Optimizing recovery is crucial for maintaining performance and reducing fatigue and injury risk in youth football players. This study applied machine learning (ML) models to classify mental fatigue in U15, U17, and U19 male players using wearable signals, tracking data, and psychophysiological features. Over six weeks, training loads were monitored via GPS, psychophysiological scales, and heart rate sensors, analyzing variables such as total distance, high-speed running, recovery state, and perceived exertion. The data preparation process involved managing absent values, applying normalization techniques, and selecting relevant features. A total of five ML models were evaluated: K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). XGBoost, RF, and DT achieved high accuracy, while KNN underperformed. Using a correlation matrix, average speed (AvS) was the only variable significantly correlated with the rating of perceived exertion (RPE) (r = 0.142; p = 0.010). After dimensionality reduction, ML models were re-evaluated, with RF and DT performing best, followed by XGBoost and SVM. These findings confirm that tracking and wearable-derived data are effectively useful for predicting RPE, providing valuable insights for workload management and personalized recovery strategies. Future research should integrate psychological and interpersonal factors to enhance predictive modeling in the individual long-term health and performance of young football players.

Aging is associated with physiological changes that increase the risk of falls, impacting functional independence and quality of life. Multicomponent exercise training has emerged as an effective strategy for mitigating these risks by enhancing strength, balance, flexibility, and aerobic capacity. This study aimed to evaluate the effects of a 30-week multicomponent training program on functional fitness and fall prevention in older women. A parallel, single-blind randomized controlled trial was conducted with 40 participants (aged ≥ 65 years), divided into an exercise group and a control group. The intervention combined strength, balance, coordination, and aerobic training, following international exercise guidelines for older adults. Functional fitness was assessed using validated tests, including the Timed Up and Go (TUG) test, lower limb strength, flexibility, and aerobic endurance measures. Results demonstrated significant improvements in the intervention group, particularly in TUG performance (p < 0.001), lower limb strength (p < 0.001), and flexibility (p < 0.05), indicating enhanced mobility and reduced fall risk. These findings reinforce the importance of structured, multicomponent training programs for aging populations, particularly women, who experience greater musculoskeletal decline due to menopause-related hormonal changes. Future research should explore long-term retention of benefits and optimize intervention strategies. This study highlights the critical role of tailored exercise programs in promoting active aging, improving functional capacity, and reducing healthcare burdens associated with fall-related injuries.

Background/Objectives: Executive function, which includes inhibitory control, working memory, and cognitive flexibility, tends to decline with aging. While traditional resistance training (TRT) has shown positive effects in mitigating these declines, limited evidence is available regarding flywheel resistance training (FRT). This study aimed to evaluate and compare the effects of TRT and FRT on executive function in older women. Methods: In this randomized controlled trial (clinicaltrials.gov NCT05910632), 29 older women were allocated into two groups: TRT (n = 15) and FRT (n = 14). The intervention lasted eight weeks with two weekly sessions conducted at the Federal University of Viçosa. The TRT group performed exercises using machines and free weights, while the FRT group used a multi-leg isoinertial device. Executive function was assessed using the Victoria Stroop Test (inhibitory control), Digit Span Test (working memory), and Trail Making Tests A and B (cognitive flexibility). Data were analyzed using a Multivariate Analysis of Covariance (p < 0.05). Results: No significant changes were observed in inhibitory control (p = 0.350). Working memory improved significantly within both groups in forward (p = 0.002) and backward (p = 0.002) span tasks. For cognitive flexibility, Trail Making Test A showed no significant changes (p > 0.05), but Test B showed significant within-group (p = 0.030) and between-group (p = 0.020) improvements. The B-A difference was also significant (p = 0.040). Conclusions: Both resistance training modalities enhanced working memory and cognitive flexibility. However, FRT produced greater improvements in cognitive flexibility, suggesting potential advantages in cognitive aging interventions.

Osteoporosis is a progressive degenerative bone condition leading to increased fracture risk. Therefore, osteoporosis acknowledges around 200 million cases

The irreversible and progressive decline of physiological functions is known as aging. Among these changes is brain aging, which leads to cognitive decline and the onset of dementia. This directly affects memory, learning, and motor skills, reducing gait efficiency. This study aimed to investigate the feasibility of identifying and classifying the risk of dementia based on the analysis of kinematic variables related to gait in older adults using machine learning algorithms. This cross-sectional observational study examined a sample of 59 individuals aged 60 ± 8 years, divided into two groups: 26 institutionalized older adults (GI) and 33 non-institutionalized older adults (GNI), all residing in Bragança, Portugal. Gait data were collected during a 10-m walk, recorded on video, and analyzed using Kinovea software. Cognitive status was assessed using the Mini-Mental State Examination (MMSE). Python™ was used for statistical analysis and to develop machine learning models to classify dementia risk based on gait variables. The results showed that the algorithmic models achieved an overall accuracy of 74.6%, with the AdaBoost algorithm performing best at 83.5%. Cross-validation revealed an overall accuracy of 72%, with the Support Vector Machine (SVM) classifier achieving the highest individual performance at 80%, correctly classifying 80% of cases across different data subsets. In conclusion, gait analysis combined with machine learning algorithms demonstrated a strong relationship between gait variables and dementia, proving to be a safe and efficient technique for dementia classification. This approach offers a low-cost and accessible early identification and intervention method, with potential applications in clinical and public health settings.