Burn injuries result in complex physiological and psychological sequelae, including hypermetabolism, muscle wasting, mobility impairment, scarring, and disrupted body image. While advances in acute care have improved survival, comprehensive rehabilitation strategies are critical for restoring function, appearance, and psychosocial well-being. Structured physical activity, including resistance and aerobic training, plays a central role in counteracting muscle atrophy, improving cardiovascular function, enhancing scar quality, and promoting psychological resilience and body image restoration. This narrative review synthesizes the current evidence on the effects of exercise-based interventions on post-burn recovery, highlighting their therapeutic mechanisms, clinical applications, and implementation challenges. In addition to physical training, emerging technologies such as virtual reality, aquatic therapy, and compression garments offer promising adjunctive benefits. Notably, artificial intelligence (AI) is gaining traction in burn rehabilitation through its integration into wearable biosensors and telehealth platforms that enable real-time monitoring, individualized feedback, and predictive modeling of recovery outcomes. These AI-driven tools have the potential to personalize exercise regimens, support remote care, and enhance scar assessment and wound tracking. Overall, the integration of exercise-based interventions with digital technologies represents a promising, multimodal approach to burn recovery. Future research should focus on optimizing exercise prescriptions, improving access to personalized rehabilitation tools, and advancing AI-enabled systems to support long-term recovery, functional independence, and positive self-perception among burn survivors.

The analysis of basketball performance has increasingly incorporated advanced analytics and machine learning methods to understand better the factors that influence offensive efficiency and game dynamics. The present study aimed to identify performance profiles in basketball using unsupervised machine learning techniques and to examine the physical load and performance indicators that differentiate these profiles. Quarters from Greek U16 basketball matches were analyzed in two contexts: quarters from games with large score differences and quarters from games with small score differences. K-means clustering was applied separately to each dataset to identify latent performance patterns. The optimal number of clusters was determined using the Elbow method and silhouette analysis, which indicated a two-cluster solution for both datasets. Cluster visualization using t-distributed stochastic neighbor embedding (t-SNE) confirmed the presence of distinct performance profiles. Independent samples t-tests revealed significant differences between clusters across several physical load indicators, including jump load, total distance covered, accumulated acceleration load, and distance covered across different speed zones (p < .001). Clusters characterized by higher movement intensity also exhibited higher basketball performance efficiency indicators. Although higher-performance clusters showed numerically higher winning proportions in both contexts (large score differences: 66.7% vs. 42.9%; small score differences: 59.4% vs. 36.7%), chi-square analyses indicated that cluster membership was not significantly associated with game outcomes. Overall, the findings suggest that performance profiles in basketball are primarily differentiated by movement intensity and physical load characteristics, highlighting the importance of integrating both physical and technical performance indicators in basketball performance analysis.

Objectives: The analysis of basketball performance has increasingly incorporated advanced analytics and machine learning methods to better understand the factors that influence offensive efficiency and match dynamics. The present study aimed to identify performance profiles in basketball using unsupervised machine learning techniques and to examine the physical load and performance indicators that differentiate these profiles. Methods: Team-quarter observations from the Final 8 phase of the Greek U16 Basketball Championship were stratified into quarters with large score differences and quarters with small score differences according to the quarter-specific score differential and the sample median of 5 points. K-means clustering was applied separately to each dataset to identify latent performance patterns. Candidate solutions were evaluated using the Elbow method, Silhouette coefficient, Calinski–Harabasz Index, and Davies–Bouldin Index. Based on their combined interpretation, together with considerations of parsimony and practical interpretability, two-cluster solutions were retained for both datasets. Cluster stability was assessed using the Adjusted Rand Index (ARI), while t-distributed stochastic neighbor embedding (t-SNE) was used exclusively for visualization of the identified clusters. Results: Welch’s independent-samples t-tests with Benjamini–Hochberg false discovery rate (FDR) correction identified significant differences between clusters across several external load variables, including jump load, total distance covered, accumulated acceleration load, and distance covered in different speed zones (pFDR < 0.001). Clusters characterized by higher movement intensity also exhibited higher values for basketball performance and offensive-efficiency indicators. Although higher-performance clusters showed numerically higher winning proportions in both contexts (large score differences: 70.0% vs. 45.7%; small score differences: 56.7% vs. 40.6%), chi-square analyses indicated that cluster membership was not significantly associated with quarter outcomes. Conclusions: Overall, the findings suggest that performance profiles in basketball are primarily differentiated by external-load characteristics, particularly movement intensity, and offensive-performance indicators, highlighting the importance of integrating both physical and technical performance indicators in basketball performance analysis.

The aim of the present study was to examine the potential effects of circadian rhythmicity on specific external load metrics in professional male soccer players. Twenty players, members of a Greek Super League team, participated in the study. Data were collected from twenty match-day-minus-one (MD-1) training sessions, performed either in the morning (10:30 h, n = 10) or afternoon (15:00 h, n = 10). All sessions followed identical structure, volume, and intensity, ensuring internal validity. The external load metrics examined were total distance (TD), high-speed running (HSR; >19.8 km/h), high-intensity-acceleration count (HIACC; >3 m/s²), high-intensity-deceleration count (HIDEC; <−3 m/s²), and sprint distance (SD; >25.2 km/h). Statistical analysis was conducted using paired t-tests with Bonferroni correction for multiple comparisons (α = 0.01). Our results revealed significantly higher TD, HSR, HIACC, and HIDEC values (p < 0.01) during morning sessions compared to afternoon, suggesting a diurnal pattern favoring morning performance. No significant difference was found for SD (p > 0.01) despite the observed tendency for higher morning-values. These findings support the notion that time of day influences training output and that habitual morning training may lead to improved physical performance due to training-induced adaptations. Since in elite soccer even minor changes in performance could be decisive for the outcome of a competition, accounting for circadian factors in training planning could be crucial for competition performance.