Multiple regression models to predict electrical bioimpedance variables through anthropometric and simple measures in children aged 6-12 years.

The study evaluated 128 children subdivided into eutrophic, overweight, and obese groups matched by sex and age.

• Eutrophic group: N = 64
• Overweight group: N = 30
• Obese group: N = 34
• Groups were matched by sex and age
• Age range was 6-12 years

Significant correlations were observed between BIA variables and simple clinical and anthropometric measurements.

• Pearson's correlation coefficients were calculated to assess linear associations between each pair of variables
• Significance level was set at p ≤ 0.05
• Non-parametric statistical tests were applied after a normality test
• Variables evaluated included body mass, height, anthropometric circumferences, and skinfolds

Body fat percentage, skeletal muscle mass, total body water, and fat-free mass were the BIA variables with the highest correlations with simple clinical and anthropometric measurements.

• These four variables were ranked as having the highest correlations in descending order: body fat percentage, skeletal muscle mass, total body water, and fat-free mass
• Correlations were assessed using Pearson's correlation coefficients
• Both anthropometric circumferences and skinfolds were among the simple measurements evaluated

Multiple regression models using a stepwise method with the Akaike information criterion indicated that simple clinical and anthropometric measurements could estimate BIA variables in children.

• The stepwise method with Akaike information criterion was used to adjust the multiple regression models
• Anthropometric indices were calculated and included as potential predictors
• The models were developed and internally validated
• The study employed an observational cross-sectional design