Background and Objectives: Pilates training intervention programs have gained attention as a potential approach to enhancing balance in older adults, thereby reducing the risk of falls. In light of these considerations, this systematic review and meta-analysis aimed to critically evaluate the existing evidence and determine the effect of Pilates training intervention programs on older adults’ balance. Materials and Methods: The literature was searched through the PubMed, Web of Science, and Scopus databases from inception until July 2023. The primary keywords used for the literature search included “elderly” or “older adults” and “pilates training” and “balance”. Results: The systematic review through qualitative analysis showed robust evidence about the efficacy of Pilates intervention programs in improving older adults’ balance. The pooled meta-analysis of static and dynamic balance showed that eight (53%) out of a total fifteen analyzed interventions presented a significant effect of Pilates in improving the participants’ balance, without between-study heterogeneity. In addition, the meta-analysis regarding dynamic balance showed that six (67%) out of nine analyzed interventions presented a significant effect of Pilates in improving the participants’ balance, without heterogeneity between studies. Similarly, the meta-analysis regarding static balance showed that four (50%) out of eight analyzed studies presented significant effects on the older adults’ balance, where moderate between-study heterogeneity was found. Sensitivity analysis showed that three studies reduced the between-study heterogeneity (19, 17.6, and 17%), regressing from moderate to low heterogeneity, p < 0.05. Conclusions: This systematic review and meta-analysis underscores the potential of Pilates training as a valuable intervention to enhance balance in the elderly population.

The increasing prevalence of overweight and obesity among adults is a risk factor for many chronic diseases and death. In addition, obesity among children and adolescents has reached unprecedented levels and studies show that obese children and adolescents are more likely to become obese adults. Therefore, both the prevention and treatment of obesity in adolescents are critical. This study aimed to develop an artificial intelligence (AI) neural network (NNET) model that identifies the risk of obesity in Portuguese adolescents based on their body mass index (BMI) percentiles and levels of physical fitness. Using datasets from the FITescola^® project, 654 adolescents aged between 10–19 years old, male: 334 (51%), female: n = 320 (49%), age 13.8 ± 2 years old, were selected to participate in a cross-sectional observational study. Physical fitness variables, age, and sex were used to identify the risk of obesity. The NNET had good accuracy (75%) and performance validation through the Receiver Operating Characteristic using the Area Under the Curve (ROC AUC = 64%) in identifying the risk of obesity in Portuguese adolescents based on the BMI percentiles. Correlations of moderate effect size were perceived for aerobic fitness (AF), upper limbs strength (ULS), and sprint time (ST), showing that some physical fitness variables contributed to the obesity risk of the adolescents. Our NNET presented a good accuracy (75%) and was validated with the K-Folds Cross-Validation (K-Folds CV) with good accuracy (71%) and ROC AUC (66%). According to the NNET, there was an increased risk of obesity linked to low physical fitness in Portuguese teenagers.

The aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data were collected from the target variables of accelerations (ACCs), decelerations (DECs), and dynamic stress load (DSL) using an 18 Hz global positioning system (GPS). Additionally, we monitored the perceived exertion and biological characteristics using total quality recovery (TQR), rating of perceived exertion (RPE), session RPE (sRPE), chronological age, maturation offset (MO), and age at peak height velocity (APHV). The ML model was computed by a feature selection process with a linear regression forecast and bootstrap method. The predictive analysis revealed that the players’ MO demonstrated varying degrees of effectiveness in predicting their DEC and ACC across different ranges of IQR. After predictive analysis, the following performance values were observed: DEC ($\overline{\mathrm{x}}$_predicted = 41, β = 3.24, intercept = 37.0), lower IQR (IQR_predicted = 36.6, β = 3.24, intercept = 37.0), and upper IQR (IQR_predicted = 46 decelerations, β = 3.24, intercept = 37.0). The player’s MO also demonstrated the ability to predict their upper IQR (IQR_predicted = 51, β = 3.8, intercept = 40.62), lower IQR (IQR_predicted = 40, β = 3.8, intercept = 40.62), and ACC ($\overline{\mathrm{x}}$_predicted = 46 accelerations, β = 3.8, intercept = 40.62). The ML model showed poor performance in predicting the players’ ACC and DEC using MO (MSE = 2.47–4.76; RMSE = 1.57–2.18: R² = −0.78–0.02). Maturational concerns are prevalent in football performance and should be regularly checked, as the current ML model treated MO as the sole variable for ACC, DEC, and DSL. Applying ML models to assess automated tracking data can be an effective strategy, particularly in the context of forecasting peak ACC, DEC, and bodily effects in sub-elite youth football training.

Background/Objectives: This study examined the effects of an intervention on anthropometrics, body composition, physical fitness, and sleep quality in aged individuals, comparing a control group (N = 11) and an experimental group (N = 13) across two measurement points. Methods: A multicomponent training program of 8 months was adopted as the intervention group. A bioimpedance balance, functional fitness test, and Pittsburgh Sleep Quality Index measured body composition, functional fitness, and sleep quality. Results: Both groups showed minimal changes in body mass and hand grip strength. However, the experimental group experienced significant improvements in physical fitness, including a 26% increase in arm curl repetitions, an 18% reduction in 5 times sit-to-stand (5TSTS) completion time, and a 29% rise in 2-min step test (2MST) steps, indicating enhanced muscle endurance and cardiovascular fitness. Flexibility decreased significantly in the experimental group, while body fat percentage was reduced by 10%. Sleep quality improved by 47% in the experimental group but declined by 14% in the control group. Correlational analysis revealed that better sleep quality was linked to improved fitness performance and reduced body fat in the experimental group, with post-intervention results further confirming the connection between sleep and fat reduction. In the control group, improved sleep quality was associated with higher metabolic rates after 8 months. Conclusions: These findings suggest that the intervention positively impacted physical fitness and sleep quality, with potential benefits for overall health.

The aim of this study was to analyze the match-to-match variation in high-intensity demands from one Portuguese professional football team according to playing positions. Twenty-three male outfield professional football players were observed during eighteen matches of the Portuguese Second League. Time–motion data were collected using Global Positioning System (GPS) technology. Match running performance was analyzed based on the following three playing positions: defenders (DF), midfielders (MF), and forwards (FW). Repeated measures ANOVA was utilized to compare match running performance within each position role, and seasonal running variation. Practical differences were assessed using the smallest worthwhile change (SWC), coefficient of variation (CV), and twice the coefficient of variation (2CV). Significant differences were found among playing positions in total distance covered (F = 15.45, p < 0.001, η² = 0.33), average speed (F = 12.79, p < 0.001, η² = 0.29), high-speed running (F = 16.93, p < 0.001, η² = 0.36), sprinting (F = 13.49, p < 0.001, η² = 0.31), accelerations (F = 4.69, p = 0.001, η² = 0.132), and decelerations (F = 12.21, p < 0.001, η² = 0.284). The match-to-match running performance encompassed TD (6.59%), AvS (8.67%), HSRr (37.83%), SPR (34.82%), ACC (26.92%), and DEC (27.85%). CV values for total distance covered ranged from 4.87–6.82%, with forwards and midfielders exhibiting the greatest and smallest variation, respectively. Midfielders demonstrated the highest match-to-match variation for all other analyzed variables (8.12–69.17%). All playing positions showed significant variation in high-demanding variables (26.94–37.83%). This study presents the initial analysis of match-to-match variation in high-intensity demands within a Portuguese professional football team. Thus, the position’s specificity and context can provide a helpful strategy for evaluating match-to-match running performance, and for recommending individualized training exercises based on the peak and high-intensity demands for each player’s role within the game.

Background: Assessing sleep quality is essential in sports science, particularly in ultra-endurance sports, where recovery is critical for performance and health. Objective: This study aimed to identify sleep quality patterns among endurance and ultra-endurance athletes using hierarchical clustering analysis, with comparisons by sex and modality. Method: Data were collected during the La Misión Brasil competitions in 2023 and 2024, using the Pittsburgh Sleep Quality Index (PSQI). The questionnaire was emailed to all registered runners two weeks before the event. A total of 490 athletes participated, including 276 men (mean ± SD age: 43 ± 11 years) and 214 women (mean ± SD age: 43 ± 13 years). Statistical analyses included Cohen’s d and r effect sizes, and a 95% confidence interval for hypothesis testing. Residuals between-cluster proportions were assessed within a range of −3.3, ensuring a 99.7% confidence level for significant differences. Results: The results showed that endurance runners had better sleep quality, with most scoring low on the PSQI. In contrast, ultra-endurance athletes displayed greater variability, with a higher prevalence of poor sleep quality, particularly in women. Conclusions: The hierarchical clustering method effectively identified distinct sleep patterns, providing insights into the dynamics of recovery and performance. These findings highlight the impact of increased physical and psychological demands in ultra-endurance sports and emphasize the need for tailored sleep monitoring strategies to optimize the recovery and performance of athletes.