Adapt2Quit - An Adaptive Motivational System for Socio-Economically Disadvantaged Smokers
A2Q
Adapt2Quit - A Machine-Learning, Adaptive Motivational System: RCT for Socio-Economically Disadvantaged Smokers
2 other identifiers
interventional
757
1 country
3
Brief Summary
The goal of this research is to test the Adapt2Quit computer program that uses participant input (message rating on how much the text motivational message might influence one to quit smoking) to select and text motivational messages that are more likely to help a user stop smoking. This Adapt2Quit system will be compared with a quitline facilitation-only control (text messages will be sent to facilitate quitline use). The primary research hypothesis is that the Adapt2Quit recommender-selected messages will be more effective than a texting quitline facilitation-only control for smoking cessation among socioeconomically disadvantaged (SED) smokers.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Jun 2021
Longer than P75 for not_applicable
3 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
January 19, 2021
CompletedFirst Posted
Study publicly available on registry
January 22, 2021
CompletedStudy Start
First participant enrolled
June 28, 2021
CompletedPrimary Completion
Last participant's last visit for primary outcome
October 10, 2024
CompletedStudy Completion
Last participant's last visit for all outcomes
March 31, 2025
CompletedMay 1, 2025
April 1, 2025
3.3 years
January 19, 2021
April 28, 2025
Conditions
Outcome Measures
Primary Outcomes (2)
Self-Reported Participant Tobacco Cessation Rate (quit rate)
Self-reported 6-month point prevalence cessation (yes/no) will be assessed at 6-month follow-up.
6-months post-randomization
Biochemical Carbon Monoxide (CO) Verification of Tobacco Cessation
Biochemical carbon monoxide (CO) verification testing will also be done for a small subset of participants, where 0-6 parts per million (PPM) measures a non-smoker and 7+ PPM indicates a current smoker.
6-months post-randomization
Secondary Outcomes (1)
Time to First Quit Attempt
From randomization to 6-months post-randomization
Study Arms (2)
Adapt2Quit
EXPERIMENTALThese participants will receive Adapt2Quit motivational messaging and quitline facilitation messaging for 6 months.
Control
ACTIVE COMPARATORThese participants will receive quitline facilitation-only messaging for 6 months.
Interventions
Motivational text messages and quitline facilitation text messages will be sent to participants; these participants will receive Adapt2Quit (experimental) messages as well as quitline facilitation messages.
Quitline facilitation text messages will be sent to participants; these participants will receive quitline facilitation-only messages.
Eligibility Criteria
You may qualify if:
- Current smoker
- Socioeconomically disadvantaged (SED) (using the following criteria: unemployed or underemployed, low income as defined by the federal poverty level guidelines, uninsured or underinsured, and/or have less than a high school education)
- English-speaking
- Active in care (at least two clinical visits in the last year)
- Have a texting-enabled cell phone
You may not qualify if:
- Not a current smoker
- Adults unable to consent
- Individuals who are not yet adults (infants, children, teenagers)
- Prisoners
- Pregnant women
- Pilot study participants
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- University of Massachusetts, Worcesterlead
- National Institutes of Health (NIH)collaborator
- National Cancer Institute (NCI)collaborator
- Johns Hopkins Universitycollaborator
- Baystate Healthcollaborator
Study Sites (3)
Johns Hopkins University
Baltimore, Maryland, 21287, United States
Baystate Health
Springfield, Massachusetts, 01199, United States
University of Massachusetts Medical School
Worcester, Massachusetts, 01655, United States
Related Publications (1)
Kamberi A, Weitz B, Flahive J, Eve J, Najjar R, Liaghat T, Ford D, Lindenauer P, Person S, Houston TK, Gauvey-Kern ME, Lobien J, Sadasivam RS, Balakrishnan K. Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial. JMIR Res Protoc. 2025 Apr 16;14:e63693. doi: 10.2196/63693.
PMID: 40239194DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Rajani Sadasivam, PhD
Associate Professor
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- TRIPLE
- Who Masked
- PARTICIPANT, INVESTIGATOR, OUTCOMES ASSESSOR
- Masking Details
- After completing the screening, informed consent, and baseline data collection, study staff will enter the participant identification number and participant mobile phone number from the survey into an online system, and the system will assign allocation based on the randomization table. Using this technique, both smokers and the staff will be blinded to allocation during the initial session. Then, the research staff will be un-blinded to provide personalized training for intervention and control (note that staff who complete follow-up will be different and blinded to group assignment).
- Purpose
- PREVENTION
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Associate Professor
Study Record Dates
First Submitted
January 19, 2021
First Posted
January 22, 2021
Study Start
June 28, 2021
Primary Completion
October 10, 2024
Study Completion
March 31, 2025
Last Updated
May 1, 2025
Record last verified: 2025-04
Data Sharing
- IPD Sharing
- Will not share