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Internet and computer based interventions for cannabis use: A meta-analysis
Open accessAbstract
Background Worldwide, cannabis is the most prevalently used illegal drug and creates demand for prevention and treatment services that cannot be fulfilled using conventional approaches. Computer and Internet-based interventions may have the potential to meet this need. Therefore, we systematically reviewed the literature and conducted a meta-analysis on the effectiveness of this approach in reducing the frequency of cannabis use.Methods We systematically searched online databases (Medline, PubMed, PsychINFO, Embase) for eligible studies and conducted a meta-analysis. Studies had to use a randomized design, be delivered either via the Internet or computer and report separate outcomes for cannabis use. The principal outcome measure was the frequency of cannabis use.Results Data were extracted from 10 studies and the meta-analysis involved 10 comparisons with 4,125 participants. The overall effect size was small but significant, g=0.16 (95% confidence interval (CI) 0.09-0.22, P<0.001) at post-treatment. Subgroup analyses did not reveal significant subgroup differences for key factors including type of analysis (intention-to-treat, completers only), type of control (active, waitlist), age group (11-16, 17+ years), gender composition (female only, mixed), type of intervention (prevention, 'treatment'), guided versus unguided programs, mode of delivery (Internet, computer), individual versus family dyad and venue (home, research setting). Also, no significant moderation effects were found for number of sessions and time to follow-up. Finally, there was no evidence of publication bias.Conclusions Internet and computer interventions appear to be effective in reducing cannabis use in the short-term albeit based on data from few studies and across diverse samples.
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“In addition, the participants of these studies have heterogeneous alcohol/drug use and gambling habits; ranging from abstinent, recreational and non-problem users, to those presenting a risk or disorder related to alcohol/drug use or gambling (for example, Tait and Christensen, 2010; Tait et al., 2013).”
From Online and Mobile Interventions for Problem Gambling, Alcohol, and Drugs: A Systematic Review (2017) · cites this paper as Tait et al., 2013
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Metadata
- Authors
- Tait, R. J., Spijkerman, R., & Riper, H.
- Affiliations
- Australian National University (AU)
- Parnassia Groep (NL)
- Leuphana University of Lüneburg (DE)
- Corresponding author
- Tait, Robert J.
- Venue
- Drug and Alcohol Dependence
- Publisher
- Elsevier BV
- Published
- 2013-12
- Volume
- 133
- Issue
- 2
- Pages
- 295-304
- ISSN
- 0376-8716 (ISSN-L)
- OpenAlex
- W2059029447
- Cited by
- 144
- References
- 79
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 4 · 2023: 8 · 2022: 9 · 2021: 12 · 2020: 20 · 2019: 11 · 2018: 17 · 2017: 19 · 2016: 14 · 2015: 12 · 2014: 7 · 2013: 2
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Drug and Alcohol Dependence (publishedVersion)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- VU Research Portal (publishedVersion)
- eSpace (Curtin University) (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Multilingual Matters (Channel View Publications) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Pharmacology (medical) › Cannabis and Cannabinoid Research
- Keywords
- cannabis use, online intervention, computer-based intervention, digital health interventions, cannabis use frequency, substance use prevention, addiction treatment, intervention effectiveness, adolescents
- MeSH terms
- Adult, Marijuana Abuse, Data Interpretation, Statistical, Female, Humans, Male, Microcomputers, Regression Analysis, Publication Bias, Internet, Consumer Health Information
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