Muscular Strength of Upper and Lower Limbs and Self-Esteem in Chilean SchoolChildren: Independent Associations with Body Composition Indicators

A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies

The increasing prevalence of overweight and obesity among adults is a risk factor for many chronic diseases and death. In addition, obesity among children and adolescents has reached unprecedented levels and studies show that obese children and adolescents are more likely to become obese adults. Therefore, both the prevention and treatment of obesity in adolescents are critical. This study aimed to develop an artificial intelligence (AI) neural network (NNET) model that identifies the risk of obesity in Portuguese adolescents based on their body mass index (BMI) percentiles and levels of physical fitness. Using datasets from the FITescola^® project, 654 adolescents aged between 10-19 years old, male: 334 (51%), female: n = 320 (49%), age 13.8 ± 2 years old, were selected to participate in a cross-sectional observational study. Physical fitness variables, age, and sex were used to identify the risk of obesity. The NNET had good accuracy (75%) and performance validation through the Receiver Operating Characteristic using the Area Under the Curve (ROC AUC = 64%) in identifying the risk of obesity in Portuguese adolescents based on the BMI percentiles. Correlations of moderate effect size were perceived for aerobic fitness (AF), upper limbs strength (ULS), and sprint time (ST), showing that some physical fitness variables contributed to the obesity risk of the adolescents. Our NNET presented a good accuracy (75%) and was validated with the K-Folds Cross-Validation (K-Folds CV) with good accuracy (71%) and ROC AUC (66%). According to the NNET, there was an increased risk of obesity linked to low physical fitness in Portuguese teenagers.

School and non-school day screen time profiles and their differences in health and educational indicators in adolescents

Sedentary behavior and screen-based devices in particular have been negatively associated with a wide range of health and educational indicators. However, few have examined these relationships separately for school days and non-school days, and none have used a person-centered approach. This study aimed to identify school and non-school day screen time profiles, as well as examine possible differences in health indicators (physical fitness, fatness, physical activity, sleep duration, and Mediterranean diet) and academic performance. This study involved the participation of 1573 Spanish adolescents aged 12-16 years (54.73% girls). Academic performance was measured through grades in Mathematics, Language, English, and Physical Education. Physical fitness was measured through a battery of tests (cardiorespiratory fitness was measured using the 20 m shuttle run test, and muscular strength with both handgrip and standing long jump tests), while fatness (skinfold thicknesses) was assessed with calipers. Finally, physical activity, screen time, sleep duration, and adherence to the Mediterranean diet were measured using self-reported questionnaires. Hierarchical cluster analyses based on square Euclidian distances and Ward's method were performed based on daily minutes of screen time recorded on school and non-school days. We identified four clusters labeled and described as: (1) "High-high": highest screen time on school and non-school days; (2) "High-low": high screen time on school days and low screen time on non-school days; (3) "Low-high": low screen time on school days and high screen time on non-school days; (4) "Low-low": lowest screen time on school and non-school days. Adolescents who belonged to the "High-high" profile had worse health-related behaviors (i.e., physical activity, sleep duration, and adherence to Mediterranean diet) and academic performance than most other profiles, while adolescents who belonged to "Low-low" profile showed the opposite pattern. Adolescents in the "Low-high" profile had a higher sleep duration on school days and better academic performance than those in the "High-low" profile. No differences in body fat, cardiorespiratory fitness, and muscular strength were found between the four different profiles. The results suggest that adolescents who accumulated a large amount of screen time on school and non-school days reported worse health-related behaviors and academic performance. Moreover, adolescents who had high screen time on school days reported only a short sleep duration on school days and worse academic performance than on non-school days. Conducting interventions to reduce screen time in these four profiles, particularly in the groups of students with more screen time on school days, becomes essential to improving adolescents' healthy lifestyles and academic performance.

Food insecurity and its determinants in a vulnerable area of Santiago, Chile

This study aims to identify the determinants associated to food insecurity in a group of households composed of schoolchildren and their mothers/caregivers, who lived in a low-to-medium community development index area of the city of Santiago, Chile with a high presence of migrant population. The non-probabilistic and convenience sample was made up of 646 people, who answered a series of surveys with the aim of characterizing this group in sociodemographic terms (sex, age, number of inhabitants in the household, place of food purchase, conditional or non-conditional state transfer program beneficiary status, persons in charge of purchasing food for the household, mothers/caregivers education level and basic knowledge of food and nutrition). To assess moderate-to-severe food insecurity and severe food insecurity, the Food Insecurity Experience Scale-FIES was applied between September and October 2021. Logistic regression analysis were used to carry out multivariate analyses, with the use of stepwise back-and-forward strategies for the selected variables and defining p < 0.05. These models were adjusted per number of inhabitants in the household. The results indicate that 25.4% of households presented moderate-to-severe food insecurity, and 6.4% severe food insecurity experience. The variables that presented significant odds of risk to food insecurity were being a migrant, low maternal education level, low performance on basic knowledge in nutrition and when the father was responsible for food purchases. Several public policies have been implemented in Chile during the most recent decades aimed at increasing access to healthier foods and the implementation of healthier food environments. Despite this, there are still social and economic health determinants that contribute to the risk of odds insecurity for the most vulnerable groups in the country, thus putting at risk the fulfillment of the human right to adequate food at risk.

A Better Cardiopulmonary Fitness Is Associated with Improved Concentration Level and Health-Related Quality of Life in Primary School Children

This study aimed to examine the physical fitness (PF) levels of primary school children and to determine the associations among PF, concentration, and health-related quality of life (HRQOL) in a subcohort. PF was assessed in 6533 healthy primary school-age children (aged 6−10 years, 3248 boys and 3285 girls) via standardized test batteries. Concentration was measured with the d2-R test, and KINDL questionnaires were used to determine HRQOL. Analysis of variance showed an increase in PF with age in all PF dimensions (all p < 0.001), except cardiopulmonary fitness (estimated VO2max) in girls (p = 0.129). Boys performed better in nearly all PF dimensions, except curl-ups, in all children aged ≥7 years (p < 0.05). Concentration levels increased in boys and girls aged 7−9 years (p < 0.001), whereas HRQOL did not (p = 0.179). The estimated VO2max had a strong impact on concentration (β = 0.16, p < 0.001) and HRQOL (β = 0.21, p < 0.001) in 9- to 10-year-olds. Cardiopulmonary fitness is important for improved concentration and better HRQOL in primary school-age children. However, longitudinal data are needed to provide further insight into the intraindividual relationships of PF and concentration over the course of child development and set up targeted prevention programs.