Introduction: The Metabolic Syndrome (MetS) describes the clustering of cardio-metabolic risk factors—including abdominal obesity, insulin resistance, elevated blood pressure, high levels of triglycerides, and low levels of high-density lipoproteins—that increase the risk for developing cardiovascular diseases and type 2 diabetes mellitus. However, a generally accepted definition of MetS in pediatric patients is still lacking.Objectives: The aim was to summarize current prevalence data of childhood MetS as well as to discuss the continuing disagreement between different pediatric definitions and the clinical importance of such diagnosis.Methodology: A systematic literature search on the prevalence of pediatric MetS was conducted. Articles that were published during the past 5 years (2014–2019), using at least one of four predetermined classifications (International Diabetes Federation, Cook et al., Ford et al., and de Ferranti et al.), were included.Results: The search resulted in 1167 articles, of which 31 publications met all inclusion criteria.Discussion: The prevalence of MetS ranged between 0.3 and 26.4%, whereby the rising number of children and adolescents with MetS partly depended on the definition used. The IDF definition generally provided the lowest prevalences (0.3–9.5%), whereas the classification of de Ferranti et al. yielded the highest (4.0–26.4%). In order to develop a more valid definition, further research on long-term consequences of childhood risk factors such as abdominal obesity, insulin resistance, hypertension, and dyslipidemia is needed. There is also a temptation to suggest one valid, globally accepted definition of metabolic syndrome for pediatric populations but we believe that it is more appropriate to suggest definitions of MetS that are specific to males vs. females, as well as being specific to race/ethnicity or geographic region. Finally, while this notion of definitions of MetS specific to certain subgroups is important, it still needs to be tested in future research.

The prevalence of non-communicable diseases (NCDs) is increasing worldwide, also among children. Information about primary prevention of NCD’s is increasing; however, convincing strategies among children is needed. The present paper describes the design and methods in the Health Oriented Pedagogical Project (HOPP) study. The main objective is to evaluate the effects of a school-based physical activity intervention program on cardio-metabolic risk factors. Secondary objectives include assessment of physical, psychological and academic performance variables. The HOPP study is a 7 years longitudinal large-scale controlled intervention in seven elementary schools (n = 1545) with two control schools (n = 752); all aged 6–11 years at baseline. The school-based physical activity intervention program includes an increase in physical activity (PA) of 225 min/week as an integrated part of theoretical learning, in addition to the curriculum based 90 min/week of ordinary PA. Primary outcomes include cardio-metabolic risk factors measured as PA level, BMI status, waist circumference, muscle mass, percent fat, endurance test performance, total serum cholesterol, high-density lipoprotein (HDL), non-HDL, micro C-reactive protein (mCRP) and long-term blood sugar (HbA1c). In addition, secondary outcomes include anthropometric growth measures, physical fitness, quality of life (QoL), mental health, executive functions, diet and academic performance. HOPP will provide evidence of effects on cardio-metabolic risk factors after a long-term PA intervention program in elementary schoolchildren. School-based PA intervention programs may be an effective arena for health promotion and disease prevention. The study is registered in Clinical trials (ClinicalTrials.gov Identifier: NCT02495714 ) as of June 20th – 2015, retrospectively registered. The collection of baseline values was initiated in mid-January 2015.

Aims: The aim of the study was to describe the natural course of handgrip strength development in primary school children and to establish a reference material to be used in future screening studies. In addition, the study aims to investigate a possible association between handgrip strength and cardiovascular risk factors. Methods: Anthropometric measures along with results for handgrip strength, endurance tests, blood pressure and cholesterol were measured on 2272 children of both sexes. An ROC analysis was used to estimate the suitability of handgrip strength as a predictor for known cardiometabolic risk factors. Results: A reference material for handgrip strength is presented for boys and girls aged 6–12 years. The results indicate that handgrip strength is unsuitable as a predictor for cardiometabolic risk factors in children. Conclusions: The results may be used as reference values for handgrip strength in 6–12-year-old children of both sexes. Handgrip strength may not be used as a screening tool for cardiometabolic risk factors in pre-pubertal children.

Aims: With overweight and obesity increasing worldwide, it has become ever more important to monitor the development and distribution of adiposity in children. This study investigated how the measurements of waist circumference (WC) and waist-to-height ratio (WHtR) in children 6–12 years old relate to earlier studies. Methods: In 2015, 2271 children (boys, n = 1150) were measured for height, weight, and WC. Parental education level was used as a measure of socioeconomic status. Results: A significant increase in WC with age was revealed for both sexes (p < .0001). Boys at 10 and 12 years had a larger WC than girls; otherwise no difference between sexes was found. The WHtR decreased with age for girls (p < .0001); 14% of the sample displayed a WHtR ≥ 0.50. Comparison with earlier studies showed a higher WC and WHtR despite no change in weight and body mass index. Conclusion: WC and WHtR are recommended as tools for identifying central obesity in children. The results indicate increased WC in 6–12-year-old children compared with earlier findings.

Aims: The objective was to investigate moderate-to-vigorous physical activity levels (MVPA) of primary school children at baseline of the Health Oriented Pedagogical Project (HOPP), Norway. Methods: Data on 2123 children aged 6–12 years were included for analysis (75% participation rate). Average minutes per day in MVPA was objectively measured using accelerometry based on seven-day averages. The sample was analysed for age-, sex-, socioeconomic-, and season-related patterns. A linear regression investigated the moderating effect of these factors as well as body mass index and waist circumference. Results: Some 86.5% of the sample had at least 60 min/day MVPA, averaging 90.7 min/day. The main differences in daily averages were between age groups 6½–9 and 10–12 ( p < .05). Boys (95.8 min/day, 95% CI: 94.1–97.5) were more active than girls (85.6 min/day, 95% CI: 83.9–87.2) in all age groups ( p < .0001). MVPA was lower by 3.5 min ( p < .0001) per additional year of age in the linear regression (R² = 0.176) and was reduced by 20 min less per day in MVPA in the winter months compared with the summer months ( p < .0001). Conclusions: Physical activity levels are already in decline from 6–7 years old and are likely to continue to decline into adolescence. Interventions must therefore focus on primary school children.

Background: Machine learning techniques are increasingly being applied in health research. It is not clear how useful these approaches are for modeling continuous outcomes. Child quality of life is associated with parental socioeconomic status and physical activity and may be associated with aerobic fitness and strength. It is unclear whether diet or academic performance is associated with quality of life. Objective: The purpose of this study was to compare the predictive performance of machine learning techniques with that of linear regression in examining the extent to which continuous outcomes (physical activity, aerobic fitness, muscular strength, diet, and parental education) are predictive of academic performance and quality of life and whether academic performance and quality of life are associated. Methods: We modeled data from children attending 9 schools in a quasi-experimental study. We split data randomly into training and validation sets. Curvilinear, nonlinear, and heteroscedastic variables were simulated to examine the performance of machine learning techniques compared to that of linear models, with and without imputation. Results: We included data for 1711 children. Regression models explained 24% of academic performance variance in the real complete-case validation set, and up to 15% in quality of life. While machine learning techniques explained high proportions of variance in training sets, in validation, machine learning techniques explained approximately 0% of academic performance and 3% to 8% of quality of life. With imputation, machine learning techniques improved to 15% for academic performance. Machine learning outperformed regression for simulated nonlinear and heteroscedastic variables. The best predictors of academic performance in adjusted models were the child’s mother having a master-level education (P<.001; β=1.98, 95% CI 0.25 to 3.71), increased television and computer use (P=.03; β=1.19, 95% CI 0.25 to 3.71), and dichotomized self-reported exercise (P=.001; β=2.47, 95% CI 1.08 to 3.87). For quality of life, self-reported exercise (P<.001; β=1.09, 95% CI 0.53 to 1.66) and increased television and computer use (P=.002; β=−0.95, 95% CI −1.55 to −0.36) were the best predictors. Adjusted academic performance was associated with quality of life (P=.02; β=0.12, 95% CI 0.02 to 0.22). Conclusions: Linear regression was less prone to overfitting and outperformed commonly used machine learning techniques. Imputation improved the performance of machine learning, but not sufficiently to outperform regression. Machine learning techniques outperformed linear regression for modeling nonlinear and heteroscedastic relationships and may be of use in such cases. Regression with splines performed almost as well in nonlinear modeling. Lifestyle variables, including physical exercise, television and computer use, and parental education are predictive of academic performance or quality of life. Academic performance is associated with quality of life after adjusting for lifestyle variables and may offer another promising intervention target to improve quality of life in children.