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Harmful alcohol use is a global risk factor for disease, injuries and death. Research on treatment of Alcohol use disorders (AUDs) indicates that different treatment modalities are equally effective, but also that a large group of patients do not change their drinking pattern despite being in treatment. It is assumed that it is not random who benefits from treatment. Thirty to forty percent of outcome variance in treatment is probably explained by patient factors, and we need more knowledge on how different patient factors moderate treatment effects.
Further, clinicians also need more knowledge about selecting patients to different therapies. The present study will investigate how patient factors predict outcome in group treatment of AUDs, and what predicts positive treatment outcomes over time. The study is designed as a quasi-experimental, multi-centre, follow-up study. Patients will be included from Vestfold Hospital Trust, Borgestadklinikken, Blue Cross Clinic, Behandlingssenteret Eina, Blue Cross Clinic and A-senteret, Oslo, Church City Mission. The Project will provide more knowledge about patients seeking treatment for AUDs, and specifically how patient factors predict outcome in group treatment. These results will in turn lead to better selection of treatment modalities, and patients will receive a more effective treatment earlier on.
Main aims: 1) How do patient factors predict outcome in group treatment of alcohol use disorders (AUDs)? 2) Do positive treatment outcomes last over time? Specifically, do the following factors: a) psychiatric comorbidity b) severity of alcohol use pre-treatment c) personality disorders and d) cognitive impairments predict 1) completion of group treatment and 2) positive outcome after 1 year. As an additional aim, we will investigate if the Montreal Cognitive Assessment test (MoCa) is feasible as a brief screening instrument for mild cognitive impairments for AUD patients.
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HYPOTHESES, AIMS AND OBJECTIVES
The general objective of the study is to increase the knowledge of addiction treatment, and how therapy can be made more effective, especially in the case of AUD. In particular, the project will study how patient characteristics interact with treatment and influence therapy outcome. The main research questions are:
The project has two main aims. The first primary aim is predictors of successful treatment completion operationalized as percentage of participation in therapeutic activities. The second primary aim is predictors for effect of group therapy one year after treatment termination. Primary outcome variable is alcohol and substance use reduction, measured with AUDIT and DUDIT. Secondary outcomes are symptom level measured with SCL-90 and quality of life measured with WHOQOL-bref. In addition, register data concerning use of health services after finishing treatment, and participation in working life, will be collected three years after treatment completion.
PROJECT METHODOLOGY
The present study is designed as a quasi-experimental, multi-centre study on treatment in ordinary clinical practice. The study will include at least 120 patients (approximately 40 participants pr. fall and spring-inclusion term). Four data collection sites are included in the study:
Participants in the study will be patients group treatment for a primary diagnosis of AUD. Treatment is administered in a time-limited format at all sites. The therapy format may vary to some degree, but is similar in overall structure and content. In this quasi-experimental study research is carried out in an ordinary clinical situation, and study attempts to investigate the complexity of clinical practice. As a result, the researchers do not have control over all of the variables, but the results will be more ecologically valid with a higher degree of generalizibility.
PROJECT ARRANGEMENTS, METHOD SELECTION AND ANALYSES
The study will use well-researched tools and methods, used in both research and ordinary clincial practice. The following information will be entered in the registry:
Patient pre-therapy background: Earlier treatment episodes and diagnoses, demographic variables (age, sex, education, marital status, economical situation) will be retrieved from participants' journal.
Screening pre-treatment:
Screening pre- and post-treatment, and after 1 and 3 years:
Screening post-treatment:
1) Treatment satisfaction: 10 questions about treatment satisfaction.
Register data:
In addition, after 3 years, register data from patient journals and from NAV will be collected concerning working status, economical benefits and if the patient have undergone more treatment.
ANALYSES
Both intention to treat and per protocol analyses will be carried out on all participants and those who complete the treatment and data collection, until the last point of assessment. Mixed methods analyses may be used to include patients with incomplete protocols.
The outcome measures will be subjected to regression analyses to test:
The study will seek to use hierarchical regression analyses transcending the single predictor domains by entering those measures from each domain that correlates highest with outcome and lowest with predictors from other domains. For the first analyses there are only one outcome measure (participation in group treatment). For the second set of analyses (function after one year) there are multiple outcome measures (drinking/substance abuse, symptom level, function and quality of life). Thus, we will seek to construct a gross overall composite outcome measure based on normative data from the tests used.
For non-continuous data (axis 1 diagnosis, gender and marital status) analyses of variance will be computed with group-characteristics as independent variable and outcome as dependent. It is reasonable to assume that patients with combinations of substance abuse profiles, symptom level, cognitive impairments and demographic factors may constitute specific groups (clusters) with different prognosis. Cluster analysis will be applied to unravel distinct combinations of factors associated with good or poor prognosis. As an experiment, one substance use measure, one symptom distress measure, one cognitive functioning measure and one or two demographic markers will be included in the analysis. Cluster analysis is merely an explorative method designed to uncover post-hoc empirical groups with combinations of features, but may discern individual patterns that will not be evident in analyses of group means.
STATISTICAL POWER
It is important that the planned number of participants is sufficiently large to answer the research questions. It is a quasi-experimental design in the sense that division into subgroups are determined by attributes of the participants not under complete control by the researchers. Regarding statistical power, the the following examples are computed: If it is expected that 35 % (n=42) of the participants have an axis 1 diagnosis of major depression and that this group will have somewhat over half a standard deviation (.55) higher score on AUDIT/DUDIT one year after completed treatment, this difference will be statistically significant on the five percent alpha-level if there are 41 and 76 persons in the two groups (depression vs. absence of depression), which will then be within the planned sample size. Continuous data will be analyzed with correlations and hierarchical regressions. Small effect sizes (Cohen's d: .30) will be statistically significant on the five percent level with 85 participants.
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Exclusion criteria
- Lack of primary AUD-diagnosis
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Central trial contact
Kristoffer Høiland, Cand psychol; Jens Egeland, Phd
Data sourced from clinicaltrials.gov
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