Search Results (418)

Objective: To explore the lived experiences of type 2 diabetes mellitus (T2DM) patients undergoing peritoneal dialysis (PD) or hemodialysis (HD) using continuous glucose monitoring (CGM). Research Design and Methods: A qualitative phenomenological study was conducted with 50 adult T2DM patients on PD or HD who used CGM for at least 14 days. Semi-structured interviews were audio-recorded and transcribed verbatim. A thematic analysis framework was applied to identify major themes regarding insulin management, CGM utilization, and emotional and social dimensions. Results: Four main themes emerged, each with multiple subthemes. PD patients emphasized enhanced autonomy and frequent insulin adjustments due to dialysate glucose absorption. Conversely, HD patients reported severe post-dialysis fatigue, emotional distress, and limited social engagement often associated with intra-dialytic hypoglycemia. CGM was valued by 85% of participants for improving metabolic awareness and self-management. However, 15% reported barriers such as device cost and technical difficulties. The insights clearly distinguish the differential impact of dialysis modality on daily glucose control and patient well-being. Conclusions: These findings underscore the critical need for patient-centered care incorporating access to CGM and tailored insulin regimens. Equitable implementation of CGM in dialysis settings could significantly enhance glycemic control, emotional resilience, and overall quality of life. Full article

To address the challenges of slow synthesis speed, unstable quality, limited emotional expressiveness, and the lack of controllable speaking rate in Mongolian emotional speech synthesis, this paper proposes a speech Rate-Controllable Mongolian emotional speech synthesis model based on improved Tacotron2 (SRC-IT2). First, an end-to-end Mongolian speech synthesis module is constructed based on an improved Tacotron2 framework, incorporating the unique linguistic characteristics of the Mongolian script. The front-end processing is optimized accordingly, and a G2P-Seq2Seq model is employed to achieve accurate grapheme-to-phoneme conversion for Mongolian characters. Next, on top of the end-to-end synthesis framework, a joint text-audio emotion analysis module is integrated to effectively learn and represent emotional style features specific to Mongolian speech. Finally, a style encoder and speaking rate control variable are embedded into the acoustic modeling process, further enhancing Tacotron2’s ability to dynamically adjust the speaking rate during emotional speech generation. Experimental results demonstrate that the proposed model produces more natural-sounding speech with improved emotional expressiveness and enables effective real-time control over speaking rate in Mongolian emotional speech synthesis. Full article

Background: While survival outcomes for breast and cervical cancer have improved in Saudi Arabia, little is known about the long-term sexual and psychosocial well-being of survivors. This study aimed to assess sexual satisfaction, emotional health, and social relationship quality among Saudi women diagnosed with cervical and breast cancers and to identify sociodemographic predictors of quality of life (QoL) across these domains. Methods: A cross-sectional survey was administered to 129 women with a history of breast or cervical cancer during May–July 2021. The instrument combined validated tools measuring three core QoL domains: sexual function and satisfaction, psychological and emotional well-being, and social and relationship qualities. Multivariable ordinal logistic regression was used. Results: A total of 129 women with cervical and breast cancers (51.2% cervical, 48.8% breast) participated. Most were aged 31–45 years (45.7%), married (83.0%), with 48.1% holding a bachelor’s degree. Overall, 74.4% of participants reported high to moderate emotional well-being; 48.8% reported satisfactory sexual function, and only 41.1% perceived high quality in social relationships. Younger age (21–30 years), higher education, and having more children were significantly associated with lower emotional well-being (p < 0.05). Conversely, current treatment status and higher parity were associated with better sexual function. Social and relationship quality was significantly higher among younger and employed women. Perceived importance of domains varied, with emotional well-being rating the highest (82.9%). Conclusions: Sexual and social QoL are underserved yet critical components of survivorship care. Cultural, familial, and educational contexts play significant roles in shaping post-treatment experiences. Interventions tailored to age, family dynamics, and treatment stage are needed to improve the holistic well-being of survivors in Saudi Arabia. Full article

Background/Objectives: Sjögren’s disease (SjD) flares are incompletely understood. The patient perspective is critical to closing this gap. This retrospective social media listening (SML) study characterized the flare lexicon within the online Reddit SjD community using novel machine learning and natural language processing. Methods: Documents (posts/comments) were analyzed from the subreddit group “r/Sjogrens” (October 2012 to August 2023). Outcomes were as follows: (1) Frequency of documents mentioning flare, and contexts in which flare was mentioned; (2) clinical concepts associated with flare (analyzed using co-occurrence and pointwise mutual information [PMI]); (3) proportion of flare vs. non-flare documents relevant to SYMPTOMS or TESTING (compared using a two-proportion z-test); and (4) primary emotions mentioned in flare documents. Results: Of 59,266 documents with 5025 authors, flare was mentioned 3330 times (4.4% of documents from 19.1% of authors). Flare was discussed as a symptom (1423 instances), disease (13), or with no clinical category (1890). Flare-associated clinical concepts (co-occurrence > 100 and PMI² > 3) included SYMPTOMS (pain, fatigue, dryness of eye, xerostomia, arthralgia, stress) and BODY PARTS (eye, mouth, joints, whole body). More flare vs. non-flare documents mentioned a SYMPTOM, whereas fewer mentioned a TEST (p < 0.001 for both). Within flare documents, 36.5% expressed emotions, primarily fear (40.5% of primary emotions), happiness (17.8%), sadness (15.7%), and anger (15.5%). Conclusions: The SjD community discusses flare frequently and in context with symptoms, specifically pain, eye and mouth dryness, and fatigue. Flare conversations frequently involve negative emotions. Additional research is required to clarify the patient experience of flare, its clinical parameters, and implications. Full article

As a pivotal carrier of emerging human–computer interaction technologies, artificial intelligence (AI) conversational agents (CAs) hold critical significance for research on the mechanisms of users’ continuance usage behaviour, which is essential for technological optimization and commercial transformation. However, the differential impact pathways of multidimensional perceived interactivity on continuance usage intention, particularly the synergistic mechanisms between technical and affective dual-path dimensions, remain unclear. This study investigates the personalized AI-based CAs project “Dialogue with Great Souls,” launched on a Chinese social platform, using survey data from 305 users. A hybrid approach combining partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) was employed for empirical analysis. The results indicate that technical dimensions, such as control and responsiveness, are key factors influencing trust, while affective interactive dimensions, including communication, personalization, and playfulness, significantly affect social presence, thereby shaping users’ continuance usage intention. ANN results corroborated most PLS-SEM findings but revealed inconsistencies in the predictive importance of personalization and communication on social presence, highlighting the complementary nature of linear and nonlinear interaction mechanisms. By expanding the interactivity model and adopting a hybrid methodology, this study constructs a novel framework for AI CAs. The empirical findings suggest that developers should strengthen socio-emotional bonds in anthropomorphic interactions while ensuring technical credibility to enhance users’ continuance usage intention. This research not only advances theoretical perspectives on the integration of technical and affective dimensions in agent systems but also provides practical recommendations for optimizing the design and development of AI CAs. Full article

This study models how designable cues in digital heritage promotion shape advocacy through affect and memory. Relying on the stimulus–organism–response paradigm, we argue that three stimuli, Visual Sensory Appeal (VSA), Narrative Immersion (NI), and Perceived Authenticity (PA), trigger Emotional Engagement (EE) and become Destination Memory (DM), leading to Intention to Recommend (IR). A cross-sectional quantitative design with an online self-report survey was employed. Using Structural Equation Modeling (SEM) we modeled 653 usable responses to test hypothesized stimulus–organism–response processes and Multi-Group Analysis (MGA) tested heterogeneity across gender, age, education, recent contact, cultural-travel frequency, preservation interest, prior heritage experience, and technology use. Direct associations revealed VSA was a strong predictor of IR, and EE and DM predicted IR positively. NI and PA were not incrementally directly affecting IR. Mediation tests revealed partial mediation for VSA (through EE and DM) and complete mediation for NI and PA; across all stimuli, DM far surpassed EE, suggesting memory consolidation as the overall mechanism. MGA revealed systematic segmentation: women preferred visual and authenticity approaches; men used affective conversion, narrative, and authenticity-to-memory more; young adults preferred story/memory levers; higher education made authenticity pathways legitimate; exposure, experience, sustainability interest, and technology use further conditioned strength of paths. Results sharpen S–O–R accounts by ranking visual design as a proximal driver and placing EE on DM as the central channel through which narrative and authenticity have their influence. In practice, the research supports visually consistent, memory-backed, segment-specific strategies for sustainable, inclusive heritage communication. Full article

Background/Objectives: Suicide is the leading cause of death among South Korean military soldiers, accounting for more than 70% of all deaths. This issue is particularly relevant in the military context due to the nature of living in groups in a controlled environment. This study was conducted active-duty south Korean male soldiers aged 18 to 28 who were performing mandatory military service for one year and six months. Additionally, it compares and analyzes the differences in suicidal ideation and risk factors between military soldiers and a comparison group consisting of males in their 20s without military experience. Methods: This study included 248 Korean soldiers and 292 general controls, totaling 540 participants. The research instruments used for evaluation included the Beck Scale of Suicide Ideation (BSI), the Childhood Trauma Questionnaire (CTQ-SF), the Perceived Stress Scale (PSS), the Difficulties in Emotion Regulation Scale (DERS-16), the Barratt Impulsiveness Scale Version 11 (BIS-11), the Rosenberg Self-Esteem Scale (RSES), the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder 7-item scale (GAD-7), the UCLA Loneliness Scale (UCLAS), and the State-Beck Hopelessness Scale (S-BHS). Results: The results of this study showed that suicidal ideation, depression, anxiety, impulsivity, and self-esteem were significantly higher in the military group compared to the comparison group. Conversely, emotional dysregulation was considerably lower in the soldiers than in the comparison group. No significant differences were found in childhood trauma, stress, loneliness, and hopelessness between the two groups. Multiple regression analysis within the military group revealed that childhood trauma, hopelessness, and depression were major factors influencing suicidal ideation. Conclusions: These findings will help identify risk factors for suicide among soldiers and develop effective intervention strategies to prevent it. Full article

This paper presents a real-time smart mirror system combining multiple AI modules for multimodal health monitoring. The proposed platform integrates three core components: facial expression analysis, remote photoplethysmography (rPPG), and conversational AI. A key innovation lies in transforming the Moving Average rPPG (MA-rPPG) model—originally developed for offline batch processing—into a real-time, continuously streaming setup, enabling seamless heart rate and peripheral oxygen saturation (SpO₂) monitoring using standard webcams. The system also incorporates the DeepFace facial analysis library for live emotion, age detection, and a Generative Pre-trained Transformer 4o (GPT-4o)-based mental health chatbot with bilingual (English/Korean) support and voice synthesis. Embedded into a touchscreen mirror with Graphical User Interface (GUI), this solution delivers ambient, low-interruption interaction and real-time user feedback. By unifying these AI modules within an interactive smart mirror, our findings demonstrate the feasibility of integrating multimodal sensing (rPPG, affect detection) and conversational AI into a real-time smart mirror platform. This system is presented as a feasibility-stage prototype to promote real-time health awareness and empathetic feedback. The physiological validation was limited to a single subject, and the user evaluation constituted only a small formative assessment; therefore, results should be interpreted strictly as preliminary feasibility evidence. The system is not intended to provide clinical diagnosis or generalizable accuracy at this stage. Full article

UPB elicits various and heterogeneous subsequent behaviors through positive and negative emotions, a phenomenon that warrants a comprehensive meta-analysis. This study synthesized 34 studies from both English and Chinese databases (49 independent samples, N = 83,810), published between 2016 and 2024. The results reveal that positive emotions (e.g., pride, psychological entitlement) trigger the moral licensing effect of rationalizing further unethical conduct and the conscientiousness effect of enhancing organizational identification and promoting positive behaviors. Conversely, negative emotions (e.g., guilt, shame) drive the moral cleansing effect of motivating reparative moral behaviors. Additionally, negative emotions can also lead to the moral slippery slope effect of inducing unethical conduct. Moreover, moral disengagement was identified as a self-regulatory mechanism that permeates this entire process, enabling employees to navigate the moral conflicts arising from UPB. This study uncovers the dual nature of UPB from an emotional perspective. Full article

Despite the growing recognition of emotional intelligence (EI) and its significant associations with academic outcomes, less is known about the underlying mechanisms through which EI mindsets affect academic engagement and burnout. Drawing on regulatory focus theory and social comparison theory, this study aims to reveal how different types of EI mindsets influence college students’ academic engagement and burnout through regulatory focus (i.e., promotion and prevention focus) and further examines the moderating role of performance-prove goal orientation—defined as the motivation to demonstrate competence and outperform others—in these pathways. To test these associations, we conducted two studies. A scenario experiment (Study 1) indicates that a growth mindset of EI (GMOE) has the potential to enhance academic engagement while reducing academic burnout, whereas a fixed mindset of EI (FMOE) exhibits the opposite pattern. Study 2, based on three-wave data, demonstrates that GMOE is positively associated with academic engagement and negatively associated with academic burnout via promotion focus, whereas FMOE is positively associated with academic burnout and negatively associated with academic engagement through prevention focus. Of note, performance-prove goal orientation moderates these pathways: Individuals with higher levels of performance-prove goal orientation exhibit a weakened indirect effect of GMOE on academic engagement via promotion focus, whereas those with lower levels of performance-prove goal orientation display a strengthened version of this pathway. Conversely, the indirect effect of FMOE on academic burnout through prevention focus is stronger when performance-prove goal orientation is high and weaker when it is low. Theoretical and practical implications are also discussed. Full article

This study investigates how AI-driven virtual anchors affect consumers’ purchase intentions by identifying their key attributes, underlying mechanisms, and configurational interplay. We integrate latent Dirichlet allocation (LDA), structural equation modeling (SEM), and fuzzy-set qualitative comparative analysis (fsQCA) into a unified methodological framework. Empirical evidence demonstrates that the public visibility of virtual anchors exerts a significant positive impact on purchase intention, whereas professionalism, responsiveness, and personalization primarily cultivate consumer pleasure and trust, yet exert limited direct influence on purchase decisions. Emotional states—arousal, pleasure, and trust—mediate the relationship between anchor characteristics and purchase intention. fsQCA further reveals that high purchase intention emerges when responsiveness serves as a necessary condition, trust operates as a pivotal hub, and arousal/pleasure function as emotional conduits; conversely, low purchase intention is chiefly attributable to deficiencies in visibility, responsiveness, and trust. By synthesizing the SOR (stimulus-organism-response) model with the PAD (Pleasure-Arousal-Dominance) emotion theory, this research extends theoretical insights into consumer behavior within e-commerce live-streaming contexts and provides actionable guidance for optimizing virtual anchor strategies, thereby advancing both standardization and innovation in the industry. Full article

This study explores the practice of “soundtalking”, a novel method of participant-led sound practice, across the Dee Estuary in the UK. Over the course of twelve months, the Our Dee Estuary Project facilitated monthly meetings where participants engaged in sound workshops, in-depth discussions, and sound-making activities, with the aim of fostering a deeper connection with both their local and sonic environments. This longitudinal practice-based research study created an environment of sonic learning and listening development, documenting how participants’ interactions and narratives both shape and are shaped by the estuarial environment, its soundscape, and their sense of place. Participant-led conversations formed the basis of the methodology, providing rich qualitative data on how individuals perceive, interpret, and interact with their surroundings and the impact that the soundscape has on the individual. The regular and unstructured discussions revealed the intrinsic value of soundscapes in participants’ lives, emphasising themes of memory, reflection, place attachment, environmental awareness, and well-being. The collaborative nature of the project allowed for the co-creation of a film and a radio soundscape, both of which serve as significant outputs, encapsulating the auditory and emotional essence of the estuary. The study’s initial findings indicate that “soundtalking” as a practice not only enhances participants’ auditory perception but also fosters a sense of community and belonging. The regularity of monthly meetings facilitated the development of a shared acoustic vocabulary and experience among participants, which in turn enriched their collective and individual experiences of the estuary. Soundtalking is proposed as an additional tool in the study of soundscapes to complement and extend more commonly implemented methods, such as soundwalking and soundsitting. Soundtalking demonstrates the efficacy of longitudinal, participant-led approaches in capturing the dynamic and lived experiences of soundscapes and their associated environments, over methods that only create fleeting short-term engagements with the soundscape. In conclusion, the Our Dee Estuary Project demonstrates the transformative potential of soundtalking in deepening our understanding of human–environment interactions and, in addition, has shown that there are both health and well-being aspects that arise from the practice. Beyond this, the project has output a film and a radio sound piece, which not only document but also celebrate the intricate and evolving relationship between the participants and the estuarine soundscape, offering valuable insights for future soundscape research and community engagement initiatives. Full article

Generating realistic and contextually appropriate gestures is crucial for creating engaging embodied conversational agents. Although speech is the primary input for gesture generation, adding controls like gesture velocity, hand height, and emotion is essential for generating more natural, human-like gestures. However, current approaches to controllable gesture generation often utilize a limited number of control parameters and lack the ability to activate/deactivate them selectively. Therefore, in this work, we propose the Cont-Gest model, a Transformer-based gesture generation model that enables selective control activation through masked training and a control fusion strategy. Furthermore, to better support the development of such models, we propose a novel evaluation-driven development (EDD) workflow, which combines several iterative tasks: automatic control signal extraction, control specification, visual (subjective) feedback, and objective evaluation. This workflow enables continuous monitoring of model performance and facilitates iterative refinement through feedback-driven development cycles. For objective evaluation, we are using the validated Kinetic–Hellinger distance, an objective metric that correlates strongly with the human perception of gesture quality. We evaluated multiple model configurations and control dynamics strategies within the proposed workflow. Experimental results show that Feature-wise Linear Modulation (FiLM) conditioning, combined with single-mask training and voice activity scaling, achieves the best balance between gesture quality and adherence to control inputs. Full article

AI has the potential to enhance mental health by scaling support. However, its implementation brings uncertainties and challenges that require careful review to ensure safety. This study examined evolving public views on AI mental health support by analyzing relevant Reddit posts (n = 517). Following the release of ChatGPT in 2022, discussions about AI in the context of mental health surged, with a noticeable shift in preference toward large language models (LLMs) over conventional therapy chatbots. Users appreciated AI for its emotional support, companionship, and accessibility, while also expressing concerns about adverse effects and lack of conversational depth and emotional connection. Distinct patterns in how benefits and challenges were discussed emerged between experienced and non-experienced AI users, as well as between AI-focused and mental health-focused communities. AI-experienced users acknowledged both the benefits and limitations, whereas AI communities emphasized the positives and mental health communities highlighted the lack of conversational depth. These findings underscore the need for tailored communication strategies to set realistic expectations about the utility of AI in mental healthcare among different stakeholders. This research provides insights into developing ethical AI systems that complement traditional care while addressing current limitations. Full article

This paper presents a modular framework for character-coherent, emotion-aware role-playing dialogue with large language models (LLMs), centered on a novel Verifiable Emotion Reward (VER) objective. We introduce VER as a reinforcement-style signal derived from frozen emotion classifiers to provide both turn-level and dialogue-level alignment, effectively mitigating emotional drift across long interactions. To amplify VER’s benefits, we construct Character-Coherent Dialogues (CHARCO), a large-scale multi-turn dataset of over 230,000 dialogues, richly annotated with persona profiles, semantic contexts, and ten emotion labels. Our experiments show that fine-tuning LLMs on CHARCO significantly enhances VER’s impact, driving marked improvements in emotional consistency, role fidelity, and dialogue coherence. Through the evaluation that integrates lexical diversity metrics, automatic scoring with GPT-4, and human assessments, we demonstrate that the collaboration between a purpose-built multi-turn dataset and the VER objective leads to significant advancements in the field of persona-aligned conversational agents. Full article