Adaptive Mobile Interventions to Reduce Cancer Risk Behaviors
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 9
- 试验地点
- 2
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Treatment
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 40 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- 未提供
研究组 & 干预措施
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.
干预措施: Smartphone-based intervention messages (Behavioral)