Understanding how young adults can use strategies to reduce cannabis-related harms
Measurement and Modeling of Within-Person Variability in Cannabis Protective Behavioral Strategies: A Novel Approach Using Scale Development, Daily Data, and Machine Learning Methods
['FUNDING_FELLOWSHIP'] · UNIVERSITY OF WASHINGTON · NIH-11046625
This study is looking at how young adults aged 18-25 can use smart strategies to reduce the downsides of using cannabis, and it will help figure out which strategies work best for different people in different situations.
Quick facts
| Phase | ['FUNDING_FELLOWSHIP'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UNIVERSITY OF WASHINGTON (nih funded) |
| Locations | 1 site (SEATTLE, UNITED STATES) |
| Trial ID | NIH-11046625 on ClinicalTrials.gov |
What this research studies
This research investigates how young adults, particularly those aged 18-25, can effectively use protective behavioral strategies (PBS) to minimize the negative effects of cannabis use. By employing daily data collection and advanced machine learning methods, the study aims to identify which strategies are most effective for different individuals in various situations. Participants will be asked to report their cannabis use and the strategies they employ, allowing researchers to analyze patterns and outcomes over time.
Who could benefit from this research
Good fit: Ideal candidates for this research are young adults aged 18-25 who use cannabis and are interested in reducing their consumption or its negative effects.
Not a fit: Patients who do not use cannabis or are outside the age range of 18-25 may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could provide young adults with tailored strategies to reduce cannabis use and its associated harms.
How similar studies have performed: Previous research has shown mixed results regarding protective behavioral strategies, indicating that while some approaches have been effective, this specific methodology using daily data and machine learning is relatively novel.
Where this research is happening
SEATTLE, UNITED STATES
- UNIVERSITY OF WASHINGTON — SEATTLE, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: SMITH-LECAVALIER, KIRSTYN NICOLE — UNIVERSITY OF WASHINGTON
- Study coordinator: SMITH-LECAVALIER, KIRSTYN NICOLE
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.
Conditions: addictive disorder