Search Results (1,336)

Background/Objectives: Despite existing guidelines on limiting screen time, children and adolescents are spending an increasing number of hours in front of digital devices. This increase raises concerns about long-term health consequences, particularly in the context of the growing prevalence in childhood overweight and obesity. The aim of this review is to present the current state of knowledge regarding the relationship between increased screen time and weight-related health outcomes in the pediatric population. It also highlights the need for health education targeting both children and parents, based primarily on developing skills to manage time spent in front of electronic screens. Methods: A narrative review of the literature was conducted in the MEDLINE (PubMed), Web of Science and Scopus databases, including studies published between 2015 and 2025. Results: Available evidence suggests that prolonged screen exposure may be associated with reduced physical activity, circadian rhythm disturbances, including sleep problems, increased stress levels, and adverse mental health outcomes. These factors may interact and reinforce one another, potentially contributing to a positive energy balance and an increased risk of overweight and obesity among young people. Conclusions: The evidence reviewed highlights the ongoing digitalisation of younger generations and the potential consequences of excessive screen use. The underlying mechanisms appear complex and multidirectional. However, methodological heterogeneity across studies underscores the need for well-designed longitudinal and intervention research. Full article

Sleep-related breathing disorders (SRBDs) encompasses a spectrum of conditions that disrupt ventilation during sleep, leading to fragmented sleep and impaired gas exchange. Their high prevalence and substantial neurocognitive and mental health outcomes make SRBD clinically significant across multiple medical disciplines. Traditional management includes lifestyle modifications and positive airway pressure (PAP). When non-surgical measures fail or anatomical factors predominate, a range of surgical approaches may be employed, such as uvulopalatopharyngoplasty (UPPP) or maxillomandibular advancement (MMA). There are many notable emerging surgical advancements, such as hypoglossal nerve stimulation (HNS), transoral robotic surgery (TORS), and minimally invasive radiofrequency technologies (RFA), that have offered improved outcomes for select patients. Advances in diagnostic tools, such as portable home sleep technologies and drug-induced sleep endoscopy (DISE), further support precision-based care. Collectively, the expanding range of therapeutic and diagnostic innovations is enabling clinicians to deliver individualized care and improve long-term outcomes for patients with SRBD. Full article

Background: Stroke remains a leading cause of death and long-term disability worldwide. Sleep disturbances are increasingly recognized as potential factors influencing recovery after stroke. Therefore, in this study we examined the associations of sleep disturbances and metabolic markers with post-stroke outcomes. Methods: We conducted a retrospective study of adult patients with stroke-related presentations. The primary analysis included 270 patients with follow-up mRS data. Extracted variables included demographics, vascular risk factors, stroke subtype, imaging findings, sleep features, and selected laboratory markers. Functional outcome was classified as favorable, mRS 0–2, or unfavorable, mRS 3–6. Recurrent stroke burden was analyzed as 0–1 versus ≥2 documented events. Associations and predictive performance were assessed using group comparisons, LASSO logistic regression, and random forest models with repeated 10-fold cross-validation. Results: Among 270 stroke patients, 214 (79.3%) had favorable outcomes and 56 (20.7%) had unfavorable outcomes. Sleep disturbances were common, especially nocturnal awakenings (59.6%), increased sleep apnea risk (44.4%), circadian rhythm disturbances (28.9%), and insomnia (23.7%). Unfavorable outcomes were linked to older age, cardio-aortic embolism, large vessel/cortical stroke, abnormal vascular imaging, insomnia, and lower HDL. In LASSO analysis, age, steno-occlusive/atherosclerotic imaging, cardio-aortic embolism, and insomnia predicted unfavorable outcome, while HDL was protective. For recurrent stroke, small artery occlusion and hypertension with diabetes were retained. In predictive modeling, the best random forest model showed good discrimination AUC values of 0.791 ± 0.0126. Conclusions: Poorer stroke outcomes were associated with vascular factors, insomnia, and low HDL; recurrent events were mainly associated with small artery occlusion. Full article

Urban environments expose individuals to multiple stressors, including air pollution and noise, which significantly impact health by causing cardiovascular and respiratory diseases and sleep disruption. Effective monitoring of these stressors through intelligent sensing technologies can support the mitigation of long-term deterioration in both physical and mental health. In this context, this pilot study presents a multimodal approach that integrates environmental sensing and physiological monitoring to assess stress responses of the human body to urban conditions. Indoor and outdoor air quality were measured using smart sensor nodes that captured particulate matter (PM₁, PM_2.5, PM₄, PM₁₀), air temperature and relative humidity. The physiological response to urban noise exposure was evaluated using electrodermal activity (EDA) and heart rate variability (HRV) acquired via a wearable biomedical device, while sound pressure levels (dBA) were measured using a professional sound level meter. Preliminary results indicate that indoor particulate matter concentrations greatly exceeded outdoor levels, despite outdoor sensors being deployed in a high-traffic urban environment. Physiological analysis revealed increased tonic electrodermal activity under noise exposure, indicating increased sympathetic activation. Complementary HRV analysis showed elevated heart rate (HR), reduced parasympathetic activity, and increased sympathetic dominance under high-noise conditions, confirming a measurable physiological stress response to urban environmental exposure. Full article

Wearable electroencephalography (EEG) is rapidly evolving toward lightweight, user-friendly systems that enable brain monitoring in naturalistic settings. Traditional multi-channel, gel-based systems provide broad scalp coverage and high signal fidelity but are impractical for unsupervised or long-term use. This review focuses on the emerging generation of smart wearable EEG devices that are easy to wear, require minimal setup, and typically integrate additional physiological sensors such as photoplethysmography (PPG), temperature, or motion sensors. We review wearable EEG systems across four main form factors: head-worn EEG devices, smart EEG patches and tattoos, in-ear and headphone-based EEG, and glasses-integrated EEG. Head-worn systems offer broader signal coverage and support more complex applications such as sleep staging, human–machine interaction, and epilepsy monitoring. Patch-based systems are well suited to comfortable long-term monitoring, particularly in sleep-related applications. Ear-center systems provide high user comfort and stable signal acquisition from non-traditional electrode locations. Glasses-integrated devices represent an emerging option for unobtrusive daytime neurophysiology. Each category is examined in terms of sensor fusion, technical parameters, and embedded algorithms, with particular emphasis on automated signal analysis. We conclude with a discussion on current limitations, regulatory and usability challenges, and future directions toward unobtrusive, AI-powered neurotechnology for home and clinical use. Full article

Insufficient sleep represents a public health challenge affecting the majority of adolescents in industrialized societies, with fewer than 30% of high school students obtaining the recommended 8–10 h of nightly sleep. Chronic effects of insufficient sleep have been shown to affect a myriad of functions including brain development, metabolism, mental health, school performance, and social relationships. While these biological and functional consequences of adolescent insufficient sleep have been addressed in prior reviews, no review has systematically examined the effects of insufficient sleep on structural brain development. Herein, we address this gap by reviewing the growing magnetic resonance imaging (MRI) literature linking adolescent sleep patterns to brain morphology. This review systematically synthesizes study findings from MRI sequences spanning diffusion tensor imaging (DTI), voxel-based morphometry, and structural volumetrics, including gray matter, white matter, and cortical thickness as it relates to sleep duration, sleep regularity, and sleep quality. In summary, results from these studies link insufficient sleep to measurable alterations in white matter microstructure, cortical thickness, and gray matter volume during adolescence, establishing detectable effects of brain morphology during a period of heightened neurobiological vulnerability. Protecting adolescent sleep represents an investment in both immediate well-being and long-term health outcomes extending into adulthood. Full article

Objective: Childhood abuse and neglect can have lasting impacts on mental and physical health. These effects may be amplified in rural and/or under-resourced communities, resulting in heightened anxiety, depression, and physical health problems. This study tested associations between childhood abuse/neglect with self-reported physical symptoms, with depressive and anxiety symptoms serving as potential mediators in this association. Participants: Participants (N = 606) were undergraduate college students living in the southeastern United States. Methods: Participants completed measures assessing childhood abuse/neglect, anxiety and depressive symptoms, and self-rated health, pain, fatigue, and sleep quality. Results: Anxiety symptoms mediated the association between emotional, physical, and sexual abuse with all self-reported health outcomes. Depressive symptoms mediated associations between emotional and physical abuse with all health outcomes. Conclusions: Results highlight the importance of increasing treatment access among college-aged adults with abuse histories to help mitigate its long-term effects on physical and mental health as individuals age. Full article

Background/Objectives: Atrial fibrillation (AF) imposes a substantial long-term clinical and healthcare system burden, recurrent hospitalizations, impaired quality of life, and increasing long-term healthcare costs. Although patient-reported outcome measures (PROMs) are increasingly used in AF research and clinical practice, their broader role in healthcare delivery, implementation, and system-level decision-making remains insufficiently defined. Existing assessment strategies frequently prioritize symptom burden while underrepresenting cognitive, emotional, social, and functional dimensions of AF-related impairment. This narrative implementation review examines the current role of PROMs in AF management from a healthcare system and implementation perspective. Methods: Literature addressing AF-specific and generic PROM instruments, implementation strategies, health system integration, value-based care, and digital health approaches was reviewed and synthesized across PubMed, Scopus, and Google Scholar. Particular emphasis was placed on implementation barriers, workflow integration, evidence strength, and challenges encountered across diverse healthcare settings. Results: Current PROM frameworks incompletely capture several important dimensions of AF burden, including cognitive dysfunction, sleep disturbance, emotional distress, social participation, sexual health, and productivity loss. Beyond conventional symptom assessment, PROMs may support longitudinal patient monitoring, treatment evaluation, shared decision-making, and patient-centred care. Emerging evidence also suggests potential roles in outpatient prioritization, healthcare quality assessment, and value-based healthcare initiatives, although prospective AF-specific implementation studies remain limited. Mapping PROM applications to the 2024 ESC AF-CARE pathway demonstrates the strongest alignment with the Evaluation and Reducing symptoms domains while supporting patient engagement, comorbidity management, and individualized care planning. Implementation remains constrained by clinician workload, questionnaire fatigue, limited interoperability, heterogeneous digital infrastructure, and variability in organizational resources, with these challenges potentially being more pronounced in smaller or resource-limited healthcare systems. Conclusions: PROM integration in AF care may provide opportunities to strengthen patient-centered management and improve healthcare system responsiveness beyond conventional rhythm- and symptom-focused approaches. Successful implementation may require careful adaptation to local healthcare infrastructure, workflow feasibility, and long-term sustainability. Future developments involving digital platforms, wearable technologies, and artificial intelligence-assisted interpretation may further expand the clinical and operational relevance of PROM-guided AF care. Full article

Brain-derived neurotrophic factor (BDNF) is one of the most pleiotropic signaling molecules in mammalian biology, regulating processes ranging from neuronal survival and synaptic plasticity to metabolic homeostasis. Under physiological conditions, BDNF expression is tightly regulated; however, it may be disrupted by a variety of adverse factors, including chronic psychological stress, sleep deprivation, oxidative stress, inflammation, aging, and metabolic imbalance. Prolonged exposure to any of these factors can chronically reduce BDNF levels, contributing to numerous disorders whose systemic consequences remain difficult to define conclusively. This uncertainty arises because the available evidence is drawn from heterogeneous sources including many species, wild-type and various gene-knockout models, and pharmacological studies of differing specificity—yielding findings that are often inconsistent and difficult to compare. Consequently, the full spectrum of multisystemic effects resulting from long-term partial BDNF deficiency remains incompletely characterized. The SD-BDNFtm1sage rat line, developed by SAGE/Envigo/Inotiv using zinc finger nuclease technology, was created to fill this gap. Sprague–Dawley rats with a heterozygous genotype retain one functional allele of the Bdnf gene, resulting in a partial, permanent reduction in BDNF expression that persists throughout life. This chronic and moderate BDNF deficiency allows the animal to survive but is insufficient to maintain normal homeostasis, disrupting many physiological systems and behavioral responses. This review summarizes findings from studies using the SD-BDNFtm1sage rat line and shows that its phenotypic spectrum—susceptibility to mental disorders, sleep disturbances, metabolic abnormalities, altered nociception, and impaired neuromuscular adaptation—closely reflects the multisystemic consequences of chronic BDNF deficiency. This broad relevance makes the model particularly useful for research with potential medical applications. Full article

Exercise-induced reactive oxygen and nitrogen species (RONS) serve as crucial signaling molecules for training adaptation, mitochondrial biogenesis, and inflammatory resolution, rather than being mere markers of oxidative damage. Chronic or excessive high-dose antioxidant supplementation may suppress these vital redox-sensitive pathways. Consequently, this narrative review examines food-based antioxidant strategies as approaches for redox modulation, meaning support for recovery and redox homeostasis without indiscriminately suppressing exercise-induced redox signals that may contribute to training adaptation, while emphasizing the distinction between whole-food matrices and isolated supplements. A structured literature search was conducted across major electronic databases, including PubMed, Web of Science, Scopus, and SPORTDiscus. The search focused on intersecting themes of exercise physiology, redox biology, and sports nutrition. The reviewed evidence includes short-term human intervention studies, systematic reviews, meta-analyses, and mechanistic studies examining tart cherry, berries, pomegranate, cocoa, green tea, beetroot, extra virgin olive oil, and Mediterranean-style dietary patterns. Overall, the evidence suggests that these food-based strategies may influence recovery-related outcomes through mechanisms extending beyond direct radical scavenging, including inflammatory regulation, vascular function, and gut-derived metabolism; however, the strength and consistency of findings vary by food source, outcome, dose, timing, study population, dietary matrix, and bioavailability. Current literature does not support universal, fixed daily antioxidant use. Food-based strategies appear most appropriate during periods of elevated recovery demands, such as heavy training blocks, congested competition, muscle damage, or environmental stress. Food-based antioxidant nutrition should therefore be interpreted as a conceptual, evidence-informed approach to periodized and context-specific recovery support, rather than as a universal or evidence-graded guideline, because much of the available evidence derives from short-term and heterogeneous intervention studies. These strategies should complement foundational sports nutrition practices (energy availability, macronutrient distribution, hydration, and sleep) when balancing the preservation of long-term training adaptations with the need for acute recovery. Full article

Sleep monitoring and analysis are essential for understanding overall health, improving sleep quality, and detecting potential disorders early. This study presents a multimodal approach for automatic sleep staging and quality assessment using a reduced set of bio-signals: a single electroencephalographic (EEG) lead (F4–F3), a single EOG lead, and the photo-plethysmographic (PPG) signal. The proposed methodology includes a hierarchical sleep staging classifier, an automatic sleep staging algorithm, and a subject-specific Sleep Quality Index (SQI) for objective sleep quality assessment. The 5-class sleep staging classifier employs a cascaded architecture of two sequential 3-class models (Wake-REM-NREM and N1-N2-N3), trained and tested on multimodal features derived from physiological signals (EEG, EOG, and PPG) of the BOAS (Bitbrain Open Access Sleep) dataset. The resulting 5-class classifier achieved 90.8% accuracy with a reduced memory footprint (3.14 MB). To assess subject-independent generalization and prevent data leakage between training and test sets, a Leave-One-Subject-Out (LOSO) validation was performed, confirming the robustness of the proposed classifier across unseen subjects. The classifier was subsequently integrated into an automatic sleep staging algorithm. Validation on 14 unseen subjects yielded accuracies ranging from 80.26% to 91.99% using heuristic post-processing rules, while a Hidden Markov Model (HMM)-based approach further improved performance, reaching a peak accuracy of 91.99%. The proposed SQI combines sleep-related metrics extracted from staging, considering multiple sleep aspects (i.e., duration, intensity, and continuity-fragmentation). A calibration strategy was proposed to customize the SQI based on sleep scoring parameters and the subjective quality score derived from sleep diaries and questionnaires (PSQI). This subject-specific strategy was validated on a public dataset, optimizing weights across multiple nights, followed by an independent test on a subsequent night and demonstrating strong alignment between the calculated SQI and the subjective sleep quality score (MAE = 10.81). Finally, the framework provides resource-efficient sleep staging and custom quality estimation, validating its readiness for practical, long-term sleep monitoring. Full article

Background: Tick-borne encephalitis (TBE) is an endemic neuroinfectious disease prevalent in parts of Europe and is often associated with persistent neurological and cognitive sequelae. The aim of this study was to evaluate the long-term outcomes and predictors of post-encephalitic sequelae in adult patients with TBE in Latvia. Methods: A retrospective cohort with prospective follow-up was used that included 105 adult patients hospitalized with laboratory-confirmed TBE between 2018 and 2024. The patients’ clinical and demographic data were extracted from medical records, and reassessments were performed ≥6 months after discharge using structured clinical and neurological evaluations for neurocognitive, subjective, and neurological sequelae. Disease severity was classified using the Mickienė and Bogovič criteria, and sequelae severity was defined according to the Bohr criteria for post-encephalitic syndrome (PES). Results: Sequelae were observed in 52/105 (49.5%) patients and were more frequent in meningoencephalitis than in meningitis cases (18/25 [72.0%] vs. 33/77 [42.9%]). The most common persistent symptoms were impaired concentration (33/52 [63.5%]), fatigue (29/52 [55.8%]), and sleep disturbances (21/52 [40.4%]). Neurological sequelae included tremor (23/52 [44.2%]), vertigo (11/52 [21.2%]), and hearing impairment (5/52 [9.8%]). According to the Bohr criteria, most of the patients had mild sequelae (42/52 [80.8%]), while 10/52 [19.2%] had moderate sequelae; no severe cases were observed. In the multivariable analysis, increasing age was independently associated with greater sequelae severity (OR = 1.045 per year; 95% CI, 1.015–1.073; p = 0.003). Sex, comorbidities, biphasic disease, and length of hospital stay were not significant predictors. Acute neurological manifestations, particularly paresis (p = 0.002) and tremor (p = 0.019), were associated with worse outcomes. Although the disease severity scores correlated with sequelae in unadjusted analyses, neither the Mickienė nor the Bogovič classification independently predicted outcomes after adjustment. Conclusions: Nearly half of the hospitalized patients with TBE included in this study developed long-term sequelae, which were predominantly neurocognitive and mild in severity. Age was the primary independent predictor of worse outcomes, while acute neurological deficits such as paresis and tremor also indicated increased risk. These findings highlight the substantial burden of post-encephalitic syndrome and the need for structured long-term follow-up in TBE survivors. Full article

Background: Sedentary behavior is a major modifiable risk factor for chronic metabolic disorders, particularly type 2 diabetes mellitus (T2DM). Despite recommendations promoting regular physical activity (PA), adherence remains low. DIA/01 is a multidisciplinary study designed to promote healthy lifestyles for the prevention and management of T2DM, supporting healthcare systems. Methods: A total of 123 adults with T2DM diagnosed will be enrolled at the Diabetes Center of the University Hospital of Perugia throughout 2025. Inclusion criteria are age 25–80 years, ability to walk independently, being inactive, and BMI 18.5–40 kg/m². Exclusion criteria include severe cardiovascular, central nervous system, or musculoskeletal diseases contraindicating PA. Participants will be randomized into three groups: (1) standard care (SC); (2) SC plus theoretical PA counseling (TCPA); and (3) SC plus TCPA plus a 3-month supervised mixed exercise program. The assessment, conducted at baseline and at 6 and 12 months, includes total weekly PA (WPA) time, using IPAQ-SF and actigraphy. Moreover, glycated hemoglobin, sedentary time (ST), functional capacity, body composition, cardiometabolic risk factors, dietary adherence, perceived barriers and willingness to initiate PA, readiness to change, health-related quality of life, and sleep quality will be studied. This study is registered in the Clinical Trials Registry on 13 May 2026, with the identifier NCT07583355. Conclusions: Participants in groups (2) and (3) are expected to show greater improvements in WPA, reductions in ST, and favorable changes in metabolic and functional outcomes compared with SC. This approach may support long-term engagement in regular PA and contribute to improving the clinical management of T2DM. Full article

Background/Objectives: Childhood overweight and obesity represent a major global public health challenge, with increasing prevalence and significant long-term metabolic, cardiovascular, and psychosocial consequences. Standard pediatric weight-management strategies based on lifestyle modification often achieve modest and variable results, highlighting the need for more personalized and scalable approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance prevention, early risk stratification, and management of pediatric overweight and obesity. Methods: This narrative review was conducted through a structured search of PubMed, Scopus, and Web of Science for English-language studies published up to January 2026. The main search terms included “artificial intelligence”, “machine learning”, and “deep learning”, combined with “child”, “adolescent”, “pediatric”, “childhood obesity”, “pediatric overweight”, “body mass index”, “weight management”, “nutrition”, “diet”, “physical activity”, “lifestyle”, and “behavior change”. After title/abstract and full-text screening according to predefined eligibility criteria, the included studies were qualitatively synthesized and grouped by main application domains. The initial database search identified 412 records. After removal of 96 duplicates, 316 records were screened by title and abstract. Full-text assessment was subsequently performed for 175 potentially eligible articles. Following this evaluation, 51 studies met the eligibility criteria and were retained from the database search. Additional relevant articles were identified through manual screening of reference lists and related reviews, resulting in the final set of studies included in the narrative synthesis. Results: The review identified five main domains of AI application in pediatric weight management: risk assessment and prediction, dietary assessment and nutritional support, physical activity and lifestyle monitoring, behavioral and psychological support, and clinical decision support. Across the included literature, AI-based approaches were most frequently applied to predictive modeling using longitudinal BMI or growth trajectories, birth characteristics, parental BMI, sleep duration, physical activity, sedentary behavior, and family or socioeconomic factors. However, the evidence base was largely composed of observational and predictive-modeling studies, whereas interventional studies, real-world implementation studies, and long-term pediatric weight-outcome data remained limited. Conclusions: This narrative review indicates that AI has potential as a complementary tool within multidisciplinary, family-centered pediatric weight-management pathways, particularly for early risk stratification, personalized monitoring, and behavioral support. However, the findings also highlight that current evidence remains mainly exploratory and predictive rather than interventional. Further longitudinal, real-world, and ethically grounded research is required to confirm effectiveness, safety, clinical usefulness, and equitable implementation in pediatric populations. Full article

Background: Insomnia and stress-related sleep disorders are increasingly recognized as systemic conditions linked to the microbiota–gut–brain axis (MGBA). With growing clinical interest in natural products that modulate the gut environment, this systematic review evaluates the efficacy and mechanisms of non-pharmacological interventions, specifically probiotics, prebiotics, dietary indices, and botanicals, in alleviating insomnia, restoring circadian rhythms, and modulating neurochemical markers. Methods: In strict accordance with PRISMA 2020 guidelines, we searched PubMed, ScienceDirect, Scopus, and The Cochrane Library for English language studies published from inception to March 31, 2026. Eligibility was restricted to studies with rigorously controlled designs, specifically randomized controlled trials (RCTs) and controlled in vivo animal studies. Interventions had to target the gut microbiota, with primary outcomes measuring sleep quality (subjective or objective) or sleep-related neurochemical markers. We excluded uncontrolled, single-arm, or observational designs; in vitro studies; non-original research; and studies involving subjects with severe medical or psychiatric comorbidities (e.g., cancer, ADHD, severe psychiatric disorders) to prevent confounding variables, though mild-to-moderate anxiety and depression were permitted. Risk of bias was assessed using the Cochrane RoB 2.0 and SYRCLE tools. Due to significant methodological heterogeneity, a narrative synthesis stratified by intervention and population was conducted. This review was not registered in PROSPERO. Results: A total of 56 studies (33 humans, 23 animals) met the inclusion criteria. Taxonomic nomenclature was updated to reflect 2020 reclassifications (e.g., Lactiplantibacillus plantarum). In human trials, interventions significantly improved subjective sleep metrics (PSQI, ISI). Recent additions demonstrated the efficacy of the Dietary Index for Gut Microbiota (DI-GM) and the improvement in N3 sleep latency by yeast mannan. Furthermore, whole-food patterns (e.g., the MIND diet) and Traditional Chinese Medicine (TCM) decoctions successfully enriched beneficial taxa, such as Bacteroides coprophilus, and increased short-chain fatty acid (SCFA) production. Animal models demonstrated that “psychobiotic” strains (Bifidobacterium breve, Lacticaseibacillus paracasei), prebiotics (GOS/PDX), and TCM formulas effectively restored GABA/5-HT profiles, lowered morning cortisol, and facilitated REM rebound in PCPA-induced models, while also consolidating non-rapid eye movement (NREM) sleep and downregulating clock genes (Per1/Per2). Conclusions: Psychobiotics, prebiotics, and botanicals represent a highly viable non-pharmacological strategy for treating insomnia. However, current evidence is constrained by a heavy reliance on subjective human questionnaires, short follow-up durations limiting insight into long-term stability, and a substantial translational gap between mechanistic rodent models and human clinical outcomes. Full article