The addictive use of video games is recognized as a problem with clinical relevance and is included in international diagnostic manuals and classifications of diseases. The association between “Internet addiction” and mental health has been well documented across a range of investigations. However, a major drawback of these studies is that no controls have been placed on the type of Internet use investigated. The aim of this study is to review systematically the current literature in order to explore the association between Internet Gaming Disorder (IGD) and psychopathology. An electronic literature search was conducted using PubMed, PsychINFO, ScienceDirect, Web of Science and Google Scholar (r.n. CRD42018082398). The effect sizes for the observed correlations were identified or computed. Twenty-four articles met the eligibility criteria. The studies included comprised 21 cross-sectional and three prospective designs. Most of the research was conducted in Europe. The significant correlations reported comprised: 92% between IGD and anxiety, 89% with depression, 85% with symptoms of attention deficit hyperactivity disorder (ADHD), and 75% with social phobia/anxiety and obsessive-compulsive symptoms. Most of the studies reported higher rates of IGD in males. The lack of longitudinal studies and the contradictory results obtained prevent detection of the directionality of the associations and, furthermore, show the complex relationship between both phenomena.

Internet Gaming Disorder is an increasingly prevalent disorder, which can have severe consequences in affected young people and in their families. There is an urgent need to improve existing treatment programs; these are currently hampered by the lack of research in this area. It is necessary to more carefully define the symptomatic, psychosocial and personality characterization of these patients and the interaction between treatment and relevant variables. The objectives of this study were three: 1) to analyze the symptomatic and personality profiles of young patients with Internet Gaming Disorder in comparison with healthy controls; 2) to analyze the effectiveness of a cognitive behavioral treatment on reducing symptomatology; and 3) to compare the results of that treatment with or without the addition of a psychoeducational group offered to the parents. The final sample consisted of 30 patients consecutively admitted to a specialized mental health unit in Spain, and 30 healthy controls. The experimental group received individual cognitive-behavioral therapy. The experimental group was divided into two subgroups (N=15), depending on the addition or not of a psychoeducational group for their parents (consecutively admitted). Scores on the Millon Adolescent Personality Inventory (MACI), the Symptom Checklist-Revised (SCL-90-R), the State-Trait Anxiety Index (STAI), and other clinical and psychopathological measures were recorded. The patients were re-assessed post treatment (except for the MACI questionnaire). Compared with healthy controls, patients did not differ in symptomatology at baseline, but scored significantly higher in the personality scales: Introversive and Inhibited, and in the expressed concerns scales: Identity Confusion, Self-Devaluation, and Peer Insecurity and scored significantly lower in the Histrionic and Egotistic scale. In the experimental group, pre-post changes differed statistically on SCL-90-R scales Hostility, Psychoticism and Global Severity Index and on the diagnostic criteria for Internet Gaming Disorder, regardless of the addition of a psychoeducational group for parents. Pre-post changes did not differ between experimental subgroups. However, the subgroup without psychoeducation for parents presented statistically higher drop-out rates during treatment. The results of this study are based on a sample of patients seeking treatment related to problems with online gaming, therefore, they may be of value for similar patients.

Introduction: Previous research indicates that social isolation, loneliness, physical dysfunction and depressive symptoms are interrelated factors, little is known about the potential pathways among them. The aim of the study is to analyse simultaneously reciprocal relationships that could exist between the four factors to clarify potential mediation effects.Methods: Within a large representative sample of older people in the Longitudinal Aging Study Amsterdam (LASA), participants aged 75 and over were followed up over a period of 11 years (four waves). We tested cross‐lagged and autoregressive longitudinal associations of social network size, loneliness, physical functioning and depressive symptoms using structural equation modelling (SEM).Results: Several statistically significant cross‐lagged associations were found: decreasing physical functioning (Coef. = −0.03; p < 0.05), as well as social network size (Coef. = −0.02; p < 0.05), predicted higher levels of loneliness, which predicted an increase in depressive symptoms (Coef. = 0.17; p < 0.05) and further reduction of social network (Coef. = −0.20; p < 0.05). Decreasing physical functioning also predicted an increase in depressive symptoms (Coef. = −0.08; p < 0.05). All autoregressive associations were statistically significant.Conclusion: Interventions focused on promoting social activities among older adults after negative life events, such as loss of social contacts or declining physical function, may alleviate feelings of loneliness and act as mental health protector.

Background: There is a substantial proportion of patients who drop out of treatment before they receive minimally adequate care. They tend to have worse health outcomes than those who complete treatment. Our main goal is to describe the frequency and determinants of dropout from treatment for mental disorders in low-, middle-, and high-income countries.Methods: Respondents from 13 low- or middle-income countries (N = 60 224) and 15 in high-income countries (N = 77 303) were screened for mental and substance use disorders. Cross-tabulations were used to examine the distribution of treatment and dropout rates for those who screened positive. The timing of dropout was examined using Kaplan–Meier curves. Predictors of dropout were examined with survival analysis using a logistic link function.Results: Dropout rates are high, both in high-income (30%) and low/middle-income (45%) countries. Dropout mostly occurs during the first two visits. It is higher in general medical rather than in specialist settings (nearly 60% v. 20% in lower income settings). It is also higher for mild and moderate than for severe presentations. The lack of financial protection for mental health services is associated with overall increased dropout from care.Conclusions: Extending financial protection and coverage for mental disorders may reduce dropout. Efficiency can be improved by managing the milder clinical presentations at the entry point to the mental health system, providing adequate training, support and specialist supervision for non-specialists, and streamlining referral to psychiatrists for more severe cases.

Objectives Our aim was to determine clusters of non-communicable diseases (NCDs) in a very large, population-based sample of middle-aged and older adults from low- and middle-income (LMICs) and high-income (HICs) regions. Additionally, we explored the associations with several covariates. Design The total sample was 72 140 people aged 50+ years from three population-based studies (English Longitudinal Study of Ageing, Survey of Health, Ageing and Retirement in Europe Study and Study on Global Ageing and Adult Health) included in the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and representing eight regions with LMICs and HICs. Variables were previously harmonised using an ex-post strategy. Eight NCDs were used in latent class analysis. Multinomial models were made to calculate associations with covariates. All the analyses were stratified by age (50–64 and 65+ years old). Results Three clusters were identified: ‘cardio-metabolic’ (8.93% in participants aged 50–64 years and 27.22% in those aged 65+ years), ‘respiratory-mental-articular’ (3.91% and 5.27%) and ‘healthy’ (87.16% and 67.51%). In the younger group, Russia presented the highest prevalence of the ‘cardio-metabolic’ group (18.8%) and England the ‘respiratory-mental-articular’ (5.1%). In the older group, Russia had the highest proportion of both classes (48.3% and 9%). Both the younger and older African participants presented the highest proportion of the ‘healthy’ class. Older age, being woman, widowed and with low levels of education and income were related to an increased risk of multimorbidity. Physical activity was a protective factor in both age groups and smoking a risk factor for the ‘respiratory-mental-articular’. Conclusion Multimorbidity is common worldwide, especially in HICs and Russia. Health policies in each country addressing coordination and support are needed to face the complexity of a pattern of growing multimorbidity.

Fruit juices are one of the most adulterated beverages, usually because of the addition of water, sugars, or less expensive fruit juices. This study presents a method based on Fourier transform infrared spectroscopy (FT-IR), in combination with machine learning methods, for the correct identification and quantification of adulterants in juices. Thus, three types of 100% squeezed juices (pineapple, orange, and apple) were evaluated and adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The results of the exploratory data analysis revealed a clear clustering trend of the samples according to the type of juice analyzed. The supervised learning analysis, based on the development of models for the detection of adulteration, obtained significant results for all tested methods (i.e., support-vector machines or SVM), random forest or RF, and linear discriminant analysis or LDA) with an accuracy above 97% on the test set. Regarding quantification, the best results are obtained with the support vector regression and with partial least square regression showing an R² greater than 0.99 and a root mean square error (RMSE) less than 1.4 for the test set.