Background: Maturation is a complex biological process affecting all tissues, organs, and systems, particularly during adolescence. The Peak Height Velocity (PHV) period, a hallmark of adolescent growth spurts, is associated with individual differentiations in the development of performance attributes amongst youth. Understanding the influence of sports participation on strength and power during the PHV period is essential for optimizing training outcomes and reducing injury risk. This scoping review synthesizes the literature on the strength and power development in athletes during the PHV period across various sports, highlighting the interaction between maturation, training, and performance outcomes. Methods: A systematic search of PubMed and Scopus, supplemented by manual searches, identified peer-reviewed studies from 2004 to 2025. The included longitudinal studies involved structured training and assessed strength-related performance during and around the PHV period. PRISMA-ScR guidelines were followed. Results: Twelve studies met the inclusion criteria. It is found that strength and power are significantly affected during the PHV period and participation in sports mitigates these effects. Training characteristics such as training frequency/volume and sport specificity were key factors. Early maturers often outperformed late maturers, though adolescent awkwardness temporarily reduced performance around PHV. Conclusions: Systematic participation in sports training can limit the phenomenon of adolescent awkwardness in the performance of strength and power. Hence, training programs should be tailored to maturity status, emphasizing skill development, strength training, and injury prevention. Future research should explore individualized training and the mechanisms underlying performance variability during the PHV period.

Maturation is a key factor in sports participation and often determines the young athletes’ characterization as a talent. However, there is no evidence of practical models for understanding the factors that discriminate children according to maturity. Hence, this study aims to deepen the understanding of the factors that affect maturity in 11-year-old Team Sports Athletes by utilizing explainable artificial intelligence (XAI) models. We utilized three established machine learning (ML) classifiers and applied the Sequential Forward Feature Selection (SFFS) algorithm to each. In this binary classification task, the logistic regression (LR) classifier achieved a top accuracy of 96.67% using the seven most informative factors (Sitting Height, Father’s Height, Body Fat, Weight, Height, Left and Right-Hand Grip Strength). The SHapley Additive exPlanations (SHAP) model was instrumental in identifying the contribution of each factor, offering key insights into variable importance. Independent sample t-tests on these selected factors confirmed their significance in distinguishing between the two classes. By providing detailed and personalized insights into child development, this integration has the potential to enhance the effectiveness of maturation prediction significantly. These advancements could lead to a transformative approach in young athletes’ pediatric growth analysis, fostering better sports performance and developmental outcomes for children.

Occupational therapy (OT) is vital in improving functional outcomes and aiding recovery for individuals with long-term disabilities, particularly those resulting from neurological diseases. Traditional assessment methods often rely on clinical judgment and individualized evaluations, which may overlook broader, data-driven insights. The integration of artificial intelligence (AI) presents a transformative opportunity to enhance assessment precision and personalize therapeutic interventions. Additionally, advancements in human–computer interaction (HCI) enable more intuitive and adaptive AI-driven assessment tools, improving user engagement and accessibility in OT. This scoping review investigates current applications of AI in OT, particularly regarding the evaluation of functional outcomes and support for clinical decision-making. The literature search was conducted using the PubMed and Scopus databases. Studies were included if they focused on AI applications in evaluating functional outcomes within OT assessment tools. Out of an initial pool of 85 articles, 13 met the inclusion criteria, highlighting diverse AI methodologies such as support vector machines, deep neural networks, and natural language processing. These were primarily applied in domains including motor recovery, pediatric developmental assessments, and cognitive engagement evaluations. Findings suggest that AI can significantly improve evaluation processes by systematically integrating diverse data sources (e.g., sensor measurements, clinical histories, and behavioral analytics), generating precise predictive insights that facilitate tailored therapeutic interventions and comprehensive assessments of both pre- and post-treatment strategies. This scoping review also identifies existing gaps and proposes future research directions to optimize AI-driven assessment tools in OT.

The purpose of this study was to compare the internal and external load in continuous and intermittent small-sided games (SSG) formats. Eight semi-professional soccer players participated in the study, and they completed three protocols: (a) I-intermittent SSG protocol (Int-I, 4 sets of 4 min with a 3 min recovery); (b) Continuous SSG protocol (Con, 2 sets of 8 min with a 3 min recovery); (c) II-SSG protocol (Int-II, 4 sets of 4 min, where each set includes 1 min of exercise with varying recovery periods (10, 20, 30 s), with a 3 min recovery period between sets). A one-way analysis of variance (ANOVA) was used to analyze the dependent variables, with significance determined at p < 0.05. The three protocols differed in total distance covered and in distance covered at speeds >19 km/h, with the Int-II protocol resulting in the greatest distance covered (p < 0.05). Additionally, players in the Con protocol exercised at a higher percentage of their maximum heart rate (%HRmax) (p < 0.05), while the highest RPE value was observed in the Int-I interval protocol (p < 0.05). The external load experienced by players in intermittent SSG protocols is higher, while internal load (%HRmax) remains relatively low. This effect is especially notable in the new intermittent exercise model proposed in this study, which incorporates progressively increasing recovery times within each exercise set. Coaches can use this model to increase players’ external load without causing a heightened perception of fatigue.

Occupational therapy (OT) is a client-centered health profession focused on enhancing individuals’ ability to perform meaningful activities and daily tasks, particularly for those recovering from injury, illness, or disability. As a core component of rehabilitation, it promotes independence, well-being, and quality of life through personalized, goal-oriented interventions. Identifying and measuring the role of artificial intelligence (AI) in the human–computer interaction (HCI) within OT is critical for improving therapeutic outcomes and patient engagement. Despite AI’s growing significance, the integration of AI-driven HCI in OT remains relatively underexplored in the existing literature. This scoping review identifies and maps current research on the topic, highlighting applications and proposing directions for future work. A structured literature search was conducted using the Scopus and PubMed databases. Articles were included if their primary focus was on the intersection of AI, HCI, and OT. Out of 55 retrieved articles, 26 met the inclusion criteria. This work highlights three key findings: (i) machine learning, robotics, and virtual reality are emerging as prominent AI-driven HCI techniques in OT; (ii) the integration of AI-enhanced HCI offers significant opportunities for developing personalized therapeutic interventions; (iii) further research is essential to evaluate the long-term efficacy, ethical implications, and patient outcomes associated with AI-driven HCI in OT. These insights aim to guide future research efforts and clinical applications within this evolving interdisciplinary field. In conclusion, AI-driven HCI holds considerable promise for advancing OT practice, yet further research is needed to fully realize its clinical potential.

Objectives: The early detection of low bone mineral density (BMD) is essential for preventing osteoporosis and related complications. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its cost and limited availability restrict its use in large-scale screening. This study investigated whether raw bioelectrical impedance analysis (BIA) data combined with explainable machine learning (ML) models could accurately classify osteopenia in women aged 40 to 55. Methods: In a cross-sectional design, 138 women underwent same-day BIA and DXA assessments. Participants were categorized as osteopenic (T-score between −1.0 and −2.5; n = 33) or normal (T-score ≥ −1.0) based on DXA results. Overall, 24.1% of the sample were classified as osteopenic, and 32.85% were postmenopausal. Raw BIA outputs were used as input features, including impedance values, phase angles, and segmental tissue parameters. A sequential forward feature selection (SFFS) algorithm was employed to optimize input dimensionality. Four ML classifiers were trained using stratified five-fold cross-validation, and SHapley Additive exPlanations (SHAP) were applied to interpret feature contributions. Results: The neural network (NN) model achieved the highest classification accuracy (92.12%) using 34 selected features, including raw impedance measurements, derived body composition indices such as regional lean mass estimates and the edema index, as well as a limited number of categorical variables, including self-reported physical activity status. SHAP analysis identified muscle mass indices and fluid distribution metrics, features previously associated with bone health, as the most influential predictors in the current model. Other classifiers performed comparably but with lower precision or interpretability. Conclusions: ML models based on raw BIA data can classify osteopenia with high accuracy and clinical transparency. This approach provides a cost-effective and interpretable alternative for the early identification of individuals at risk for low BMD in resource-limited or primary care settings.