Adaptive Mobile Interventions to Reduce Cancer Risk Behaviors

Adaptive Mobile Interventions to Reduce Cancer Risk Behaviors

Tobacco use remains the leading cause of preventable death, causing over 400,000 annual deaths in the United States alone. Smartphone-based interventions, particularly those leveraging real-time adaptive messaging, represent a promising yet underutilized approach to delivering personalized tobacco and cannabis treatment. The investigator's ongoing NCI funded micro-randomized trial (MRT; R01 CA246590) has shown initial feasibility in reducing smoking urges through situationally tailored cognitive-behavioral therapy (CBT) and mindfulness-based acceptance and commitment-based therapy (ACT) messages triggered by real-time contextual data (e.g., geolocation, momentary stress). To advance from a static MRT framework to a dynamic, data-driven just-in-time adaptive intervention (JITAI), this project aims to develop, test, and refine a reinforcement learning (RL) algorithm that can continuously adapt to user needs in real-time, enhancing treatment outcomes for various tobacco and cannabis products.

To ensure optimal usability and engagement, the investigators will conduct user-centered testing with the developed RL-based intervention delivery in one cohort (N=7) over 45 days. This will include usability assessment via the System Usability Scale, analysis of app interaction metrics, and semi-structured interviews to gather feedback for refining message content, timing, and design.

Inclusion Criteria:

• live in the U.S.;
• are between 18 and 40 years of age;
• own a smartphone with iOS and Android operating system and GPS capabilities;
• are carrying smartphone every day;
• are willing to participate in the study for 44 days and give the research team access to the phone GPS data;
• have smoked ≥100 cigarettes in the participant's life and currently smoke at least 3 cigarettes per day on 5 or more days of the week;
• are planning to quit smoking within the next 30 days.

Exclusion Criteria:

• None