Search Results (175)

Background/Objectives: Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and early dementia, affecting up to 20% of older adults. Sleep disturbances, particularly insomnia, affect around 60% of individuals with MCI, contributing to declines in cognitive and physical function. Although Cognitive Behavioral Therapy for Insomnia (CBT-I) is an evidence-based non-pharmacological treatment, few studies have adapted it for individuals with MCI. This pilot study developed and evaluated Slumber, a clinician-supported mobile CBT-I app tailored for older adults with MCI and insomnia. Methods: The study had three aims: (1) to develop the app for delivering CBT-I to individuals with MCI; (2) to evaluate its usability and refine smart messaging prompts; and (3) to assess the feasibility of outcome measurement while detecting exploratory signals of change through a 6-week pilot trial. N = 19 participants completed the trial. Results: A significant reduction in insomnia severity was observed (mean difference = −2.06; p = 0.0131), while changes in cognitive and physical functioning were not statistically significant. Participants reported high satisfaction with the app’s tracking features and motivational reminders, though some noted technical challenges with presenting and interpreting sleep analysis charts. Conclusions: Findings support the usability of the Slumber app and the feasibility of outcome measurement in this population. The observed improvement in sleep quality provides an initial signal of promise. Future studies should address user feedback, enhance technical features, and evaluate clinical effectiveness in a larger randomized trial. Full article

Hypoglycaemia in patients with type 1 diabetes mellitus (T1DM) remains a major clinical burden and, beyond its metabolic complications, can cause serious cardiac arrhythmias. Multiple mechanisms lead to different types of arrhythmias during hypoglycaemia. However, existing studies often involve mixed diabetes populations, small cohorts, or limited monitoring during nocturnal periods, leaving a critical gap in understanding the links between glucose fluctuations and arrhythmic events. This review provides an updated combination of experimental and clinical evidence describing how autonomic dysfunction and ionic imbalances lead to electrophysiological instability and structural remodelling of the myocardium during hypoglycaemia. Continuous glucose monitoring (CGM) combined with electrocardiographic or wearable rhythm tracking may enable early detection of glycemic and cardiac disturbances and help identify high-risk individuals. Future prospective studies using combined CGM–ECG monitoring, particularly during sleep, are essential to clarify the relationship between hypoglycaemia and arrhythmic events. Full article

Continuous respiratory rate (RR) monitoring can improve the detection of clinical events, such as pulmonary infections, cardiac arrests, and sleep apnoea. Wi-Fi-based systems offer a low-cost, contactless alternative to radar and video. However, existing studies are limited to narrow respiratory ranges and small-scale validation. We present VitalCSI, a vital sign monitoring system using off-the-shelf, low-power Wi-Fi hardware. We recorded 15 healthy university athlete volunteers and developed RR estimation algorithms benchmarked against nasal airflow sensors. VitalCSI uses a consumer Wi-Fi access point and a Raspberry Pi computer to capture channel state information (CSI). We estimated the RR from CSI via principal component analysis (PCA), spectral peak detection, and breath (counting in 30 s windows), which were then fused by a multidimensional Kalman filter. VitalCSI showed strong agreement with airflow references ($r^2=0.93$, MAE = 1.20 brpm), tracking RR across 6–33 brpm and outperforming prior Wi-Fi studies. VitalCSI demonstrates the feasibility of RR monitoring with a single-antenna, single-board microcomputer as the Wi-Fi transmitter. It is the first validated system for continuous, contactless RR monitoring using consumer-grade Wi-Fi over an extended respiratory range, paving the way for use in both home and sports monitoring contexts. Full article

Background: According to the National Institute for Health and Care Excellence (NICE), long COVID refers to symptoms persisting for four weeks or more after acute infection, with over 100 identified, including fatigue, cognitive dysfunction, and breathlessness. Women aged 45–54 are disproportionately affected, overlapping with the typical age for perimenopause and menopause. This scoping review aimed to provide an overview of existing research on the intersection between long COVID and the menopausal transition. Methods: Five database (CINAHL ultimate, MEDLINE, ScienceDirect, Cochrane, and Scopus) searches yielded 387 articles; after removing 40 duplicates and screening 347 titles and abstracts, fourteen studies were reviewed in full, with seven meeting the inclusion criteria (examined both long COVID and menopause in their scope and are written in English language). Results: This scoping review identified a significant symptomatic overlap between long COVID and menopause reported by participants, particularly fatigue, cognitive difficulties, mood changes, and sleep disturbances. Preliminary evidence also suggests that hormonal fluctuations may influence symptom severity, though biological mechanisms remain insufficiently understood. Methodological limitations restrict generalisability, underscoring the need for longitudinal symptom tracking, diverse samples, and biomarker-informed studies. Recognising the intersection of long COVID and menopausal transition is essential for improving assessment, management, and targeted care for affected women. Full article

Emergency Departments (EDs) are high-pressure environments that place significant psychological and physiological stress on both patients and healthcare staff. Despite increasing awareness of stress-related impacts, proactive stress management interventions have limited uptake in healthcare. This proof-of-concept study will evaluate WeCare: a 6-week, wearable-integrated, self-guided program grounded in a “Learn–Track–Act” framework to support stress regulation, resilience, and wellbeing. The study will examine four key aspects of implementing the program: (1) feasibility, (2) acceptability and usability, (3) preliminary clinical effectiveness (self-report and physiological outcomes), and (4) preliminary economic impacts. Using a mixed-methods, multiple-baseline N-of-1 design, the program will be trialled with up to 32 participants across four ED-exposed groups: patients with non-severe or severe injuries, patients with acute medical presentations, and ED staff. The intervention includes digital psychoeducation, continuous biofeedback via a smart ring, personalised guidance, and evidence-based self-regulation strategies. Assessments will include standardised questionnaires combined with continuous physiological monitoring via a smartwatch, and interviews. Quantitative outcomes include heart rate variability, sleep patterns, perceived stress, wellbeing, healthcare use, and time off work. Qualitative interviews will explore user experience, usability, and perceived barriers. The findings will inform the refinement of the intervention and co-design of a larger-scale trial, contributing valuable evidence to support low-cost, wearable-enabled proactive mental healthcare in high-stress healthcare environments. Full article

Background: Clozapine remains the only antipsychotic with proven efficacy in treatment-resistant schizophrenia (TRS). However, it is effective in only about 40% of patients and is associated with numerous adverse drug reactions. Personalization of clozapine therapy is therefore of critical importance in clinical psychiatry. MiRNA expression may serve as a promising exploratory marker for understanding individual variability in clozapine efficacy and safety. Methods: In this study, we determined the complete miRNA expression profile in TRS patients before initiation of clozapine and after four weeks of treatment. Results: In 15 inpatients with TRS receiving 4-week clozapine monotherapy, PANSS total decreased from 98.8 ± 13.19 to 80.47 ± 14.63 (p = 0.001). The most frequent adverse drug reactions were hypersalivation (n = 13), drowsiness/sedation (n = 12), and prolonged sleep (n = 12). We detected 24 differentially expressed miRNAs after clozapine. Changes in hsa-miR-129-5p, hsa-miR-6068, and hsa-miR-6814-5p correlated with improvements in positive symptoms; hsa-miR-128-1-5p tracked general psychopathology; and hsa-miR-6814-5p aligned with global improvement (lower PANSS total, higher PSP). Safety signals included associations of hsa-miR-4472 with asthenia/fatigue and prolonged sleep, hsa-miR-4510 with prolonged sleep, hsa-miR-615-3p and hsa-miR-4715-3p with tachycardia, and hsa-miR-329-1-5p with weight gain. Conclusions: Because miRNAs regulate the expression of a wide range of genes, including those involved in clozapine’s efficacy and safety, these findings underscore the need for further studies integrating pharmacoepigenetic and pharmacogenetic biomarkers. Our preliminary findings suggest that specific miRNAs could be candidate biomarkers associated with clozapine response in TRS, although these results require validation in larger and controlled studies. Full article

Background: Smart rings enable continuous physiological monitoring through finger-worn sensors. Despite growing consumer adoption, their clinical utility beyond sleep tracking remains unclear. Objectives: To systematically review evidence for smart ring applications in clinical medicine, assess measurement accuracy, and evaluate clinical outcomes. Methods: We searched PubMed/MEDLINE, Embase, Cochrane Library, and Web of Science through 31 July 2025. Two reviewers independently screened studies and extracted data. Risk of bias was assessed using ROBINS-I and RoB 2.0. Results: From 862 citations, 107 studies met inclusion criteria including approximately 100,000 participants. Studies were equally distributed between sleep (47.7%) and non-sleep applications (52.3%). Smart rings demonstrated high accuracy: heart rate r² = 0.996, heart rate variability r² = 0.980, and sleep detection 93–96% sensitivity. Predictive capabilities included COVID-19 detection 2.75 days pre-symptom (82% sensitivity), inflammatory bowel disease flare prediction 7 weeks early (72% accuracy), and bipolar episode detection 3–7 days early (79% sensitivity). However, 65% of studies had moderate-to-high bias risk. Limitations included small samples, proprietary algorithms (89%), poor diversity reporting (35%), and declining adherence (80% at 3 months to 43% at 12 months). Conclusion: Smart rings have evolved into clinical tools capable of early disease detection. However, algorithmic opacity, population homogeneity, and adherence challenges require attention before widespread implementation. Full article

Background: Although the participation of children with autism spectrum disorder (ASD) in team sports presents challenges, group-based physical activities could offer specific benefits for their core symptoms. Therefore, the aim of this systematic review was to analyze the benefits of mini-basketball for children with ASD. Methods: A systematic review was conducted following PRISMA guidelines and was registered in PROSPERO (CRD420251144800). Four databases (Web of Science, SPORTDiscus, PubMed, and Scopus) were searched to select randomized controlled trials reporting the effects of mini-basketball interventions on children with ASD from their inception to August 2025. Results: Eight randomized controlled trials involving 436 participants (aged 3–12 years, 87.3% male) met the inclusion criteria. All studies were conducted in China using 12-week interventions (40–45 min, 2–5 days/week at moderate intensity). The quality was rated as good in two studies and fair in six. Five studies assessed social responsiveness, with four showing significant pre–post reductions in the experimental groups and all demonstrating superior outcomes versus those of the controls. One study reported significant reductions in repetitive behaviors, self-injurious behaviors, and restricted behaviors compared to that of the controls. Joint attention improvements were observed through eye-tracking measures, with increased fixation counts, shorter time to first fixation, and more accurate gaze shifts. Physical fitness benefits included improved shuttle run times and standing long jump performance. Finally, one study demonstrated better inhibition control and improvements in sleep quality, including increased sleep duration and efficiency. Conclusions: Mini-basketball interventions can improve social responsiveness and related outcomes in children with ASD. These findings support mini-basketball as a feasible, safe, and effective intervention that could be integrated with existing therapeutic approaches. Full article

Psychiatric illness during childhood and adolescence is a growing concern, placing increased pressure on psychiatric services. Reliance on an evidence-based assessment tool may facilitate the identification and management of symptoms and may facilitate accountability. The purposes for this study were to characterize the psychiatric symptoms of a sample of pediatric patients accessing outpatient psychiatry using the DSM Level 1 Cross-Cutting Measure (CCSM), compare patient and caregiver ratings (CCSM), and explore patients’ acceptability of using the CCSM. The sample consisted of 51 patients (mean age 14 years) and 46 caregivers (mean age 43 years). Patient and caregiver ratings suggested problems with sleep, inattention, depression, irritability/anger, and anxiety. The most common discordance concerned suicide ideation. Most patients (34 of 38) reported that the assessment tool was easy to complete. The CCSM may be a useful, evidence-based, standardized, transdiagnostic assessment tool aligned with the DSM-5 that can be used in a variety of mental health settings to identify symptoms, inform treatment planning, and track progress. Full article

Catheter ablation (CA) has become a validated technique for treating patients with symptomatic or paroxysmal atrial fibrillation (AF), as recommended by the latest 2024 European society of cardiology (ESC) guidelines, class II level A. The procedure is also recommended for patients with persistent AF without major risk factors for AF recurrence, as an alternative to antiarrhythmic medications class I or III. However, CA carries the risk of AF recurrence in 30–35% of patients, sometimes after the procedure. Multiple factors impact the onset, maintenance, and recurrence of AF after CA, including clinical, biohumoral, echocardiographic, genetic, and lifestyle factors. Beyond traditional predictors, emerging factors such as obstructive sleep apnea syndrome, chronic renal failure, chronic lung disease, physical activity patterns, gut microbiota composition, and epicardial fat thickness significantly influence outcomes. Therefore, optimizing patient’s selection for CA is an important strategy to minimize the risk of AF recurrence. Many echocardiographic parameters emerged as predictors of AF recurrence post-CA, but none stood out as a potential single factor. These factors include traditional markers such as left atrial size by 2D echocardiography, LV ejection fraction, LV diastolic function parameters as well as myocardial deformation addressed by the recently developed speckle tracking analysis. Additionally, the duration and type of AF represent fundamental risk factors, with longstanding persistent AF showing significantly higher recurrence rates compared to paroxysmal forms. Novel biomarkers including MR-proANP, caspase-8, hsa-miR-206, and neurotrophin-3 show promise in enhancing risk prediction capabilities. The aim of this review is to explore the most relevant echocardiographic parameters, including myocardial deformation, that could accurately predict recurrence of AF after CA, while also examining the role of emerging clinical and biochemical predictors in comprehensive patient selection strategies. Full article

Understanding the influence of weather on human well-being and mobility is essential to promoting healthier lifestyles. In this study we employ data collected from 151 participants over a continuous 30-day period in Switzerland to examine the effects of weather on well-being and mobility. Physiological data were retrieved through wearable devices, while mobility was automatically tracked through Google Location History, enabling detailed analysis of participants’ mobility behaviors. Mixed effects linear models were used to estimate the effects of temperature, precipitation, and sunshine duration on well-being and mobility while controlling for potential socio-demographic confounders. In this work, we demonstrate the feasibility of combining multi-source physiological and location data for environmental health research. Our results show small but significant effects of weather on several well-being outcomes (activity, sleep, and stress), while mobility was mostly affected by the level of precipitation. In line with previous research, our findings confirm that normal weather fluctuations exert significant but moderate effects on health-related behavior, highlighting the need to shift research focus toward extreme weather variations that lie beyond typical seasonal ranges. Given the potentially severe consequences of such extremes for public health and health-care systems, this shift will help identify more consistent effects, thereby informing targeted interventions and policy planning. 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: Mandibular advancement devices (MADs) are widely used for mild-to-moderate obstructive sleep apnea (OSA). We aimed to synthesize recent evidence on their clinical effectiveness and tolerability. Methods: A systematic review was conducted. Ten studies were included, evaluating MAD therapy in adults with mild-to-moderate OSA. The review reported on standard outcomes, including the apnea-hypopnea index (AHI), oxygenation, daytime sleepiness (Epworth Sleepiness Scale, ESS), quality of life, adherence, and adverse events. Risk of bias was also assessed. Results: Across the included studies, MADs consistently reduced AHI from baseline and improved ESS and/or snoring. In head-to-head comparisons, MADs generally yielded smaller reductions in AHI than CPAP but achieved comparable improvements in symptoms and quality of life, with higher nightly adherence. Reported adverse effects were mostly mild and transient. Conclusions: MAD therapy is an effective and generally well-tolerated option for adults with mild-to-moderate OSA and for the patients intolerant to CPAP, although average AHI reduction is smaller than with CPAP. Given the low certainty and heterogeneity of current evidence, high-quality randomized trials with objective adherence tracking and standardized titration are needed. Full article

The wire tightener is a critical tool in the construction and maintenance of power lines. Failure to detect tension overload in a timely manner may lead to plastic deformation or even breakage of the tool, potentially causing serious safety accidents. To address this issue, a force monitoring sensor was developed to track the real-time load on wire tighteners. In terms of hardware design, a foil strain gauge was integrated with an ultra-low-power mixed-signal microcontroller based on the mechanical characteristics of the wire tightener, enabling accurate acquisition and processing of load data. Low-power LoRa technology was employed for wireless data transmission, and an adaptive sleep–wake strategy was implemented to optimize power efficiency during data collection. The sensor’s material, geometry, and structure were tailored to the tool’s composition and working environment. Experimental results showed that the average relative error between the sensor readings and the reference values was less than 0.5%. The sensor has been successfully deployed in practical engineering applications, consuming approximately 4500 mWh over an 8 h continuous monitoring period. Full article

Dementia is a progressive condition that affects cognitive and functional abilities. Psycho-motor agitation represents a frequent and challenging manifestation in People Living with Dementia (PLwD). This behavior contributes to heightened distress and increased risk of harm for patients, while posing a significant burden for caregivers, who must navigate the complexities of managing unpredictable and potentially harmful agitation episodes. Accurately predicting and promptly responding to agitation events is thus critical for enhancing the safety and well-being of PLwD. Leveraging artificial intelligence, tools can be used to monitor behavioral patterns and alert healthcare providers about potential agitation to facilitate timely and effective interventions. Despite the link between poor sleep quality and the likelihood of agitation, there remains a gap in utilizing sleep parameters for predictive analytics in this domain. This study explores the potential of integrating sleep and associated physiological data to predict the risk of agitation in dementia patients the next day, leveraging the Technology Integrated Health Management (TIHM) dataset. Our analysis reveals that the LightGBM model, enhanced with combined feature sets, delivers superior performance, achieving a weighted F1 score of 93.6% compared to standard baseline models. The findings underscore the value of incorporating sleep data into automated models and advocate for continued efforts to develop long-term agitation prediction methods. Full article