ArticleInternet interventions2026
Engagement patterns among users of a digital intervention to reduce cannabis use: a latent class analysis.
Article in Internet interventions, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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Abstract
Background: Understanding and optimizing engagement in digital interventions remains a significant challenge. This study aims to identify different patterns of engagement among users of a digital intervention to reduce cannabis use. Method: We conducted a secondary analysis of engagement data from the intervention group of a study on the effectiveness of a digital cannabis intervention (ICan). Engagement patterns were identified through a latent class analysis using eight engagement indicator variables. The bias-adjusted three-step approach was used to examine differences in baseline characteristics across classes and associations with average change-from-baseline scores in cannabis use frequency and quantity. Results: Three latent classes were identified: Class 1 (32%), 'non-engagers', showed minimal to no engagement; Class 2 (41%), 'shorter-term engagers', showed moderate engagement in the intervention, but with a shorter duration than recommended; Class 3 (27%), 'long-term engagers', showed high engagement in the intervention with a duration meeting the recommendations. The proportion of males was significantly higher in the 'non-engagers' class compared to the others. The 'long-term engagers' class reported fewer tobacco use days at baseline compared to the others. No differences were found between classes regarding average change-from-baseline scores in cannabis use frequency and quantity. Conclusions: Users of digital interventions show distinct engagement patterns, and characteristics such as male gender and tobacco use may predict sub-optimal engagement. Importantly, also sub-optimal exposure to a digital intervention may be associated with changes in cannabis use, as higher engagement does not necessarily lead to greater effectiveness.
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