Use of predictive models in anthropometric measurements in workers standing upright

Abstract

This study proposes the use of predictive models as an efficient and economical alternative to anthropometric measurements in workers who stand for long periods, especially in contexts with technological and budgetary limitations. The objective of this research is to propose the use of predictive models as a viable alternative to anthropometric measurements.  A quantitative, cross-sectional, and descriptive study was conducted between 2021 and 2023, using the state of Aragua and its industrial areas as the population base. Within this survey, 3,125 workers were obtained, with a sample of 185 industrial workers. The sampling for this research is simple random probability sampling stratified by age and sex. The observation technique was used through an estimation scale, anthropometric tables for measurement, and Minitab 17 software to obtain multiple linear regression models, considering variables such as height at the acromion, elbow, and eye, categorized by sex and age group. The results showed high accuracy in the predictions, with coefficients of determination (adjusted R²) between 75.68% and 98.73%, and statistical significance of 95% (p < 0.05). The equations generated allow key measurements to be estimated from height, validated by comparison with actual data without significant differences. It is concluded that the use of predictive models is a technically and scientifically viable alternative to traditional anthropometric measurements.