[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100625860":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":11,"centralContacts":27,"locations":11,"responsibleParty":37,"collaborators":41,"id":46,"slug":47,"hasResults":48,"nctId":49,"briefTitle":50,"officialTitle":51,"acronym":52,"eligibilityCriteria":53,"healthyVolunteers":54,"sex":55,"minAge":56,"maxAge":11,"enrollmentInfo":57,"targetDuration":11,"studyType":60,"phases":61,"briefSummary":63,"conditions":64,"keywords":67,"overallStatus":75,"whyStopped":11,"lastUpdateSubmitDate":76,"lastUpdatePostDateStruct":77,"startDateStruct":80,"completionDateStruct":82,"leadSponsor":84,"locationsCount":11},{"fullName":5,"class":6},"Stanford University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Personalized LLM Coaching","ACTIVE_COMPARATOR",null,[13],"Behavioral: Personalized LLM Coaching",{"label":15,"type":16,"description":11,"interventionNames":17},"Generic Activity Prompts","PLACEBO_COMPARATOR",[18],"Behavioral: Generic Activity Prompts",[20,24],{"type":21,"name":9,"description":22,"armGroupLabels":23,"otherNames":11},"BEHAVIORAL","Participants receive daily text messages containing exercise coaching prompts generated by a large language model (LLM) that are personalized to the user's demographic information for a period of 7 days.",[9],{"type":21,"name":15,"description":25,"armGroupLabels":26,"otherNames":11},"Participants receive generic daily activity prompts for a period of 7 days (e.g., \"Push yourself! Reach 10,000 steps today.\")",[15],[28,33],{"name":29,"role":30,"phone":31,"phoneExt":11,"email":32},"Anders Johnson","CONTACT","8016984141","acjohn@stanford.edu",{"name":34,"role":30,"phone":35,"phoneExt":11,"email":36},"Daniel Seung Kim,, MD, PhD, MPH","(206) 465-5858","myheartcounts@stanford.edu",{"type":38,"investigatorFullName":39,"investigatorTitle":40,"investigatorAffiliation":5,"oldNameTitle":11,"oldOrganization":11},"PRINCIPAL_INVESTIGATOR","Euan Ashley","Chair, Department of Medicine",[42,44],{"name":43,"class":6},"Imperial College London",{"name":45,"class":6},"University of Washington","100625860","my-heart-counts-cardiovascular-health-study-next-gen-100625860",false,"NCT07428122","My Heart Counts Cardiovascular Health Study: Next Gen","My Heart Counts Cardiovascular Health Study","MHC","Inclusion Criteria:\n\n* 18 years of age or older\n* Resident of the United States\n* Able to read, understand and consent to study\n\nExclusion Criteria:\n\n• Not owning a compatible iOS or Android smartphone",true,"ALL","18 Years",{"count":58,"type":59},15000,"ESTIMATED","INTERVENTIONAL",[62],"NA","The My Heart Counts Cardiovascular Health Study will utilize mobile health capabilities of smartphones to assess daily activity measures and compare these to measures of cardiovascular health risk factors and fitness. The study aims to collect cardiovascular health data on a diverse population by making the application available on both iOS and Android platforms. Using smartphone sensors and connected devices, the study will collect physical activity metrics, heart rate data, and responses to health questionnaires. The study includes a randomized crossover trial component examining the effectiveness of personalized activity coaching prompts generated by a language model compared to generic prompts.",[65,66],"Cardiovascular Health","Physical Activity",[68,69,70,71,72,73,74],"physical activity","fitness","heart health","mobile health","digital health","cardiovascular risk","behavioral interventions","NOT_YET_RECRUITING","2026-02-19",{"date":78,"type":79},"2026-02-23","ACTUAL",{"date":81,"type":59},"2026-04-30",{"date":83,"type":59},"2035-12-31",{"name":5,"class":6}]