This clinical trial develops a chatbot smartphone application (app), QuitBot, and text messaging to help American Indians (AI) and Alaska Natives (AN) to quit smoking commercial tobacco (smoking cessation), and evaluates two remote smoking cessation programs to see how well they work for helping AI/AN people quit smoking commercial tobacco. AI/AN populations have one of the highest rates of commercial cigarette smoking of any racial and ethnic group in the United States (US). They also have a higher rate of developing smoking-related cancer but are less likely to quit smoking. The two programs are designed to provide personalized support in setting a smoking cessation goal, tasks to reach the smoking cessation goal, and motivation to remain smoke-free. This may help to keep participants engaged and support them in their quit efforts, and may improve smoking cessation among AI and AN.
OUTLINE: Participants are randomized to 1 of 2 arms. Both groups receive access to a 42-day quit smoking program. ARM I: Participants receive daily QuitBot program chatbot messages about the importance of quitting smoking, setting a quit date, preparing to quit, quitting, and maintaining abstinence over 42 days. ARM II: Participants receive daily QuitBot text messages about the importance of quitting smoking, setting a quit date, preparing to quit, quitting, and maintaining abstinence over 42 days. After completion of study intervention, participants are followed up at 3, 6, and 12 months and may be contacted thereafter for up to 24 months.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
TRIPLE
Enrollment
772
Participate in Quitbot chatbot program
Participate in Quitbot texting program
Ancillary studies
Ancillary studies
Ancillary studies
Fred Hutch/University of Washington Cancer Consortium
Seattle, Washington, United States
30-day point prevalence abstinence (PPA)
Will use a logistic regression model and standard smoking cessation trial intent-to-treat analysis, with all missing outcomes will be coded as smoking. The model will adjust for stratification factors and baseline factors that are significantly related to the outcome. Sensitivity analyses will include: (1) multiple imputation of missing outcomes, (2) complete case analysis, and (3) carbon monoxide (CO)-verified results adjusted for use of other combustible nicotine-containing tobacco products at the 12-month follow-up-in order to differentiate them from use of cigarettes at 12 months.
Time frame: At 12-months post-randomization
30-day PPA
Also 24-hour and 7-day PPA at the 3-, 6-, and 12- month follow-ups. Will use a logistic regression model and standard smoking cessation trial intent-to-treat analysis, with all missing outcomes will be coded as smoking. The model will adjust for stratification factors and baseline factors that are significantly related to the outcome. Sensitivity analyses will include: (1) multiple imputation of missing outcomes, (2) complete case analysis, and (3) CO-verified results adjusted for use of other combustible nicotine-containing tobacco products at the 12-month follow-up-in order to differentiate them from use of cigarettes at 12 months.
Time frame: At 3- and 6- months post-randomization
Measures of bond between user and conversational chatbot
Will be measured via four subscales of the 12-item Working Alliance Inventory for Tobacco (WAIT-12), with technology intervention adaptations similar to those of prior chatbot research. Will calculate the proportion of treatment effect explained by each of the mediators. Number of times participants engaged with the intervention will be adjusted in Poisson regressions that account for the number of prompts a participant received.
Time frame: At 3- and 6-months post-randomization
Agreement on goals of treatment
Will be measured via four subscales of the WAIT-12, with technology intervention adaptations similar to those of prior chatbot research. Will calculate the proportion of treatment effect explained by each of the mediators.
Time frame: At 3- and 6-months post-randomization
Agreement on tasks of treatment
Will be measured via four subscales of the WAIT-12, with technology intervention adaptations similar to those of prior chatbot research. Will calculate the proportion of treatment effect explained by each of the mediators.
Time frame: At 3- and 6-months post-randomization
User's sense that QuitBot understands their needs
Will be measured via four subscales of the WAIT-12, with technology intervention adaptations similar to those of prior chatbot research. Will calculate the proportion of treatment effect explained by each of the mediators.
Time frame: At 3- and 6-months post-randomization
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