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.

The development of chronic, low-grade systemic inflammation in the elderly (inflammaging) has been associated with increased incidence of chronic diseases, geriatric syndromes, and functional impairments. The aim of this study was to examine differences in habitual physical activity (PA), dietary intake patterns, and musculoskeletal performance among community-dwelling elderly men with low and elevated systemic inflammation. Nonsarcopenic older men free of chronic diseases were grouped as ‘low’ (LSI: n = 17; 68.2 ± 2.6 years; hs-CRP: 1 mg/L) systemic inflammation according to their serum levels of high-sensitivity CRP (hs-CRP). All participants were assessed for body composition via Dual Emission X-ray Absorptiometry (DEXA), physical performance using the Short Physical Performance Battery (SPPB) and handgrip strength, daily PA using accelerometry, and daily macro- and micronutrient intake. ESI was characterized by a 2-fold greater hs-CRP value than LSI (p < 0.01). The two groups were comparable in terms of body composition, but LSI displayed higher physical performance (p < 0.05), daily PA (step count/day and time at moderate-to-vigorous PA (MVPA) were greater by 30% and 42%, respectively, p < 0.05), and daily intake of the antioxidant vitamins A (6590.7 vs. 4701.8 IU/day, p < 0.05), C (120.0 vs. 77.3 mg/day, p < 0.05), and E (10.0 vs. 7.5 mg/day, p < 0.05) compared to ESI. Moreover, daily intake of vitamin A was inversely correlated with levels of hs-CRP (r = −0.39, p = 0.035). These results provide evidence that elderly men characterized by low levels of systemic inflammation are more physically active, spend more time in MVPA, and receive higher amounts of antioxidant vitamins compared to those with increased systemic inflammation.

This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method ($ReSuMe$), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of $83.4\%$, demonstrating the approach’s efficacy in precise movement activity classification. This method’s significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs’ superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare.

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.

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.

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.