The onset of substance use mostly occurs during adolescence. The aim of the present study is to investigate the relevance of personality on the basis of the NEO-Five-Factor Inventory (NEO-FFI) to future experiences with tobacco, alcohol and cannabis.
The test data were derived from the baseline assessment and first follow-up of the IMAGEN study, a European multicenter and multidisciplinary research project on adolescent mental health. In the present study 1004 participants were tested. The characterization of personality was conducted with the NEO-FFI at the age of 14 (T1). The data on substance use were collected with the European School Survey Project on Alcohol and Other Drugs (ESPAD) questionnaire at the age of 16 (T2). For the statistical analysis, t-tests and univariate analyses of variance were performed.
The scores of Conscientiousness at T1 were significantly lower for adolescents with tobacco, alcohol and cannabis experiences at T2. We found lower scores of Agreeableness at T1 in participants with tobacco and cannabis use at T2. Extraversion at T1 was significantly higher for adolescents with smoking experiences at T2. No significant associations between Neuroticism or Openness and future substance use were observed.
Low scores of Conscientiousness and Agreeableness seem to have the greatest value for a prediction of later experiences with substance use. As the present study is the first one to examine the predictive value of the NEO-FFI for future substance use in an adolescent sample, further studies are necessary to enable a better applicability in a clinical context.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).