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Comparison of Total and Segmental Body Composition Using DXA and Multifrequency Bioimpedance in Collegiate Female Athletes
Abstract
The purpose of this investigation was to determine the agreement between multi-frequency bioelectrical impedance analysis (BIA) and dual-energy x-ray absorptiometry (DXA) for measuring body fat percentage (BF%), fat-free-mass (FFM), and total body and segmental lean soft tissue (LST) in collegiate female athletes. Forty-five female athletes (age = 21.2 ± 2.0 years, height = 166.1 ± 7.1 cm, weight = 62.6 ± 9.9 kg) participated in this study. Variables measured via BIA and DXA were as follows: BF%; FFM; and LST of the arms (ARMSLST), the legs (LEGSLST), the trunk (TRUNKLST), and the total body (TOTALLST). Compared to the DXA, the InBody 720 provided significantly lower values for BF% (-3.3%, p < 0.001) and significantly higher values for FFM (2.1 kg, p < 0.001) with limits of agreement (1.96 SD of the mean difference) of ± 5.6% for BF% and ± 3.7 kg for FFM. No significant differences (p < 0.008) existed between the two devices (InBody 720 - DXA) for ARMSLST (0.05 kg), TRUNKLST (0.14 kg), LEGSLST (-0.4 kg), and TOTALLST (-0.21 kg). The limits of agreement were ± 0.79 kg for ARMSLST, ± 2.62 kg for LEGSLST, ± 3.18 kg for TRUNKLST, and ± 4.23 kg for TOTALLST. This study found discrepancies in BF% and FFM between the two devices. However, the InBody 720 and DXA appeared to provide excellent agreement for measuring total body and segmental LST. Therefore, the InBody 720 may be a rapid non-invasive method to assess LST in female athletes when DXA is not available.
... Numerous studies have compared BIA and DXA for total and regional body composition metrics such as body fat, lean mass, and bone mineral content (13)(14)(15)(16)(17)(18)(19) . For example, research has shown the accuracy of single-frequency BIA for predicting appendicular lean and fat mass varies based on sex and segmental mass (15) . ...
... In addition, researchers have shown that BIA is more https://doi.org/10.1017/S000711452400076X Published online by Cambridge University Press Accepted manuscript accurate when utilized to predict lean mass instead of fat mass (15,19,20) . Lastly, validation research has shown BIA can be used to estimate bone mineral content, when compared to DXA, in healthy populations (16,17) . ...
... Correlations between MQI metrics ranged from 0.71 to 0.94 ( Figure 1). Strong, statistically significant correlations were observed for all ALM variables (0.84 < R 2 < 0.93; al. (19) found MFBIA and DXA had excellent agreement when used to predict appendicular lean soft tissue (i.e., arms and legs) in collegiate female athletes. It is worth noting the lean soft tissue measures from Esco et al. (19) excluded bone tissue. ...
The purpose of this study was to compare single- and multi-frequency bioimpedance (BIA) devices against dual energy X-ray absorptiometry (DXA) for appendicular lean mass (ALM) and muscle quality index (MQI) metrics in Hispanic adults. One-hundred thirty-one Hispanic adults (18–55 yrs.) participated in this study. ALM was measured with single-frequency (SFBIA), multi-frequency (MFBIA), and DXA. ALM TOTAL (left arm + right arm + left leg + right leg) and ALM ARMS (left arm + right arm) were computed for all three devices. Handgrip strength (HGS) was measured using a dynamometer. The average HGS was used for all MQI models (highest left hand + highest right hand)/2. MQI ARMS was defined as the ratio between HGS and ALM ARMS . MQI TOTAL was established as the ratio between HGS and ALM TOTAL . SFBIA and MFBIA had strong correlations with DXA for all ALM and MQI metrics (CCC values ranged from 0.86 [MQI MFBIA-ARMS ] to 0.97 [Arms LM SFBIA ]; all p < 0.001). Equivalence testing varied between methods (e.g., SFBIA vs. DXA) when examining the different metrics (i.e., ALM TOTAL , ALM ARMS , MQI TOTAL , and MQI ARMS ). MQI ARMS was the only metric that did not differ from the line of identity and had no proportional bias when comparing all the devices against each other. The current study findings demonstrate good overall agreement between SFBIA, MFBIA, and DXA for ALM TOTAL and ALM ARMS in a Hispanic population. However, SFBIA and MFBIA have better agreement with DXA when used to compute MQI ARMS than MQI TOTAL .
... and valid when compared to dualenergy x-ray absorptiometry (1,20). Additionally, BIA has been suggested in law enforcement populations because of its practicality (29), and has been used in sport conditioning programs as a method of segmental body composition analysis (14). The test was administered by trained staff from InBody, done first in the testing session, and similar to previously established methods (2,14,38). ...
... Additionally, BIA has been suggested in law enforcement populations because of its practicality (29), and has been used in sport conditioning programs as a method of segmental body composition analysis (14). The test was administered by trained staff from InBody, done first in the testing session, and similar to previously established methods (2,14,38). Recruits were asked to remove any jewelry and metallic wear before their palms and soles were cleaned with an electrolyte tissue. Recruits then stood on the InBody scale with their soles in contact with the electrodes and body mass was measured by the device. ...
... Next, recruits grasped hand electrodes with palms and fingers making contact, elbows extended and shoulders slightly abducted. The InBody 720 measured entire body composition of five segments (both arms, both legs, and trunk) (14). For analysis, body composition measures for both arms were added together for upper extremities, and both legs added together for lower extremities. ...
Law enforcement agencies often test the fitness performance and body composition of incoming recruits. This study investigated the relationships between whole and segmental body composition, and fitness tests in law enforcement recruits. A retrospective analysis of 72 male and 11 female recruits was performed. Bioelectrical impedance analysis (BIA) variables were: lean mass (LM), upper-extremity lean mass (UELM), trunk LM, lower-extremity lean mass (LELM), fat mass (FM), upper-extremity fat mass (UEFM), trunk FM, and lower-extremity fat mass (LEFM). Fitness tests included: vertical jump (VJ), peak anaerobic power (PAPw), 75-yard pursuit run (75PR), push-ups, sit-ups, 2-kg medicine ball throw (MBT), and the multi-stage fitness test (MSFT). Partial correlations and ANCOVAs between quartiles assessed relationships between body composition and performance. Significant moderate-to-large relationships were found; LM, UELM, trunk LM, LELM all related to PAPw (r = 0.500-0.558) and MBT (r = 0.494-0.526). FM, UEFM, trunk FM, LEFM all related to VJ (r = -0.481 to -0.493), 75PR (r = 0.533-0.557), push-ups (r = -0.484 to -0.503), sit-ups (r = -0.435 to -0.449), and MSFT (r = -0.371 to -0.423). The highest LM quartile (4) had superior PAPw and MBT than LM quartiles 1-3. Higher FM quartiles performed poorer in VJ, push-ups, and sit-ups. The 75PR quartiles 2, 3, and 4 were slower than quartile 1, and MSFT quartile 4 completed less shuttles. Total and segmental measures of LM and FM shared the same relationships; lower FM and higher LM related to better performance. Monitoring body composition could help guide training to optimize performance.
... All 8 studies were assessed for risk of bias. Five of the included articles were rated as positive, [19][20][21][22][23] and the remaining articles were deemed to be neutral. [24][25][26] All studies had clearly stated the research question, clearly defined the outcomes, the measurements were valid and reliable, procedure and comparison described in detail, and conclusions were supported by results with biases and limitations taken into consideration. ...
... The 8 included articles reported on 461 athletes who took part in sports and athletic activities including rock climbing, baseball, cross-country running, soccer, basketball, tennis, gymnastics, American football, field hockey, lacrosse, and rugby league. [19][20][21][22][23][24][25][26] The number of participants in each study ranged from 22 to 160, with an average of 58 6 44 (mean 6 SD) athletes. On average, men made up 51.4% of the participants. ...
... On average, men made up 51.4% of the participants. Five studies 19,21,[23][24][25] reported on men only, and 1 study exclusively investigated female participants. 23 The mean age of the participants ranged from 19.7 to 26.8 years, and the mean BMI ranged from 21.7 to 26.9 kg/m 2 . ...
Objective:
To compare dual-energy x-ray absorptiometry (DXA) and bioelectric impedance analysis (BIA) in the assessment of body composition in athletes.
Data sources:
A systematic review and meta-analysis was conducted collating peer-reviewed studies that compared BIA with DXA for the assessment of body composition in athletes that indexed in MEDLINE, CINAHL, EMBASE, and PsycINFO databases.
Main results:
After duplicate removal, 267 articles remained for full-text screening. Sixty-three studies remained for the final inclusion, with 8 focused on athletes (n = 461). Five studies were included in the meta-analysis and were rated as positive after risk of bias assessment, whereas the remaining were neutral. BIA overestimated fat-free mass (FFM) over DXA 2.78 (1.38-4.18) (mean difference ± 95% CI) with an effect size of 3.9(P < 0.001).
Conclusions:
BIA was found to overestimate total FFM when compared with DXA. Correlations are high between BIA and DXA; however, the limits of agreement are wide. Hence, BIA may not be a suitable substitute for DXA body composition scanning of athletes. Because of the low level of ionizing radiation exposure, the use of DXA should always be medically justified, and therefore, it is not recommended for repeat, longitudinal measurements in healthy subjects.We recommend that clinicians do not use BIA interchangeably with DXA in the assessment of body composition in athletes. Considerations should be made over the safety and appropriateness of DXA in young healthy adults. For long-term use, BIA can be warranted for athletes.PROSPERO Registration Number: CRD42020183777.
... The selected articles included different devices and technologies as shown in Fig. 2. Considering the four different technologies, 4 articles used the hand to hand (Esco et al. 2011;Loenneke et al. 2013;Graybeal et al. 2020;Syed-Abdul et al. 2021), 6 the leg to leg (Civar et al. 2003(Civar et al. , 2006Dixon et al. 2005;Loenneke et al. 2013;Domingos et al. 2019;Graybeal et al. 2020), 29 the foot to hand (Birzniece et al. 2015;Colville et al. 1989;Lukaski et al. 1990;Kirkendall et al. 1991;Hortobágyi et al. 1992;Pichard et al. 1997;Williams and Bale 1998;Fornetti et al. 1999;De Lorenzo et al. 2000;Houtkoopr et al. 2001;Andreoli et al. 2004;Svantesson et al. 2008;Company and Ball 2010;Matias et al. 2016aMatias et al. , b, 2021Deminice et al. 2016;Krzykała et al. 2016;Arias Téllez et al. 2019;Campa et al. 2020Campa et al. , 2021aMarini et al. 2020;Graybeal et al. 2020;Silva et al. 2020;Sardinha et al. 2020;Shiose et al. 2020;Stagi et al. 2021;Francisco et al. 2021;Coratella et al. 2021), and 9 the direct segmental technology (Loenneke et al. 2012(Loenneke et al. , 2013Esco et al. 2015;Krzykała et al. 2016;Raymond et al. 2018;Brewer et al. 2019;Hartmann Nunes et al. 2020;Graybeal et al. 2020;Lee et al. 2021). Particularly, more than one technology was used in some studies and for each technology, different devices were used. ...
... Particularly, more than one technology was used in some studies and for each technology, different devices were used. Considering the dependent variables, 30 articles (Birzniece et al. 2015;Colville et al. 1989;Lukaski et al. 1990;Kirkendall et al. 1991;Hortobágyi et al. 1992;Pichard et al. 1997;Williams and Bale 1998;Fornetti et al. 1999;De Lorenzo et al. 2000;Houtkoopr et al. 2001;Civar et al. 2003Civar et al. , 2006Andreoli et al. 2004;Dixon et al. 2005;Svantesson et al. 2008;Company and Ball 2010;Esco et al. 2011Esco et al. , 2015Loenneke et al. 2012Loenneke et al. , 2013Krzykała et al. 2016;Raymond et al. 2018 Shiose et al. 2020;Coratella et al. 2021;Francisco et al. 2021) assessed body fluids comparing BIA with reference methods, using the foot to hand technology. A total of five articles (Campa et al. , 2021aMarini et al. 2020;Silva et al. 2020;Stagi et al. 2021) assessed body composition comparing BIVA with reference methods, using the foot to hand technology (Fig. 3). ...
... Of these six studies, one used a regional approach for investigating the legs FM (Brewer et al. 2019), and one assessed the visceral FM ). Three studies (Esco et al. 2015;Krzykała et al. 2016;Raymond et al. 2018) showed an underestimation of %FM, and one of them used a regional approach measuring the arms and legs FM (Raymond et al. 2018), three studies (Raymond et al. 2018;Brewer et al. 2019;Graybeal et al. 2020) showed no difference between BIA and the reference methods. The study by Graybeal et al. (2020) found higher %FM only in men, while they reported a good agreement in women. ...
The present systematic review aimed to compare the accuracy of Bioelectrical Impedance Analysis (BIA) and Bioelectrical Impedance Vector Analysis (BIVA) vs. reference methods for the assessment of body composition in athletes. Studies were identified based on a systematic search of internationally electronic databases (PubMed and Scopus) and hand searching of the reference lists of the included studies. In total, 42 studies published between 1988 and 2021 were included. The meth-odological quality was assessed using the Quality Assessment Tool for Observational Cohort and Cross-sectional Studies as recommended by the National Institute of Health. Twenty-three studies had an overall good rating in terms of quality, while 13 were rated as fair and 6 as poor, resulting in a low to moderate risk of bias. Fat mass was inconsistently determined using BIA vs. the reference methods, regardless of the BIA-technology. When using the foot to hand technology with predictive equations for athletes, a good agreement between BIA and the reference methods was observed for fat-free mass, total body, intra and extra cellular water. However, an underestimation in fat-free mass and body fluids was found when using generalized predictive equations. Classic and Specific BIVA represented a valid approach for assessing body fluids (Classic BIVA) and percentage of fat mass (Specific BIVA). The present systematic review suggests that BIA and BIVA can be used for assessing body composition in athletes, provided that foot-to-hand technology, predictive equations, and BIVA references for athletes are used.
... From 2000 to 2014, BIA was used in 15 studies [34,35,[37][38][39][40][41][42][43][44][45][46][47][48][49], with a peak of 11 articles published in 2015 [50][51][52][53][54][55][56][57][58][59][60], followed by a decline 2 years later [24,[61][62][63][64], before undergoing a progressive increase beginning in 2018 up until 2020 . Possibly, all articles published before 2018 mainly used the quantitative assessment of body composition, i.e., the simple estimation of the different body composition parameters using prediction equations. ...
... These perplexities have pushed researchers to develop specific equations [24][25][26] or use alternative evaluation approaches [31,45,56,78,109,110]. From 2000 to 2014, BIA was used in 15 studies [34,35,[37][38][39][40][41][42][43][44][45][46][47][48][49], with a peak of 11 articles published in 2015 [50][51][52][53][54][55][56][57][58][59][60], followed by a decline 2 years later [24,[61][62][63][64], before undergoing a progressive increase beginning in 2018 up until 2020 . Possibly, all articles published before 2018 mainly used the quantitative assessment of body composition, i.e., the simple estimation of the different body composition parameters using prediction equations. ...
... However, no specific formulas developed and validated for athletes were available at that time; thus, the equations used in these studies were those proposed for the general population. This may have led to inaccurate values, generating doubts about the The first alternative approach came in 2015, when the segmental BIA was used to estimate the body composition of different body segments in athletes for the first time [51]. Indeed, the segmental BIA allows for the independent assessment of the individual body segments, defined as the four limbs and the trunk [110]. ...
Body composition is acknowledged as a determinant of athletic health and performance. Its assessment is crucial in evaluating the efficiency of a diet or aspects related to the nutritional status of the athlete. Despite the methods traditionally used to assess body composition, bioelectric impedance analysis (BIA) and bioelectric impedance vector analysis (BIVA) have recently gained attention in sports, as well as in a research context. Only until recently have specific regression equations and reference tolerance ellipses for athletes become available, while specific recommendations for measurement procedures still remain scarce. Therefore, the present narrative review summarizes the current literature regarding body composition analysis, with a special focus on BIA and BIVA. The use of specific technologies and sampling frequencies is described, and recommendations for the assessment of body composition in athletes are provided. Additionally, the estimation of body composition parameters (i.e., quantitative analysis) and the interpretation of the raw bioelectrical data (i.e., qualitative analysis) are examined, highlighting the innovations now available in athletes. Lastly, it should be noted that, up until 2020, the use of BIA and BIVA in athletes failed to provide accurate results due to unspecific equations and references; however, new perspectives are now unfolding for researchers and practitioners. In light of this, BIA and especially BIVA can be utilized to monitor the nutritional status and the seasonal changes in body composition in athletes, as well as provide accurate within- and between-athlete comparisons.
... Data have compared similar MF-BIA devices (Seca mBCA; full range frequencies 1-500 kHz) to a 4C model in male and female body builders, reporting MF-BIA (Seca) is not a valid method to assess total body per cent fat (%fat), FM and FFM estimates (Graybeal et al., 2020). Two previous studies evaluating the validity of a MF-BIA devices against DXA in male and female athletes also demonstrated poor validity for body composition outcomes (Brewer et al., 2019;Esco et al., 2015); higher error reported in these previous studies may be related to higher muscle mass and lower adiposity in these populations (Forslund, 1998). Total body water accounts for 50-75% of body weight and is directly influenced by the amount of muscle mass (Ritz et al., 2008); higher muscle mass alters standard hydration assumptions in BIA techniques. ...
... Significant differences were found between MF-BIA and the 4C criterion model estimates for %fat (p < 0.001), FM (p < 0.001) and FFM (p < 0.001) for the total sample (Table 3). to conduct the test, the number of devices needed and accuracy (Heymsfield et al., 1990). When multiple devices are not accessible, 2C model devices are commonly used to estimate body composition (Esco et al., 2015). Results from the present study reveal discrepancies between the MF-BIA and 4C model, which ultimately demonstrate greater agreement in men compared to women. ...
... Although the present study also reported significant overestimation of %fat by a MF-BIA device, our results produced greater agreement between devices (MD: 1.40 ± 2.1%) (Heymsfield & Waki, 1991). Another study utilizing a single-frequency BIA reported that the largest error in %fat occurred when assessing lean men (average %fat = 16.6 ± 3.8%) (Van Marken Lichtenbelt et al., 2004); the current investigation found excellent agreement for %fat estimates between the MF-BIA and 4C model when assessing (Bolanowski & Nilsson, 2001;De Lorenzo et al., 2000;Esco et al., 2015;Ling et al., 2011;Shafer et al., 2009). One previous study comparing a MF-BIA device to a 4C model in female body builders reported a small overestimation of %fat (MD: 0.9 ± 0.5%), similar to the present study evaluating normal-weight females (MD: 1.7 ± 4.5%) (Graybeal et al., 2020). ...
Background
Multi‐frequency bioelectrical impedance analysis (MF‐BIA) offers enhanced body composition outcomes in a time efficient manner. The accuracy of stand‐up MF‐BIA compared against a four‐compartment (4C) criterion lacks evidence.
Objectives
To validate a stand‐up MF‐BIA compared to a 4C criterion for fat mass (FM), fat free mass (FFM), and body fat percentage (%fat).
Subjects/Methods
Eighty‐two healthy (32% men) normal weight (BMI: 18.5 – 24.9 kg/m²) young adults were measured for body composition determined from a stand‐up MF‐BIA and 4C model. Validity statistics included total error (TE) and standard error of the estimate (SEE) to examine prediction error between methods.
Results
For the total sample, prediction error was the highest for %fat (TE=4.2 %; SEE=3.9 %) followed by FM (TE=2.4 kg; SEE=2.2 kg) and FFM (TE=2.4 kg; SEE=2.2 kg). In men, %fat (TE=2.5 %; SEE=2.2 %) and FM (TE=1.9 kg; SEE=1.6 kg) were ideal; FFM was similar to FM (TE=1.9 kg; SEE=1.6 kg). In women, %fat (TE=4.7 %; SEE=4.4 %) ranged from good to fairly good, and FM was very good to excellent (TE=2.6 kg; SEE=2.4 kg); FFM was similar to FM (TE=2.6 kg; SEE=2.3 kg).
Conclusions
Stand‐up MF‐BIA may overestimate %fat and FM, and underestimate FFM compared to a 4C model. FM and FFM estimates from MF‐BIA demonstrate good agreement to a 4C model and may be a practical measure of body composition in normal weight adults. The highest error was seen in %fat for both sexes, with greater error in women.
... The MF-BIA typically uses 8-electrodes and measures the impedance of electrical currents at multiple frequencies to estimate body composition (5,6). Advanced MF-BIA techniques have the ability to also measure impedance and resistance separately across 5 different cylinders within the human body which allow for whole and segmental (legs, arms, and trunk) FFM analysis (9). ...
... The results of the comparison of fat mass (FM), FFM, and segmental FFM while comparing DEXA and MF-BIA, however, have remained conflicting. For example, multiple studies have shown MF-BIA underestimates FM and overestimates FFM (4,5,9,13,21,22,23). In contrast, other studies demonstrated that MF-BIA overestimates FM and underestimates FFM (11,14,17,20,24). ...
... There have also been inconsistent results with examination of segmental FM and FFM. Some studies show that the MF-BIA overestimates appendicular FM while others showed an underestimation of appendicular FM and FFM (1,5,9,18). Due to conflicting findings found in these previous studies (1,4,5,9,11,13,14,17,18,(20)(21)(22)(23)(24) additional data using other MF-BIA and DEXA models are needed to clarify previous findings. ...
The purpose of this study was to evaluate the validity of whole body percent fat (%BF) and segmental fat-free mass (FFM) using multi-frequency bioelectrical impedance analysis (MF-BIA) and dual-energy x-ray absorptiometry (DEXA) in college-aged adults. Sixty-two participants male (n = 32) and female (n = 30) completed MF-BIA and DEXA measurements following established pre-test guidelines. %BF and segmental FFM (right arm, left arm, trunk, right leg, and left leg) were collected and analyzed. The MF-BIA significantly (p < 0.05) underestimated %BF for all participants, females, and males compared to DEXA. In addition, MF-BIA significantly (p < 0.05) underestimated FFM in the arms and legs in all participants and males with the exception of the left arm in all subjects while significantly overestimating FFM in the trunk. In females, the MF-BIA overestimated FFM in the arms and trunk while significantly (p < 0.05) underestimating FFM in the legs. Difference plots also indicated that the underestimation of FFM from MF-BIA in the arms and legs increased as the amount of FFM increased. Thus, our findings suggested that the MF-BIA may not be accurate for measuring whole %BF and segmental FFM in the college-aged population.
... The process of measuring body composition using the S-MFBIA scale used in the present study (InBody 700 series) has been described elsewhere. 10,11 Briefly, the participants grasped the handles, with the palms and thumbs making contact with the electrodes. The S-MFBIA uses 8 polar tactile electrodes, with 2 that are in contact with the palms, 2 with each thumb, 2 with the anterior, and 2 with the posterior aspect of each foot (Figure 1, right). ...
... Although there are a number of available techniques for measuring 15 There are a number of studies that have compared the S-MFBIA with DXA in varying patient populations. 10,[12][13][14][15][16][17][18] A previous study used an earlier version of the S-MFBIA tested in the present study (InBody 720 vs 770) in patients with obesity (>35). 12 One of the main objectives of this study was to conduct a validation test for S-MFBIA in patients with a BMI > 35, given the question of decreased accuracy of BIA as BMI increases. ...
... Another trial compared S-MFBIA (InBody 720) with DXA in college women athletes (n = 45; average age, 21.2 ± 2 years; average weight, 62.6 ± 9.9 kg). 10 Compared with DXA, the S-MFBIA provided slightly lower PBF levels (−3.3%) and higher FFM (+2.1 kg). The authors concluded that the S-MFBIA and DXA had excellent agreement for measuring total and segmental FFM. 10 18 As the results of these studies reveal, BIA technology has improved significantly over the past 30 years with the introduction of MF segmental devices. ...
Background
Despite malnutrition being associated with increased mortality and morbidity, there continues to be great difficulty in defining criteria and implementing widespread screening. Tools used to diagnose decreased Fat Free Mass (FFM; sarcopenia) should be easy to use, relatively inexpensive, and safe. Bioelectrical impedance analysis (BIA) has the potential to meet these criteria but reliability across Body Mass Index (BMI) classes is a concern. Current study compared FFM and Percent Body Fat (PBF) measurements using Segmental Multi‐Frequency BIA (S‐MFBIA) versus dual‐energy X‐ray absorptiometry (DXA) in participants across BMI categories.
Methods
A total of 176 healthy ambulatory participants (18‐65 years of age) were recruited equally (n = 44) in four BMI (kg/m²) categories: 1) 18.5‐24.9, 25.0‐29.9, 30–34.9, ≥35.0. Participants were fasting overnight and had S‐MFBIA (InBody 770) measurements the next morning with DXA being performed subsequently within 30 minutes.
Results
The measurements (mean ± SD) for FFM with DXA was 52.8 ± 11.0 and BIA was 53.6 ± 11.0. Delta (S‐MFBIA‐DXA) was 0.8 ± 2.2 (5% limits of agreement ‐3.5 to +5.2) and concordance correlation coefficient (CCC) was 0.98 (95% CI 0.97,0.98). The measurements (mean ± SD) for PBF with DXA was 37.5 ± 10.6% and S‐MFBIA was 36.6 ± 11.3%. Delta (S‐MFBIA‐DXA) was ‐0.9 ± 2.6 (5% limits of agreement ‐6.0 to +4.2) and concordance correlation coefficient (CCC) was 0.97 (95% CI 0.96,0.98). The CCC according to the four body mass groups for FFM and PBF was between 0.96‐0.98 and 0.90‐0.94 respectively.
Conclusions
FFM and PBF measured by S‐MFBIA had good agreement with DXA across all BMI categories measured in the current study of ambulatory participants.
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... Current literature suggests that BIA may be a usable alternative to DXA for the estimation of (segmental) body composition as this method is less expensive, faster and more applicable under field conditions compared to DXA [15]. In this respect, Esco et al. [16] investigated the (dis)agreement between BIA (i.e., InBody 720) and DXA (i.e., GE Lunar Prodigy) test outcomes for estimating body fat% and fat-free mass in addition to wholebody, arm and leg lean soft tissue in 45 female collegiate athletes. These authors demonstrated that BIA resulted in significantly lower whole-body fat% values and significantly higher whole-body fat-free mass values when compared to DXA, although there was an excellent agreement between both methods for whole-body and segmental lean soft tissue outcomes. ...
... Although comparison with previous literature is challenging due to the use of different devices and study populations, the findings of the current study are more or less in agreement with the results of previous work in a similar study population in terms of age [16,[23][24][25]. However, our results contradict those of the study performed by Jayanama et al. [26] in which no significant difference was found between the Inbody S10 and the Hologic Discovery DXA for whole-body fat%, whole-body fat mass and whole-body fat-free mass, regardless of sex. ...
Bio-electrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) are methods to estimate human body composition. This study aimed to compare sex-specific outcomes for estimating segmental and whole-body composition in 83 healthy participants (21.9 ± 1.5 years, 56% men) using Inbody S10 BIA and Norland Elite DXA devices. One-way repeated measures ANOVAs showed significantly lower whole-body fat% and whole-body fat mass values alongside higher whole-body lean mass values resulting from BIA when compared to DXA (both sexes: p < 0.001). In men, whole-body bone mineral content was significantly higher using BIA against DXA (p < 0.001). Regardless of sex, no significant BIA versus DXA difference was found in arm fat mass (men: p = 0.180, women: p = 0.233), whereas significantly lower leg fat mass values were found with BIA versus DXA (both sexes: p < 0.001). Additionally, significantly higher arm lean mass (both sexes: p < 0.001) and leg lean mass (only women: p < 0.001) were found in BIA versus DXA. Moderate to very strong positive associations (p < 0.05) between BIA and DXA outcome measures were found, except for arm fat mass (men: p = 0.904, women: p = 0.130) and leg fat mass (only men: p = 0.845). This study highlights (sex-dependent) differences in corresponding test outcomes between BIA and DXA both at the segmental and whole-body level.
... Compared to DXA, BOD POD has been shown to overestimate %BF in thinner participants and underestimate %BF in heavier individuals [14]. Compared to DXA, BIA may overestimate fat mass and lean mass in men with different BC [12], underestimate %BF [15], offer an accurate estimate of %BF [16] or show different limits of agreement [17]. However, these studies were conducted with the general population, including normal weight, overweight and obese male individuals [12,[14][15][16][17]. ...
... Compared to DXA, BIA may overestimate fat mass and lean mass in men with different BC [12], underestimate %BF [15], offer an accurate estimate of %BF [16] or show different limits of agreement [17]. However, these studies were conducted with the general population, including normal weight, overweight and obese male individuals [12,[14][15][16][17]. In a study conducted with healthy active males, NIR showed a good concordance with DXA, but tended to overestimate %BF in leaner subjects and underestimate %BF in those with a higher %BF [13]. ...
Combat sports athletes competing in the same discipline exhibit notable and substantial differences in body weight, body composition (BC) and adiposity. No studies have considered the influence of adiposity levels in the agreement between different BC assessment methods. The aim of this study was to analyze the influence of adiposity in the agreement between different methods used to estimate relative body fat (%BF) in Olympic combat sport athletes. A total of 38 male athletes were evaluated using air displacement plethysmography and dual-energy X-ray absorptiometry (DXA) as laboratory methods, and bioelectrical impedance analysis (BIA), near-infrared interactance (NIR) and anthropometry as field methods. All methods were compared to DXA. Agreement analyses were performed by means of individual intraclass correlation coefficients (ICCs) for each method compared to DXA, Bland–Altman plots and paired Student t-tests. The ICCs for the different methods compared to DXA were analyzed, considering tertiles of %BF, tertiles of body weight and type of sport. For the whole group, individual ICCs oscillated between 0.806 for BIA and 0.942 for anthropometry. BIA showed a statistically significant underestimation of %BF when compared to DXA. The agreement between every method and DXA was not affected by %BF, but it was highest in athletes at the highest %BF tertile (>13%). The ICC between NIR and DXA was poor in 72–82 kg athletes. Our results indicate that field methods are useful for routine %BF analysis, and that anthropometry is particularly appropriate, as it showed the highest accuracy irrespective of the athletes’ adiposity.
... BIA does not measure muscle mass directly, instead provides an estimation of muscle mass based on whole body resistance to an electrical current . Lean tissue supplies the least resistance to the current because of its high water content, with the speed of the current being converted to estimate body fat percentage and fat free mass (Esco et al., 2015). Although inexpensive and easy to use in clinical practice, the method is seen as less accurate than other methods available due to its dependence on the hydration status of the individual, and tends to overestimate muscle mass and underestimate fat mass (Esco et al., 2015;Reiss et al., 2016). ...
... Lean tissue supplies the least resistance to the current because of its high water content, with the speed of the current being converted to estimate body fat percentage and fat free mass (Esco et al., 2015). Although inexpensive and easy to use in clinical practice, the method is seen as less accurate than other methods available due to its dependence on the hydration status of the individual, and tends to overestimate muscle mass and underestimate fat mass (Esco et al., 2015;Reiss et al., 2016). ...
Purpose: Pathological and age-related declines in both physical fitness and muscle function are well established; however, the role mitochondrial function plays in these changes is less understood. With low skeletal muscle mass and function associated with poorer surgical outcomes, treatments and interventions that can limit the decrease in muscle mass and function seen in the elderly, known as sarcopenia, and in those with cancer, known as cancer cachexia, is vital. Methods: A systematic review and meta-analysis was conducted to determine the impact of sarcopenia on overall and disease-free survival in patients with locally advanced rectal cancer. In addition, young and older healthy volunteers were recruited to determine links between advancing age and declines in global physical fitness and muscle function, as well as investigate if similar declines in mitochondrial function occur. Results: Our systematic review and meta-analysis established that pre-existing sarcopenia was associated with shorter overall and disease-free survival in patients with locally advanced rectal cancer. Within our healthy volunteer cohort, age significantly impacted global physical function (HGS, 1-RM and VO2max) and measures of muscle architecture, with reduced status in older adults. Conversely, mitochondrial function was not different between the age-groups. Conclusions: There is clearly an age-related decline in global physical fitness and muscle function, however it remains unknown to what degree mitochondrial function is implicated in these changes. With sarcopenia and cachexia both having a negative impact on various prognostic outcomes, interventions such as exercise training regimes show promising results in improving cardiovascular fitness and muscle mass/ function in both elderly and cancer patients undergoing surgery. Despite this, if or how these interventions may modify any mitochondrial dysfunction that may exist, especially in cancer patients undergoing neoadjuvant treatment prior to surgery is wholly unknown. More research is required to understand the complex relationship between mitochondrial function and the changes seen in the skeletal muscle of both the elderly and cancer patients.
... Bioimpedance (BIA) is another method to assess the body composition and the RMR, usually applied by physicians, nutritionists, and physiologists [18,19]. BIA uses a low-intensity electric current passing through body tissues and estimates the total body water and fat-free mass [20]. ...
... BIA uses a low-intensity electric current passing through body tissues and estimates the total body water and fat-free mass [20]. Several studies showed a high accuracy for body composition results [19,21,22]. Nevertheless, no studies verified the accuracy of BIA to evaluate RMR. ...
Background and aims
Precise evaluation of resting metabolic rate (RMR) is critical, especially for seniors in syndromes conditions. The study aimed to compare different methods and devices to evaluate the resting metabolic rate and assess them’ reliability in Brazilian women with metabolic syndrome.
Methods
A single-center prospective study with forty elderly postmenopausal women was performed to verify the reliability of indirect calorimetry (IC) versus Bioimpedance (BIA) on RMR fluctuations for an interval length of six months.
Results
Measurements showed a high correlation between devices at baseline [BIA vs IC, intraclass correlation coefficient (ICC) = 0.906 (0.822–0.950)]. Surprisingly, a high correlation was kept between BIA and IC after six months [BIA vs. IC, ICC = 0.909 (0.829–0.952)]. The results suggest that both BIA and IC are excellent strategies to measure RMR in elderly postmenopausal women and with metabolic syndrome.
Conclusions
However, the BIA method presents greater convenience, optimizes patients' time, and does not require prolonged fasting to obtain good reliable results compared to IC.
... Body composition analyzers, e.g., different versions of BIA technology, such as InBody ® , have been utilized in the last decade in clinical research, including on cardiovascular disease [40,41]. The validity of BIA has been compared to Dual-Energy X-ray Absorptiometry (DEXA) and found equally strong in multiple studies [42,43]. To the best of our knowledge, BIA has never before been used in a neurosurgical setting. ...
Background
Rupture of an intracranial aneurysm resulting in a subarachnoid hemorrhage (SAH) is a life-threatening situation. Obesity is an increasing health challenge associated with numerous comorbidities. However, recent studies have shown a surprising decreased risk of SAH with increasing body mass index (BMI). The aim was to explore associations between other anthropometric variables and the rupture risk of an intracranial aneurysm, which to our knowledge is lacking in present literature.
Methods
Using a bioelectrical impedance analysis device, we performed body composition analyses on 31 patients admitted with aneurysmal SAH (aSAH) and 28 patients with planned intervention on their unruptured aneurysm. We also collected information on comorbidities and relevant risk factors. Logistic regression was used to explore associations between anthropometric variables and patients with ruptured versus unruptured aneurysms.
Results
Unadjusted estimates showed a significant inverse relationship between body fat percent and aneurysmal rupture (OR [95% CI]: 0.92 [0.86, 0.97], P = 0.009), and between body fat mass and aneurysmal rupture (OR [95% CI]: 0.95 [0.90, 0.99], P = 0.047). These risk relationships remained significant in age- and sex-adjusted analyses for body fat percent (OR [95% CI]: 0.93, [0.87, 0.97], P = 0.028), and body fat mass (OR [95% CI]: 0.95 [0.90, 0.99], P = 0.041).
Conclusions
In recent studies showing a paradoxical relation between aSAH and obesity, BMI was the only parameter investigated. We further explored this “obesity paradox” and found lower body fat in aSAH patients compared to UIA. Future studies should investigate these relationships in larger samples.
Clinical Trial Registration NCT04613427, November 3, 2020, retrospectively registered
... More recently, the incorporation of segmental bioimpedance analysis has enabled targeted evaluation of specific body segments, such as the arms and the legs (Cornish et al. ,1999;Lorenzo & Andreoli ,2003). This technique involves the use of multiple electrodes strategically placed on the body to measure impedance at various points along the body (Esco et al., 2015;Campa et al., 2021). By analyzing impedance data from different segments Both methodologies are deemed "doubly indirect", and their precision may be subject to various influencing factors including temperature, hydration status, skin elasticity, and operator technique. ...
Introduction: Currently, there are no formulas to estimate the percentage of fat by segments of the body from anthropometric measurements. The objective of this work was to correlate the percentage of arm fat mass (obtained through segmental bioimpedance) with anthropometric measurements, to generate a prediction formula valid for both genders. Methods: A sample of 100 individuals (50 women and 50 men) from 18 to 70 years old was analyzed in this observational study. A bioimpedance analysis was performed along with anthropometric determinations according to ISAK standards. Results: The percentage of arm fat mass estimated by bioimpedance strongly and positively correlated with the triceps and biceps skin folds, the arm fat area, and its percentage of fat area, in both sexes. In women, the percentage of arm fat mass also correlated with body mass index, arm circumference, and arm muscle area. Conclusion: Through a linear regression formula applicable to both sexes, the percentage of arm fat can be estimated from three anthropometric measurements.
... This instrument has previously proven reliable for the measurement of body composition when more accurate methods (e.g. dual-energy X-ray absorptiometry) are not available [35][36][37]. Height was measured using a stadiometer (Harpender, Holtain Limited; Crymich, UK). ...
Background
Growing evidence supports the ergogenic effects of creatine supplementation on muscle power/strength, but its effects on endurance performance remain unclear. We assessed the effects of high-dose short-term creatine supplementation in professional cyclists during a training camp.
Methods
The study followed a double-blind, randomized parallel design. Twenty-three professional U23 cyclists (19 ± 1 years, maximum oxygen uptake: 73.0 ± 4.6 mL/kg/min) participated in a 6-day training camp. Participants were randomized to consume daily either a recovery drink (containing carbohydrates and protein) with a 20-g creatine supplement (creatine group, n = 11) or just the recovery drink (placebo group, n = 12). Training loads and dietary intake were monitored, and indicators of fatigue/recovery (Hooper index, countermovement jump height), body composition, and performance (10-second sprint, 3-, 6-, and 12-minute time trials, respectively, as well as critical power and W’) were assessed as study outcomes.
Results
The training camp resulted in a significant (p < 0.001) increase of training loads (+50% for total training time and + 61% for training stress score, compared with the preceding month) that in turn induced an increase in fatigue indicators (significant time effect [p < 0.001] for delayed-onset muscle soreness, fatigue, and total Hooper index) and a decrease in performance (significant time effect [p = 0.020] for critical power, which decreased by −3.8%). However, no significant group-by-time interaction effect was found for any of the study outcomes (all p > 0.05).
Conclusions
High-dose short-term creatine supplementation seems to exert no consistent beneficial effects on recovery, body composition or performance indicators during a strenuous training period in professional cyclists.
... The interclass correlation (ICC) was reported at 0.99, indicating strong reliability [59]. Additionally, significant correlations were found with the reference measure, dual-energy X-ray absorptiometry (DXA), with a correlation coefficient (r) of 0.95 and the reported standard error of estimate (SEE) of 1.8 [60]. Using the InBody 720, we measured the following body composition parameters: body mass, body mass index (BMI), skeletal muscle mass (absolute and relative) and body fat (absolute and relative). ...
Judo elements rely on lower and upper body muscle power, supported by the ATP-PCr energy system, which is crucial in high-intensity tasks. This study aims to assess the anaerobic status of young male competitive judokas using the upper body Wingate test and explore associations with competition performance and individual morphological characteristics. A total of 29 male judokas from the U18 and U20 age categories were tested, all actively participating in top-tier national and international competitions. Anthropometric characteristics and body composition measurements were obtained for all participants through bioelectrical impedance analysis. Anaerobic testing was conducted using the upper body Wingate test with a hand ergometer. Competition performance was recorded from the final national cup ranking list. The results presented no statistically significant correlations between morphological and anaerobic variables and competitive performance among selected participants. This highlights the importance of the necessity of updated training programs to increase the anaerobic performance of young Slovenian judokas. Additionally, it shows that in these age groups, anaerobic performance is not the crucial factor but just one piece of the puzzle in young judokas’ successful competition performance; therefore other variables should be further researched.
... Civar et al. 8 compared a leg-to-leg BIA to underwater weighing in women athletes and found no significant difference in %fat between the two methods (11.8 ± 2.4% vs 11.6 ± 2.4%, respectively) with a moderate correlation between them (r = 0.67). Using a handto-hand single-frequency BIA, Esco et al. 9 showed mean %fat for a group of women athletes to be 5.1% (±3.6%) lower than DXA, which caused FFM to be overestimated by an average of 3.4 kg (±2.5 kg) with a high correlation between the two methods (r = 0.84) but a fairly large limits of agreement (LoA) of -8.4 to 2.4 kg. Nickerson et al. 10 used a handto-foot single-frequency BIA device compared to DXA for determining %fat and FFM in 44 college women athletes. ...
Background: Body composition is frequently measured in women athletes to evaluate training changes, assist in dietary planning, and avoid the female athlete triad. Measurements to monitor %fat and fat-free mass (FFM) can provide valuable information for coaches and athletes throughout the training process. However, questions remain concerning the accuracy of various methods used to measure %fat. The purpose of this study was to assess the accuracy of bioelectric impedance analysis (BIA) devices to estimate %fat and FFM compared to dual-energy X-ray absorptiometry (DXA) in college women athletes. Methods: A cross-section design was employed to assess %fat and FFM among college women athletes. Fiftyseven athletes (age = 20.0 1.4 yrs, height = 179.2 6.0 cm, weight = 74.3 4.4 kg) from soccer (n = 29), basketball (n = 15), and swimming (n = 13) had %fat estimated from four single-frequency BIA devices. Two BIA devices had general population equations (BIA1 and BIA2) and two had athletespecific equations (BIA3 and BIA4). Each device had proprietary equations for estimating %fat and was not capable of being updated. Each device had a 2-point electrode contact with either hands or feet. DXA %fat served as the criterion measurement. Percent fat was estimated directly by each device, and FFM was calculated as body mass minus fat mass. All measures were completed in single sessions for each athletic group with different sports groups being measured at the onset of their competitive season. Athletes were measured between 1400 and 1600 hours in a rested stated with hydration assumed and after voiding the bladder. A repeated-measures one-way analysis of variance (ANOVA) with Bonferroni post hoc testing was used to evaluate differences among measurement techniques with significance set at p<0.05. Results: Three arm-to-arm BIA devices (BIA1, BIA2, and BIA3) were not significantly different in %fat estimates (23.1 ± 5.0%, 23.7 ± 4.7%, and 23.6 ± 4.3%, respectively) but were significantly lower than DXA (29.5 ± 5.1%). The leg-to-leg athletic BIA (BIA4) had a significantly higher %fat estimate (24.6 ± 5.7%) than BIA1 but was not significantly different from BIA2 and BIA3. The correlation of DXA %fat with BIA1 (r = 0.84), BIA2 (r = 0.85), and BIA3 (r = 0.85) were significant but not statistically different across the 3 devices. BIA4 had a significantly lower correlation (r = 0.66) with DXA %fat. The lower estimates in %fat resulted in significantly higher calculated FFM values for BIA1 (51.1 ± 5.5 kg), BIA2 (50.8 ± 59 kg), BIA3 (50.9 ± 6.9 kg), and BIA4 (50.1 ± 5.8 kg) than for DXA (47.5 ± 5.9 kg). However, all BIA estimates of FFM were highly correlated with DXA FFM (r = 0.90-0.93). Limits of agreement analysis indicatedthe average bias ranged from 2.2 kg (BIA4) to 3.4 kg (BIA1). Conclusion: Single-frequency BIA devices utilized in this study tend to underestimate %fat and overestimate FFM compared to DXA in college women athletes. However, high correlations between predicted and actual FFM values indicate that single-frequency BIA devices may be useful for tracking changes in women athletes across seasons.
... However, BIA may lack sensitivity to detect small changes in body composition, may overestimate fat mass and underestimate lean body mass compared with DXA, and may be affected by hydration status and recent dietary intake. 71,72 Furthermore, BIA is not suitable for patients with electronic implants such as pacemakers or other active prostheses or portable electronic devices. As such, BIA may be considered suitable for classification of a population in a research context, particularly where large differences in body composition are anticipated, however significant intraindividual variability in measurements limits the clinical utility of BIA. ...
Esophagectomy is an exemplar of complex oncological surgery and is associated with a relatively high risk of major morbidity and mortality. In the modern era, where specific complications are targeted in prevention and treatment pathways, and where the principles of enhanced recovery after surgery are espoused, optimum outcomes are targeted via a number of approaches. These include comprehensive clinical and physiological risk assessment, specialist perioperative care by a high-volume team, and multimodal inputs throughout the patient journey that aim to preserve or restore nutritional deficits, muscle mass and function.
... BIA has been proposed as a safe noninvasive simple portable quick and low-cost alternative to DXA, and it can provide acceptable body composition reports in terms of accuracy and reliability 16,18,19 . BIA devices have become more sophisticated in recent years and now include devices using multiple frequencies, which have been used in investigations on the similarity of body composition estimates between DXA and BIA 17,[20][21][22][23][24][25][26] . However, although such multiple frequency bioelectrical impedance analyzers less expensive than DXA, but they are not necessarily intended for routine measurements in homes and schools. ...
A variety of easy-to-use commercial bioelectrical impedance appliances are available. The aim of this study was to examine the usefulness of a commercially available body composition meter using bioelectrical impedance analysis (BIA) by comparing its measurement results with those obtained from dual-energy X-ray absorptiometry (DXA). The participants were 443 children aged from 10 to 14 years (226 boys and 217 girls). Fat mass, fat-free mass, lean body mass, percentage of body fat, and bone mineral contents were evaluated for all participants using BIA and DXA. The agreement in the anthropometric data obtained from both devices was analyzed using correlation analysis, intraclass correlation coefficient (ICC), Lin’s concordance correlation coefficient (CCC), Bland–Altman plots, and ordinary least products regression analysis. Equivalence between both devices was tested by two one-sided t -test. All measured indicators showed strong linear correlations between the two measurement systems (r, 0.853–1.000). Fat mass, fat-free mass, and lean body mass showed absolute concordance (ICC, 0.902–0.972; Lin’s CCC, 0.902–0.972). BIA overestimated bone mineral content (62.7–66.5%) and underestimated percentage of body fat (− 8.9 to − 0.8%), lean body mass (− 3.5 to − 1.8%), and body mass (− 0.8 to − 0.5%). For fat mass and fat-free mass, the overestimate or underestimate varied according to the sex and statistical analysis test. Bland–Altman analysis and ordinary least products analysis showed fixed bias and proportional bias in all indicators. Results according to quartiles of body mass index showed poor agreement for fat mass and percentage of body fat in both boys and girls in the lowest body mass index quartile. The present results revealed strong linear correlations between BIA and DXA, which confirmed the validity of the present single-frequency BIA-derived parameters. Our results suggest that BIA cannot provide the exact same values as DXA for some body composition parameters, but that performance is sufficient for longitudinal use within an individual for daily health management and monitoring.
... Body composition fat, bone, muscle, and water mass are very important in sports [22], namely when considering athletes' health and performance [1]. In recent years, bioelectrical impedance analysis (BIA), especially direct segmental multifrequency methods, have been widely used in science and sports practice-along with other traditional body composition methods such as skinfold measurements and dual-energy X-ray absorptiometry-and have become a standard method for the determination of complete body structure according to the body segments [23][24][25]. Moreover, it was indicated in the past that the morphological characteristics of young athletes may influence swimming performance and vary by events [26]; namely, regional and wholebody lean mass (LM) influence short-term performance, anaerobic reserves, and fat-free mass for upper limbs (UL), and, consequently, exercise intensity at VO2max (iVO2max), which will naturally influence swimming performance [27]. ...
This study sought to analyze the relationship between regional body composition, swimming performance, and aerobic and force profile determined through tethered swimming in well-trained swimmers. Eleven male and five female swimmers were involved in the study and underwent the following evaluations: (1) body composition, assessed by the dual-energy X-ray absorptiometry method (DXA); (2) swimming performance, determined for 200, 400, 800, and 1.500 m front-crawl swimming; (3) a tethered swimming force test to determine maximum and mean force (Fmax and Fmean); and (4) an incremental tethered swimming test for the aerobic profile determination of the swimmers. Oxygen uptake (VO2) was directly measured by an automatic and portable system (K4b2 Cosmed, Italy). The fat-free mass (lean mass + bone mineral content, LM+BMC) in lower and upper limbs (UL_LM+BMC: 6.74 ± 1.57 kg and LL_LM+BMC: 20.15 ± 3.84 kg) positively correlated with all indexes of aerobic conditioning level, showing higher coefficients to the indexes representing the ability to perform at high aerobic intensities (VO2max: 49.2 ± 5.9 mL·kg−1·min−1 and respiratory compensation point (RCP): 43.8 ± 6.0 mL·kg−1·min−1), which attained 0.82 and 0.81 (with VO2max), 0.81 and 0.80 (with RCP). The S200 (1.48 ± 0.13 m·s−1) was significantly correlated to Trunk_LM+BMC (r = 0.74), UL_LM+BMC (r = 0.72), Total_LM+BMC (r = 0.71), and LL_LM+BMC (r = 0.64). This study highlights that regional body composition plays an important role in swimming, and body segment analysis should be considered instead of the total body. Tethered swimming may represent a useful method for force and aerobic assessment, aiming at training control and performance enhancement.
... The percentage of body fat and lean body mass was measured using bioelectrical impedance analysis (InBody 720, InBody, Tokyo, Japan) with the participants in the upright position. The validity of the device for measuring body composition has been confirmed by comparison with dualenergy x-ray absorptiometry, which is the gold standard for measuring body composition [16][17][18]. ...
Although social jetlag (SJL) is generally considered a chronic condition, even acute SJL may have unfavorable effects on the cardiovascular system. We focused on the acute effects of SJL on morning blood pressure (BP) surge. This randomized crossover trial recruited 20 healthy men. In the SJL trial, participants delayed their bedtime by three hours on Friday and Saturday nights. Participants in the control (CON) trial implemented the same sleep-wake timing as on weekdays. Pre- and post-intervention measurements were performed to evaluate resting cardiovascular variables on Friday and Monday mornings, respectively. The ambulatory BP was automatically measured during the sleep and awake periods for 2 h after the participant woke up at night before pre- and post-intervention measurements. SJL (average mid-sleep time on weekends – average mid-sleep time on weekdays) occurred only in the SJL trial (SJL: 181 ± 24 min vs. CON: 8 ± 47 min). Carotid-femoral pulse wave velocity (cfPWV) and morning BP surge on Monday in the SJL trial were significantly higher than those on Friday in the SJL trial (cfPWV: P = 0.001, morning BP surge: P < 0.001), and those on Monday in the CON trial (cfPWV: P = 0.007; morning BP surge: P < 0.001). Furthermore, a significant positive correlation was found between ΔcfPWV and Δmorning BP surge ( R = 0.587, P = 0.004). These results suggest that even acute SJL augments morning BP surge. This phenomenon may correspond to increased central arterial stiffness.
State the details of Clinical Trials: Name: Effect of acute social jetlag on risk factors of lifestyle-related diseases. URL: https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000053204 . Unique identifier: UMIN000046639. Registration date: 17/01/2022
... A third limitation of the study is that it relied on BIA as a method for FFM determination. Previous studies have shown BIA to overestimate FFM relative to DEXA [48,49], which may also bias the low EA data. Finally, future research should analyze changes in EA over time and its impact on physical performance and sleep in cadets. ...
The purpose of this study was to assess the dietary habits, prevalence of low energy availability (EA), and sleep quality in a cohort of male army Reserve Officer Training Corps (ROTC) cadets, and to investigate the relationship between EA and sleep quality as well as EA and various body composition variables that are important for tactical readiness. Thirteen male army ROTC cadets (22.2 ± 4.1 yrs; BMI: 26.1 ± 2.3) had their EA and body composition assessed using diet and exercise records alongside bioelectrical impedance analysis. Cadets also completed a validated sleep questionnaire. Sixty-two percent of participants presented with clinically low EA (<30 kcal/kg fat-free mass [FFM]) and none met the optimum EA threshold (≥45 kcals/kg FFM). Dietary analysis indicated that 15%, 23%, 46%, 23%, and 7% of cadets met the Military Dietary Reference Intakes (MDRI) for calories, carbohydrates, protein, fat, and fiber, respectively. Additionally, 85% of cadets exhibited poor sleep quality. Significant associations between EA and fat mass/percent body fat were shown (p < 0.05). There was, however, no statistically significant correlation between EA and sleep quality. The present study found a high prevalence of low EA and sleep disturbance among male army ROTC cadets and that many were unable to meet the MDRIs for energy and macronutrient intake. Further, low EA was associated with higher percent body fat and fat mass but not sleep quality.
... Another limitation of the current study is the use of BIA to estimate FFM. Previous research has shown that BIA tends to overestimate FFM in athletic populations [38,39], thus potentially overestimating low EA in our cohort. Using a more objective measure such as RMR ratio (i.e., the ratio of resting metabolic rate measured divided by the predicted rate) could help to solve this issue, as well as underreporting, by directly assessing an individuals' metabolism that is indicative of EA status. ...
There is limited information regarding the dietary habits and energy availability (EA) of collegiate athletes. Therefore, the purpose of the present study is to assess the nutrient intakes, dietary habits, and prevalence of low EA ( 0.05). The present findings show that there was a high prevalence of low EA during the pre-season among male and female collegiate swimmers that was not fully captured using a validated screening tool for females. Low EA occurred alongside lower intakes of calories, carbohydrates, and proteins, and the majority of swimmers did not meet the United States Department of Agriculture recommendations for fruit and vegetable intake. These data stress the need for improved dietary intakes in NCAA Division III collegiate swimmers.
... One benefit of these methods is the ability to assess total and regional body composition. For instance, the ability to carry out appendicular and trunk analysis of body composition is likely a reason why these clinical methods are highly desired and utilized in validation research and clinical settings [8,9]. Unfortunately, the assessment of body composition in special populations, such as individuals with DS, can be problematic with imaging-based methods. ...
The primary aim of this study was to evaluate the accuracy of skinfold thickness (SFT) measurements for the estimation of %Fat when compared to dual energy X-ray absorptiometry (DXA) in individuals with Down syndrome (DS). The secondary aim was to develop a new SFT-based body fat equation (SFT NICKERSON). SFT-based %Fat was estimated using a body fat equation from González-Agüero (SFT G-A) and body density conversion formulas from Siri (SFT SIRI) and Brozek (SFT BROZEK). Criterion %Fat was measured via DXA. SFT G-A , SFT SIRI , and SFT BROZEK were significantly lower than DXA (mean differences ranged from −7.59 to −13.51%; all p < 0.001). The SEE values ranged from 3.47% (SFT BROZEK) to 8.60% (SFT G-A). The 95% limits of agreement were greater than ±10% for all comparisons. Mid-axilla and suprailium were significant predictors of %Fat (both p < 0.05). %Fat SFT NICKERSON = 10.323 + (0.661 × mid-axilla) + (0.712 × suprailium). Age and all other skinfold sites were not statically significant in the regression model (all p > 0.05). Current findings indicate that SFT G-A , SFT SIRI , and SFT BROZEK erroneously place an individual with excessive adiposity in a normal healthy range. Accordingly, the current study developed a new equation (SFT NICKERSON) that can easily be administered in people with DS in a quick and efficient time frame. However, further research is warranted in this area.
... Second, this study used BIA instead of dual energy X-ray absorptiometry (DEXA), which is the standard method for the measurement of body composition. BIA is a noninvasive, cost-effective, and less time-consuming approach that has been validated in several studies [28,29]. However, compared with DEXA, BIA may overestimate the lean mass of the upper extremities and trunk and underestimate the fat mass of the extremities. ...
Purpose:
This study analyzed changes in body composition and physical fitness in men with testosterone deficiency (TD) after testosterone treatment (TT) and examined the correlations of body composition and physical fitness with serum testosterone levels and hypogonadal symptoms.
Materials and methods:
Seventy patients with TD were divided into control (group I, n=23) and experimental (group II, n=47) groups. Patients in the experimental group were administered intramuscular testosterone enanthate (250 mg) for six months. The aging males symptom scale (AMS) score, international prostate symptom score (IPSS), body mass index, waist circumference, and serum laboratory values were measured at baseline and at the end of the study. Bioelectrical impedance analysis was used to assess the patients' body composition. Seven types of basic exercise tests were used to evaluate the patients' physical fitness.
Results:
After six months, there were no significant differences in group I, while group II had significantly improved IPSS and AMS scores; increased hemoglobin, hematocrit, prostate-specific antigen, and testosterone levels and skeletal muscle mass; and waist circumference, and body fat mass. All elements of the physical fitness test were significantly improved in group II, with the exceptions of flexibility and endurance. Decreased waist circumference was correlated with changes in testosterone levels in group II, and the IPSS, cardiorespiratory fitness, and agility were correlated with improved hypogonadal symptoms.
Conclusions:
TT improved the hypogonadal and lower urinary tract symptoms in patients with TD and improved body composition, physical fitness, and metabolic syndrome parameters. Increased testosterone and improved hypogonadal symptoms were correlated with a decrease in waist circumference and an improvement in cardiorespiratory fitness and agility. As such, when implementing TT, we should consider whether these areas may be improved, as this can help to predict the effect.
... These results are compatible with several studies investigated the MF-BIA accuracy and reliability. Previous studies on the Asian population revealed high accuracy of MF-BIA for body composition measurement compared to DXA (12,19,20); however, research on Caucasian revealed a low accuracy of MF-BIA (18,21,22). These disparate results support the proposition that a population-specific database of muscle mass measured by BIA should be obtained. ...
Background:
Dual-frequency bioelectrical impedance analysis (DF-BIA) devices are more accessible and affordable than dual-energy X-ray absorptiometry (DXA); however, no studies have reported the accuracy of DF-BIA in body composition measurement, especially in the Thai elderly. The aims of this study were to (1) compare the accuracies of lean muscle masses measured by DF-BIA devices and DXA and (2) assess the reliability of the DF-BIA device.
Methods:
This cross-sectional study was conducted on participants older than 60 years who visited the Orthopedic Clinic of Siriraj Hospital. Whole-body and appendicular skeletal muscle masses (ASMs) were measured using DF-BIA (Tanita RD-545), with DXA (GE Lunar iDXA) as the standard reference. The test-retest reliability of the DF-BIA and the agreement between the devices were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Regression analysis was used to develop an equation to estimate ASM values from BIA close to those from DXA.
Results:
The mean age of 88 participants was 73.8 (SD 8.0) years, with women predominating (84.1%). The agreement of BIA and DXA was very high for whole-body lean mass (ICC = 0.954) and ASM (ICC = 0.954), but the mean difference in muscle mass from DF-BIA was overestimated. The ICCs of test-retest reliability for whole-body muscle mass and ASM were 0.987 and 0.988, respectively. The equation for corrected ASM was formulated from a linear equation (R2 = 0.93).
Conclusions:
Although lean muscle mass from DF-BIA was minimally overestimated relative to DXA, this device had high accuracy and reliability for lean muscle mass evaluation in the elderly. DXA and DF-BIA are interchangeable for the assessment of muscle mass.
... Those authors, therefore, did not recommend the use of InBody770 for body composition measurement in athletes. Esco et al. [36] used DXA to compare InBody720 body composition measurements of the whole body and each limb for 45 female college athletes. For whole PBF, FFM, and lean mass, the correlation coefficients of the two devices were r = 0.94, 0.95, 0.92, respectively. ...
We investigated differences in body composition measurements for the whole body and limb segments in elite male wrestlers between results of multi-frequency bioelectrical impedance analyses (MFBIA) and dual energy X-ray absorptiometry (DXA). Sixty-six elite male wrestlers from Taiwan were recruited. Wrestlers’ body fat percentage (PBFWB), whole body fat-free mass (FFMWB), whole body lean soft tissue mass (LSTMWB), and fat-free mass of arms, legs and trunk (FMArms, FFMLegs, FFMTrunk) were measured by MFBIA and DXA, and analyzed using Pearson correlation coefficient and Bland–Altman plot. Correlations of FFMWB, LSTMWB, and PBFWB between devices were 0.958, 0.954, and 0.962, respectively. Limits of agreement (LOA) of Bland–Altman plot were −4.523 to 4.683 kg, −4.332 to 4.635 kg and −3.960 to 3.802%, respectively. Correlations of body composition parameters FFMArms, FFMLegs and FFMTurnk between devices in each limb segment were 0.237, 0.809, and 0.929, respectively; LOAs were −2.877 to 2.504 kg, −7.173 to −0.015 kg and −5.710 to 0.777 kg, respectively. Correlation and consistency between the devices are high for FFM, LSTM and PBF but relatively low for limb segment FFM. MFBIA may be an alternative device to DXA for measuring male wrestlers’ total body composition but limb segment results should be used cautiously.
... As an alternative, the use of a tetrapolar BIA approach with eight pre-gelled Ag/AgCl surface electrodes, while performed in a supine position, has been proposed to overcome some of these issues and compensates for differences in distinct body types. Currently, there is a research gap regarding the validation of regional BIA measurements for predicting DXA regional LSTM, with most of the equations being developed in specific populations, such as athletes [14,15] and older adults [16][17][18]. Even though some recent equations predicting the overall LSTM of arms, legs and trunk have been developed in adults [19,20], to the best of our knowledge, no previous investigations using tetrapolar BIA measurements with eight point electrodes have validated equations predicting DXA-derived LSTM of each body segment independently using healthy adults as the reference population. ...
Background/Objectives
Bioelectrical impedance (BIA) whole-body and regional raw parameters have been used to develop prediction models to estimate whole-body lean soft tissue (LSTM), with less attention being given to the development of models for regional LSTM. Therefore, we aimed to develop and validate BIA-derived equations predicting regional LSTM against dual x-ray absorptiometry (DXA) in healthy adults.
Subjects/Methods
149 adults were included in this cross-sectional investigation. Whole-body and regional LSTM were assessed by DXA, and raw bioelectrical parameters of distinct body regions were measured using a 50 kHz phase sensitive BIA analyzer. BIA-derived equations were developed using a stepwise multiple linear regression approach in 2/3 of the sample and cross-validated in the remaining sample.
Results
Slopes and intercepts of predicted LSTM and DXA measured LSTM did not differ from 1 and 0, respectively, for each region (p ≥ 0.05), with the exception for the trunk (p < 0.05). The BIA-derived equations exhibited a strong relationship (p < 0.001) between the predicted and measured LSTM for each of the following body regions: right and left arms (R = 0.94; R = 0.96), right and left legs (R = 0.88; R = 0.88), upper body (R = 0.96), lower body (R = 0.89), right and left sides of the body (R = 0.94; R = 0.94), and trunk (R = 0.90). Agreement analyses revealed no associations between the differences and the means of the predicted and DXA-derived LSTM.
Conclusion
The developed BIA-derived equations provide a valid estimate of regional LSTM in middle-aged healthy adults, representing a cost-effective and time-efficient alternative to DXA for the assessment and identification of LSTM imbalances in both clinical and sport-specific contexts.
... scale and height was entered based on the anthropometric assessment. Complete details of the octapolar bioimpedance system and testing procedure have been described elsewhere (24). Briefly, participants grasped the handles with the palms and thumbs contacting the electrodes, extending the arms outwards from the torso at an angle of 15-45 degrees to ensure no contact between the arms and the torso. ...
Background
Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of devices are unknown.
Objectives
This study evaluated smart watches with integrated bioimpedance (BIA) sensors for their ability to measure and monitor change in body composition.
Design
Participants recruited across body mass indexes received duplicate body composition measures using two wearable smart watch (W-BIA) models in sitting and standing positions and multiple versions of each watch were used to evaluate inter- and intra-model precision. Duplicate laboratory-grade octapolar bioimpedance (8-BIA) and criterion dual-energy X-ray absorptiometry (DXA) scans were acquired to compare estimates between the watches and laboratory methods. Test-retest precision and least significant changes assessed the ability to monitor change in body composition.
Results
Of 109 participants recruited, 75 subjects completed the full manufacturer-recommended protocol. No significant differences were observed between W-BIA watches in position or between watch models. Significant fat-free mass (FFM) differences (p < 0.05) were observed between both W-BIA and 8-BIA when compared to DXA, though the systematic biases to the criterion were correctable. No significant difference was observed between the W-BIA and the laboratory-grade BIA technology for FFM (55.3 ± 14.5 kg for W-BIA versus 56.0 ± 13.8 kg for 8-BIA, p > 0.05, CCC = 0.97). FFM was less precise on the watches than DXA (CV = 0.7%, RMSE = 0.4 kg versus CV = 1.3%, RMSE = 0.7 kg for W-BIA), requiring more repeat measures to equal the same confidence in body composition change over time as DXA.
Conclusions
After systematic correction, smart watch BIA devices are capable of stable, reliable and accurate body composition with precision comparable but lower than laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems such as the home, training centers, and geographically remote locations.
... The measurement, comparison and validation studies of commercial BIA devices in LSTM or FFM for the whole body and limbs of athletes have been proposed and have gradually captured increasing attention [10][11][12] . The physical characteristics of athletes are different from those of the general population based on long-term trends in speci c sports 13 . ...
The lean soft tissue mass (LSTM) of the limbs is approximately 63% of total skeletal muscle mass. For athletes, measurement of limb LSTM is the basis for rapid estimation of skeletal muscle mass. This study aimed to establish the estimation equation of limb lean soft tissue mass in Asian athletes using bioimpedance analysis (BIA). A total of 198 athletes (121 males, 77 females; mean age 22.04 ± 5.57 years) from different sports in Taiwan were enrolled. A modeling group (MG) of 2/3 (n = 132) of subjects and a validation group (VG) of 1/3 (n = 68) were randomly assigned. Resistance (R) and reactance (Xc) were measured using 50KHz current measurement in whole-body mode. Predictor variables were height, weight, age, gender, Xc, resistance index (RI; RI = h 2 / R). Lean soft tissue mass of upper and lower limbs measured by dual-energy X-ray absorptiometry (DXA) was the response variable. Multivariate stepwise regression analysis method was used to establish BIA estimation equations as ArmsLSTM BIA-Asian and LegsLSTM BIA-Asian . Measurement equations performance was confirmed by cross-validation. The established single-frequency BIA estimation equation quickly and accurately measures lean soft tissue mass of the arms and legs of Asian athletes.
... The structural and bone mineral parameters of each athlete were converted to z-scores relative to age-and sex-specific mean and standard deviation values estimated for the total sample (z = (individual value-age-group mean) / age-group standard deviation). Bone mineral densities of the upper extremities, lower extremities, and trunk were also expressed as a percentage of total BMD to address asymmetries in BMD by type of sport [26]. ...
In an earlier report, bone mineral reference values for young athletes were developed. This study addressed variations in bone mineral parameters of young athletes participating in sports with different mechanical loads. The bone mineral status of 1793 male and female athletes, 11 to 20 years of age, in several sports was measured with DEXA. Specific bone mineral parameters were converted to z-scores relative to age- and sex-specific reference values specified by the DEXA software. Z-score profiles and principal components analyses were used to identify body structural components in the young athletes and to evaluate the associations between the identified component and type of sport defined by mechanical load. A unique skeletomuscular robusticity of male wrestlers, pentathletes, and cyclists was noted: wrestlers had significantly more developed skeletomuscular robusticity and bone mineral density compared to the age-group average among elite athletes, while pentathletes and cyclists had lower bone mineral parameters than the age-group references among elite athletes. Among female athletes, bone mineral parameters of both the trunk and extremities of rhythmic gymnasts and pentathletes were significantly lower compared to the age-group means for elite athletes. The bone mineral development of elite young athletes varies with the impact forces associated with their respective sports. The skeletal development of cyclists, pentathletes, and rhythmic gymnasts should be monitored regularly as their bone development lags behind that of their athlete peers and the reference for the general population.
... Body composition measurements were performed in the standing position, following all necessary accurate measurement guidelines [48,49]: (1) the measurements were taken in the morning (between 8 and 10 AM); (2) the participants were asked to abstain from large meals after 9 PM the evening before the test, and on the day of the measurement they neither ate nor drank before the end of the procedure; (3) participants were asked to refrain from extreme physical exertions 24 h prior to measuring, and last training should have been performed at least 12 h prior to testing; (4) the respondents did not consume alcohol 48 h before the measurement; (5) the respondents were asked to empty their bowels and bladder at least 30 min before the measurement; (6) the respondents were in the standing position for at least 5 min before the measurement to redistribute the tissue fluids; (7) the measurement was performed in the standing position by the procedure recommended by the manufacturer (hands aside placed 15 cm laterally from the body). The high test-retest, reliability, and accuracy of InBody 720 was previously assessed, with interclass correlation (ICC) reported at 0.99 [50] and correlations with the reference measure (dual-energy X-ray absorptiometry-DXA) were shown to be significant r = 0.95, with the reported standard error of estimate (SEE) of 1.8 [51]. With InBody 720, we measured body weight, body mass index (BMI), skeletal muscle mass, trunk lean mass, left and right arm lean mass, left and right leg lean mass, and body fat mass. ...
The aims of this study are: (1) to identify morphological asymmetries in road cycling by using a novel 3D scanning method and electrical bioimpedance, (2) to investigate possible asymmetries in road cyclists of low (LPG) and high (HPG) performance group, (3) to compare the number of morphological asymmetries between HPG and LPG of cyclists, and (4) to explore correlations between asymmetry scores and competition performance. Body composition and 3D anthropometric measurements were conducted on 48 top-level male road cyclists (178.98 ± 5.39 cm; 68.37 ± 5.31 kg) divided into high (n = 22) and low (n = 26) performance groups. Competition performance (CP) is represented through racing points gathered at the end of the competition season. The latter was used to divide road cyclists into low- and high-performing groups. One-way ANOVA was used to determine differences between groups, while paired-samples T-test and Absolute Asymmetry index (AA) were calculated (p ≤ 0.05) for paired variables inside the groups, and the Spearman correlation coefficient was used to explore correlations between AA and CP. Results showed statistically significant differences between the left and right side of different body segments (16 paired variables) among low-performing road cyclists in five paired variables of the upper body: elbow girth (4.35, p = 0.000), forearm girth (6.31, p = 0.000), arm surface area (2.54, p = 0.018), and arm volume (2.71, p = 0.012); and six paired variables of the lower body: leg lean mass (5.85, p = 0.000), leg length (3.04, p = 0.005), knee girth (4.93, p = 0.000), calf girth (5.25, p = 0.000), leg surface area (4.03, p = 0.000), and leg volume (5.3, p = 0.000). Altogether, the high-performing group of road cyclists statistically differed only in 2 out of 16 paired variables of the upper body: elbow girth (4.93, p = 0.000) and in forearm girth (5.12, p = 0.000). Low- and high-performing groups were statistically significantly different in the asymmetry of leg lean mass F(1,46) = 6.25, p = 0.016 and asymmetry of the calf girth F(1,46) = 7.44, p = 0.009. AA of calf girth on the total sample (n = 48) showed a significant correlation with CP (r = −0.461; p = 0.001). In conclusion, the study’s main finding was that high-performance road cyclists are more symmetrical than the low-performance group, for which it is significant to have a higher amount of morphological asymmetries.
... . 그렇기 때문에 현재 다양한 연령과 집단 의 건강 상태 및 영양 상태를 평가하기 위해 임상 및 현장에서 광범위 하게 사용되고 있다 [8,9]. BIA는 신뢰성 및 타당성이 검토되었으며 [10][11][12][13], 신체조성 측정의 표준방법(gold standard)으로 알려진 DEXA와 높 은 상관계수를 가지고 있어 높은 정밀도를 증명하였다 [14][15][16]. ...
PURPOSE: Body composition is strongly associated with cardiometabolic risk factors, and accurate measurement of body composition is vital for the management of chronic diseases. In this study, we assessed whether major factors such as urination, aerobic exercise, food, and water consumption had significant effects on body composition by segmental bioelectrical impedance analysis (BIA).METHODS: To achieve the goal of this study, research was conducted on 32 healthy young males (n=18) and females (n=14). All participants underwent body composition analysis in four different conditions (both pre- and post-urination, moderate-intensity aerobic exercise for 30 minutes, immediately after food and water consumption, 30, 60, and 120 minutes after each treatment), and segmental BIA was performed using Inbody720.RESULTS: We found that after urination, body weight, skeletal muscle mass, and basal metabolic rate (BMR) significantly decreased. However, water intake significantly increased body weight, body fat mass, and body fat percentage. Furthermore, an acute 30-minutes aerobic exercise significantly decreased body weight, fat mass, and fat percentage, and increased skeletal muscle mass and BMR. In addition, impedance decreased immediately and increased 120 minutes after the acute aerobic exercise. Finally, food ingestion significantly increased the body weight, skeletal muscle mass, and BMR.CONCLUSIONS: Our study suggests that variables such as urination, exercise, food consumption, and water intake should be considered to accurately assess body composition.
... This finding supports earlier analyses in a variety of collegiate athletes [38] and generic populations [39] in which leg FM was similarly underestimated in BIA relative to DXA. Interestingly, in similar investigations in female collegiate athletes [40], this underestimation of leg FM by BIA was not apparent. These findings may indicate that this differential attribution is only demonstrable with a larger segmental mass or that sex-based differences could be present [41]. ...
Tracking changes in body composition may provide key information about the effectiveness of training programs for athletes. This study reports on the agreement between bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) for tracking body composition changes during a seven-week offseason training program in 29 NCAA collegiate American football players. Body composition in subjects (mean ± SD; age: 19.7 ± 1.5 y; height: 179.8 ± 6.6 cm; body mass (BM: 96.1 ± 12.6 kg; DXA body fat: 20.9 ± 4.4%) was estimated using BIA (InBody 770) and DXA (Hologic Horizon) before and after the training intervention. Repeated measures ANOVA and post hoc comparisons were performed. Longitudinal agreement between methods was also examined by concordance correlation coefficient (CCC) and Bland–Altman analysis alongside linear regression to identify bias. Significant method by time interactions were observed for BM (DXA: 1.1 ± 2.4 kg; BIA: 1.4 ± 2.5 kg; p < 0.03), arms fat-free mass (FFM) (DXA: 0.4 ± 0.5 kg; BIA: 0.2 ± 0.4 kg; p < 0.03), and legs FFM (DXA: 0.6 ± 1.1 kg; BIA: 0.1 ± 0.6 kg; p < 0.01). Post hoc comparisons indicated that DXA—but not BIA—detected increases in FFM of the arms and legs. Time main effects, but no method by time interactions, were observed for total FFM (DXA: 1.6 ± 1.9 kg; BIA: 1.2 ± 2.1 kg; p = 0.004) and trunk FFM (DXA: 0.7 ± 1.3 kg; BIA: 0.5 ± 1.0 kg; p = 0.02). Changes in total BM (CCC = 0.96), FFM (CCC = 0.49), and fat mass (CCC = 0.50) were significantly correlated between BIA and DXA. DXA and BIA may similarly track increases in whole-body FFM in American collegiate football players; however, BIA may possess less sensitivity in detecting segmental FFM increases, particularly in the appendages.
... Following the completion of informed consent and questionnaires, participant's body composition and mass were measured using an InBody 770 device (InBody USA, Cerritos, CA). The InBody is a multi-frequency bio-electrical impedance device that has been validated against both dualenergy X-ray absorptiometry and a four-compartment body composition model with an accuracy of ± 1.0% for body fat, 0.34 kg for fat mass and 0.87 kg for fat-free mass [27,28]. The participant's total and regional body fat and lean tissue mass were recorded. ...
The purpose of this study was to test the hypothesis that under controlled surf conditions, sex differences in skin temperature exist, but core temperature would not vary between sexes when performing a simulated surf session while wearing a 2-mm wetsuit. Twenty male and 13 female surfers engaged in a 60-min simulated surf protocol using a custom 2-mm wetsuit in an Endless Pool Elite Flume with water temperature set to 15.6 °C. Participants were instrumented with a heart rate monitor, eight skin temperature sensors, and a disposable sensor for measurement of core temperature. The surf simulation consisted of paddling, duck-diving and stationary activities at three paddling speeds (1.2, 1.4 and 1.6 m/s). Participants were asked their thermal sensation periodically during the protocol, and all data were collected at 1-min intervals. Results indicated no significant differences in core temperature between males (37.31 ± 0.35 °C) and females (37.32 ± 0.48 °C, p = 0.995). Upper arm and thigh skin temperatures were significantly lower in females (27.45 ± 1.04 °C and 23.53 ± 0.78 °C, respectively) than males (28.61 ± 1.32 °C and 24.73 ± 0.68 °C; p = 0.012 and p = 0.000, respectively). Conversely, skin temperatures in the abdomen were significantly lower in males (26.57 ± 1.44 °C) than females (27.75 ± 1.50 °C; p = 0.035). Meanwhile, perceptual data were inconclusive. The results suggest that although regional differences in skin temperature may exist between male and female surfers, they may be too small to translate into perceptual differences and are unnecessary when considering wetsuit design.
... First, it is non-invasive and easy to use [25,29,30] . Second, previous studies have demonstrated excellent correlations between InBody 720 data and that of DEXA and CT for evaluating skeletal muscle mass and visceral fat [31][32][33] . Furthermore, recent studies have used the InBody 720 to assess skeletal muscle mass and to diagnose sarcopenia [25,34] . ...
Background:
Gallbladder polyps (GBPs) are known to be associated with obesity and metabolic diseases. However, to date, the relationship between GBPs and abnormal body fat distribution, such as fatty liver, visceral obesity, or sarcopenia, has not yet been established.
Aim:
To evaluate whether GBPs are associated with fatty liver, visceral obesity, or sarcopenia.
Methods:
We retrospectively reviewed the medical records of subjects who underwent various laboratory tests, body composition measurement with a non-invasive body composition analyzer, and abdominal ultrasonography during health checkups. A total of 1405 subjects with GBPs were compared with 2810 age- and sex-matched controls.
Results:
The mean age of the subjects was 46.8 ± 11.7 years, and 63.8% were male. According to multiple logistic regression analysis, the presence of fatty liver [odds ratio (OR) 1.413; 95% confidence interval (CI) 1.218-1.638; P < 0.001] was an independent risk factor for GBP, together with low levels of alanine aminotransferase (OR 0.993; 95%CI 0.989-0.996; P < 0.001). Additionally, fatty liver showed both independent (OR 1.629; 95%CI, 1.335-1.988; P < 0.001) and dose-dependent (moderate to severe fatty liver; OR 2.137; 95%CI, 1.662-2.749; P < 0.001) relationship with large GBPs (≥ 5 mm). The presence of sarcopenia and high visceral fat area were not significantly associated with GBPs.
Conclusion:
Fatty liver was found to be closely associated with GBPs irrespective of sarcopenia and visceral obesity.
... Body Composition: Height was recorded using a height measurement tape (Seca stadiometer, model 206, Germany), with bodyweight and body composition recorded using a bioelectrical impedance analyzer (InBody 720, InBody, Korea). This protocol was adopted from Esco et al [19]. Before each measurement, player's palms and soles were wiped with electrolyte tissue. ...
Objective: This study aims to develop a physical profile of international cricketers, and investigate if positional differences exist between bowlers and batters. Methods: Nineteen, international male cricketers, eleven bowlers (age 24.1 ± 5.2 years; height 179.73 ± 5.27 cm; weight 73.64 ± 6.65 kg), and eight batters (age 22.9 ± 3.8 years; height 180.25 ± 5.57 cm; weight 77.01 ± 8.99 kg) participated in this study. The physical test battery included; power, speed, strength and aerobic fitness tests. Results: Batters demonstrated significantly higher scores for the countermovement jump (p < 0.03; ES =-1.55) and squat jump (p < 0.03; ES =-0.98). Batters showed non-significant but small ES for faster 0-5 m (ES = 0.40) and 0-10 m (ES = 0.35) sprint times, superior hand grip strength (ES =-0.20), and higher Yo-Yo intermittent recovery test scores (ES =-0.46). Bowlers showed non-significant but small ES for faster 5 km time trials (ES =-0.51), lower bodyweight (ES =-0.42) and body fat percentage (ES =-0.30). Intra-positional (i.e., seam and spin bowlers) and individual differences amongst players were observed. Conclusion: This study provides a physical profile of international cricketers. Batters demonstrated superior lower-body power compared to bowlers and other physical test results were similar across positions. However, individual scores for each physical test demonstrated that differences exist amongst players. This should be accounted for by strength and conditioning coaches when using physical profiling data to inform the design and evaluation of their programs.
... En 2015 por Camina-Martin et al. (24), en hombres mayores con y sin demencia, para comparar la antropometría y la impedancia bioeléctrica, así como para verificar la relación entre la demencia y la composición corporal. Finalmente, en el mismo año por Esco et al. (25), en atletas universitarias del sexo femenino, para evaluar la fiabilidad de este método para medir la composición corporal en los distintos segmentos corporales. ...
This study aimed to investigate the effects of aging on body composition analyses detected by a single-frequency (SF-BIA) and a multifrequency bioelectrical impedance analyzer (MF-BIA). Forty older patients and 42 young subjects were included in the study. Body composition was measured using an SF-BIA and an MF-BIA consecutively. Significant differences were found between the analyses of SF-BIA and MF-BIA in older patients. Handgrip strength values were found to correlate more strongly with the skeletal muscle mass index detected by MF-BIA. MF-BIA may be preferable to SF-BIA in older adults due to its higher correlation coefficient with handgrip strength.
This study was initiated in 2022 in Miyaki Town, Saga Prefecture, Japan, to determine the impact of an intervention that combined brain and physical function training and health education in older residents. Miyaki has a population of approximately 26,000, 35% of whom are considered to be aging. A 14-week program consisting of strength training, brain function training, and health lectures was conducted with 34 older residents of the community. Body composition, motor function, brain function, and various blood tests were evaluated before and after the intervention. Brain function was assessed using the Trail Making Test-A. Physical function was assessed by Open-Close Stepping, Functional Reach Test, Open-Leg Standing Time, and Two-Step Test. The intervention group showed significant improvements in brain function (p< 0.0001), physical function (p = 0.0037), body composition (p = 0.0053), and LDL-C (p = 0.017). This study provides substantial evidence that community-based combined programs can be beneficial for older adults.
The body composition phenotype of an athlete displays the complex interaction among genotype, physiological and metabolic demands of a sport, diet, and physical training. Observational studies dominate the literature and describe the sport-specific physique characteristics (size, shape, and composition) of adult athletes by gender and levels of competition. Limited data reveal how body composition measurements can benefit an athlete. Thus, the objective is to identify purposeful measurements of body composition, notably fat and lean muscle masses, and determine their impact on the health and performance of athletes. Areas of interest include relationships among total and regional body composition measurements, muscle function, sport-specific performance, risk of injury, return to sport after injury, and identification of activity-induced fluid shifts. Discussion includes the application of specific uses of dual X-ray absorptiometry and bioelectrical impedance including an emphasis on the need to minimize measurement errors and standardize protocols, and highlights opportunities for future research. This focus on functional body composition can benefit the health and optimize the performance of an athlete.
Background and aim
Relative fat mass (RFM) is a new method to estimate whole-body fat percentage in adults using an anthropometric linear equation. We aimed to assess the association between RFM and body fat (BF), evaluated by dual x-ray absorptiometry (DXA) or bioelectrical impedance (BIA), in young male adults.
Methods
Eighty-one young males were assessed for BF fat and free fat mass (by BIA and DXA), waist circumference. BMI and RFM were then calculated from data collected from the subjects. The agreement between BMI and RFM or BIA/DXA was assessed by Pearson's Correlation and Kappa index. Univariate and multivariate linear regression were applied.
Results
Analyzing all the participants together, the correlation between RFM and DXA (rDXA = 0.90) or RFM and BIA (rBIA = 0.88) were slightly higher than the correlation between BMI and DXA (rDXA = 0.79) or BMI and BIA (rBIA: 0.82). When analyzed by BF, low BF (LBF) individuals showed a much higher correlation with RFM (rDXA = 0.58; rBIA = 0.73) than BMI (rDXA = 0.24; rBIA: 0.46). However, subjects with excess BF (EBF) presented similar correlations when comparing RFM (rDXA = 0.80; rBIA = 0.64) and BMI (rDXA = 0.78; rBIA = 0.64). In general, RFM presented a higher strength of agreement with DXA and BIA (kDXA = 0.75; kBIA = 0.67) than BMI (kDXA = 0.63; kBIA = 0.60). Multivariable linear regression also revealed high associations between RFM and DXA or RFM and BIA (r²DXA = 0.85; r²BIA = 0.81).
Conclusion
Our findings suggest that RFM shows a good correlation and association with BF measured by DXA and BIA in young male adults. Furthermore, RFM seems to be better correlated to BF in LBF individuals when compared to BMI. Therefore, further studies investigating RFM as a tool to assess BF and obesity are motivated.
Fleck, SJ, Hayes, A, Stadler, G, Goesch, T, Goldammer, M, and Braun, S. Urine specific gravity effect on total and segmental body composition validity of multifrequency bioelectrical impedance analysis compared with dual energy x-ray absorptiometry. J Strength Cond Res XX(X): 000-000, 2020-The purposes were to compare body composition measures between a specific multifrequency bioelectrical impedance analyzer (InBody770) and dual-energy x-ray absorptiometry (DXA) and determine if hydration status within a specified range affected these measures. Methods included determining urine specific gravity before testing. Urine specific gravity needed to be within typical well-hydrated (n = 37), euhydrated (n = 45), or slightly dehydrated (n = 20) ranges. Segmental and total body composition measures were determined with the InBody770 and by DXA within the same testing session. Paired sample t-tests revealed significant differences (p < 0.005) between InBody770 and DXA for all body composition variables for all 3 hydration statuses, except for trunk fat-free mass (FFM) and trunk fat mass (FM) of the well-hydrated and euhydrated groups and right leg FM and trunk FFM of the slightly dehydrated group. For the total sample (n = 102), InBody770 significantly underestimated total body FM, right arm FFM, left arm FFM, right leg FFM, and left leg FFM with the range of underestimation being between 0.16 and 2.87 kg. The total body FFM by InBody770 was overestimated by 2.33 ± 2.80 kg or 3.6%. Bland-Altman plots supported these results. The major conclusions are that differences between the InBody770 and DXA segmental and total body FFM and FM are not significantly affected by hydration status in the range investigated, and the FM and FFM determined by the 2 devices are generally significantly different.
Objective
To investigate the association of phase angle(PA) with sacropenia and its components and to evaluate the effectiveness of PA in sarcopenia diagnosis in elderly male with cancer.
Methods
Cases of elderly male patients (>65y) with non-small cell lung cancer and digestive tract cancer who were hospitalized in the past 3 years were included. General characteristics such as age, BMI and tumor stage were gathered. Mid-upper arm muscle circumstance(MAMC), calf circumstance(CC) and hand grind strength(HGS) were measured. PA and appendicular skeletal muscle mass(ASM) were examined by BIA. According to the diagnostic criteria of the 2019 consensus of Asian Sarcopenia Working Group, the patients were divided into two groups: non-sarcopenia group and sarcopenia group.
Results
445 cases were included with a 22.2% prevalence of sarcopenia. PA was different in non-sarcopenia and sarcopenia group(5.02° vs. 4.18°, P<0.001]. Pearson correlation showed PA was related to diagnostic and confounding factors of sarcopenia. After adjusted for all potential confounding factors, multiple linear regression analysis showed diagnostic components of sarcopenia[HGS and skeletal muscle mass index(SMI)] could predict 25.3% of PA variation and logistic regression analysis showed PA(OR=0.309, P<0.001) were related to sarcopenia. Then ROC curve showed the cut-off value of 4.25° with AUC=0.785 for PA.
Conclusions
PA is related to diagnostic components of sarcopenia, HGS and SMI. PA can be useful for the diagnosis of sarcopenia in elderly male patients with cancer. The cutoff value proposed in this study was 4.25 °.
The paper addresses relations between the characteristics of body composition in international sprint swimmers and sprint performance. The research included 82 swimmers of international level (N = 46 male and N = 36 female athletes) from 8 countries. We measured body composition using multifrequency bioelectrical impedance methods with "InBody 720" device. In the case of male swimmers, it was established that the most important statistically significant correlation with sprint performance is seen in variables, which define the quantitative relationship between their fat and muscle with the contractile potential of the body (Protein-Fat Index, r = 0.392, p = 0.007; Index of Body Composition, r = 0.392, p = 0.007; Percent of Skeletal Muscle Mass, r = 0.392, p = 0.016). In the case of female athletes, statistically significant relations with sprint performance were established for variables that define the absolute and relative amount of a contractile component in the body, but also with the variables that define the structure of body fat characteristics (Percent of Skeletal Muscle Mass, r = 0.732, p = 0.000; Free Fat Mass, r = 0.702, p = 0.000; Fat Mass Index, r = −0.642, p = 0.000; Percent of Body Fat, r = −0.621, p = 0.000). Using Multiple Regression Analysis, we managed to predict swimming performance of sprint swimmers with the help of body composition variables, where the models defined explained 35.1 and 75.1% of the mutual variability of performance, for male and female swimmers, respectively. This data clearly demonstrate the importance of body composition control in sprint swimmers as a valuable method for monitoring the efficiency of body adaptation to training process in order to optimize competitive performance.
Objective:
The study aimed to determine the proportion of the body segments in relation to the total body mass in healthy people, as well as analyze the composition of each segment and compare these results between sexes.
Methods:
A total of 60 young adults (30 men and 30 women) were subjected to a full-body scan by dual energy Xrays absorptiometry (DXA) under standardized conditions. The regions of interest (ROI) were determined by a single trained evaluator. The body was divided into 16 segments to obtain values of total mass, lean mass (LM), fat mass, bone mineral content (BMC), lean mass percentage (%LM) and fat mass percentage (%FM) of each body segment represented by the 16 ROI.
Results:
Men presented higher absolute mass in the upper limbs (Δ= 32.87%; p <0.05). The proportion of the lower limbs (Δ= 6.83%; p<0.05) and trunk (Δ= 5.07%; p <0.05) of men is higher than women. In addition, males have more LM in the upper limbs (Δ= 42.19%; p<0.05) and trunk (Δ= 26.46%; p<0.001) and more BMC in the trunk (Δ= 18.78%; p<0.05) and forearms (Δ= 32.21%; p<0.05). They also present higher %LM (Δ= 6.48%; p<0.001) and lower %FM (Δ= 54.43%; p<0.001) than women in the forearms.
Conclusions:
The different body segments represent a different percentage of the total body mass in men than in women, as well as, men present more LM and BMC in the trunk and upper limbs.
The diagnostic accuracy of clinical-based body composition methods such as body mass index (BMI), waist circumference (WC), bioimpedance analysis (BIA), and dual energy X-ray absorptiometry (DXA) has yet to be evaluated in Hispanic adults. Moreover, it has also been suggested that previously established obesity cutoff values may need adjusting.
Purpose:
The primary aim of this study was to investigate the diagnostic accuracy of BMI, WC, BIA, and DXA for obesity classification in Hispanic adults. The secondary aim was to internally derive obesity cutoff values producing equal sensitivity and specificity for the respective tests.
Methods:
Hispanic females (n = 101) and males (n = 90) volunteered to participate in this study (18-45 years). Body fat percentage was estimated with BIA, DXA, and a 4-compartment (4C) model. Obesity-defined criteria was employed as follows: (Body fat percentage ≥ 25% and 35% and WC ≥ 102cm and 88cm for males and females, respectively; BMI ≥ 30 kg/m2). A 4C model was used as a criterion to evaluate BMI, WC, DXA, and BIA.
Results:
Sensitivity of DXA and BIA (74.1%-96.9%) was greater than BMI and WC (25.8%-51.9%) when using previously established standards. However, specificity was poor for DXA (<70%), but considered good to excellent for BMI, WC, and BIA (83.1%-96.6%) when using previously established standards. Internally derived cutoff values improved sensitivity for BMI and WC (74.2%-81.5%) and improved specificity for DXA (>80.0%).
Conclusion:
The internally derived cutoff values, producing identical sensitivity, and specificity, were developed and shown to improve the diagnostic performance of the body composition methods compared to previously established obesity cutoff standards. Consequently, the internally derived obesity cutoff values are recommended for use by allied health professionals in clinical practice when equal sensitivity and specificity is desired.
Bioelectrical impedance analysis (BIA) is a common practice to assess body composition in athletes, however, when measuring athletes with specific body geometry, its accuracy may decrease. In this study we examined how length dimensions affect body composition estimation and we compared BIA and dual-energy X-ray absorptiometry (DXA) assessments in three sports. 738 male adolescent athletes (15.8 ± 1.4 years) from three sports (soccer, basketball, and handball) were measured. Body composition was estimated by BIA (InBody 720) and by DXA (Lunar Prodigy). Differences between the two methods were tested by Bland-Altman analysis and by paired t-test. ANOVA was used for inter-group comparisons. Pearson correlation and multivariate linear regression was used to look for the relationship between segmental lean body mass and length dimensions. BIAInBody 720 consistently underestimated percent body fat (PBF) and overestimated lean body mass (LBM) than DXA. The magnitude of the differences between the two methods varied among the examined sports. Handball (PBF = 8.3 ± 2.4 %; LBM = -5.0 ± 2.1 kg) and basketball players (PBF = 8.8 ± 2.3 %; LBM = -5.3 ± 1.8 kg) had significantly larger differences between the two methods than soccer players (PBF = 6.4 ± 2.2 %; LBM = -3.1 ± 1.4 kg). There was a negative correlation between differences in segmental LBM estimation and length sizes (trunk length, upper extremity length, lower extremity length). The highest correlation was found for lower extremity (r = -0.4). Longer lower extremity resulted in greater difference in LBM estimation. The differences between the sport disciplines are most probably attributed to body height differences. Length dimensions result in overestimation of LBM with BIA, thus body composition assessment with BIAInBody 720 needs to be carefully interpreted in athletes with extreme length sizes, especially, with basketball players.
Key words: Young athletes, DXA, bioimpedance method, lean mass, body fat
The purpose of this study was to evaluate the inter-device reliability of three VERT devices (Mayfonk Athletic, Florida, USA) when worn on the waist (W), left-hip (LH), and right-hip (RH) during single- and double-leg counter movement jumps (CMJ) in collegiate athletes. METHODS: Thirty-two female and twenty-eight male NCAA Division II athletes (n = 60) participated in the present study. Jump height (JH) values for double-leg CMJs were analyzed by each device using a one-way repeated measures ANOVA whereas a 2 (jump leg) x 3 (wear location) repeated measures ANOVA was employed to evaluate single-leg CMJs. Reliability of the VERT devices were based upon intraclass correlation coefficients (ICC). RESULTS: Double-leg CMJs revealed an excellent ICC between all three VERT devices (ICC=0.969). However, JH for RH and LH (45.69±9.84 and 45.82±10.45cm, respectively) were on average lower than W (50.44±12.37cm; both p<0.001). The ICCs were excellent for right- and left-leg CMJs (ICC=0.939 and 0.941, respectively). However, an interaction was observed (p<0.001). No differences existed for left- or right-leg when VERT was worn on the waist. However, JH was higher when VERT devices were worn on the opposite hip of the jump leg (i.e., LH>RH for right-leg CMJs; RH>LH for left-leg CMJs; all p<0.001). CONCLUSIONS: Results suggest that LH and RH are interchangeable for double-leg CMJs, but not with waist despite excellent reliability. In addition, all wear locations provided excellent ICCs for single-leg CMJs. However, waist provides more consistent JH values for right- and left-leg CMJs while RH and LH show more variability.
Previous studies show that body composition is related to injury risk and physical performance in soldiers. Thus, valid methods for measuring body composition in military personnel are needed. The frequently used body mass index method is not a valid measure of body composition in soldiers, but reliability and validity of alternative field methods are less investigated in military personnel. Thus, we carried out test and retest of skinfold (SKF), single frequency bioelectrical impedance analysis (SF-BIA), and multifrequency bioelectrical impedance analysis measurements in 65 male and female soldiers. Several validated equations were used to predict percent body fat from these methods. Dual-energy X-ray absorptiometry was also measured, and acted as the criterion method. Results showed that SF-BIA was the most reliable method in both genders. In women, SF-BIA was also the most valid method, whereas SKF or a combination of SKF and SF-BIA produced the highest validity in men. Reliability and validity varied substantially among the equations examined. The best methods and equations produced test-retest 95% limits of agreement below ±1% points, whereas the corresponding validity figures were ±3.5% points. Each investigator and practitioner must consider whether such measurement errors are acceptable for its specific use.
Objective:
This study compared bioimpedance analysis (BIA) in the assessment of body composition with dual-energy X-ray absorptiometry (DXA) in 18- to 88-year-old adults.
Design and methods:
Body composition of 882 adults was estimated by eight-polar BIA and DXA. In addition, estimates of lean mass, fat mass, and percentage of fat were investigated across a range of age and leisure time physical activity (LTPA) groups.
Results:
Compared to DXA, larger lean masses (mean difference 2.9 and 1.6 kg) and smaller fat masses (3.1 and 2.6 kg) were estimated by BIA in both women and men, respectively. Differences between the methods' mean values were evident in all age and LTPA groups, except in the oldest men (over 70 years). Age, waist circumference, grip strength, and LTPA explained 21% or less of the variance observed in the differences between methods.
Conclusions:
Compared to DXA, BIA provided systematically different body composition estimates throughout the adult age span with considerable amount of intraindividual variation. The differences between estimates may be related to the BIAs' algorithm or body geometry or composition of the population used in this study. Knowledge about the methodological limitations and device comparability is essential for researchers, clinicians, and persons working in rehabilitation and sport centers.
Background/objectives:
The purpose of the current review was to evaluate how body composition can be utilised in athletes, paying particular attention to the bioelectrical impedance analysis (BIA) technique.
Subjects/methods:
Various body composition methods are discussed, as well as the unique characteristics of athletes that can lead to large errors when predicting fat mass (FM) and fat-free mass (FFM). Basic principles of BIA are discussed, and past uses of the BIA technique in athletes are explored. Single-prediction validation studies and studies tracking changes in FM and FFM are discussed with applications for athletes.
Results:
Although extensive research in the area of BIA and athletes has been conducted, there remains a large gap in the literature pertaining to a single generalised athlete equation developed using a multiple-compartment model that includes total body water (TBW).
Conclusions:
Until a generalised athlete-specific BIA equation developed from a multiple-compartment is published, it is recommended that generalised equations such as those published by Lukaski and Bolonchuk and Lohman be used in athletes. However, BIA equations developed for specific athletes may also produce acceptable values and are still acceptable for use until more research is conducted. The use of a valid BIA equation/device should produce values similar to those of hydrostatic weighing and dual-energy X-ray absorptiometry. However, researchers and practitioners need to understand the individual variability associated with BIA estimations for both single assessments and repeated measurements. Although the BIA method shows promise for estimating body composition in athletes, future research should focus on the development of general athlete-specific equations using a TBW-based three- or four-compartment model.
The purpose of this investigation was to compare a practical measurement of fat free mass index (FFMI) from bioelectrical impedance analysis (BIA) to the dual energy X-ray absorptiometry (DEXA) value in collegiate athletes.
Thirty-three male baseball players and 16 female gymnasts volunteered to participate in this study during their respective pre-season. Subjects visited the laboratory once and had their measurements taken in the following order: weight, height, DEXA, and Omron HBF-500.
The BIA device investigated was not a valid estimate of FFMI when compared to the DEXA. The TE was 0.93 kg/ m(2) for males and 0.78 kg/ m(2) for females. There were also significant mean differences between the BIA prediction and the DEXA value for males (BIA=20.6 kg/m(2) vs. DEXA=21.1 kg/m(2), P=0.007) and females (BIA=16.2 kg/m(2) vs. DEXA=17.5 kg/m(2), P=0.001).
The BIA device investigated in this study did not provide a valid estimate of FFMI in male and female collegiate athletes. Although there was a general tendency for the BIA to underestimate FFMI compared to DEXA, 98% of the estimates were within plus or minus 2 kg/ m(2). Therefore, while slightly biased, BIA may provide a reasonable (± 2 kg/ m(2)) estimate of nutritional status for practitioners who are unable able to afford more expensive equipment.
The purpose of the present study was to determine the validity of various laboratory methods for estimating percent body fat (%fat) in NCAA Division I college female athletes (n = 29; 20 +/- 1 year). Body composition was assessed via hydrostatic weighing (HW), air displacement plethysmography (ADP), and dual-energy X-ray absorptiometry (DXA), and estimates of %fat derived using 4-compartment (C), 3C, and 2C models were compared to a criterion 5C model that included bone mineral content, body volume (BV), total body water, and soft tissue mineral. The Wang-4C and the Siri-3C models produced nearly identical values compared to the 5C model (r > 0.99, total error (TE) < 0.40%fat). For the remaining laboratory methods, constant error values (CE) ranged from -0.04%fat (HW-Siri) to -3.71%fat (DXA); r values ranged from 0.89 (ADP-Siri, ADP-Brozek) to 0.93 (DXA); standard error of estimate values ranged from 1.78%fat (DXA) to 2.19%fat (ADP-Siri, ADP-Brozek); and TE values ranged from 2.22%fat (HW-Brozek) to 4.90%fat (DXA). The limits of agreement for DXA (-10.10 to 2.68%fat) were the largest with a significant trend of -0.43 (P < 0.05). With the exception of DXA, all of the equations resulted in acceptable TE values (<3.08%fat). However, the results for individual estimates of %fat using the Brozek equation indicated that the 2C models that derived BV from ADP and HW overestimated (5.38, 3.65%) and underestimated (5.19, 4.88%) %fat, respectively. The acceptable TE values for both HW and ADP suggest that these methods are valid for estimating %fat in college female athletes; however, the Wang-4C and Siri-3C models should be used to identify individual estimates of %fat in this population.
We compared body composition estimates using an eight-electrode, segmental, multiple-frequency bioelectrical impedance analysis (segmental MF-BIA) and dual x-ray absorptiometry (DXA) in a group of healthy adults with a range of body mass indexes (BMIs).
Percentage of body fat (%BF), fat-free mass, and fat mass assessed by DXA and segmental MF-BIA in 132 healthy adults were classified by normal (N; 18.5-24.9 kg/m(2)), overweight (OW; 25-29.9 kg/m(2)), and obese (OB; 30-39.9 kg/m(2)) BMI.
Compared with DXA, segmental MF-BIA overestimated %BF in the OB BMI group (3.4%; P < 0.0001). MF-BIA overestimated %BF among men (0.75%; P < 0.006) and women (0.87%; P < 0.006) and underestimated it in the N BMI group (-1.56%; P < 0.0001); %BF was not different between methods in the OW BMI group. Error in %BF determined by segmental MF-BIA and DXA increased as %BF increased (r = 0.42, P < 0.0001). Waist circumference was the only significant predictor of systematic error in %BF between MF-BIA and DXA (r = 0.60, P < 0.0001).
Eight-electrode, segmental MF-BIA is a valid method to estimate %BF in adults with BMI classified as N and OW, but not as OB. Estimation of trunk resistance with current segmental MF-BIA devices may explain the underestimation of %BF in the adults with OB BMI. Further examination of the effect of waist circumference and body fat distribution on the accuracy of BIA measurements is warranted.
The purpose of this study was to use estimates of body composition from a four-component model to determine whether the density of the fat-free mass (D(FFM)) is affected by muscularity or musculoskeletal development in a heterogenous group of athletes and nonathletes. Measures of body density by hydrostatic weighing, body water by deuterium dilution, bone mineral by whole body dual-energy X-ray absorptiometry (DXA), total body skeletal muscle estimated from DXA, and musculoskeletal development as measured by the mesomorphy rating from the Heath-Carter anthropometric somatotype were obtained in 111 collegiate athletes (67 men and 44 women) and 61 nonathletes (24 men and 37 women). In the entire group, D(FFM) varied from 1.075 to 1.127 g/cm3 and was strongly related to the water and protein fractions of the fat-free mass (FFM; r = -0.96 and 0.89) and moderately related to the mineral fraction of the FFM (r = 0.65). Skeletal muscle (%FFM) varied from 40 to 68%, and mesomorphy varied from 1.6 to 9.6, but neither was significantly related to D(FFM) (r = 0.11 and -0.14) or to the difference between percent fat estimated from the four-component model and from densitometry (r = 0.09 and -0.16). We conclude that, in a heterogeneous group of young adult athletes and nonathletes, D(FFM) and the accuracy of estimates of body composition from body density using the Siri equation are not related to muscularity or musculoskeletal development. Athletes in selected sports may have systematic deviations in D(FFM) from the value of 1.1 g/cm3 assumed in the Siri equation, resulting in group mean errors in estimation of percent fat from densitometry of 2-5% body mass, but the cause of these deviations is complex and not simply a reflection of differences in muscularity or musculoskeletal development.
Abdominal fat is of major importance in terms of body fat distribution but is poorly reflected in conventional body impedance measurements. We developed a new technique for assessing the abdominal subcutaneous fat layer thickness (SFL) with single-frequency determination of the electrical impedance across the waist (SAI).
The method uses a tetrapolar arrangement of surface electrodes which are placed symmetrically to the umbilicus in a plane perpendicular to the body axis. Twenty-four test subjects (12 male, 12 female) underwent SAI and abdominal magnetic resonance imaging (MRI). The SFL below the sensing electrodes was determined from MRI and correlated with the SAI data at four different frequencies (5, 20, 50 and 204 kHz).
A highly significant linear correlation (r2=0.99) between SFL and SAI over a wide range of the abdominal SFL was found. Separate regression models for female and male subjects did not differ significantly, except at 50 kHz.
SAI represents a good predictor of the SFL and provides an excellent tool for the assessment of central obesity.
Bioelectrical impedance analysis (BIA) has been suggested as a simple, rapid method to assess changes in hydration status. BIA measures the electrical impedance to a low amperage current that is affected by both water and electrolyte content of the body. While BIA can reliably estimate total body water and body density in euhydrated individuals under standardized clinical conditions, changes in fluid and electrolyte content can independently alter bioimpedance measurements. Because hydration changes typically involve concomitant changes in fluid and electrolyte content, the interpretation of a change in bioimpedance will often be confounded. This paper examines the assumptions underlying estimations of total body water from BIA and addresses the factors known to influence bioimpedance independently from actual change in total body water. The results indicate that current BIA methodology may not provide valid estimates of total body water when hydration state is altered.
The female athlete triad (Triad) refers to the interrelationships among energy availability, menstrual function, and bone mineral density, which may have clinical manifestations including eating disorders, functional hypothalamic amenorrhea, and osteoporosis. With proper nutrition, these same relationships promote robust health. Athletes are distributed along a spectrum between health and disease, and those at the pathological end may not exhibit all these clinical conditions simultaneously. Energy availability is defined as dietary energy intake minus exercise energy expenditure. Low energy availability appears to be the factor that impairs reproductive and skeletal health in the Triad, and it may be inadvertent, intentional, or psychopathological. Most effects appear to occur below an energy availability of 30 kcal.kg(-1) of fat-free mass per day. Restrictive eating behaviors practiced by girls and women in sports or physical activities that emphasize leanness are of special concern. For prevention and early intervention, education of athletes, parents, coaches, trainers, judges, and administrators is a priority. Athletes should be assessed for the Triad at the preparticipation physical and/or annual health screening exam, and whenever an athlete presents with any of the Triad's clinical conditions. Sport administrators should also consider rule changes to discourage unhealthy weight loss practices. A multidisciplinary treatment team should include a physician or other health-care professional, a registered dietitian, and, for athletes with eating disorders, a mental health practitioner. Additional valuable team members may include a certified athletic trainer, an exercise physiologist, and the athlete's coach, parents and other family members. The first aim of treatment for any Triad component is to increase energy availability by increasing energy intake and/or reducing exercise energy expenditure. Nutrition counseling and monitoring are sufficient interventions for many athletes, but eating disorders warrant psychotherapy. Athletes with eating disorders should be required to meet established criteria to continue exercising, and their training and competition may need to be modified. No pharmacological agent adequately restores bone loss or corrects metabolic abnormalities that impair health and performance in athletes with functional hypothalamic amenorrhea.
New, vertical, 8-electrode bioimpedance spectroscopy (BIS) analyzers provide detailed body-composition and nutritional information within 2 min. This is the first report on BIS's accuracy in predicting relative fatness [percentage body fat (%BF)] in a heterogeneous sample according to a multicomponent model criterion.
We compared %BF measurements from 2 BIS devices with those from a multicomponent model in a sample of Hispanic, black, and white adults.
Equal numbers of apparently healthy men and women (n = 75 of each) from each racial-ethnic group, diverse in body mass index and age, volunteered. Reference %BF (%BF(4C)) was computed by using a 4-component (4C) model with total bone mineral content obtained from dual-energy X-ray absorptiometry, body density from underwater weighing with measured residual lung volume, and total body water from traditional BIS. Estimations from InBody 720 (%BF(720)) and InBody 320 (%BF(320)) BIS analyzers were validated against %BF(4C).
The %BF(720) (r = 0.85, SEE = 5.19%BF) and %BF(320) (r = 0.84, SEE = 5.17%BF) correlations were significant (P < 0.05) in the men; main effects were nonsignificant. Correlations for %BF(720) (r = 0.88, SEE = 4.85%BF) and %BF(320) (r = 0.89, SEE = 4.82%BF) also were significant in the women (P < 0.05); there was a main effect for method but not race-ethnicity. There were no sex-specific overestimations or underestimations at the extremes of the distributions.
BIS estimates of %BF(4C) were well correlated in men and women. There were no significant methodologic differences in the men. The %BF(4C) was significantly underestimated by %BF(720) and %BF(320) in the women.
The purpose of this study was to validate the assessment of %Fat measured by two commercial grade BIA devices against the gold standard of dual x-ray absorptiometry (DEXA). Twenty-one subjects were measured for %Fat using three devices: an octopolar, multi-frequency BIA device (BIA8, BioSpace InBody R20); a quadripolar, single frequency BIA device (BIA4, Tanita BC-590BT); and a whole body DEXA (Hologic 4500). Mean ± SD differences in %Fat between the devices and DEXA were 0.14 ± 0.04 (P=0.80) (BIA8) and 1.77 ± 0.54 (P=0.76) (BIA4). Correlations with DEXA were r=0.98 (BIA8) and r=0.92 (BIA4). Bland-Altman analyses revealed a systematic bias for both BIA instruments vs. DEXA in which %Fat was underestimated in leaner subjects and overestimated in fatter subjects. All subjects had individual differences of ≤±3.0 %Fat for BIA8 vs. DEXA while 43% had differences of ≤3.0 %Fat for BIA4 vs. DEXA. Our data suggest that the use of an octopolar BIA device yields more than twice as many subjects within a ±3% error compared with BIA4; a value that might be considered appropriate for clinical use.
SEASONAL ALTERATIONS IN BODY COMPOSITION AND SPRINT PERFORMANCE OF ELITE SOCCER PLAYERS. Sergej M. Ostojic. JEPonline. 2003;6(3):11-14. The purpose of the present study was to examine the effects of training and competition on body fat content and sprint performance in elite professional soccer players. Thirty professional male soccer players (1 st National league) participated in the study. Anthropometric measurements were collected at the start of the first conditioning period, at the start of season, in the mid-season, end-season and at the start of the second conditioning period. Body composition was assessed by skinfold measurements. Estimated body fat percentage at the end of the season was significantly lower than levels at the start of the first conditioning period, mid-season, second conditioning period and at the start of the season (9.6±2.5% vs. 11.5±2.1, 10.2±2.9, 12.6±3.3 and 10.9±2.4% respectively; p<0.05; values are mean±SD). There were no significant differences in fat-free mass between measurements performed during the season. Be tter 50 m sprint times were achieved at the end of season as compared to the start of the first conditioning period, at the beginning of the season and at the start of the second conditioning period (7.1±0.5 s vs. 7.5±0.6, 7.3±0.6, 7.6±0.5 s, respectively; p < 0.05). The main finding of the present study was that body fat content of professional soccer players significantly dropped during the conditioning and competitive periods and increased during the off-season.
Multifrequency bioelectrical impedance analysis of body composition may be an appropriate alternative to dual-energy x-ray absorptiometry. We hypothesized that there would be no significant differences between dual-energy x-ray absorptiometry and either the Biospace (Los Angeles, CA, USA) InBody 520 or 720 multifrequency bioelectrical impedance analysis devices for total lean body mass (LBM), appendicular lean mass (ALM), trunk lean mass (TM), and total fat mass (FM) in 25 men and 25 women (including lean, healthy, and obese individuals according to body mass index), age 18 to 49 years, weight of 73.6 ± 15.4 kg. Both devices overestimated LBM in women (~2.5 kg, P < .001) and underestimated ALM in men (~3.0 kg, P < .05) and women (~1.0 kg, P < .05). The 720 overestimated FM in men (1.6 kg, P < .05) and underestimated TM in women (0.6 kg, P ≤ .05). Regression analyses in men revealed R² (0.87-0.91), standard error of the estimate (SEE; 2.3-2.8 kg), and limits of agreement (LOAs; 4.5-5.7 kg) for LBM; R(2) (0.62-0.87), SEE (1.5-2.6 kg), and LOA (3.2-6.0 kg) for ALM; R² (0.52-0.71), SEE (2.4-3.0 kg), and LOA (4.6-6.1 kg) for TM; and R(2) (0.87-0.93), SEE (1.9-2.6 kg), and LOA (5.9-6.2 kg) for FM. Regression analyses in women revealed R² (0.87-0.88), SEE (1.8-1.9 kg), and LOA (4.1-4.2 kg) for LBM; R² (0.78-0.79), SEE (1.4-1.5 kg), and LOA (2.7-2.9 kg) for ALM; R² (0.76-0.77), SEE (1.0 kg), and LOA (2.2-2.3 kg) for TM; and R² (0.95), SEE (2.2 kg), and LOA (4.3-4.4 kg) for FM. The InBody 520 and 720 are valid estimators of LBM and FM in men and of LBM, ALM, and FM in women; the 720 and 520 are valid estimators of TM in men and women, respectively.
In 1994 the WHO proposed guidelines for the diagnosis of osteoporosis based on measurement of bone mineral density. They
have been widely used for epidemiological studies, clinical research and for treatment strategies. Despite the widespread
acceptance of the diagnostic criteria, several problems remain with their use. Uncertainties concern the optimal site for
assessment, thresholds for men and diagnostic inaccuracies at different sites. In addition, the development of many new technologies
to assess the amount or quality of bone poses problems in placing these new tools within a diagnostic and assessment setting.
This review considers the recent literature that has highlighted the strengths and weaknesses of diagnostic thresholds and
their use in the assessment of fracture risk, and makes recommendations for actions to resolve these difficulties.
Body composition measurement is a valuable tool for assessing nutritional status and physical fitness in a variety of clinical settings. Although bioimpedance analysis (BIA) can easily assess body composition, its accuracy remains unclear. We examined the accuracy of direct segmental multi-frequency BIA technique (DSM-BIA) in assessing different body composition parameters, using dual energy X-ray absorptiometry (DEXA) as a reference standard.
A total of 484 middle-aged participants from the Leiden Longevity Study were recruited. Agreements between DSM-BIA and DEXA for total and segmental body composition quantification were assessed using intraclass correlation coefficients and Bland-Altman plots.
Excellent agreements were observed between both techniques in whole body lean mass (ICC female = 0.95, ICC men = 0.96), fat mass (ICC female = 0.97, ICC male = 0.93) and percentage body fat (ICC female = 0.93, ICC male = 0.88) measurements. Similarly, Bland-Altman plots revealed narrow limits of agreements with small biases noted for the whole body lean mass quantification but relatively wider limits for fat mass and percentage body fat quantifications. In segmental lean muscle mass quantification, excellent agreements between methods were demonstrated for the upper limbs (ICC female≥0.91, ICC men≥0.87) and lower limbs (ICC female≥0.83, ICC male≥0.85), with good agreements shown for the trunk measurements (ICC female = 0.73, ICC male = 0.70).
DSM-BIA is a valid tool for the assessments of total body and segmental body composition in the general middle-aged population, particularly for the quantification of body lean mass.
The primary purpose of this study was to determine if muscular endurance is affected by referenced waist circumference groupings, independent of body mass and subcutaneous abdominal fat, in women. This study also explored whether selected body composition measures were associated with muscular endurance. Eighty-four women were measured for height, weight, body mass index (BMI), waist circumference (WC), and abdominal skinfold thickness (SFAB) and performed 60-s sit-ups (SU) and maximal push-ups (PU) tests. Mean differences in SU and PU scores were tested across three groups based on WC as follows: WCG1 < 70 cm; WCG2 between 70 and 89 cm; WCG3 > or = 90 cm. There were no significant differences in SU and PU scores between WCG1 and WCG2. WCG3 had significantly lower SU and PU scores compared to the other groups. After adjusting for the influence of SFAB, BMI, and weight, the differences disappeared. The regression analysis revealed a two-variable (BMI and SFAB) model that accounted for the variation in SU performance. For PU, only BMI loaded into the regression model. The results of this study suggest that women with a WC > or = 90 cm have decreased muscular endurance compared to their lower WC counterparts. This difference is related to higher body masses.
The purpose of this investigation was to determine the accuracy of hand-to-hand bioelectrical impedance analysis (BIA) for estimating body composition in college-age female athletes using dual-energy X-ray absorptiometry (DEXA) as the criterion measure. Forty National Association for Intercollegiate Athletics college female athletes volunteered to participate in this study. For each participant, total body fat percentage (BF%) and fat-free mass (FFM) were obtained via BIA and DEXA. The mean BF% and FFM values obtained by BIA were compared with the criterion DEXA measure. The DEXA strongly correlated to the BIA for BF% (r = 0.74, R2 = 0.55, SEE = 3.60, and p < 0.01) and FFM (r = 0.84, R2 = 0.71, SEE = 2.45, p < 0.01). However, when compared with the DEXA, the mean values for BIA were significantly lower for BF% (DEXA = 27.6 ± 5.3%, BIA = 22.5 ± 3.5%, p < 0.01) and higher for FFM (DEXA = 47.2 ± 4.5 kg, BIA = 50.6 ± 4.6 kg, p < 0.01). The results of this investigation indicate that hand-to-hand BIA significantly underestimates BF% and overestimated FFM in college-age female athletes when compared with the criterion DEXA. Practitioners should use caution when analyzing body composition with hand-held BIA in a population of athletic women.
In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
The aim of this study was to examine variations in measures of body composition in elite soccer players. Skinfolds and measures of body mass (BM) recorded on a monthly basis across an entire competitive season in a group of senior professional players (n = 26) were used to estimate percentage body fat (%BF) and provide fat-free body mass (FFBM) values. Mean values in players were compared between 6 specific positional roles (goalkeepers, central and lateral defenders/midfielders and center-forwards). In-season variations in measures were studied by comparing values at 5 separate points across the season. The effects of positional group (goalkeepers, defenders, midfielders, and forwards) and exposure time to play (participation time in training and matches) in relation to in-season variations were also examined. To investigate interseasonal changes, repeated measures were taken in players (n = 9) over 3 consecutive seasons. In relation to positional role, a difference in average %BF and BM values was observed (p < 0.001), with substantial differences observed in goalkeepers, lateral midfielders, and forwards. Across all players, there were significant in-season variations in %BF (between start- and mid-season and mid- and end-season, p < 0.001) and FFBM (between start- and mid-season and start- and end-season, p < 0.001), whereas BM remained unchanged. Further analysis of these fluctuations in %BF and FFBM at different points of the season showed that variations differed across the positional groups (p < 0.01), especially in defenders and midfielders. In contrast, no association was observed between measures and exposure time and no differences were reported across seasons. Practitioners should consider individual positional role when interpreting mean body composition data. They should also take into account positional groups when in-season variations in body composition are identified.
Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.
The purpose of this study was to explore whether selected anthropometric measures such as specific skinfold sites, along with weight, height, body mass index (BMI), waist and hip circumferences, and waist/hip ratio (WHR) were associated with sit-ups (SU) and push-ups (PU) performance, and to build a regression model for SU and PU tests. One hundred apparently healthy adults (40 men and 60 women) served as the subjects for test validation. The subjects performed 60-second SU and PU tests. The variables analyzed via multiple regression included weight, height, BMI, hip and waist circumferences, WHR, skinfolds at the abdomen (SFAB), thigh (SFTH), and subscapularis (SFSS), and sex. An additional cohort of 40 subjects (17 men and 23 women) was used to cross-validate the regression models. Validity was confirmed by correlation and paired t-tests. The regression analysis yielded a four-variable (PU, height, SFAB, and SFTH) multiple regression equation for estimating SU (R2 = 0.64, SEE = 7.5 repetitions). For PU, only SU was loaded into the regression equation (R2 = 0.43, SEE = 9.4 repetitions). Thus, the variables in the regression models accounted for 64% and 43% of the variation in SU and PU, respectively. The cross-validation sample elicited a high correlation for SU (r = 0.87) and PU (r = 0.79) scores. Moreover, paired-samples t-tests revealed that there were no significant differences between actual and predicted SU and PU scores. Therefore, this study shows that there are a number of selected, health-related anthropometric variables that account significantly for, and are predictive of, SU and PU tests.
Bioelectrical impedance analysis (BIA) has potential in the area of sports and exercise as a method for evaluating body composition in groups of athletes. BIA probably holds less promise for detecting small changes in percentage fat within an individual. Available data in athletes have indicated an urgent need to control for testing conditions such as hydration, temperature, glycogen stores, and preceding diet and exercise. There are almost no data available for female athletes, but acceptable results have been reported in males when conditions are well controlled. There is, however, a tendency for BIA to overestimate percentage body fat, and more so in African American athletes. BIA is also potentially useful for assessing the hydration status in wrestlers, but it is advisable to use untransformed BIA measurements rather than to convert resistance measurements to body fat because of the questionable hydration status in these athletes. Untransformed results are potentially useful in evaluating the clinical status of athletes at risk for abnormal hydration because of extreme dieting practices.
Dual-energy X-ray absorptiometry (DXA) is rapidly gaining acceptance as a reference method for analyzing body composition. An important and unresolved concern is whether and to what extent variation in soft tissue hydration causes errors in DXA fat estimates. The present study aim was to develop and validate a DXA physical hydration model and then to apply this model by simulating errors arising from hypothetical overhydration states. The DXA physical hydration model was developed by first linking biological substance elemental content with photon attenuation. The validated physical model was next extended to describe photon attenuation changes anticipated when predefined amounts of two known composition components are mixed, as would occur when overhydration develops. Two overhydration models were developed in the last phase of study, formulated on validated physical models, and error was simulated for fluid surfeit states. Results indicate that systematic errors in DXA percent fat arise with added fluids when fractional masses are varied as a percentage of combined fluid + soft tissue mass. Three independent determinants of error magnitude were established: elemental content of overhydration fluid, fraction of combined fluid + soft tissue as overhydration fluid, and initial soft tissue composition. Small but systematic and predictable errors in DXA soft tissue composition analysis thus can arise with fluid balance changes.
The purpose of this study was to determine how differences in hydration states and ion content of hydrating fluids affected bioelectrical impedance (BI) and hydrostatic weighing (HW) measurements.
Fifteen athletic subjects aged 19-56 yr were recruited. Relative body fat (%), fat-weight (FW), and fat-free weight (FFW) were assessed using BI and HW under normal conditions (N), hypohydration (HPO), rehydration (RHY), and superhydration (SHY) states. During the RHY and SHY trial periods, subjects were hydrated with either distilled water or an electrolyte solution (ELS). HPO and SHY levels were set at 3% of each person's normally hydrated body weight.
Comparison between the distilled water and the ELS trials indicated that hydration solution had no effect on BI or HW. Thus, the results presented are the trial means of both hydration solutions combined. Both BI and HW were shown to be highly test-retest reliable (r-values: 0.96 and 0.99, respectively). The effects of exercise induced HPO followed by RHY on body composition values indicated that HW was very stable across measurement periods while BI was not. From N to the HPO state, BI %BF declined from 14.4 +/- 5.3% to 12.3 +/- 5.3%, respectively. After RHY, BIA %BF increased to 15.5 +/- 5.8%. Similar findings occurred when subjects were superhydrated (N-BI = 13.2 +/- 5.3%; SHY-BI = 15.4 +/- 5.6%). With a comparison of the intercepts and slopes of HW and BIA for the N and SHY states, it was clear hydration status significantly affected the intercepts (HW: 0.37 vs. BI: 1.85) and not the slopes (HW: 1.00 vs BI: 0.99). As a result, a majority of all fluid changes were interpreted as FW by BI. During HPO, 82% of the weight loss was considered FW while during RHY or SHY, 128% and 85% of the water weight regain/gain was considered FW.
These results indicate that BI is not a valid technique in athletes, especially when wanting to determine body composition effects of training/detraining. This study indicates that even small fluid changes such as those that occur with endurance training may be interpreted incorrectly as changes in an athlete's body fat content.
OBJECTIVE: Despite widespread use of skinfolds to estimate body fatness, few prediction models have been validated on female athletes. Most skinfold models have been validated with hydrodensitometry, which does not account for the variability in bone density that may exist among female athletes. Our purpose was to develop a skinfold model that predicts fat-free mass (FFM) in female collegiate athletes. DESIGN AND SETTING: A skinfold model was developed using dual-energy x-ray absorptiometry (DEXA) as the criterion method. Four skinfold measures (abdominal, suprailiac, thigh, triceps), height, and weight were entered into a regression model. The best model was developed and validated by calculating the predicted error sum of squares statistic. SUBJECTS: Study participants included 101 National Collegiate Athletic Association Division I female athletes (age = 20.3 +/- 1.4 years, height = 166.7 +/- 7.8 cm, mass = 63.1 +/- 8.1 kg) from several sports. MEASUREMENTS: Each participant's FFM was measured via DEXA. Skinfold thicknesses were measured and entered into the regression model. RESULTS: The final regression model included mass and abdominal and thigh skinfolds: FFM = 8.51 + (0.809 x mass) - (0.178 x abdominal skinfold) - (0.225 x thigh skinfold). The model showed excellent predictive ability (R = 0.98, standard error of the estimate = 1.1 kg). Pairwise comparisons indicated that prediction error showed no overprediction or underprediction bias. CONCLUSIONS: In female collegiate athletes, FFM can be predicted accurately from body mass and abdominal and thigh skinfolds. This model is practical and can be used in most athletic settings.
The purpose of this study was to examine changes in body composition (BC) and physical performance tests (PT) resulting from a competitive season in soccer. Twenty-five male collegiate players (age = 19.9 +/- 1.3 years; height = 177.6 +/- 6.4 cm; body mass = 77.6 +/- 8.6 kg, and percentage body fat = 12.8 +/- 5.2%) were tested before (PRE) and after (POST) the 2003-2004 National Collegiate Athletic Association season. The following tests were performed: BC (anthropometric and dual energy x-ray absorptiometry measurements), vertical jump (VJ), 9.1-m (9 m) and 36.5-m (36 m) sprint, lower-body power (LP), total body power (TP), and cardiorespiratory endurance (VO(2)max). Training was divided into soccer-specific training: field warm-up drills, practices, games, and additional conditioning sessions. A daily, unplanned, nonlinear periodization model was used to assign session volume and intensity for strength sessions (total repetitions < or =96 and workload was > or =80% of 1 repetition maximum). For the entire team, body mass significantly increased by 1.5 +/- 0.4 kg from PRE to POST due to a significant increase in total lean tissue (0.9 +/- 0.2 kg). Regionally, lean tissue mass significantly increased in the legs (0.4 +/- 0.0 kg) and trunk (0.3 +/- 0.1 kg). Physical performance variables were very similar for the entire team at PRE and POST; VJ (cm) = 61.9 +/- 7.1 PRE vs. 63.3 +/- 8.0 POST, 9.1-m (s) = 1.7 +/- 0.1 PRE and POST, 36.5-m (s) = 5.0 +/- 0.2 PRE and POST, predicted VO(2)max (ml.kg.min(-1))= 59.8 +/- 3.3 PRE vs. 60.9 +/- 3.4 POST. The only significant improvements across the season were for TP (17.3%) and for LP (10.7%). In conclusion, soccer athletes who begin a season with a high level of fitness can maintain, and in some cases improve, body composition and physical performance from before to after a competitive season. A correct combination of soccer-specific practices and strength and conditioning programs can maintain and develop physical performance, allowing a soccer athlete to perform optimally throughout pre-, in-, and postseason play.
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