Using Machine Learning to Develop Just-in-Time Adaptive Interventions for Smoking Cessation
Purpose
The purpose of this study is to evaluate the feasibility and preliminary effectiveness of delivering a personalized, just-in-time adaptive intervention driven by machine learning prediction of smoking lapse risk in real time.
Condition
- Smoking Cessation
Eligibility
- Eligible Ages
- Over 18 Years
- Eligible Genders
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- a score greater than or equal to 4 on the Rapid Estimate of Adult Literacy in Medicine Short Form (REALM-SF),12 - willingness to quit smoking 14 days after the baseline visit - no contraindications to using Nicotine replacement therapy (NRT). - If participants would like to use their own phone to complete the EMAs, they must additionally have an Android smartphone (Android 5.2 or higher), and be willing to install the InsightTM mHealth app on their phone.
Exclusion Criteria
- currently smoking less than 5 cigarettes per day
Study Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Parallel Assignment
- Primary Purpose
- Treatment
- Masking
- None (Open Label)
Arm Groups
Arm | Description | Assigned Intervention |
---|---|---|
Experimental Adaptive Treatment plus usual care |
|
|
Active Comparator Usual care |
|
Recruiting Locations
The University of Texas Health Science Center at Houston
Houston, Texas 77030
Houston, Texas 77030
More Details
- Status
- Recruiting
- Sponsor
- The University of Texas Health Science Center, Houston