Search Results (1,058)

Rett syndrome (RTT) presents with a wide range of symptoms spanning various clinical areas. Capturing symptom change as the disorder progresses is challenging. Wearable sensors offer a non-invasive and objective means of monitoring disease states in neurodevelopmental disorders. The goal of this study was to conduct a systematic literature review to critically appraise the literature on the use of wearable sensors in individuals with RTT. The PRISMA criteria were used to search four databases without time restriction and identified 226 records. After removing duplicates, the titles and abstracts of 184 records were screened, 147 were excluded, and 37 were assessed for eligibility. Ten (10) articles remained, and a further two were included after additional searching. In total, 12 articles were included in the final analysis. The sample size ranged from 7 to 47 subjects with an age range of 1 to 41 years. Different wearable biosensor devices were used across studies, with the Empatica E4 wearable device being most frequently used in 33% (4/12) of the studies. All the studies demonstrated a high methodological quality with a low risk of bias. Evidence from wearable sensors, combined with machine learning methods, enabled the prediction of different sleep patterns and clinical severity in RTT. Given the small sample size and the limitations of available data for training machine learning models, we highlight areas for consideration. The review emphasises the need to enhance research on the application of wearable sensors in epilepsy and gastrointestinal manifestations/morbidity in RTT. Increased electrodermal activity (EDA), % of maximum heart rate (HRmax%) and the heart rate to low-frequency power (HR/LF) ratio were identified as physiological measures potentially associated with disease states. Based on the evidence synthesis, the role of physiological parameters and their association with symptom management in RTT is discussed. Full article

Sleep is a fundamental biological process essential for health and homeostasis. Traditionally investigated through laboratory-based polysomnography (PSG), sleep research has undergone a paradigm shift with the advent of wearable technologies that enable non-invasive, long-term, and real-world monitoring. This review traces the evolution from early analog and actigraphic methods to current multi-sensor and AI-driven wearable systems. We summarize major technological milestones, including the transition from movement-based to physiological and biochemical sensing, and the growing role of edge computing and deep learning in automated sleep staging. Comparative studies with PSG are discussed, alongside the strengths and limitations of emerging devices such as wristbands, rings, headbands, and camera-based systems. The clinical applications of wearable sleep monitors are examined in relation to remote patient management, personalized medicine, and large-scale population research. Finally, we outline future directions toward integrating multimodal biosensing, transparent algorithms, and standardized validation frameworks. By bridging laboratory precision with ecological validity, wearable technologies promise to redefine the gold standard for sleep monitoring, advancing both individualized care and population-level health assessment. Full article

Inactivity and increases in psychological and behavioral symptoms are common for people with dementia, and current assessment relies on proxy-rated tools. We investigate the feasibility and adherence of the use of sensor technology by exploring the relationship between daytime activity and sleep quality. For a total of 42 day–night data pairs in nursing home residents with dementia (N = 11), Garmin Vivoactive5 and Somnofy monitored continuous physical activity levels, sleep efficiency (SE), sleep score, sleep regularity index (SRI), and wake after sleep onset (WASO). Using the Spearman coefficient, we explored correlations between digital and proxy-rated tools (Personal Self Maintenance Scale (PSMS) and Neuropsychiatric Inventory–Nursing Home version (NPI-NH)) and the relationships between the digital biomarkers (SE, SRI, WASO, sleep score, physical activity). Participants (mean age 84 years) had moderate to severe degrees of dementia. Daytime activity levels correlated to sleep quality parameters WASO (−0.34, p = 0.03), and SRI (0.43, p = 0.01), and traditional sleep measures were associated with digital biomarkers (WASO/NPI-NH-K, p = 0.03). We found a relationship between daytime activity and sleep quality; however, the bidirectional relationship remains ambiguous and should be further investigated. The use of sensing technologies for people with dementia residing in a nursing home is feasible, although not without limitations, and has the potential to identify subtle changes, improving clinical assessment and the corresponding care recommendations. Full article

The LRRK2+ SAA− cohort of Parkinson’s disease (PD), characterized by the absence of hallmark α-synuclein pathology, remains under-explored. This limits opportunities for early detection and targeted intervention. This study analyzes data from this under-characterized subgroup and compares it with the LRRK2+ SAA+ cohort using longitudinal data from the Parkinson’s Progression Markers Initiative (PPMI). The PPMI dataset includes 115 LRRK2+ patients (70 SAA+, 45 SAA−) across 52 features encompassing clinical assessments, cognitive scores, DaTScan SPECT imaging, and motor severity. DaTScan binding ratios were selected as imaging-based indicators of early dopaminergic loss, while NP3TOT (MDS-UPDRS Part III total score) was used as a gold-standard clinical measure of motor symptom severity. Linear mixed-effects models were then applied to evaluate longitudinal predictors of DaTScan decline and NP3TOT progression, and statistical analyses of group comparisons revealed distinct drivers of symptoms differentiating SAA− from SAA+ patients. In SAA− patients, a decline in DaTScan was significantly associated with thermoregulatory impairment (p-value = 0.019), while NP3TOT progression was predicted by constipation (p-value = 0.030), sleep disturbances (p-value = 0.046), and longitudinal time effects (p-value = 0.043). In contrast, SAA+ patients showed significantly lower DaTScan values compared to SAA− (p-value = 0.0004) and stronger coupling with classical motor impairments, including freezing of gait (p-value = 0.016), rising from a chair (p-value = 0.007), and turning in bed (p-value = 0.016), along with cognitive decline (MoCA clock-hands test, p-value = 0.037). These findings support the hypothesis that LRRK2+ SAA− patients follow a distinct pathophysiological course, where progression is influenced more by autonomic and non-motor symptoms than by typical motor dysfunction. This study establishes a robust, multimodal modeling framework for examining heterogeneity in genetic PD and highlights the utility of combining DaTScan, NP3TOT, and symptom-specific features for early subtype differentiation. These findings have direct clinical implications, as stratifying LRRK2 carriers by SAA status may enhance patient monitoring, improve prognostic accuracy, and guide the design of targeted clinical trials for disease-modifying therapies. Full article

Background: The human gastrointestinal tract harbors a complex microbiota that plays a vital role in metabolic health. Dysbiosis of the gut microbiome has been linked to metabolic syndrome (MetS), a growing health concern characterized by obesity, hypertension, and dyslipidemia, all of which are strongly associated with insulin resistance and low-grade inflammation. This study aimed to analyze changes in gut microbiome composition and metabolic parameters in individuals with MetS following a 3-month shared medical appointment program driven by a patient-centered agenda with an emphasis on lifestyle pillars of diet, activity, sleep, and stress management. Methods: Thirty-six individuals with MetS were recruited. Of these, 14 completed a structured metabolic health program with facilitated group appointments, including personalized dietary adjustments, increased physical activity, stress management, and clinical monitoring, while 22 served as an untreated group. Fecal samples were collected for full-length 16S rRNA sequencing. Clinical and biochemical parameters, including body weight, blood pressure, HbA1c, triglycerides, and liver enzymes, were assessed. Microbiome data were analyzed for alpha and beta diversity and differential abundance. Correlations between microbial genera and clinical parameters were evaluated using Spearman correlation. Results: Post-intervention, significant improvements were observed in body weight (p = 0.0061), HbA1c (p = 0.033), triglycerides (p = 0.047), AST (p = 0.016), and systolic blood pressure (p = 0.020). Alpha and beta diversity of the gut microbiome showed no significant changes. However, differential abundance analysis revealed increased levels of butyrate-producing and anti-inflammatory genera including Duncaniella, Megasphaera, Pseudoruminococcus, and Oliverpabstia. Conclusions: A 3-month lifestyle intervention in individuals with MetS was associated with marked improvements in metabolic health and beneficial shifts in gut microbiota composition. These findings suggest that even small lifestyle modifications may be a potential therapeutic target for metabolic syndrome management, highlighting the need for personalized approaches in future research. Full article

Background: Burnout is a widespread concern across frontline professions, with healthcare, education, and emergency services workers experiencing particularly high rates of stress and emotional exhaustion. Passive artificial intelligence (AI) technologies may provide novel means to monitor and predict burnout risk using data collected continuously and non-invasively. Objective: This review aims to synthesize recent evidence on passive AI approaches for detecting stress and burnout among frontline workers, identify key physiological and behavioral biomarkers, and highlight current limitations in implementation, validation, and generalizability. Methods: A narrative review of peer-reviewed literature was conducted across multiple databases and digital libraries, including PubMed, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science. Eligible studies applied passive AI methods to infer stress or burnout in individuals in frontline roles. Only studies using passive data (e.g., wearables, Electronic Health Record (EHR) logs) and involving healthcare, education, emergency response, or retail workers were included. Studies focusing exclusively on self-reported or active measures were excluded. Results: Recent evidence indicates that biometric data (e.g., heart rate variability, skin conductance, sleep) from wearables are most frequently used and moderately predictive of stress, with reported accuracies often ranging from 75 to 95%. Workflow interaction logs (e.g., EHR usage patterns) and communication metrics (e.g., email timing and sentiment) show promise but remain underexplored. Organizational network analysis and ambient computing remain largely conceptual in nature. Few studies have examined cross-sector or long-term data, and limited work addresses the generalizability of demographic or cultural findings. Challenges persist in data standardization, privacy, ethical oversight, and integration with clinical or operational workflows. Conclusions: Passive AI systems offer significant promise for proactive burnout detection among frontline workers. However, current studies are limited by small sample sizes, short durations, and sector-specific focus. Future work should prioritize longitudinal, multi-sector validation, address inclusivity and bias, and establish ethical frameworks to support deployment in real-world settings. Full article

Forest fire monitoring is of great significance for ecological protection and public safety. This study proposes a monitoring technology based on LoRa-Mesh (Long Range-Mesh) wireless communication, integrating temperature and humidity sensing, image acquisition, fire identification, data transmission, and energy-saving optimization. To address the limitations of traditional LoRa networks in flexibility and energy consumption, a Layered Dynamic Synchronization Energy-saving (LDSE) protocol is designed. By constructing a hierarchical network, employing implicit route exploration, multi-channel and multi-path communication, and time synchronization optimization, the protocol significantly reduces packet loss rate and system energy consumption. Experimental results demonstrate that the LDSE protocol outperforms the traditional Ad hoc On-Demand Distance Vector Routing Protocol (AODV) in terms of packet loss rate, energy consumption, and latency. Additionally, the proposed energy-saving algorithm significantly reduces system power consumption, with the node sleep-relay mode exhibiting optimal energy efficiency. Experimental verification confirms that the system achieves high reliability, low power consumption, and efficient data transmission, providing an effective IoT solution for forest fire prevention. Full article

Background: Primary headaches in children and adolescents impose emotional, cognitive, and functional burdens beyond pain. This review synthesizes their psychopathological and neuropsychological dimensions and outlines implications for assessment and care. Methods: We performed a comprehensive review with systematic searches of PubMed, Scopus, and Embase (2015–2025). We included observational/experimental studies and evidence syntheses on 0–18-year-olds with migraine, tension-type, or cluster headache; treatment-only reports were excluded. Results: Across population and clinic samples, primary headaches co-occur with elevated anxiety/depression, frequent ADHD/learning problems, and pervasive sleep disturbances with likely bidirectionality. Interictally, small to moderate neurocognitive differences are most consistent in attention/executive control, processing speed, and verbal memory. Quality of life and school participation are reduced. Standardized tools (e.g., PedMIDAS, PedsQL/KIDSCREEN, SDQ/CBCL, SDSC±actigraphy, NEPSY-II/BRIEF) support multidisciplinary assessment. Conclusions: Care should look beyond pain counts, integrating routine screening of mood, sleep, and cognition; active family involvement; and school–healthcare coordination within stepped-care pathways (education and sleep hygiene for all; targeted CBT for catastrophizing/avoidance) with monitoring that pairs headache frequency with functional outcomes. Full article

Background: Heart rate variability (HRV) is a well-established marker of autonomic nervous system (ANS) activity. It is also an important tool for investigating cardiovascular and neurological health. Changes in HRV have been associated with epilepsy and sudden unexpected death in epilepsy (SUDEP), conditions in which autonomic dysregulation is believed to play a significant role. HRV is traditionally measured using electrocardiography (ECG) under standardized laboratory conditions. Recently, however, wearable devices such as the Empatica E4 wristband have emerged as promising tools for continuous, noninvasive HRV monitoring in real-life, ambulatory, and clinical settings where laboratory infrastructure may be lacking. Methods: We evaluated the validity and clinical utility of the Empatica E4 wristband in two cohorts. In the first cohort of healthy controls (n = 29), we compared HRV measures obtained with the E4 against those obtained with a gold-standard laboratory ECG device under seated rest and metronomic breathing conditions. In persons with epilepsy (PWE, n = 42), we assessed HRV across wake and sleep states, as well as during exposure to sodium channel blockers. This was done to determine whether the device could detect physiologically and clinically meaningful changes in autonomic nervous system (ANS) function. Results: In healthy participants, the Empatica E4 provided heart rate (HR), root mean square of successive R-R intervals (RMSSD), and standard deviation of all interbeat intervals (SDNN) values that were strongly correlated with laboratory measurements. Both devices detected the expected increase in RMSSD during metronomic breathing; however, the E4 consistently reported higher absolute values than the ECG. In patients with epilepsy (PWE), the E4 reliably captured parasympathetic activation during sleep and detected a significant reduction in heart rate variability (HRV) in patients taking sodium channel blockers, demonstrating its sensitivity to clinically relevant autonomic changes. Conclusions: The Empatica E4 wristband is valid for measuring HRV in research and clinical contexts. It can detect modulations of ANS activity that are physiologically meaningful. While HRV metrics were robust, other signals, such as electrodermal activity and temperature, were less reliable. These results highlight the potential of wearable devices as practical alternatives to laboratory-based autonomic testing, especially in emergency and resource-limited settings, and emphasize their importance in epilepsy care risk assessment. Full article

Background: Habitual breakfast skipping is associated with an increased risk of cardiovascular and cardiometabolic diseases. However, the effects of skipping breakfast regularly versus irregularly on vascular endothelial function (VEF), a key marker of cardiovascular health, remain unclear. This study aimed to investigate the effects of eight-Day regular or irregular breakfast skipping on brachial artery VEF in healthy habitual breakfast eaters using a three-condition, randomized controlled crossover trial. Methods: Ten young healthy adults (seven females, three males) completed three randomized nine-Day trials: (1) Eat (three meals per day), (2) Skip (breakfast skipped on days 1–8, consumed on Day 9), and (3) Eat/Skip (alternating breakfast consumption and skipping). Flow-mediated dilation (FMD) of the right brachial artery was assessed at 7:45–55 am on days 1, 2, 5, and 9, expressed as the percentage change in the brachial artery diameter normalized to the shear rate area under the curve (Δ%FMD/SR_AUC). Blood samples were collected before and 30 min after breakfast or lunch for glucose, insulin, free fatty acids, and triglyceride analyses. Insulin resistance was estimated using the homeostasis model assessment of insulin resistance calculated from fasting glucose and fasting insulin values. Objective measurements of sleep, physical activity, and continuous glucose monitoring were obtained. Results: On Day 9, the Skip and Eat/Skip trials had significantly lower %FMD/SR_AUC and significantly higher levels of fasting plasma insulin than the Eat trial. Exploratory analyses within the Skip and Eat/Skip trials suggested a weak negative association between changes in %FMD/SR_AUC and fasting blood glucose and insulin from day 1 to day 9. Conclusions: These findings suggest that both regular and irregular breakfast skipping may impair early morning VEF, possibly through alterations in glucose metabolism, whereas regular breakfast consumption may help preserve VEF and support cardiovascular health. Clinical Trial Registry: Clinical Trial Registry: University Hospital Medical Information Network (UMIN000053117, registered 20 December 2023). Full article

IoT (Internet of Things)-enabled solar irradiance sensors are evolving toward energy harvesting, interoperability, and open-source availability, yet current solutions remain either costly, closed, or limited in robustness. Based on a thorough literature review and identification of future trends, we propose an open-source smart wireless sensor that employs a small photovoltaic module simultaneously as sensing element and energy harvester. The device integrates an ESP32 microcontroller, precision ADC (Analog-to-Digital converter), and programmable load to sweep the PV (photovoltaic) I–V (Current–Voltage) curve and compute irradiance from electrical power and solar-cell temperature via a calibrated third-order polynomial. Supporting Modbus RTU (Remote Terminal Unit)/TCP (Transmission Control Protocol), MQTT (Message Queuing Telemetry Transport), and ZigBee, the sensor operates from batteries or supercapacitors through sleep–wake cycles. Validation against industrial irradiance meters across 0–1200 W/m² showed average errors below 5%, with deviations correlated to irradiance volatility and sampling cadence. All hardware, firmware, and data-processing tools are released as open source to enable reproducibility and distributed PV monitoring applications. Full article

Chronic insomnia is a prevalent and disabling sleep disorder with growing evidence linking it to cardiovascular and cerebrovascular morbidity. Stroke, a leading cause of mortality and a long-term disability worldwide, may be influenced by sleep disturbances through multiple physiological mechanisms. While traditional risk factors such as hypertension, atrial fibrillation, diabetes, obesity, smoking, and sedentary lifestyle remain dominant drivers of stroke burden, accumulating evidence suggests that sleep disturbances, particularly chronic insomnia, may act as both independent risk factors for incident stroke and as outcomes of cerebrovascular injury. Chronic insomnia, affecting approximately 10% of the global population, is characterized by persistent difficulties with sleep initiation, maintenance, or quality, accompanied by daytime dysfunction. Beyond its impact on quality of life and mental health, insomnia has been linked to cardiometabolic dysregulation, inflammation, and vascular dysfunction. Importantly, sleep disturbances after stroke can impair recovery and functional outcomes, underscoring a bidirectional relationship between stroke and sleep. Several recent reviews have examined the connection between insomnia and stroke. Our review differs by focusing specifically on (1) the stroke-specific epidemiological evidence for chronic insomnia as a risk factor, (2) the bidirectional interplay between insomnia and post-stroke sleep disturbances, and (3) the role of emerging technologies in monitoring and prognosis. By addressing these gaps, we aim to refine the current understanding and highlight priorities for future research and clinical translation. Full article

Sleep monitoring is critical for diagnosing and treating sleep disorders. Although polysomnography (PSG) remains the clinical gold standard, its complexity, discomfort, and lack of portability limit its applicability for long-term and home-based monitoring. To overcome these challenges, this study introduces a novel flexible headband system designed for multi-modal physiological signal acquisition, incorporating dry electrodes, a six-axis inertial measurement unit (IMU), and a temperature sensor. The device supports eight EEG channels and enables wireless data transmission via Bluetooth, ensuring user convenience and reliable long-term monitoring in home environments. To rigorously evaluate the system’s performance, we conducted comprehensive assessments involving 13 subjects over two consecutive nights, comparing its outputs with conventional PSG. Experimental results demonstrate the system’s low power consumption, ultra-low input noise, and robust signal fidelity, confirming its viability for overnight sleep tracking. Further validation was performed using the self-collected HBSleep dataset (over 184 h recordings of the 13 subjects), where state-of-the-art sleep staging models (DeepSleepNet, TinySleepNet, and AttnSleepNet) were applied. The system achieved an overall accuracy exceeding 75%, with AttnSleepNet emerging as the top-performing model, highlighting its compatibility with advanced machine learning frameworks. These results underscore the system’s potential as a reliable, comfortable, and practical solution for accurate sleep monitoring in non-clinical settings. Full article

Background/Objectives: Adequate sleep has a fundamental role in human health, mainly in cognitive and physiological functions. However, the daily demands of modern society have led to a constant pursuit of better living conditions, requiring more active hours at the expense of sleeping hours. This sleep deprivation has been associated with human health deterioration, namely an increase in Diabetes Mellitus incidence. This metabolic disease is a chronic pathology that imposes a big burden on health systems and is associated with the rise in insulin resistance. In this sense, the aim of this review is to analyze the relation between sleep deprivation and insulin resistance, emphasizing the metabolic parameters and hormones that may be involved in the subjacent mechanism. Methods: A literature review of the last 10 years was performed with specific terms related to “sleep deprivation” and “insulin resistance”. Results: Overall, the studies analyzed showed a decrease in insulin sensitivity in cases of sleep deprivation, even with different study protocols. In addition, an association between sleep deprivation and increased non-esterified fatty acids was also noticeable; however, other parameters such as cortisol, metanephrines, and normetanephrines showed no consistent results among the studies. Conclusions: This review allowed us to confirm the relationship between sleep deprivation and insulin resistance; however, despite the difficulties to monitor sleep, more research is needed to understand the related mechanisms that have not yet been clarified. Full article

Accurate assessment of sleep architecture is critical for diagnosing and managing sleep disorders, which significantly impact global health and well-being. While polysomnography (PSG) remains the clinical gold standard, its inherent intrusiveness, high cost, and logistical complexity limit its utility for routine or home-based monitoring. Recent advances highlight that subtle variations in respiratory dynamics, such as respiratory rate and cycle regularity, exhibit meaningful correlations with distinct sleep stages and could serve as valuable non-invasive biomarkers. In this work, we propose a framework for estimating sleep stage distribution—specifically Wake, Light (N1+N2), Deep (N3), and REM—based on respiratory audio captured over a single sleep episode. The framework comprises three principal components: (1) a segmentation module that identifies distinct respiratory cycles in respiratory sounds using a fine-tuned Transformer-based architecture; (2) a feature extraction module that derives a suite of statistical, spectral, and distributional descriptors from these segmented respiratory patterns; and (3) stage-specific regression models that predict the proportion of time spent in each sleep stage. Experiments on the public PSG-Audio dataset (287 subjects; mean 5.3 h per subject), using subject-wise cross-validation, demonstrate the efficacy of the proposed approach. The segmentation model achieved lower RMSE and MAE in predicting respiratory rate and cycle duration, outperforming classical signal-processing baselines. For sleep stage proportion prediction, the proposed method yielded favorable RMSE and MAE across all stages, with the TabPFN model consistently delivering the best results. By quantifying interpretable respiratory features and intentionally avoiding black-box end-to-end modeling, our system may support transparent, contact-free sleep monitoring using passive audio. Full article