ArticleDrug and alcohol dependence2023
Predicting quantity of cannabis smoked in daily life: An exploratory study using machine learning.
Article in Drug and alcohol dependence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Ecological momentary insights into cannabis use and its contexts: a pre-post legalization study in Germany.Scientific reports · 2026Article
- Measuring impulsivity in daily life: A systematic review and recommendations for future research.Psychological assessment · 2026Article
- Facial expression analysis and machine learning for objective assessment of alcohol craving in alcohol use disorder: a study protocol.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundCannabis use is prevalent in the United States and is associated with a host of negative consequences. Importantly, a robust indicator of negative consequences is the amount of cannabis consumed.
methodsData were obtained from fifty-two adult, regular cannabis flower users (3+ times per week) recruited from the community; participants completed multiple ecological momentary assessment (EMA) surveys each day for 14 days. In this exploratory study, we used various machine learning algorithms to build models to predict the amount of cannabis smoked since participants' last report including forty-three EMA measures of mood, impulsivity, pain, alcohol use, cigarette use, craving, cannabis potency, cannabis use motivation, subjective effects of cannabis, social context, and location in daily life.
resultsOur best-fitting model (Gradient Boosted Trees; 71.15% accuracy, 72.46% precision) found that affects, subjective effects of cannabis, and cannabis use motives were among the best predictors of cannabis use amount in daily life. The social context of being with others, and particularly with a partner or friend, was moderately weighted in the final prediction model, but contextual items reflecting location were not strongly weighted in the final prediction model, the one exception being not at work.
conclusionsMachine learning approaches can help identify additional environmental and psychological phenomena that may be clinically-relevant to cannabis use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.