Personality psychopathology, drug use and psychological symptoms in adolescents with substance use disorders and community controls

The significance of information variables in polydrug use by adolescents: insights from a cross-sectional study in Tarragona (Spain)

Substance use, especially among adolescents, is a significant public health concern, with profound implications for physical and psychological development. This study aimed to evaluate the quantity and sources of information available to adolescents regarding polydrug use. A cross-sectional survey was conducted in Tarragona involving adolescents with an average age of 16.44 years. This study assessed the number of substances used (alcohol, cigarettes, and cannabis) in the past month, along with information sources related to substance use. Monitored sources (e.g., schools, parents, and mass media) and unmonitored sources (e.g., peers, siblings, internet) were distinguished. In addition, four individual and four environmental control variables were considered. Multinomial logistic regression analysis revealed that incorporating variables related to adolescents' substance use information and its sources enhanced the explanatory model, surpassing control variables. The degree of information about substance use did not significantly explain consumption patterns, but the number of information sources, both monitored and unmonitored, did. The unmonitored sources were associated with increased polydrug use. Conversely, greater reliance on supervised sources for information was linked to reduced single-substance and polydrug use. This protective effect increased with an increase in the number of substances used. In conclusion, information obtained from monitored sources acts as a deterrent to substance consumption, consistent with findings suggesting that greater health literacy among adolescents discourages substance use. Conversely, this study suggests that information from more informal sources may encourage heavier polydrug use, aligning with reports indicating that adolescents with a more comprehensive understanding of substance use consequences tend to engage in heavier drug use.

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.

Reassessing Fitness-to-Drive in Drinker Drivers: The Role of Cognition and Personality

Drunken driving is among the main challenges for road safety by causing worldwide motor-vehicle crashes with severe injuries and deaths. The reassessment of fitness-to-drive in drivers stopped for drunken driving includes mainly psychological examinations. The present study aimed to investigate the effectiveness and the consistency of selected variables of different psychological driving-related dimensions (i.e., cognitive skills and personality) in discriminating 90 male drinker drivers (DD) from matched non-drinkers controls. The Montreal Cognitive Assessment (MoCA), the Mental Rotation Test (MRT), and the Perspective-Taking Test (PT) were administered to assess overall cognitive functioning, and object- and self-based spatial transformation abilities, respectively. Participants completed a computerized test measuring resilience of attention (DT), reaction times (RT), and perceptual speed (ATAVT). The Personality Psychopathology Five scales (i.e., PSY-5: Aggressiveness-AGGR, Psychoticism-PSYC, Disconstraint-DISC, Negative-Emotionality-NEGE, and Introversion-INTR) the validity scale (L) and the dissimulation index (F-K) were scored from the Minnesota Multiphasic Personality Inventory (MMPI-2). A logistic binomial regression analysis (backward subtraction method) was used to identify discriminant predictors. A prediction analysis (ROC curve method) was performed on the final model. Results showed that the scores obtained in MRT, DT, and the personality measures of PSYC, DISC, NEGE, and INTR significantly discriminated DD from their matched controls with moderate-to-good values of accuracy (0.79), sensitivity (0.80), and specificity (0.79), as well as a good AUC value (0.89). In some cases, the personality dimensions provided—reliable—unexpected results. Low scores of PSYC, NEGE, and INTR were found to predict the membership to the DD group; results are discussed with reference to response management. Personality measures should be assessed with particular attention in a forensic context because they are more prone to be feigned than cognitive ones. Overall, the present study confirmed the relevance of integrating different driving-related psychological dimensions in the evaluation of fitness-to-drive showing the usefulness of standardized tools for the reassessment of drinker drivers.

Distinct psychological profiles among college students with substance use: A cluster analytic approach

Substance use in youth is a central public health concern, related to deleterious consequences at psychological, social, and cognitive/cerebral levels. Previous research has identified impulsivity and consumption motives as key factors in the emergence of excessive substance use among college students. However, most studies have focused on a specific substance and have considered this population as a unitary group, ignoring the potential heterogeneity in psychological profiles. We used a cluster analytic approach to explore the heterogeneity in a large sample (N = 2741) of substance users (i.e., tobacco, alcohol, cannabis, ecstasy, cocaine, heroin) on impulsivity and consumption motives. We identified four clusters: The first two clusters, associated with good self-esteem, low anxiety, and moderate substance use, were respectively characterized by low impulsivity and consumption motives (Cluster 1) and by high social and enhancement motives without marked impulsivity (Cluster 2). The two other clusters were conversely related to low self-esteem and high anxiety, and characterized by high consumption motives (particularly conformity) together with elevated urgency (Cluster 3) and by globally increased impulsivity and consumption motives (Cluster 4). These two clusters were also associated with higher substance use. These results highlight the existence of distinct psychological profiles of substance users and underline the need to develop targeted prevention and intervention programs (e.g., focusing on the specific impulsivity facets and consumption motives presented by each subgroup). Based on these findings, we also suggest extending the exploration of distinct profiles of substance users by targeting other psychological variables (e.g., self-esteem).

Ecological Momentary Assessment with smartphones for measuring mental health problems in adolescents

AIM

To analyze the viability of Ecological Momentary Assessment (EMA) for measuring the mental states associated with psychopathological problems in adolescents.

METHODS

In a sample of 110 adolescents, a sociodemographic data survey and an EMA Smartphone application over a one-week period (five times each day), was developed to explore symptom profiles, everyday problems, coping strategies, and the contexts in which the events take place.

RESULTS

The positive response was 68.6%. Over 2250 prompts about mental states were recorded. In 53% of situations the smartphone was answered at home, 25.5% of cases they were with their parents or with peers (20.3%). Associations were found with attention, affective and anxiety problems (P < 0.001) in the participants who took longer to respond to the EMA app. Anxious and depressive states were highly interrelated (rho = 0.51, P < 0.001), as well as oppositional defiant problems and conduct problems (rho = 0.56, P < 0.001). Only in 6.2% of the situations the subjects perceived they had problems, mainly associated with inter-relational aspects with family, peers, boyfriends or girlfriends (31.2%). We also found moderate-high reliability on scales of satisfaction level on the context, on positive emotionality, and on the discomfort index associated with mental health problems.

CONCLUSION

EMA methodology using smartphones is a useful tool for understanding adolescents’ daily dynamics. It achieved moderate-high reliability and accurately identified psychopathological manifestations experienced by community adolescents in their natural context.