In Comprehensive psychiatry
BACKGROUND : Despite limited clinical evidence of its efficacy, cannabis use has been commonly reported for the management of various mental health concerns in naturalistic field studies. The aim of the current study was to use machine learning methods to investigate predictors of perceived symptom change across various mental health symptoms with acute cannabis use in a large naturalistic sample.
METHODS : Data from 68,819 unique observations of cannabis use from 1307 individuals using cannabis to manage mental health symptoms were analyzed. Data were extracted from Strainprint®, a mobile app that allows users to monitor their cannabis use for therapeutic purposes. Machine learning models were employed to predict self-perceived symptom change after cannabis use, and SHapley Additive exPlanations (SHAP) value plots were used to assess feature importance of individual predictors in the model. Interaction effects of symptom severity pre-scores of anxiety, depression, insomnia, and gender were also examined.
RESULTS : The factors that were most strongly associated with perceived symptom change following acute cannabis use were pre-symptom severity, age, gender, and the ratio of CBD to THC. Further examination on the impact of baseline severity for the most commonly reported symptoms revealed distinct responses, with cannabis being reported to more likely benefit individuals with lower pre-symptom severity for depression, and higher pre-symptom severity for insomnia. Responses to cannabis use also differed between genders.
CONCLUSIONS : Findings from this study highlight the importance of several factors in predicting perceived symptom change with acute cannabis use for mental health symptom management. Mental health profiles and baseline symptom severity may play a large role in perceived responses to cannabis. Distinct response patterns were also noted across commonly reported mental health symptoms, emphasizing the need for placebo-controlled cannabis trials for specific user profiles.
Kuhathasan Nirushi, Ballester Pedro L, Minuzzi Luciano, MacKillop James, Frey Benicio N
2023-Feb-10
Anxiety, Depression, Gender, Insomnia, Machine learning, Mental health, SHAP, Symptom severity, Therapeutic cannabis
