Internet-Delivered Tobacco Treatment for People Using Cannabis: A Randomized Trial in Two Australian Cannabis Clinics
Bibliographic record
Abstract
BACKGROUND: Tobacco use is disproportionately higher in people who smoke cannabis than in the general population, increasing the severity of dependence for cannabis use, decreasing the likelihood of successful quit attempts for both cannabis and tobacco, and increasing the risk of relapse for both substances. Opportunities to address tobacco use in people using cannabis are being missed. OBJECTIVE: This study aims to investigate the feasibility of engaging tobacco smokers who were accessing treatment for cannabis, with a tobacco-focused internet-based Healthy Lifestyle Program (iHeLP; 4 modules). It was hypothesized that iHeLP completion would be associated with decreases in tobacco use and improved quality of life (QoL) and psychological health. It was also hypothesized that iHeLP completion would be higher in those who additionally received telephone support. Given that iHeLP aimed to improve healthy lifestyle behaviors, it was also hypothesized that there would be reductions in cannabis use. METHODS: A total of 13 smokers seeking treatment for cannabis use were randomly allocated to iHeLP alone or iHeLP plus telephone support. Participants were engaged in iHeLP over 8 weeks and completed a 12-week follow-up assessment. RESULTS: Results from 10 participants who completed the follow-up indicated that the acceptability of iHeLP was high-very high in terms of general satisfaction, appropriateness of services, effectiveness, and met need. Additional telephone support increased modal module completion rates for iHeLP from 0 to 2 but did not provide any other significant advantages over iHeLP alone in terms of cannabis use, tobacco use, QoL, or psychological health. Participants in the iHeLP-alone condition (n=4) reported a mean reduction of 5.5 (SD 9.00) tobacco cigarettes per day between baseline and follow-up, with a concomitant mean reduction in expired carbon monoxide (CO) of 5.5 parts per million (ppm, SD 6.91). The iHeLP plus telephone support group (n=6) reported a mean reduction of 1.13 (SD 4.88) tobacco cigarettes per day and a mean reduction of 9.337 ppm of expired CO (SD 5.65). A urinalysis indicated that abstinence from cannabis was achieved by 2 participants in the iHeLP-alone group and three participants in the iHeLP plus telephone support group. Between baseline and follow-up assessments, iHeLP-alone participants reported a mean reduction in days of use of cannabis in the prior month of 6.17 days (SD 13.30). The average reduction in the number of days of cannabis use for the iHeLP plus telephone support group was also 6.17 days (SD 13.59). CONCLUSIONS: Despite the small sample size, this study provides preliminary support for the use of internet-delivered, tobacco-focused interventions in tobacco smokers seeking treatment for cannabis use.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".