Motives for Cannabis Use and Readiness to Change Among Users of the “Stop-Cannabis” Mobile App: Cluster Analysis
Bibliographic record
Abstract
Background: Cannabis use is widespread and driven by diverse motives, ranging from recreational purposes to coping with psychological distress. Understanding the underlying reasons for cannabis use, their distribution across different subgroups of people who use cannabis, and how they relate to possible behavior change is essential for developing effective prevention and intervention strategies such as smartphone apps designed to support change. Objective: The primary objective of the study was to determine whether analyzing profiles on the "Stop-cannabis" app (Institute of Global Health, University of Geneva, Switzerland) could reveal subgroups based on motives for cannabis use and readiness to change. A secondary objective was to explore differences among these subgroups in terms of problematic use and other indicators of change readiness. Methods: This study analyzed data from 2578 individuals using the "Stop-cannabis app", a mobile app developed in Switzerland to support those seeking to manage their cannabis use. Participants completed validated questionnaires assessing motives for use (Marijuana Motives Measure [MMM]), readiness to change (Stages of Change Readiness and Treatment Eagerness Scale [SOCRATES]), and risk of problematic use (Alcohol, Smoking, and Substance Involvement Screening Test [ASSIST]). They also self-rated their "readiness for action," the "importance of change," and their "confidence in their ability to change." These assessments were part of the app's intervention model, with personalized feedback delivered based on participants' responses; no external incentives were offered. Cluster analysis was conducted to identify subgroups based on MMM and SOCRATES scores. Results: In total, 3 distinct profiles emerged: the "individually coping users" (ICU), the "social and coping users" (SCU), and the "enhancement-seeking users" (ESU). ICU and SCU scored higher on coping motives compared with ESU, along with greater ambivalence and stronger recognition of problematic use, as measured by SOCRATES. They also scored higher on the ASSIST (indicating greater risk of problematic cannabis use), placed more importance on making behavioral changes, yet reported lower confidence in their ability to enact those changes. By contrast, ESU primarily used cannabis for recreational reasons and had low recognition of problematic use, despite being at moderate risk. Conclusions: This research highlights that while motives for cannabis use are varied and individually nuanced, distinct subgroups can be identified, each with specific challenges. The findings align with previous research emphasizing the importance of coping motives in behavior change. Tailoring app content to reflect the unique profiles and needs of each subgroup may improve intervention outcomes. For instance, SCU and ICU may benefit from strategies targeting emotion regulation and alternative coping mechanisms, whereas ESU may respond better to brief motivational feedback and harm reduction strategies. Such tailored approaches can enhance the effectiveness of digital tools in promoting meaningful and long-term behavior change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".