Search Results (1,177)

Sleep quality is closely associated with cardiovascular, metabolic, and mental health outcomes, yet the clinical gold standard, polysomnography (PSG), is costly and intrusive for long-term home monitoring. Ballistocardiography (BCG) enables unobtrusive in-bed sensing and is therefore attractive for low-burden sleep assessment in natural environments. However, most existing BCG studies are PSG-referenced and mainly focus on sleep staging, while movement and out-of-bed episodes are often treated as artifacts rather than modeled jointly. In this study, we propose an interpretable unsupervised proxy-state modeling framework for three-state in-bed monitoring from BCG signals under an unlabeled setting. BCG recordings were segmented into 30 s windows with 50% overlap, and multi-domain features were extracted from waveform morphology, spectral power, heart rate-related dynamics, and wavelet energy distribution. K-means clustering (K = 3) was used to construct cluster-derived proxy labels, TreeSHAP-based feature ranking together with inner-CV-guided Top-N subset selection was used for training-only feature screening, and multiple classifiers were compared under a strict leave-one-subject-out protocol, with an ROA-optimized RBF-SVM achieving the best overall performance. Using data from 32 volunteers, the framework achieved an accuracy of 0.9932 ± 0.0047 (mean ± SD), together with consistently strong Macro-F1 and MCC scores. Overall, it outperformed the alternative methods compared in this study. Full article

Sleep plays a fundamental role in human health and cognitive functioning, motivating the development of reliable and scalable methodologies for sleep stage classification (SSC). Recent advances in non-invasive and economically sustainable sensing technologies enable continuous sleep monitoring beyond laboratory settings. However, SSC remains a challenging data analytics task due to the intrinsic class imbalance among sleep stages. This study investigates the effectiveness of different imbalanced data management strategies within a machine learning framework for non-invasive SSC. The proposed approach relies exclusively on heart rate and motion signals, which can be acquired through wearable devices or contactless under-mattress sensors, making it suitable for longitudinal monitoring scenarios. Using the PhysioNet DREAMT dataset, 32 experimental scenarios are defined by combining data-level techniques (ADASYN oversampling with different balancing weights), algorithm-level strategies (cost-sensitive learning), and hybrid solutions. Four model families are evaluated—Decision Tree, k-Nearest Neighbors, Ensemble Classifiers, and Artificial Neural Networks—across classification tasks involving 2, 3, 4, and 5 sleep stages. The experimental results show that ensemble-based models provide robust and consistent performance under severe class imbalance, achieving macro accuracies of 82% for sleep–wake detection, 73% for 3-stage classification, 72% for 4-stage classification, and 64% for 5-stage classification. These findings confirm the relevance of imbalance-aware analytics and demonstrate the feasibility of accurate, minimally invasive SSC within big data and cognitive computing paradigms. Full article

Restless legs syndrome (RLS) is one of the most prevalent sleep disorders, yet its diagnosis continues to rely almost entirely on subjective symptom descriptions. This persistent dependence on phenomenology reflects the absence of reliable biological markers to aid in the process of diagnosis or monitoring. However, there is accumulating molecular evidence that suggests that RLS is associated with systemic biological alterations. These extend beyond the traditional paradigm of iron deficiency. The present scoping review synthesizes the current research on circulating serum biomarkers investigated in RLS outside classical iron indices. A comprehensive search of PubMed, Scopus, and Web of Science databases identified 1050 records, of which 50 studies met eligibility criteria and were included. In the processing of data, clusters emerged into several recurring biological domains, including dysregulated iron regulatory signaling (hepcidin), low-grade immune activation, oxidative stress, and neuroaxonal injury markers. High-throughput omics studies reveal molecular network perturbations involving inflammatory pathways, complement activation, metabolic signaling, and cellular stress responses. Biomarker associations appear stronger when linked to objective motor burden. These findings suggest that RLS may involve multifarious molecular changes detectable in the serum. Consequently, this can support the transition from symptom-based diagnosis toward biomarker-informed stratification, which may enable more precise disease characterization and improved diagnostic accuracy. Full article

Major social, cultural, and sociopolitical events routinely disrupt daily life, yet their effects on sleep are rarely conceptualized at the population level beyond anecdotal sharing. The purpose of this Opinion piece is to initiate a preliminary discussion of “public sleep” as a novel construct describing systematic, event-related changes in sleep timing, duration, and quality that emerge coherently within communities in response to shared social experiences. Drawing on similarities with the well-established concept of public mood, we posit that sleep can be shaped by social environments in which shared attention, emotional climate, and coordinated schedules exert systematic influence. In support of this claim, we describe preliminary evidence from diverse domains demonstrating population-level sleep disruption following major events, including the transition to Daylight Saving Time, national elections, prolonged crises such as the COVID-19 pandemic and armed conflicts, and highly salient cultural activities such as major sporting events. These reports from disparate fields provide an initial indication that public sleep disruptions can be acute or prolonged, geographically localized or global, and may be shaped by the duration, emotional intensity, and perceived importance of the associated event. We further highlight the potential public health, safety, social, and economic consequences of collective sleep loss, underscoring its relevance beyond individual well-being. Finally, we outline key directions for future research, emphasizing the need for systematic reviews, mechanistic studies, longitudinal designs, and policy-relevant recommendations. Recognizing public sleep as a measurable population phenomenon would provide a foundation for anticipating, monitoring, and mitigating sleep disruption during periods of collective strain, with implications for both individual health and societal resilience. Full article

Infant respiration is a physiological marker of health and wellbeing that can provide insight into sleep and wake patterns. Technological innovation presents opportunities to enhance measurements of physiological signals, which improves ecological validity and participant experiences. This is particularly true in the context of studying infant sleep, as it can be disrupted by changes in the environment and the physical sensation of unfamiliar or uncomfortable sensors. The goal of this study was to examine if a commercially available video baby monitor (Nanit system) can accurately estimate respiration during a nap relative to a commonly used cardiorespiratory sensor (Isansys Lifetouch sensor). Thirty-three infants (M = 9.7 months; range = 1–22 months) took a nap while wearing the Lifetouch sensor and Nanit Breathing Band. Infants slept in view of the Nanit camera. A computer vision algorithm applied to the video detected movement of the patterns on the fabric band worn around the infant’s torso to determine respiratory rates. The results showed strong consistency between the devices. More than 95% of the minute-by-minute respiration data fell within the limits of agreement, with little bias. Agreement was not influenced by age or nap duration, suggesting the Nanit Breathing Band provides a valid measure of respiration across infancy. Full article

As a reliable and high-potential wireless communication technology for the Internet of Things (IoT), LoRa excels in long-distance and low-power transmission. The star topology adopted by traditional LoRaWAN suffers from poor deployment flexibility and insufficient scalability in scenarios with complex terrain or harsh environments. LoRa-Mesh networks can effectively solve coverage challenges through characteristics such as multi-hop and self-organization; however, the relay and forwarding requirements of nodes also introduce new challenges in energy consumption management. To address the energy consumption management challenges of LoRa-Mesh, this paper proposes a Data-Driven Energy Saving (DDES) protocol. It flexibly sets and dynamically fine-tunes node sleep durations based on data changes, constructs an efficient energy-saving framework through uplink data streams, and implements precise control over nodes via downlink post-analysis messages to achieve on-demand energy saving. Simulation results in the smart agriculture scenario of soil moisture monitoring and irrigation show that compared with protocols without a sleep mechanism, the battery life of the LoRa-Mesh network using the DDES protocol is extended by approximately 20 times. The proposed protocol breaks through the limitations of fixed sleep schemes, realizes refined and flexible division of sleep regions, and exhibits significant advantages in LoRa network energy saving. Full article

Background: Monitoring health-related variables across a competitive season is essential to understand factors associated with performance in Paralympic athletes. However, evidence on the interplay between sleep, mood states, nutritional factors, injuries, and performance remains limited. Objective: To examine the associations between injuries, sleep parameters, nutritional factors, mood states, and performance in Para powerlifting athletes during a competitive cycle. Methods: Twenty-four athletes from the Brazilian National Para powerlifting team were assessed at three time points: baseline (~3 months pre-competition), pre-competition (upon arrival), and post-competition (day after the event). Data were collected using standardized instruments and analyzed in R. Descriptive statistics, Mann–Whitney U tests, Spearman’s correlations, Friedman tests, and individual delta values (Δ) were applied. Results: No significant between-group differences were observed in pre-competition cross-sectional analyses. Longitudinally, sleep duration was the only variable consistently differing between performance groups. Athletes who matched or improved performance showed greater sleep stability, whereas those who did not improve exhibited larger post-competition increases in sleep duration. Negative mood states decreased over time, and baseline vigor was higher in the higher-performing group. Sleep duration changes were negatively correlated with performance variation (ρ = −0.575, p = 0.003). Conclusions: Sleep duration was the variable most consistently associated with performance variation. Mood changes reflected reduced negative affect over time. Findings support longitudinal monitoring in Para powerlifting, although caution is warranted due to the observational design and small sample. Full article

Background: Gaucher disease (GD) is a rare lysosomal storage disorder caused by mutations in the GBA1 gene. Traditionally, GD is classified into three subtypes based on the severity of neurological involvement; however, overlapping clinical features increasingly suggest a continuum of phenotypes rather than distinct categories. In this prospective observational cohort study, we conducted a multidisciplinary assessment of patients with GD to identify and monitor neurological, cognitive, auditory, and visual impairments. Materials and Methods: A comprehensive clinical and instrumental evaluation was performed at baseline and repeated at follow-up, with a median interval of 37 months (IQR 36–38). Neurological assessments included physical examination, clinical rating scales, video-EEG, and brain MRI. Cognitive status was assessed using a standardized battery of neuropsychological tests. Detailed audiological and ophthalmological evaluations were also conducted. Paired parametric or non-parametric tests were applied as appropriate, with Bonferroni correction for cognitive outcomes (p < 0.05). Results: Of the 22 patients assessed at baseline, 18 completed the follow-up evaluation. Neurological assessments showed a worsening of subtle parkinsonian signs, with significant increases in Movement Disorder Society–Unified Parkinson’s Disease Rating Scale Part III scores (p = 0.04) and non-motor symptom scores (p = 0.01). Two of the eighteen patients developed epilepsy during follow-up. A high prevalence of sleep disturbances was confirmed, with 27.8% exhibiting excessive daytime sleepiness and 16.7% reporting REM sleep behaviour disorder on standardized questionnaires. Compared with baseline, cognitive assessments revealed a higher proportion of patients with performance below normative population scores in at least one cognitive domain, particularly memory. Sensorineural hearing loss was confirmed in 11 of 15 patients (73.3%) who underwent audiological evaluation, with progressive worsening of audiometric thresholds observed in 7 of 11 (64%). Ophthalmological evaluations showed no changes in visual acuity or OCT findings; however, multifocal electroretinography abnormalities were detected in 12 of 13 patients. Conclusions: Through in-depth phenotyping, this study identifies measurable neurological, cognitive, and sensory progressive changes in patients with GD over time, supporting the value of tailored, multidisciplinary long-term care strategies to monitor and address emerging clinical needs in this rare disease. Full article

Sleep disturbances are early hallmarks of Alzheimer’s disease (AD) and other dementias, yet the molecular mechanisms remain poorly understood. We previously showed that dipeptidyl aminopeptidase-like protein 6-knockout (DPP6-KO) mice exhibit accelerated neurodegeneration with synaptic loss, neuronal death, and circadian dysfunction resembling AD pathology. Here, we investigate whether DPP6 deficiency directly causes sleep dysregulation and assess age-dependent effects using wireless EEG/EMG telemetry, behavioral monitoring, and body temperature recordings. We found striking age-dependent sleep phenotypes in DPP6-KO mice. Adult (3-month) DPP6-KO mice showed hyperactivity-driven REM sleep increases, while aged (12-month) DPP6-KO mice developed insomnia with fragmented sleep architecture. Critically, aged DPP6-KO mice exhibited decreased REM latency, a biomarker of depression, which we confirmed by behavioral assays. Conversely, DPP6 overexpression in aged wild-type mice increased NREM duration and reduced sleep fragmentation, demonstrating a protective effect. Throughout aging, DPP6-KO mice showed dysregulated locomotor activity and body temperature rhythms, suggesting broader disruption of circadian and metabolic homeostasis. These findings establish DPP6 as a critical regulator of sleep architecture whose loss recapitulates key sleep disturbances observed in AD/dementia. The progressive nature of sleep dysfunction in DPP6-KO mice, from REM abnormalities to insomnia, parallels human disease progression and positions DPP6 as a potential therapeutic target for sleep-related symptoms in neurodegenerative disorders. Full article

Commercially available wearable health devices (WHDs) carry the potential to decentralize healthcare systems. These devices can empower individuals with health knowledge by offering a low-cost and accessible way to monitor physical activity, sedentary behavior, cardiac health, and sleep. However, a lack of standardization in design, health, and safety regulations means that consumer-grade WHDs on the market vary in efficacy to affect positive behavior change in users, as user compliance alone does not indicate whether these devices actually influence wellbeing outcomes long term. We use a grounded theory analysis of the experiences of seven long-term informed users of the same wearable, the Oura Ring, to propose a substantive theory describing the tacit challenges that these users face in order to truly benefit from their device even after extended use. We provide recommendations as to how designers of wearable devices can facilitate the user’s journey to surpass these obstacles. Full article

Paralympic athletes are challenged by unique systemic strain due to impairment-related physiological and psychological stressors. This study aims to synthesize the current evidence regarding post-exercise recovery modalities in Paralympic athletes, providing an overview of their physiological considerations and practical applications. A narrative review was conducted across PubMed/MEDLINE, Scopus, and Web of Science (inception to December 2025). Inclusion criteria prioritized original research on competitive para-athletes evaluated through physiological or performance-based markers. Evidence identifies four critical domains: (1) Thermoregulation: In spinal cord injury (SCI), upper-body cooling is significantly more effective than lower-body strategies for core temperature reduction; objective monitoring of playing time is essential, as subjective perception is unreliable. (2) Systemic recovery: Sleep quality is compromised by secondary complications (e.g., nocturia and spasticity), and heart rate variability (HRV) serves as a sensitive autonomic marker to validate readiness. (3) Neuromuscular restoration: The early-phase rate of force development (RFD ≤ 50 ms) is more sensitive than the peak strength for detecting neural fatigue, particularly in SCI. (4) Contextual modulators: Infrastructure accessibility and psychological resilience are primary determinants of intervention efficacy. Effective recovery in para-sports requires a shift toward “active-assisted” impairment-specific interventions. Future research must validate specialized monitoring tools and longitudinal impacts on long-term health. Full article

Sleep abnormalities and circadian rhythm disruptions are frequently observed in psychiatric disorders such as depression and schizophrenia. However, most previous studies have examined circadian rhythms and sleep separately, limiting understanding of how these processes interact within individuals. This study examined circadian and sleep characteristics in depression and schizophrenia compared with healthy controls using multi-day wrist actigraphy. Circadian rhythms were assessed using parametric and non-parametric measures of rest–activity patterns, and sleep metrics were derived using a validated actigraphy-based algorithm. Distinct patterns were observed across diagnostic groups. Schizophrenia showed widespread disruption in daily activity patterns, with altered timing and reduced rhythm strength. Sleep was longer but highly fragmented, with frequent awakenings despite increased time in bed. In contrast, depression showed more limited changes, mainly in activity timing and overall activity levels, while sleep and daily patterns remained closer to controls. A key finding was the identification of distinct circadian–sleep profiles for each condition, with global disruption in schizophrenia and more selective alterations in depression. These findings show that combining circadian and sleep measures provides a clearer understanding of psychiatric disorders and may support monitoring and targeted interventions based on daily behavioral rhythms. Full article

Background: Unwanted loneliness and social isolation in older adults are public health problems with negative effects on physical and mental health. The usual assessment tools, based on self-report questionnaires, have limitations in capturing these phenomena continuously and objectively. Objective: We aimed to critically analyze recent scientific evidence on the use of passive sensor technologies combined with artificial intelligence for the detection of unwanted loneliness and social isolation in older adults. Methods: Studies were reviewed in databases (PubMed, Scopus, Web of Science, and IEEE Xplore) that used wearable devices, environmental sensors in the home, smartphones, and multimodal fusion approaches. This systematic review was conducted following the PRISMA 2020 guidelines. Results: Behavioral variables derived from passive monitoring, such as mobility, time away from home, sleep patterns, and digital interactions, are consistently associated with measures of loneliness and social isolation. Likewise, artificial intelligence models based on the combination of multiple data sources show better predictive performance than unimodal approaches. Conclusions: Sensor-based technologies can complement traditional assessment methods, although their practical application requires overcoming challenges related to methodological validation, user acceptance, and ethical considerations. Full article

Background/Objectives: The global incidence of diabetes in childhood is increasing, raising concern about its long-term effects on the developing brain. Although paediatric diabetes research has traditionally focused on microvascular and macrovascular complications, accumulating evidence indicates that the brain is also a vulnerable target. Methods: This narrative review synthesizes current knowledge on the impact of diabetes on brain health in children and adolescents, with emphasis on epidemiology, neuroimaging and cognitive outcomes, underlying mechanisms, risk and protective factors, and clinical implications. Results: In type 1 diabetes (T1D), studies consistently demonstrate subtle but measurable alterations in brain structure, including reduced growth of total, grey, and white matter volumes, alongside functional and microstructural changes. These neurobiological differences are associated with mild deficits in cognition, particularly in attention, executive function, memory, and processing speed. While clinically significant impairment affects a minority, subclinical alterations are common and may accumulate over time. Key risk factors include chronic hyperglycaemia, glycaemic variability, severe hypoglycaemia, diabetic ketoacidosis, and younger age at onset, whereas good glycaemic stability, diabetes technologies, supportive psychosocial environments, and adequate sleep appear protective. Proposed mechanisms involve oxidative stress, neuroinflammation, disrupted insulin signalling, altered cerebral metabolism, and vulnerability of the immature brain during critical developmental windows. Type 2 diabetes (T2D), increasingly diagnosed in youth, is also associated with adverse brain outcomes. Emerging data link early-onset T2D to alterations in brain structure and connectivity, poorer cognitive performance, and increased mental health burden, mediated by hyperglycaemia, insulin resistance, inflammation, and psychosocial stressors. Conclusions: Overall, childhood diabetes—both T1D and T2D—is associated with meaningful effects on brain development and function. Longitudinal and interventional studies are needed to establish causality and determine whether optimizing glycaemic control and psychosocial support can mitigate neurocognitive risk. Recognizing brain health as a potential complication of paediatric diabetes has important implications for monitoring, prevention, and clinical care. Full article

Background/Objectives: Acute total sleep deprivation (TSD) is known to impair working memory capacity. However, the specific relationship between alterations in the brain’s electrical power spectrum following TSD and working memory deficits remains poorly understood. Methods: In this study, 30 healthy young adults (14 males and 16 females) were enrolled, and 28 participants were finally included in the analysis after excluding EEG data with excessive noise, who underwent a verbal working memory task under two conditions: baseline sleep (BL) and 36 h of TSD. EEG data were recorded concurrently. Results: We observed a significant decrease in working memory accuracy and a significant prolongation of reaction time after TSD. Furthermore, TSD led to a significant enhancement of parietal alpha-ERD (at electrodes P3/Pz/P4) and theta-ERS, accompanied by a reduction in N2 and P3 wave amplitudes. Conclusions: These findings suggest that TSD may impair working memory by weakening parietal alpha-ERD and early conflict monitoring and late attention evaluation processes. The enhanced theta-ERS might represent a compensatory mechanism. Full article