Search Results (545)

Background/Objectives: Family caregivers of individuals with schizophrenia often face considerable psychological and physical strain due to the complexity of caregiving. Although psychoeducation has demonstrated benefits in alleviating this burden, its provision via telenursing remains underexplored in Saudi Arabia. This study evaluated the effect of a psychoeducational program delivered via telenursing on reducing caregiver burden. Methods: A quasi-experimental design was used with 60 caregivers from a tertiary mental health hospital in northern Saudi Arabia, who were divided equally into intervention and control groups. The intervention group participated in a structured four-week psychoeducational program via Zoom, while the control group received routine care. Caregiver burden was assessed using the Family Burden Interview Schedule (FBIS), a validated tool designed to measure the objective and subjective burden experienced by family members caring for individuals with mental illness. The FBIS was administered before and three months after the intervention. The statistical analysis included independent and paired t-tests and ANOVA. Results: The pre-intervention scores showed no significant differences, confirming baseline equivalence. The post-intervention scores showed a significant reduction in burden among the intervention group (p < 0.001), while no meaningful change occurred in the control group. Additionally, a lower burden was associated with higher education, sufficient income (i.e., the caregiver’s perception of being able to meet essential household expenses without financial strain), strong family support, and absence of caregiver illness. Conclusions: These findings suggest that psychoeducation through telenursing is an effective strategy for reducing caregiver burden and improving support accessibility, particularly for those in remote areas. Full article

Heart failure (HF) is a major global health challenge, characterized by high morbidity, mortality, and frequent hospital readmissions. Despite the advent of guideline-directed medical therapies (GDMTs), the burden of HF continues to grow, necessitating a shift toward comprehensive, multidisciplinary care models. Heart Failure Disease Management Programs (HF-DMPs) have emerged as structured frameworks that integrate evidence-based medical therapy, patient education, telemonitoring, and support for social determinants of health to optimize outcomes and reduce healthcare costs. This review outlines the key components of HF-DMPs, including patient identification and risk stratification, pharmacologic optimization, team-based care, transitional follow-up, remote monitoring, performance metrics, and social support systems. Incorporating tools such as artificial intelligence, pharmacist-led titration, and community health worker support, HF-DMPs represent a scalable approach to improving care delivery. The success of these programs depends on tailored interventions, interdisciplinary collaboration, and health equity-driven strategies. Full article

Background/Objectives: Improving health outcomes for Indigenous people by strengthening the cultural safety of care is a vital challenge for the health sector. University Departments of Rural Health (UDRH), academic centres based in regional, rural, and remote (RRR) locations across Australia, are uniquely positioned to foster a culturally safe rural health workforce through training, education, and engagement with Indigenous communities. This narrative review examines the contributions of UDRHs to health workforce issues through analysis of their publications focused on Indigenous health. Methods: Research articles relating to workforce were identified from an established database of UDRH Indigenous health-related publications published 2010–2021. Results: Of 46 articles identified across the 12 years, 19 focused on developing the understanding and cultural safety skills of university students studying in a health field, including campus-based Indigenous health education and support for students undertaking rural clinical placements. Twelve articles investigated cultural safety skills and recruitment and retention of the rural health workforce. Fifteen articles focused on Indigenous people in the health workforce, examining clinical training and resources, and the enablers and barriers to retaining Indigenous students and workers. Conclusions: This analysis highlights the sustained efforts of UDRHs to improve Indigenous health through multiple areas within their influence, including curriculum design, health student training on campus, and rural placement opportunities to transform understanding of Indigenous strengths and disadvantages and rural health workforce development. A continuing effort is needed on ways UDRHs can support Indigenous health students during their studies and while on placement, how to improve cultural safety in the health workforce, and ways to better support Indigenous health professionals. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Total Knee Arthroplasty (TKA) is a well-established surgical intervention for the management of end-stage knee osteoarthritis. While the procedure is generally successful, postoperative rehabilitation remains a key determinant of long-term functional outcomes. Traditional rehabilitation protocols, particularly those requiring in-person clinical visits, often encounter limitations in accessibility, patient adherence, and personalization. In response, emerging sensor technologies have introduced innovative solutions to support and enhance recovery following TKA. This review provides a thematically organized synthesis of the current landscape and future directions of sensor-assisted rehabilitation in TKA. It examines four main categories of technologies: wearable sensors (e.g., IMUs, accelerometers, gyroscopes), smart implants, pressure-sensing systems, and mobile health (mHealth) platforms such as ReHub^® and BPMpathway. Evidence from recent randomized controlled trials and systematic reviews demonstrates their effectiveness in tracking mobility, monitoring range of motion (ROM), detecting gait anomalies, and delivering real-time feedback to both patients and clinicians. Despite these advances, several challenges persist, including measurement accuracy in unsupervised environments, the complexity of clinical data integration, and digital literacy gaps among older adults. Nevertheless, the integration of artificial intelligence (AI), predictive analytics, and remote rehabilitation tools is driving a shift toward more adaptive and individualized care models. This paper concludes that sensor-enhanced rehabilitation is no longer a future aspiration but an active transition toward a smarter, more accessible, and patient-centered paradigm in recovery after TKA. Full article

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. Full article

Diabetic foot ulcers (DFUs) are a serious complication of diabetes, associated with high recurrence and amputation rates. Adherence to offloading devices is critical for wound healing but remains inadequately monitored in real-world settings. This study evaluates the SmartBoot edge-computing system—a wearable, real-time remote monitoring solution integrating an inertial measurement unit (Sensoria Core) and smartwatch—for its validity in quantifying cadence and step count as digital biomarkers of frailty, and for detecting adherence. Twelve healthy adults wore two types of removable offloading boots (Össur and Foot Defender) during walking tasks at varied speeds; system outputs were validated against a gold-standard wearable and compared with staff-recorded adherence logs. Additionally, user experience was assessed using the Technology Acceptance Model (TAM) in healthy participants (n = 12) and patients with DFU (n = 81). The SmartBoot demonstrated high accuracy in cadence and step count across conditions (bias < 5.5%), with an adherence detection accuracy of 96% (Össur) and 97% (Foot Defender). TAM results indicated strong user acceptance and perceived ease of use across both cohorts. These findings support the SmartBoot system’s potential as a valid, scalable solution for real-time remote monitoring of adherence and mobility in DFU management. Further clinical validation in ongoing studies involving DFU patients is underway. Full article

Quality assurance programs are critical to ensuring the consistency and reliability of point-of-care surveillance test results. In 2022, we launched Laos’ inaugural quality control (QC) and external quality assessment (EQA) program for national HIV recent infection surveillance. Our study aims to implement the first QC and EQA program for national HIV recent infection surveillance in Laos, utilizing non-infectious dried tube specimens (DTS) for quality control testing. This initiative seeks to monitor and assure the quality of HIV infection surveillance. We employed the Asante HIV-1 Rapid Test for Recent Infection (HIV-1 RTRI) point-of-care kit, using plasma specimens from the Thai Red Cross Society to create dried tube specimens (DTS). The DTS panels, including HIV-1 negative, HIV-1 recent, and HIV-1 long-term samples, met ISO 13528:2022 standards to ensure homogeneity and stability. These panels were transported from the Thai National Institute of Health (Thai NIH) to the Laos National Center for Laboratory and Epidemiology (NCLE) and subsequently shipped to 12 remote laboratories at ambient temperature. The laboratory results were electronically transmitted to Thai NIH 15 days after receiving the panel for performance analysis. The concordance results with the sample types were scored, and laboratories that achieved 100% concordance across all sample panels were considered to have satisfactorily met the established standards. Almost all laboratories demonstrated satisfactory results with 100% concordance across all sample panels during all three rounds of QC: 11 out of 12 (92%) in June, 10 out of 12 (83%) in July, and 11 out of 12 (91%) in August. The two rounds of EQA performed in June and August 2022 were satisfied by 8 out of 11 (72%) and 5 out of 10 (50%) laboratories, respectively. QC and EQA monitoring identified errors such as testing protocol mistakes and insufficient DTS panel dissolution, leading to improvements in HIV recency testing quality. Laboratories that reported errors were corrected and implemented further preventive actions. The QC and EQA program for HIV-1 RTRI identified errors in HIV recent infection testing. Implementing a specialized QC and EQA program for DTS marks a significant advancement in improving the accuracy and consistency of HIV recent infection surveillance. Continuous assessment is vital for addressing recurring issues. Full article

Background/Objectives: Mobile healthcare units (MHUs) comprise flexible, ambulatory healthcare teams that deliver community care services, particularly in underserved or remote areas. In Greece, MHUs were pivotal in epidemiological surveillance during the COVID-19 pandemic and are now evolving into a sustainable and integrated service for much-needed community-based healthcare. To support this expanded role, targeted, competency-based training is essential; however, this can pose challenges, especially in coordinating synchronous learning across geographically dispersed teams and in ensuring engagement using an online format. Methods: A nationwide, online training program was developed to improve the knowledge of the personnel members of the Hellenic National Public Health Organization’s MHUs. This program was structured focusing on four core themes: (i) prevention–health promotion; (ii) provision of care; (iii) social welfare and solidarity initiatives; and (iv) digital health skill enhancement. The program was implemented by the University of Crete’s Center for Training and Lifelong Learning from 16 January to 24 February 2025. A multidisciplinary team of 64 experts delivered 250 h of live and on-demand educational content, including health screenings, vaccination protocols, biomarker monitoring, chronic disease management, treatment adherence, organ donation awareness, counseling on social violence, and eHealth applications. Knowledge acquisition was assessed through a pre- and post-training multiple-choice test related to the core themes. Trainees’ and trainers’ qualitative feedback was evaluated using a 0–10 numerical rating scale (Likert-type). Results: A total of 873 MHU members participated in the study, including both healthcare professionals and administrative staff. The attendance rate was consistently above 90% on a daily basis. The average assessment score increased from 52.8% (pre-training) to 69.8% (post-training), indicating 17% knowledge acquisition. The paired t-test analysis demonstrated that this improvement was statistically significant (t = −8.52, p < 0.001), confirming the program’s effectiveness in enhancing knowledge. As part of the evaluation of qualitative feedback, the program was positively evaluated, with 75–80% of trainees rating key components such as content, structure, and trainer effectiveness as “Very Good” or “Excellent.” In addition, using a 0–10 scale, trainers rated the program relative to organization (9.4/10), content (8.8), and trainee engagement (8.9), confirming the program’s strength and scalability in primary care education. Conclusions: This initiative highlights the effectiveness of a structured, online training program in enhancing MHU knowledge, ensuring standardized, high-quality education that supports current primary healthcare needs. Future studies evaluating whether the increase in knowledge acquisition may also result in an improvement in the personnel’s competencies, and clinical practice will further contribute to assessing whether additional training programs may be helpful. Full article

Background: Spinal cord injury is a multisystem disease which compromises independence and quality of life; remote care models represent an opportunity for long-term management of complications. The aim of this study was to explore remote care models for chronic spinal cord injury patients. Methods: A systematic review of the literature was carried out. Five databases (PubMed, CINAHL, Web of Science, Cochrane Library, Google Scholar) were systematically explored with a time limit of five years. Included studies were assessed using Jadad Score and PEDro Scale. Results: Four RCTs were included in this systematic review. In all studies, multidisciplinary home care supported by technology were compared with in-person models. Remote care models were effective in managing pressure injury, infection, and muscle atrophy and improve quality of life. Conclusions: Remote care models can be a key tool for improving self-efficacy, decreasing hospitalizations and preventing long-term mortality. Full article

The COVID-19 pandemic exposed the urgent need for scalable, reliable telemedicine tools to manage mild cases remotely and avoid overburdening healthcare systems. This study evaluates StepCare, a remote monitoring medical device, during the first pandemic wave at a single center in Spain. Among 35 patients monitored, StepCare showed high clinical reliability, aligning with physician assessments in 90.4% of cases. Patients and clinicians reported excellent usability and satisfaction. The system improved workflow efficiency, reducing triage time by 25% and associated costs by 84%. These results highlight StepCare’s value as a scalable, patient-centered solution for remote care during health crises. Full article

Background: Antimicrobial resistance (AMR) poses a significant threat to public health, especially in low- and middle-income countries (LMICs), where surveillance infrastructure is underdeveloped. Bihar, India’s third most populous state and one of its least-resourced states, has remained largely absent from national AMR monitoring initiatives. Methods: This study aimed to characterize the AMR infection landscape across five public tertiary care hospitals in Bihar over three years (2022–2024) and to assess the feasibility of integrating digital workflows for real-time microbiological reporting. Standardized antimicrobial susceptibility testing (AST) was performed on >48,000 urine, pus, and blood samples using CLSI guidelines. Facility-level data were digitized into an open-source AMR reporting system, enabling automated antibiogram generation. Results: The findings revealed substantial resistance: high resistance to beta-lactams, carbapenems, and fluoroquinolones across pathogens. For instance, E. coli sensitivity to nitrofurantoin varied from 86.5% at NMCH (Patna) to 44.7% at JLNMCH (Bhagalpur), while cephalosporin sensitivity in Klebsiella spp. dropped below 2% in several hospitals. MRSA prevalence exceeded 65% in two facilities, far above the national average of 47.8%. Digital integration led to a four-fold increase in culture testing in all facilities and improved data completeness and turnaround times. Spatial analysis and microbiology laboratory assessment revealed significant geographic disparities in diagnostic access, with facilities in remote districts facing delays of over four hours for basic testing. Conclusions: Our study is the first study from India to create such a broad, facility-associated AMR picture over time at a state level. Policy implications include the need for a state-level AMR surveillance dashboard, alignment of procurement with facility-specific resistance patterns, and routine stewardship audits. Clinically, this study demonstrates the utility of localized antibiograms for guiding empirical therapy in resource-limited settings. This study provides a scalable framework for embedding AMR surveillance into routine health system workflows in LMICs. Full article

Urban green spaces (UGSs), including parks, forests, and community gardens, play a critical role in enhancing public health and well-being by providing essential ecosystem services such as improving air quality, reducing surface temperatures, and mitigating harmful substances. As urbanization accelerates, especially in rapidly growing cities like Jinan, China, the demand for UGSs is intensifying, necessitating careful urban planning to balance development and environmental protection. While previous studies have often focused on city-level green coverage, this study shifts the analytical focus from UGS as a whole to urban functional units (UFUs), allowing for a more detailed examination of how green space is distributed across different land use types. We investigate UGS changes in Jinan over the past two decades and assess the influence of socio-economic factors—such as housing prices, land use types, and building age—on UGS distribution within UFUs. Remote sensing technology was employed to analyze the spatiotemporal dynamics of UGS and its correlation with these variables. Our findings reveal a significant shift in UGS distribution, with parks and leisure areas becoming primary drivers of UGS expansion. This study also highlights the growing influence of economic factors, particularly housing prices, on UGS distribution in more affluent UFUs. Additionally, while UGS in Jinan has generally expanded, challenges remain in balancing green space with urban expansion, especially in commercial and residential UFUs. This paper contributes to a more nuanced understanding of UGS distribution by integrating the UFU framework and identifying socio-economic drivers—including housing prices, construction age, and land use type—that shape green space patterns in Jinan. Our findings demonstrate that the spatial pattern of UGS in Jinan mirrors socio-economic and land use disparities observed in other global cities, highlighting both the universality of these patterns and the need for targeted planning in rapidly urbanizing contexts. Full article

The challenges and increasing number of elderly individuals requiring remote monitoring at home highlight the need for technological innovations. This study devised an eHealth mobile application designed to detect abnormal movement behavior and alert caregivers when a lack of movement is detected for an abnormal period. By utilizing the built-in accelerometer of a conventional mobile phone, an application was developed to accurately record movement patterns and identify active and idle states. Fuzzy logic, an artificial intelligence (AI)-inspired paradigm particularly effective for real-time reasoning under uncertainty, was integrated to analyze activity data and generate timely alerts, ensuring rapid response in emergencies. The approach reduced development costs while leveraging the widespread familiarity with mobile phones, facilitating easy adoption. The approach involved collecting real-time accelerometry data, analyzing movement patterns using fuzzy logic-based inferencing, and implementing a rule-based decision system to classify user activity and detect inactivity. This pilot study primarily validated the devised fuzzy logic method and the functional prototype of the mobile application, demonstrating its potential to leverage universal smartphone accelerometers for accessible remote monitoring. Using fuzzy logic, temporal and behavioral symmetry in movement patterns were adapted to detect asymmetric anomalies, e.g., abnormal inactivity or falls. The study is particularly relevant considering lonely individuals found deceased in their homes long after dying. By providing real-time monitoring and proactive alerts, this eHealth solution offers a scalable, cost-effective approach to improving elderly care, enhancing safety, and reducing the risk of unnoticed deaths through fuzzy logic. Full article