Effectiveness of Digitally Delivered Interventions for Cannabis Use Reduction in Adults: An Individual Participant Data Meta-Analysis
| Summary | An individual participant data meta-analysis investigating the effectiveness of digitally delivered interventions for reducing cannabis use among adults and examining which participants benefit most from these interventions. |
| Project manager(s) | Nikolaos Boumparis |
| Duration | 10.2023 - present |
| Funding / commissioning body | The project is currently being conducted without external funding. |
| Cooperation partners | Louisiana State University, Trimbos Institute, Karolinska Institutet, Université de Montréal, Auburn University, Yale University, University of Amsterdam and Harvard Medical School. Additional institutions are currently in the process of joining the collaboration. |
Background
Digitally delivered interventions, including web-based programmes, mobile applications and text-message interventions, offer a promising and easily accessible way to support people who want to reduce their cannabis use. Such interventions may reach individuals who do not use conventional treatment services because of concerns about stigma, limited availability of services or a preference for managing their cannabis use independently.
Randomized controlled trials have examined a growing number of digital interventions for cannabis use. However, conventional meta-analyses based on published study-level results provide only limited information about which participants are most likely to benefit and which intervention characteristics contribute to better outcomes.
Objectives
The project examines the overall effectiveness of digitally delivered interventions in reducing the frequency of cannabis use compared with non-active control conditions. It also investigates their effects on cannabis-related problems and abstinence, as well as on related outcomes such as alcohol and tobacco use, anxiety, depression and ADHD symptoms.
A particular focus is placed on individual and intervention-related factors that may influence treatment outcomes. These include participants’ baseline cannabis use and mental health, their engagement with the intervention, and whether the intervention includes guidance from a professional. The project will also examine factors associated with study dropout and with an increase in cannabis use during the intervention period.
Methodology
A systematic review will identify randomized controlled trials of digitally delivered interventions for adults who use cannabis. The investigators of eligible trials will be invited to provide anonymized participant-level data.
The data from the participating studies will be harmonized using a standardized data collection protocol and analysed jointly in an individual participant data meta-analysis. Multilevel statistical models will be used to account for differences between studies and to examine both overall intervention effects and differences in treatment response between participants.
Trials for which participant-level data cannot be obtained will be included in a parallel meta-analysis based on published study results, where sufficient information is available. This will help determine whether studies contributing participant-level data differ systematically from those that do not.
The study protocol and statistical analysis plan have been preregistered on the Open Science Framework (OSF) and are publicly available at: https://doi.org/10.17605/OSF.IO/W2FNR
Value of the project
By combining the original participant-level data from multiple randomized controlled trials, this project will provide a more precise assessment of the effectiveness of digital interventions for cannabis use than conventional evidence syntheses.
The findings may clarify whether guided interventions are more effective than unguided programmes, whether intervention engagement is associated with better outcomes, and whether people with more severe cannabis-related problems require additional support. The results can therefore contribute to the development of more effective and better targeted digital interventions and support evidence-based decisions about their implementation in prevention and treatment services.