Search Results (63)

The extensive use of digital media among adolescents has raised concerns about its impact on emotional development and mental health. Understanding the psychological factors behind problematic digital media use is essential for effective prevention. This study examined whether the relationships between emotion regulation (negative and positive), compulsive use, cognitive preoccupation, and negative outcomes linked to digital media are consistent across sex and academic year. We used a cross-sectional design with 2357 adolescents (12–16 years old) from Compulsory Secondary Education. Participants completed validated self-report questionnaires assessing problematic digital media use, and associated consequences in four domains: internet, video games, social networking, and messaging. Four structural equation models (SEMs), each focused on a media type, tested whether these relationships remained stable across sex and academic year. All models showed good fit, and differences between groups were minimal, supporting valid comparisons. Results confirm that emotion regulation difficulties and problematic digital media use are consistently associated with negative outcomes in all adolescents, regardless of sex or academic level. Preventive strategies targeting emotional regulation and digital media behaviors may be broadly applied to reduce emotional and functional problems related to excessive media use. Full article

This study aims to explore the interrelationships among key factors influencing learning burnout, such as motivation and negative emotions (depression, anxiety, and stress) along with other factors influencing including problematic mobile phone use, nomophobia, and interactive learning, as well as whether their pathways of influence on learning burnout differ between online and offline learning contexts. Using the convenience sampling method, data from 293 college students were collected. Measurements were carried out using the Nomophobia Scale, the Problematic Mobile Phone Use Scale, the Depression Anxiety Stress Scale (DASS), the Interactive Learning Scale, the Learning Burnout Scale, and the Scale of Motivation for Activity Participation. By applying network analysis and network comparison methods, and based on the Social Comparison Theory and the Affective Socialization Heuristics Model, it was found that under the online learning condition the motivation to pursue value directly affects learning burnout. In contrast, under the offline learning condition learning motivation indirectly affects learning burnout through negative emotions. This study posits that this difference is caused by peer comparison. In a collective learning atmosphere, students’ comparison with their peers triggers negative emotions such as anxiety and stress. These negative emotions weaken the learning motivation to pursue value, ultimately resulting in an elevated level of learning burnout. Full article

Identifying when and which adolescents are at increased risk of developing problematic social media use (PSMU) is critical for effective prevention and early intervention. Previous research has examined risk and protective factors using theory-driven (confirmatory-explanatory) approaches, such as regression models. However, few studies have simultaneously considered personal, peer, and parent characteristics to assess their relative contributions, and none have explored how these factors are structurally interrelated using data-driven (inductive–exploratory) approaches. To address these gaps, this study combines logistic regression and psychological network analysis to examine which personal, parent, and peer factors are most relevant in identifying at-risk/problematic social media use among adolescents. Using three waves of data analyzed cross-sectionally from N = 2441 secondary school students, adolescents were classified as normative (0–1 symptoms) or at-risk/problematic (2+ symptoms) users based on the Social Media Disorder Scale. Logistic regression showed that fear of missing out, impulsivity, depressive symptoms, intensity of meeting with friends, and reactive parental rules uniquely predicted at-risk/problematic use. Psychological network analysis identified self-esteem, attention problems, impulsivity, depressive symptoms, and life satisfaction as central, highly interconnected nodes. These findings show that theory- and data-driven approaches illuminate different aspects of PSMU risk, and that network analysis can generate novel hypotheses about underlying processes. Full article

The rapid growth of cities significantly impacts the development of transport and logistics infrastructure (TLI), creating substantial challenges for the transport network and quality of life. To enhance the efficiency and sustainability of TLI, various approaches, planning methods, and management strategies are employed at the city or agglomeration level. The objective of this study was to investigate, using graph theory and correlation analysis, the relationship between the polarity and logistic flow of the city’s meso-districts. Based on these findings, recommendations for the development of the city’s transport and logistics infrastructure were proposed. The logistic flow, influenced by social, economic, institutional, and environmental factors, plays a critical role in the planning and operation of transport and logistics infrastructure within each meso-district of the city. The determination of the polarity of meso-districts was conducted based on expert assessments by specialists, while the indicators of logistic flow were derived from the average values of statistical data for the period 2021–2023. The results demonstrated that a reduction in the polarity of meso-districts—characterized by multilateral connections between meso-districts and key indicators of logistic flows—can positively influence the quality and accessibility of the city’s transport and logistic infrastructure. This approach enables the identification of the most problematic meso-districts within the city, the mapping of logistic flow directions, and the determination of strategic development pathways for the city’s transport and logistics infrastructure (TLI). Furthermore, it was established that the polarity of the meso-district graph reflects the state of traffic congestion within the districts and its environmental impact. This correlation provides valuable insights into refining the planning and development of the city’s TLI, ensuring a more sustainable and efficient urban transport system. This study contributed to the development of the city’s transport and logistics infrastructure by proposing a comprehensive model that enhances the understanding and strengthens the interconnections between meso-districts and urban logistics. The findings hold significant implications for urban planning, as they highlight the necessity of a detailed consideration of the role of meso-districts, as well as targeted investments in transport and logistics infrastructure to ensure its sustainable development in the future. Full article

Climate change has increased the frequency of extreme weather events, challenging traditional flood risk assessments. New methods, like InVEST’s urban flood risk mitigation model (UFRM), are crucial for assessing flood-prone areas, especially those vulnerable to pluvial flooding. This systematic review examines the utilization and limitations of the UFRM model for identifying flood-prone areas and designing adaptation plans in response to climate change, based on research articles published between 2019 and 2024. The articles were identified through Scinapse and Google Scholar using predefined criteria, including relevance to UFRM applications and publication within the specified timeframe. An analysis of the 21 selected papers using bibliometric techniques reveals geographic concentrations of the case studies that utilized the UFRM model primarily in Italy, Turkey, China, and the United States, with notable research gaps in Central Europe. The journal publication trends highlight the prominence of certain journals, such as the Journal of Land. A network analysis using VOSviewer identifies thematic clusters aligned with the UFRM research areas, including Urban Planning and Nature-Based Solutions (NBSs), Urbanization and Sustainable Development, the Economic and Social Effects of Flood Mitigation, Adaptation and Risk Management, and Resilience and Vulnerability. This review contributes to gaining a deeper understanding of the research landscape regarding the utilization of the UFRM model and provides recommendations for advancing sustainable and resilient urban development practices. The review was not preregistered in a formal registry. The finding reveals that integrating automation and optimization tools to suggest different solutions for problematic nodes in urban areas is crucial for addressing multiple issues simultaneously and optimizing adaptation plans effectively. Full article

Background/Objectives: Problematic online gaming and gambling tend to co-occur. The exact mechanisms underlying this phenomenon and the potential effects of gender differences remain unknown. This study aimed to identify the early clustering patterns of problematic online gaming and gambling in a community sample of young adults without a lifetime history of psychiatric treatment. Methods: Data were collected through an online survey and analyzed using partial correlations and Bayesian networks. Results: Altogether, 1441 individuals (aged 18–40 years, 51.4% females) participated in the survey. Both problematic online behaviors were weakly interrelated, suggesting that they serve as distinct constructs. Men’s networks appeared to be more complex and had significantly higher global connectivity. Moreover, men and women differed with respect to the specific nodes that bridged both constructs. In men, the bridge nodes were “being criticized because of betting or being told about gambling problems”, “loss of previous interests due to gaming”, “deceiving other people because of gaming”, and “health consequences of gambling”. Among women, the bridge nodes were “feeling guilty because of gambling”, “loss of previous interests because of gaming”, “social consequences of gaming”, and “continued gaming problems with other people”. In men, the strongest edge was found between “borrowing money/selling anything to gamble” and “financial problems because of gambling”, while in women, the strongest edge appeared between “betting more than afforded to be lost” and “tolerance symptoms of gambling”. Conclusions: The findings indicate that problematic online gaming and gambling tend to emerge in different ways among men and women. Therapeutic interventions should be planned considering gender differences. Full article

(1) Background: This study investigates the influence of social networks on young adults, focusing on both positive and negative impacts. It considers problematic social networking site use (PSNSU), emotional intelligence, empathy, and the phenomenon of Fear of Missing Out (FoMO). (2) Methods: A comprehensive online survey was conducted with 442 participants aged 18 to 30 years that used various psychometric tools to assess emotional intelligence, empathy, internet and social media use, and FoMO. (3) Results: The study revealed that young adults display low competencies in emotional intelligence and empathy, with a tendency towards problematic internet and social media use. High FoMO scores were observed, correlating with negative internet use outcomes. Gender differences in these aspects were also explored. (4) Conclusions: The findings suggest a complex interaction between social network use, emotional skills, and FoMO that impacts young adults’ mental health and social behaviors. This study highlights the need for a nuanced understanding of these relationships and their implications for well-being and social interaction in the digital age. Full article

This study aims to validate the impact of sociodemographic factors and differentiate the influence of social variables on the correlation between the number of years of formal education an individual has and the time they allocate to internet usage. To achieve this, we utilized a publicly available database, extracting relevant indicators for our investigation. Subsequently, we conducted analyses involving associations, regressions, and moderations among the variables under scrutiny. The results revealed statistically significant variations in daily internet usage time across different countries, residences, age groups, educational levels, and marital statuses. Factors such as living in an urban or suburban environment, being in the youth demographic, possessing a higher education, maintaining single status, having an extensive social network, holding a negative perception of health, lacking home internet access but having access at work and on the go, along with the facilitation of online communication and remote work, collectively explain the variance in daily internet usage time. The relationship between the number of years of education and the duration of internet usage is moderated by sociodemographic variables (gender, age, and marital status) as well as social variables (locations of internet usage and social contacts). These findings enable us to identify a user profile at a higher risk of developing problematic behavior in relation to internet usage, as indicated by the time invested. Full article

As behavioral addictions (BAs) significantly affect well-being, paying attention to the characteristics associated with their onset is relevant. Current studies suggest that BAs should be addressed from an adaptive–maladaptive continuum perspective to define what and how some behaviors became problematic. The multi-faceted nature of behaviors attributed to possible BA involves psychological and individual backgrounds (e.g., attachment style). Given its role in affecting personality processes, social development, and motivational drives, the attachment style would be involved in addiction-like behaviors from adolescence, defined as a critical period for BA onset. This study analyzed the association between the attachment dimensions and two possible BAs that can be included in an adaptive–maladaptive continuum (i.e., social network use and TV series watching). A sample consisting of 493 late adolescents/emerging adults (age range: 18–24) completed questionnaires assessing social network use, TV series watching, and attachment style. The results showed a positive association between problematic attachment styles and BAs. High worry and need for relationships (anxious attachment) would be risk factors for problematic social network use and relationships as secondary (avoidant attachment) would be a risk factor for problematic TV series watching. These findings suggest the importance of further analyzing the role of attachment styles and their dimensions in influencing behavioral expression early to prevent the occurrence of BA. Full article

The concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch of Artificial Intelligence (AI) and develops algorithms that can learn from data and generalize their judgment to new observations by exploiting primarily statistical methods. The new millennium has seen the proliferation of Artificial Neural Networks (ANNs), a formalism able to reach extraordinary achievements in complex problems such as computer vision and natural language recognition. In particular, designers claim that this formalism has a strong resemblance to the way the biological neurons operate. This work argues that although ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science, at least from a methodological perspective. The main reason is that ML algorithms have notable prediction power although they cannot necessarily provide a causal explanation about the achieved predictions. For example, an ANN could be trained on a large dataset of consumer financial information to predict creditworthiness. The model takes into account various factors like income, credit history, debt, spending patterns, and more. It then outputs a credit score or a decision on credit approval. However, the complex and multi-layered nature of the neural network makes it almost impossible to understand which specific factors or combinations of factors the model is using to arrive at its decision. This lack of transparency can be problematic, especially if the model denies credit and the applicant wants to know the specific reasons for the denial. The model’s “black box” nature means it cannot provide a clear explanation or breakdown of how it weighed the various factors in its decision-making process. Secondly, this work rejects the belief that a machine can simply learn from data, either in supervised or unsupervised mode, just by applying statistical methods. The process of learning is much more complex, as it requires the full comprehension of a learned ability or skill. In this sense, further ML advancements, such as reinforcement learning and imitation learning denote encouraging similarities to similar cognitive skills used in human learning. Full article

To understand the factors related to suicidal ideation in university students, we examined individual and microsystemic variables with an ecological model organizing bidirectional influences between different dimensions. Suicidal ideation, psychological distress, multidimensional self-concept, loneliness, community social support, life satisfaction, family functioning, bullying victimization, and problematic use of virtual social networks were measured in a sample of 376 Mexican university students (67% women) from 18 to 34 years of age (M = 20.8). Data were collected in March 2020, before mandatory confinement for COVID started in Mexico. Discriminant analyses showed that psychological distress, loneliness, self-concept, life satisfaction, family functioning, internet violence/rejection, and informal social support predicted belonging to high or no suicidal ideation groups with 88% accuracy. Only psychological distress and family self-concept predicted suicidal ideation in multilinear regression analyses. There were differences by gender in multilinear regression, with family self-concept significant for women and physical self-concept, but not family self-concept for boys. Conclusions: Early Suicide prevention in universities should include periodic screening of psychological distress, loneliness, and virtual social media use to identify students that need further evaluation and intervention. University-based strategies of mental health promotion that strengthen family relationships and the sense of community, including gender-sensitive orientation, could enhance the effect of protective factors. Full article

Based on the interaction of person-affect-cognition-execution model, this study examined the association between stress perception and problematic social network use among Chinese college students and explored the mediating effect of the fear of missing out (FoMO) on the relationship between stress perception and problematic social network use. A questionnaire survey was conducted among 554 students from nine universities in China. We found that stress perception was significantly positively correlated with problematic social network use and FoMO (r = 0.38, 0.46; p < 0.001), and FoMO was significantly positively correlated with problematic social network use (r = 0.45, p < 0.001). FoMO mediated the relationship between stress perception and problematic social network use. Conclusion: Stress perception has a negative impact on college students’ problematic social network use via the mediating effect of FoMO. Practical implications that reveal the college students’ problematic social network use were discussed as well. Full article

Young individuals frequently share and encounter alcohol-related content (i.e., alcohol posts) on social networking sites. The prevalence of these posts is problematic because both the sharing of and exposure to these posts can increase young individuals’ alcohol (mis)use. Consequently, it is essential to develop effective intervention strategies that hinder young individuals from sharing these posts. This study aimed to develop such intervention strategies by following four steps: (1) assessing young individuals’ problem awareness of alcohol posts, (2) unraveling individuals’ own intervention ideas to tackle the problem of alcohol posts, (3) examining their evaluations of theory/empirical-based intervention ideas, and (4) exploring individual differences in both problem awareness and intervention evaluations. To reach these aims, a mixed-method study (i.e., focus-group interviews and surveys) among Dutch high-school and college students (N_total = 292, Age_range = 16–28 years) was conducted. According to the results, most youth did not consider alcohol posts to be a problem and were, therefore, in favor of using automated warning messages to raise awareness. However, these messages might not work for every individual, as group differences in problem awareness and intervention evaluations exist. Overall, this study puts forward potential intervention ideas to reduce alcohol posts in digital spheres and can therefore serve as a steppingstone to test the actual effects of the ideas. Full article

An understanding of the neurocognitive profile underlying the use of social networking sites (SNSs) can help inform decisions about the classification of problematic SNS use as an addictive disorder and elucidate how/when ‘SNS addiction’ might develop. The present review aimed to synthesize structural and functional MRI research investigating problematic/compulsive forms of SNS use or regular (non-addicted) SNS use behaviours. We conducted a systematic search for research articles published in English using the Web of Science, PubMed, and Scopus databases up to October 2022. Studies meeting our inclusion criteria were assessed for quality and a narrative synthesis of the results was conducted. Twenty-eight relevant articles were identified comprising structural MRI (n = 9), resting-state fMRI (n = 6) and task-based fMRI studies (n = 13). Current evidence suggests that problematic SNS use might be characterised by (1) reduced volume of the ventral striatum, amygdala, subgenual anterior cingulate cortex, orbitofrontal cortex and posterior insula; (2) increased ventral striatum and precuneus activity in response to SNS cues; (3) abnormal functional connectivity involving the dorsal attention network; (4) inter-hemispheric communication deficits. Regular SNS use behaviours appear to recruit regions involved in the mentalising network, the self-referential cognition network, the salience network, the reward network and the default mode network. Such findings are at least partially consistent with observations from the substance addiction literature and provide some provisional support for the addictive potential of SNSs. Nonetheless, the present review is limited by the small number of eligible studies and large heterogeneity in the methods employed, and so our conclusions should remain tentative. Moreover, there is a lack of longitudinal evidence suggesting SNSs cause neuroadaptations and thus conclusions that problematic SNS use represents a disease process akin to substance use addictions are premature. More well-powered longitudinal research is needed to establish the neural consequences of excessive and problematic SNS use. Full article

Transformability is one of the essential attributes of social systems. To improve transformability, one should create the preconditions for strategic intervention on the underlying social structures. This paper proposes a design-driven conflict (DDC) methodology in response to the limitation of the systemic design approach by aggregating a network of allies essential for the paradigmatic shifts. The proposed methodology has more strategic implications. It starts with unfolding the actors and shared resources (phase one context mapping). It continues with defining the power relations between them, drivers, and spillovers that cause conflicts and disagreements (phase two analysis). After this, it shows how one can synthesize the commonalities and the core narratives of actors in the form of boundary objects (phase three synthesis). By using the existing narratives and the commonalities between actors as inputs for the translation phase, DDC creates the preconditions for a network of allies construction. Next, the methodology uses translation as a method, in relation to the four moments of a ‘sociology of translation’, problematization, interessement, enrolment, and mobilization, in order to gradually change the learning paradigm of the system. In the scaling-up phase (phase five), DDC proposes ways of creating a narrative platform, shedding light on how to mobilize the results of translation from the community level onto a broader social scale. The framework for the design methodology has been evaluated based on a method content analysis and by a group of experts from diverse backgrounds and disciplines. The results show, except for the efficiency of the method, which requires additional investigation in a real-life context, the efficacy and effectiveness of the method have been elaborated in a sufficient way. Full article