Drug and alcohol dependence · 2024
Predicting cannabis use moderation among a sample of digital self-help subscribers: A machine learning study.
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Olthof Marleen I A, Ramos Lucas A, van Laar Margriet W, Goudriaan Anna E, Blankers Matthijs
Аннотация
BACKGROUND: For individuals who wish to reduce their cannabis use without formal help, there are a variety of self-help tools available. Although some are proven to be effective in reducing cannabis use, effect sizes are typically small. More insight into predictors of successful reduction of use among individuals who frequently use cannabis and desire to reduce/quit could help identify factors that contribute to successful cannabis use moderation.
METHODS: We analyzed data taken from a randomized controlled trial comparing the effectiveness of the digital cannabis intervention ICan to four online modules of educational information on cannabis. For the current study, we included 253 participants. Success was defined as reducing the grams of cannabis used in the past 7 days at baseline by at least 50 % at 6-month follow-up. To train and evaluate the machine learning models we used a nested k-fold cross-validation procedure.
RESULTS: The results show that the two models applied had comparable low AUROC values of .61 (Random Forest) and .57 (Logistic Regression). Not identifying oneself as a cannabis user, not using tobacco products, high levels of depressive symptoms, high levels of psychological distress and high initial cannabis use values were the relatively most important predictors for success, although overall the associations were not strong.
CONCLUSIONS: Our study found only modest prediction accuracy when using machine learning models to predict success among individuals who use cannabis and desire to reduce/quit and show interest in digital self-help tools.
METHODS: We analyzed data taken from a randomized controlled trial comparing the effectiveness of the digital cannabis intervention ICan to four online modules of educational information on cannabis. For the current study, we included 253 participants. Success was defined as reducing the grams of cannabis used in the past 7 days at baseline by at least 50 % at 6-month follow-up. To train and evaluate the machine learning models we used a nested k-fold cross-validation procedure.
RESULTS: The results show that the two models applied had comparable low AUROC values of .61 (Random Forest) and .57 (Logistic Regression). Not identifying oneself as a cannabis user, not using tobacco products, high levels of depressive symptoms, high levels of psychological distress and high initial cannabis use values were the relatively most important predictors for success, although overall the associations were not strong.
CONCLUSIONS: Our study found only modest prediction accuracy when using machine learning models to predict success among individuals who use cannabis and desire to reduce/quit and show interest in digital self-help tools.