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Background Physical education classes are widely accepted as one of the most effective settings for promoting physical activity and health and have often been used to implement physical activity interventions. The aim of this pilot study was to test a physical education intervention program on physical activity levels and physical fitness in a sample of school-age children. Methods Participants were a convenience sample of 50 children (34 experimental group and 16 in the comparative group) aged between 6 and 11 years old (Mean = 8.28 years). A 21-week intervention was implemented, consisting of high-intensity and physical fitness-focused exercises, in addition to a once-a-month extra class nutritional education. The following variables were evaluated before and post-intervention: physical fitness, sedentary behavior (SB), light physical activity (LPA), moderate physical activity (MVA), and vigorous physical activity (VPA). Propensity score analyses calculated the average treatment effect on the treated (ATET) within a quasi-experimental framework. Results Physical fitness variables showed improvements after the intervention, specifically for agility (ATET = -0.67 s; p  < 0.001), cardiorespiratory fitness (ATET = 89.27 m; p  = 0.045), lower limbs power (ATET = 4.47 centimeters; p  = 0.025), and speed (ATET = -1.06 s; p  < 0.001). For physical activity and SB levels, there were no improvements after intervention implementation. Conclusion The intervention program showed preliminary effectiveness to improve physical fitness of children, but not SB nor physical activity.

Aim To verify the association between ideal sleep time and physical literacy components while also considering multiple mediators, such as quality of life and obesity, using a sample of adolescents. Methods This cross-sectional study consisted of 470 adolescents aged 11–17 years from southern Brazil. Sleep time, health-related quality of life, and physical literacy components (i.e., physical education enjoyment, sports participation, sedentary behavior, moderate to vigorous physical activity, sex, and age) were evaluated through self-reported questionnaires. In addition, body mass index (BMI), and waist circumference were determined. The theoretical/statistical support of the structural equation model was evaluated according to fit parameters and strength of association. Results A direct association was observed between more sleep time and lower levels of obesity. The obesity indicators also had a negative association with HqOL, and HqOL had a positive association with physical literacy. The indirect associations indicated that the ideal sleep time was positively associated with HqOL and physical literacy components, considering the negative mediation effect of obesity. The model explains physical literacy in 31% of the variance ( R  = 0.31). Conclusion There was an indirect association between ideal sleep duration and quality of life and between both variables with physical literacy. These relationships occur even considering the negative influence of obesity. Therefore, a child who sleeps adequately has a higher likelihood of being physically active, regardless of obesity, potentially enhancing overall quality of life across various domains.

Background Considering the previous research that suggested that screen time (ST), sleep duration, physical activity (PA), obesity and cardiometabolic risk factors are related, it is essential to identify how these variables are associated over time, to provide knowledge for the development of intervention strategies to promote health in pediatric populations. Also, there is a lack of studies examining these associations longitudinally. The aims of the present study were: (1) to investigate the longitudinal relationships between ST, sleep duration, leisure PA, body mass index (BMI), and cardiometabolic risk score (cMetS) in children and adolescents; and (2) to verify scores and prevalence of cMetS risk zones at baseline and follow-up. Methods This observational longitudinal study included 331 children and adolescents (aged six to 17 years; girls = 57.7%) from schools in a southern city in Brazil. ST, sleep duration, and leisure PA were evaluated by a self-reported questionnaire. BMI was evaluated using the BMI z-scores (Z_BMI). The cMetS was determined by summing sex- and age-specific z-scores of total cholesterol/high-density lipoprotein cholesterol (HDL-C) ratio, triglycerides, glucose, and systolic blood pressure and dividing it by four. A two-wave cross-lagged model was implemented. Results ST, sleep duration, and leisure PA were not associated with cMetS after 2-years. However, it was observed that higher ST at baseline was associated with shorter sleep duration at follow-up (B=-0.074; 95%IC=-0.130; -0.012), while higher Z_BMI from baseline associated with higher cMetS of follow-up (B = 0.154; 95%CI = 0.083;0.226). The reciprocal model of relationships indicated that the variance of ST, sleep time, leisure PA, Z_BMI, and cMetS explained approximately 9%, 14%, 10%, 67% and 22%, respectively, of the model. Individual change scores and prevalence indicated that cMetS had individual changes from 2014 to 2016. Conclusion Sleep duration, ST and leisure PA were not associated with cMetS after 2 years. ST showed an inverse association with sleep duration, and Z_BMI was positively associated with cMetS after a 2-year follow-up. Finally, the prevalence of no clustering of risk factors increased after two years. These findings suggest the need to promote healthy lifestyle habits from childhood and considering individual factors that can influence cardiometabolic health in children and adolescents.

Purpose The aim of this study was to examine the interaction between screen time and cardiorespiratory fitness (CRF) in their longitudinal association with waist circumference (WC) during a follow-up of 3 years from childhood to adolescence. Methods Observational 3-year longitudinal study with 401 students (224 females), seven to 15 years of age at baseline. The CRF was evaluated by estimating peak oxygen uptake (VO 2peak ) from an indirect field-based test and body mass index. Screen time was assessed using self-reported questionnaires. Moderation analyses were tested using a multiple linear regression model with adjustments for sex, age, puberty stage, and ethnicity. Results A statistically significant interaction term was observed (B = -0.0003; 95% CI: -0.007; -0.0001). Since screen time was contextualized as the independent variable, the results show that relationship between screen time and WC varies across different CRF levels. Conclusion The findings suggest that higher CRF can attenuate the harmful association that increased sedentary behavior might have on abdominal adiposity.

The purpose of this study was to apply compositional data analysis (CoDA) for the analysis of segmented school step counts and associate the school step count composition to body mass index (BMI) z-scores in a sample of children. Participants were 855 (51.8% female) children recruited from the fourth and fifth grades from four schools following a 7-h school schedule. Using piezoelectric pedometers, step count data were collected during physical education, recess, lunch, and during academic class time. A multi-level mixed effects model associated the step count composition with BMI z-scores. Compositional isotemporal substitution determined changes in BMI z-scores per reallocation of steps between pairs of school segments. A higher percentage of steps accrued during physical education (b = −0.34, 95%CI: −0.65–−0.03, p = 0.036) and recess (b = −0.47, 95%CI: −0.83–−0.11, p = 0.012), relative to other segments, was associated with lower BMI z-scores. Specifically, a 5% to 15% reallocation of steps accrued during lunchtime to either physical education or recess was associated with lower BMI z-scores, ranging from −0.07 to −0.25 standard deviation units. Focusing school-based promotion of physical activity during physical education and recess may have greater relative importance if targeted outcomes are weight-related.

The purpose of this study was to examine agreement in energy expenditure between the Apple Series 1 Watch, LifeTrak Core C200, and Fitbit Charge HR with indirect calorimetry during various treadmill speeds in young adults. Participants were a sample of college-aged students (mean age = 20.1 (1.7) years; 13 females, 17 males). Participants completed six structured 10-minute exercise sessions on a treadmill with speeds ranging from 53.6 m·min−1 to 187.7 m·min−1. Indirect calorimetry was used as the criterion. Participants wore the Apple Watch, LifeTrak, and Fitbit activity monitors on their wrists. Group-level agreement was examined using equivalence testing, relative agreement was examined using Spearman’s rho, and individual-level agreement was examined using Mean Absolute Percent Error (MAPE) and Bland-Altman Plots. Activity monitor agreement with indirect calorimetry was supported using the Apple Watch at 160.9 m·min−1 (Mean difference = −2.7 kcals, 90% C.I.: −8.3 kcals, 2.8 kcals; MAPE = 11.9%; rs = 0.64) and 187.7 m·min−1 (Mean difference = 3.7 kcals, 90% C.I.: −2.2 kcals, 9.7 kcals; MAPE = 10.7%; rs = 0.72) and the Fitbit at 187.7 m·min−1 (Mean difference = −0.2 kcals, 90% C.I.: −8.8 kcals, 8.5 kcals; MAPE = 20.1%; rs = 0.44). No evidence for statistical equivalence was seen for the LifeTrak at any speed. Bland-Altman Plot Limits of Agreement were narrower for the Apple Series 1 Watch compared to other monitors, especially at slower treadmill speeds. The results support the utility of the Apple Series 1 Watch and Fitbit Charge HR for assessing energy expenditure during specific treadmill running speeds in young adults.

To evaluate the simultaneous moderating role of sleep duration and screen time in the relationship between waist circumference (WC) and clustered cardiometabolic risk score (cMetS) according to children and adolescents' physical activity. A cross‐sectional study was conducted on 3072 children and adolescents (aged 6–17 years, 57.5% girls). Physical activity, sleep duration, and screen time were assessed through a self‐report questionnaire. The cMetS was determined by averaging the z‐scores of risk factors and dividing it by four. Moderation analyses were tested through multiple linear regression models. Among physically active individuals, sleep duration (p = 0.85) and screen time (p = 0.96) had no influence on the relationship between WC and cMetS. However, a positive interaction between WC x screen time and cMetS (p = 0.04) was observed for physically inactive participants. Concerning sleep duration, there was no interaction with WC. Participants who spent 60 min of screen time presented lower cMetS, even presenting high WC, compared to the higher tertiles of screen time (180 and 360 min). However, although the interaction between sleep duration and WC was not significant, it was observed that the lowest tertile of sleep duration (482 min) combined with 60 min of screen time presented lower cMetS even with the presence of high WC. Our findings encourage compliance with physical activity guidelines associated with the adoption of adequate screen time to minimize the influence of waist circumference on cMetS. Highlights Sleep duration and screen time influence adiposity and cardiometabolic risk in physically inactive children and adolescents; Meeting the PA guidelines seems crucial in preventing cardiometabolic risk factors; In inactive individuals, the screen has a deleterious effect on cardiometabolic health.

Objective The present study aims to verify the odds of remaining with the clustering of 3 or more, 4 or more, and 5 or more risk factors across a 2-year time span. Methods Observational longitudinal study that included 358 children and adolescents (10.96 ± 2.28 years of age at baseline). Cardiorespiratory fitness, glucose, systolic blood pressure, total cholesterol/high-density lipoprotein cholesterol ratio, triglycerides, and waist circumference were assessed. The number of children in whom the risk factors were not independently distributed was analyzed. Odds ratios of presenting n risk factors clustered at follow-up according to the number of risk factors observed at baseline were calculated. Results More participants than expected were found presenting clustering of 4 or more and 5 or more risk factors at both baseline (11.7% and 5.6%, respectively) and follow-up (9.5% and 5.6%, respectively). The odds ratios calculated demonstrated that the odds of presenting the same number of risk factors clustered or more at follow-up increased according to the number of risk factors clustered at baseline. Conclusion The higher the number of risk factors a child had at baseline, the higher the odds of presenting the same number of risk factors or more after two years of follow-up.

The human locus FNDC5 rs16835198 contributes positively to anthropometric phenotypes in children and adolescents. However, the role of specific components of physical fitness in this relationship is not known. The present study aimed to verify the moderator role of cardiorespiratory fitness (CRF) and muscular strength in the relationship between rs16835198 polymorphism FNDC5 and adiposity in children and adolescents. This cross-sectional study was carried out by genotyping the rs16835198 FNDC5 polymorphism in 1701 children and adolescents (mean age 11.73 ± 2.75 years). Obesity was assessed using waist circumference and body mass index (BMI) z-scores. To evaluate CRF and muscular strength, the 6 min run/walk test and lower limb strength (LLS) were used. Linear regression models were applied, and all analyses were adjusted for age, sex, skin color, living area, and school type. A significant interaction term for CRF (p = 0.038) and LLS (p = 0.040) × rs16835198 FNDC5 with WC was identified. Regarding BMI, a significant interaction term for CRF (p = 0.007) and LLS (p = 0.044) × rs16835198 FNDC5 was observed. Moreover, medium and high CRF and LLS levels protected against higher WC and BMI. In conclusion, adiposity levels of children and adolescents with a genetic predisposition to obesity might be modified by improving CRF and muscular strength.

Cardiorespiratory endurance is a major component of health-related fitness testing in physical education. FITNESSGRAM recommends the 1-mile Run/Walk (1-MRW) or the Progressive Aerobic Cardiovascular Endurance Run (PACER) to assess cardiorespiratory endurance by estimating aerobic capacity, or VO 2Peak. No research to date has cross-validated prediction models from both 1-MRW and PACER using current FITNESSGRAM criterion-referenced (CR) standards. Additionally, new prediction models for 1-MRW without a body mass index (BMI) term are needed to attenuate the problems incorporating this index into an aerobic capacity model. The purpose of this dissertation was to cross-validate various prediction models using 1-MRW and PACER and to develop alternative 1-MRW aerobic capacity prediction models for adolescent youth. Participants included 90 students aged 13 to 16 years. Each student completed the 1-MRW and PACER, in addition to a maximal treadmill test to measure VO2Peak . Multiple correlations among various models with measured VO2Peak were considered strong (R = 0.74 to 0.78). CR validity, examined using modified kappa (Kq), percentage of agreement (Pa), and phi was considered moderate among all models ( Kq = 0.25 to 0.49; Pa = 72% to 79%; phi = 0.38 to 0.65). Two new models were developed from 1-MRW times, one linear and one quadratic model. The linear and quadratic models displayed multiple correlations of R = 0.77 and R = 0.82 with measured VO2Peak, respectively. CR validity evidence was considered moderate with ( Kq = 0.38; Pa = 73%; phi = 0.57) using the linear model and (Kq = 0.34; Pa = 70%; phi = 0.54) using the quadratic model. The accuracy of these models was confirmed using k-fold cross-validation. In conclusion, the prediction models demonstrated strong linear relationships with measured VO2Peak, acceptable prediction error, and moderate CR agreement with measured VO2Peak using FITNESSGRAM's CR standards to categorize health groups. The new 1-MRW models displayed good predictive accuracy and moderate CR agreement with measured VO2Peak without using a BMI predictor. Despite evidence for predictive utility of the new models, they must be externally validated to ensure they can be generalizable to larger populations of students.