\u003c/p\u003e \u003cp\u003eDual-energy X-ray Absorptiometry (DXA) is renowned as the gold standard in body composition assessment and is recognized for its precision and applicability in various settings (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Unlike conventional X-ray systems, DXA requires special beam filtering and precise spatial registration to measure both whole-body bone mass and soft tissue composition (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). DXA utilizes a 3-compartment model, with compartments encompassing fat mass (FM), FFM, and bone mineral content. Increasing the number of compartments enhances accuracy, lowers the chances of measurement errors, and reduces the need for assumptions in determining body composition (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). DXA assessments are rapid, non-invasive, and entail minimal inconvenience for patients, further enhancing its appeal in clinical practice and research (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, despite its advantages, DXA does have drawbacks, including the requirement for expensive specialized radiology equipment, small radiation exposure, and the need for trained technicians, which may limit its feasibility in routine clinical practice (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these challenges, BIA has been suggested to assess body composition. BIA serves as an indirect method for assessing body composition, relying on parameters such as impedance and phase angle to calculate various body compartments (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). It utilizes the body's electrical properties to gauge resistance to an electric current, factoring in weight, height, and age to estimate total body water (TBW). Then applies equations to accurately determine BF% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). BIA, a two-compartment model, operates under the assumption of constant FFM density. However, significant water fluctuations during growth and development can lead to inaccuracies in body composition measurements (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Other factors including device type, water distribution, hydration status, weight, and height, may increase the risk of inaccurately assessing FFM (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). BIA is safe, simple, non-invasive, and cost-effective, making it increasingly popular for evaluating body composition in clinical and research settings (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) It has emerged as a popular alternative to DXA for assessing body composition (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). BIA\u0026rsquo;s advantages, such as portability, affordability, minimal training requirements, and lack of radiation exposure, make it a practical option for assessing body composition in both clinical practice and large-scale epidemiological studies (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, the accuracy of predictive equations for estimating TBW depends on factors such as the type of BIA device used to record impedance data, the reference method employed to assess FFM, and the characteristics of the population in which the equation was developed (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBIA devices incorporate predictive equations for body fat that were originally developed using data from specific demographic groups (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Many of these equations were initially derived from studies involving predominantly white populations and subsequently applied to individuals from diverse ethnic backgrounds. However, research indicates that there are variations in body composition among different ethnic groups, potentially impacting the precision of BIA measurements (\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Hence, past research suggests the necessity for customized BIA equations tailored to specific ethnic groups (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Currently, there is a lack of research comparing BIA and DXA in the UAE. Therefore, the aim of this study was to examine the agreement between BIA and DEXA measurements of BF% among females.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects\u003c/h2\u003e \u003cp\u003eIn a retrospective analytical cross-sectional study, data, including questionnaire responses and measurements, were collected initially between March 2016 and March 2017. This study involved analyzing a comprehensive dataset obtained from a sample of 170 females aged 17 to 27 years. The participants were healthy Emirati women living in the UAE, selected using a convenience snowball sampling method. Participants were selected based on specific inclusion criteria, including female Emiratis aged between 17 and 28, having fasted for 12 hours, and not currently menstruating. Exclusion criteria encompassed males, non-Emiratis, individuals with metabolic disorders (such as diabetes, kidney disease, and hypertension), those taking certain medications, pregnant or lactating females, and those reporting weight fluctuations exceeding 3 kg. Following the application of these criteria, 95 out of the initial 170 participants were included in the analysis. A minimum sample of 91 participants was determined (power\u0026thinsp;=\u0026thinsp;0.8, alpha\u0026thinsp;=\u0026thinsp;0.05) to achieve a medium effect size for the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) increases, in a regression equation with up to 5 predictors. Therefore, our sample of 95 participants was enough to ensure adequate power analysis in the equation development (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Participation in the study was voluntary and informed written consent was obtained from all participants. Ethical approval for the study was obtained from the Zayed University Research Ethics Committee (ZU16_016_F). Participants who agreed to take part were provided with a consent form to review and sign before the assessment session. To maintain confidentiality, the names and contact details of the participants were kept separately in a secure cabinet, and each participant was assigned a unique code. Various communication channels, such as email, text messages, and campus word of mouth, were utilized to disseminate information about the study and recruit female university students. Interested females were provided with either an electronic or paper copy of the information sheet upon expressing their willingness to participate.\u003c/p\u003e \u003cp\u003eMeasurements were conducted in the body composition laboratory at Zayed University in Dubai, UAE. All participants needed to arrive at the laboratory after fasting for at least 8\u0026ndash;12 hours and with an empty stomach. All the measurements were taken on the same day. Participants were instructed to wear light clothing, avoid any physical activity, refrain from consuming caffeinated beverages, and empty their bladders before measurements were taken. A checklist was used to ensure that the participants met the eligibility criteria for the examination. The session, which lasted between 60 and 90 minutes, involved the collection of data and measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eQuestionnaire\u003c/h2\u003e \u003cp\u003eA trained research assistant administered a structured questionnaire to gather personal information, including age, sex, ethnicity, marital status, body weight, height, medical history, and any medications used, if relevant. Participants were also asked about weight fluctuations over the past 3 months, with the question: \"Have you experienced any changes in your weight? If so, how many kilograms?\"\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric Measurements and Body Fat Analysis\u003c/h2\u003e \u003cp\u003eBody weight was measured to the nearest 0.01 kg using the BodPod System electronic scale (Body Composition System; Life Measurement, Incorporated, Concord, CA). Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer (TANITA HR-200, China). The waist circumference was measured at the narrowest point to the nearest 0.1 cm over light clothing using a non-elastic, flexible tape (SECA 201) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The hip circumference was measured at the widest portion of the hip (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). BMI was calculated by dividing the weight in kilograms by the square of the height in meters (kg/m\u003csup\u003e2\u003c/sup\u003e). WHO defines overweight as a BMI between 25 and 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e, and the classification of obesity is defined as a BMI greater than 30 kg/m\u003csup\u003e2\u003c/sup\u003e (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The DXA scanner provides accurate data on bone and soft tissue composition, including bone mineral density (BMD), lean and fat tissue mass, and fat percentage. The DXA scanner was calibrated daily according to the manufacturer\u0026rsquo;s instructions. Participants removed any objects and clothing containing metal before undergoing the scan. Whole-body scans were performed on participants who were instructed to lie flat on the scanning bed with their hands at their sides and to refrain from moving throughout the measurement. BIA was measured using a TANITA BC-418 MA hand-foot impedance machine, which prints out FM, FFM, and TBW measurements based on manufacturer-specific equations and whole-body resistance (Ω). For this device, impedance is recorded with the subject standing and holding hand grips. The machine requires the entry of the subject's age, gender, height, and standard body type. Inaccuracies in these inputs affect the body composition estimates but not the resistance measurement. Subjects were then asked to stand barefoot on the metal footplates while holding the handles for approximately one minute.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics including means and standard deviations were calculated for participant characteristics. Paired samples t-tests were used to assess if measures of %fat, FM, and FFM were significantly different between BIA and DXA. The level of statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Cohen\u0026rsquo;s d for paired samples was calculated as a measure of effect size for differences between BIA and DXA measures. An effect size of d\u0026thinsp;=\u0026thinsp;0.2 was considered a small effect size, d\u0026thinsp;=\u0026thinsp;0.5 was considered a moderate effect size, and d\u0026thinsp;=\u0026thinsp;0.8 was considered a large effect size (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Pearson correlation coefficients were used to assess the correlation between fat, FM, and FFM measured by BIA and DXA. Stepwise multiple regression analysis was used to develop a predictive equation for FFM, incorporating resistance index (RI), weight, and age as independent variables, with FFM from DXA as the dependent variable. A tolerance and variance inflation factor (VIF) analysis was conducted to ensure no multicollinearity. A Durbin-Watson statistic was calculated to assess the assumption that the residuals are independent. Residual normality and variance homogeneity were evaluated during the model development phase.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe descriptive characteristics of all 95 participants included in the analysis are shown in Table 1. The average age of the subjects was 19.7 years, and the mean BMI was 22.9 kg/m². Out of the 95 participants, 19 (20%) were classified as underweight, 52 (54.7%) had a normal weight, 14 (14.7%) were overweight, and 10 (10.5%) were obese.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA paired samples t-test was conducted to assess differences in % Fat, FM, and FFM between BIA and DXA (Table 2). Results indicate that BIA significantly underestimated % Fat (14.1%) and FM (5.8 kg) compared to DXA, with a large effect size and a strong correlation between the two methods. Conversely, BIA significantly overestimated FFM by 8.2 kg compared to DXA, also showing a large effect size and strong correlation. All three body composition parameters measured by DXA and BIA showed significant positive correlations (p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003eThe Bland–Altman (B\u0026amp;A) plots were used to determine the limits of agreement (LoA) between BIA and DXA (Fig 1). The B\u0026amp;A plot for %fat and FM indicates that BIA tends to underestimate %fat to a greater extent among those with greater %fat compared to those with lower %fat (LoA: -25.1 to -3.1). A similar observation was seen for FM, where the discrepancy between BIA and DXA increases with higher FM, indicating proportional bias for both %fat and FM. For FM (LoA: -10.0 to -1.6). For FFM, the non-zero slope of the trend line also indicates a proportional bias. The mean difference in FFM estimates was larger for individuals with lower FFM, with BIA overestimating FFM in participants with lower FFM compared to those with higher FFM (LoA: 3.3 to 13.2). Across all measures, the greatest overestimate was 13.37 for FFM, and the greatest underestimate was -27.5 for %fat. These results show that the in-built prediction equations cannot adequately predict the %fat, FM, and FFM for this sample.\u003c/p\u003e\n\u003cp\u003eTable 3 displays the results of the stepwise linear regression analysis investigating the predictor variables influencing FFM (DXA). The optimal model includes the resistance index, weight, and age as predictors, resulting in an R² of 0.906, indicating that these variables collectively explain 90.6% of the variance in FFM. The resistance index (β = 0.494), weight (β = 0.485), and age (β = 0.171) all demonstrate statistically significant contributions (p \u0026lt; 0.001) to predicting FFM.\u003c/p\u003e\n\u003cp\u003eThe final model retained weight (kg), age (years), and RI as significant predictors of FFM. The stepwise regression analysis yielded the following FFM prediction equation for this Emirati female population:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFFM = - 4.599 + 0.171 (age) + 0.485 (weight) + 0.494 (RI)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe increasing prevalence of obesity in the UAE highlights the urgent need for precise methods to assess body composition. This study addresses this need by evaluating the agreement between BIA and DXA measurements of %BF in a sample of 95 Emirati females living in the UAE. Accurate assessment methods are vital for developing effective public health interventions and clinical strategies to combat obesity and its associated health risks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite strong correlations between BIA and DXA, this study revealed poor agreement in %BF measurements between the two methods. Specifically, BIA underestimated %BF by 14.1%, while overestimating FFM by 8.2 kg. At the individual level, differences in %fat ranged from -3.1% to -25.1%, potentially leading to misclassification based on BMI category. Although there was a strong correlation overall, the agreement at the individual level was not clinically acceptable. The consistent underestimation of %BF and FM by BIA aligns with findings from other studies comparing BIA with DXA (22,33\u0026ndash;36). A study by Lopes et al. in a sample of 121 adults observed a strong positive correlation between BIA and DXA in measuring FFM and %BF yet emphasized that these methods are not interchangeable. BIA consistently underestimated %BF by 5.56% and overestimated FFM by 2.90 kg, which could potentially impact nutritional planning by clinical dietitians and patient outcomes (37). Leahy et al.\u0026apos;s study, conducted on a sample of 403 healthy young Irish adults, similarly found that BIA consistently underestimated both %BF and FM compared to DXA, with a more pronounced effect in women and individuals with higher total %BF (38). Another study conducted on Turkish students reported comparable findings that BIA consistently underestimated %BF compared to DXA, with the degree of underestimation remaining consistent across varying levels of body fat (39). With numerous studies suggesting that BIA tends to underestimate %BF, particularly in individuals with higher BMI, its utility remains viable as a practical option for body composition assessment in the absence of more precise methods (24).\u003c/p\u003e\n\u003cp\u003eVariations in BIA results are influenced by differences in device parameters, including the path of the electrical current, electrode configuration, participant positioning, frequency settings, and specific equations used (13). Another factor contributing to variability in BIA measurements is its susceptibility to bias in populations with altered hydration levels, such as during fasting, pregnancy, medication intake, and obesity. These biases arise primarily due to assumptions of consistent properties in FFM. However, the use of age, gender, and population-specific equations can enhance the accuracy of BIA in estimating body composition (24). One study reported a strong agreement between BIA and other methods for measuring FFM and FM but noted that BIA equations are specific to manufacturers and populations, often derived from data of healthy individuals with normal weight (14). These assumptions are critical for accurate BIA outcomes, and deviations can impact measurement precision. Developing tailored prediction equations for specific ethnic populations may further improve agreement between BIA and DXA (20).\u003c/p\u003e\n\u003cp\u003eThe primary reason for these ethnic-specific equations in BIA is likely due to recognized differences in physique and body proportions between ethnic groups (26). These differences include variations in stature and lean mass. Moreover, there are notable differences in the amount and distribution of body fat across ethnicities (28). Because impedance measurements are influenced by the body\u0026apos;s cross-sectional area and the length of conducting segments, ethnic differences in body size and proportions may contribute to the ethnic-specific relationship observed between bioelectrical data and body composition (26).\u003c/p\u003e\n\u003cp\u003eThe inaccuracies observed in assessing body composition in Emirati females using built-in equations stem from their development based on populations of white ethnicity. Although numerous studies have proposed new predictive equations to enhance the accuracy of body composition assessments using BIA, no studies have been conducted in the UAE. Therefore, this study proposes a new prediction equation for body composition using BIA. While our study provides valuable insights into the agreement between BIA and DXA measurements of BF% in Emirati females, several limitations should be acknowledged. The study\u0026apos;s reliance on convenience sampling may introduce selection bias, potentially limiting the generalizability of the findings to the broader population of Emirati females. The small sample size restricted our ability to compare the agreement between BIA and DXA across different BMI categories and hindered the capacity to validate the proposed equation in an independent sample.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study identified substantial discrepancies between BIA and DXA in measuring %BF among Emirati females. BIA consistently underestimated %BF and FM while overestimating FFM. Despite these discrepancies, BIA remains a valuable tool for obesity assessment due to its ease of use, non-invasiveness, and cost-effectiveness. Future research should focus on validating new BIA-specific equations tailored for females in the UAE to enhance its accuracy and reliability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available within the published article. Additional data can be available from the corresponding author upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge all the participants who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDH designed the study. DH and AE analyzed the data. AE wrote the first draft of the manuscript. DH reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the Zayed University Research Ethics Committee (ZU16_016_F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad Q, Shaima A, Haifa M, Sangameshwar MB. UAE National Health Survey Report 2017-2018 [Internet]. 2018 [cited 2024 April 28]. 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Eur J Sport Sci. 2009;9(3):181\u0026ndash;90. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Descriptive characteristics of the study sample (n = 95)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e19.7\u0026nbsp;\u0026plusmn;\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e139.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e158.4\u0026nbsp;\u0026plusmn;\u0026nbsp;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight \u0026nbsp;(kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e113.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e58\u0026nbsp;\u0026plusmn; 15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e22.9\u0026nbsp;\u0026plusmn;\u0026nbsp;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist Circumference (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e68.7\u0026nbsp;\u0026plusmn;\u0026nbsp;10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTanita % Fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e24.9\u0026nbsp;\u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTanita Fat mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e15.9\u0026nbsp;\u0026plusmn; 10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTanita Fat free mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e34.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e61.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e42.1\u0026nbsp;\u0026plusmn; 4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistance (Ω)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e432.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e920.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e622.9\u0026nbsp;\u0026plusmn; 93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistance index (cm\u003csup\u003e2\u003c/sup\u003e/Ω)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e74.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e41.4\u0026nbsp;\u0026plusmn; 7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDXA % Fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e23.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e56.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e39.1\u0026nbsp;\u0026plusmn; 6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDXA Fat mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e22.1\u0026nbsp;\u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.096774193548384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDXA Fat free mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\"\u003e\n \u003cp\u003e22.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e57.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.838709677419356%\"\u003e\n \u003cp\u003e33.9\u0026nbsp;\u0026plusmn; 6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Resistance index was calculated as height in cm\u0026sup2; divided by resistance (Ω)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePaired samples t-test results comparing % Fat, FM, and FFM between BIA and DXA\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.792937399678973%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean difference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep - value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohen\u0026rsquo;s d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.792937399678973%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\" valign=\"top\"\u003e\n \u003cp\u003e-14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\" valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e0.855\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.792937399678973%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFat mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\" valign=\"top\"\u003e\n \u003cp\u003e-5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\" valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e-2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e0.984\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.792937399678973%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFat free mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\" valign=\"top\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\" valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e0.929\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMean difference = BIA \u0026ndash; DXA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*Correlation is significant at the 0.01 level\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eStepwise linear regression analysis of predictors influencing FFM measured by DXA\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.657276995305164%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.485133020344287%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.89358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.710485133020343%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep - value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.657276995305164%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistance index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.485133020344287%\" valign=\"top\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.89358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.710485133020343%\" valign=\"top\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"top\"\u003e\n \u003cp\u003e4.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.657276995305164%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistance index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.485133020344287%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.89358372456964%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.710485133020343%\" valign=\"top\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.52095808383233%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.143712574850298%\" valign=\"top\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.33532934131737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.657276995305164%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistance index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.485133020344287%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.89358372456964%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.710485133020343%\" valign=\"top\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" rowspan=\"3\" valign=\"top\"\u003e\n 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\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4636500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4636500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground/Objectives:\u003c/h2\u003e \u003cp\u003eObesity is a significant health issue in the UAE. Accurate body composition assessment is crucial for managing obesity-related health risks. This study aimed to evaluate the agreement between Bioelectrical Impedance Analysis (BIA) and Dual-Energy X-ray Absorptiometry (DXA) in measuring body fat percentage (%BF) among Emirati females.\u003c/p\u003e\u003ch2\u003eSubjects/Methods:\u003c/h2\u003e \u003cp\u003eThis cross-sectional study involved 95 healthy Emirati females aged 17\u0026ndash;27 years. Paired samples t-tests, correlation analyses, and Bland-Altman plots were used to compare the two methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBIA significantly underestimated %BF and fat mass (FM) while overestimating fat-free mass (FFM) compared to DXA. The mean difference in %BF was \u0026minus;\u0026thinsp;14.1% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the mean difference in FFM was +\u0026thinsp;8.2 kg (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Despite strong correlations between BIA and DXA measurements (r\u0026thinsp;=\u0026thinsp;0.855 for %BF, r\u0026thinsp;=\u0026thinsp;0.984 for FM, and r\u0026thinsp;=\u0026thinsp;0.929 for FFM), Bland-Altman plots indicated poor agreement, with wide limits of agreement.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBIA remains valuable for obesity assessment in large-scale studies and clinical settings due to its non-invasive, easy-to-use, and cost-effective characteristics. The results show that the in-built prediction equations cannot adequately predict the %fat, FM, and FFM for this sample. Future research should focus on developing and validating BIA-specific equations tailored for Emiratis.\u003c/p\u003e","manuscriptTitle":"Assessment of Body Fat Percentage in Emirati Females: A Comparative Analysis of BIA vs DXA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 22:18:57","doi":"10.21203/rs.3.rs-4636500/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e570dd24-d4f5-4d02-894f-d97acf8d1225","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33752714,"name":"Health sciences/Health care/Diagnosis/Body mass index"},{"id":33752715,"name":"Health sciences/Health care/Nutrition"}],"tags":[],"updatedAt":"2024-08-07T10:40:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-23 22:18:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4636500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4636500","identity":"rs-4636500","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}