Effectiveness of a Multi-Component Smoking Cessation Support Programme (McSCSP) for Patients with Severe Mental Disorders: Study Design

Only a few studies have examined the efficacy and safety of smoking cessation programmes in patients with mental disorders. The aim of this paper is to describe in detail the methodology used in the study as well as the Multi-component Smoking Cessation Support Programme in terms of pharmacological treatments and psychological interventions. An open-label 9-month follow-up study was conducted in Spain. A total of 82 clinically stable outpatients with schizophrenia, schizoaffective or bipolar disorder were enrolled. Treatment consisted of a programme specifically developed by the research team for individuals with severe mental disorders. The programme consisted of two phases: (1) weekly individual motivational therapy for 4-12 weeks, and (2) a 12-week active treatment phase. During this phase, at each study visit patients received a one- or two-week supply of medication (transdermal nicotine patches, varenicline or bupropion) with instructions on how to take it, in addition to group psychotherapy for smoking cessation. Evaluations were performed: (1) at the time of enrolment in the study, (2) during the 12-week active treatment phase of the study (weekly for the first 4 weeks and then biweekly), and (3) after the end of this phase (two follow-up assessments at weeks 12 and 24). Evaluations included: (1) smoking history, (2) substance use, (3) psychopathology, (4) adverse events, and (5) laboratory tests. The importance of this study lies in addressing a topical issue often ignored by psychiatrists: the unacceptably high rates of tobacco use in patients with severe mental disorders.

An investigation of imputation methods for discrete databases and multi-variate time series

When it comes to information sets in real life, often pieces of the whole set may not be available. This problem can find its origin in various reasons, describing therefore different patterns. In the literature, this problem is known as Missing Data. This issue can be fixed in various ways, from not taking into consideration incomplete observations, to guessing what those values originally were, or just ignoring the fact that some values are missing. The methods used to estimate missing data are called Imputation Methods. The work presented in this thesis has two main goals. The first one is to determine whether any kind of interactions exists between Missing Data, Imputation Methods and Supervised Classification algorithms, when they are applied together. For this first problem we consider a scenario in which the databases used are discrete, understanding discrete as that it is assumed that there is no relation between observations. These datasets underwent processes involving different combina- tions of the three components mentioned. The outcome showed that the missing data pattern strongly influences the outcome produced by a classifier. Also, in some of the cases, the complex imputation techniques investigated in the thesis were able to obtain better results than simple ones. The second goal of this work is to propose a new imputation strategy, but this time we constrain the specifications of the previous problem to a special kind of datasets, the multivariate Time Series. We designed new imputation techniques for this particular domain, and combined them with some of the contrasted strategies tested in the pre- vious chapter of this thesis. The time series also were subjected to processes involving missing data and imputation to finally propose an overall better imputation method. In the final chapter of this work, a real-world example is presented, describing a wa- ter quality prediction problem. The databases that characterized this problem had their own original latent values, which provides a real-world benchmark to test the algorithms developed in this thesis.