Search Results (118)

Background/Objectives: Health literacy represents the ability to access, understand, appraise, and apply health information for making appropriate health decisions. It is closely linked to education, income, employment, and overall health outcomes. Limited health literacy is associated with poor self-care, inadequate treatment adherence, and increased healthcare utilization. This study aimed to assess the level of health and medication adherence behaviors among primary health care users in Montenegro and examine its association with key demographic and socioeconomic factors. Methods: A cross-sectional, multicenter study was conducted among 202 primary health care users at the Primary Healthcare Center Danilovgrad, Plav and Ulcinj, Montenegro. Data were collected using a demographic questionnaire, the standardized European Health Literacy Questionnaire (HLS-EU-Q-47), and the Attitudes towards Medication Adherence Self-Reported Questionnaire (ADHERE-7). Statistical analyses included descriptive statistics, χ² tests, and univariate and multivariate regression. Results: The mean HLS-EU-Q Index was 33.55 ± 8.05. Significant differences in literacy levels were observed by age (p = 0.022), material status (p = 0.043), and self-rated health (p = 0.020). In multivariate ordinal regression analysis, lower income (<400 €) was associated with lower odds of belonging to a higher health literacy category (OR = 0.22, 95% CI: 0.02–0.92, p = 0.039), while no statistically significant associations were observed for gender, education level, or employment status after adjustment. The mean ADHERE-7 score of the study population was 21.78 ± 5.19. When analyzed in relation to the level of health literacy, the highest mean ADHERE-7 score was observed among participants with excellent health literacy (24.28 ± 4.90). Lower levels of health literacy were associated with lower odds of belonging to higher health literacy categories among participants reporting selected non-adherence behaviors, including missing therapy 3–4 times per week (OR = 0.30), frequently skipping prescribed medication when feeling well (OR = 0.03), and reducing or omitting therapy due to perceived lack of benefit or high costs (OR range: 0.10–0.31). Conclusions: Health literacy among primary care users in Montenegro is moderate, with a substantial proportion exhibiting limited literacy. Low income is a key determinant of limited literacy, and limited health literacy was associated with poorer medication adherence. Targeted educational and policy interventions are needed to improve health literacy and reduce health inequalities. Full article

Virtual reality (VR) offers new opportunities for delivering psychologically meaningful conversations in digitally mediated settings. This study examined how environmental designs influence user experience during emotionally relevant self-disclosure. Fifty university students completed a within-subjects experiment in which they engaged in a structured positive and negative self-disclosure task across four immersive environments (seaside, garden, urban, and sci-fi). After each interaction, participants rated six experiential dimensions relevant to therapeutic communication: comfort, calmness, pleasantness, focus, privacy, and perceived overall suitability for psychological therapy. Repeated-measures analyses showed that nature-themed environments were rated more positively than non-nature environments across all dimensions. Although the seaside and garden environments did not differ in overall composite ratings, the seaside setting was most frequently preferred for comfort, calmness, and pleasantness in participants’ final rankings. These findings demonstrate that virtual environment design meaningfully shapes users’ emotional and interpersonal experience in VR, highlighting the value of nature-based environments for VR counselling systems and digital mental-health applications. Full article

Background/Objectives: Cochlear implants (CIs) are a common treatment of severe-to-profound hearing loss and provide reasonable speech understanding, at least in quiet situations. However, their limited spectro-temporal resolution restricts sound quality, which is especially crucial for music appraisal. Many CI recipients wear a hearing aid (HA) on the non-implanted ear (bimodal users), which may enhance music perception by adding acoustic fine structure cues. Since it is unclear how the HA should be fitted in conjunction with the CI to achieve optimal benefit, this study aimed to systematically vary HA fitting parameters and assess their impact on music sound quality in bimodal users. Methods: Thirteen bimodal CI recipients participated in a listening experiment using a master hearing aid that allowed controlled manipulation of HA settings. Participants evaluated three music excerpts (pop with vocals, pop without vocals, classical) using the multiple-stimulus with hidden reference and anchor (MUSHRA) test. To assess the reliability of individual judgments, each participant repeated the test, and responses were analyzed with the eGauge method. Results: Most participants provided reliable and consistent sound quality ratings. Compared to a standard DSL v5.0 prescriptive fitting, modifications in compression settings and low-frequency gain significantly influenced perceived music quality. The effect of low-frequency gain adjustments was especially pronounced for pop music with vocals, indicating stimulus-dependent benefits. Conclusions: The study demonstrates that HA fitting for bimodal CI users can be optimized beyond standard prescriptive rules to enhance music sound quality by increasing low-frequency gain, particularly for vocal-rich pieces. Additionally, the testing method shows promise for clinical application, enabling individualized HA adjustments based on patient-specific listening preferences, hence fostering personalized audiology care. Full article

Background: Large language models (LLMs) are increasingly considered in dentistry, yet their accuracy in orthodontic radiology remains uncertain. This study evaluated the performance of ChatGPT-4 on questions aligned with current radiology guidelines. Methods: Fifty short, guideline-anchored questions were authored; thirty were pre-selected a priori for their diagnostic relevance. Using the ChatGPT-4 web interface in March 2025, we obtained 30 answers per item (900 in total) across two user accounts and three times of day, each in a new chat with a standardised prompt. Two blinded experts graded all responses on a 3-point scale (0 = incorrect, 1 = partially correct, 2 = correct); disagreements were adjudicated. The primary outcome was strict accuracy (proportion of answers graded 2). Secondary outcomes were partial-credit performance (mean 0–2 score) and inter-rater agreement using multiple coefficients. Results: Strict accuracy was 34.1% (95% CI 31.0–37.2), with wide item-level variability (0–100%). The mean partial-credit score was 1.09/2.00 (median 1.02; IQR 0.53–1.83). Inter-rater agreement was high (percent agreement: 0.938, with coefficients indicating substantial to almost-perfect reliability). Conclusions: In the conditions of this study, ChatGPT-4 demonstrated limited strict accuracy yet substantial reliability in expert grading when applied to orthodontic radiology questions. These findings underline its potential as a complementary educational and decision-support resource while also highlight its present limitations. Its role should remain supportive and informative, never replacing the critical appraisal and professional judgement of the clinician. Full article

As mental health issues such as stress, anxiety, and depression become increasingly prevalent in urban populations, there is a critical need to embed restorative functions into the built environment. Urban parks, as integral components of ecological infrastructure, play a vital role in promoting psychological well-being. This study explores how diverse park environments facilitate mental health recovery through multi-sensory engagement, using integrated psychophysiological assessments in a wetland park in Zhengzhou, China. Electroencephalography (EEG) and perceived restoration scores were employed to evaluate recovery outcomes across four environmental types: waterfront, wetland, forest, and plaza. Key perceptual factors—including landscape design, spatial configuration, biodiversity, and facility quality—were validated and analyzed for their roles in shaping restorative experiences. Results reveal significant variation in recovery effectiveness across environments. Waterfront areas elicited the strongest physiological responses, while plazas demonstrated lower restorative benefits. Two recovery pathways were identified: a direct, sensory-driven process and a cognitively mediated route. Biodiversity promoted physiological restoration only when mediated by perceived restorative qualities, whereas landscape and spatial attributes produced more immediate effects. Facilities supported psychological recovery mainly through cognitive appraisal. The study proposes a smart park framework that incorporates environmental sensors, adaptive lighting, real-time biofeedback systems, and interactive interfaces to enhance user engagement and monitor well-being. These technologies enable urban parks to function as intelligent, health-supportive infrastructures within the broader built environment. The findings offer evidence-based guidance for designing responsive green spaces that contribute to mental resilience, aligning with the goals of smart city development and healthy life-building environments. Full article

This paper examines how artificial intelligence (AI) can be strategically deployed to enhance urban planning and environmental livability in Riyadh by generating data-driven, people-centric design interventions. Unlike previous studies that concentrate primarily on visualization, this research proposes an integrative appraisal framework that combines AI-generated design with site-specific environmental data and native vegetation typologies. This study was conducted across key jurisdictional areas including the Northern Ring Road, King Abdullah Road, Al Rabwa, Al-Malaz, Al-Suwaidi, Al-Batha, and King Fahd Road. Using AI tools, urban scenarios were developed to incorporate expanded pedestrian pathways (up to 3.5 m), dedicated bicycle lanes (up to 3.0 m), and ecologically adaptive green buffer zones featuring native drought-resistant species such as Date Palm, Acacia, and Sidr. The quantitative analysis of post-intervention outcomes revealed surface temperature reductions of 3.2–4.5 °C and significant improvements in urban esthetics, walkability, and perceived safety—measured on a five-point Likert scale with 80–100% increases in user satisfaction. Species selection was validated for ecological adaptability, minimal maintenance needs, and compatibility with Riyadh’s sandy soils. This study directly supports the Kingdom of Saudi Arabia’s Vision 2030 by demonstrating how emerging technologies like AI can drive smart, sustainable urban transformation. It aligns with Vision 2030’s urban development goals under the Quality-of-Life Program and environmental sustainability pillar, promoting healthier, more connected cities with elevated livability standards. The research not only delivers practical design recommendations for planners seeking to embed sustainability and digital innovation in Saudi urbanism but also addresses real-world constraints such as budgetary limitations and infrastructure integration. Full article

This study is titled “Success Factors in Transport Interventions: A Mixed-Method Systematic Review (1990–2022)”. The purpose of the systematic review is to (1) identify effective interventions for transitioning individuals from private car reliance to sustainable transport, (2) summarise psychosocial theories shaping transportation choices and identify enablers and barriers influencing sustainable mode adoption, and (3) determine the success factors for interventions promoting sustainable transport choices. The last search was conducted on 18 November 2022. Five databases (Scopus, Web of Science, MEDLINE, APA PsycInfo, and ProQuest) were searched using customised Boolean search strings. The identified papers were included or excluded based on the following criteria: (a) reported a modal shift from car users or cars to less CO₂-emitting modes of transport, (b) covered the adoption of low-carbon transport alternatives, (c) comprised interventions to promote sustainable transport, (d) assessed or measured the effectiveness of interventions, or (e) proposed behavioural models related to mode choice and/or psychosocial barriers or drivers for car/no-car use. The identified papers eligible for inclusion were critically appraised using Sirriyeh’s Quality Assessment Tool for Studies with Diverse Designs. Inter-rater reliability was assessed using Cohen’s Kappa to evaluate the risk of bias throughout the review process, and low-quality studies identified by the quality assessment were excluded to prevent sample bias. Qualitative data were extracted in a contextually relevant manner, preserving context and meaning to avoid the author’s bias of misinterpretation. Data were extracted using a form derived from the Joanna Briggs Institute. Data transformation and synthesis followed the recommendations of the Joanna Briggs Institution for mixed-method systematic reviews using a convergent integrated approach. Of the 7999 studies, 4 qualitative, 2 mixed-method, and 30 quantitative studies successfully passed all three screening cycles and were included in the review. Many of these studies focused on modelling individuals’ mode choice decisions from a psychological perspective. In contrast, case studies explored various transport interventions to enhance sustainability in densely populated areas. Nevertheless, the current systematic reviews do not show how individuals’ inner dispositions, such as acceptance, intention, or attitude, have evolved from before to after the implementation of schemes. Of the 11 integrated findings, 9 concerned enablers and barriers to an individual’s sustainable mode choice behaviour. In addition, two integrated findings emerged based on the effectiveness of the interventions. Although numerous interventions target public acceptance of sustainable transport, this systematic review reveals a critical knowledge gap regarding their longitudinal impact on individuals and effectiveness in influencing behavioural change. However, the study may be affected by language bias as it only included peer-reviewed articles published in English. Due to methodological heterogeneity across the studies, a meta-analysis was not feasible. Further high-quality research is needed to strengthen the evidence. This systematic review is self-funded and has been registered on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY; Registration Number INPLASY202420011). Full article

Background: The increasing prevalence of dementia and mild cognitive impairment (MCI) among the elderly population is a global health issue. Information and Communication Technology (ICT)-based interventions hold promises for maintaining cognition, but their viability is affected by several challenges. Objectives: This study aimed to significantly assess the effectiveness of ICT-based cognitive and memory aid technology for individuals with MCI or dementia, identify adoption drivers, and develop an implementation model to inform practice. Methods: A PRISMA-based systematic literature review, with the protocol registered in PROSPERO (CRD420251051515), was conducted using seven electronic databases published between January 2015 and January 2025 following the PECOS framework. Random effects models were used for meta-analysis, and risk of bias was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklists. Results: A total of ten forms of ICT interventions that had proved effective to support older adults with MCI and dementia. Barriers to adoption included digital literacy differences, usability issues, privacy concerns, and the lack of caregiver support. Facilitators were individualized design, caregiver involvement, and culturally appropriate implementation. ICT-based interventions showed moderate improvements in cognitive outcomes (pooled Cohen’s d = 0.49, 95% CI: 0.14–1.03). A sensitivity analysis excluding high-risk studies yielded a comparable effect size (Cohen’s d = 0.50), indicating robust findings. However, trim-and-fill analysis suggested a slightly reduced corrected effect (Cohen’s d = 0.39, 95% CI: 0.28–0.49), reflecting potential small-study bias. Heterogeneity was moderate (I² = 46%) and increased to 55% after excluding high-risk studies. Subgroup analysis showed that tablet-based interventions tended to produce higher effect sizes. Conclusions: ICT-based interventions considerably enhance cognition status, autonomy, and social interaction in older adults with MCI and dementia. To ensure long-term scalability, future initiatives must prioritize user-centered design, caregiver education, equitable access to technology, accessible infrastructure and supportive policy frameworks. Full article

Aims: Cue exposure therapy (CET) is a promising treatment approach for cocaine substance use disorder (SUD). CET specifically targets the psychological and physiological responses elicited by drug-related cues, aiming to reduce their motivational impact. To advance understanding of CET for cocaine treatment, this systematic review aims to categorise the range of cocaine cues used in research. Methods: A systematic review of the existing literature with searches conducted on PubMed and Web of Science bibliographic databases with no time constraints in August 2024 (PROSPERO: CRD42024554361). Three reviewers were independently involved in the screening, review and data extraction process, in line with PRISMA guidelines. Data extracted included participant demographics, study design, data on the cocaine cue task, and examples (if provided). Each study was appraised and received a quality score. The secondary outcome was to summarise examples for each category type identified. The data are presented as a narrative synthesis. Results: 3600 articles were identified and screened. 235 articles were included in the analysis. Cues identified included images, paraphernalia, drug-related words, cocaine smell, auditory stimuli presented via audiotapes, video recordings, scripts, and virtual reality environments, often combining multiple modalities. Included studies recruited cocaine-dependent individuals, recreational users, polydrug users, and non-cocaine-using controls. The sample sizes of the studies ranged from a single case study to a study including 1974 participants. Conclusions: This review found that studies employed a wide range of cue categories, but detailed examples were often lacking, limiting replication. The number and combination of cues varied: some studies used only cocaine-related images, while others included images, videos, physical items, and audiotapes. The level of immersion and personalisation also differed considerably. All studies used cocaine-specific cues, most commonly images or representations of cocaine substance, cocaine use or drug paraphernalia, drug preparation items, or conversations of cocaine use and its effects. The overall quality of the included studies was deemed good, with all adhering to standard research norms. While this review highlights the breath of cue types used in the literature, further research should focus on enhancing cue exposure techniques by incorporating more immersive and personalised stimuli, and by providing clearer documentation of cue characteristics to support replication and clinical translation. Full article

This systematic review examines the application of learning analytics to enhance user experience within Learning Management Systems in higher education institutions. Addressing a salient knowledge gap regarding the optimal integration of learning analytics for diverse learner populations, this study identifies analytical approaches and delineates implementation challenges that contribute to data misinterpretation and underutilisation. Consequently, the absence of a systematic evaluation of analytical methodologies impedes the capacity of higher education institutes to tailor learning processes to individual student needs. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a search was conducted across five academic databases. Studies employing learning analytics within Learning Management Systems environments to improve user experience in higher education institutions were included, while purely theoretical or non-higher education institution studies were excluded, resulting in a final corpus of 41 studies. Methodological rigour was assessed using the Critical Appraisal Skills Programme Checklist. This study revealed diverse learning analytics methodologies and a dual research focus on specific platforms and broader impacts on Learning Management Systems. However, ethical, implementation, generalisability, interpretation, personalisation, and system quality challenges impede effective learning analytics integration for user experience improvement, demanding rigorous and contextually aware strategies. This study’s reliance on existing literature introduces potential selection and database biases. As such, future research should prioritise empirical validation and cross-institutional studies to address these limitations. Full article

Background: Interactive conversational agents (chatbots) simulate human conversation using natural language processing and artificial intelligence. They enable dynamic interactions and are used in various fields, including education and healthcare. Objective: This systematic review aims to identify and synthesize studies on chatbots for women and expectant parents in the preconception, pregnancy, and postnatal period through 12 months postpartum. Methods: We searched in six electronic bibliographic databases (MEDLINE (Ovid), CINAHL (EBSCO), Embase, Web of Science, Inspec, and IEEE Xplore) using a pre-defined search strategy. We included sources if they focused on women in the preconception period, pregnant women and their partners, mothers, and fathers/coparents of babies up to 12 months old. Two reviewers independently screened studies and all disagreements were resolved by a third reviewer. Two reviewers independently extracted and validated data from the included studies into a standardized form and conducted quality appraisal. Results: Twelve studies met the inclusion criteria. Seven were from the USA, with others from Brazil, South Korea, Singapore, and Japan. The studies reported high user satisfaction, improved health intentions and behaviors, increased knowledge, and better prevention of preconception risks. Chatbots also facilitated access to health information and interactions with health professionals. Conclusion: We provide an overview of interactive conversational agents used in the perinatal period and their applications. Digital interventions using interactive conversational agents have a positive impact on knowledge, behaviors, attitudes, and the use of health services. Interventions using interactive conversational agents may be more effective than those using methods such as individual or group face-to-face delivery. Full article

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations. Full article

Despite advances in conservation–restoration treatments, most surface cleaning tests are subjectively evaluated. Scores according to qualitative criteria are employed to assess results, but these can vary by user and context. This paper presents a range of cleaning efficacy and homogeneity evaluation metrics for appraising cleaning trials, which minimise user bias by measuring quantifiable changes in the appearance and characteristic spectral properties of surfaces. The metrics are based on various imaging techniques (optical imaging by photography using visible light (VIS); spectral imaging in the visible-to-near-infrared (VNIR) and shortwave infrared (SWIR) ranges; chemical imaging by Fourier transform infrared (FTIR) spectral mapping in the mid-infrared (MIR) range; and scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX) element mapping). They are complemented by appearance measurements (glossimetry and colourimetry). As a case study showcasing the low-cost to high-end metrics, agar gel spray cleaning tests on exposed ground and unvarnished oil paint mock-ups are reported. The evaluation metrics indicated that spraying agar (prepared with citric acid in ammonium hydroxide) at a surface-tailored pH was as a safe candidate for efficacious and homogenous soiling removal on water-sensitive oil paint and protein-bound ground. Further research is required to identify a gel-based cleaning system for oil-bound grounds. Full article

High-income countries like Aotearoa New Zealand are grappling with inequitable access to healthcare services. Out-of-pocket payments can lead to the reduced use of appropriate healthcare services, poorer health outcomes, and catastrophic health expenses. To advance our knowledge, this systematic review asks, “What interventions aim to reduce cost barriers for health users when accessing primary healthcare in high-income countries?” The search strategy comprised three bibliographic databases (Dimensions, Embase, and Medline Web of Science). Two authors selected studies for inclusion; discrepancies were resolved by a third reviewer. All articles published in English from 2000 to May 2022 and that reported on outcomes of interventions that aimed to reduce cost barriers for health users to access primary healthcare in high-income countries were eligible for inclusion. Two blinded authors independently assessed article quality using the Critical Appraisal Skills Program. Relevant data were extracted and analyzed in a narrative synthesis. Forty-three publications involving 18,861,890 participants and 6831 practices (or physicians) met the inclusion criteria. Interventions reported in the literature included removing out-of-pocket costs, implementing nonprofit organizations and community programs, additional workforce, and alternative payment methods. Interventions that involved eliminating or reducing out-of-pocket costs substantially increased healthcare utilization. Where reported, initiatives generally found financial savings at the system level. Health system initiatives generally, but not consistently, were associated with improved access to healthcare services. Full article

The abundance of publicly available data on the internet within the e-marketing domain is consistently expanding. A significant portion of this data revolve around consumers’ perceptions and opinions regarding the goods or services of organizations, making it valuable for market intelligence collectors in marketing, customer relationship management, and customer retention. Sentiment analysis serves as a tool for examining customer sentiment, marketing initiatives, and product appraisals. This valuable information can inform decisions related to future product and service development, marketing campaigns, and customer service enhancements. In social media, predicting ratings is commonly employed to anticipate product ratings based on user reviews. Our study provides an extensive benchmark comparison of different deep learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), and bi-directional long short-term memory (Bi-LSTM). These models are evaluated using various word embedding techniques, such as bi-directional encoder representations from transformers (BERT) and its derivatives, FastText, and Word2Vec. The evaluation encompasses two setups: 5-class versus 3-class. This paper focuses on sentiment analysis using neural network-based models for consumer sentiment prediction by evaluating and contrasting their performance indicators on a dataset of reviews of different products from customers of an online women’s clothes retailer. Full article