Aim: This scoping review examines the application of artificial intelligence (AI) in sports biomechanics, with a focus on enhancing performance and preventing injuries. The review addresses key research questions, including primary AI methods, their effectiveness in improving athletic performance, their potential for injury prediction, sport-specific applications, strategies for translating knowledge, ethical considerations, and remaining research gaps. Following the PRISMA-ScR guidelines, a comprehensive literature search was conducted across five databases (PubMed/MEDLINE, Web of Science, IEEE Xplore, Scopus, and SPORTDiscus), encompassing studies published between January 2015 and December 2024. After screening 3248 articles, 73 studies met the inclusion criteria (Cohen’s kappa = 0.84). Data were collected on AI techniques, biomechanical parameters, performance metrics, and implementation details. Results revealed a shift from traditional statistical models to advanced machine learning methods. Based on moderate-quality evidence from 12 studies, convolutional neural networks reached 94% agreement with international experts in technique assessment. Computer vision demonstrated accuracy within 15 mm compared to marker-based systems (6 studies, moderate quality). AI-driven training plans showed 25% accuracy improvements (4 studies, limited evidence). Random forest models predicted hamstring injuries with 85% accuracy (3 studies, moderate quality). Learning management systems enhanced knowledge transfer, raising coaches’ understanding by 45% and athlete adherence by 3.4 times. Implementing integrated AI systems resulted in a 23% reduction in reinjury rates. However, significant challenges remain, including standardizing data, improving model interpretability, validating models in real-world settings, and integrating them into coaching routines. In summary, incorporating AI into sports biomechanics marks a groundbreaking advancement, providing analytical capabilities that surpass traditional techniques. Future research should focus on creating explainable AI, applying rigorous validation methods, handling data ethically, and ensuring equitable access to promote the widespread and responsible use of AI across all levels of competitive sports.

Background: Healthcare data interoperability faces significant barriers, including regulatory compliance complexities, institutional trust deficits, and technical integration challenges. Current centralized architectures demonstrate inadequate mechanisms for balancing data accessibility requirements with patient privacy protection, as mandated by HIPAA and GDPR frameworks. Traditional compliance approaches rely on manual policy implementation and periodic auditing, which are insufficient for dynamic, multi-organizational healthcare data-sharing scenarios. Objective: This study develops and proposes a blockchain-based healthcare data management framework that leverages Hyperledger Fabric, IPFS, and the HL7 FHIR standard and incorporates automated regulatory compliance mechanisms via smart contract implementation to meet HIPAA and GDPR requirements. It assesses the theoretical system architecture, security characteristics, and scalability considerations. Methods: We developed a permissioned blockchain architecture that employs smart contracts for privacy policy enforcement and for patient consent management. The proposed system incorporates multiple certification authorities for patients, hospitals, and research facilities. Architectural evaluation uses theoretical modeling and system design analysis to assess a system’s security, compliance, and scalability. Results: The proposed framework demonstrated enhanced security through decentralized control mechanisms and cryptographic protection protocols. Smart contract-based compliance verification can automate routine regulatory tasks while maintaining human oversight in complex scenarios. The architecture supports multi-organizational collaboration with attribute-based access control and comprehensive audit-trail capabilities. Conclusions: Blockchain-based healthcare data-sharing systems provide enhanced security and decentralized control compared with traditional architectures. The proposed framework offers a promising solution for automating regulatory compliance. However, implementation considerations—including organizational readiness, technical complexity, and scalability requirements—must be addressed for practical deployment in healthcare settings.

This study explores the association of respiratory muscle strength with aerobic endurance kinetics among athletes, with a specific focus on maximal oxygen consumption (VO₂max). Previous research has elucidated the complex interactions between respiratory and skeletal muscles during exercise, highlighting the critical role of efficient respiration in maximizing athletic performance. The interplay between active skeletal muscles and respiratory muscles, especially the influence of respiratory muscle fatigue on exercise capacity, is well-documented. High-intensity exercise has been shown to activate the respiratory muscle metaboreflex, which can restrict blood flow to working muscles, thereby impacting the energy required for respiration. A total of 41 athletes, drawn from the disciplines of biathlon, judo, and cross-country, participated in this study. Respiratory function tests (RFTs) were administered to assess various respiratory parameters, including changes in chest circumference. Additionally, maximal oxygen consumption (VO₂max) and heart rate were measured during a treadmill test. To explore the associations between VO2max and ventilatory parameters—namely, ventilation (VE), oxygen consumption (VO₂), carbon dioxide production (VCO₂)—as well as respiratory metrics, linear regression analysis was employed. Based on the standardized regression coefficients (β), it was found that maximum expiratory pressure (MEP) (mean ± SD: 130.95 ± 42.82) and inspiratory diaphragmatic circumference values were significantly associated with VE, VO₂, and VCO₂. Conversely, the other predictor variables did not exhibit a significant effect on VE (mean ± SD: 134.80 ± 36.69), VO₂ (mean ± SD: 3877.52 ± 868.47 mL), and VCO₂ (mean ± SD: 4301.27 ± 1001.07 mL). Similarly, measurements of chest circumference (mean ± SD: 91.40 ± 10.72 cm), MEP, and diaphragmatic circumference during inspiration (mean ± SD: 95.20 ± 10.21 cm) were significantly associated with VO₂max (mean ± SD: 58.52 ± 10.74 mL/kg/min), while the remaining predictor variables did not demonstrate a significant effect on VO₂max. Additionally, a multiple linear regression analysis was conducted to examine the combined effects of respiratory muscle strength and ventilatory factors on VO₂max. The model, which included interaction terms, explained 89.9% of the variance in VO₂max (R² = 0.899, adjusted R² = 0.859). Significant interactions were found between MIP and VE (B = −0.084, p = 0.006), as well as MEP and VE (B = 0.072, p = 0.012). These findings suggest that respiratory muscle strength plays a more substantial role in determining VO₂max in individuals with higher ventilatory efficiency, highlighting the importance of both respiratory strength and breathing efficiency in aerobic performance. Our findings underscore the importance of considering respiratory muscle strength in assessing and enhancing athletes’ aerobic performance. Integrating objective measurements such as maximal inspiratory and expiratory pressure assessments into routine performance evaluations allows coaches and sports scientists to monitor changes in respiratory function over time and adjust training protocols accordingly.

Background: Healthcare 5.0 represents a shift toward intelligent, human-centric care systems. Intensive care units generate vast amounts of data that require real-time decisions, but current decision support systems lack comprehensive frameworks for safe integration of artificial intelligence. Objective: We developed and validated the Learn–Predict–Monitor–Detect–Correct (LPMDC) framework as a methodology for systematic artificial intelligence integration across the critical care workflow. The framework improves predictive analytics, continuous patient monitoring, intelligent alerting, and therapeutic decision support while maintaining essential human clinical oversight. Methods: Framework development employed systematic theoretical modeling integrating Healthcare 5.0 principles, comprehensive literature synthesis covering 2020–2024, clinical workflow analysis across 15 international ICU sites, technology assessment of mature and emerging AI applications, and multi-round expert validation by 24 intensive care physicians and medical informaticists. Each LPMDC phase was designed with specific integration requirements, performance metrics, and safety protocols. Results: LPMDC implementation and aggregated evidence from prior studies demonstrated significant clinical improvements: 30% mortality reduction, 18% ICU length-of-stay decrease (7.5 to 6.1 days), 45% clinician cognitive load reduction, and 85% sepsis bundle compliance improvement. Machine learning algorithms achieved an 80% sensitivity for sepsis prediction three hours before clinical onset, with false-positive rates below 15%. Additional applications demonstrated effectiveness in predicting respiratory failure, preventing cardiovascular crises, and automating ventilator management. Digital twins technology enabled personalized treatment simulations, while the integration of the Internet of Medical Things provided comprehensive patient and environmental surveillance. Implementation challenges were systematically addressed through phased deployment strategies, staff training programs, and regulatory compliance frameworks. Conclusions: The Healthcare 5.0-enabled LPMDC framework provides the first comprehensive theoretical foundation for systematic AI integration in critical care while preserving human oversight and clinical safety. The cyclical five-phase architecture enables processing beyond traditional cognitive limits through continuous feedback loops and system optimization. Clinical validation demonstrates measurable improvements in patient outcomes, operational efficiency, and clinician satisfaction. Future developments incorporating quantum computing, federated learning, and explainable AI technologies offer additional advancement opportunities for next-generation critical care systems.

Background: Exercise capacity is markedly impaired in patients with end-stage renal disease (ESRD) due to neuromuscular dysfunction and reduced oxygen delivery. This study aimed to investigate the effects of NMES during HD on exercise capacity in patients with ESRD. It specifically examined neuromuscular and hemodynamic adaptations. Methods: Twenty-two patients with ESRD were randomized to a neuromuscular electrical stimulation training group (NSTG, n = 11) or a control group (CG, n = 11). The NSTG underwent intradialytic quadriceps NMES three times per week for 12 weeks (40 min/session). Exercise capacity was evaluated via sustained isometric contraction at 50% of maximal voluntary contraction (MVC) until exhaustion. Neuromuscular function was assessed through voluntary activation (ΔVA) and potentiated twitch force (ΔQ_tw,pot), while muscle oxygenation (ΔO₂Hb, ΔHHb, ΔTHb) of the vastus lateralis was continuously monitored using near-infrared spectroscopy. Results: After the intervention, the NSTG showed a significant +20% increase in Tlim (103.9 ± 14.4 s to 123.3 ± 16.6 s; p = 0.01) and +30% improvement in MVC (421.3 ± 24.9 N to 550.4 ± 20.3 N; p < 0.01), while no improvements were observed in CG. Resting VA increased by ≈7% in NSTG (90.2 ± 3.7% to 96.8 ± 2.5%; p = 0.012). Improved muscle oxygenation and reduced twitch force suggest enhanced oxidative capacity and greater peripheral fatigue tolerance. Conclusions: Intradialytic NMES elicited robust improvements in exercise capacity, muscle strength, and oxygen utilization in ESRD patients by enhancing both central activation and peripheral oxidative adaptations. These findings support NMES as a feasible and effective rehabilitative strategy to counteract fatigue and functional decline in the ESRD population.

The influence of circadian rhythms and menstrual cycle phases on athletic performance and psychological responses is critical for optimizing training and competition strategies for female athletes. This study aimed to investigate the effects of time of day and menstrual cycle phases on the physical performance and psychological responses of elite female Tunisian volleyball players. Thirteen elite female volleyball players were assessed during three different phases of their menstrual cycle (menstrual, follicular, and luteal) and at two different times of day (morning and evening). Physical performance was evaluated using the Modified Agility Test (MAT), Reactive Agility Test (RAT), and Repeated Sprint Ability (RSA) Test. Psychological responses were measured using the Profile of Mood States (POMS), Hooper’s Questionnaire, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, Vis-Morgen Questionnaire, and Spiegel Questionnaire. Significant effects of menstrual cycle, time of day, and competition on physical performance, cognitive function, mood, and sleep parameters were found. Physical performance, including the Countermovement Jump (CMJ), the Modified Agility T-test (MAT) and the Reactive Agility test (RAT), was higher in the afternoon than in the morning across all menstrual phases (CMJ: p < 0.001, η² = 0.836; MAT: p < 0.001, η² = 0.777; RAT: p < 0.001, η² = 0.859). After the competition, performance decreased significantly, especially in the follicular and luteal phases. As measured by the Stroop test, cognitive function showed significant diurnal effects (p < 0.001, η² = 0.910), with pre-competition performance being better in the afternoon. Mood disturbances (POMS) increased after the competition, especially in the morning and during the luteal phase (p < 0.001, η² = 0.961). Sleep parameters were significantly influenced by time and menstrual cycle, with higher fatigue (Hooper score: p < 0.001, η² = 0.754) and poorer sleep quality (PSQI: p < 0.001, η² = 0.627) in the morning, especially after the competition. Our results suggest that aligning high-intensity training and competitions with afternoon circadian peaks may enhance physical and cognitive performance in elite female athletes. Recovery strategies and workload adjustments should account for menstrual phases, particularly reducing morning demands during the luteal phase to mitigate fatigue and mood disruptions. Integrating circadian timing with menstrual cycle monitoring offers a practical, evidence-based approach to optimize athlete readiness and resilience.