Background: Smartwatches, equipped with advanced sensors, have become increasingly prominent in health and fitness domains. Their integration with machine learning (ML) algorithms presents novel opportunities for personalized exercise prescription and physiological monitoring. Objective: This systematic review aimed to evaluate the effectiveness, limitations, and practical applications of smartwatch-ML systems in delivering tailored fitness interventions and health tracking. Methods: Following PRISMA guidelines, five databases (PubMed, Scopus, IEEE Xplore, Web of Science, and SPORTDiscus) were searched for studies published from January 2000 to December 2023. Inclusion criteria required empirical studies involving human participants, the use of smartwatches for exercise monitoring or prescription, and the application of ML algorithms. Forty-nine studies met the eligibility criteria and were synthesized narratively using thematic clustering. Results: The majority of included studies demonstrated high algorithmic performance in activity recognition (>98% accuracy) and vital sign tracking. However, external validity was often limited due to lab-based testing, narrow demographic representation, and lack of standardized evaluation frameworks. Few studies incorporated explainable artificial intelligence, behavioral adaptation, or longitudinal validation. Ethical and regulatory considerations were rarely addressed. Conclusion: Smartwatch-ML integration holds substantial promise for individualized, real-time health support, especially in fitness and rehabilitation. To ensure broader impact and clinical adoption, future research must address generalizability, ethical data governance, interpretability, and interdisciplinary system design.

Pediatric neurological disorders such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are increasingly understood to arise from complex interactions between genetics and environmental factors. Emerging evidence highlights the role of epigenetic mechanisms, particularly DNA methylation, histone modification, and non-coding RNAs, in mediating how early-life exposures shape brain development. This review synthesizes findings from 48 recent studies, including human cohorts and animal models, linking prenatal stress, maternal nutrition, endocrine disruptors, and air pollution to epigenetic alterations that disrupt neurogenesis, synaptic plasticity, and neural differentiation. These mechanisms offer insight into clinical phenomena and open potential for early screening and epigenetic interventions in pediatric neurology. The main finding reveals that early environmental exposures can leave lasting epigenetic marks that significantly influence neurodevelopment and contribute to pediatric neurological disorders.

Neurofeedback, a technique enabling individuals to regulate their brain activity in real time, has gained momentum as both a clinical intervention and a tool for cognitive and performance enhancement. This review synthesizes findings from 65 studies to evaluate the current state of neurofeedback research. We outline its historical development, methodological approaches, and technological innovations, including advances in connectivity‐based and multimodal feedback paradigms. Applications across clinical disorders, such as attention‐deficit/hyperactivity disorder (ADHD), post‐traumatic stress disorder (PTSD), depression, and autism, as well as performance optimization, are critically examined, with emphasis on efficacy, limitations, and translational challenges. To enhance transparency, we summarize methodological trends and provide integrative insights that cut across individual studies. We further discuss persistent limitations, including methodological heterogeneity and placebo‐related concerns, and highlight future directions such as personalization, multimodal integration, and interdisciplinary collaboration. By consolidating evidence across diverse domains, this review positions neurofeedback as a rapidly evolving field with significant therapeutic and translational potential.

This review explores the integration of artificial intelligence (AI) in supply chain management (SCM), providing a comprehensive overview of its applications across key functions such as procurement, production, inventory management, logistics, and customer service. AI technologies, including machine learning, predictive analytics, robotics, natural language processing, computer vision, blockchain, and the Internet of Things, are highlighted for their potential to enhance efficiency, reduce costs, and improve decision-making within supply chains. The paper also examines the challenges associated with AI adoption, including technical difficulties, data privacy concerns, and ethical considerations. Through a critical analysis of current literature and case studies, this review identifies key research gaps and suggests future directions for the development and implementation of AI in SCM. The findings underscore the transformative potential of AI while emphasizing the need for responsible adoption to address technical, organizational, and ethical issues.

Neurodegenerative dementias, including Alzheimer’s disease and vascular dementia, have long been viewed through a neuron-centric lens. However, growing evidence highlights the indispensable and multifaceted roles of glial cells, astrocytes, microglia, and oligodendrocytes in both the onset and progression of these disorders. While prior reviews have cataloged glial dysfunction in isolation, this review offers a novel, integrative framework that maps the interconnected roles of glial subtypes across molecular, cellular, and circuit-level pathology in dementia. We critically synthesize recent advances in single-cell RNA sequencing, spatial transcriptomics, and glial imaging to redefine glial heterogeneity and function in disease states. Special emphasis is placed on the dynamic cross talk between glial populations and the feedback loops that govern their dual roles in neuroprotection and neurodegeneration. Furthermore, we examine emerging therapeutic strategies targeting glial-specific pathways, including NF-κB, JAK/STAT, CSF1R, and TREM2 signaling, as well as remyelinating agents and stem cell–based interventions. By integrating glial biology with therapeutic innovation, this review positions glial cells not as supporting actors but as central regulators and potential gatekeepers of dementia pathogenesis and treatment.

This review paper examines the intersection of wearable technology, machine learning algorithms, and exercise prescription, focusing on the utilization of smartwatches to monitor physiological data during workouts. With the proliferation of smartwatches equipped with sensors capable of capturing various biometric parameters, alongside the advancements in machine learning, personalized exercise recommendations have become increasingly feasible. Through a synthesis of existing literature and analysis of recent developments, this paper explores the potential of integrating wearable technology and artificial intelligence to optimize exercise routines tailored to individual needs and goals. Key topics covered include the types of sensors found in smartwatches, machine learning algorithms used for exercise prescription, practical applications, challenges, and future directions. By providing insights into the current landscape and emerging trends, this review aims to inform researchers, practitioners, and policymakers on the opportunities and challenges in leveraging wearable technology and machine learning for personalized fitness monitoring and exercise prescription.