Encarnação Irismar Gonçalves Almeida da, Cerqueira Matheus Santos, Almeida Paulo Henrique Ribeiro Fernandes, Oliveira Cláudia Eliza Patrocínio de, Silva Analiza Mónica Lopes de Almeida, Silva Diego Augusto Santos, Heymsfield Steven B, Moreira Osvaldo Costa
Department of Physical Education, Center for Biological and Health Sciences, Federal University of Viçosa, Viçosa, Brazil.
Academic Department of Education, Federal Institute Southeast of Minas Gerais, Campus Rio Pomba, Brazil.
Eur J Clin Nutr. 2025 Apr 7. doi: 10.1038/s41430-025-01613-1.
This scoping review aimed to assess the repeatability and accuracy of Digital Anthropometry by Mobile Application (DAM) compared to reference methods for estimating anthropometric dimensions, body volume (BV), and body composition. A comprehensive search was conducted on December 8th, 2024, without restrictions on language, time, sex, ethnicity, age, or health condition. We found 14 different DAMs across the 23 included studies. Reference methods for each estimated variable were: (a) Body circumferences-tape measure; (b) body mass-calibrated scale; (c) body length-stadiometer; (d) BV-Underwater Weighing; (e) percentage of body fat-Dual energy x-ray absorptiometry (DXA), BOD POD, 3, 4, and 5-compartment models; (f) fat mass and fat-free mass-DXA, 3 and 4-compartment models; (g) appendicular Lean Mass-DXA. DAMs demonstrated high repeatability and accuracy at a mean level in most studies. However, their accuracy is lower at individual-level analysis and for tracking changes over time. Estimated BV showed high accuracy compared to UWW (SEE = 0.68; MD = 0.04 to 0.1; LoA = 2.86), including the BV-derived DAMs integrated into alternative multi-compartment models compared to reference methods. As relatively new methods, DAMs offer numerous possibilities and areas for exploration in future studies. However, caution is advised due to their potentially low or unknown accuracy at the individual level.
本综述旨在评估通过移动应用程序进行的数字人体测量(DAM)与用于估计人体测量尺寸、身体体积(BV)和身体成分的参考方法相比的可重复性和准确性。于2024年12月8日进行了全面搜索,对语言、时间、性别、种族、年龄或健康状况没有限制。我们在纳入的23项研究中发现了14种不同的DAM。每个估计变量的参考方法为:(a)身体周长——卷尺;(b)体重——校准秤;(c)身体长度——身高计;(d)BV——水下称重;(e)体脂百分比——双能X线吸收法(DXA)、BOD POD、3、4和5室模型;(f)脂肪量和去脂体重——DXA、3和4室模型;(g)四肢瘦体重——DXA。在大多数研究中,DAM在平均水平上表现出高可重复性和准确性。然而,在个体水平分析和跟踪随时间的变化时,它们的准确性较低。与水下称重相比,估计的BV显示出高准确性(SEE = 0.68;MD = 0.04至0.1;LoA = 2.86),包括与参考方法相比集成到替代多室模型中的BV衍生DAM。作为相对较新的方法,DAM在未来研究中提供了许多可能性和探索领域。然而,由于它们在个体水平上的准确性可能较低或未知,建议谨慎使用。