Adaptive Mobile Interventions to Reduce Cancer Risk Behaviors

Part of paid clinical trials in Baltimore, Maryland.

Sponsor
Johns Hopkins Bloomberg School of Public Health
Study ID
NCT07585357
Status
Not Yet Recruiting

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Conditions

  • Smoking Cessation

Eligibility Criteria

Sex
ALL
Age
18 Years - 40 Years
Healthy Volunteers
Accepted

Interventions

  • Smartphone-based intervention messages — BEHAVIORAL
    Intervention messages will suggest strategies of coping with smoking urges in the moment.

Study Details

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.

Key Dates

Start date
May 31, 2026
Status verified
May 2026
Primary completion
Jul 31, 2026
Completion
Jul 31, 2026

Study Design

Enrollment
7 participants (estimated)
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
TREATMENT

Arms

  • Experimental: RL-informed intervention
    Participants complete a 14-day Ecological Momentary Assessment (EMA) training phase using a smartphone app (MetricWire), during which the participant responds to up to 3 randomly prompted and cigarette-triggered EMA surveys per day while the app passively collects GPS data. These data are used to identify high-risk locations and time periods and to inform a previously trained reinforcement learning (RL) algorithm. During the subsequent 30-day intervention phase, the RL algorithm delivers personalized intervention messages (cognitive-behavioral therapy \[CBT\], acceptance and commitment therapy \[ACT\], or attention control) triggered by geofence entry at high-risk locations.

Primary Outcome Measure

Change in smoking urge as assessed by a single item [ Time Frame: 15 minutes after message delivery ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
Johns Hopkins Bloomberg School of Public HealthBaltimoreMaryland21205
Johannes Thrul, PhD
[email protected]
443-318-6633

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Johns Hopkins Bloomberg School of Public Health· Baltimore, MD

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