A pilot adaptive trial of FITtrack chatbot, nurse navigator, and peer navigator to promote FIT screening among rural residents.

& Abstract Background: Rural residents experience higher colorectal cancer (CRC) incidence and mortality rates than their urban counterparts. Adherence to CRC screening is essential for early detection, timely treatment, and reduced mortality. The fecal immunochemical test (FIT) is a preferred screening method among rural populations due to its non-invasive nature, affordability, and at-home convenience. Patient navigation plays a crucial role in increasing FIT completion rates but faces structural and logistical challenges. While adaptive, stepped-care patient navigation interventions have been developed to improve scalability and cost-effectiveness, they have not been failed to account for individual sociodemographic factors and evolving patient needs in real time, and have not determined the optimal timing or type of person-led navigation. The primary goal of this study is to optimize a novel adaptive patient navigation model using a two-stage Sequential Multiple Assignment Randomized Trial (SMART) to promote FIT screening among rural residents. In Stage 1, this study will evaluate chatbot-based navigation (FITtrack) versus standard SMS reminders as an initial assistive tool and determine the optimal timing for introducing additional person-led support. In Stage 2, this study will assess the optimal type of person-led support, comparing nurse navigators and peer navigators. Specific Aims: 1) Evaluate the feasibility and acceptability of the digital intervention (4-week SMS reminders, 4-week FITtrack chatbot, and 12- week FIT track chatbot), supplemental person-led navigation (nurse vs. peer), and adaptive interventions (digital intervention plus person-led navigation); 2) Evaluate the preliminary effectiveness of these interventions; 3) Understand step-specific challenges and supports needed in completing FIT. Innovation: This study is highly innovative as it tailors to individual sociodemographic factors and real-time needs through the integration of AI- based chatbot technology, utilizes a novel trial design (i.e., SMART) to inform the development of an adaptive model, and directly addresses the unique challenges faced by rural residents. Significance: Aligned with PA- 25-295, this study accelerates priority initiatives by identifying optimal type and timing of digital and person- assisted navigation approaches to improve CRC screening adherence among rural populations. Ultimately, these findings will support the development of a scalable, adaptive, and cost-effective patient navigation intervention, advancing cancer prevention and early detection efforts in populations with high cancer burden. Project Number: 1R21CA309305-01 | Fiscal Year: 2026 | NIH Institute/Center: National Cancer Institute (NCI) | Principal Investigator: Bingjing Mao | Institution: LSU HEALTH SCIENCES CENTER, NEW ORLEANS, LA | Award Amount: $380,554 | Activity Code: R21 | Study Section: Molecular Cancer Diagnosis and Classification Study Section[MCDC] View on NIH RePORTER: https://reporter.nih.gov/project-details/11288100