Sesbreno Erik, Slater Gary, Mountjoy Margo, Galloway Stuart D R
Canadian Sport Institute Ontario.
l'Institut National du Sport du Québec.
Int J Sport Nutr Exerc Metab. 2020 Mar 1;30(2):174–181. doi: 10.1123/ijsnem.2019-0232. Epub 2020 Feb 7.
The monitoring of body composition is common in sports given the association with performance. Surface anthropometry is often preferred when monitoring changes for its convenience, practicality, and portability. However, anthropometry does not provide valid estimates of absolute lean tissue in elite athletes. The aim of this investigation was to develop anthropometric models for estimating fat-free mass (FFM) and skeletal muscle mass (SMM) using an accepted reference physique assessment technique. Sixty-four athletes across 18 sports underwent surface anthropometry and dual-energy X-ray absorptiometry (DXA) assessment. Anthropometric models for estimating FFM and SMM were developed using forward selection multiple linear regression analysis and contrasted against previously developed equations. Most anthropometric models under review performed poorly compared with DXA. However, models derived from athletic populations such as the Withers equation demonstrated a stronger correlation with DXA estimates of FFM (r = .98). Equations that incorporated skinfolds with limb girths were more effective at explaining the variance in DXA estimates of lean tissue (Sesbreno FFM [R2 = .94] and Lee SMM [R2 = .94] models). The Sesbreno equation could be useful for estimating absolute indices of lean tissue across a range of physiques if an accepted option like DXA is inaccessible. Future work should explore the validity of the Sesbreno model across a broader range of physiques common to athletic populations.
鉴于身体成分与运动表现之间的关联，在体育领域对身体成分进行监测很常见。在监测变化时，体表人体测量法因其便利性、实用性和便携性而常被优先选用。然而，人体测量法无法对精英运动员的绝对瘦体重提供有效的估计。本研究的目的是使用一种公认的参考体格评估技术来开发用于估计去脂体重（FFM）和骨骼肌质量（SMM）的人体测量模型。来自18个运动项目的64名运动员接受了体表人体测量和双能X线吸收法（DXA）评估。使用向前选择多元线性回归分析开发了用于估计FFM和SMM的人体测量模型，并与先前开发的方程进行了对比。与DXA相比，大多数被审查的人体测量模型表现不佳。然而，源自运动员群体的模型，如威瑟斯方程，与DXA对FFM的估计显示出更强的相关性（r = 0.98）。将皮褶厚度与肢体围度相结合的方程在解释DXA对瘦体重估计的方差方面更有效（塞斯布雷诺FFM [R2 = 0.94]和李SMM [R2 = 0.94]模型）。如果无法使用像DXA这样公认的方法，塞斯布雷诺方程可能有助于估计一系列体格的瘦体重绝对指标。未来的工作应探索塞斯布雷诺模型在更广泛的运动员群体常见体格范围内的有效性。
