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Purpose

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.

Condition

Eligibility

Eligible Ages
Between 18 Years and 40 Years
Eligible Sex
All
Accepts Healthy Volunteers
Yes

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

Study Design

Phase
N/A
Study Type
Interventional
Allocation
N/A
Intervention Model
Single Group Assignment
Primary Purpose
Treatment
Masking
None (Open Label)

Arm Groups

ArmDescriptionAssigned Intervention
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.
  • Behavioral: Smartphone-based intervention messages
    Intervention messages will suggest strategies of coping with smoking urges in the moment.

Recruiting Locations

Johns Hopkins Bloomberg School of Public Health
Baltimore, Maryland 21205
Contact:
Johannes Thrul, PhD
443-318-6633
jthrul@jhu.edu

More Details

Status
Recruiting
Sponsor
Johns Hopkins Bloomberg School of Public Health

Study Contact

Johannes Thrul, PhD
443-318-6633
jthrul@jhu.edu

Notice

Study information shown on this site is derived from ClinicalTrials.gov (a public registry operated by the National Institutes of Health). The listing of studies provided is not certain to be all studies for which you might be eligible. Furthermore, study eligibility requirements can be difficult to understand and may change over time, so it is wise to speak with your medical care provider and individual research study teams when making decisions related to participation.