Xu Taojin, Li Xu, Wang Dingfang, Zhang Yi, Zhang Qinghua, Yan Jianyin, Jiang Junhao, Liu Wenbin, Chen Jing
School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.
Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, Chongqing, China.
Front Nutr. 2023 Jan 18;9:1063939. doi: 10.3389/fnut.2022.1063939. eCollection 2022.
Hand grip strength (HGS) is a fast, useful, and inexpensive outcome predictor of nutritional status and muscular function assessment. Numerous demographic and anthropometric factors were reported to be associated with HGS, while which one or several factors produce greater variations in HGS has not been discussed in detail. This is important for answering how should HGS be normalized for eliminating the influence of individual differences in clinical practice.
To compare the contribution of age, sex, height, weight, and forearm circumference (FCF) to variations in HGS based on a large-scale sample.
We enrolled 1,511 healthy undergraduate students aged 18-23 years. Age, weight, height, and sex were obtained. HGS was measured using a digital hand dynamometer, and FCF was measured at the point of greatest circumference using a soft ruler in both hands. Pearson's or Spearman's correlation coefficients were calculated with data of women and men separated and mixed for comparison. Partial correlation analysis and multivariate linear regression were used to compare the effect of variables on HGS.
Analysis results confirmed the correlates of higher HGS include higher height, heavier weight, being men and dominant hand, and larger FCF. The correlation between HGS and FCF was the highest, and the bivariate correlation coefficient between weight and HGS was largerata of women and men were mixed, than that between height and HGS. When data of women and men were mixed, there were moderate correlations between HGS and height and weight ( = 0.633∼0.682). However, when data were separated, there were weak correlations ( = 0.246∼0.391). Notably, partial correlation analysis revealed no significant correlation between height and HGS after eliminating the weight effect, while the correlation between weight and HGS was still significant after eliminating the height effect. Multivariate linear regression analyses revealed sex was the most significant contributor to the variation in HGS (Beta = -0.541 and -0.527), followed by weight (Beta = 0.243 and 0.261) and height (Beta = 0.102 and 0.103).
HGS and FCF reference values of healthy college students were provided. Weight was more correlate with hand grip strength, at least among the healthy undergraduates.
http://www.chictr.org.cn/showproj.aspx?proj=165914, identifier ChiCTR2200058586.
握力(HGS)是营养状况和肌肉功能评估的一种快速、有用且廉价的结果预测指标。据报道,许多人口统计学和人体测量学因素与握力有关,但尚未详细讨论其中哪一个或哪几个因素会导致握力产生更大的差异。这对于回答在临床实践中应如何对握力进行标准化以消除个体差异的影响很重要。
基于大规模样本比较年龄、性别、身高、体重和前臂围(FCF)对握力变化的贡献。
我们招募了1511名年龄在18 - 23岁的健康本科生。获取了他们的年龄、体重、身高和性别信息。使用数字握力计测量握力,并用软尺在双手最大围度处测量前臂围。分别计算并比较了女性和男性分开及混合的数据的Pearson或Spearman相关系数。采用偏相关分析和多元线性回归来比较各变量对握力的影响。
分析结果证实,较高握力的相关因素包括较高的身高、较重的体重、男性以及优势手,还有较大的前臂围。握力与前臂围的相关性最高,体重与握力的双变量相关系数在女性和男性混合数据时大于身高与握力的双变量相关系数。当女性和男性的数据混合时,握力与身高和体重之间存在中等程度的相关性(= 0.633∼0.682)。然而,当数据分开时,相关性较弱(= 0.246∼0.391)。值得注意的是,偏相关分析显示在消除体重影响后身高与握力之间无显著相关性,而在消除身高影响后体重与握力之间仍具有显著相关性。多元线性回归分析显示,性别是握力变化的最主要贡献因素(β = -0.541和 -0.527),其次是体重(β = 0.243和0.261)和身高(β = 0.102和0.103)。
提供了健康大学生的握力和前臂围参考值。至少在健康本科生中,体重与握力的相关性更强。
http://www.chictr.org.cn/showproj.aspx?proj=165914,标识符ChiCTR2200058586 。