Positive Imagery-Based Cognitive Bias Modification as a Web-Based Treatment Tool for Depressed Adults: A Randomized Controlled Trial.

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Abstract

Depression is a global health problem requiring treatment innovation. Targeting neglected cognitive aspects may provide a useful route. We tested a cognitive-training paradigm using positive mental imagery (imagery cognitive bias modification, imagery CBM), developed via experimental psychopathology studies, in a randomized controlled trial. Training was delivered via the Internet to 150 individuals with current major depression. Unexpectedly, there was no significant advantage for imagery CBM compared with a closely matched control for depression symptoms as a whole in the full sample. In exploratory analyses, compared with the control, imagery CBM significantly improved anhedonia over the intervention and improved depression symptoms as a whole for those participants with fewer than five episodes of depression and those who engaged to a threshold level of imagery. Results suggest avenues for improving imagery CBM to inform low-intensity treatment tools for depression. Anhedonia may be a useful treatment target for future work.

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Cognition and depression: current status and future directions.

Ian Gotlib, Jutta Joormann (2010)

Cognitive theories of depression posit that people's thoughts, inferences, attitudes, and interpretations, and the way in which they attend to and recall information, can increase their risk for depression. Three mechanisms have been implicated in the relation between biased cognitive processing and the dysregulation of emotion in depression: inhibitory processes and deficits in working memory, ruminative responses to negative mood states and negative life events, and the inability to use positive and rewarding stimuli to regulate negative mood. In this review, we present a contemporary characterization of depressive cognition and discuss how different cognitive processes are related not only to each other, but also to emotion dysregulation, the hallmark feature of depression. We conclude that depression is characterized by increased elaboration of negative information, by difficulties disengaging from negative material, and by deficits in cognitive control when processing negative information. We discuss treatment implications of these conclusions and argue that the study of cognitive aspects of depression must be broadened by investigating neural and genetic factors that are related to cognitive dysfunction in this disorder. Such integrative investigations should help us gain a more comprehensive understanding of how cognitive and biological factors interact to affect the onset, maintenance, and course of depression.

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Advantages and limitations of Internet-based interventions for common mental disorders.

Gerhard Andersson, Nickolai Titov (2014)

Several Internet interventions have been developed and tested for common mental disorders, and the evidence to date shows that these treatments often result in similar outcomes as in face-to-face psychotherapy and that they are cost-effective. In this paper, we first review the pros and cons of how participants in Internet treatment trials have been recruited. We then comment on the assessment procedures often involved in Internet interventions and conclude that, while online questionnaires yield robust results, diagnoses cannot be determined without any contact with the patient. We then review the role of the therapist and conclude that, although treatments including guidance seem to lead to better outcomes than unguided treatments, this guidance can be mainly practical and supportive rather than explicitly therapeutic in orientation. Then we briefly describe the advantages and disadvantages of treatments for mood and anxiety disorders and comment on ways to handle comorbidity often associated with these disorders. Finally we discuss challenges when disseminating Internet interventions. In conclusion, there is now a large body of evidence suggesting that Internet interventions work. Several research questions remain open, including how Internet interventions can be blended with traditional forms of care. Copyright © 2014 World Psychiatric Association.

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Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry.

Ralitza Gueorguieva, John Krystal (2004)

The analysis of repeated-measures data presents challenges to investigators and is a topic for ongoing discussion in the Archives of General Psychiatry. Traditional methods of statistical analysis (end-point analysis and univariate and multivariate repeated-measures analysis of variance [rANOVA and rMANOVA, respectively]) have known disadvantages. More sophisticated mixed-effects models provide flexibility, and recently developed software makes them available to researchers. To review methods for repeated-measures analysis and discuss advantages and potential misuses of mixed-effects models. Also, to assess the extent of the shift from traditional to mixed-effects approaches in published reports in the Archives of General Psychiatry. The Archives of General Psychiatry from 1989 through 2001, and the Department of Veterans Affairs Cooperative Study 425. Studies with a repeated-measures design, at least 2 groups, and a continuous response variable. The first author ranked the studies according to the most advanced statistical method used in the following order: mixed-effects model, rMANOVA, rANOVA, and end-point analysis. The use of mixed-effects models has substantially increased during the last 10 years. In 2001, 30% of clinical trials reported in the Archives of General Psychiatry used mixed-effects analysis. Repeated-measures ANOVAs continue to be used widely for the analysis of repeated-measures data, despite risks to interpretation. Mixed-effects models use all available data, can properly account for correlation between repeated measurements on the same subject, have greater flexibility to model time effects, and can handle missing data more appropriately. Their flexibility makes them the preferred choice for the analysis of repeated-measures data.