Home > Predictive and explanatory models of cigarette smoking: computational approaches to understanding nicotine addiction.

Jollans, Lee (2019) Predictive and explanatory models of cigarette smoking: computational approaches to understanding nicotine addiction. PhD thesis, Trinity College Dublin.

URL: http://www.tara.tcd.ie/handle/2262/86096


Smoking is the leading cause of preventable death worldwide, causing 6 million deaths every year (WHO, 2011). Most people try smoking for the first time in adolescence (O’Loughlin et al., 2014), making this a critical period for research regarding risk factors for progressing into nicotine addiction. As with other substance use disorders, much is known about how nicotine-induced changes in neurotransmitter systems and sensitivity to drug- and non-drug rewards lead from recreational to habitual and finally to compulsive use. Differences in personality, life history, environment, behavioural responding, and neurobiology between non-smokers, smokers, and smokers who manage to quit are also known. However, there is very little evidence as to what pre-existing neurobiological factors make adolescents vulnerable to smoking behaviour. Using a large sample of 548 14-year old non-smokers, machine learning was used to predict smoking behaviour in the next four years.

Item Type:Thesis (PhD)
Date:2019
EndNote:View
Accession Number:HRB (Electronic Only)
Subjects:B Substances > Tobacco (cigarette smoking)
J Health care, prevention and rehabilitation > Risk and protective factors > Risk factors
VA Geographic area > Europe > Ireland

Repository Staff Only: item control page