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.
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
|
|
-
Behavioral: Android Wear smartwatch
All participants will wear an Android Wear smartwatch, and will complete ecological momentary assessments (EMA).
-
Behavioral: Adaptive Treatment
Participants will have access to a "Dashboard" button in the InsightTM app that displays personalized statistics based on their progress and patterns in the study.The dashboard will update as more data is collected about the participant's smoking habits, starting in the pre-quit period and continuing through the post-quit period. Second, in the post-quit period, participants will receive treatment messages when the machine learning algorithm determines that they are at high risk for lapse.At the follow-up visit, participants will complete a survey to evaluate what they liked and disliked about the intervention, how accurate they thought the app was in predicting their risk, and how useful they found the dashboard.
-
Drug: Nicotine Patch
At Visit 2, participants will be asked to begin their attempt to quit smoking and will receive a 12-week supply of nicotine replacement therapy (i.e., nicotine patches and gum)
-
Behavioral: interviewing-based counseling
At Visit 2, participants will be asked to begin their attempt to quit smoking and meet with a Tobacco Treatment Specialist, who will provide motivational interviewing-based counseling, help the participants develop a quit plan, and discuss relapse prevention. During the 6-weeks of the study, participants will have the option of attending up to 6 counseling sessions with the Tobacco Treatment Specialist, either in-person or via phone or web.
|
Active Comparator Usual care
|
|
-
Behavioral: Android Wear smartwatch
All participants will wear an Android Wear smartwatch, and will complete ecological momentary assessments (EMA).
-
Drug: Nicotine Patch
At Visit 2, participants will be asked to begin their attempt to quit smoking and will receive a 12-week supply of nicotine replacement therapy (i.e., nicotine patches and gum)
-
Behavioral: interviewing-based counseling
At Visit 2, participants will be asked to begin their attempt to quit smoking and meet with a Tobacco Treatment Specialist, who will provide motivational interviewing-based counseling, help the participants develop a quit plan, and discuss relapse prevention. During the 6-weeks of the study, participants will have the option of attending up to 6 counseling sessions with the Tobacco Treatment Specialist, either in-person or via phone or web.
|
Recruiting Locations
The University of Texas Health Science Center at Houston
Houston,
Texas
77030
More Details
- Status
- Recruiting
- Sponsor
- The University of Texas Health Science Center, Houston
Study Contact
Emily Hebert, DrPH
(512) 391-1351
Emily.T.Hebert@uth.tmc.edu