Search Results (1,227)

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

Background/Objectives: SPINEHealthie is a smartphone-based application enabling remote patient monitoring, care delivery and standardized collection of patient-reported outcome measures (PROMs). This registry-based analysis evaluates patient satisfaction with the platform across a large, heterogeneous spine surgery cohort. Methods: A total of 1729 thoracolumbar spine surgery cases were eligible for analysis. Among these, 881 (50.9%) completed the in-app feedback survey and constituted the primary feedback cohort. The global app experience was categorized as “Very Good” or “Poor”. Clinical outcomes, i.e., the Visual Analogue Scale (VAS) for back and leg pain and the Oswestry Disability Index (ODI) were assessed at baseline and in the postoperative period; Δ denotes the change from baseline to two weeks postoperation. Results: Among feedback responders, 800 patients (90.8%) rated their app experience as “Very Good”, while 80.8% of 639 patients with surgical outcome data reported that the surgery met their expectations. However, app experience and surgical outcome expectations were assessed using different items at different postoperative time points. Among patients whose surgery did not meet expectations, 84.6% rated their app experience as “Very Good”. Patients with a “Very Good” app-experience rating demonstrated significantly greater improvement in back pain (ΔVAS Back pain 2.94 vs. 2.13, p = 0.027), leg pain (ΔVAS Leg pain 3.16 vs. 2.14, p = 0.019) and disability (ΔODI 11.00 vs. 2.41, p < 0.001). In the primary model, older age, shorter follow-up duration and smaller ODI improvement were associated with an unfavorable app experience; only the association with age remained consistent across sensitivity analyses. Conclusions: Among feedback responders, SPINEHealthie was associated with a high proportion of favorable global app-experience ratings. Favorable app-experience ratings remained high, even among patients with unmet surgical expectations or failure to achieve the exploratory two-week ODI-improvement threshold, suggesting that perceived platform value is not solely explained by surgical outcome. Older patients and those with limited functional recovery were identified as subgroups that may benefit from targeted onboarding and engagement strategies. Full article

Stress–strain detection serves as a common inspection technique in engineering integrity management, enabling the prediction of the health status and remaining service life of materials or structures. This study systematically investigates the performance and application effectiveness of Digital Image Correlation (DIC) and Alternating Current Stress Measurement (ACSM) technologies in the field of stress–strain detection. Based on the inverse magnetostriction effect and Maxwell’s equations, an ACSM detection system was developed, achieving the conversion of stress signals into electromagnetic signals. Simultaneously, DIC technology combined with a high-resolution binocular vision system was employed to realize non-contact measurement of full-field strain distribution. Finite element analysis using COMSOL V6.1 software was conducted to simulate the stress–strain distribution of ferrous specimens under axial tensile load, identifying the range of the gauge section with uniform stress–strain distribution. An experimental platform was established to perform tensile tests on flat specimens. The results demonstrated that the stress detection error of the ACSM system was less than 42.93 MPa, the strain measurement error of the DIC system was below 2.14722 × 10⁻⁴, and the stress inversion error was less than 31 MPa. A comprehensive comparison indicates that DIC technology offers superior performance in measurement accuracy and resolution, while ACSM technology provides advantages in operational convenience and rapid response, making it suitable for rapid screening in industrial settings. Full article

The coronavirus disease 2019 (COVID-19) pandemic highlighted the critical need for rapid, accessible, and accurate diagnostic tools to support timely clinical decision-making, outbreak control, and public health surveillance. Point-of-care testing (POCT) has emerged as an essential component of decentralized healthcare by enabling diagnostic testing outside conventional laboratory settings. This review was based on a structured literature search of PubMed, Scopus, and Web of Science databases covering studies published between January 2020 and January 2026. The review evaluates current advances in COVID-19 POCT technologies, including molecular assays, antigen-based tests, antibody-based assays, biosensor platforms, and artificial intelligence (AI)-assisted diagnostic approaches. Molecular POCT methods, including rapid reverse transcription polymerase chain reaction (RT-PCR), loop-mediated isothermal amplification (LAMP), and CRISPR-based technologies, provide high analytical sensitivity and specificity and are increasingly suitable for decentralized diagnostic applications. Antigen-based assays offer rapid and cost-effective screening solutions, although diagnostic performance may vary depending on viral load, symptom onset, and circulating variants. Antibody-based POCT remains valuable for seroprevalence studies, retrospective diagnosis, and immune-response monitoring rather than acute infection detection. Emerging biosensor technologies and AI-enabled diagnostic systems demonstrate promising analytical capabilities and operational advantages; however, many remain at the prototype or early-validation stage and require further clinical evaluation before widespread implementation. The findings indicate that no single POCT modality is optimal for all clinical scenarios. Instead, molecular, antigen, antibody, biosensor, and AI-assisted approaches provide complementary strengths that support different diagnostic and public health objectives. Continued advances in assay design, digital connectivity, multiplex testing, and variant-resilient detection strategies are expected to further enhance the role of POCT in COVID-19 management and future infectious disease preparedness. Full article

Urban environments concentrate spatially distributed stressors, including heat, noise, pollution, and biodiversity loss, that are increasingly recognized as determinants of population mental health. However, current approaches rarely integrate environmental structure, lived exposure, and climate-related emotional responses within a unified spatial framework applicable to public health. This article introduces Eco-Affective Health Mapping (EAHM) as a conceptual, spatially explicit framework, grounded in the recently formalized Eco-Affective Health theoretical model, for understanding how environmental conditions may shape climate-related emotional responses, including eco-anxiety, solastalgia, and ecological grief, across urban socio-ecological systems. Drawing on evidence from spatial epidemiology, landscape ecology, environmental mental health, and digital phenotyping, I argue that affective responses to environmental stressors are not randomly distributed but are hypothesized to exhibit spatial clustering in relation to environmental exposures, landscape configuration, and mobility-based interactions. I propose the Eco-Affective Health Mapping (EAHM) framework, which integrates four spatial layers: environmental exposures, landscape configuration, person–place interaction, and affective indicators, together with a companion composite metric, the Eco-Affective Load Index (EALI), for which I provide a formal multi-domain specification and a purely illustrative, non-empirical worked example. EAHM is presented here as a theoretical and methodological proposal rather than as a validated instrument: no primary environmental, mobility, or affective data were collected or analyzed for this article, and the framework’s constituent relationships require prospective empirical testing, for which I outline a companion measurement strategy grounded in the Eco-Affective Health Assessment Protocol (EAHAP). By conceptualizing climate-related emotional responses as candidate measurable public health signals, EAHM is intended to support a future shift toward prevention-oriented, population-level mental health strategies aligned with planetary health and sustainable development agendas. Full article

Background/Objectives: The objective evaluation of regional dietary intake remains a core challenge in personalized health management due to complex plate presentations and a lack of culturally specific dataset benchmarks. Methods: This study introduces a confidence-aware hybrid vision–language framework engineered for traditional Turkish food recognition and structured nutritional assessment. Results: We curate a balanced dataset containing 14,711 verified images spanning 40 representative Turkish culinary classes to train and evaluate seven deep learning architectures. Among the visual models, EfficientNet V2-L achieved the highest standalone performance with an accuracy of 93.47%, 0.92 macro-precision, 0.92 macro-recall, and a 0.92 F1 score. To overcome visual ambiguity and automate content analysis, a confidence-aware routing strategy escalates uncertain predictions ($\tau <0.70$) or user-rejected classifications to the Google Gemini 2.5 Flash multimodal large language model (MLLM). Conclusions: This hybrid paradigm yields a combined classification accuracy of 95.50% while validating portion weight estimations within a mean absolute error (MAE) of 18.42 g and total energy within 36.75 kcal. Fully realized as a cross-platform Flutter mobile application, the end-to-end pipeline demonstrates localized plate detection, adaptive portion analysis, and structured nutrient tracking, providing a scalable design for consumer-facing digital nutrition platforms. Full article

Background/Objectives: Germany’s 2019 Digital Healthcare Act allows manufacturers to integrate digital health applications (DiGAs) into statutory health insurance if they demonstrate a medical benefit or patient-relevant structural or procedural improvement, substantiated by comparative studies. However, existing reviews indicate that the quality of evidence in these studies is inconsistent. Methods: A scoping review following the PRISMA-ScR guidelines was conducted using MEDLINE on 16 December 2024, via PubMed, Scopus, EMBASE, and Cochrane Library. Studies published between 2020 and 2024 assessing DiGA evaluation tools, study quality, and the strength of evidence were included, while non-DiGA studies, telemedicine research, and register-based studies were excluded. Results: Out of the 4628 publications screened, 16 met the inclusion criteria. Across the nine dimensions of evidence quality, deficits were most frequently identified in relation to validity (nine publications), transparency (seven), objectivity (six), consistency (five), and reliability (five), followed by relevance, generalizability, and accuracy (four each) and timeliness (two). Conclusions: The findings highlight substantial limitations in the quality of DiGA studies, including high dropout rates, a lack of blinding, gender imbalance, a high risk of bias, small sample sizes, and biased control-group designs. Additionally, DiGA usage durations were often shorter than recommended. Strengthening the evidence base for DiGAs requires balancing methodological rigor with the practical realities of digital health development. Sustainable integration into routine care depends on feasible evaluation pathways that enable continuous, real-world evidence generation and address the financial and operational challenges of high-quality evaluations. Full article

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, driven by the increasing availability of large-scale clinical data and advances in machine learning. Early computational applications in cardiology were primarily limited to rule-based electrocardiogram interpretation systems. Over time, these approaches have evolved into sophisticated deep learning models capable of analysing complex cardiovascular signals and imaging data. In parallel with the broader development of digital health technologies, including wearable devices, electronic health records, and remote monitoring systems, AI applications have expanded across multiple domains of cardiovascular care. These now include electrocardiographic (ECG) and electrophysiological analysis, cardiovascular imaging, surgical planning, and multimodal risk prediction. More recently, multimodal AI models have emerged that integrate heterogeneous data sources such as imaging, physiological signals, clinical records, and genomic information, enabling more comprehensive characterisation of cardiovascular disease. Beyond diagnostic applications, AI is increasingly influencing system-level aspects of cardiovascular medicine, including clinical decision support, workflow optimisation, medical education, and clinical trial design. This narrative review traces the historical and clinical evolution of artificial intelligence in cardiovascular medicine from early automated ECG interpretation systems to contemporary multimodal and system-level applications. It highlights key technological developments, current clinical applications, translational challenges, and the emerging role of AI within digital cardiovascular health ecosystems, with particular emphasis on early disease detection, risk stratification, prognostic modelling, and personalised cardiovascular care. Full article

The transition to more-electric aircraft (MEA) is driving the replacement of centralized hydraulic and pneumatic systems with electrically powered alternatives. Within this paradigm, flight-control actuation remains one of the most demanding subsystems, combining strict requirements in force density, dynamic response, reliability, fault tolerance, thermal performance, and certification. Electromechanical actuators (EMAs) are a key enabling technology due to their potential for power-on-demand operation, reduction or elimination of hydraulic infrastructure, improved maintainability, and compatibility with distributed electrical architectures. However, their broader use in safety-critical flight-control applications remains constrained by mechanical jamming, thermal management, power-electronics robustness, fault tolerance, health monitoring, and system-level integration. This paper presents a structured, design-oriented review of electromechanical flight-control actuation systems within the MEA framework. It analyzes the evolution from hydraulic to fully electromechanical actuation, examines EMA architectures and subsystems, reviews energy-efficiency trade-offs at actuator and aircraft levels, and discusses fault modes, redundancy strategies, fault-tolerant design, and health-monitoring approaches. Finally, it identifies open challenges related to certification, jamming mitigation, high-voltage electrical architectures, wide-bandgap power electronics, thermal management, and digital twin-based health monitoring. The review provides a unified system-level perspective that supports the development of energy-efficient, fault-tolerant, and certifiable flight-control actuation systems. Full article

Green and Blue Infrastructure (GBI) is increasingly addressed within climate adaptation and mitigation policies as a strategic operational measure for reducing climate-related impacts in urban environments. However, GBI effectiveness relies strictly on the biophysical condition of natural assets and on the capacity to monitor and interpret ecosystem processes over time, specifically vegetation quality and its physiological response to climatic stressors within complex urban fabrics. This variability emphasizes the need to integrate ecosystem performance into decision-making through digital frameworks capable of quantifying and accounting for ecological resources across temporal scales. In this context, Remote Sensing (RS) technologies provide a structured informational basis for assessing vegetation health and surface thermal patterns in relation to climatic stress thresholds and human exposure. This paper presents a policy-aligned geospatial evidence framework to bridge the gap between environmental monitoring and urban climate action. By integrating high-resolution multispectral remote sensing with heterogeneous spatial datasets and climate models, the framework enables the multitemporal assessment of ecological conditions to inform where GBI measures can support SECAP implementation, project refinement, and monitoring activities. Developed within the Horizon Europe KNOWING project and applied to the Naples East district, Italy, the framework was operationalized within the city’s Sustainable Energy and Climate Action Plan (SECAP). The application produces scenario-oriented outputs for interpreting the potential contribution of GBI and NbS measures to outdoor heat-stress reduction under SECAP conditions. Its practical value lies in translating biophysical data into reusable GIS/WMS layers that connect ecological performance, climate exposure, socio-energetic vulnerability, and planned urban transformations, thereby supporting SECAP implementation, project refinement, and monitoring. Full article

Virtual reality (VR) has increasingly been explored to support mental health interventions, including those informed by Cognitive Behavioural Therapy (CBT). However, many VR-based CBT systems report limited usability evaluation and minimal practitioner involvement, raising questions about their applicability beyond clinical settings. This study aimed to conduct a usability-focused validation of a gamified VR platform translating selected CBT-informed techniques for non-clinical use. Selected CBT techniques were implemented as short VR mini-games guided by a design rationale emphasising simplicity, symbolic interaction, and ease of use. A user study involving 58 university students evaluated usability using the System Usability Scale (SUS), ISO 9241-11-informed usability categories, and brief anxiety-related self-report measures. In addition, three psychological practitioners reviewed the platform and assessed therapeutic coherence and design alignment. Findings demonstrated good overall usability, positive immediate experiential responses, and practitioner support for the platform’s therapeutic coherence and suitability as a supportive digital wellbeing tool. The study demonstrates a structured usability-based validation approach for VR systems translating CBT-informed techniques and offers practical guidance for the design and evaluation of immersive mental health technologies. Full article

Offshore wind farms are rapidly expanding into deeper and more remote ocean regions. Their structural safety and operational reliability in harsh marine environments have garnered widespread global attention. Sensing technologies capture structural and environmental conditions and are indispensable to structural monitoring. Accordingly, this review examines the applications of environmental monitoring, supervisory control and data acquisition, condition monitoring, and structural health monitoring systems covering both the horizontal-axis and vertical-axis types of fixed and floating offshore wind turbines. It also summarizes key technologies for data transmission and optimal sensor placement. However, uncertainty in sensing data can significantly affect monitoring results, yet existing studies lack an adequate summary and in-depth discussion. We therefore focus on sources of sensing uncertainty, including the marine environment, the host platform, variations in environmental and operational conditions, and sparse sensing. By analyzing their effects on monitoring data, we explore key methods for overcoming data uncertainties and improving sensing accuracy. This paper also evaluates the application potential of cutting-edge artificial intelligence and digital twin technologies. Furthermore, the study points out that fusing multi-source signal data to establish a highly reliable intelligent decision-making and early warning framework is likely to become an important development direction for offshore wind power monitoring. This review aims to provide valuable support for the safe development of offshore wind farms towards deep-sea regions over the coming decades. Full article

Primary prevention is essential to reduce disease burden before clinical onset, yet it remains less systematically integrated into care than diagnosis and treatment. Although artificial intelligence (AI) is increasingly used in medicine, most applications have focused on secondary and tertiary prevention, including diagnosis, prognostic stratification, and disease management, while its role in primary prevention remains less defined. This narrative review examines current AI applications across four modifiable lifestyle domains relevant to prevention and healthspan promotion: nutrition, physical activity, sleep, and mental health. We synthesize evidence on machine-learning models, wearable-derived algorithms, computer-vision tools, just-in-time adaptive interventions, and conversational agents used in consumer, community, and hybrid clinical–digital settings. AI applications support postprandial glycemic prediction, automated dietary assessment, meal-planning adherence, sedentary-pattern detection, personalized exercise recommendations, adaptive behavioral nudges, sleep monitoring, circadian-aware recommendations, psychoeducation, stress-management support, and early identification of psychological vulnerability. Collectively, these tools may extend prevention beyond episodic clinical encounters toward continuous, personalized, and context-aware support. However, evidence remains limited by short follow-up, reliance on surrogate or engagement outcomes, digitally literate populations, and insufficient validation in real-world preventive-care pathways. AI is therefore a promising enabling technology for proactive, healthspan-oriented medicine, provided future studies demonstrate long-term effectiveness, equity, safety, and responsible implementation. Full article

Background/Objectives: Disaster preparedness is central to public health resilience. This study examined the association between disaster-related digital technology engagement and Health Belief Model-based preparedness beliefs among adults attending a primary care clinic in Türkiye. Methods: In this cross-sectional study, 538 participants completed a sociodemographic form, a researcher-developed 10-item Disaster and Technology Use Questionnaire, and the 31-item General Disaster Preparedness Belief Scale. The technology questionnaire used five-point Likert-type response categories and was treated as a multidomain questionnaire; its prespecified equal-weighted sum formed an operational composite index rather than a unidimensional scale score. Supplementary internal-structure analyses described item covariance and clustering. Spearman correlations, false-discovery-rate correction, and hierarchical multiple linear regression with HC3 robust standard errors were used, adjusting for age, gender, education, income, marital status, and occupation. Results: The technology composite showed α = 0.793 and ω = 0.799, and exploratory and confirmatory analyses identified two correlated empirical item domains with adequate model fit (CFI = 0.952, TLI = 0.936, RMSEA = 0.055). Technology engagement was independently associated with total preparedness beliefs (B = 0.888, 95% CI 0.672–1.103; standardized β = 0.397; p < 0.001) and increased explained variance by 13.3% beyond sociodemographic factors. Adjusted associations remained significant for perceived susceptibility, perceived benefits, perceived low barriers, cues to action, and self-efficacy, but not for perceived severity. Conclusions: Greater disaster-related digital technology engagement was associated with stronger preparedness beliefs, but causal direction cannot be inferred. Primary care initiatives may promote official alert tools, practical application use, and digital-information literacy while maintaining non-digital alternatives for people with limited digital access. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly used in youth mental health services, including clinical decision support, risk prediction, and digital therapeutics. However, existing frameworks provide limited guidance for integrating ethical, cultural, and relational considerations into the design, governance, and implementation of AI-enabled mental health technologies. This scoping review examined how digital humanities-informed approaches have been incorporated into AI-supported mental health interventions for children and adolescents. Methods: A scoping review was conducted following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. Peer-reviewed literature published between 2015 and 2025 was searched using PubMed and supplemented by semantic searches through Elicit. Systematic reviews, scoping reviews, and meta-analyses examining AI-enabled digital mental health interventions and digital humanities perspectives were included. Data were synthesized using inductive thematic analysis. Results: Seventeen review-level studies met the inclusion criteria. Six recurring themes were identified: engagement, participatory co-design, human oversight, equity, ethical governance, and implementation. Across the included reviews, humanities-informed approaches were associated with greater attention to relational engagement, stakeholder participation, transparency, contextual adaptation, and culturally responsive implementation. Evidence supporting intervention effectiveness was strongest in systematic reviews and meta-analyses, whereas findings related to ethics, governance, equity, and implementation were derived primarily from scoping reviews and conceptual syntheses. Conclusions: This review suggests that digital humanities provides a valuable interdisciplinary perspective for informing the design, governance, and implementation of AI-enabled youth mental health interventions. Although the current evidence base remains heterogeneous, integrating humanities-informed approaches may support the development of AI systems that are more ethical, equitable, and developmentally responsive. Future research should evaluate these approaches through empirical implementation studies and emerging generative AI applications. Full article