搜索结果：Integrative psychological & behavioral science

This study aimed to examine the psychological and behavioral determinants of AI-assisted academic dishonesty among university students through an integrated model. Specifically, the study investigated whether academic procrastination, learned helplessness, and academic self-efficacy predict cheating tendency; whether cheating tendency predicts AI-assisted academic dishonesty; whether AI use moderates the relationship between cheating tendency and academic dishonesty; and whether social and contextual factors significantly predict AI-assisted academic dishonesty. The study employed a quantitative, cross-sectional, correlational survey design. A total of 1,045 undergraduate students from different academic disciplines participated voluntarily in the study. Data were collected using seven measurement instruments and a personal information form. Descriptive statistics, Pearson correlation analyses, multiple regression analyses, and Hayes' PROCESS Model 14 were used to test the proposed moderated mediation model. Bootstrap resampling with 5,000 samples was applied to estimate indirect effects and 95% confidence intervals. The findings showed that academic procrastination and learned helplessness positively predicted cheating tendency, whereas academic self-efficacy negatively predicted it. Cheating tendency significantly predicted AI-assisted academic dishonesty, and the interaction term indicated that the association between cheating tendency and AI-assisted academic dishonesty was stronger at higher levels of AI use. Conditional indirect effect analyses further demonstrated that cheating tendency mediated the effects of academic procrastination, learned helplessness, and academic self-efficacy on AI-assisted academic dishonesty, and these indirect effects became stronger at higher levels of AI use. In addition, social norms, peer behaviors, family attitudes, insufficient sanctions, teacher attitude, high expectations, and adverse conditions significantly predicted AI-assisted academic dishonesty, whereas ethical and moral education emerged as a negative predictor. The findings indicate that AI-assisted academic dishonesty should be understood as a multilevel outcome shaped by the interaction of psychological vulnerabilities, cognitive tendencies, technological affordances, and socio-contextual influences. The study contributes to the academic integrity literature by showing that AI use does not merely accompany dishonest tendencies but amplifies their translation into behavior. These results highlight the need for psychologically informed, ethically grounded, and institutionally supported interventions to reduce academic dishonesty in AI-enhanced higher education environments.

Poststroke dysphagia (PSD) affects nearly half of stroke survivors and impairs health-related quality of life. Nursing interventions increasingly integrate psychological support, yet their effectiveness remains unclear. This systematic review and meta-analysis evaluated the impact of nursing strategies incorporating psychological support on QOL in PSD patients and examined the influence of intervention duration and type. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, EMBASE, Scopus, Web of Science, and CINAHL were searched up to August 2025. Randomized controlled trials (RCTs) assessing nursing interventions with psychological components, such as counseling or education, cognitive behavioral therapy-informed strategies, relaxation or stress management, emotional support, and motivational interviewing, and reporting QOL outcomes were included. Standardized mean differences (SMDs) were pooled using random-effects models. Subgroup analyses compared short (&#x2264;4 weeks) versus longer (>4 weeks) interventions and psychological-only versus combined approaches. Nine RCTs involving 693 participants were included. Nursing interventions improved QOL compared with control conditions (SMD = 0.95; 95% confidence interval [CI]: 0.48-1.41; P<0.001), with substantial heterogeneity (I2 = 84.5%). Interventions lasting &#x2264;4 weeks showed larger effects (SMD = 1.21; 95% CI: 0.13-2.29) than longer interventions (SMD = 0.82; 95% CI: 0.32-1.31). Psychological-only interventions produced greater benefits (SMD = 1.25; 95% CI: 0.68-1.82) than combined approaches (SMD = 0.82; 95% CI: 0.20-1.43). No publication bias was detected (Egger's test, P = 0.79). Nursing interventions incorporating psychological support enhance QOL in PSD. Short-duration and psychological-only approaches appear most effective, supporting integration into stroke rehabilitation.

This study examined the mediating role of negative stress in the relationship between Psychological Capital (PsyCap) and psychological distress indicators among Ecuadorian university students. PsyCap was conceptualized as a higher-order construct composed of hope, self-efficacy, resilience, and optimism. A cross-sectional study was conducted with 1732 university students (55% women; M = 20.44, SD = 2.29) from three Ecuadorian universities using validated self-report measures. Structural equation modeling supported the proposed mediational model and demonstrated an adequate fit to the data, &#x3c7;2(367) = 1732, p < 0.001, CFI = 0.972, TLI = 0.969, RMSEA = 0.061 (90% CI [0.058, 0.063]), and SRMR = 0.041. PsyCap showed a significant negative association with negative stress (&#x3b2; = -0.311, p < 0.001). In turn, negative stress was positively associated with anxiety-depression symptoms (&#x3b2; = 0.785, p < 0.001) and psychological inflexibility (&#x3b2; = 0.774, p < 0.001). Mediation analyses revealed significant indirect effects of PsyCap on anxiety-depression (&#x3b2; = -0.244, p < 0.001) and psychological inflexibility (&#x3b2; = -0.241, p < 0.001) through negative stress. Direct effects remained significant but smaller in magnitude (&#x3b2; = -0.131 and &#x3b2; = -0.107, respectively), supporting a partial mediation model. The model explained 69.7% of the variance in anxiety-depression and 66.3% of the variance in psychological inflexibility. These findings suggest that PsyCap functions primarily as a protective psychological resource through its capacity to reduce maladaptive stress responses, which subsequently influence broader transdiagnostic indicators of psychological distress. The study highlights the relevance of integrating strengths-based approaches and stress-reduction strategies in university mental health interventions. Furthermore, it provides empirical evidence from a Latin American context, contributing to the understanding of mechanisms linking positive psychological resources and mental health among university students.

This research examines consumer pro-environmental behavior and loyalty to energy-efficient appliances, aiming to mitigate the effects of climate change by integrating construal level theory and environmental responsibility foundational theory as the underlying frameworks. This study further develops a behavioral model to better understand how consumers perceive climate change and how their subjective psychological distance cognitively interprets objects, influencing their purchase behavior for energy-efficient appliances. This study also examines the mediating roles of the environmental responsibility chain with proximal psychological distance and behavioral intention. A structural equation model was applied to explore the model and hypotheses for 1020 valid respondents from Bangladesh. Findings reveal that proximal psychological distance has a significant positive relationship with environmental attitude, environmental ethics, self-responsibility, social responsibility, moral norms and behavior intention. Nevertheless, proximal psychological distance, self-responsibility, and moral norms have an insignificant relationship with consumer loyalty. To our knowledge, it is the first empirical study in Bangladesh to comprehensively explain consumer purchasing behavior intention and loyalty to energy-efficient appliances. Theoretical, managerial, and social contributions of consumers' pro-environmental behavior in climate change mitigation are discussed.

Burning mouth syndrome (BMS) is a complex chronic orofacial pain condition characterized by a multifactorial etiology and poorly understood pathophysiology. Conventional monotherapies often have limited efficacy, necessitating a transition toward personalized management. Accordingly, we searched PubMed, Web of Science, Embase, and the Cochrane Library for articles published up to January 2026 using keywords related to BMS. Following screening, we synthesized the available evidence to propose a psychological classification framework for BMS, identifying four distinct subtypes: emotion-dominant, stressor-related, cognitive-distortion, and personality-based. These four subtypes were derived from a synthesis of existing literature on BMS comorbidities and clinical heterogeneity, providing potential insights for clinical practice. We delineated the core clinical features and putative neurobiological mechanisms and validated the psychometric assessment tools corresponding to each phenotype. We also evaluated evidence-based interventions tailored to these psychological profiles. Current evidence supports cognitive behavioral therapy (CBT) as a well-supported first-line intervention for emotional- and personality-based dimensions, biofeedback for stressor-related symptoms, and mindfulness-based cognitive therapy (MBCT) for addressing cognitive distortions. We posit that a stratified, integrated therapeutic strategy grounded in rigorous psychological subtyping can address the limitations of traditional "one-size-fits-all" approaches, thereby potentially helping to guide individualized treatment strategies and improve clinical outcomes, pending prospective validation. Future research should prioritize the clinical validation and feasibility of this precision-oriented paradigm to establish a new standard of care for patients with BMS.

Research on arts engagement and mental health has expanded rapidly in recent years, and a growing body of evidence suggests that participation in artistic and cultural activities is associated with improved well-being, reduced psychological distress, and stronger social connectedness. However, theoretical explanations remain insufficiently integrated. Existing studies identify a wide range of possible mechanisms, including emotional expression, stress reduction, social interaction, identity formation, and meaning-making, yet these processes are often discussed in parallel rather than synthesized within a coherent explanatory framework. In addition, the literature frequently conflates everyday arts engagement with community-based programmes and formal therapeutic interventions, making it difficult to determine what is distinctive about arts engagement as a mental health resource in ordinary social life. In response to these limitations, this article offers a conceptual review and develops a social psychological framework for understanding how arts engagement may influence mental health. It focuses primarily on everyday and community-facing forms of arts engagement in ordinary social life, while drawing selectively on therapeutic and programme-based literature to clarify conceptual boundaries and mechanisms. The article argues that arts engagement should be understood not merely as a leisure activity or cultural exposure, but as a socially organized practice through which individuals gain access to psychosocial resources. To advance this argument, it proposes a four-layer framework comprising socially scaffolded affect regulation, connectedness and belonging, social identity, collective meaning, and the social cure, and agency, recognition, and narrative repair. By integrating these processes within a single conceptual model, the article clarifies how arts engagement may influence mental health, under what conditions these effects are most likely to emerge, and why a social psychological perspective provides a valuable framework for understanding these associations.

While offering separation between vulnerable road users and motorized vehicles, the presence of non-motorized traffic facilities, such as bikelanes, introduces complex interaction dynamics among pedestrians, conventional bicycles, and e-bikes. Existing surrogate measures inadequately quantify conflict risks in these environments due to oversimplified assumptions and limited consideration of contextual factors. This study proposes a novel safety field-based surrogate measure (Bikelane Safety Field-based Measure, BSFM) that integrates physical dynamics (i.e., road environmental characteristics, kinematic interactions) and behavioral dynamics (i.e., psychological comfort, risk perception, evasion behaviors) to quantify bikelane conflict risk. Utilizing drone-collected trajectory data (203 conflict groups, 37,652&#xa0;s) from three Tianjin intersections, a bikelane-specific safety field model was developed. Subsequently, the BSFM was proposed, and the threshold of the surrogate measure was determined using extreme value theory. Validation of the BSFM yielded the following key findings: (i) The BSFM demonstrated superior conflict identification recall (81.3%) compared to Time-to-Collision (TTC) (34.5%) and Projected Time-to-Collision (PTTC) (41.4%). (ii) Significant sensitivity to evasive actions was observed, with Kolmogorov-Smirnov and Mann-Whitney U tests confirming statistically significant changes in BSFM values during swerving and deceleration maneuvers (p&#xa0;<&#xa0;0.001). (iii) Real-time risk tracking was effectively achieved through dynamic visualizations of the safety performance envelope. (iv) The model exhibited robust applicability across diverse conflict participants, including pedestrians, bicycles, and e-bikes. The BSFM provides a validated framework for real-time safety assessment in shared micro-mobility environments, advancing proactive traffic management strategies.

This method article presents a reproducible cross-sectional self-report survey protocol for examining associations among psychological resilience, sense of school belonging (SSB), higher-order thinking, and internet gaming disorder (IGD) in college students. In this protocol, new employment patterns are treated as a contextual background shaped by generative artificial intelligence, platform-based work, and changing graduate skill expectations rather than as a participant-level exposure variable. The protocol integrates institutional sampling, questionnaire administration, translation documentation, response-quality screening, missing-data handling, scale scoring, common-method-bias screening, variable centering, and conditional process analysis into one standardized workflow. SSB is specified as a statistical mediator, and IGD is specified as a moderator within an association-based moderated mediation framework. Representative outputs include sample-flow records, descriptive statistics, correlation matrices, mediation and moderated mediation estimates, conditional indirect effects, the index of moderated mediation, and interaction plots. Because the representative dataset is cross-sectional and self-reported, the protocol supports transparent estimation of statistical associations rather than causal or longitudinal inference. By standardizing preprocessing decisions and model specifications before analysis, this workflow improves transparency, comparability, and reproducibility in educational and behavioral survey research.

The San Diego Consensus for Laryngopharyngeal Symptoms (LPS) and Laryngopharyngeal Reflux Disease (LRPD) describes a broad-based multidisciplinary management paradigm that focuses on mechanisms underlying symptoms to improve treatment outcomes. This review expands on the San Diego Consensus framework to discuss multidisciplinary management of LPS and LPRD. LPS manifest as persistent or disproportionate symptoms despite minimal or inconsistent evidence of reflux exposure. Emerging evidence suggests that hyperresponsiveness, hypervigilance, and symptom-specific anxiety are more strongly associated with LPS than objective reflux metrics and may contribute to symptom persistence through heightened attention to perceived irritation and protective behavioral responses. Repeated peripheral sensory input may further contribute to central sensitization and lower perceptive thresholds. While these behavioral and neurophysiological processes may be partially improved by reducing reflux exposure, behavioral therapies that address the multidimensional nature of LPS may provide additional benefit. Laryngeal Recalibration Therapy addresses LPS by retraining maladaptive laryngeal behaviors, enhancing vagal tone via heart rate variability biofeedback, and cognitive reframing to reduce symptom amplification. Meta-therapy, a clinical dialogue approach used in speech-language pathology to facilitate behavioral change, alongside psychological interventions such as mindfulness meditation, cognitive-behavioral therapy, and gut-directed hypnotherapy, may further target cognitive-affective processes that shape perception. Behavioral interventions can be combined with neuromodulators, particularly delta ligands such as gabapentin in patients with chronic cough, and tricyclic antidepressants or selective serotonin reuptake inhibitors in select cases. Effective management relies on multidisciplinary collaboration, integration of reflux-directed and behavioral therapies, and patient-centered education that supports adaptive symptom interpretation.

Emojis, as emerging paralinguistic cues in computer-mediated communication, are increasingly integrated into daily digital interactions and are known to be efficiently stored as targets in working memory (WM). Despite their pervasive use, the cognitive mechanisms underlying the filtering of emojis as distractors remains unclear. The present study combined behavioral measures and event-related potentials to investigate how emojis are filtered in WM. Participants performed a WM task in which emojis served as distractors. The results showed that emojis, compared with other types of distractors, could be efficiently filtered, as evidenced by reduced unnecessary storage (US) and lower contralateral delay activity (CDA) amplitudes. Moreover, a positive correlation between US and CDA emerged only in the emoji distractor condition, especially among high-frequency emoji users (r = 0.517, p = 0.023), suggesting that prior experience with emojis modulates the link between behavioral and neural indices of filtering. These findings provide preliminary evidence that emojis can be effectively filtered in WM and underscore the modulatory role of usage experience in shaping cognitive processing during digital communication.

The purpose of this study is to examine the relationship between body image distress and disordered eating behaviors (DEBs) using peer-based social network characteristics during adolescence. Data were drawn from the Korean Study of Adolescent Health, a longitudinal cohort investigating mental and behavioral health in Korean youth. The analytic sample comprised 315 female high school students from two cohorts. Eating behaviors, body image distress, depressive and anxiety symptoms, perceived stress, personality traits, and social network centrality were assessed using selfreport questionnaires. Ordinal and binary logistic regression analyses were conducted. Students in Cohort 23 reported lower body image distress and binge eating compared with those in Cohort 22 (p<0.05). Underweight status (body mass index [BMI] <18.5) was associated with lower body image distress and reduced binge eating, whereas obesity was linked to body image distress but not dieting behaviors. Depressive symptoms predicted both binge eating and dieting (p<0.01). Social network indices indicated that higher in-degree centrality was protective against body image distress, while higher out-degree centrality was associated with body image distress but reduced dieting behaviors. In addition to BMI and depressive symptoms, social network centrality emerged as a correlate of DEBs among adolescent girls. These findings highlight the importance of integrating emotional regulation and supportive peer network structures into interventions aimed at promoting healthy body image and eating behaviors.

To explore the associations between mindfulness, psychological distress, and health-related quality of life (HRQOL), and examine whether psychological distress mediates the association between mindfulness and HRQOL in individuals with Parkinson's disease (PD). This is a secondary analysis of baseline data from a clinical trial investigating mindfulness-based interventions in patients with mild-to-moderate PD (n = 159). Participants completed validated questionnaires assessing mindfulness, psychological distress (anxiety and depression), disease-specific HRQOL, and assessor-rated motor symptom severity. The sample consisted of 159 participants, including 83 female (52.2%) with a mean age of 64.8 years (SD = 7.9). Of these, 107 participants (67.3%) were classified at Hoehn and Yahr stage 3. Independent t-tests showed that psychologically distressed individuals had significantly lower mindfulness levels and poorer HRQOL than non-distressed individuals. Multiple linear regression analyses showed that higher mindfulness levels were associated with reduced anxiety and depression and improved HRQOL, particularly in the non-judging and acting with awareness facets. Mediation analyses demonstrated that anxiety and depression fully mediated the relationship between the observing and non-judging facets of mindfulness and HRQOL. In contrast, acting with awareness facet was partially mediated by both anxiety and depression, while describing facet was fully mediated by depression alone. Integrating mindfulness interventions into clinical pathways for neurodegenerative diseases could offer patients with effective tools to improve their quality of life. These interventions, focused on observing, non-judging, and acting with awareness, could enhance psychological care. Notably, for individuals at risk of comorbid depression, the 'describing' facet of mindfulness may play a particularly critical role in mitigating psychological distress.

The integration of artificial intelligence (AI) into addiction research has expanded rapidly, yet it remains unclear how psychosocial, behavioral, and social-structural determinants are incorporated into predictive models of addiction treatment outcomes. Because recovery is strongly shaped by psychological, social, and environmental context, assessing how AI approaches operationalize these dimensions is essential for developing clinically meaningful and equitable tools. We conducted a systematic review of peer-reviewed studies published from January 1, 2020 to October 30, 2025 across PubMed, Scopus, and Web of Science. Eligible studies applied artificial intelligence or machine learning (ML) models to addiction treatment outcomes and explicitly included psychosocial, behavioral, or social-structural predictors. Two reviewers independently screened studies, extracted data, and evaluated methodological quality using Joanna Briggs Institute (JBI) and Cochrane Risk of Bias 2 (RoB-2) domain structures. The protocol was prospectively registered on the Open Science Framework (OSF) and in PROSPERO. Fifteen studies met inclusion criteria, including electronic health record (EHR), administrative-, claims-based, program-level clinical models and psychosocial assessment datasets, digital phenotyping/ecological momentary assessment (EMA) studies, natural language processing/large language model (NLP/LLM) approaches, and one causal ML analysis of randomized controlled trial data. Across modalities, models consistently identified housing instability, psychiatric comorbidity, employment status, craving, stress, legal involvement, prior overdose, treatment history, medication adherence, and neighborhood disadvantage as influential predictors of treatment dropout, discontinuation, overdose risk, relapse, or poor engagement-often adding prognostic value beyond medication-related, diagnostic, and routinely available clinical variables. EMA and digital phenotyping showed the highest short-term predictive accuracy for near-term risk prediction, whereas structured EHR-, administrative-, claims-based, and program-level clinical models achieved moderate but clinically actionable performance. Methodological quality was moderate overall, with limited external validation and infrequent assessment of calibration, fairness, transportability, or reproducibility practices. Current evidence indicates that psychosocial, behavioral, and social-structural determinants are central to AI-based prediction of addiction treatment outcomes. Although findings are promising, existing models remain preliminary and should not yet guide clinical decisions without external validation and implementation evaluation. Future work should prioritize multi-site validation, transparent reporting, fairness evaluation, and co-development with clinicians and individuals with lived experience to ensure that AI tools strengthen person-centered and equitable addiction care.

Background/Objectives: Insomnia is one of the most prevalent and persistent symptoms among patients with breast cancer, yet it remains under-recognized and undertreated in routine clinical practice. Beyond its impact on sleep quality, insomnia is increasingly understood as a multidimensional condition involving neurobiological, psychological, and behavioral mechanisms, closely intertwined with cancer-related stress and psychiatric comorbidities. This narrative review aims to provide a comprehensive and integrative overview of insomnia in breast cancer, focusing on its epidemiology, pathophysiological underpinnings, neuropsychiatric correlates, and clinical implications, while highlighting gaps in current research and management. Methods: A narrative review of the literature was conducted, including studies published in major medical databases (PubMed, Scopus, and Web of Science) up to 2025. Relevant articles addressing insomnia, sleep disturbances, psychiatric symptoms, and neurobiological mechanisms in breast cancer populations were selected and synthesized. Results: Insomnia affects a substantial proportion of breast cancer patients across the disease trajectory, from diagnosis to survivorship. Its etiology is multifactorial, involving dysregulation of the hypothalamic-pituitary-adrenal axis, inflammatory processes, and circadian rhythm, as well as treatment-related factors such as chemotherapy, endocrine therapy, and menopausal symptoms. Insomnia frequently co-occurs with depression, anxiety, fatigue, and pain, forming symptom clusters that significantly impair quality of life and may influence clinical outcomes. Emerging evidence supports a bidirectional relationship between insomnia and psychiatric vulnerability, suggesting a shared neurobiological substrate within the brain-body stress axis. Conclusions: Insomnia in breast cancer should be conceptualized as a neuropsychiatric condition embedded within a broader stress-related symptom network rather than as an isolated sleep disturbance. Improved screening, interdisciplinary management, and the integration of evidence-based interventions such as cognitive behavioral therapy for insomnia are essential. Research should focus on personalized and mechanistically informed approaches to better address this highly prevalent yet insufficiently managed condition.

Fear of Missing Out (FoMO) has been increasingly recognized as a psychological driver of problematic mobile phone use and sleep disturbances among young adults. However, existing research is fragmented, with limited integration of cognitive-affective and behavioral mechanisms within a unified theoretical framework. Drawing on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, a process-oriented framework in which problematic smartphone use mediates the association between FoMO and sleep quality, with sensation seeking examined as a boundary condition. Data from 1124 Chinese undergraduate students showed that FoMO was associated with poor sleep quality. Problematic smartphone use partially mediated this association, suggesting that FoMO is linked to sleep outcomes through both direct cognitive-affective processes and indirect behavioral pathways. Sensation seeking significantly strengthened the associations between FoMO and problematic smartphone use, as well as between problematic smartphone use and sleep quality, whereas it was not significantly associated with the direct FoMO-sleep link, indicating pathway-specific moderation primarily operating at the behavioral execution level. These findings provide empirical support for an I-PACE-based process model of FoMO-related sleep problems and highlight behavioral engagement processes as a key target for interventions among high-risk individuals.

Farmers decision-making processes are critical to the implementation of technologies and climate-resilient sustainable practices. These decisions are subject to complex thinking processes, which include risk perception and belief systems. This systematic review explains the decision-making process of farmers, through cognitive mapping. Moreover, it examines mental models, perception, belief system and cause-effect relationship of farmers with particular attention given to their behavior and practices. The research adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework. It utilized Scopus and Web of Science databases to retrieve 80 articles. The main aim was to identify research trends and gaps through bibliometric analysis and the TCCM framework by focusing on major theories, contexts, characteristics, and methodologies of agricultural cognitive-mapping research. The major trends identified in this study are risk perception in agricultural economics, climatic change, technology adoption, conservative agricultural and sustainability. The major stakeholders considered included farmers (cattle, pig, rice, and date farmers), extension agents, policymakers, NGOs, rural households, agro-industries, technology providers, and the holders of indigenous knowledge. There are still major gaps in understanding the psychological and cognitive processes underlying the decisions of farmers: longitudinal studies are limited, the role of gender is not studied thoroughly, particularly in the conditions of climate-change effects and policy shifts. Though, the mental model, perceptions, and belief systems have a significant impact on agricultural decision-making, there are still gaps in the comprehension of psychological and cognitive mechanisms involved since it is a persistent problem in the agricultural decision-making process. Future studies must incorporate the behavioral psychology, mixed-methods and cross-cultural research designs to develop integrated models that support agricultural sustainability and resilience.

Digital health interventions are emerging as an approach to support obesity management through self-management and remote care. However, utilization, impact, and practicality remain unclear. This study aims to map research on digital health interventions for obesity management among adults, describing their characteristics, uses, and outcomes, and identifying gaps. A comprehensive scoping review following Arksey and O'Malley's methodological framework, in accordance with the Joanna Briggs Institute's guidelines and reported in line with the PRISMA-ScR guidelines, examined studies published between 2015 and 2025 across four databases: PubMed, Google Scholar, Scopus, and APA PsycNet. A total of 43 studies met the eligibility criteria. Digital health interventions for obesity encompassed consultations and education on healthy lifestyle, behavioral change strategies, physical activity, dietary management, weight goal setting, intermittent fasting, gamification, and psychological support. Digital health interventions were delivered through telehealth, mobile apps, web-based programs, multicomponent digital approaches integrating several digital tools, and hybrid models combining digital delivery with face-to-face communication. These interventions often supported by devices such as digital scales, wearable trackers, and telemonitoring units to enhance self-monitoring, adherence, and engagement. The interventions were implemented across clinical, workplace, and community settings, including adaptations developed during the COVID-19 pandemic. The included studies showed varying and inconsistently reported outcomes, with some showing significant weight loss, improvements in metabolic markers and behavioral outcomes, including dietary adherence, physical activity, and self-monitoring, as well as favorable feasibility outcomes and the potential to maintain continuity of service delivery during the COVID-19 outbreak. Digital health interventions used telehealth, mobile applications, web-based, multicomponent, and hybrid models across different settings, including healthcare settings (e.g., clinical and primary care) and community settings. Digital devices, such as scales, wearables, and activity trackers, were often incorporated to support self-tracking, adherence, and engagement. Reported outcomes included weight loss, improved self-monitoring, and behavioral changes such as enhanced dietary adherence and increased physical activity, and a favorable feasibility outcome; however, these outcomes were not consistently reported across all studies. Key gaps included short follow-up periods and limited evidence from LMICs. Future research should prioritize sustainable, equitable, scalable, culturally adapted, and cost-effective digital interventions.

Rapid urbanization and unequal urban-rural development have driven large-scale internal migration in low- and middle-income countries, placing internal migrant children at increased risk for psychological distress and sociocultural marginalization. Although schools are critical settings for prevention, targeted interventions that support migrant students' psychological ("feeling well") and sociocultural ("doing well") adaptation remain limited. Mindfulness training shows promise for youth mental health, yet its feasibility among socioeconomically disadvantaged migrant youth remains underexamined. This randomized controlled trial evaluated a 16-week school-based mindfulness program against an active psychoeducation control among 259 internal migrant students in China (ages 9-12; 47.1% female; 68.7% low-income) at risk for anxiety-depression. Psychological outcomes (anxiety-depression symptoms, resilience, emotional states) and sociocultural outcomes (school and sociocultural adjustment), along with parent-reported behavioral functioning, were assessed at baseline, midpoint, postintervention, and 1-year follow-up. Both programs were implemented with high fidelity. Between-group differences were not statistically significant for most outcomes. Compared with psychoeducation, mindfulness showed greater improvements only in positive affect and learning autonomy at mid-test and mindful acceptance at follow-up. Subgroup analyses indicated that effects were confined to a few outcomes in specific groups: reduced internalizing problems among students with clinical-level symptoms and selective sociocultural gains among fifth graders and girls. As schools worldwide serve growing numbers of migrant and immigrant students, equitable approaches are needed to support their adaptation amid structural disadvantage. In internal migrant students, mindfulness training showed no consistent advantage over psychoeducation and only modest, outcome- and subgroup-specific benefits, highlighting the need for context-sensitive, targeted prevention strategies.

Nurse burnout and workforce attrition represent critical challenges for healthcare organizations worldwide. This study examines how job demands and job resources are associated with burnout and how burnout relates to psychological factors shaping nurses' intention to remain in the profession. Drawing on the Job Demands-Resources (JD-R) model and the theory of planned behavior (TPB), the research integrates organizational and behavioral perspectives to explain retention processes in hospital nursing. Empirical data were collected through a cross-sectional quantitative survey among 288 hospital nurses employed in public and private healthcare institutions in Hungary. Structural equation modelling was applied to test the relationships between relevant variables. The results indicate that workload has a strong positive association with burnout, while autonomy and regular supportive feedback function as important protective resources. Burnout was significantly associated with less favorable attitudes toward remaining in the profession and lower perceived behavioral control, both of which were strong predictors of intention to stay. The findings highlight the importance of addressing both organizational working conditions and psychological mechanisms when seeking to improve nurse retention.