Search Results (3,952)

Introduction: Multiple sclerosis (MS) is a chronic neurodegenerative disease that entails high costs, progressive disability, and reduced quality of life (QoL). Telerehabilitation (TR), supported by new technologies, is emerging as an alternative or complement to in-person rehabilitation, potentially lowering socioeconomic impact and improving QoL. Aim: The objective of this study was to evaluate the effect of TR on the QoL of people with MS compared with in-person rehabilitation or no intervention. Materials and methods: A systematic review of randomized clinical trials was conducted (March–May 2025) following PRISMA guidelines. Searches were run in the PubMed-Medline, EMBASE, PEDro, Web of Science, and Dialnet databases. Methodological quality was assessed with the CASP scale, risk of bias with the Risk of Bias 2 tool, and evidence level and grade of recommendation with the Oxford Classification. The protocol was registered in PROSPERO (CRD420251110353). Results: Of the 151 articles initially found, 12 RCTs (598 total patients) met the inclusion criteria. Interventions included (a) four studies employing video-controlled exercise (one involving Pilates to improve fitness, another involving exercise to improve fatigue and general health, and two using exercises focused on the pelvic floor muscles); (b) three studies using a monitoring app to improve manual dexterity, symptom control, and increased physical activity; (c) two studies implementing an augmented reality system to treat cognitive deficits and sexual disorders, respectively; (d) one platform with a virtual reality headset for motor and cognitive training; (e) one study focusing on video-controlled motor imagery, along with the use of a pain management app; (f) a final study addressing cognitive training and pain reduction. Studies used eight different scales to assess QoL, finding similar improvements between groups in eight of the trials and statistically significant improvements in favor of TR in four. The included trials were of good methodological quality, with a moderate-to-low risk of bias and good levels of evidence and grades of recommendation. Conclusions: TR was more effective in improving the QoL of people with MS than no intervention, was as effective as in-person treatment in patients with EDSS ≤ 6, and appeared to be more effective than in-person intervention in patients with EDSS between 5.5 and 7.5 in terms of QoL. It may also eliminate some common barriers to accessing such treatments. Full article

Social farming combines agricultural, social, and healthcare functions, and Animal-Assisted Interventions (AAIs) are increasingly being applied within this framework. Despite their potential, cattle are excluded from Italian guidelines and rarely studied. This pilot study explored the feasibility, effects, and economic sustainability of cow-assisted interventions within social farming in Umbria, Italy. It represents an original and innovative contribution, drawing attention to the therapeutic potential of the human–cow relationship. The study presents an experimental cow therapy protocol and proposes behavioral monitoring tools designed both for people with different disabilities and for the animals involved. Four Red Pied Valdostana cows were involved in structured sessions with three groups: adolescents removed from families, young adults with mental health disorders, and individuals with eating disorders. Activities included observation, feeding, grooming, problem solving, and leading. Human outcomes were assessed regarding emotional, relational, and behavioral dimensions, and animal welfare was continuously monitored. A cost analysis was also conducted for Animal-Assisted Activity (AAA), Animal-Assisted Education (AAE), and Animal-Assisted Therapy (AAT). Participants reported improved self-esteem, emotional expression, and social interaction; the eating disorder group showed greater openness toward dairy consumption. Animal welfare remained stable with high tolerance to handling. Costs were driven mainly by professional staff rather than animal care, with average hourly costs of €74.51 (AAA), €144.99 (AAE), and €172.41 (AAT). The comparative analysis demonstrates a clear trade-off: as the intervention shifts from recreational (AAA) to educational (AAE) and finally to therapeutic (AAT), the financial investment increases in parallel with the level of professionalization, personalization, and expected clinical outcomes. Cow-assisted interventions proved to be safe, feasible, and beneficial, supporting their potential inclusion in Italian guidelines on AAIs. Full article

Background: Vitamin K antagonist oral anticoagulant (VKA) therapy, using warfarin or acenocoumarol in our health system, is indicated, according to clinical guidelines, for the prophylaxis of thromboembolic events. In Málaga, the VKA patient management program currently includes a total of 856 patients. Hypothesis: The use of an AI-based application can enhance treatment adherence among VKA patients participating in self-monitoring and self-management programs. Furthermore, it can support the comprehensive implementation of the system, leading to reduced costs and fewer interventions for anticoagulated patients. Methods: The study will be conducted in several phases. The first phase involves the development of the application and the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second phase includes preliminary testing and validation of the developed application. The third phase consists of full implementation, along with an assessment of user-identified needs and potential quality improvements. Expected Results: The implementation of the AIto-Control app is expected to reduce healthcare-related costs by decreasing primary care visits and hospital admissions due to thromboembolic or bleeding events. Additionally, it aims to ease the workload on both primary care and hospital services. These outcomes will be achieved through the involvement of advanced practice nurses who will supervise app-based monitoring and patient education. Full article

Achieving environmentally sustainable growth is a core challenge for developing economies, yet the welfare consequences of green development policies for vulnerable populations remain understudied. This article investigates the distributional impacts of one of the world’s largest development interventions: China’s energy transition. By integrating provincial-level energy metrics with a decade-long household panel survey (CFPS), we employ a fixed-effects model to provide a holistic assessment of the policy’s effects on household well-being. The analysis reveals a stark trade-off: a 10% increase in clean energy adoption generates significant non-monetary well-being gains, equivalent to a 190,000 CNY annual income rise, primarily through improved environmental quality and cleaner cooking fuel access. However, these benefits are partially offset by rising energy costs. Our heterogeneity analysis reveals a clear regressive burden: the transition significantly increases energy expenditures for rural and low-income households, while having a negligible or even cost-reducing effect on their urban and high-income counterparts. Our findings demonstrate that while the energy transition promotes aggregate welfare, its benefits are unevenly distributed, potentially exacerbating energy poverty and inequality. This underscores a critical development challenge: green growth is not automatically inclusive. We argue that for the energy transition to be truly pro-poor, it must be accompanied by robust social protection mechanisms, such as targeted subsidies, to shield the most vulnerable from the adverse economic shocks of the policy. Full article

Background: The rising prevalence of neurodegenerative conditions such as dementia underscores the impact of population aging. Consequently, long-term care needs have increased and are often met by family members through informal caregiving, thereby supporting formal care systems by reducing associated costs. These caregivers face physical and mental health challenges, raising concerns about their psychological well-being and prompting interest in both clinical and psychosocial research. Ryff’s eudaimonic model offers a robust framework for the assessment of psychological well-being; yet, in Romania, data on this population segment remain limited. Objective: This study aimed to compare the psychological well-being of Romanian dementia family caregivers with a reference population from the Romanian adaptation of the 54-item Ryff Psychological Well-Being Scale, and to explore how sociodemographic characteristics relate to relevant differences across well-being dimensions. Methods: A cross-sectional study was conducted among 70 Romanian family caregivers recruited from a single clinical hospital in Bucharest, Romania. Caregivers completed the 54-item Ryff Scale (Romanian adaptation), and scores were compared to reference values using one-sample t-tests with bootstrap confidence intervals. The most relevant dimension (purpose in life) was dichotomized and further examined in relation to sociodemographic and caregiving variables using Chi-squared and Fisher’s exact tests. Results: Caregivers reported significantly lower scores compared to the reference population in purpose in life (p < 0.001, d = −1.01), personal growth (p < 0.001, d = −0.91), and positive relations (p = 0.01, d = −0.30). The most pronounced deficit was observed in purpose in life, with 85.7% of caregivers scoring below the reference mean. This dimension was further examined in relation to caregiver characteristics. Retirement status showed a statistically significant association with Purpose in Life, with retired caregivers more likely to report lower scores (χ² (1) = 4.04, p = 0.04), supported by the likelihood ratio test (p = 0.01) and a linear trend (p = 0.05). Additional marginal associations were found for household income (p = 0.14) and whether the patient slept in a separate room (p = 0.15), suggesting possible links between caregiver well-being and economic or environmental conditions. Conclusions: The study findings highlight notable psychological vulnerabilities among Romanian dementia caregivers, particularly in purpose in life and personal growth. Associations with structural and contextual factors such as retirement status, income, and caregiving environment suggest that caregiver well-being is shaped by broader socioeconomic conditions. While the magnitude of these deficits may be underestimated due to elevated stress levels in the reference group, the findings underscore the need for targeted clinical, social, and policy-level interventions aimed at strengthening existential meaning and personal development in culturally specific settings. Full article

Background/Objectives: Postoperative complications such as wound infections and sepsis are common in diabetic patients, often resulting in longer hospital stays and higher morbidity. This study hypothesizes that a Random Forest Classifier can accurately predict these complications, enabling early clinical interventions. The model utilizes ensemble learning to integrate diverse patient data and improve predictive accuracy beyond traditional risk assessments. Methods: A comprehensive retrospective analysis was performed using data extracted from the National Surgical Quality Improvement Program (NSQIP) database. The dataset encompassed a wide array of variables, including demographic factors, clinical markers, and detailed surgical data (specialty, type of anesthesia, duration of surgery). Each variable was meticulously encoded into numerical formats, with categorical variables transformed through one-hot encoding, and continuous variables were normalized. The dataset was partitioned into training (80%) and testing (20%) subsets, ensuring a balanced representation of the target outcomes. The Random Forest Classifier was selected due to its robustness in handling high-dimensional data and its ability to model complex interactions between variables. Results: The Random Forest model showed accuracy rates of 94.38% for wound infection and 94.94% for sepsis. Precision and recall metrics also exceeded 94%, highlighting the model’s accuracy in identifying true positives and reducing false positives. ROC curve analysis yielded AUC values of 0.92 for wound infection and 0.95 for sepsis, indicating strong discriminative capability. Feature importance analysis further identified key predictors, including surgical duration, specific laboratory markers, and patient comorbidities. Conclusions: This study demonstrates the Random Forest Classifier’s strong predictive ability for postoperative wound infections and sepsis in diabetic patients. The model’s high-performance metrics indicate its potential for real-time risk stratification in clinical workflows. Future research should validate these findings in diverse populations and surgical settings. Incorporating this predictive model into clinical practice has the potential to significantly improve patient outcomes and reduce healthcare costs. Full article

Cardiovascular diseases claim millions of lives each year, yet timely diagnosis remains a significant challenge due to the high number of patients and associated costs. Although various machine learning solutions have been proposed for this problem, most approaches rely on careful data preprocessing and feature engineering workflows that could benefit from more comprehensive documentation in research publications. To address this issue, this paper presents a machine learning framework for predicting heart attack risk online. Our systematic methodology integrates a unified pipeline featuring advanced data preprocessing, optimized feature selection, and an exhaustive hyperparameter search using cross-validated grid evaluation. We employ a metamodel ensemble strategy, testing and combining six traditional supervised models along with six stacking and voting ensemble models. The proposed system achieves accuracies ranging from 90.2% to 98.9% on three independent clinical datasets, outperforming current state-of-the-art methods. Additionally, it powers a deployable, lightweight web application for real-time decision support. By merging cutting-edge AI with clinical usability, this work offers a scalable solution for early intervention in cardiovascular care. Full article

Objectives: Hearing aids (HAs) are the most common intervention recommended for hearing loss (HL). Many adults with HL do not seek HA rehabilitation. Several studies have attempted to identify barriers and facilitators to using HAs. Different bureaucratic processes for acquiring HAs may lead to different barriers and facilitators. In addition, studies have not yet explored the factors influencing compliance with a recommendation for an HA consultation. This study focuses on the stage prior to consultation in a context where HAs are heavily subsidized. Methods: 148 patients who had undergone a hearing test during 2022 at Hadassah University Medical Center and received a recommendation to undergo a hearing aid consultation were contacted for a telephone survey. Seventy-two adults, 48 male and 24 female, aged 25–85 years, with HL ranging from slight to profound, responded to a telephone questionnaire. The questionnaire, based on two previously published English questionnaires and translated and adapted into Hebrew, was used to assess the main reasons a person did or did not comply with the recommendation to pursue an HA consultation. Results: HL was more severe in those who sought hearing rehabilitation. The main reasons for seeking hearing rehabilitation are the need and desire to hear better and pressure from others. The foremost reasons for not pursuing hearing rehabilitation are feeling that there is currently no need, esthetics, lack of time, and self-consciousness. No significant gender- or age-based differences were found. Conclusions: There are additional barriers to seeking HAs aside from cost and accessibility. Understanding the reasons for avoidance of hearing rehabilitation may help in developing strategies that encourage people to seek hearing rehabilitation and use HAs when the need exists. Full article

To address the global challenge of sustainable energy transition in cities, there is a growing demand for innovative solutions to provide flexible, low-carbon, and socio-economically profitable energy systems. In this context, there is a need for holistic evaluation frameworks for the prioritization and economic optimization of interventions. This paper provides a literature review on sustainable planning and economic impact assessment of innovative urban areas, such as Positive Energy Districts (PEDs), to analyze research trends in terms of evaluation methods, impacts, system boundaries, and identify conceptual and methodological gaps. A dedicated search was conducted in the Scopus database using several query strings to conduct a systematic review. At the end, 57 documents were collected and categorized by analysis approach, indicators, project interventions, and other factors. The review shows that the Cost–Benefit Analysis (CBA) is the most frequently adopted method, while Life Cycle Costing and Multi-Criteria Analysis result in a more limited application. Only in a few cases is the reduction in GHG emissions and disposal costs a part of the economic model. Furthermore, cost assessments usually do not consider the integration of the district into the wider energy network, such as the interaction with energy markets. From a more holistic perspective, additional costs and benefits should be included in the analysis and monetized, such as the co-impact on the social and environmental dimensions (e.g., social well-being, thermal comfort improvement, and biodiversity preservation) and other operational benefits (e.g., increase in property value, revenues from Demand Response, and Peer-To-Peer schemes) and disposal costs, considering specific discount rates. By adopting this multi-criteria thinking, future research should also deepen the synergies between urban sectors by focusing more attention on mobility, urban waste and green management, and the integration of district heating networks. According to this vision, investments in PEDs can generate a better social return and favour the development of shared interdisciplinary solutions. Full article

Health Technology Assessment (HTA) is a multidimensional and multidisciplinary approach for analyzing the medical–clinical, social, organizational, economic, ethical, and legal implications of a technology, through the evaluation of multiple dimensions such as efficacy, safety, costs, and social–organizational impact. In the healthcare context, “technology” refers to any tool—including pharmaceuticals (or, in this case, vaccines)—that is applied to healthcare practice. HTA focuses on assessing both the real and potential effects of a given technology, either prospectively or throughout its life cycle, as well as the consequences that the introduction or exclusion of an intervention may have on the healthcare system, the economy, and society at large. Full article

Background/Objectives: Surgical site infections (SSIs) following pancreaticoduodenectomy contribute to significant morbidity and healthcare costs. Negative pressure wound therapy (NPWT) has emerged as a potential preventive intervention; however, evidence regarding its efficacy in pancreatic surgery remains limited. This systematic review and meta-analysis aimed to evaluate the efficacy of NPWT compared to conventional dressings in preventing SSI following pancreaticoduodenectomy. Methods: PubMed, Scopus, BASE, Cochrane CENTRAL, and ClinicalTrials.gov were systematically searched from their inception to 2 April 2025. Randomized clinical trials and observational studies comparing NPWT with conventional dressings in patients undergoing pancreaticoduodenectomy were included. Two independent reviewers extracted the data and assessed the methodological quality. Random-effects meta-analysis was performed to calculate the pooled relative risks (RRs) with 95% CIs. The primary outcome was the incidence of SSI. The secondary outcomes included pancreatic fistula, seroma formation, incisional hernia, and readmission rates. Results: Nine studies (three randomized clinical trials and six observational studies) comprising 1247 patients were included. NPWT was associated with a significant reduction in SSI compared with conventional dressings (RR, 0.61; 95% CI, 0.41–0.90). Subgroup analysis revealed varying effects by study design: retrospective cohort studies showed a nonsignificant trend toward SSI reduction (RR, 0.53; 95% CI, 0.19–1.48), randomized clinical trials demonstrated a nonsignificant trend favoring NPWT (RR, 0.67; 95% CI, 0.37–1.23), and the single prospective cohort study showed significant SSI reduction (RR, 0.48; 95% CI, 0.28–0.84). No significant differences were observed in pancreatic fistula rates between the NPWT and conventional dressing groups. Prophylactic NPWT application, longer duration (≥5 days), and higher negative pressure settings (−125 mmHg) appeared more effective than therapeutic application, shorter duration, and lower-pressure settings, respectively. Conclusions: This systematic review and meta-analysis suggests that NPWT is associated with a reduced SSI risk following pancreaticoduodenectomy. The greatest benefit may be achieved with prophylactic application in high-risk patients, longer therapy duration, and higher negative pressure settings. These findings support the consideration of NPWT as part of SSI prevention strategies in pancreatic surgery, particularly for patients with identified risk factors. Full article

Public educational buildings, particularly schools, are often overlooked in energy efficiency initiatives, despite their potential for substantial energy and cost savings. This study presents an integrative, measurement-informed, calibrated model-based approach for assessing and enhancing energy performance in elementary schools located in Israel’s hot-arid climate. By combining multiscale environmental monitoring with a rigorously calibrated Energy Plus simulation model, the study evaluates the impact of three demand-side management (DSM) strategies: night ventilation, external envelope insulation, and a combination of the two. Quantitative results show that night ventilation reduced average indoor temperatures by up to 3.3 °C during peak occupancy hours and led to daily energy savings of 10–15%, equating to approximately 1500–2200 kWh annually per classroom. Envelope insulation further reduced diurnal temperature fluctuations from 7.75 °C to 1.0 °C and achieved an additional 9% energy savings. When combined, the two strategies yielded up to 20% energy savings and improved thermal comfort. The findings provide a transferable framework for evaluating retrofitting options in public buildings, offering actionable insights for policymakers and facility managers aiming to implement scalable, cost-effective energy interventions in educational environments. Full article

We evaluated the effectiveness of three locally available disinfectants in reducing Escherichia coli (E. coli) contamination of wastewater-irrigated lettuce while preserving structural integrity. We conducted a quasi-experimental study using lettuce from two farms (Accra and Tamale) in Ghana. Disinfectants tested included (i) salt combined with vinegar, (ii) sequential salt and potassium permanganate, and (iii) sequential vinegar and potassium permanganate. Structural integrity (stem crispness and leaf mushiness) was assessed at 5, 10, and 15 min. E. coli counts and antibiotic resistance were determined pre- and post-disinfection. All three disinfectants preserved structural integrity of lettuce at 5 and 10 min. At 15 min, sequential disinfectants preserved 100% structural integrity, while the salt–vinegar mix caused mushiness in 16%. Pre-disinfection E. coli counts were 9720 cfu/g for Accra (Inter Quartile range, IQR: 3915–14,175) and 72 cfu/g (IQR: 36–189) for Tamale. All disinfectants eliminated E. coli after 15 min. Multi-drug-resistant isolates were common (45% in Accra and 30% in Tamale), particularly against “Watch, restricted use” antibiotics. A 15 min exposure of lettuce to locally available disinfectants, particularly when used sequentially, can eliminate E. coli contamination while preserving structural quality. This practical, low-cost intervention can empower households, vendors, and farmers to limit lettuce-borne diarrheal diseases and antimicrobial resistance transmission. Full article

Viral metagenomic next-generation sequencing (vmNGS) has transformed our capacity for the untargeted detection and characterisation of (re)emerging zoonotic viruses, surpassing the limitations of traditional targeted diagnostics. In this review, we critically evaluate the current landscape of vmNGS, highlighting its integration within the One Health paradigm and its application to the surveillance and discovery of (re)emerging viruses at the human–animal–environment interface. We provide a detailed overview of vmNGS workflows including sample selection, nucleic acid extraction, host depletion, virus enrichment, sequencing platforms, and bioinformatic pipelines, all tailored to maximise sensitivity and specificity for diverse sample types. Through selected case studies, including SARS-CoV-2, mpox, Zika virus, and a novel henipavirus, we illustrate the impact of vmNGS in outbreak detection, genomic surveillance, molecular epidemiology, and the development of diagnostics and vaccines. The review further examines the relative strengths and limitations of vmNGS in both passive and active surveillance, addressing barriers such as cost, infrastructure requirements, and the need for interdisciplinary collaboration. By integrating molecular, ecological, and public health perspectives, vmNGS stands as a central tool for early warning, comprehensive monitoring, and informed intervention against (re)emerging viral threats, underscoring its critical role in global pandemic preparedness and zoonotic disease control. Full article

Reducing food loss and waste (FLW) is a global priority under UN SDG 12.3, yet Taiwan has lacked stage-specific FLW data and systematic valuation of its environmental and economic implications. This study addresses these gaps by integrating localized FLW estimates from the APEC-FLOWS database with an enhanced analytical framework—the Environmentally Extended Input–Output Valuation (EEIO-V) model. The EEIO-V extends conventional input–output analysis by monetizing multiple environmental burdens, including greenhouse gases, air pollutants, wastewater, and solid waste, thereby linking FLW reduction to tangible economic benefits and policy design. The simulations reveal substantial differences in environmental cost reductions across supply chain stages, with downstream interventions delivering the largest benefits, particularly in reducing air pollution and greenhouse gases. By contrast, upstream measures contribute relatively smaller improvements. These findings highlight the novelty of EEIO-V in bridging environmental valuation with system-level FLW analysis, and they provide actionable insights for designing cost-effective, stage-specific strategies that prioritize downstream interventions to advance Taiwan’s sustainability and policy goals. Full article