Using Machine Learning to Develop Just-in-Time Adaptive Interventions for Smoking Cessation

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.

Conditions

• Smoking Cessation

Interventions

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.

Sponsors & Collaborators

• National Institute on Drug Abuse (NIDA)

  collaborator NIH

• The University of Texas Health Science Center, Houston

  lead OTHER

Principal Investigators

• Emily Hebert, DrPH · The University of Texas Health Science Center, Houston

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2024-02-06

Primary Completion

2024-11-22

Completion

2024-11-22

Countries

• United States

Study Locations