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European Journal of Clinical Nutrition https://doi.org/10.1038/s41430-017-0046-1 BRIEF COMMUNICATION Validation of a three-dimensional body scanner for body composition measures Michelle M. Harbin1 ● Alexander Kasak1 ● Joseph D. Ostrem2 ● Donald R. Dengel1 Received: 12 March 2017 / Revised: 5 October 2017 / Accepted: 20 November 2017 © Macmillan Publishers Limited, part of Springer Nature 2017 Abstract The accuracy of an infrared three-dimensional (3D) body scanner in determining body composition was compared against hydrostatic weighing (HW), bioelectrical impedance analysis (BIA), and anthropometry. A total of 265 adults (119 males; age= 22.1± 2.5 years; body mass index= 24.5± 3.9 kg/m2) had their body fat percent (BF%) estimated from 3D scanning, HW, BIA, skinfolds, and girths. A repeated measures analysis of variance (ANOVA) indicated significant differences among methods (p o 0.001). Multivariate ANOVA indicated a significant main effect of sex and method (p o 0.001), with a non- significant interaction (p= 0.101). Bonferroni post-hoc comparisons identified that BF% from 3D scanning (18.1 ± 7.8%) was significantly less than HW (22.8 ± 8.5%, p o 0.001), BIA (20.1 ± 9.1%, p o 0.001), skinfolds (19.7± 9.7%, p o 0.001), and girths (21.2 ± 10.4%, p o 0.001). The 3D scanner decreased in precision with increasing adiposity, potentially resulting from inconsistences in the 3D scanner’s analysis algorithm. A correction factor within the algorithm is required before infrared 3D scanning can be considered valid in measuring BF%. Introduction Two-compartment models, which separate the human body into fat mass and fat-free mass, are widely used methods of determining body composition and include techniques such as hydrostatic weighing (HW) and air displacement ple- thysmography (ADP) [1, 2]. Multi-compartment body composition models (e.g., dual X-ray absorptiometry [DXA]) offer greater accuracy than traditional two- component methodology. However, the high cost of using such methods and amount of radiation exposure limit their use in certain settings and populations [1, 2]. Current advances in scanning technology allow for the ability to capture a three-dimensional (3D) image of the human body and digitally extract numerous anthropometric measurements [1–4]. To date, few studies have investigated the accuracy of infrared 3D technology in estimating body composition. As such, the aim of this study was to compare and validate the accuracy of an infrared 3D body scanner in determining body composition against hydrostatic weighing (HW), bioelectrical impedance analysis (BIA), and anthro- pometry (e.g., skinfold thickness and girths). Methods Subjects Healthy college students (N= 265; males= 119) at the University of Minnesota, Twin Cities were recruited from December 2015 to December 2016. The study protocol was reviewed and approved by the University of Minnesota, Twin Cities Institutional Review Board (IRB). All proce- dures were followed in accordance with the IRB and HIPAA guidelines. Exclusion criteria included claus- trophobia, hypersensitivity to chlorine, and medical con- traindications that limit water submersion (pregnancy, orthopedic casting, open lacerations). * Michelle M. Harbin harb0085@umn.edu 1 Laboratory of Integrative Human Physiology, School of Kinesiology, University of Minnesota, Minneapolis, MN 55455, USA 2 Kinesiology and Health Sciences, College of Education and Science, Concordia University - St. Paul, St. Paul, MN 55104, USA 12 34 56 78 90 Procedures Testing was performed at the Laboratory of Integrative Human Physiology on the University of Minnesota, Twin Cities campus. Subjects wore tight-fit clothing (compres- sion shorts, spandex). BIA and anthropometry measures Body mass and BF% were obtained via a BIA scale (Tanita Corporation, Tokyo, Japan). Height was obtained from a stadiometer (Seca GmbH & Co. KG, Hamburg, Germany). Body mass index (BMI) was calculated as body mass in kilograms (kg) divided by height in squared meters (m2). Lange C-120 skinfold calipers (Beta Technology Inc., Cambridge, Maryland) measured skinfold thickness. The Jackson and Pollock equation estimated body density from the sum of three skinfold sites [5]. A 60-inch Gulick mea- suring tape assessed girth (Creative Health Products, Ann Arbor, MI). The Navy circumference-based equation esti- mated body density from the average of three girths taken at each gender specific site [6]. Infrared 3D body scanner measures The 3D body scanner (MYBODEE™; Styku, Los Angeles, CA) estimated BF% via automated girths and an undi- sclosed linear regression algorithm. The scanner consisted of a Microsoft Kinect V2 infrared depth sensor (Microsoft Corporation, Redmond, WA) positioned 157 centimeters (cm) away from a rotating cir- cular platform. Subjects stood erect on the circular platform with their feet flat and directed forward. Subjects were required to remain motionless with arms abducted at a 45- degree angle away from the torso and aligned within the coronal plane of their body. Once scanning initiated, the circular platform rotated at a constant speed for approxi- mately 30 seconds while the infrared depth sensor simulta- neously relayed information to a compatible computer. Hydrostatic weighing measures Hydrostatic weight was measured via a wireless underwater weighing system (Exertech®, Dresbach, MN). An estimate of residual volume (RV) was made based on the subject’s height, age, and sex [7]. The average of three hydrostatic weights was used to calculate body density and to predict BF% using the estimated RV and the Siri equation [8]. Statistical analysis IBM SPSS Statistics 23 (IBM Corp. Armonk, NY) was used for statistical analysis. Descriptive characteristicsTa bl e 1 C om pa ri so n of bo dy co m po si tio n fr om 3D bo dy sc an ni ng , hy dr os ta tic w ei gh t, B IA , an d an th ro po m et ry M ea n ± SD C oh en ’s d r S E M L im its of A gr ee m en t T ot al M al es F em al es C E ± 1. 96 × SD L ow er U pp er T re nd 3D bo dy sc an ne r B F % , % 18 .1 ± 7. 8 10 .7 ± 4. 0 24 .2 ± 3. 9‡ H yd ro st at ic w ei gh t B F % , % 22 .8 ± 8. 5† 16 .4 ± 6. 2* 28 .0 ± 6. 3‡ ° 0. 94 0 0. 81 6 0. 30 7 − 4. 70 4 ± 9. 80 8 − 14 .5 1 5. 10 4 − 0. 09 8 B IA B F % , % 20 .1 ± 9. 1† 12 .6 ± 5. 0* 26 .2 ± 6. 8‡ ° 0. 46 7 0. 88 8 0. 46 9 − 1. 95 4 ± 8. 16 7 − 10 .1 2 6. 21 3 − 0. 15 7 S ki nf ol ds B F % : Ja ck so n an d P ol lo ck eq ua tio n, % 19 .7 ± 9. 7† 11 .8 ± 5. 6* 25 .9 ± 7. 5‡ ° 0. 25 6 0. 81 7 0. 25 6 − 1. 74 3 ± 1. 13 3 − 12 .8 8 9. 39 1 − 0. 21 6 G ir th s B F % : N av y eq ua tio n, % 21 .2 ± 10 .4 † 13 .9 ± 4. 6* 27 .0 ± 9. 9‡ ° 0. 46 9 0. 87 5 0. 46 7 − 3. 20 ± 9. 02 2 − 12 .2 2 5. 82 2 − 0. 20 3 V al ue s ar e m ea n ± S D an d re pr es en t es tim at es of bo dy fa t pe rc en t m ea su re d by 3D bo dy sc an ni ng , hy dr os ta tic w ei gh t, B IA , sk in fo ld th ic kn es s us in g th e Ja ck so n an d P ol lo ck su m of th re e sk in fold s si te s, an d gi rt hs us in g th e N av y ci rc um fe re nc e- ba se d eq ua tio n. † R ep re se nt s si gn ifi ca nt ly gr ea te r B F % co m pa re d to 3D bo dy sc an ni ng (p o 0. 00 1) .‡ R ep re se nt s si gn ifi ca nt ly gr ea te r B F % am on g fe m al es co m pa re d to m al es w ith in bo dy co m po si tio n m et ho d (p o 0. 00 1) . *R ep re se nt s si gn ifi ca nt ly gr ea te r B F % co m pa re d to 3D bo dy sc an ni ng w ith in m al es (p o 0. 00 1) . ° R ep re se nt s si gn ifi ca nt ly gr ea te r B F % co m pa re d to 3D bo dy sc an ni ng w ith in fe m al es (p o 0. 00 1) .B F % bo dy fa tp er ce nt ,r P ea rs on ’s co rr el at io n co ef fi ci en t, SE M st an da rd er ro r of th e m ea n, C E co ns ta nt er ro r. T re nd w as ca lc ul at ed as th e sl op e fr om th e le as t sq ua re lin ea r re gr es si on of di ff er en ce s of th e av er ag es MM Harbin et al. between sexes were compared by a one-way analysis of variance (ANOVA). Bland-Altman plots evaluated the level of agreement between the 3D body scanner against other body composition methods. A repeated measures ANOVA evaluated mean differences in estimating BF% according to the different body composition methods with Bonferroni post-hoc analysis, and a multivariate ANOVA tested for an interaction. Results The subject population was predominately Caucasian (87%) and consisted of 119 males and 147 females (males vs. females: age= 22.4± 2.7 vs. 21.8± 2.4 years, p= 0.071; body mass= 81.4± 12.8 vs. 66.6± 11.8 kg, p o 0.001; height= 179.3± 7.3 vs. 165.5± 11.1 cm, p o 0.001; BMI = 25.2± 3.3 vs. 23.9± 4.2 kg/m2, p= 0.007). There were significant differences among the body composition methods (p o 0.001). Bonferroni post-hoc analysis revealed that BF% estimated by 3D body scanning was significantly less than all the other techniques (Table 1). The multivariate ANOVA showed a significant effect between each body composition method (p o 0.001). As expected, males had significantly lower BF% compared to females on all body composition methods (p o 0.001), though the interaction between body composition method and sex was not (p= 0.101). Body fat percentages for both males and females from the 3D body scanner were sig- nificantly less than HW (p o 0.001), BIA (p o 0.001), skinfolds (p o 0.001), and girth measures (p o 0.001) (Table 1). Bland-Altman plots exhibited proportional bias in 3D scanning and reduced precision among subjects with increased adiposity (Fig. 1). Discussion Three-dimensional body scanners using infrared technology underestimated BF% among healthy, young adults when compared to HW, BIA, and anthropometry. The non- significant interaction from multivariate analysis revealed that the underestimation in BF% from the 3D body scanner was consistent among both males and females. Similarly, Ryder and Ball [9] observed that a fan-beam 3D body scanner underestimated BF% across all BMI categories when compared to both DXA and ADP. In addition to the 3D body scanner underestimating BF %, proportional bias occurred. Proportional bias occurs when two methods have unequal agreement through the range of measurements. The 3D scanner had decreased accuracy among subjects with increased adiposity, which is largely attributable to inconsistencies with landmark and partition positioning in the 3D surface scan analysis algo- rithms [10]. Software updates in infrared technology and a correction factor are warranted to resolve biases [9, 10]. A possible limitation includes inconsistences of manu- ally measuring skinfold thickness and girths between the subjects. Another limitation was that HW with estimated RV was used instead of DXA or other multi-compartment models. However, 3D body scanning underestimated BF compared to HW, BIA and anthropometry. In conclusion, 3D body scanners have the potential application for mon- itoring body composition. Advancements in technology are Fig. 1 Bland-Altman plots for differences in body fat percent comparing the 3D body scanner to hydrostatic weighing a, bioe- lectrical impedance analysis b, skinfold thickness c, and girth measures d Validation of Infrared Three-Dimensional Body Scanning for Body Composition required before infrared 3D body scanners can be desig- nated as an accurate method for assessing body composition. Acknowledgements The authors wish to thank the University of Minnesota, Twin Cities. Funding The authors report no funding source. Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest. References 1. Lee SY, Gallagher D. 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Standardized lung function testing. Bull Eur Phy- siopathol Respir. 1983;19:1–95. 8. Siri WE. Body composition from fluid spaces and density: Ana- lysis of methods. Nutrition. 1961;9:480–491. 9. Ryder JR, Ball SD. Three-dimensional body scanning as a novel technique for body composition assessment: a preliminary investigation. J Exerc Physiol Online. 2012;15:1–14. 10. Ng BK, Hinton BJ, Fan B, Kanaya AM, Shepherd JA. Clinical anthropometrics and body composition from 3d whole-body sur- face scans. Eur J Clin Nutr. 2016;70:1265–1270. MM Harbin et al. Validation of a three-dimensional body scanner for body composition measures Abstract Introduction Methods Subjects Procedures BIA and anthropometry measures Infrared 3D body scanner measures Hydrostatic weighing measures Statistical analysis Results Discussion Compliance with ethical standards ACKNOWLEDGMENTS References
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