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      The Potential Role of Learning Capacity in Cognitive Behavior Therapy for Depression: A Systematic Review of the Evidence and Future Directions for Improving Therapeutic Learning

      1 , 2 , 3 , 4 , 1 , 2 , 3
      Clinical Psychological Science
      SAGE Publications

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          Abstract

          Insight into how cognitive behavioral therapy (CBT) works is urgently needed to improve depressive outcome. First, we discuss the role of learning in CBT for depression by reviewing evidence for learning processes involved in the development and maintenance of depression. Second, we investigate the role of learning capacity as a moderator by reviewing empirical evidence for the relation between (a) CBT procedures and learning processes, (b) learning processes and CBT treatment processes, and (c) learning processes and CBT outcome. We propose that learning capacity moderates the relation between CBT procedures and change in CBT treatment processes and explains why therapeutic procedures lead to process change and long-term success in some but not all patients. Third, we identify procedures that lead to successful therapeutic learning and describe how experimental studies help to better explain causal mechanisms of change and the role of learning capacity in CBT for depression.

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          Most cited references147

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          The effect of exercise-induced arousal on cognitive task performance: a meta-regression analysis.

          The effects of acute exercise on cognitive performance were examined using meta-analytic techniques. The overall mean effect size was dependent on the timing of cognitive assessment. During exercise, cognitive task performance was impaired by a mean effect of -0.14. However, impairments were only observed during the first 20min of exercise. Otherwise, exercise-induced arousal enhanced performance on tasks that involved rapid decisions and automatized behaviors. Following exercise, cognitive task performance improved by a mean effect of 0.20. Arousal continued to facilitate speeded mental processes and also enhanced memory storage and retrieval. Positive effects were observed following exercise regardless of whether the study protocol was designed to measure the effects of steady-state exercise, fatiguing exercise, or the inverted-U hypothesis. Finally, cognitive performance was affected differentially by exercise mode. Cycling was associated with enhanced performance during and after exercise, whereas treadmill running led to impaired performance during exercise and a small improvement in performance following exercise. These results are indicative of the complex relation between exercise and cognition. Cognitive performance may be enhanced or impaired depending on when it is measured, the type of cognitive task selected, and the type of exercise performed. Published by Elsevier B.V.
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            Decreased serum brain-derived neurotrophic factor levels in major depressed patients.

            Recent findings with animal models have suggested a possible role for brain-derived neurotrophic factor (BDNF) in depression. We have therefore hypothesized that depression could be characterized by low levels of serum BDNF. Major depressed patients (15F + 15M) diagnosed according to DSM-IV criteria and healthy controls (15F + 15M) participated in the study. Serum BDNF was assayed with the ELISA method and the severity of depression was evaluated with Montgomery-Asberg-Depression Rating Scale (MADRS). BDNF levels were significantly lower in patients than in controls: 22.6 +/- 3 and 26.5 +/- 7 ng/ml (t-test = 2.7; d.f. = 58; P < 0.01). They were negatively correlated to the MADRS scores (r = -0.55; P < 0.02). Female patients were more depressed and released less BDNF than men. Analysis of covariance (MADRS and gender as independent variable vs. BDNF as dependent variable) indicated that depression severity mainly accounted for the negative correlation. These results suggest that major depression is characterized by low serum BDNF levels and support the hypothesis of neurotrophic factor involvement in affective disorders.
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              Cognitive neuroscience of self-regulation failure.

              Self-regulatory failure is a core feature of many social and mental health problems. Self-regulation can be undermined by failures to transcend overwhelming temptations, negative moods and resource depletion, and when minor lapses in self-control snowball into self-regulatory collapse. Cognitive neuroscience research suggests that successful self-regulation is dependent on top-down control from the prefrontal cortex over subcortical regions involved in reward and emotion. We highlight recent neuroimaging research on self-regulatory failure, the findings of which support a balance model of self-regulation whereby self-regulatory failure occurs whenever the balance is tipped in favor of subcortical areas, either due to particularly strong impulses or when prefrontal function itself is impaired. Such a model is consistent with recent findings in the cognitive neuroscience of addictive behavior, emotion regulation and decision-making. Copyright © 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Clinical Psychological Science
                Clinical Psychological Science
                SAGE Publications
                2167-7026
                2167-7034
                May 06 2019
                July 2019
                March 26 2019
                July 2019
                : 7
                : 4
                : 668-692
                Affiliations
                [1 ]Department of Clinical Psychology, Vrije Universiteit Amsterdam
                [2 ]Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam
                [3 ]Department of Psychology, University of Pennsylvania
                [4 ]Department of Psychology, Vanderbilt University
                Article
                10.1177/2167702619830391
                41348468-4156-4d67-9725-07a7019949ac
                © 2019

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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