SummaryBackground: Diabetes and cardiovascular disease have emerged as key threats to human health, and the risk is increased in individuals with visceral obesity. The consensus is that physical exercise should be part of the treatment of diabetes mellitus (DM).

BackgroundOur purpose was to develop and test a predictive model of the acute glucose response to exercise in individuals with type 2 diabetes.Design and methodsData from three previous exercise studies (56 subjects, 488 exercise sessions) were combined and used as a development dataset. A mixed-effects Least Absolute Shrinkage Selection Operator (LASSO) was used to select predictors among 12 potential predictors. Tests of the relative importance of each predictor were conducted using the Lindemann Merenda and Gold (LMG) algorithm. Model structure was tested using likelihood ratio tests. Model accuracy in the development dataset was assessed by leave-one-out cross-validation.Prospectively captured data (47 individuals, 436 sessions) was used as a test dataset. Model accuracy was calculated as the percentage of predictions within measurement error. Overall model utility was assessed as the number of subjects with ≤1 model error after the third exercise session. Model accuracy across individuals was assessed graphically. In a post-hoc analysis, a mixed-effects logistic regression tested the association of individuals’ attributes with model error.ResultsMinutes since eating, a non-linear transformation of minutes since eating, post-prandial state, hemoglobin A1c, sulfonylurea status, age, and exercise session number were identified as novel predictors. Minutes since eating, its transformations, and hemoglobin A1c combined to account for 19.6% of the variance in glucose response. Sulfonylurea status, age, and exercise session each accounted for <1.0% of the variance. In the development dataset, a model with random slopes for pre-exercise glucose improved fit over a model with random intercepts only (likelihood ratio 34.5, p < 0.001). Cross-validated model accuracy was 83.3%.In the test dataset, overall accuracy was 80.2%. The model was more accurate in pre-prandial than postprandial exercise (83.6% vs. 74.5% accuracy respectively). 31/47 subjects had ≤1 model error after the third exercise session. Model error varied across individuals and was weakly associated with within-subject variability in pre-exercise glucose (Odds ratio 1.49, 95% Confidence interval 1.23-1.75).ConclusionsThe preliminary development and test of a predictive model of acute glucose response to exercise is presented. Further work to improve this model is discussed.

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Introduction For individuals diagnosed with diabetes mellitus, the practice of properly oriented physical exercises brings significant benefits to the individual's health and is considered an indispensable tool for metabolic management. The individualization of exercise routines is an essential aspect for therapeutic success, despite the need to consider some general recommendations. This review is an authorized literal translation of the Brazilian Society of Diabetes (SBD) Guidelines 2021–2022, which is based on scientific evidence and provides guidance on physical activities and exercises aimed at individuals with type 1 and 2 diabetes. Methods SBD designated 9 specialists from its “Department of Diabetes, Exercise & Sports” to author chapters on physical activities and exercises directed to individuals with type 1 and 2 diabetes. The aim of these chapters was to highlight recommendations in accordance with Evidence Levels, based on what is described in the literature. These chapters were analyzed by the SBD Central Committee, which is also responsible for the SBD 2021–2022 guidelines. Main clinical inquiries were selected to perform a narrated review by using MEDLINE via PubMed. Top available evidence, such as high-quality clinical trials, large observational studies and meta-analyses related to physical activity and exercise advisory, were analyzed. The adopted MeSh terms were [diabetes], [type 1 diabetes], [type 2 diabetes], [physical activity] [physical exercise]. Results 17 recommendations were defined by the members. For this review, it was considered different Evidence Levels, as well as different Classes of Recommendations. As to Evidence Levels, the following levels were contemplated: Level A) More than one randomized clinical trial or a randomized clinical trial meta-analysis with low heterogeneity. Level B) Meta analysis with observational studies, one randomized clinical trial, sizeable observational studies and sub-groups analysis. Level C) Small non-randomized studies, cross-sectional studies, case control studies, guidelines or experts’ opinions. In respect to Recommendation Classes, the following criteria were adopted: I. “Recommended”: Meaning there was a consent of more than 90% of the panel; IIa. “Must be considered”: meaning there is a general preference of the panel which 70–90% agrees; IIb. “Can be considered”. 50–70% agrees; III Not recommended: There is a consensus that the intervention should not be performed. Conclusion Physical exercise aids on the glycemic control of type 2 diabetes individuals while also decreasing cardiovascular risk in individuals with type 1 and 2 diabetes. Individuals diagnosed with diabetes should perform combined aerobic and resistance exercises in order to manage the disease. In addition, exercises focusing on flexibility and balance should be specially addressed on elderly individuals. Diabetes individuals using insulin as therapeutic treatment should properly monitor glycemia levels before, during and after exercise sessions to minimize health incidents, such as hypoglycemia.

Introdução: O sono é um estado de proteção para o organismo que, durante esse estágio, passa por diversas alterações fisiológicas. É um processo de reorganização da atividade do sistema nervoso. Distúrbios do sono acometem pessoas das mais diversas faixas etárias, podendo atingir desde crianças a adultos. Objetivo: Verificar a prevalência dos distúrbios do sono na população infantil residentes no Distrito Sanitário II do município do Recife. Métodos: Aplicou-se um questionário, baseado no Índice de Qualidade de Sono Pittsburgh, aos pais das crianças ou aos responsáveis por elas. Resultados: A análise das respostas mostrou que 16,9% das crianças se enquadravam como “maus dormidores”, apresentando escore entre 6 e 15, sendo 57% do sexo feminino; e 43%, do masculino. Conclusão: Os resultados apresentaram uma parcela considerável de crianças com algum tipo de distúrbio do sono, indicando uma possível subnotificação da má qualidade do sono da população infantil no atendimento básico de saúde.