Using Machine Learning to Develop Just-in-Time Adaptive Interventions for Smoking Cessation
Not Applicable
Completed
- Conditions
- Smoking Cessation
- Interventions
- Behavioral: Android Wear smartwatchBehavioral: Adaptive TreatmentBehavioral: interviewing-based counseling
- Registration Number
- NCT04839198
- Brief Summary
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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 60
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
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Adaptive Treatment plus usual care Android Wear smartwatch - Adaptive Treatment plus usual care Adaptive Treatment - Adaptive Treatment plus usual care Nicotine Patch - Adaptive Treatment plus usual care interviewing-based counseling - Usual care Android Wear smartwatch - Usual care interviewing-based counseling - Usual care Nicotine Patch -
- Primary Outcome Measures
Name Time Method Number of patients who quit smoking as confirmed by absence of salivary cotinine 4-weeks after quit day
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
The University of Texas Health Science Center at Houston
🇺🇸Houston, Texas, United States