Heymsfield SB, Bourgeois B, Ng BK, Sommer MJ, Li X, Shepherd JA. Digital anthropometry: a critical review. Eur J Clin Nutr. 2018;72:680–7. https://doi.org/10.1038/s41430-018-0145-7

Article  Google Scholar 

Mocini E, Cammarota C, Frigerio F, Muzzioli L, Piciocchi C, Lacalaprice D, et al. Digital anthropometry: a systematic review on precision, reliability and accuracy of most popular existing technologies. Nutrients 2023;15:302. https://doi.org/10.3390/nu15020302

Graybeal AJ, Brandner CF, Tinsley GM. Visual body composition assessment methods: a 4-compartment model comparison of smartphone-based artificial intelligence for body composition estimation in healthy adults. Clin Nutr. 2022;41:2464–72. https://doi.org/10.1016/j.clnu.2022.09.014

Article  Google Scholar 

Graybeal AJ, Brandner CF, Tinsley GM. Evaluation of automated anthropometrics produced by smartphone-based machine learning: a comparison with traditional anthropometric assessments. Br J Nutr. 2023;130:1077–87. https://doi.org/10.1017/s0007114523000090

Article  CAS  Google Scholar 

Smith B, McCarthy C, Dechenaud ME, Wong MC, Shepherd J, Heymsfield SB. Anthropometric evaluation of a 3D scanning mobile application. Obesity. 2022;30:1181–8. https://doi.org/10.1002/oby.23434

Article  Google Scholar 

Tinsley GM, Harty PS, Siedler MR, Stratton MT, Rodriguez C. Improved precision of 3-dimensional optical imaging for anthropometric measurement using non-rigid avatar reconstruction and parameterized body model fitting. Clin Nutr Open Sci. 2023;50:40–45. https://doi.org/10.1016/j.nutos.2023.07.002

Article  Google Scholar 

Tinsley GM, Moore ML, Dellinger JR, Adamson BT, Benavides ML. Digital anthropometry via three-dimensional optical scanning: evaluation of four commercially available systems. Eur J Clin Nutr. 2020;74:1054–64. https://doi.org/10.1038/s41430-019-0526-6

Article  Google Scholar 

Walter SD, Eliasziw M, Donner A. Sample size and optimal designs for reliability studies. Stat Med. 1998;17:101–10. https://doi.org/10.1002/(sici)1097-0258(19980115)17:1<101::aid-sim727>3.0.co;2-e

Article  CAS  Google Scholar 

Arifin WN. Sample size calculator. Retrieved from http://wnarifin.github.io, 2023.

Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86:420–8. https://doi.org/10.1037//0033-2909.86.2.420

Article  CAS  Google Scholar 

Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15:155–63. https://doi.org/10.1016/j.jcm.2016.02.012

Article  Google Scholar 

Wong MC, Ng BK, Kennedy SF, Hwaung P, Liu EY, Kelly NN, et al. Children and adolescents’ anthropometrics body composition from 3-D optical surface scans. Obesity. 2019;27:1738–49. https://doi.org/10.1002/oby.22637

Article  Google Scholar 

Ng BK, Sommer MJ, Wong MC, Pagano I, Nie Y, Fan B, et al. Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies. Am J Clin Nutr. 2019;110:1316–26. https://doi.org/10.1093/ajcn/nqz218

Article  Google Scholar 

Sullivan K, Metoyer CJ, Hornikel B, Holmes CJ, Nickerson BS, Esco MR, et al. Agreement between a 2-dimensional digital image-based 3-compartment body composition model and dual energy X-ray absorptiometry for the estimation of relative adiposity. J Clin Densitom. 2022;25:244–51. https://doi.org/10.1016/j.jocd.2021.08.004

Article  Google Scholar 

Sullivan K, Hornikel B, Holmes CJ, Esco MR, Fedewa MV. Validity of a 3-compartment body composition model using body volume derived from a novel 2-dimensional image analysis program. Eur J Clin Nutr. 2022;76:111–8. https://doi.org/10.1038/s41430-021-00899-1

Article  Google Scholar 

Fedewa MV, Sullivan K, Hornikel B, Holmes CJ, Metoyer CJ, Esco MR. Accuracy of a mobile 2D imaging system for body volume and subsequent composition estimates in a three-compartment model. Med Sci Sports Exerc. 2021;53:1003–9. https://doi.org/10.1249/mss.0000000000002550

Article  CAS  Google Scholar 

Sobhiyeh S, Kennedy S, Dunkel A, Dechenaud ME, Weston JA, Shepherd J, et al. Digital anthropometry for body circumference measurements: toward the development of universal three-dimensional optical system analysis software. Obes Sci Pract. 2021;7:35–44. https://doi.org/10.1002/osp4.467

Article  Google Scholar 

Harty PS, Sieglinger B, Heymsfield SB, Shepherd JA, Bruner D, Stratton MT, et al. Novel body fat estimation using machine learning and 3-dimensional optical imaging. Eur J Clin Nutr. 2020;74:842–5. https://doi.org/10.1038/s41430-020-0603-x

Article  Google Scholar 

Tinsley GM. Five-component model validation of reference, laboratory and field methods of body composition assessment. Br J Nutr. 2021;125:1246–59. https://doi.org/10.1017/S0007114520003578

Article  CAS  Google Scholar 

Tinsley GM, Moore ML, Benavides ML, Dellinger JR, Adamson BT. 3-dimensional optical scanning for body composition assessment: a 4-component model comparison of four commercially available scanners. Clin Nutr. 2020;39:3160–7. https://doi.org/10.1016/j.clnu.2020.02.008

Article  CAS  Google Scholar 

Tinsley GM, Harty PS, Stratton MT, Smith RW, Rodriguez C, Siedler MR. Tracking changes in body composition: comparison of methods and influence of pre-assessment standardisation. Br J Nutr. 2022;127:1656–74. https://doi.org/10.1017/S0007114521002579

Article  CAS  Google Scholar 

McCarthy C, Tinsley GM, Yang S, Irving BA, Wong MC, Bennett JP, et al. Smartphone prediction of skeletal muscle mass: model development and validation in adults. Am J Clin Nutr. 2023. https://doi.org/10.1016/j.ajcnut.2023.02.003

Kennedy S, Hwaung P, Kelly N, Liu YE, Sobhiyeh S, Heo M, et al. Optical imaging technology for body size and shape analysis: evaluation of a system designed for personal use. Eur J Clin Nutr. 2020;74:920–9. https://doi.org/10.1038/s41430-019-0501-2

Article  Google Scholar 

Cabre HE, Blue MNM, Hirsch KR, Brewer GJ, Gould LM, Nelson AG, et al. Validity of a three-dimensional body scanner: comparison against a 4-compartment model and dual energy X-ray absorptiometry. Appl Physiol Nutr Metab. 2021;46:644–50. https://doi.org/10.1139/apnm-2020-0744

Article  CAS  Google Scholar 

Ng BK, Hinton BJ, Fan B, Kanaya AM, Shepherd JA. Clinical anthropometrics and body composition from 3D whole-body surface scans. Eur J Clin Nutr. 2016;70:1265–70. https://doi.org/10.1038/ejcn.2016.109

Article  CAS  Google Scholar 

Tian IY, Wong MC, Kennedy S, Kelly NN, Liu YE, Garber AK, et al. A device-agnostic shape model for automated body composition estimates from 3D optical scans. Med Phys. 2022. https://doi.org/10.1002/mp.15843

Wong MC, Ng BK, Tian I, Sobhiyeh S, Pagano I, Dechenaud M, et al. A pose-independent method for accurate and precise body composition from 3D optical scans. Obesity. 2021;29:1835–47. https://doi.org/10.1002/oby.23256

Article  CAS  Google Scholar 

Ashby N, Jake LaPorte G, Richardson D, Scioletti M, Heymsfield SB, Shepherd JA, et al. Translating digital anthropometry measurements obtained from different 3D body image scanners. Eur J Clin Nutr. 2023;77:872–80. https://doi.org/10.1038/s41430-023-01289-5

Article  ADS  Google Scholar 

Sobhiyeh S, Borel N, Dechenaud M, Graham CA, Wong M, Wolenski P, et al. Fully automated pipeline for body composition estimation from 3D optical scans using principal component analysis: a shape up study. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:1853–8. https://doi.org/10.1109/embc44109.2020.9175211

Article  Google Scholar 

Morse S, Talty K, Kuiper P, Scioletti M, Heymsfield SB, Atkinson RL, et al. Machine learning prediction of combat basic training injury from 3D body shape images. PLoS ONE. 2020;15:e0235017. https://doi.org/10.1371/journal.pone.0235017

Article  CAS  Google Scholar 

Nelson R, Cheatham J, Gallagher D, Bigelman K, Thomas DM. Revisiting the United States Army body composition standards: a receiver operating characteristic analysis. Int J Obes. 2019;43:1508–15. https://doi.org/10.1038/s41366-018-0195-x

Article  Google Scholar 

Harty PS, Friedl KE, Nindl BC, Harry JR, Vellers HL, Tinsley GM. Military body composition standards and physical performance: historical perspectives and future directions. J Strength Cond Res. 2022;36:3551–61. https://doi.org/10.1519/jsc.0000000000004142

Article  Google Scholar 

Keith DS, Scherrer D, Nunley B, Boykin JR, Green JJ, Siedler MR, et al. Anthropometric predictors of conventional deadlift kinematics and kinetics: a preliminary study. Int J Exerc Sci. 2023;16:429–47.

Google Scholar 

Smith M, Turner D, Spencer C, Gist N, Ferreira S, Quigley K, et al. Body shape and performance on the US Army Combat Fitness Test: insights from a 3D body image scanner. PLoS ONE. 2023;18:e0283566. https://doi.org/10.1371/journal.pone.0283566

Article  CAS  Google Scholar 

Bennett JP, Liu YE, Quon BK, Kelly NN, Leong LT, Wong MC, et al. Three-dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults. Obesity. 2022;30:1589–98. https://doi.org/10.1002/oby.23470

Article  CAS  Google Scholar 

Pleuss JD, Talty K, Morse S, Kuiper P, Scioletti M, Heymsfield SB, et al. A machine learning approach relating 3D body scans to body composition in humans. Eur J Clin Nutr. 2019;73:200–8. https://doi.org/10.1038/s41430-018-0337-1

Article  Google Scholar