Search Results (6,566)

Dental caries remains one of the most prevalent yet preventable non-communicable diseases worldwide, disproportionately affecting populations with limited access to dental care and persistent socioeconomic inequalities. Early-stage lesions frequently remain undetected because of their asymptomatic nature, inadequate screening infrastructure, and the absence of continuous monitoring technologies, resulting in preventable complications and increased healthcare costs. To address these challenges, this study proposes an Internet of Things (IoT)-enabled intraoral wearable sensing device (I-OWSD) for continuous, quantitative, real-time monitoring of biomarkers associated with caries progression. The proposed framework integrates intraoral wearable sensing, cloud-based telemedicine services, and artificial intelligence (AI)-assisted analytics to support preventive oral healthcare and remote clinical decision-making. Two primary contributions are presented. First, a fractional-order delay-type model (FODM) based on the Caputo–Fabrizio derivative is proposed to capture the memory-dependent and nonlocal dynamics of caries progression. Mathematical analysis establishes the model’s non-negativity, boundedness, existence, uniqueness, and stability properties. Second, a biocompatible intraoral sensor interface is designed to enable continuous data acquisition and secure wireless communication with digital health platforms. Simulation results based on the proposed FODM suggest that, under an estimated adoption rate of 67.49%, the I-OWSD framework could reduce caries prevalence by approximately 15% while improving opportunities for early intervention and preventive care. The findings demonstrate the potential of combining fractional-order modelling, wearable sensing, and AI-driven teledentistry to advance continuous oral health monitoring and preventive dental care. Full article

Public lighting can account for nearly 40% of municipal energy consumption in some European cities and plays a vital role in road safety, mobility, and the quality of public spaces. Despite notable efficiency gains from the widespread adoption of light-emitting diode (LED) technologies, the technical outputs of standards-based and installation-level assessment methods are not usually simple and communicable energy-performance labels for municipal decision-making. This study addresses this issue by introducing an algorithm-based framework for classifying energy performance in public lighting at the road-segment level. This approach translates existing lighting standards and efficiency indicators into a straightforward and understandable energy label, adapting the energy labelling concept, commonly used for buildings and appliances, to public space infrastructure. This framework is implemented through a national digital platform for public lighting classification, which has already attracted formal interest from more than 100 municipalities, indicating strong institutional uptake. The results indicate that road-segment-level energy classification is feasible and scalable as a voluntary tool to enhance municipal accountability and support informed decision-making. This study concludes that algorithmic energy labels for public lighting can support sustainable urban governance transparency, comparability and decision-making capacity, with future research aimed at building capacity for large-scale implementation and incorporating environmental, human health, and ecological impact considerations into the classification system. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly embedded within diagnostic imaging workflows, reshaping clinical decision-making, health system governance, and regulatory oversight. While technical advances in radiological AI have accelerated, governance mechanisms have struggled to keep pace with issues of bias, transparency, accountability, and lifecycle oversight. This study examines ethical, regulatory, and implementation challenges in AI-enabled diagnostic imaging, building on prior reviews that have often emphasised technical performance by integrating ethical risk domains with governance responses across the AI lifecycle. Methods: This study presents a PRISMA-ScR-informed systematic survey of 156 sources, including peer-reviewed publications, regulatory documents, policy reports, and professional guidance materials (2018–2025), synthesised through thematic analysis and lifecycle mapping spanning data acquisition, model development, deployment, monitoring, and continuous learning. Results: Drawing on both thematic insights derived from the reviewed literature and established ethical and regulatory frameworks, we propose a literature-derived conceptual ethical-governance framework organised around five pillars: equity and bias mitigation, explainability and transparency, accountability and oversight, privacy-preserving infrastructure, and adaptive regulatory alignment. Although illustrated through the Australian healthcare context, the framework is designed to be transferable to federated and multi-jurisdictional health systems. This review further identifies trust quantification as an underdeveloped but essential dimension of clinical AI governance, emphasising the need to integrate measurable indicators such as calibration, clinician–AI concordance, and patient acceptance into lifecycle-based evaluation. Conclusions: By bridging technical, ethical, and policy perspectives, this review proposes a structured conceptual governance framework to support safe, equitable, and trustworthy AI integration in digital health systems. Full article

Background: Anxiety and depression are major contributors to mental-health burden and frequently co-occur in clinical practice. In tele–mental health, routinely captured operational variables such as consultation duration, visit frequency, and follow-up cadence may provide clinical digital phenotypes that complement conventional symptom scales. This study aimed to characterize anxiety–depression comorbidity in a large real-world tele–mental health cohort and to determine whether symptom severity was associated with distinct patterns of healthcare utilization. Methods: We conducted a retrospective real-world study of 3467 patients followed in psychiatry and psychology teleconsultations. Patients were classified as anxiety only, depression only, comorbid anxiety–depression, or neither. Symptom severity was categorized as mild, moderate, or severe using validated questionnaire-based measures; to improve comparability across instruments, scores were additionally harmonized using z-score normalization. Associations between anxiety and depression severity within the comorbid subgroup were examined using a chi-square framework. Telehealth utilization endpoints included consultation duration, number of consultations, and inter-visit interval, analysed overall and stratified by sex, age group, and symptom severity. Results: Anxiety and/or depression were present in 61.7% of the cohort (2140/3467), and anxiety–depression comorbidity accounted for 43.8% of all patients (1520/3467), indicating substantial real-world overlap. Within comorbid cases, anxiety and depression severity were strongly coupled, with depression severity varying systematically across anxiety severity strata (chi-square p = 9.88 × 10⁻¹⁰²). Compared with isolated anxiety or depression, comorbidity was associated with a more intensive healthcare-utilization profile, characterized by a higher mean number of consultations and shorter inter-visit intervals. Among comorbid patients, females showed greater longitudinal service use than males, with more visits and closer follow-up. Resource use also varied according to symptom burden, mainly in depression, supporting a graded relationship between clinical severity and operational care demand. Conclusions: In this large real-world tele–mental health cohort, anxiety–depression comorbidity was highly prevalent, clinically structured, and associated with distinct and measurable resource-use signatures. These findings highlight the novelty and practical value of integrating symptom severity with operational telehealth data to derive pragmatic digital phenotypes of care intensity. Such phenotypes may support risk stratification, triage, follow-up scheduling, and capacity planning in tele–mental health, with potential translational relevance for broader mental healthcare systems. However, these findings should be considered descriptive and hypothesis-generating and warrant further longitudinal validation in other clinical settings. Full article

Background and Aim: While the proliferation of digital health technologies and wearable devices provides nursing professionals with constant access to biometric data, the pathological reliance on these metrics represents an emerging, yet empirically unexamined, digital anxiety framework. This study aims to theoretically define and systematically analyze this theorized phenomenon—termed “Technohypochondria”—within the context of nursing management and clinical practice. Methods: Utilizing Walker and Avant’s eight-stage concept analysis method, a systematic literature search was conducted across PubMed, CINAHL, Scopus, and Web of Science databases. Following strict inclusion and exclusion criteria, a total of 1240 data sources spanning nursing, management, psychology, and informatics literature were analyzed. Results: Three defining attributes of Technohypochondria emerged inductively from the literature: (1) Biometric data obsession, (2) Digital misinterpretation and catastrophizing, and (3) Need for algorithmic feedback. Unlike the general informational search patterns of cyberchondria, these attributes specifically capture a continuous, device-driven feedback loop. Ownership of wearable technology and inadequate digital health literacy were identified as primary antecedents. The analysis revealed significant managerial consequences, including loss of clinical focus, increased risk of medical errors, and weakened professional autonomy. Conclusions: Technohypochondria operationalizes a specific anxiety framework driven by constant biometric monitoring, conceptually diverging from cyberchondria’s focus on online health-information seeking. For nursing managers, addressing the psychological relationship between staff and technology is a strategic necessity for patient safety and workforce productivity. A primary limitation of this study is its theoretical nature; however, this study provides the essential conceptual foundation awaiting future empirical validation and scale development. Full article

The quick transition of the healthcare industry to digital during the era of the Internet of Medical Things and Artificial Intelligence has ignited the demand for frameworks for data sharing while retaining safety and patient privacy. Centralized learning models place potentially sensitive patient data at risk of leakage, regulatory violation, and cyber-attacks which undermine receptivity and responsible ownership of big medical data. Federated learning is a novel paradigm that allows patients from various healthcare entities to train machine learning models while maintaining the ability to leverage their data without sharing their direct data. This study proposes a systematic literature review of approaches of privacy-preserving federated learning frameworks in healthcare applications. Following PRISMA guidelines, searches were conducted across Web of Science, Scopus, IEEE Xplore, ScienceDirect, PubMed, and ACM Digital Library with predefined query strings, explicit inclusion/exclusion criteria, and quality appraisal procedures. A total of 80 peer-reviewed studies, published from January 2015 to December 2025, were included in this systematic review, which examined cryptographic, architectural and algorithmic methods including differential privacy, homomorphic encryption, and Secure Multi-Party Computation, along with integrations using blockchain to enhance trust and confidence in distributed healthcare systems. The findings indicate a gradual shift towards hybrid privacy-preserving federated learning architectures which combined multiple security mechanisms to improve trust, confidentiality and robustness. Although significant progress has been achieved, the real-world deployment of such systems is heavily affected due to the challenges in communication efficiency, non-IID data distribution, adversarial attacks, and regulatory requirements. This research highlights future research directions for scalable, explainable and interoperable federated architectures that strike an optimal balance of privacy, utility and system performance for next-gen health intelligence. Trial registration: PROSPERO (CRD420261401073). Full article

Objective: To map the scientific evidence and identify knowledge gaps regarding the health of Black and Sexual and Gender Minority populations within the global context of health education. Introduction: Health education curricula should explicitly recognize, define, and address the specific needs and health disparities affecting Black and Sexual and Gender Minority populations to ensure that healthcare provision is comprehensive and inclusive in diverse settings. Eligibility criteria: Studies related to professional health training at undergraduate and graduate levels, as well as other educational modalities addressing healthcare provision for Black and Sexual and Gender Minority populations, were included. Methods: This scoping review was conducted following the JBI methodology. Studies were retrieved from Scopus, Web of Science, PubMed, Embase, MEDLINE, Virtual Health Library, CINAHL, ERIC, Cochrane Library, Brazilian Digital Library of Theses and Dissertations, ProQuest Dissertations & Theses Global, EBSCO databases, and the Networked Digital Library of Theses and Dissertations, without language or time restrictions. Two independent reviewers screened the studies and extracted data using a standardized form developed for this review. Concepts, definitions, structures, results, and applications of professional health education for the care of Black and Sexual and Gender Minority populations were systematically synthesized. The results were organized and presented in tabular and graphical formats, accompanied by a narrative summary. Results: A total of 104 studies were included. The evidence was predominantly concentrated in North America, particularly in the United States, with limited representation from other regions. Most studies were published after 2020, indicating a recent expansion of research interest. The methodological profile was characterized by a predominance of quantitative and descriptive designs, alongside qualitative and mixed-methods approaches. Thematic analysis revealed a concentration of studies addressing gender-affirming care, workforce diversity, social determinants of health, and discrimination, while intersectional approaches and long-term educational outcomes remained less explored. Conclusions: The available evidence indicates that health education has increasingly incorporated themes related to equity and diversity; however, the integration of structured and mandatory curricular approaches addressing the intersectional health needs of Black and Sexual and Gender Minority populations remains limited. The findings highlight the need for broader geographic representation, stronger methodological designs, and the development of comprehensive educational strategies capable of addressing structural inequalities within health training contexts. Full article

Background: With the vast and fast-growing number of mHealth applications supporting health, disease management and self-care for older people, the usability of these applications has become a critical factor determining their acceptance and usage. In order to develop mHealth applications suitable for the aging population, it is important to investigate the relationship between older people’s experience with mobile technology in the past, their perception of the usability of mHealth applications and their subsequent use of these applications. Objective: This study investigated the impact of the length of mobile usage on the perceived mHealth application usability of older adults, and the impact of mHealth application usability on the mHealth application user satisfaction and frequency of use of older adults. Methods: This study is based on a cross-sectional survey among older individuals in Al-Ahsa, Saudi Arabia. The measurement model consisted of five distinct constructs with fifteen corresponding indicators including efficiency, learnability, memorability, error handling, and user satisfaction. In terms of analysis, this study included reliability and descriptive statistics as well as correlation and regression analysis, as well as simple and bootstrapped mediation analysis, and, finally, confirmatory factor analysis (CFA) and structural equation modeling (SEM). Based on discriminant validity, the findings suggest that four first-order dimensions, efficiency, learnability, memorability, and error handling, constitute second-order usability dimensions. Results: A total of 271 older adults were included in the final analysis. All constructs demonstrated satisfactory reliability and convergent validity, with Cronbach’s alpha values ranging from 0.797 to 0.862, Composite Reliability values ranging from 0.798 to 0.860, and Average Variance Extracted values ranging from 0.568 to 0.673. Structural equation modeling revealed that mobile usage duration significantly influenced usability (β = 0.616, p < 0.001), usability significantly influenced user satisfaction (β = 0.953, p < 0.001), and user satisfaction significantly influenced use frequency (β = 0.193, p = 0.002). The second-order structural model demonstrated excellent fit to the data (χ²/df = 1.824, CFI = 0.972, TLI = 0.966, GFI = 0.940, AGFI = 0.928, RMSEA = 0.055). Conclusions: Usability plays a central role in explaining the satisfaction of older people with mHealth services and their continuous use of applications. Older people’s experience with their smartphones is associated with their perceptions of the usability of mHealth applications. Higher perceived usability of mHealth applications is positively associated with greater user satisfaction and more frequent use of these applications among older adults. The findings are in line with a usability-centered technology acceptance model. Design of mHealth services should be based on user-centered design principles. In addition to other design principles, efficiency, learnability, memorability, error handling and other usability principles should be particularly addressed in order to increase acceptance of mHealth services by older people. Full article

Foodborne bacterial pathogens, including S. aureus, L. monocytogenes, C. sakazakii, and M. bovis, pose significant threats to dairy safety and public health. Current detection methods, such as culture-based techniques and real-time quantitative PCR (qPCR), are either time-consuming or limited in absolute quantification accuracy. Herein, we developed and validated a novel quadruplex droplet digital PCR (ddPCR) assay for simultaneous detection and absolute quantification of these four pathogens in dairy products. The assay targets the femA, hly, ompA, and esxA genes, respectively, with optimized primer/probe concentrations of 500 nM/400 nM and an annealing temperature of 58 °C. The established method demonstrated high specificity, with no cross-reactivity against common dairy-associated bacteria. The limits of detection (LoDs) ranged from 7.04 to 10.31 copies/reaction, with coefficients of variation (CVs) below 14% for intra-assay and 10% for inter-assay repeatability. Notably, the ddPCR assay detected a 25% co-contamination rate compared to 13% by qPCR among 120 dairy samples, suggesting higher sensitivity for low-abundance targets. This quadruplex ddPCR platform offers a rapid, sensitive, and high-throughput solution for food safety surveillance, particularly in high-risk dairy matrices such as infant formula. Full article

Background: Driven by Vision 2030, Saudi Arabia is rapidly integrating Artificial Intelligence into its healthcare ecosystem. This study investigates the patterns, predictors, and sociodemographic determinants of AI reliance and dependence in healthcare decision making, focusing on how trust influences the shift toward personalized digital diagnosis. Methods: A cross-sectional study was conducted with 627 adults in Saudi Arabia using convenience sampling. Data collected via online questionnaires were analyzed using JMP student edition version 18 software to evaluate user interaction with symptom checkers, wearables, and generative AI. A multidimensional framework assessed how trust and dependence influence health-seeking behaviors. Results: The findings reveal high AI engagement, with 63.7% of respondents using AI tools weekly. Conversational AI and LLMs are the dominant interfaces (92.2%), primarily serving as “gatekeepers” for personalized diagnosis (71.6%) and treatment suggestions (76.9%) before formal consultations. While gender significantly impacts reliance (p = 0.0037), trust was identified as the only significant predictor of overall engagement (p < 0.0001). Notably, age, education, and income had no statistical impact (p > 0.05), indicating uniform adoption across groups. Conclusions: For surveyed cohorts, trust is the primary determinant of AI reliance, overriding traditional demographic factors. Fostering user trust is essential for the successful implementation of AI-driven personalized healthcare solutions. Full article

The digital sharing of healthcare data necessitates a careful balance between the need for verifiability and the protection of patient privacy. In many real-world scenarios, particularly in employer and third-party verification processes, excessive clinical information is disclosed beyond what is strictly required. This practice introduces significant privacy risks and conflicts with data minimization principles. To address this problem, this study proposes a blockchain-based, privacy-preserving system architecture that enables health report verification without revealing diagnosis information. The proposed system is built upon a dual-layer architecture that structurally separates clinical data from verification processes. In the clinical data layer, health reports are encrypted on the client side and stored in off-chain environments, while only reference data and access control information are recorded on the blockchain. The system further integrates revocation mechanisms, role-based access control, and auditability through a modular smart contract design. In conclusion, this study introduces a modular, privacy-oriented, and practically applicable solution for secure healthcare data verification. By eliminating the need for clinical data disclosure during verification, the proposed architecture offers a novel design perspective and contributes both conceptually and technically to the development of blockchain-based healthcare information systems. Full article

Background/Objectives: Long-term care (LTC) is becoming a strategic priority for health systems facing population ageing, multimorbidity, frailty, and increasing demand for coordinated medical and social support. Greece faces these pressures in a context of fragmented governance, limited formal LTC capacity, heavy reliance on family care, and quality oversight that remains largely compliance-oriented rather than performance-oriented. This policy review aims to translate international and national evidence into an operational framework for measuring and improving LTC quality in Greece. Methods: The review combined a structured search of peer-reviewed literature, international policy reports, statistical sources, and Greek legislative and regulatory texts with a pragmatic feasibility assessment of candidate indicators. Results: Evidence from OECD and EU systems suggests that mature LTC quality systems share four operational features: legally mandated reporting, standardised indicators, public transparency, and use of data for provider-level improvement. For Greece, the analysis identifies major gaps in legal reporting obligations, data interoperability, workforce monitoring, public reporting, and user-experience measurement. We propose a three-tier indicator framework covering safety, clinical care processes, workforce and staffing, person-centredness, access and equity, efficiency, governance, and digital readiness. Implementation should proceed through a five-year roadmap: foundation, scale-up, and consolidation. Provider-level dashboards and a National LTC Quality Observatory are recommended as key mechanisms for transforming data into continuous quality improvement. Conclusions: A phased, feasible, and legally anchored approach could strengthen patient safety, dignity, operational efficiency, and accountability in Greek LTC. Full article

Background: Research on social media use, fear of missing out (FoMO), sleep disturbance, and health complaints has been dominated by survey-based studies, particularly among adolescents and university students. Less is known about how users spontaneously describe these experiences in naturalistic online settings. This exploratory pilot study examined how publicly available Reddit discussions narrate the relationship between social media use, FoMO-related concern, sleep disruption, and self-reported physical complaints. Methods: A total of 30 publicly available English-language Reddit posts and comments were purposively sampled from 11 threads dated August 2022 to March 2026. The study used exploratory qualitative content analysis supported by reflexive thematic interpretation. Structured indicators were used to describe whether each unit contained explicit FoMO language, implicit FoMO-related concern, sleep disturbance, physical health complaints, and nighttime use or sleep loss. Thematic coding was used to identify dominant discourse patterns. All counts and percentages are reported only to characterize the analytic corpus and should not be interpreted as prevalence estimates. Results: Within the corpus, sleep disturbance appeared in 16 of 30 units, nighttime use or sleep loss in 15, physical health complaints in 11, explicit FoMO language in 6, and implicit FoMO-related concern in 3. The dominant themes were delayed sleep and bedtime displacement, somatic and cognitive overload, self-regulation and recovery, and compulsive monitoring and comparison. Sleep-related complaints were usually described alongside bedtime scrolling, delayed disengagement, or lost sleep opportunity. FoMO-related concern was less often expressed through formal terminology and more often appeared through everyday descriptions of checking, comparison, and difficulty disconnecting. Conclusions: This small exploratory corpus suggests that Reddit users often describe social media-related strain through practical behavioral language, such as late-night scrolling, inability to stop, lost sleep, next-day fatigue, headache, and brain fog. The findings are descriptive, discourse-focused, and hypothesis-generating. They do not estimate population prevalence or establish causal health effects. To improve transparency, the revised study provides a de-identified analytic matrix of all 30 coded Reddit units and reports a strengthened coding procedure with independent second-coder checking. Naturally occurring online discourse may complement survey-based digital-health research by showing how users themselves frame the embodied experience of digital over-engagement. Full article

Purpose: This study aimed to determine levels of stress, anxiety, depression and digital fatigue among nursing students and to examine the associations between digital fatigue components and these psychological outcomes. Methods: This descriptive and correlational cross-sectional study was conducted with 543 nursing students from a nursing faculty in Türkiye in June 2025. Data were collected face-to-face using a Personal Information Form, the Digital Fatigue Scale (DFS) and the Depression Anxiety Stress Scale-21 (DASS-21). Descriptive statistics, t-test, Mann–Whitney U, ANOVA, Kruskal–Wallis, correlation, and multiple regression analyses were performed. Results: Students reported elevated levels of depression, anxiety and stress, and a moderate level of digital fatigue (DFS total mean = 2.82 ± 0.68). Female students had significantly higher anxiety, stress and digital fatigue scores than males. Daily internet use of ≥6 h was associated with higher depression, stress and digital fatigue. DASS-21 total scores were positively correlated with DFS total and all subscales. In multivariate models, digital addiction (β = 0.178), online pressure (β = 0.104), and psychosomatic problems (β = 0.174) significantly predicted depression. Psychosomatic problems (β = 0.236) and physical–mental fatigue (β = 0.156) predicted anxiety, while digital addiction (β = 0.180), psychosomatic problems (β = 0.210) and physical–mental fatigue (β = 0.157) predicted stress. These models explained 16.2%, 17.6%, and 20.1% of the total variance, respectively (all p < 0.05). Conclusions: Digital fatigue is positively associated with depression, anxiety, and stress in nursing students. High daily internet use and female gender relate to higher symptom and fatigue scores. Incorporating digital well-being, screen-time management, and mental health support into curricula, along with institutional strategies to reduce digital burden, may protect future nurses’ psychological well-being. Full article

Background: The COVID-19 pandemic was accompanied by an infodemic that promoted the use of so-called “miracle products” lacking scientific evidence, posing significant public health risks. Despite increasing concern, evidence on effective educational strategies to counteract this misinformation remains limited, particularly in Latin America. Methods: A quasi-experimental pre–post study without a control group was conducted among university students in Mexico City between February and June 2021. Participants were recruited via Facebook using a snowball sampling approach. A validated nine-item questionnaire assessed knowledge about miracle products before and after exposure to an educational video intervention. Paired statistical analyses were performed to evaluate changes in knowledge. Results: A total of 157 participants completed the pre-test, and 103 completed the post-test. The intervention resulted in a significant increase in knowledge scores, from a mean of 5.98 (SD = 1.73) to 9.05 (SD = 1.54) (p < 0.001). Significant improvements were observed in eight of nine items, with the largest increases in knowledge related to high-risk substances and reporting mechanisms. No significant baseline differences were found between participants who completed and those who did not complete the post-test. Conclusions: The video-based educational intervention was effective in improving knowledge about miracle products during COVID-19. These findings support the use of digital health education strategies as scalable tools to combat misinformation, particularly in resource-constrained settings. However, further research using controlled designs is needed to assess long-term effects and behavioral outcomes. Full article