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Cognitive training and remediation interventions for substance use disorders: a Delphi consensus study.

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Title: Cognitive training and remediation interventions for substance use disorders: a Delphi consensus study.
Authors: Verdejo‐Garcia, Antonio, Rezapour, Tara, Giddens, Emily, Khojasteh Zonoozi, Arash, Rafei, Parnian, Berry, Jamie, Caracuel, Alfonso, Copersino, Marc L., Field, Matt, Garland, Eric L., Lorenzetti, Valentina, Malloy‐Diniz, Leandro, Manning, Victoria, Marceau, Ely M., Pennington, David L., Strickland, Justin C., Wiers, Reinout, Fairhead, Rahia, Anderson, Alexandra, Bell, Morris
Source: Addiction. May2023, Vol. 118 Issue 5, p935-951. 17p. 2 Diagrams, 2 Charts, 2 Graphs.
Subjects: Consensus (Social sciences), Neurosciences, Executive function, Substance abuse, Surveys, Research funding, Cognitive remediation, Emotions, Cognitive therapy, Delphi method
Abstract: Aims: Substance use disorders (SUD) are associated with cognitive deficits that are not always addressed in current treatments, and this hampers recovery. Cognitive training and remediation interventions are well suited to fill the gap for managing cognitive deficits in SUD. We aimed to reach consensus on recommendations for developing and applying these interventions. Design, Setting and Participants: We used a Delphi approach with two sequential phases: survey development and iterative surveying of experts. This was an on‐line study. During survey development, we engaged a group of 15 experts from a working group of the International Society of Addiction Medicine (Steering Committee). During the surveying process, we engaged a larger pool of experts (n = 54) identified via recommendations from the Steering Committee and a systematic review. Measurements: Survey with 67 items covering four key areas of intervention development: targets, intervention approaches, active ingredients and modes of delivery. Findings Across two iterative rounds (98% retention rate), the experts reached a consensus on 50 items including: (i) implicit biases, positive affect, arousal, executive functions and social processing as key targets of interventions; (ii) cognitive bias modification, contingency management, emotion regulation training and cognitive remediation as preferred approaches; (iii) practice, feedback, difficulty‐titration, bias modification, goal‐setting, strategy learning and meta‐awareness as active ingredients; and (iv) both addiction treatment work‐force and specialized neuropsychologists facilitating delivery, together with novel digital‐based delivery modalities. Conclusions: Expert recommendations on cognitive training and remediation for substance use disorders highlight the relevance of targeting implicit biases, reward, emotion regulation and higher‐order cognitive skills via well‐validated intervention approaches qualified with mechanistic techniques and flexible delivery options. [ABSTRACT FROM AUTHOR]
Copyright of Addiction is the property of Wiley-Blackwell 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: Cognitive training and remediation interventions for substance use disorders: a Delphi consensus study.
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  Data: <searchLink fieldCode="JN" term="%22Addiction%22">Addiction</searchLink>. May2023, Vol. 118 Issue 5, p935-951. 17p. 2 Diagrams, 2 Charts, 2 Graphs.
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  Data: Aims: Substance use disorders (SUD) are associated with cognitive deficits that are not always addressed in current treatments, and this hampers recovery. Cognitive training and remediation interventions are well suited to fill the gap for managing cognitive deficits in SUD. We aimed to reach consensus on recommendations for developing and applying these interventions. Design, Setting and Participants: We used a Delphi approach with two sequential phases: survey development and iterative surveying of experts. This was an on‐line study. During survey development, we engaged a group of 15 experts from a working group of the International Society of Addiction Medicine (Steering Committee). During the surveying process, we engaged a larger pool of experts (n = 54) identified via recommendations from the Steering Committee and a systematic review. Measurements: Survey with 67 items covering four key areas of intervention development: targets, intervention approaches, active ingredients and modes of delivery. Findings Across two iterative rounds (98% retention rate), the experts reached a consensus on 50 items including: (i) implicit biases, positive affect, arousal, executive functions and social processing as key targets of interventions; (ii) cognitive bias modification, contingency management, emotion regulation training and cognitive remediation as preferred approaches; (iii) practice, feedback, difficulty‐titration, bias modification, goal‐setting, strategy learning and meta‐awareness as active ingredients; and (iv) both addiction treatment work‐force and specialized neuropsychologists facilitating delivery, together with novel digital‐based delivery modalities. Conclusions: Expert recommendations on cognitive training and remediation for substance use disorders highlight the relevance of targeting implicit biases, reward, emotion regulation and higher‐order cognitive skills via well‐validated intervention approaches qualified with mechanistic techniques and flexible delivery options. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Addiction is the property of Wiley-Blackwell 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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        Value: 10.1111/add.16109
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        Text: English
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      – SubjectFull: Delphi method
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