Search Results (451)

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) affects an estimated 6% of adults in the United States and contributes to a significant economic burden. Telehealth has emerged as a vital tool in the management of ADHD, offering improved access to care, especially for individuals in underserved communities. Despite its growing role, there remain critical gaps in understanding how social determinants of health (SDOH) are associated with disparities in telehealth utilization for ADHD treatment. Objectives and Methods: This study analyzed data from the National Center for Health Statistics (NCHS) Rapid Surveys System (RSS) Round 2: ADHD (October–November 2023), a nationally fielded survey of U.S. adults. Respondents were classified into three groups: never diagnosed, previously diagnosed, and currently diagnosed with ADHD. The study aimed to (1) compare the distribution of SDOH across ADHD status groups and the general adult population to identify factors associated with ADHD diagnosis; (2) assess the homogeneity of SDOH distributions across ADHD groups; (3) evaluate telehealth utilization among adults currently diagnosed with ADHD; and (4) examine the relationship between SDOH and telehealth use for ADHD treatment. Multivariable logistic regression (MVLR) served as a benchmark model, while machine learning (ML) models—including regularized linear regression, support vector machine (SVM), random forest (RF), LightGBM, multilayer perceptron (MLP), and Few-Shot Learning (FSL)—were trained to identify key predictors. Results: A total of 7009 survey responses were analyzed: 124 had a past diagnosis, 444 were currently diagnosed, and the remainder had never been diagnosed with ADHD, corresponding to a current ADHD prevalence of 6.3%. Adults with current ADHD were more likely to be male, single, younger, white, non-homeowners, and frequent users of online health resources. They also reported lower education, income, and financial security. About 70% used telehealth for counseling and prescriptions; insurance covered telehealth visits for 82.32% of users, yet 38.76% reported no coverage of ADHD-related diagnostic or treatment costs. Nineteen SDOH elements across four domains—demographic, socioeconomic, neighborhood/built environment, and healthcare access—were identified as predictors. ML models outperformed MVLR, with SVM and FSL achieving the highest F1 (both 0.63), and FSL the highest recall (0.69). Age, race, marital status, difficulty paying bills, home ownership, education, and household size were the most consistently important variables. Limitations: This study is limited by a cross-sectional design, reliance on self-reported ADHD diagnoses, and a lack of genetic or family-history measures. Additionally, the omission of complex sampling weights limits the national representativeness of these findings. Finally, the small effective sample size poses risks of model overfitting, and the generalizability of the models could not be externally validated due to the unavailability of comparable independent datasets. Conclusions: Despite widespread internet access, disparities in telehealth use for ADHD persist. Among 19 SDOH predictors, age (aOR = 0.56), difficulty paying medical bills (aOR = 2.52), and race (aOR = 1.37) were significantly associated with telehealth use, and all ML models outperformed the MVLR benchmark, though bootstrap CIs overlapped. Future research should incorporate inclusive data collection and stratified modeling to better represent disadvantaged populations and inform equitable access strategies. Full article

Background: The health sector is getting transformed with the usage of AI, be it diagnosis, treatment planning, disease prediction, and or health system management. Research in this field has picked up in the last few years, which was made possible with the emergence of machine learning, natural language processing and the increasing number of e-health records. Objectives: The study aims to investigate the current trends in the implementation of artificial intelligence (AI) applications in medical settings by investigating the global scientific output/landscape on this theme, such as the annual publication trends, country-wise contributions, and publishing patterns. Methods: The current study is based on systematic review by combining bibliometric analysis and cluster analysis using VOSviewer version 1.6.20, R software version 4.5.0, and Biblioshiny (Bibliometrix package in R) along with preferred reporting items for systematic reviews and meta analyses (PRISMA), 2020 which provides transparency and rigorous visualization to examine the articles published in English on the use of AI in healthcare, after the onset of COVID-19 till date i.e., from 2020 to 2026 on the Scopus database. Results: Using the relevant search string, 5940 documents were identified between 2020 and 2026, 1434 were included for analysis after screening and relevant filters. The publications have increased remarkably after 2020 on this theme and more than half of the publications have their roots in the discipline of Medicine. The USA, China, and the United Kingdom have contributed the most to the volume of research. Natural language processing and diagnosis are the emerging themes. The Journal of Medical Internet Research, BMC Medical Informatics and Decision Making, Computers in Biology and Medicine, IEEE Journal of Biomedical and Health Informatics, Frontiers in Public Health, and Digital Health are some of the most influential sources in the field. Li J and Liu X are among the authors with remarkable local impact. Conclusions: The work aims to assist investigators, health care professionals, and policymakers to learn about modern trends and focus on critical areas of future research and collaboration in AI-enhanced health care. The limitation of the study is that it considered only the Scopus database but it has opened up opportunities for researchers for analysis using other databases such as Dimensions, Lens, and PubMed. Also, this review is considering the publication record since the onset of COVID-19 but a comparative analysis of pre and post-pandemic studies can also be conducted to get a holistic view of drastic collaboration of research in this field. Discussions: The findings suggest that the role of artificial intelligence in health care has paramount over recent years, with other supporting technologies but a technologically hesitant population as well as low acceptance of AI due to ethical issues, cannot be ignored for ensuring efficiency in the health sector. Full article

Background: Depression and anxiety disorders remain among the leading contributors to global disability and represent a major public health challenge. Although evidence-based psychotherapies are available, access to treatment remains limited due to structural, economic, geographical, and workforce-related barriers. Digital mental health interventions have emerged as scalable approaches to reducing this treatment gap, with artificial intelligence (AI)-guided cognitive behavioral therapy (CBT) representing a rapidly developing and clinically relevant extension of digital psychotherapy. Objective: This review aims to synthesize current evidence on digital and AI-guided CBT interventions for depression and anxiety, with a focus on clinical utility, scalability, mechanisms of change, safety considerations, and public health relevance. In addition, the review proposes a clinically oriented conceptual framework for understanding the role of AI-guided CBT within contemporary digital psychiatry. Methods: A focused narrative review was conducted using PubMed, Scopus, and Google Scholar databases, covering publications from 2010 to 2025. Relevant peer-reviewed studies, systematic reviews, meta-analyses, and conceptual papers addressing digital CBT, AI-assisted CBT, conversational agents, symptom monitoring, and digital mental health implementation were identified and analyzed qualitatively. Results: Existing evidence suggests that internet-delivered CBT, mobile applications, and AI-based conversational agents may reduce depressive and anxiety symptoms, particularly in individuals with mild to moderate conditions. However, the evidence base remains heterogeneous, with limitations including short follow-up periods, variability in intervention quality, reliance on self-reported outcomes, and insufficient data on long-term effectiveness, safety, and real-world implementation. Emerging concepts such as digital therapeutic alliance, continuous symptom monitoring, adaptive intervention delivery, and AI-driven personalization may represent key factors influencing engagement and clinical outcomes. Conclusions: AI-guided CBT represents a promising but still evolving component of modern mental health care. These technologies have the potential to improve accessibility, optimize resource allocation, and support stepped-care and hybrid models of treatment. Future research should prioritize rigorous clinical validation, long-term outcome evaluation, transparent safety protocols, ethical governance, and integration into real-world health systems. AI-guided CBT should not be understood as a replacement for clinicians, but as a complementary and scalable extension of evidence-based psychotherapy. Full article

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

Continuous detection of critical events such as falls and health deterioration is enabled by Internet of Things (IoT)-enabled monitoring systems in elderly care. However, system reliability is undermined by real-world sensor degradation, which produces high false-alarm rates and missed incidents. Existing systems lack differentiated governance mechanisms for acute decisions (e.g., fall detection, requiring high sensitivity and low latency) versus cumulative decisions (e.g., health deterioration monitoring, requiring stability and specificity). Conventional approaches treat data quality as a preprocessing concern rather than as a formal determinant of decision admissibility, creating a gap between data availability and decision reliability. In this paper, Decision-Critical Data Quality Contracts are proposed as a governance paradigm in which decision analytics is explicitly separated from admissibility. Symmetric (uniform) and asymmetric (adaptive) enforcement strategies are explored and implemented through a hierarchical Decision Quality Tree framework for context-aware quality assessment. A simulation-based evaluation was conducted over 72 h periods across three degradation scenarios: controlled (5% missingness), realistic (15%), and stress (30% with sensor failures). The no-contract, symmetric, asymmetric, and Decision Quality Tree approaches were compared on metrics including missed alarms, coverage, stability, false alarms, and audit trail completeness. The results demonstrate that missed fall alarms are reduced by up to 71% by the Decision Quality Tree compared to asymmetric enforcement (from 28.57% to 8.20%). Coverage improved to 97.80% and stability to 95.20%. The lowest false-alarm rates are achieved by the Decision Quality Tree (0.90% for acute decisions, 2.80% for cumulative decisions). Audit trail completeness shows a 70.6% improvement over the best baseline (score: 0.87 vs. 0.51). Ablation studies confirm that these improvements stem from synergistic combinations of fallback paths and context awareness. The Decision Quality Tree framework establishes a new balance between system availability and decision safety, providing a foundation for trustworthy IoT governance in elderly care. Full article

Population ageing presents growing challenges for health and social care systems, particularly in supporting older adults to remain independent and involved in decisions concerning their own health and wellbeing. The EMPOWERing individuals and communities to manage their own CARE (EMPOWERCARE) project evaluated asset-based initiatives designed to support older adults in managing their health and wellbeing across seven pilot sites in Belgium, France, the Netherlands and the United Kingdom. Initiatives were categorised as caring technologies, which focused on digital tools and assistive technologies to improve autonomy, promote self-management, and support independent living, and social mobilisation initiatives aimed at building stronger community networks, reducing loneliness, and fostering engagement. A multi-site, embedded case study design combined quantitative and qualitative methods. Survey data were collected at baseline (T0; n = 187) and endpoint (T2; n = 105) between July 2021 and January 2023. Outcomes included self-efficacy, mental wellbeing, loneliness and digital literacy. Descriptive statistics and repeated-measures t-tests were conducted, while Photovoice and focus group data were analysed using summative content analysis. Findings indicated improvements in self-efficacy and mental health among some participants, alongside positive trends in digital literacy and internet-based health-seeking behaviour. Qualitative findings further highlighted increased confidence, social connectedness and empowerment among participants. Full article

Brucellosis remains an important zoonotic disease in southern Saudi Arabia; however, community-level knowledge, risk-related practices, and information pathways in Najran City are insufficiently characterized. This study assessed brucellosis-related knowledge, attitudes, practices, and information pathways among adults in Najran City to inform locally relevant One Health interventions. In this cross-sectional survey, adults were recruited using stratified time–location (venue-based) sampling across community and exposure-relevant sites in Najran City. A total of 608 adults completed a structured interviewer-administered questionnaire. Composite scores were calculated for knowledge (0–21), attitude (0–22), practice (0–64), and information-source breadth (0–6). Descriptive statistics, group comparisons, correlation analyses, and multivariable linear regressions were performed. The findings suggest that participants more commonly relied on interpersonal social networks, especially family and friends, for information related to brucellosis (53.9%), whereas formal sources were less commonly reported, including health professionals (7.9%), media (4.6%), internet sources (3.3%), educational institutions (2.0%), and agricultural or veterinary organizations (1.3%). Mean knowledge scores were moderate (10.7/21), attitudes were generally favorable (19.5/22), and practice scores were moderate (36.6/64). Exposure-related behaviors remained common, particularly the consumption of unpasteurized milk or dairy products (56.6%). The breadth of information sources showed a moderate positive correlation with knowledge (rho = 0.561), whereas attitude showed only small positive correlations with knowledge and practice. Finally, knowledge was weakly and inversely correlated with practice. Among adults recruited in this venue-based sample, favorable attitudes did not consistently correspond to safer practices. These findings support practical One Health interventions, including coordinated veterinary–public health messaging on animal abortion events, safe-dairy guidance at points of sale and community venues, workplace-based training for livestock-contact groups, and referral pathways linking suspected animal cases with veterinary services and human care-seeking. Because recruitment was venue-based and non-probability, the results should be interpreted as descriptive and hypothesis-generating rather than population-representative; however, they still identify practical communication and service-delivery priorities for future intervention studies in Najran. Full article

Problematic internet use among children and adolescents is a major public health concern. In this protocol, it is defined as construct encompassing problematic gaming, social media use, smartphone use, and undifferentiated use, characterized by impaired control, prioritization, and persistence despite harm. It is associated with academic, sleep, and psychosocial difficulties. However, preventive interventions—particularly family-based—remain underexplored despite evidence linking parenting and family functioning to risk. This protocol outlines systematic review and meta-analysis of family-based preventive interventions among children and adolescents (6–18 years). Randomized controlled, quasi-randomized, cluster-randomized, and quasi-experimental studies with parallel comparison groups will be included. Comparators defined as no intervention, waitlist, usual care, or non-family-based prevention. Searches will be conducted in CENTRAL, PubMed, PsycINFO, Web of Science, and CiNii Research, supplemented by reference screening. Risk-of-bias will be assessed using RoB 2 and ROBINS-I. Primary outcomes include changes in overall and subtype-specific severity; secondary outcomes include use time, family functioning, and parental involvement. Random-effects meta-analyses with Hartung–Knapp adjustment will be conducted when ≥3 homogeneous studies are available; otherwise, findings will be synthesized narratively following the Synthesis Without Meta-analysis guideline. This review will synthesize current evidence and clarify role of family-based prevention, informing research and public health strategies. Full article

Objectives: Improving the interoperability of electronic health records is critical for efficient, cost-effective delivery of quality services, enhanced care coordination, and improved treatment outcomes within the United States healthcare system. Healthcare leaders and administrators often experience EHR interoperability issues, restricting communication between health systems and impacting electronic health data utilization. Methods: This systematic literature review explored innovative strategies to improve electronic health record interoperability between health information systems to enhance data exchange efficiency, accuracy, and security in U.S. healthcare settings. A search transpired using the Public Medline, Institute of Electrical and Electronics Engineers Xplore Digital Library, and Cumulative Index to Nursing and Allied Health Literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: Data from 24 relevant articles were analyzed using screening criteria revolving around the research question. Five themes emerged during data analysis. The themes included the utilization of blockchain-based EHR systems (67%), the drive of the Cures Act to achieve interoperability (17%), the advent of artificial intelligence and how it can be used (33%), how the Internet of Things drives the industry to strategically enhance the system (33%), and how the value of interoperability drives outcomes (79%). Conclusions: Findings indicate strategies from a technical perspective and from policy initiatives can improve communication between health information systems. Findings suggest that by strategically leveraging available resources and implementing innovative strategies, healthcare leaders can achieve comprehensive EHR interoperability long term. Full article

Background/Objectives: The digital transformation of healthcare has become a key component of building resilient and sustainable health systems. However, the long-term sustainability of digital health technologies depends not only on user acceptance but also on the institutional governance conditions that shape how these technologies are implemented and utilised in practice. This study examines how institutional factors shape the sustainable utilisation patterns of Internet of Things (IoT) technologies in regulated healthcare environments, with hospital IoT-based asset management systems, a mature and widely deployed use case in China’s public hospitals, providing the empirical context for the investigation. Methods: Drawing on institutional theory and the Technology Acceptance Model (TAM), we conceptualise user perceptions as behavioural micro-foundations through which institutional conditions influence technology utilisation. A survey of 293 healthcare professionals from two large public hospitals in China was analysed using Structural Equation Modelling (SEM), incorporating mediation and Multi-Group Analysis (MGA). Results: The results demonstrate that technical compatibility (TC) significantly enhances perceived ease of use (PEU) (β = 0.40), while organisational support (OS) positively influences both perceived usefulness (PU) (β = 0.35) and PEU (β = 0.30). Conversely, regulatory compliance (RC) negatively affects PU (β = −0.25) and PEU (β = −0.20), revealing a tension between accountability requirements and operational efficiency. The model explains between 58% and 67% of the variance in key constructs. Conclusions: Overall, the findings indicate that sustainable utilisation patterns depend on alignment between technological capabilities and institutional governance conditions, with user perceptions operating as behavioural micro-foundations through which institutional effects are transmitted. By integrating institutional theory with technology acceptance research, this study contributes a governance perspective for understanding sustainable digital transformation in healthcare systems and provides practical insights for designing interoperable, compliant, and supportive digital health infrastructures to enhance hospital operational efficiency and quality of care. Full article

Background: Telehealth has expanded access to care for people with chronic diseases, but little is known about how patients in Jordan become activated, motivated, and ready to use these services, particularly during early adoption. Aim: To explore how patients with chronic diseases in Jordan describe their initial activation, readiness, and experiences with telehealth services. Methods: This exploratory qualitative study used interviews with 14 purposively selected adults with chronic diseases from three hospitals in Jordan. Data was analyzed using Braun and Clarke’s six-step thematic analysis. Results: Four interrelated themes emerged. First, patients valued telehealth for preserving independence and ensuring continuity of care, particularly by reducing reliance on family members for transportation to health facilities. Second, readiness was shaped by geography, mobility, and finances. Although telehealth reduced transport costs and lost wages, patients still had to pay for devices and internet access, creating an economic paradox for poorer patients. Third, participation was supported by families but hindered by low digital literacy, platform changes, and unstable internet connectivity. Fourth, trust in telehealth was conditional and depended on patients’ perceptions of convenience and responsiveness. Conclusions: Readiness to use telehealth was relational, structural, experiential, and conditional rather than purely individual. Patients with chronic diseases in Jordan need hybrid care models that engage families and leverage affordable digital technologies to support sustained telehealth use for disease management. Full article

Background: Despite the introduction in 2017 of mandatory vaccination for the hexavalent and the measles–mumps–rubella–varicella vaccines, childhood vaccination coverage in Sicily (Italy) remains below the recommended and safety threshold of 95%. A catch-up vaccination intervention was implemented for the pediatric population of the 2022–2023 birth cohorts residing in a health district of Palermo (Bagheria) where in 2024, 24-month coverage for polio and measles was 77.29% and 77.62%, respectively. Methods: A cross-sectional study with a before–after component was conducted between June 2025 and December 2025, with the aim of evaluating the increase in vaccination coverage. A questionnaire was administered to the parents of non-compliant children to investigate the determinants of infant vaccine hesitancy. Results: Collaboration with primary care pediatricians and the organization of active call sessions and extra vaccination sessions resulted in an increase in vaccination coverage of approximately 10–12 percentage points in both birth cohorts. The investigation of the determinants of vaccination adherence showed some significant associations: “perception of infectious disease risk” (OR: 7.91; p = 0.009) and “expectations of a positive outcome from vaccination” (OR: 8.62; p = 0.003). Vaccine information sources such as the internet and media were associated with refusal of catch-up vaccination (OR: 0.47, p < 0.001; and OR: 0.13, p = 0.026, respectively). Conclusions: Despite methodological limitations, such as the self-reported nature of the survey data, the study demonstrated the usefulness of local strategies aimed at vaccination catch-up, representing a valuable example of local public health practice and effectively contributing to improved vaccination coverage in the pediatric population. Full article

Morocco, facing a growing prevalence of chronic diseases such as diabetes, hypertension, and cardiovascular diseases, must overcome significant challenges to modernize its healthcare system. In this context, the integration of digital technologies, including telemedicine, the Internet of Medical Things (IoMT), Artificial Intelligence (AI), and healthcare system interoperability, represents a promising solution to improve the management of chronic diseases. This article examines how these technologies can be utilized to transform the Moroccan healthcare system into a more accessible, efficient, and patient-focused model of care. The paper reviews recent pilot projects and initiatives, focusing on infrastructure development, remote monitoring, AI and IoMT integration, public health campaigns, and national health programs aimed at improving access to treatment. Building on these observations, the paper explores the potential of an integrated digital health system for managing chronic diseases and proposes a national integrated care architecture that connects Morocco’s public and private healthcare providers. These insights highlight the significance of digital health in Morocco and provide a framework for improved, more patient-centered, and more efficient advanced healthcare. Future perspectives focus on developing an adapted digital transformation approach to further enhance chronic disease management. Full article

The easily defanged myth that baby rattlesnakes (genera Crotalus and Sistrurus) are more dangerous than adults has persisted in North America despite all evidence to the contrary. The most often cited reason for the babies-more-dangerous (BMD) myth is the venom-dump (VD) hypothesis: babies, in contrast to adults, cannot control how much venom they expend, and therefore inject all of it when biting. We undertook three approaches to explore the origin, transmission, and prevalence of this myth and its most frequent explanation. First, we examined historical newspaper accounts. From 130 newspaper stories mentioning the relative danger of baby rattlesnakes, we identified a timeline in which (1) most stories prior to 1969 were factually correct; (2) the BMD myth and VD hypothesis likely originated in the mid-to-late 1960s and became entrenched in California, especially, from 1970 to 1999; (3) factually incorrect statements subsequently prevailed throughout North America from 2000 to 2014; and (4) factually correct stories regained prominence with apparent effective messaging success from 2015 onward. We further learned that general information stories about rattlesnakes, more often citing subject experts like university professors, were much more likely to provide accurate information than local snakebite stories, which more often cited health professionals (e.g., physicians, veterinarians, pharmacists) and emergency responders (e.g., police and fire officers) who frequently supplied misinformation. Second, we surveyed familiarity with the BMD myth and VD hypothesis among 53 university classrooms (including one high school) representing 3751 students across 29 states within the United States. Consistent with the California media’s outsized influence on misinformation transmission, familiarity with the myth was greatest in the southwestern states (52.6%) and declined moving north and east, with the least familiarity in the northeastern states (16.4%). Third, a small survey of 75 emergency responders and health professionals from Southern California revealed that a whopping 73.3% actually believed the BMD myth. Numerous organizations generally regarded as authoritative further amplified the misinformation, especially on the internet, where some content persists to this day. Unfortunately, belief in the BMD myth and VD hypothesis can lead to negative consequences, including misinformed risk-taking by those encountering snakes, unwarranted fear among snakebite victims, and inappropriate care delivered by misinformed or patient/family-pressured medical professionals. Our findings target health professionals and emergency responders as priority audiences for education. Full article

Background: Tinnitus, the perception of sound in the absence of an external source, is a prevalent condition that can substantially affect physical and mental health. Although tinnitus is not typically curable, it is often manageable with structured, multidisciplinary care. This pilot research describes the Sensation–Emotion–Cognition (SEC) model, a practical audiological framework developed by Danesh et al. that targets three core dimensions of the tinnitus experience. Methods: We integrate findings from an exploratory retrospective cohort and a prospective expansion study. The SEC protocol included sound therapy, counseling and relaxation training, and cognitive behavioral therapy (CBT) delivered through either unguided, module-based internet CBT, clinician-guided module-based internet CBT, or six therapist-led CBT sessions. The objective was to evaluate whether this multifactorial approach is associated with reductions in tinnitus-related distress. Results: In this prospective study, preliminary results from 16 participants who completed the study were associated with significant pre–post changes in tinnitus-related outcomes: 4C management confidence increased from M = 30.38 to 60.19 (p < 0.001; Cohen’s dz = 1.04), and SAD-T emotional distress decreased from M = 4.75 to 2.38 (p = 0.001; Cohen’s dz = 0.99). Conclusions: These findings suggest the potential value of an integrated management strategy; however, given the single-group pre–post design and attrition, the results should be interpreted as exploratory and warrant confirmation in larger controlled trials. Full article