Quality of life and sociodemographic variables, as explanatory variables of obesity
DOI:
https://doi.org/10.35622/j.rep.2024.01.001Keywords:
quality of life, sociodemographic, artificial neural networks, obesityAbstract
Objective: To identify variables related to quality of life and sociodemographics that could explain the percentage of body fat, as well as low-density lipoproteins, very low-density lipoproteins, total cholesterol, and triglycerides, all associated with obesity. Methods: This was a quantitative, non-experimental, convenience, and explanatory study. Participants included 320 adults with different body mass index levels, of both sexes, who were patients at the Higher School of Medicine of the National Polytechnic Institute from 2018 to 2020. We assessed and measured quality of life, lipoproteins, cholesterol, and body fat percentage. Linear regressions, categorical regressions, structural equations, and artificial neural networks were employed. Results: In the artificial neural network, the variables with the highest synaptic weight were marital status, occupation, and age; and in terms of quality of life, cognitive function, medical dependence, and physical performance. In linear models, explanatory factors included concerns, isolation, body perception, attitude towards treatment, leisure time, gender, and marital status. Conclusion: Body perception, age, medical dependence, marital status, and concerns were the input variables that explained the percentage of body fat and blood lipids related to obesity.
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