CONTEXT: The effect of physical activity (PA) on different areas of academic achievement and classroom behaviors and how different characteristics of PA interventions could modify the effect remain unclear. OBJECTIVE: The objective was twofold: (1) to assess the effect of PA interventions on academic achievement and classroom behaviors in childhood and (2) to determine the characteristics of individuals and PA programs that enhance academic performance. DATA SOURCES: We identified studies from the database inception to October 16, 2016. STUDY SELECTION: We selected intervention studies aimed at examining the effect of exercise on academic achievement and classroom behaviors at developmental age. DATA EXTRACTION: Random-effects models were used to calculate pooled effect size for all primary outcomes (language- and mathematics-related skills, reading, composite score, and time in on-task behavior). Positive values represent a direct relationship between PA programs and academic achievement scores or on-task behaviors. RESULTS: A total of 26 studies (10 205 children, aged from 4 to 13) were included. Pooled effect size (95% confidence interval) estimates were as follows: (1) 0.16 (−0.06 to 0.37) for language-related skills; (2) 0.21 (0.09 to 0.33) for mathematics-related skills; (3) 0.13 (0.02 to 0.24) for reading; (4) 0.26 (0.07 to 0.45) for composite scores; and (5) 0.77 (0.22 to 1.32) for time in on-task behaviors. LIMITATIONS: Limitations included the variety of tools used to measure academic achievement and the limited number of studies that reported the effect of after-school PA interventions. CONCLUSIONS: PA, especially physical education, improves classroom behaviors and benefits several aspects of academic achievement, especially mathematics-related skills, reading, and composite scores in youth.

The prevalence of obesity and overweight has markedly risen in the last 2 decades in Europe, even though noticeable variations in the estimations across countries have been reported.^1-3 According to estimates from the World Health Organization’s Childhood Obesity Surveillance Initiative, using the International Obesity Task Force (IOTF) criteria, approximately one-quarter of European children aged 6 to 9 years had overweight or obesity in 2010, with estimates ranging from 10.8% (Belgium) to 39.9% (Greece).⁴ The prevalence of overweight and obesity among children and adolescents is growing in low- and middle-income countries,⁵ although some country-specific analyses have suggested a recent plateau, or even a decrease, in the frequency of excess weight, especially in high-income countries.^3,6

Risk factors for in-hospital mortality in confirmed COVID-19 patients have been summarized in numerous meta-analyses, but it is still unclear whether they vary according to the age, sex and health conditions of the studied populations. This study explored these variables as potential mortality predictors. A systematic review was conducted by searching the MEDLINE, Scopus, and Web of Science databases of studies available through July 27, 2020. The pooled risk was estimated with the odds ratio (p-OR) or effect size (p-ES) obtained through random-effects meta-analyses. Subgroup analyses and meta-regression were applied to explore differences by age, sex and health conditions. The MOOSE guidelines were strictly followed. The meta-analysis included 60 studies, with a total of 51,225 patients (12,458 [24.3%] deaths) from hospitals in 13 countries. A higher in-hospital mortality risk was found for dyspnoea (p-OR = 2.5), smoking (p-OR = 1.6) and several comorbidities (p-OR range: 1.8 to 4.7) and laboratory parameters (p-ES range: 0.3 to -2.6). Age was the main source of heterogeneity, followed by sex and health condition. The following predictors were more markedly associated with mortality in studies with patients with a mean age ≤60 years: dyspnoea (p-OR = 4.3), smoking (p-OR = 2.8), kidney disease (p-OR = 3.8), hypertension (p-OR = 3.7), malignancy (p-OR = 3.7), diabetes (p-OR = 3.2), pulmonary disease (p-OR = 3.1), decreased platelet count (p-ES = -1.7), decreased haemoglobin concentration (p-ES = -0.6), increased creatinine (p-ES = 2.4), increased interleukin-6 (p-ES = 2.4) and increased cardiac troponin I (p-ES = 0.7). On the other hand, in addition to comorbidities, the most important mortality predictors in studies with older patients were albumin (p-ES = -3.1), total bilirubin (p-ES = 0.7), AST (p-ES = 1.8), ALT (p-ES = 0.4), urea nitrogen (p-ES), C-reactive protein (p-ES = 2.7), LDH (p-ES = 2.4) and ferritin (p-ES = 1.7). Obesity was associated with increased mortality only in studies with fewer chronic or critical patients (p-OR = 1.8). The prognostic effect of clinical conditions on COVID-19 mortality vary substantially according to the mean age of patients. CRD42020176595.

Increased arterial stiffness has been associated with an increased risk of developing cardiovascular diseases and all-cause mortality. Pulse wave velocity (PWV) is an innovative and affordable measurement of arterial stiffness which may be an accessible tool to estimate mortality risk; however, no meta-analysis has estimated its predictive performance for cardiovascular and all-cause mortality. Moreover, reference values for PWV have only been established by consensus for healthy populations. The aim of this review was to estimate PWV and especially carotid femoral PWV performance predicting cardiovascular and all-cause mortality as well as comparing the resulting cfPWV thresholds with already established values in order to increase its validity. Original studies measuring PWV thresholds and its association with cardiovascular and all-cause mortality were systematically searched. The DerSimonian and Laird method was used to compute pooled estimates of diagnostic odds ratio (dOR), and overall test performances were summarized in hierarchical summary receiver operating characteristic curves (HSROC). Six studies were included in the meta-analysis. The pooled dOR values for the predictive performance of cfPWV were 11.23 (95 % CI, 7.29–1.29) for cardiovascular mortality and 6.52 (95% CI, 4.03–10.55) for all-cause mortality. The area under the HSROC curve for cfPWV was 0.75 (95% CI, 0.69–0.81) for cardiovascular mortality and 0.78 (95% CI, 0.74–0.83) for all-cause mortality, where the closest cut-off point to the summary point was 10.7 and 11.5, respectively. This systematic review and meta-analysis demonstrates that cfPWV is a useful and accurate cardiovascular mortality predictor and that its previously estimated reference values for estimating risk may be used in high-risk populations.

Alongside an increase in obesity, society is experiencing the development of substantial technological advances. Interventions that are easily scalable, such as lifestyle (including diet and physical activity) mobile health (mHealth) self-monitoring, may be highly valuable in the prevention and treatment of excess weight. Thus, the aims of this systematic review and meta-analysis were to estimate the following: (i) the effect of behavioral weight management interventions using lifestyle mHealth self-monitoring on weight loss and (ii) the adherence to behavioral weight management interventions using lifestyle mHealth self-monitoring. MEDLINE via PubMed, EMBASE, the Cochrane Central Register of Controlled Trials and the Web of Science databases were systematically searched. The DerSimonian and Laird method was used to estimate the effect of and adherence to behavioral weight management interventions using lifestyle mHealth self-monitoring on weight loss. Twenty studies were included in the systematic review and meta-analysis, yielding a moderate decrease in weight and higher adherence to intervention of behavioral weight management interventions using lifestyle mHealth self-monitoring, which was greater than other interventions. Subgroup analyses showed that smartphones were the most effective mHealth approach to achieve weight management and the effect of behavioral weight management interventions using lifestyle mHealth self-monitoring was more pronounced when compared to usual care and in the short-term (less than six months). Furthermore, behavioral weight management interventions using lifestyle mHealth self-monitoring showed a higher adherence than: (i) recording on paper at any time and (ii) any other intervention at six and twelve months.

This study aims to quantify the association between aerobic fitness and academic achievement, and determine how different aerobic fitness tests and individual demographic characteristics may modify this association. It includes 41 cross-sectional and 7 longitudinal studies focusing on children and adolescents, from MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database and Web of Science. Pooled effect sizes (ES) were estimated for the association between aerobic fitness and specific domains of academic achievement. Subgroup analyses and meta-regressions were based on aerobic fitness tests and demographic characteristics. The ES for the association between aerobic fitness with language/reading-skills, mathematics-related skills and composite scores, were 0.23 (95% CI: 0.12; 0.34); 0.27 (95% CI: 0.19; 0.36) and 0.28 (95% CI: 0.12; 0.45), respectively. Subgroup analyses by aerobic fitness test modified these associations; additionally, analyses by sex or age showed differences between boys and girls as well as between children and adolescents. Finally, meta-regressions revealed a slight effect of body mass index on these associations. Cardiorespiratory fitness is positively associated with academic achievement. Associations were dependent on demographic and aerobic fitness test characteristics, being stronger in boys than in girls, and in children than in adolescents. Systematic review registration: PROSPERO CRD42017069677