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

ArmDescriptionAssigned 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.

More Details

Status
Active, not recruiting
Sponsor
The University of Texas Health Science Center, Houston

Study Contact