Search Results (606)

The rapidly changing dynamics of the digital age reshape the addiction relationship that high school students establish with technology. While smartphones remove boundaries in terms of communication and access to information, their usage triggers a source of anxiety and nomophobia. The increase in students’ anxiety levels because of their over-reliance on mobile phone use leads to significant behavioral changes in their mental health, academic performance, social interactions and financial dependency. This study examined the nomophobia levels of high school students according to selected socio-demographic indicators. Using the relational screening model, the multistage sampling technique was used to select a sample of 884 participants: 388 from Science High School and 496 from Anatolian High School (459 female, 425 male, Mage = 16.45 ± 1.14 year). Independent sample test and One-way ANOVA were applied. Depending on the homogeneity assumption of the data, Welch values were considered, and Tukey tests were applied as a second-level test from post hoc analyses. Comprehensive analyses of nomophobia levels revealed that young individuals’ attitudes towards digital technology differ significantly according to their demographic and behavioral characteristics. Variables such as gender, physical activity participation, grade level and duration of smartphone use are among the main factors affecting nomophobia levels. Female individuals and students who do not participate in physical activity exhibit higher nomophobia scores. Full article

Food addiction is characterized by compulsive consumption and impaired control over highly palatable foods, with neurobiological mechanisms analogous to substance use disorders. The Yale Food Addiction Scale 2.0 (YFAS 2.0) is the most widely used instrument to assess these symptoms; however, its psychometric properties have not been validated in Mexican adults. This study aimed to perform the cross-cultural adaptation of the YFAS 2.0 and validate its psychometric properties for the identification of food addiction in the Mexican adult population. A cross-sectional study was conducted in 500 Mexican adults aged 20 years or older. Participants completed the cross-culturally adapted YFAS 2.0. Exploratory and hierarchical factor analyses were conducted. Reliability was assessed using Cronbach’s alpha and omega coefficients, and model fit was evaluated through global fit indices. The scale showed high internal consistency (α = 0.88; ωt = 0.87; ωh = 0.89). The Kaiser–Meyer–Olkin index was 0.815 and Bartlett’s test was significant (χ² = 4367.88; df = 595; p < 0.001). Exploratory factor analysis supported a unidimensional structure, with the first factor explaining 21.3% of the total variance. In the hierarchical model, all items loaded substantially onto the general factor. Fit indices indicated excellent model fit (CFI = 0.99; TLI = 0.98; RMSEA = 0.001; RMR = 0.004). The YFAS 2.0 is a valid and reliable instrument for identifying food addiction symptoms in Mexican adults. It may be useful in clinical practice and research on eating disorders. Full article

Background/Objectives: In an increasingly pervasive digital environment, trait boredom has been identified as a key psychological factor in the onset and maintenance of problematic digital technology use. This systematic review aims to investigate the role of trait boredom in digital behavioral addictions, including problematic smartphone use, Internet and social media overuse, and gaming addiction, through theoretical models such as the I-PACE model and the Compensatory Internet Use Theory (CIUT). Methods: A systematic literature search was conducted across multiple scientific databases (PsycINFO, Web of Science, PubMed, and Scopus), yielding a total of 4603 records. Following the PRISMA guidelines after duplicate removal and screening based on title and abstract, 152 articles were assessed for full-text eligibility, and 28 studies met the predefined inclusion and exclusion criteria and were included in the final review. Results: Findings reveal that trait boredom functions as both a direct and indirect factor in problematic technology use. It serves as a mediator and moderator in the relationship between psychological vulnerabilities (e.g., depression, alexithymia, vulnerable narcissism) and dysfunctional digital behaviors. Furthermore, as an independent variable, it has an influence on technological variables through Fear of Missing Out (FoMO), loneliness, low self-regulation, and dysfunctional metacognitions, while protective factors such as mindfulness and attentional control mitigate its impact. Conclusions: Boredom represents a central psychological lever for understanding behavioral addictions in the digital age and should be considered a key target in preventive and therapeutic interventions focused on enhancing self-regulation and meaningful engagement with free time. Full article

Substance use is a critical public health issue in the U.S., with Black communities, particularly those with criminal justice contact, disproportionately affected. Chronic exposure to stressors can lead to substance use as a coping strategy. This study used data from 1476 Black adults with criminal justice involvement from the National Survey of American Life to examine how psychosocial stress and sleep disturbances relate to lifetime substance use and to determine if there are any sex differences. Sex-separate generalized linear models for a Poisson distribution with a log-link function estimated prevalence ratios and adjusted prevalence ratios (APRs) for lifetime alcohol abuse, lifetime cigarette, and marijuana use. Independent variables include stressors (family, person, neighborhood, financial, and work-related) and sleep problems, with covariates such as age, SES, and marital status. Lifetime alcohol abuse was associated with family stressors (APR = 2.72) and sleep problems (APR = 3.36) for males, and financial stressors (APR = 2.75) and sleep problems (APR = 2.24) for females. Cigarette use was linked to family stressors (APR = 1.73) for males and work stressors (APR = 1.78) for females. Marijuana use was associated with family stressors (APR = 2.31) and sleep problems (APR = 2.07) for males, and neighborhood stressors (APR = 1.72) for females. Lifetime alcohol abuse, as well as lifetime cigarette and marijuana use, was uniquely associated with various psychosocial stressors among Black adult males and females with criminal justice contact. These findings highlight the role of structural inequities in shaping substance use and support using a Social Determinants of Health framework to address addiction in this population. Full article

Background/Objectives: Smartphone addiction (SA) and gaming addiction (GA) have become risk factors for individuals of all ages in recent years. Especially during adolescence, it has become very difficult for parents to control this situation. Physical activity and the effective use of free time are the most important factors in eliminating such addictions. This study aimed to test a new machine learning method by combining routine regression analysis with the gradient-boosting machine (GBM) and random forest (RF) methods to analyze the relationship between SA and GA with leisure time management (LTM) and the enjoyment of physical activity (EPA) among adolescents. Methods: This study presents the results obtained using our developed GBM + RF hybrid model, which incorporates LTM and EPA scores as inputs for predicting SA and GA, following the preprocessing of data collected from 1107 high school students aged 15–19 years. The results were compared with those obtained using routine regression results and the lasso, ElasticNet, RF, GBM, AdaBoost, bagging, support vector regression (SVR), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and light gradient-boosting machine (LightGBM) models. In the GBM + RF model, probability scores obtained from GBM were used as input to RF to produce final predictions. The performance of the models was evaluated using the R², mean absolute error (MAE), and mean squared error (MSE) metrics. Results: Classical regression analyses revealed a significant negative relationship between SA scores and both LTM and EPA scores. Specifically, as LTM and EPA scores increased, SA scores decreased significantly. In contrast, GA scores showed a significant negative relationship only with LTM scores, whereas EPA was not a significant determinant of GA. In contrast to the relatively low explanatory power of classical regression models, ML algorithms have demonstrated significantly higher prediction accuracy. The best performance for SA prediction was achieved using the Hybrid GBM + RF model (MAE = 0.095, MSE = 0.010, R² = 0.9299), whereas the SVR model showed the weakest performance (MAE = 0.310, MSE = 0.096, R² = 0.8615). Similarly, the Hybrid GBM + RF model also showed the highest performance for GA prediction (MAE = 0.090, MSE = 0.014, R² = 0.9699). Conclusions: These findings demonstrate that classical regression analyses have limited explanatory power in capturing complex relationships between variables, whereas ML algorithms, particularly our GBM + RF hybrid model, offer more robust and accurate modeling capabilities for multifactorial cognitive and performance-related predictions. Full article

Food addiction (FA) is an emerging psychiatric condition that presents behavioral and neurobiological similarities with other addictions, and its early identification is essential to prevent the development of more severe disorders. The aim of the present study was to determine the ability of anthropometric measures, eating habits, symptoms related to eating disorders (ED), and lifestyle features to predict the symptoms of food addiction. Methodology: A cross-sectional study was conducted in a sample of 702 university students (77.3% women; age: 22 ± 6 years). The Food Frequency Questionnaire (FFQ), the Yale Food Addiction Scale 2.0 (YFAS 2.0), the Eating Attitudes Test (EAT-26), anthropometric measurements, and a set of self-report questions on substance use, physical activity level, and other questions were administered. A total of 6.4% of participants presented symptoms compatible with food addiction, and 8.1% were at risk for ED. Additionally, 26.5% reported daily smoking, 70.6% consumed alcohol, 2.9% used illicit drugs, and 29.4% took medication; 35.3% did not engage in physical activity. Individuals with food addiction had higher BMI (p = 0.010), waist circumference (p = 0.001), and body fat (p < 0.001) values, and a higher risk of eating disorders (p = 0.010) compared to those without this condition. In the multivariate logistic model, non-dairy beverage consumption (such as coffee or alcohol), vitamin D deficiency, and waist circumference predicted food addiction symptoms (R²_Nagelkerke = 0.349). Indeed, the machine learning approaches confirmed the influence of these variables. Conclusions: The prediction models allowed an accurate prediction of FA in the university students; moreover, the individualized approach improved the identification of people with FA, involving complex dimensions of eating behavior, body composition, and potential nutritional deficits not previously studied. Full article

Background: Insulin resistance (IR) is an underlying pathophysiology for type 2 diabetes (T2D). The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) is the simplest method for evaluating IR. At the same time, volatile organic compounds (VOCs) detected in human respiration can be correlated with specific diseases. To date, machine learning (Mach-L) has yet to be used to examine potential relationships between VOCs and IR. The present study has two aims: (1) to identify the VOCs most relevant to HOMA-IR, and (2) to use Shapley addictive explanation (SHAP) to determine the impacts of the distributions and directions of each feature in Taiwanese women. Methods: A total of 1432 Taiwanese women between the ages of 19 and 84 years were enrolled, and 344 VOCs were measured. Traditional multiple linear regression (MLR) was used as a benchmark for comparison, applying three Mach-L methods. Finally, SHAP was used to evaluate the directions of impacts of the features on HOMA-IR. Results: Six VOCs were identified as important: dimethylfuran, propanamine, aniline, butoxyethanol, and isopropyltoluene, in order from most to least important. SHAP found that dimethylfuran, isopropyltoluene, and dodecane were positively correlated to HOMA-IR, while butoxyethanol, aniline, and propanamine were negatively correlated. Conclusions: Using three different Mach-L methods, six VOCs were selected to be related to IR in Taiwanese women. According to their importance, dimethylfuran, propanamine, aniline, butoxyethanol, and isopropyltoluene could be used to help diagnose HOMA-IR. Furthermore, by using SHAP, dimethylfuran, isopropyltoluene, and dodecane had a positive and the other three had a negative influence. Full article

An accurate assessment of alcohol consumption is essential for identifying at-risk individuals and informing prevention and intervention strategies. The present study aimed to validate the Italian version of the Alcohol Use Questionnaire (AUQ), a self-report instrument designed to assess both general alcohol intake and binge drinking patterns. A sample of 378 Italian participants (54.5% female; M_age = 26.76 years, SD = 8.44) completed the AUQ along with additional measures assessing binge eating and psychological vulnerabilities related to addiction. Confirmatory factor analysis supported a bifactor model reflecting two distinct but related dimensions: general intake and binge drinking. Network analysis highlighted the central role of perceived frequency of intoxication within the structure of alcohol-related behaviors. Both AUQ indices showed good internal consistency and significant associations with external variables, particularly impulsivity, dissociation, and affect dysregulation, supporting construct validity. The Italian AUQ emerges as a valid and reliable tool for assessing alcohol use patterns and may be useful in both research and clinical practice. Full article

Background: This study investigated the prevalence and predictors of burnout among medical students at “Dunărea de Jos” University of Galați, Faculty of Medicine and Pharmacy. Methods: Burnout was measured using the School Burnout Inventory-U 9 (SBI-U 9), and potential predictors, including social media addiction (Bergen Social Media Addiction Scale—BSMAS), procrastination, age, gender, year of study, admission grade, last annual grade, hobbies, achievements, close friends, and relationship status, were assessed using appropriate instruments. Correlation and hierarchical multiple regression analyses identified predictors of burnout. Mediation analysis tested procrastination as a mediator between BSMAS and burnout, while moderation analysis examined whether procrastination moderated this relationship. Results: Social media addiction was an independent predictor of burnout. While younger age was correlated with higher burnout, it was not a significant predictor in the multivariate model. Procrastination did not significantly mediate the link between social media addiction and burnout but significantly moderated it. The effect of social media addiction on burnout was stronger for students with lower levels of procrastination. Conclusions: The study shows increased susceptibility to burnout among younger students and identifies social media addiction as a key risk factor. Procrastination moderates this relationship, indicating the need for interventions targeting both digital habits and time management in medical education. Full article

Exercise addiction (EA) is a maladaptive behavior characterized by excessive physical activity, often linked to negative psychological outcomes. This study investigated the relationships between perfectionism, sport commitment, and EA in a sample of 219 Hungarian athletes (M = 22.19 years). Using path analysis, we tested a model hypothesizing that adaptive and maladaptive perfectionism differentially predict enthusiastic and constrained commitment, which in turn influences EA. Our results showed that maladaptive perfectionism positively predicted constrained commitment (β = 0.70) and EA (β = 0.63), while negatively relating to enthusiastic commitment (β = −0.17). Conversely, adaptive perfectionism was positively associated with enthusiastic commitment (β = 0.24) and negatively with constrained commitment (β = −0.12). Moreover, enthusiastic commitment positively predicted EA (β = 0.24). We found a significant indirect effect between adaptive and maladaptive perfectionism when controlling for enthusiastic commitment, suggesting its dual role in this context. Our study suggests that enthusiastic commitment serves as a source of exercise addiction (EA) and has a dual role, acting as both a protective factor and a risk factor for it. Additionally, we found that maladaptive perfectionism is associated with higher levels of constrained commitment and EA, while correlating with lower levels of enthusiastic commitment. Conversely, adaptive perfectionism increases enthusiastic commitment and decreases constrained commitment. These findings highlight the associations between motivational and personality factors in EA, indicating that even adaptive traits can contribute to unhealthy exercise patterns in athletic environments. Full article

Background: Food addiction (FA) is an emerging construct that mirrors the behavioral and neurobiological characteristics of substance use disorders. Despite growing interest, its association with specific psychiatric disorders among bariatric patients remains understudied. Objective: Our aim was to examine the prevalence and strength of associations between FA and seven major psychiatric disorders in individuals who underwent bariatric surgery. Methods: In a sample of 100 post-bariatric patients referred for psychiatric evaluation, FA was assessed using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0), and psychiatric disorders were diagnosed using the Mini International Neuropsychiatric Interview (MINI). Logistic regression models were used to estimate adjusted odds ratios (aORs) for the association between FA and each psychiatric disorder, controlling for sex, age, body mass index (BMI), employment status, the number of children, clinical comorbidities, physical activity, family psychiatric history, and region of residence. Results: FA was present in 51% of the sample. Descriptive analyses revealed a significantly higher prevalence of major depressive disorder, panic disorder, generalized anxiety disorder, social anxiety disorder, agoraphobia, obsessive–compulsive disorder, and bulimia nervosa among individuals with FA. Multivariate models showed robust associations between FA and bulimia nervosa (aOR = 19.42, p < 0.05), generalized anxiety disorder (aOR = 2.88, p < 0.05), obsessive–compulsive disorder (aOR = 6.64, p < 0.05), agoraphobia (aOR = 3.14, p < 0.05), social anxiety disorder (aOR = 4.28, p < 0.05) and major depressive disorder (aOR = 2.79, p < 0.05). Conclusions: FA is strongly associated with a range of psychiatric comorbidities in post-bariatric patients, reinforcing the need for comprehensive mental health screening in this population. These findings underscore the potential role of FA as a clinical marker for stratified risk assessment, supporting more personalized approaches to mental health monitoring and intervention following bariatric surgery. Full article

Background/Objectives: Substance use disorders, particularly opioid addiction, continue to pose a major global health and toxicological challenge. Morphine dependence represents a significant problem in both clinical practice and preclinical research, particularly in modeling the pharmacodynamics of withdrawal. Rodent models remain indispensable for investigating the neurotoxicological effects of chronic opioid exposure and withdrawal. However, conventional behavioral assessments rely on manual observation, limiting objectivity, reproducibility, and scalability—critical constraints in modern drug toxicity evaluation. This study introduces MWB_Analyzer, an automated and high-throughput system designed to quantitatively and objectively assess morphine withdrawal behaviors in rats. The goal is to enhance toxicological assessments of CNS-active substances through robust, scalable behavioral phenotyping. Methods: MWB_Analyzer integrates optimized multi-angle video capture, real-time signal processing, and machine learning-driven behavioral classification. An improved YOLO-based architecture was developed for the accurate detection and categorization of withdrawal-associated behaviors in video frames, while a parallel pipeline processed audio signals. The system incorporates behavior-specific duration thresholds to isolate pharmacologically and toxicologically relevant behavioral events. Experimental animals were assigned to high-dose, low-dose, and control groups. Withdrawal was induced and monitored under standardized toxicological protocols. Results: MWB_Analyzer achieved over 95% reduction in redundant frame processing, markedly improving computational efficiency. It demonstrated high classification accuracy: >94% for video-based behaviors (93% on edge devices) and >92% for audio-based events. The use of behavioral thresholds enabled sensitive differentiation between dosage groups, revealing clear dose–response relationships and supporting its application in neuropharmacological and neurotoxicological profiling. Conclusions: MWB_Analyzer offers a robust, reproducible, and objective platform for the automated evaluation of opioid withdrawal syndromes in rodent models. It enhances throughput, precision, and standardization in addiction research. Importantly, this tool supports toxicological investigations of CNS drug effects, preclinical pharmacokinetic and pharmacodynamic evaluations, drug safety profiling, and regulatory assessment of novel opioid and CNS-active therapeutics. Full article

Short videos’ increasing popularity and increased user engagement have sparked concerns about time perception. While studies have linked gaming or watching TV series to time loss, research on short videos’ temporal impact is scarce. This study aims to investigate the impact of short video use on time distortion (including perceptions of time for experimental tasks and weekly usage) through an experimental design. Fifty-six college students were randomly assigned to two time duration conditions (long-duration for 16 min 9 s or short-duration for 5 min 23 s). Participants in both conditions were instructed to watch short videos and read public articles for the same duration and then estimate the time duration of the tasks. Subsequently, participants completed a questionnaire about their estimated and actual weekly short video use and problematic short watching levels. The results showed that the impact of task duration on time perception was significant. Task type had no significant impact on time perception, with no notable difference in time estimation between conditions involving watching short videos and reading. The interaction between time duration and task type was not significant. Additionally, problematic short video watching and the estimated weekly short video use were not significantly related to time distortion. This study contributes to empirical research on time distortion while watching short videos, providing insights for expanding theoretical models of addictive behaviors and interventions for problematic short video use. Full article

Emotional intelligence is a crucial determinant of socioemotional adaptation, psychological well-being and healthy habits in a population, although it has been barely studied in Generation Z. Therefore, the following research objectives were established: (1) to measure the levels of attention, clarity and emotional repair of Spanish university students in teaching undergraduates and (2) to design predictive models of emotional intelligence considering sex, anthropometric measurements, physical activity and the use of social networks as factors. A cross-sectional study was conducted with the involvement of Spanish teaching undergraduates. An online questionnaire integrating sociodemographic questions, the International Physical Activity Questionnaire Short Form, Trait Meta-State Mood Scale TMMS-24 and Social Network Addiction Scale SNAddS-6S were administered. University students exhibited higher levels of emotional attention (30.32 ± 6.08) than those of emotional clarity (28.18 ± 6.34) and emotional repair (28.51 ± 6.02). Most students use X, Pinterest, TikTok, Instagram, YouTube and WhatsApp most days of the week. There are positive relationships between attention and emotional clarity (r = 0.33; p ≤ 0.001), attention and emotional repair (r = 0.18; p ≤ 0.001) and clarity and emotional repair (r = 0.44; p ≤ 0.001). In conclusion, males have higher levels of emotional clarity and emotional repair, but females show higher levels of emotional attention. The model with the highest explanatory power is the one obtained for men’s emotional attention. Full article

In recent years, methamphetamine (METH) misuse in the US has been rapidly increasing, and there is no FDA-approved pharmacotherapy for METH use disorder (MUD). We previously determined that ubiquitin-protein ligase parkin is involved in the regulation of METH addictive behaviors in rat models of MUD. Parkin is not yet a “druggable” drug target; therefore, this study aimed to determine which biological processes, pathways, and proteins downstream of parkin are likely drug targets against MUD. Employing young adult Long Evans male rats with parkin deficit or excess in the nucleus accumbens (NAc), label-free proteomics, and molecular biology, we determined that the pathways downstream of parkin that are candidates for regulating METH addictive behaviors in young adult male rats are mitochondrial respiration, oxidative stress, AMPA receptor trafficking, GABAergic neurotransmission, and actin cytoskeleton dynamics. Full article