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Are Digitally Delivered Psychological Interventions for Depression the Way Forward? A Review.

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Title: Are Digitally Delivered Psychological Interventions for Depression the Way Forward? A Review.
Authors: Stefanopoulou, Evgenia, Lewis, David, Taylor, Matthew, Broscombe, James, Ahmad, Javid, Larkin, Jan
Source: Psychiatric Quarterly. Dec2018, Vol. 89 Issue 4, p779-794. 16p. 1 Chart.
Subjects: Mental depression, Psychotherapy, Affective disorders, National Institute for Health & Care Excellence (Great Britain), Cognitive therapy
Abstract: Digitally delivered interventions aim to make psychological treatments more widely accessible and minimize clinician input. Although their clinical efficacy against wait-list, control conditions is well established, comparative outcome studies are a much better way to examine if psychotherapies are equally effective. Such reviews are still relatively lacking. The aim of this review was therefore to evaluate the effectiveness of digitally delivered psychological therapies over traditionally delivered (face-to-face) ones to alleviate symptoms in adults experiencing sub-threshold and clinical depression. Findings showed that digital interventions produced consistently clinically significant improvements in depressive symptoms. Moreover, the level of therapist contact or expertise did not affect much treatment effectiveness. Future research is pertinent to investigate further the influence of therapist input, the reasons for dropout, how to improve users’ experience and therapeutic engagement and maintain improvements at post-treatment. [ABSTRACT FROM AUTHOR]
Copyright of Psychiatric Quarterly is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Psychotherapy%22">Psychotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Affective+disorders%22">Affective disorders</searchLink><br /><searchLink fieldCode="DE" term="%22National+Institute+for+Health+%26+Care+Excellence+%28Great+Britain%29%22">National Institute for Health & Care Excellence (Great Britain)</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+therapy%22">Cognitive therapy</searchLink>
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  Data: Digitally delivered interventions aim to make psychological treatments more widely accessible and minimize clinician input. Although their clinical efficacy against wait-list, control conditions is well established, comparative outcome studies are a much better way to examine if psychotherapies are equally effective. Such reviews are still relatively lacking. The aim of this review was therefore to evaluate the effectiveness of digitally delivered psychological therapies over traditionally delivered (face-to-face) ones to alleviate symptoms in adults experiencing sub-threshold and clinical depression. Findings showed that digital interventions produced consistently clinically significant improvements in depressive symptoms. Moreover, the level of therapist contact or expertise did not affect much treatment effectiveness. Future research is pertinent to investigate further the influence of therapist input, the reasons for dropout, how to improve users’ experience and therapeutic engagement and maintain improvements at post-treatment. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychiatric Quarterly is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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