Search Results (6,078)

Internet Gaming Disorder (IGD) has become a major behavioral health concern among adolescents, yet current assessment tools remain limited. These tools often fail to capture the disorder’s complex symptom variations and lack clinical interpretability. This study, taking an interdisciplinary approach that combines clinical psychology and psychometrics, summarizes recent progress in understanding adolescent IGD and the development of its assessment methods. We compare the diagnostic criteria of the DSM-5 TR and ICD-11 and argue that the nine DSM-5 TR criteria are particularly suited for transformation into distinct diagnostic attributes due to their detailed and actionable nature. We then review the strengths and weaknesses of Classical Test Theory (CTT), Item Response Theory (IRT), and Cognitive Diagnostic Models (CDMs) in assessing IGD. The review emphasizes the limitations of total-score and single latent-trait approaches in capturing the disorder’s multidimensional symptoms. Based on these insights, we propose a conceptual assessment framework, Cognitive Diagnosis Computerized Adaptive Testing (CD-CAT), that integrates CDMs with computerized adaptive testing. Rather than presenting an empirically validated system, this framework offers a theoretically grounded proposal that specifies the key components, logical relationships, and methodological pathways necessary for advancing precision assessment of adolescent IGD. CD-CAT uses a system of attributes and a Q-matrix based on the DSM-5 TR criteria to efficiently classify IGD symptoms in adolescents, reducing the number of items required while enhancing clinical relevance. Lastly, we discuss the theoretical contributions of the proposed framework, acknowledge its limitations as a conceptual proposal, and outline directions for future empirical research. Full article

Conventional Heating, Ventilation, and Air Conditioning (HVAC) control systems primarily rely on environmental and physiological parameters, largely ignoring the critical influence of psychological states on thermal comfort. Overlooking this factor often leads to suboptimal occupant satisfaction, energy inefficiency and thus carbon dioxide (CO₂) emissions. To this aim, this study introduces a novel mood-adaptive HVAC control system integrating psychological feedback to decrease CO₂ emissions in office buildings by reducing energy consumption and optimizing comfort. A total of 7000 thermal facial measurement records and high-resolution camera images were collected across seven mood state conditions using video stimuli and the Profile of Mood States (POMS) questionnaire to evaluate mood variations. A dual artificial intelligence system was developed: a Convolutional Neural Network (CNN) for analyzing facial expressions and an Artificial Neural Network (ANN) for processing facial temperatures via thermal imaging. These models collectively predict occupant mood in real-time, and a custom-designed wearable necklace interface transmits this data to dynamically adjust HVAC setpoints. To evaluate system performance, energy consumption was directly measured in real-life operations using an energy analyzer, without relying on simulations. Results indicate that this prototype personalized mood-driven system has the potential to enhance perceived thermal comfort while achieving up to a 20% reduction in carbon emissions compared to conventional systems. This human-centered approach significantly advances intelligent building management and climate change mitigation. Full article

Psychological stress is a major public health problem associated with adverse outcomes in physical and mental health. This study proposes an approach to predicting clinical stress levels using metabolic and endocrine biomarkers combined with machine learning models based on genetic algorithms. Data were obtained from 87 university students, including measurements of glucose, insulin, and cortisol, as well as perceived stress scores assessed using the Perceived Stress Scale (PSS). Stress levels were categorized into low ($n=5$), moderate ($n=22$), and high ($n=60$) classes, reflecting an imbalanced dataset. Feature engineering and genetic algorithm–based selection identified glucose concentration, the insulin–glucose ratio, and the insulin–cortisol ratio as the most relevant features. These were used to train XGBoost and Elastic Net models, which were evaluated using leave-one-out cross-validation. The XGBoost model achieved the best performance, with an accuracy of 0.77 and strong predictive capability for high stress levels. The results demonstrate the usefulness of machine learning based on metabolic biomarkers as an objective tool for stress assessment in psychological and clinical research. Full article

Employing a 4 (opponent type) × 2 (communication condition) between-subjects design, the study recruited 194 valid human participants to complete three rounds of game tasks. Results revealed: (1) The type of game counterpart exerted a significant main effect on participants’ remaining funds (F(3, 185) = 3.179, p = 0.025). Human participants retained significantly more funds when the counterpart was a real large model compared to other groups. (2) A significant interaction existed between the type of game counterpart and communication conditions (F(3, 185) = 3.318, p = 0.021). Specifically, when the opponent was a fake AI model (presented as human but actually an AI), human participants’ remaining funds were significantly higher under the communication condition than without communication (p = 0.012). This indicates that communication can promote rational decision-making in identity mismatch scenarios by providing additional behavioral cues. In the fake-human group (informed as human but actually AI), a numerical trend toward increased funds was also observed under communication conditions, though it did not reach statistical significance (p = 0.159); (3) The moderating effect of social value orientation did not reach significance. These findings extend the application of the theory of mind in human–machine games, revealing the complex influence mechanism of identity perception and communication dynamics on rational decision-making. Full article

Interactive digital commerce environments increasingly rely on influencers as algorithmically amplified intermediaries between brands and consumers. However, the process through which influencer attributes translate into brand trust remains theoretically underdeveloped. Drawing on Social Influence Theory and Source Credibility Theory, this study develops a process-based model in which consumer engagement operates as a psychological mechanism linking influencer characteristics, namely credibility, brand alignment, interactivity, and authenticity, to brand trust. Using survey data from 400 active social media users in Lebanon and partial least squares structural equation modeling (PLS-SEM), the findings reveal that all four influencer attributes significantly enhance consumer engagement, which in turn strongly predicts brand trust. Influencer–brand alignment emerges as the strongest driver of engagement, suggesting that value congruence functions as a heuristic cue in interactive digital commerce contexts. By conceptualizing engagement as a trust-internalization mechanism within platform-mediated environments, this study advances electronic commerce theory and provides context-sensitive insight into digital trust formation in emerging markets. Full article

Online social platforms increasingly function as informal self-help environments for individuals experiencing depression, offering spaces for emotional expression and peer support outside traditional clinical settings. However, how coping strategies and psychological engagement states—individuals’ emotional and cognitive involvement in managing their condition—are reflected through online self-disclosure remains poorly understood. We analyzed a large-scale dataset from Reddit depression-related communities to investigate how different psycho-linguistic profiles and coping orientations emerge from users’ language. We collected posts and comments from over 300,000 users across six depression-focused subreddits over two years. User-generated text was characterized through multiple psychological and linguistic dimensions capturing emotions, sentiment, subjectivity, and related features, then aggregated at the user-month level and analyzed using unsupervised clustering techniques. Our analysis identifies four distinct groups characterized by different emotional profiles and dominant coping orientations. These states exhibit meaningful correspondences with established theoretical frameworks, including the Coping Orientations to Problems Experienced model and the Patient Health Engagement model. Our findings demonstrate that large-scale textual data from online communities can provide interpretable insights into coping behaviors and engagement patterns, offering a complementary perspective to traditional approaches for studying mental health. Full article

This study examines whether improvements in quality of life (QOL) require increased household energy consumption (HEC) among low-income households in India by using a comparative analysis of Delhi and Kharagpur. A survey of 879 households (Delhi: n = 539; Kharagpur: n = 340) was conducted, and structural equation modelling (SEM) was applied to analyse the relationships between HEC and key QOL constructs, including residential satisfaction, economic satisfaction, and place attachment. The results indicate that QOL is primarily influenced by socio-psychological and housing-related factors rather than energy consumption alone. In Delhi, QOL is significantly associated with place attachment (β = 0.49, p < 0.001), economic satisfaction (β = 0.33, p < 0.001), and residential satisfaction (β = 0.13, p < 0.05), with the model explaining 42% of the variance (R² = 0.42; RMSEA = 0.048; CFI = 0.94). In Kharagpur, economic (β = 0.61) and residential satisfaction (β = 0.52, p < 0.001) show comparatively stronger effects. Although HEC is strongly associated with appliance ownership and cooling-related practices, it does not show a corresponding relationship with perceived QOL. Descriptive results further show higher well-being in Delhi (M = 3.85 vs. 3.42; d = 0.54). Overall, the findings suggest that differences in QOL between the two cities are more closely linked to socio-economic and residential conditions than to variations in household energy use, highlighting the importance of contextual factors in shaping well-being outcomes. Full article

Heavy episodic drinking (HED) is prevalent in young adulthood, yet its relationship with psychosocial well-being remains complex. This study examines the association between HED and perceived social isolation among young adults and tests whether this relationship varies by sexual orientation. Using pooled, nationally representative data from the 2022 and 2024 Health Information National Trends Survey (HINTS), this study analyzed adults aged 18–29 (N = 723). Perceived social isolation was measured using the PROMIS Social Isolation Short Form. Weighted multivariable linear regression models assessed interactions between sexual orientation and HED occasions (0 vs. 1+), adjusting for sociodemographic variables and psychological distress. 45.5% reported HED. Lesbian/gay (B = 5.62, SE = 0.58, p < 0.001) and bisexual (B = 1.66, SE = 0.34, p < 0.001) young adults reported higher isolation than straight peers; HED was inversely associated with isolation (B = −1.71, SE = 0.20, p < 0.001). A significant interaction indicated that among lesbian/gay young adults, heavy drinking was associated with lower perceived isolation (B = −5.77, SE = 0.98, p < 0.001). Interventions should account for the social meanings of alcohol use to avoid unintentionally increasing isolation among sexual minoritized populations. Full article

Background/Objectives: The COVID-19 pandemic posed significant challenges to quality of life, particularly for individuals living with chronic physical and/or mental conditions. Psychological factors such as fear of COVID-19, psychological distress, and resilience may be associated with quality-of-life outcomes during prolonged public health crises. This study aimed to examine quality of life and its psychological correlates among individuals with chronic conditions during the later phases and aftermath of the COVID-19 pandemic crisis. Methods: A cross-sectional study was conducted among 293 adults with chronic physical and/or mental conditions attending the General Hospital of Argolida, Greece. Participants completed validated self-report measures assessing quality of life (MVQOLI), fear of COVID-19 (FCV-19S), depression, anxiety, and stress (DASS-21), and psychological resilience (CD-RISC-25). Descriptive statistics, Spearman correlation analyses, and multivariable regression models were used to examine associations and identify factors associated with quality-of-life domains. Results: Higher levels of fear of COVID-19 and depressive symptoms were significantly associated with poorer quality of life across multiple domains. Depressive symptoms showed consistent negative associations with functional, interpersonal, transcendent, and overall quality-of-life scores. In contrast, psychological resilience was positively associated with interpersonal, transcendent, and overall quality of life. Regression analyses showed that depressive symptoms were negatively associated with overall quality of life, while resilience was independently associated with better quality-of-life outcomes. Conclusions: Psychological distress, particularly depressive symptoms and fear related to COVID-19, was associated with lower quality of life among individuals with chronic conditions during the later phases and aftermath of the COVID-19 crisis. Psychological resilience was positively associated with better quality-of-life outcomes, underscoring its relevance for supporting well-being during and after public health crises. Full article

Background/Objectives: Uncooperative behavior in pediatric dental settings remains a significant barrier to effective treatment. Factors such as demographics, psychological variables, and family influences may impact children’s behavior, but their relative importance is not fully understood. This study explores the emotional, familial, and demographic factors influencing cooperation among children in dental clinics in Jeddah, Saudi Arabia. Methods: A cross-sectional study was conducted among children aged 6–11 years attending dental clinics in Jeddah, Saudi Arabia. Participants undergoing non-invasive dental procedures were recruited. Behavioral cooperation was assessed using the Frankl Behavior Rating Scale, and dental anxiety was measured using the validated Abeer Children Dental Anxiety Scale (ACDAS). Data on demographic characteristics, child cognitive factors, and parental dental anxiety were collected through structured interviews. Multivariable logistic regression analysis was performed to identify independent determinants of cooperative behavior. Results: A total of 906 children were included in the analysis. Demographic variables, including gender, age, and income, were not significantly associated with child behavior (all p > 0.05). Fear of losing control emerged as the strongest predictor in the model. Children reporting fear of losing control had significantly lower odds of cooperative behavior (AOR = 0.14, 95% CI [0.10–0.22], p < 0.001). Shyness in the clinic was not statistically significant (p = 0.216). Maternal dental anxiety was significantly associated with child behavior, with higher maternal anxiety scores linked to lower odds of cooperative behavior (AOR = 0.96, 95% CI [0.93–0.997], p = 0.032). Paternal dental anxiety was not significantly associated with child behavior (p = 0.701). Conclusions: Fear of losing control and maternal dental anxiety were independently associated with children’s behavioral responses during dental visits. These findings highlight the relevance of children’s perceived control and maternal anxiety in understanding behavioral outcomes in pediatric dental settings. Full article

Background/Objectives: Patients with breast cancer show substantial heterogeneity in terms of psychological adjustment following diagnosis. We aimed to characterize longitudinal trajectories of quality of life (QoL) and depressive symptoms during the first 18 months post-diagnosis and to identify robust clinical, psychosocial, and behavioral predictors associated with distinct adjustment pathways. Methods: Women (N = 538; mean age 55.4 years; range 40–70) with operable breast cancer (stages I–III) were drawn from the multicenter BOUNCE cohort. QoL (Global Health Status/QoL scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30) and depressive symptoms (depression subscale of the Hospital Anxiety and Depression Scale) were assessed at baseline and months 3, 6, 9, 12, 15 and 18. Latent class growth analysis and growth mixture modeling identified distinct trajectory classes. Associations between early predictors and trajectory membership were examined using logistic regression combined with elastic net regularization. Results: Depression trajectories demonstrated heterogeneity, with groups characterized by persistent resilience (59.7%), stable moderate/high (25.3%), delayed onset (5.0%), and recovery (10.0%). QoL trajectories ranged from stable excellent (13.2%) and stable high (40.7%) to moderate (31.4%) and persistent low/deteriorating (6.9%), as well as a distinct recovering trajectory (7.8%). Trajectory differentiation was primarily driven by psychological resources, symptom burden, functional status, and coping processes, alongside specific contributions from clinical factors. Conclusions: Distinct subgroups of women with breast cancer follow divergent adjustment pathways. These findings highlight the multidimensional nature of resilience and support the need for tailored interventions that promote long-term well-being beyond simple risk reduction. Full article

The large-scale deployment of autonomous vehicles (AVs) in mixed-traffic environments raises an important question: how do human drivers evaluate safety when interacting with AVs under real-world uncertainty? This study aims to examine how drivers’ objective knowledge of AVs shapes their perceived safety when sharing the road with AVs in mixed-traffic environments. Using survey data from 905 licensed drivers in Wuhan, China, this study treats perceived road-sharing safety as an interaction-level evaluative outcome rather than merely a precursor of adoption intention. Latent class analysis was first used to identify knowledge-based driver segments, structural equation modeling was then applied to estimate Theory of Planned Behavior (TPB)-related psychological constructs, and ordered logit regression was finally employed to examine the determinants of perceived safety across segments. The results indicate that behavioral intention consistently shows a positive association with perceived safety; however, attitude toward AVs exhibits a significant negative association among high-knowledge drivers. This attitudinal reversal challenges the implicit homogeneity assumption embedded in conventional TPB applications and suggests that cognitive familiarity may recalibrate, rather than amplify, technological optimism. Overall, the findings show that knowledge-based heterogeneity changes the psychological mechanisms underlying safety appraisal in mixed traffic. These insights carry important implications for differentiated communication strategies and trust calibration in transitional automated mobility systems. Full article

Cancer caregiving is increasingly recognized as a major psychosocial challenge, yet the mental health needs of caregivers remain insufficiently addressed in oncology research and practice. This narrative review examines the experiences of cancer caregivers within the context of rising cancer incidence and prolonged survival, conditions frequently accompanied by sustained psychological burden and anticipatory grief, with particular attention to depressive symptoms. Relevant qualitative and quantitative studies were identified through targeted searches of major databases (PubMed, Scopus, PsycINFO, and Google Scholar), including studies published up to January 2026. Study selection was guided by conceptual relevance and applied significance to the intersection between religiosity, spirituality, caregiving, and mental health outcomes. The reviewed literature highlights substantial psychological burden among caregivers, with depression affecting approximately 20–40% of cancer caregivers and identifies religiosity and spirituality as potentially supportive resources. Across studies, recurrent themes include meaning-making, hope maintenance, emotional regulation, moral orientation, and perceived social support as mechanisms through which these dimensions are associated with lower levels of depression and improved psychological adjustment. Evidence suggests that both religiosity, understood as the lived engagement with religious values, and spirituality, defined as a broader existential orientation toward meaning and purpose, contribute to coping in caregiving contexts; however, findings remain heterogeneous and largely based on cross-sectional analyses. Notable gaps persist, including limited caregiver-specific research, conceptual imprecision, and a lack of longitudinal designs. By integrating conceptual clarification with empirical synthesis, this review outlines potential psychological pathways linking religiosity and spirituality to caregiver mental health outcomes. In summary, religiosity and spirituality are considered adjunctive, non-exclusive resources that complement conventional psychological and psychiatric care within comprehensive models of caregiver support. Full article

Architectural heritage plays a central role in shaping visitors’ emotional experiences within cultural tourism contexts. However, empirical research examining how specific architectural styles evoke emotional responses remains limited, particularly when using objective measurement techniques. This study investigates emotional reactions to architectural heritage by applying the Stimulus–Organism–Response (SOR) theoretical framework. In this model, architectural styles act as environmental stimuli, emotional processing represents the organismic state, and the resulting emotional activation constitutes the response. An experimental protocol was conducted with a sample of 645 participants exposed to a series of standardized architectural heritage images representing different architectural styles and infrastructure types. Emotional reactions were captured in real time through facial emotion recognition technology, enabling the objective measurement of eight basic emotions: neutral, happiness, sadness, surprise, fear, disgust, anger, and contempt. The collected emotional data were statistically analyzed using Analysis of Variance (ANOVA) to identify significant differences in emotional responses across architectural styles, heritage typologies, and gender. When significant differences were detected, Tukey’s HSD post hoc tests were applied to determine specific group contrasts. The findings reveal that different architectural styles generate distinct emotional patterns, highlighting the role of architectural aesthetics as a powerful mediator of affective engagement with heritage environments. From a theoretical perspective, this research contributes to heritage tourism and environmental psychology by integrating the SOR framework with real-time emotion detection technologies, providing a novel methodological approach for analyzing emotional responses to architectural heritage. Full article

This study examines the development and consequences of student–institution distrust (SID) in higher education. While prior research has predominantly focused on trust, limited attention has been given to distrust as a distinct psychological construct influencing student experiences. Guided by Institutional Logics Theory, the study investigates how perceived institutional practices, institutional support, and cost–value (ROI) perceptions shape SID and how distrust influences sense of belonging, academic engagement, and help-seeking intentions. Data were collected from 600 undergraduate students enrolled in Business Administration and Tourism programs at public universities in Saudi Arabia. Multi-Group Structural Equation Modeling (MG-SEM) was employed to examine the proposed relationships and the moderating role of academic discipline. The results indicate that institutional practices, perceived support, and ROI perceptions significantly predict student–institution distrust. In turn, distrust exerts significant negative effects on students’ sense of belonging, academic engagement, and help-seeking intentions, confirming the theorized detrimental role of distrust in shaping student outcomes. The findings further reveal that academic discipline strengthens the negative impact of distrust on student outcomes, with stronger effects observed among Tourism students. By conceptualizing distrust as a multidimensional construct rather than simply the absence of trust, this study contributes to the literature on student–institution relationships and provides practical insights for designing transparent and supportive institutional environments that reduce distrust and enhance student engagement. Full article