Background: Structured aerobic or resistance training alone seems to be a beneficial tool for improving glucose homeostasis, chronic systemic inflammation, resting cardiovascular function, and mental health in people with obesity and type 2 diabetes mellitus (T2DM). The aim of the present study was to synthesize the available data on the effectiveness of combined aerobic and resistance training (CART) on glycemic control, blood pressure, inflammation, cardiorespiratory fitness (CRF), and quality of life (QoL) in overweight and obese individuals with T2DM.Methods: A database search was carried out in PubMed, Web of Science, Scopus, Science Direct, Cochrane Library, and Google Scholar from inception up to May 2023. The Cochrane risk of bias tool was used to assess eligible studies, and the GRADE method to evaluate the reliability of evidence. A random-effects model was used, and data were analyzed using standardized mean differences and 95% confidence intervals. The study protocol was registered in the International Prospective Register of Systematic Reviews (ID: CRD42022355612).Results: A total of 21,612 studies were retrieved; 20 studies were included, and data were extracted from 1,192 participants (mean age: 57 ± 7 years) who met the eligibility criteria. CART demonstrated significant improvements in body mass index, glycated hemoglobin, systolic and diastolic blood pressure, C-reactive protein, tumor necrosis factor-alpha, interleukin-6, CRF, and QoL compared to ST. These findings highlight the significance of exercise interventions such as CART as essential elements within comprehensive diabetes management strategies, ultimately enhancing overall health outcomes in individuals with T2DM and overweight/obesity.No differences were found in resting heart rate between CART and ST. An uncertain risk of bias and poor quality of evidence were found among the eligible studies.Conclusion: These outcomes show clear evidence considering the positive role of CART in inducing beneficial changes in various cardiometabolic and mental health-related indicators in patients with T2DM and concurrent overweight/obesity. More studies with robust methodological design are warranted to examine the dose-response relationship, training parameters configuration, and mechanisms behind these positive adaptations.

Breast cancer survivors with obesity are at a high risk of cancer recurrence, comorbidity, and mortality. This review aims to systematically evaluate the effects of combined aerobic and resistance training (CART) on body composition, lipid homeostasis, inflammation, adipokines, cancer-related fatigue, sleep, and quality of life in breast cancer patients and survivors with overweight/obesity. An electronic search was conducted in PubMed, Web of Science, Scopus, Science Direct, Cochrane, and Google Scholar databases from inception up to January 8, 2024. Randomized controlled trials (RCTs) meeting the inclusion criteria were selected for the analysis. The Cochrane risk of bias tool was used to assess eligible studies, and the GRADE method to evaluate the quality of evidence. A random-effects model was used, and data were analyzed using mean (MD) and standardized mean differences (SMD) for continuous variables with 95% confidence intervals (CI). We assessed the data for risk of bias, heterogeneity, sensitivity, reporting bias, and quality of evidence. A total of 17 randomized controlled trials were included in the systematic review involving 1,148 female patients and survivors (mean age: 54.0 ± 3.4 years). The primary outcomes showed significant improvements in body mass index (SMD -0.57 kg/m2, p = 0.04), body fat (SMD -0.50%, p = 0.02), fat mass (SMD -0.63 kg, p = 0.04), hip circumference (MD -3.14 cm, p = 0.02), and fat-free mass (SMD 1.03 kg, p < 0.001). The secondary outcomes indicated significant increases in high-density lipoprotein cholesterol (MD -0.05 mmol/L, p = 0.008), natural killer cells (SMD 0.42%, p = 0.04), reductions in triglycerides (MD -81.90 mg/dL, p < 0.01), total cholesterol (SMD -0.95 mmol/L, p < 0.01), tumor necrosis factor α (SMD -0.89 pg/mL, p = 0.03), and leptin (SMD -0.63 ng/mL, p = 0.03). Also, beneficial alterations were found in cancer-related fatigue (SMD -0.98, p = 0.03), sleep (SMD -1.17, p < 0.001), and quality of life (SMD 2.94, p = 0.02) scores. There was very low to low confidence in the estimated effect of most of the outcomes. The present findings reveal that CART could be considered an adjunct therapy in supporting the conventional clinical approach observed following exercise. However, further high-quality research is needed to evaluate whether CART would be a valuable intervention to lower aggressive pharmacologic use in breast cancer patients with overweight/obesity.

The purpose of this study was to investigate the relationship between adolescents’ biological maturation level and their muscle power, as well as their overall muscle strength. Overall, 691 adolescents (414 boys and 277 girls) aged 12.01–11.96 (measured for body mass, body height as well as vertical jump, muscle power, and muscle strength). There was a statistically significant difference in terms of average right and left grip strength, vertical jump, and power in the late maturation group. For the body height and vertical jump averages in male adolescents, it was observed that the body height and vertical jump averages in the late group were significantly lower than in the early and on-time maturation groups. For female adolescents’ chronological age, sitting height, body mass, BMI, left and right grip strength, and power averages were found to be significantly higher compared with the on-time group (p < 0.05). It was established that biological maturation has a substantial link with vertical jump height and power, as well as grip strength on the right and left hands.

Obesity is the excessive accumulation of adipose tissue in the body that leads to health risks. The study aimed to classify obesity levels using a tree-based machine-learning approach considering physical activity and nutritional habits. Methods: The current study employed an observational design, collecting data from a public dataset via a web-based survey to assess eating habits and physical activity levels. The data included gender, age, height, weight, family history of being overweight, dietary patterns, physical activity frequency, and more. Data preprocessing involved addressing class imbalance using Synthetic Minority Over-sampling TEchnique-Nominal Continuous (SMOTE-NC) and feature selection using Recursive Feature Elimination (RFE). Three classification algorithms (logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost)) were used for obesity level prediction, and Bayesian optimization was employed for hyperparameter tuning. The performance of different models was evaluated using metrics such as accuracy, recall, precision, F1-score, area under the curve (AUC), and precision–recall curve. The LR model showed the best performance across most metrics, followed by RF and XGBoost. Feature selection improved the performance of LR and RF models, while XGBoost’s performance was mixed. The study contributes to the understanding of obesity classification using machine-learning techniques based on physical activity and nutritional habits. The LR model demonstrated the most robust performance, and feature selection was shown to enhance model efficiency. The findings underscore the importance of considering both physical activity and nutritional habits in addressing the obesity epidemic.

Background: Obesity, which causes physical and mental problems, is a global health problem with serious consequences. The prevalence of obesity is increasing steadily, and therefore, new research is needed that examines the influencing factors of obesity and how to predict the occurrence of the condition according to these factors. This study aimed to predict the level of obesity based on physical activity and eating habits using the trained neural network model. Methods: The chi-square, F-Classify, and mutual information classification algorithms were used to identify the most critical factors associated with obesity. The models’ performances were compared using a trained neural network with different feature sets. The hyperparameters of the models were optimized using Bayesian optimization techniques, which are faster and more effective than traditional techniques. Results: The results predicted the level of obesity with average accuracies of 93.06%, 89.04%, 90.32%, and 86.52% for all features using the neural network and for the features selected by the chi-square, F-Classify, and mutual information classification algorithms. The results showed that physical activity, alcohol consumption, use of technological devices, frequent consumption of high-calorie meals, and frequency of vegetable consumption were the most important factors affecting obesity. Conclusions: The F-Classify score algorithm identified the most essential features for obesity level estimation. Furthermore, physical activity and eating habits were the most critical factors for obesity prediction.

Background: The potential advantages of concurrent aerobic and resistance training (CART) for enhancing cardiometabolic health-related outcomes appear to surpass the outcomes of engaging in aerobic or resistance training alone. The present study aimed to synthesize the available scientific evidence on the effects of CART on body composition, lipid metabolism, and physical function in patients with type 2 diabetes and overweight/obesity. Methods: PubMed, Scopus, ScienceDirect, Cochrane Library, and Google Scholar were searched from inception to August 7, 2024. The review focused on randomized controlled trials and controlled clinical trials of CART. The Cochrane risk of bias tool was used to assess eligible studies, and the GRADE method to evaluate the reliability of evidence. A random-effects model was used and data were analyzed using standardized mean differences (SMD) and 95% confidence intervals (CI). Results: A total of 22,878 studies were retrieved; only 20 studies were included, and data were extracted from 1,289 participants (57.0 ± 7.0 years; 31.1 ± 4.6 kg/m²) who met the eligibility criteria. CART group significantly reduced body fat percentage (SMD −0.42, 95% CI [−0.70 to −0.15]), low-density lipoprotein-cholesterol (SMD −0.32, 95% CI [−0.62 to −0.02]), triglycerides (SMD −0.48, 95% CI [−0.71 to −0.24]), total cholesterol (SMD −0.35, 95% CI [−0.58 to −0.12]), and fasting blood glucose levels compared to standard treatment (non-exercising controls). CART significantly increased high-density lipoprotein-cholesterol (SMD 0.44, 95% CI [0.05–0.82]) and improved physical function (cardiorespiratory fitness: SMD 78.78, 95% CI [46.30–111.25]; muscular fitness: SMD 5.19, 95% CI [1.80–8.59]) compared to standard treatment. There were no significant differences in body mass, waist-to-hip ratio, fat mass, and lean body mass between CART and standard treatment. An uncertain risk of bias and poor quality of evidence were observed in the eligible studies. Conclusions: The present results indicate clear evidence that CART has a beneficial role in the improvement of several cardiometabolic health-related parameters in patients with type 2 diabetes and concomitant overweight/obesity. More trials with robust methodological design are needed to investigate the dose-response effects, training parameters formation, and potential mechanisms.