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

Smoking Cessation Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04839198
• Other study ID #: HSC-SPH-20-1159
• Secondary ID: R00DA046564
• Status: Recruiting
• Phase: N/A
• Start date: January 31, 2024
• Est. completion date: November 30, 2024

Study information

• Verified date: March 2024
• Source: The University of Texas Health Science Center, Houston
• Contact: Emily Hebert, DrPH
• Phone: ?(512) 391-1351
• Email: Emily.T.Hebert[@]uth.tmc.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 60
• Est. completion date: November 30, 2024
• Est. primary completion date: August 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Smoking Cessation

Intervention

Behavioral:
• Android Wear smartwatch
All participants will wear an Android Wear smartwatch, and will complete ecological momentary assessments (EMA).
• 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.

Locations

Country	Name	City	State
United States	The University of Texas Health Science Center at Houston	Houston	Texas

Sponsors (2)

Lead Sponsor	Collaborator
The University of Texas Health Science Center, Houston	National Institute on Drug Abuse (NIDA)

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Number of patients who quit smoking as confirmed by absence of salivary cotinine		4-weeks after quit day