[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100639319":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":21,"centralContacts":25,"locations":30,"responsibleParty":45,"collaborators":47,"id":51,"slug":52,"hasResults":53,"nctId":54,"briefTitle":55,"officialTitle":55,"acronym":20,"eligibilityCriteria":56,"healthyVolunteers":57,"sex":58,"minAge":59,"maxAge":60,"enrollmentInfo":61,"targetDuration":20,"studyType":64,"phases":65,"briefSummary":67,"conditions":68,"keywords":20,"overallStatus":70,"whyStopped":20,"lastUpdateSubmitDate":71,"lastUpdatePostDateStruct":72,"startDateStruct":75,"completionDateStruct":77,"leadSponsor":79,"locationsCount":80},{"fullName":5,"class":6},"Johns Hopkins Bloomberg School of Public Health","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"RL-informed intervention","EXPERIMENTAL","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.",[13],"Behavioral: Smartphone-based intervention messages",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":20},"BEHAVIORAL","Smartphone-based intervention messages","Intervention messages will suggest strategies of coping with smoking urges in the moment.",[9],null,[22],{"name":23,"affiliation":5,"role":24},"Johannes Thrul, PhD","PRINCIPAL_INVESTIGATOR",[26],{"name":23,"role":27,"phone":28,"phoneExt":20,"email":29},"CONTACT","443-318-6633","jthrul@jhu.edu",[31],{"facility":5,"status":20,"city":32,"state":33,"zip":34,"country":35,"countryCode":36,"cosmosGeoPoint":37,"geoPoint":42,"contacts":43},"Baltimore","Maryland","21205","United States","US",{"type":38,"coordinates":39},"Point",[40,41],-76.61219,39.29038,{"lat":41,"lon":40},[44],{"name":23,"role":27,"phone":28,"phoneExt":20,"email":29},{"type":46,"investigatorFullName":20,"investigatorTitle":20,"investigatorAffiliation":20,"oldNameTitle":20,"oldOrganization":20},"SPONSOR",[48],{"name":49,"class":50},"Maryland Cigarette Restitution","UNKNOWN","100639319","adaptive-mobile-interventions-to-reduce-cancer-risk-behaviors-100639319",false,"NCT07585357","Adaptive Mobile Interventions to Reduce Cancer Risk Behaviors","Inclusion Criteria:\n\n* live in the U.S.;\n* are between 18 and 40 years of age;\n* own a smartphone with iOS and Android operating system and GPS capabilities;\n* are carrying smartphone every day;\n* are willing to participate in the study for 44 days and give the research team access to the phone GPS data;\n* 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;\n* are planning to quit smoking within the next 30 days.\n\nExclusion Criteria:\n\n* None",true,"ALL","18 Years","40 Years",{"count":62,"type":63},7,"ESTIMATED","INTERVENTIONAL",[66],"NA","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.\n\nTo 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.",[69],"Smoking Cessation","NOT_YET_RECRUITING","2026-06-23",{"date":73,"type":74},"2026-06-25","ACTUAL",{"date":76,"type":63},"2026-07",{"date":78,"type":63},"2026-09",{"name":5,"class":6},1]