Computer-delivered interventions for reducing alcohol consumption: Meta-analysis and meta-regression using behaviour change techniques and theory

Nicola Danielle Black, Barbara Mullan, Louise Sharpe

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

The current aim was to examine the effectiveness of behaviour change techniques (BCTs), theory and other characteristics in increasing the effectiveness of computer-delivered interventions (CDIs) to reduce alcohol consumption. Included were randomised studies with a primary aim of reducing alcohol consumption, which compared self-directed CDIs to assessment-only control groups. CDIs were coded for the use of 42 BCTs from an alcohol-specific taxonomy, the use of theory according to a theory coding scheme and general characteristics such as length of the CDI. Effectiveness of CDIs was assessed using random-effects meta-analysis and the association between the moderators and effect size was assessed using univariate and multivariate meta-regression. Ninety-three CDIs were included in at least one analysis and produced small, significant effects on five outcomes (d+ = 0.07–0.15). Larger effects occurred with some personal contact, provision of normative information or feedback on performance, prompting commitment or goal review, the social norms approach and in samples with more women. Smaller effects occurred when information on the consequences of alcohol consumption was provided. These findings can be used to inform both intervention- and theory-development. Intervention developers should focus on, including specific, effective techniques, rather than many techniques or more-elaborate approaches.
Original languageEnglish
Pages (from-to)341-357
Number of pages17
JournalHealth Psychology Review
Volume10
Issue number3
Early online date24 Apr 2016
DOIs
Publication statusPublished - 2016

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Keywords

  • Alcohol
  • computer
  • intervention
  • behaviour change techniques
  • theory
  • meta-analysis

ASJC Scopus subject areas

  • Psychology(all)
  • Medicine(all)

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