Search Results (2,158)

Background: This study compared the performance of a Head-mounted Virtual Reality Perimeter (HVRP) with the Humphrey Field Analyzer (HFA), the standard in automated perimetry. The HFA is the established standard for automated perimetry but is constrained by lengthy testing, bulky equipment, and limited patient comfort. Comparative data on newer head-mounted virtual reality perimeters are limited, leaving uncertainty about their clinical reliability and potential advantages. Aim: The aim was to evaluate parameters such as visual field outcomes, portability, patient comfort, eye tracking, and usability. Methods: Participants underwent testing with both devices, assessing metrics like mean deviation (MD), pattern standard deviation (PSD), and duration. Results: The HVRP demonstrated small but statistically significant differences in MD and PSD compared to the HFA, while maintaining a consistent trend across participants. MD values were slightly more negative for HFA than HVRP (average difference −0.60 dB, p = 0.0006), while pattern standard deviation was marginally higher with HFA (average difference 0.38 dB, p = 0.00018). Although statistically significant, these differences were small in magnitude and do not undermine the clinical utility or reproducibility of the device. Notably, HVRP showed markedly shorter testing times with HVRP (7.15 vs. 18.11 min, mean difference 10.96 min, p < 0.0001). Its lightweight, portable design allowed for bedside and home testing, enhancing accessibility for pediatric, geriatric, and mobility-impaired patients. Participants reported greater comfort due to the headset design, which eliminated the need for chin rests. The device also offers potential for AI integration and remote data analysis. Conclusions: The HVRP proved to be a reliable, user-friendly alternative to traditional perimetry. Its advantages in comfort, portability, and test efficiency support its use in both clinical settings and remote screening programs for visual field assessment. Its portability and user-friendly design support broader use in clinical practice and expand possibilities for bedside assessment, home monitoring, and remote screening, particularly in populations with limited access to conventional perimetry. Full article

Flooding from hurricanes creates damp indoor environments that support mold growth and microbial contamination, posing long-term health risks for occupants. This pilot study evaluated TMVOCs, microbial activity, and environmental conditions in 13 Hurricane Ian-affected residences across multiple flood-affected neighborhoods. Air samples were collected using sorbent tubes and analyzed by gas chromatography–mass spectrometry, while microbial activity on surfaces was assessed via ATP bioluminescence. Visible mold and dampness were documented with the CDC/NIOSH Dampness and Mold Assessment Tool, and environmental measurements included temperature, relative humidity, and surface as well as hidden moisture. Median (IQR) TMVOC concentrations were 12 (8) µg/m³, with 61% of homes exceeding the 10 µg/m³ benchmark set by previous researchers despite minimal visible contamination. Spearman’s correlation revealed significant negative relationships between odor and surface microbial activity (ρ = −0.569, p < 0.05), indicating that organic debris may play a more crucial role in microbial activity within the tested homes, and that odors might originate from hidden microbes instead of surface microbial growth. Our study emphasizes the necessity of utilizing both chemical (TMVOC) and biological (ATP) indicators to evaluate poor air quality caused by molds in flood-affected homes, serving as a supplement to routine visible mold assessments. Full article

Formaldehyde poses a critical indoor environmental health hazard, particularly in rapidly urbanizing settings. Residential and public buildings serve as the most significant exposure sites; however, the extent of urban populations’ formaldehyde exposure in these two types of environments remains unclear, posing challenges for precise prevention and control strategies. This study employed a comprehensive exposure assessment by combining personal exposure monitoring with environmental sampling to characterize formaldehyde exposure profiles and contributions apportioned to residential and public microenvironments. The mean personal exposure concentration of formaldehyde of working adults was 36.0 μg/m³ (SD: 30.7 μg/m³). The mean chronic daily intake derived from personal data was 5.1 μg/kg/day. Residential environments were identified as the predominant contributors to overall exposure (>50% of total exposure in working adults, and >80% in children/elderly), followed by public places (contributing to 40% among employed adults). For children under 5 years and the elderly, residential settings accounted for >80% of the contribution of total intake. The home and school environments contributed to approximately 60% and 30% of exposure for children and adolescents aged 5–18 years, respectively. Other microenvironments (such as vehicular and outdoor settings) contributed to less than 10%. Simulation scenarios further suggested that reducing indoor formaldehyde concentrations by 15–30% in both residential and public buildings could avert 10–20% of associated health burdens for targeted populations. These findings underscore the continuous need for formaldehyde exposure control in both residential and public environments as well as indoor health interventions in modern urban areas. Full article

Identifying tobacco smoke exposure in indoor environments is critical for public health, especially in vulnerable populations. In this study, we developed and validated a machine learning model to detect smoking households based on indoor air quality (IAQ) data collected using low-cost sensors. A dataset of 129 homes in Spain and Austria was analyzed, with variables including PM_2.5, PM₁, CO₂, temperature, humidity, and total VOCs. The final model, based on the XGBoost algorithm, achieved near-perfect household-level classification (100% accuracy in the test set and AUC = 0.96 in external validation). Analysis of PM_2.5 temporal profiles in representative households helped interpret model performance and highlighted cases where model predictions revealed inconsistencies in self-reported smoking status. These findings support the use of sensor-based approaches for behavioral inference and exposure assessment in residential settings. The proposed method could be extended to other indoor pollution sources and may contribute to risk communication, health-oriented interventions, and policy development, provided that ethical principles such as transparency and informed consent are upheld. Full article

Introduction: Exposure to violence is an adverse experience associated with the perpetration of violent future behaviors such as child-to-parent violence. Objective: The objectives were to analyze the psychometric properties of the Violence Exposure Scale (VES) in a sample of Ecuadorian adolescents as well as its measurement invariance by sex and age; analyze the differences in exposure to violence across four settings (home, school, street, and TV), in two time frames (last year and childhood), according to sex and age; and analyze the relationship between exposure to violence in the four settings and in both time frames with child-to-parent violence. Methods: A cross-sectional study was conducted using a probabilistic sample of 2150 Ecuadorian adolescents (55% female), aged 12 to 18 years (M = 14.53; SD = 1.55). Participants completed the adapted version of the VES and the Child-to-Parent Violence Questionnaire (CPV-Q). Confirmatory factor analyses, reliability testing, convergent and discriminant validity analyses, and measurement invariance assessments were performed. Results: The VES showed excellent model fit in both versions, VES¹ (last year) and VES² (before age 10), with strong goodness-of-fit indices (VES¹: CFI = 0.988; RMSEA = 0.055; VES²: CFI = 0.994; RMSEA = 0.044). Reliability was good (α_o and ω_o ≤ 0.80; G.6 and CR ≤ 0.70). Effect sizes ranged from 0.11 to 0.31 for violence by children toward parents and reached up to 0.83 among the different forms of victimization. Conclusions: The adaptation of the VES in Ecuadorian adolescents showed validity and reliability in assessing exposure to violence. Girls were more at risk at home, while boys were more exposed at school and in the community. Full article

Background/Objectives: Cerebral palsy (CP) is one of the most common causes of permanent motor disability in children, and its consequences for upper limb function have a significant impact on the patient’s independence and quality of life. Virtual reality is attracting increasing interest as a modern, engaging and effective method of physiotherapy for children with cerebral palsy. This systematic literature review aimed to synthesize current scientific data on the impact of virtual-reality-based physiotherapy on upper limb function in children with cerebral palsy. Methods: The review was conducted in accordance with PRISMA 2020 guidelines. PubMed, Science Direct, Scopus, Web of Science, Research Gate and Google Scholar databases were searched for studies published between 2010 and 2025. Six original studies meeting the following criteria were included in the analysis: virtual reality therapy, population of children with cerebral palsy, physiotherapy goals related to the upper limb and availability of full text. Results: All included studies demonstrated a positive impact of virtual reality on at least one functional parameter of the upper limb, including range of motion, muscle strength, coordination and manual precision. Task-oriented training, immersive virtual reality environments and home-based therapy supported by remote monitoring proved to be the most effective. The effects were particularly noticeable in children with moderate impairment at GMFCS I–III. Conclusions: Virtual reality represents a safe and promising technology to support upper limb physiotherapy in children with cerebral palsy. It can be used both in clinical and home settings, contributing to increased exercise intensity and motivation. Further long-term studies using high-quality methodology are needed to determine the sustainability of the effects and their impact on everyday living. Full article

Background: Airborne transmission of bacteria, viruses, and fungal spores poses a major threat in enclosed settings, particularly nursing homes where residents are highly vulnerable. Compressive Heating Air Sterilization Technology (CHAST) applies compressive heating to inactivate microorganisms without reliance on filtration or chemicals. Methods: CHAST efficacy was evaluated in laboratory and deployed for a feasibility and performance validation study of air sterilization in a nursing home environment. Laboratory studies tested prototypes (300–5000 CFM; 220–247 °C) against aerosolized surrogates including Bacillus globigii (Bg), B. stearothermophilus (Bst), B. thuringiensis (Bt), Escherichia coli, and MS2 bacteriophage. Viral inactivation thresholds were further assessed by exposing MS2 to progressively lower treatment temperatures (64.5–143 °C). Feasibility and performance validation evaluation involved continuous operation of two CHAST units in a nursing home, with pre- and post-treatment air samples analyzed for bacterial and fungal burden. Results: Laboratory testing demonstrated consistent microbial inactivation, with most prototypes achieving > 6-log (99.9999%) reductions across bacterial spores, vegetative bacteria, and viruses. A 5000 CFM prototype achieved > 7-log (99.99999%) elimination of B. globigii. MS2 was completely inactivated at 240 °C, with modeling suggesting a threshold for total viral elimination near 170 °C. In the feasibility study, baseline sampling revealed bacterial (35 CFU/m³) and fungal (17 CFU/m³) contamination, dominated by Bacillus, Staphylococcus, Cladosporium, and Penicillium. After 72 h of CHAST operation, discharge air contained no detectable viable organisms, and fungal spore counts showed a 93% reduction relative to baseline return air. Units maintained stable operation (464 °F ± 2 °F; 329–335 CFM) throughout deployment. Conclusion: CHAST reproducibly and scalably inactivated airborne bacteria, viruses, and fungi under laboratory and feasibility field studies, supporting its potential as a chemical-free strategy to improve infection control and indoor air quality in healthcare facilities. Full article

Television commercials (TVCs) play a critical role in shaping and reflecting societal understandings of gender roles. Guided by cultivation theory and framing theory, this study examines gender representation in Indian TVCs, focusing on the gender distribution of primary characters, voiceovers, settings (home, outdoor, workplace), and product categories. A quantitative content analysis of 120 Indian TVCs from 2011 to 2020 was conducted, with coding performed by the researcher. Findings show that men were primary characters in 54.6% of ads, while women featured in 45.4%. Male voiceovers dominated at 70.1%, compared to 29.9% for females. Women appeared in home settings in 66.7% of TVCs, while men were predominant in workplace contexts (100%). No significant gender disparity was observed in outdoor settings. Product-wise, women were mostly linked with household and healthcare items, whereas men dominated sectors like banking, technology, and transport. The study highlights how repetitive portrayals of certain gender framings in TVCs contribute to the normalization of traditional gender roles, offering insights into the symbolic structures that reinforce these norms in Indian media culture. Full article

A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as ‘Watch_TV’, ‘Sleep’, ‘Work_On_Computer’, and ‘Cook_Dinner’, is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models—XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)—are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation. Full article

A critical factor contributing to the burden of childhood asthma is the lack of effective self-management in homecare settings. Artificial intelligence (AI) and lung sound monitoring could help address this gap. Yet, existing AI-driven auscultation tools focus on wheeze detection and often rely on subjective human labels. To improve the early detection of asthma worsening in children in homecare setting, we trained and evaluated a Deep Learning model based on spirometry-labelled lung sounds recordings to detect asthma exacerbation. A single-center prospective observational study was conducted between November 2020 and September 2022 at a tertiary pediatric pulmonology department. Electronic stethoscopes were used to record lung sounds before and after bronchodilator administration in outpatients. In the same session, children also underwent spirometry, which served as the reference standard for labelling the lung sound data. Model performance was assessed on an internal validation set using receiver operating characteristic (ROC) curves. A total of 16.8 h of lung sound recordings from 151 asthmatic pediatric outpatients were collected. The model showed promising discrimination performance, achieving an AUROC of 0.763 in the training set, but performance in the validation set was limited (AUROC = 0.398). This negative result demonstrates that acoustic features alone may not provide sufficient diagnostic information for the early detection of asthma attacks, especially in mostly asymptomatic outpatients typical of homecare settings. It also underlines the challenges introduced by differences in how digital stethoscopes process sounds and highlights the need to define the severity threshold at which acoustic monitoring becomes informative, and clinically relevant for home management. Full article

Background/Objectives: Substance use disorders (SUD) pose a significant public health challenge, with 47.7 million people nationwide struggling to end their addiction. Individuals in recovery from SUDs are at an elevated risk of relapses, even after an extended period of abstinence from substances. While the importance of social relationships in addiction recovery has been extensively researched, the specific ways addiction recovery differs between sex/gender within Oxford House (OH) settings needs further research. Some evidence suggests males and females experience SUD differently and respond distinctively to recovery. Methods: We recruited 229 participants from 42 OH recovery homes, with 55.5% (n = 127) male participants. Moderated mediation model seven by Andrew F. Hayes was used to determine the relationship between quality of life, length of stay, sense of community, and sex/gender. Results: Length of stay was a significant predictor of sense of community, with longer stays associated with stronger perceived community ties. Additionally, quality of life had a robust direct effect on sense of community. We found that there is a small indirect effect of quality of life on sense of community through length of stay for females. Conclusions: These findings suggest that while quality of life and length of stay both independently contribute to individuals’ sense of community, the mediating role of length of stay appears to be more pronounced among females. Further research is needed to understand and address sex/gender-specific recovery experiences. Full article

Background: There is an emerging need for new scalable behavioral assays, i.e., assays that are feasible to administer from the comfort of the person’s home, with ease and at higher frequency than clinical visits or visits to laboratory settings can afford us today. This need poses several challenges which we address in this work along with scalable solutions for behavioral data acquisition and analyses aimed at diversifying various populations under study here and to encourage citizen-driven participatory models of research and clinical practices. Methods: Our methods are centered on the biophysical fluctuations unique to the person and on the characterization of behavioral states using standardized biorhythmic time series data (from kinematic, electrocardiographic, voice, and video-based tools) in naturalistic settings, outside a laboratory environment. The methods are illustrated with three representative studies (58 participants, 8–70 years old, 34 males, 24 females). Data is presented across the nervous systems under a proposed functional taxonomy that permits data organization according to nervous systems’ maturation and decline levels. These methods can be applied to various research programs ranging from clinical trials at home, to remote pedagogical settings. They are aimed at creating new standardized biometric scales to screen and diagnose neurological disorders across the human lifespan. Results: Using this remote data collection system under our new unifying statistical platform for individualized behavioral analysis, we characterize the digital ranges of biophysical signals of neurotypical participants and report departure from normative ranges in neurodevelopmental and neurodegenerative disorders. Each study provides parameter spaces with self-emerging clusters whereby data points corresponding to a cluster are probability distribution parameters automatically classifying participants into different continuous Gamma probability distribution families. Non-parametric analysis reveals significant differences in distributions’ shape and scale (p < 0.01). Data reduction is realizable from full probability distribution families to a single parameter, the Gamma scale, amenable to represent each participant within each subclass, and each cluster of similar participants within each cohort. We report on data integration from stochastic analyses that serve to differentiate participants and propose new ways to highly scale our research, education, and clinical practices. Conclusions: This work highlights important methodological and analytical techniques for developing personalized and scalable biometrics across various populations outside a laboratory setting. Full article

Psychedelic-assisted therapy offers rapid and profound benefits for treatment-resistant psychiatric conditions but remains constrained by the need for intensive, clinic-based administration. Concurrently, advances in digital health technologies have introduced scalable tools. This systematic review evaluates the safety, efficacy, and implementation of digitally enabled psychedelic-assisted therapy delivered in non-clinical settings. A comprehensive search of five databases, registered in PROSPERO (CRD420251020968) and conducted in accordance with PRISMA guidelines, identified six eligible studies including real-world analyses, clinical trials, qualitative research, and case reports, representing a total of 12,731 participants. Most studies examined at-home ketamine or esketamine therapy supported by telehealth platforms or mobile applications. Data were synthesized narratively given the heterogeneity of designs and outcomes. Digital enablement was associated with high response rates (ranging from 56.4% to 62.8% for depression) and rapid symptom improvement, particularly in depression and anxiety. Remote monitoring and digital tools demonstrated feasibility and acceptability, but serious safety concerns—including psychiatric adverse events and one unintentional overdose—underscore the need for strict oversight. Risk of bias was moderate to serious across non-randomized studies, limiting confidence in the findings. One study on virtual ayahuasca rituals highlighted the sociocultural potential and limitations of online practices. Despite promising preliminary findings, the field is marked by low methodological rigor and absence of controlled trials. Digitally supported at-home psychedelic therapy represents a transformative but high-stakes frontier, requiring robust research and safeguards to ensure safe, equitable, and effective implementation. No funding was received for this review, and the authors declare no conflicts of interest. Full article

Advancements in electroencephalography (EEG) technology and feature extraction methods have paved the way for wearable, non-invasive systems that enable continuous sleep monitoring outside clinical environments. This study presents the development and evaluation of an EEG-based acquisition system for sleep staging, which can be adapted for wearable applications. The system utilizes a custom experimental setup with the ADS1299EEG-FE-PDK evaluation board to acquire EEG signals from the forehead and in-ear regions under various conditions, including visual and auditory stimuli. Afterward, the acquired signals were processed to extract a wide range of features in time, frequency, and non-linear domains, selected based on their physiological relevance to sleep stages and disorders. The feature set was reduced using the Minimum Redundancy Maximum Relevance (mRMR) algorithm and Principal Component Analysis (PCA), resulting in a compact and informative subset of principal components. Experiments were conducted on the Bitbrain Open Access Sleep (BOAS) dataset to validate the selected features and assess their robustness across subjects. The feature set extracted from a single EEG frontal derivation (F4-F3) was then used to train and test a two-step deep learning model that combines Long Short-Term Memory (LSTM) and dense layers for 5-class sleep stage classification, utilizing attention and augmentation mechanisms to mitigate the natural imbalance of the feature set. The results—overall accuracies of 93.5% and 94.7% using the reduced feature sets (94% and 98% cumulative explained variance, respectively) and 97.9% using the complete feature set—demonstrate the feasibility of obtaining a reliable classification using a single EEG derivation, mainly for unobtrusive, home-based sleep monitoring systems. Full article

Background: The Pharm2Home Initiative’s Community Health Arm adopts a health-equitable approach to chronic disease education and medication therapy management (MTM). We serve senior residents of Solano County, California, who live in affordable housing and have limited financial resources. Aim: This evaluation assesses the uptake of chronic disease management recommendations provided by clinical pharmacists during MTM sessions at community events. Methods: The program engaged clinical pharmacists to provide tailored education and healthcare interventions in senior housing facilities. The goal was to empower seniors to manage their health effectively. The sessions covered various topics, including expired or duplicated medications, incorrect medication use, consultations on medication management, immunizations, and lifestyle adjustments. Results: Over an 18-month period, from January 2022 to August 2023, the program involved 65 participants across ten community health events. These events provided approximately 65 h of direct intervention. Many participants reported significant improvements in understanding their treatment plans and navigating their health needs more confidently. Feedback from 60 seniors after the sessions indicated that 88% felt much better informed about their medications, and 75% expressed that their concerns were addressed extremely well. Conclusions: These outcomes demonstrate the importance of clinical pharmacist-led interventions in improving seniors’ medication use and chronic disease management. The initiative’s approach advocates for integrating clinical pharmacists into community health settings, suggesting a scalable model for enhancing person-centered care. However, further studies are necessary to assess the long-term impacts of these interventions and explore their effectiveness across diverse age groups and more complex conditions. Full article