Results from Brazil's 2022 Report Card on Physical Activity for Children and Adolescents.

This research aims to summarize the process and results of the 2022 Report Card on Physical Activity for Brazilian children and adolescents. A group of experts led by 10 PhD researchers gathered the best possible evidence on physical activity indicators. The Report Card Brazil 2022 included the top 10 indicators of physical activity and sleep, obesity, and poor mental health variables, which made up four dimensions: (I) Daily Behaviors; (II) Settings and Sources of Influence; (III) Government Strategies and Investments; and (IV) Health Outcomes. Comprehensive searches, including peer-reviewed and gray literature searches, were performed for each indicator. Data were considered from systematic reviews, local and national surveys, websites, and official information from the Brazilian Federal Government. Grades from the indicators ranged from F (Active Play) to B (School). In addition, the results found for each indicator were Overall Physical Activity (D), Organized Sport Participation (C-), Active Transportation (C), Sedentary Behaviors (D), Sleep (C), Family and Peers (C-), Community and Environment (C), Government (D+), Physical Fitness (D+), Obesity (11.7%), and Poor Mental Health (37.8%). Successfully strategies for increasing physical activity among Brazilian children and adolescents should look at the different indicators presented in this report.

A causal learning framework for the analysis and interpretation of COVID-19 clinical data.

We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph showing the causal connections among the analyzed features. The workflow consists in a multi-step approach that goes from identifying the main causes of patient's outcome through BSL, to the realization of a tool suitable for clinical practice, based on a Binary Decision Tree (BDT), to recognize patients at high-risk with information available already at hospital admission time. We evaluate our approach on a feature-rich dataset of Coronavirus disease (COVID-19), showing that the proposed framework provides a schematic overview of the multi-factorial processes that jointly contribute to the outcome. We compare our findings with current literature on COVID-19, showing that this approach allows to re-discover established cause-effect relationships about the disease. Further, our approach yields to a highly interpretable tool correctly predicting the outcome of 85% of subjects based exclusively on 3 features: age, a previous history of chronic obstructive pulmonary disease and the PaO2/FiO2 ratio at the time of arrival to the hospital. The inclusion of additional information from 4 routine blood tests (Creatinine, Glucose, pO2 and Sodium) increases predictive accuracy to 94.5%.