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http://hdl.handle.net/11375/27884
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DC Field | Value | Language |
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dc.contributor.advisor | MacKillop, James | - |
dc.contributor.author | Syan, Sabrina Kaur | - |
dc.date.accessioned | 2022-09-28T20:00:21Z | - |
dc.date.available | 2022-09-28T20:00:21Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://hdl.handle.net/11375/27884 | - |
dc.description.abstract | Substance and alcohol use disorders are highly prevalent and confer a considerable burden of morbidity and mortality including medical, psychosocial, and economic consequences. Although evidence-based treatments to treat substance and alcohol use disorders are present, treatment retention and efficacy within these treatment modalities remains low. Further, individuals with substance and alcohol use disorders are clinically complex and often present with concurrent psychiatric disorders, which makes treatment delivery and response more challenging. Developing novel treatments and improving clinical outcomes in patients with substance and alcohol use disorders is predicated upon basic scientific advances in understanding the biological and behavioural determinants of treatment response. The purpose of this dissertation is to examine the biobehavioural predictors of treatment outcome in substance and alcohol use disorders through three distinct studies that each used pre-treatment variables to predict addictions treatment response. The first study used latent profile analysis to elucidate clinical profiles and independent variables associated with premature treatment termination from clinically complex sample of individuals with concurrent disorders at a large residential addiction treatment centre. The second study systematically synthesized and critically appraised empirical findings of delayed reward discounting, a transdiagnostic behavioural economic indicator of impulsivity, as a predictor of smoking cessation treatment outcome. Finally, the third study used baseline resting state functional connectivity (rsFC) to predict response to a brief intervention to reduce alcohol consumption at three-month follow-up in individuals with alcohol use disorder. The results of this dissertation extend the current literature and highlight the utility of pre-treatment clinical, behavioural, and neuroimaging data in predicting treatment response and elucidating pre-treatment patterns of rsFC that are associated with poor prognosis. The pre-treatment variables identified in this dissertation can be used to identify high-risk patient populations that may benefit from additional care pathways, adjunctive treatment, or further resources to improve patient outcomes and prognosis. | en_US |
dc.language.iso | en | en_US |
dc.title | Biobehavioural Predictors of Treatment Outcome in Addiction | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Psychology | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Candidate in Philosophy | en_US |
dc.description.layabstract | Approximately 1 in 5 Canadians will use alcohol or substances problematically within their lifetime. While there are well-researched treatments for problematic alcohol or substance use, individuals either have considerable difficulty completing treatment, or relapse following treatment. Investigating factors that can help individuals remain in treatment and maintain progress can be challenging since individuals with substance and alcohol use challenges are clinically complex patients who often present with additional mental health disorders. Understanding the pre-treatment factors that contribute to treatment outcome is critical to improving patient outcomes during and following addictions treatment. This dissertation examined the role of pre-treatment clinical patient profiles, impulsivity, and brain functional connectivity in predicting addictions treatment outcome. Clinically, these results will help identify patients at high risk of poor prognosis, who may benefit from additional resources during treatment to improve progress through treatment and treatment outcome. | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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syan_sabrina_k_finalsubmission202209_phd.pdf | 4.02 MB | Adobe PDF | View/Open |
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