[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"artificial-intelegence\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:artificial-intelegence":32},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,53],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":19,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":24,"conditions":25,"keywords":33,"overallStatus":40,"whyStopped":4,"lastUpdateSubmitDate":41,"lastUpdatePostDateStruct":42,"startDateStruct":45,"completionDateStruct":47,"leadSponsor":49,"locationsCount":52},"100593355","fitbit-and-ai-chatbot-in-sedentary-primary-care-patients-with-t2d-100593355",false,"NCT07005362","Fitbit and AI Chatbot in Sedentary Primary Care Patients With T2D","Feasibility of Use of Fitbit, Brief DSMES, and Targeted Text Messaging in Sedentary Adults With Type 2 Diabetes in Primary Care Settings","FIT T2D","Inclusion Criteria:\n\n* Diagnosed with type 2 diabetes per investigator discretion\n* No more than 20% of the sample will have A1c \\\u003C 7.5% (confirmed by medical record review or an A1c completed within 3 months of the screening visit)\n* Age ≥18 years and ≤ 80 years\n* Does not meet ADA guidelines for physical activity (\\\u003C 150 minutes of aerobic exercise per week defined as any activity where the participant can talk but not sing)\n* Has a smartphone compatible with a Fitbit\n\nExclusion Criteria:\n\n* Completing more than 60 minutes of moderate to vigorous activity per week defined as activity where you cannot sing (moderate) or can't say more than a few words without gasping for breath (vigorous)(14)\n* Any medical condition which, in the opinion of the investigator, would put the participant at an unacceptable safety risk, such as untreated malignancy, unstable cardiac disease, unstable or end-stage renal disease, and\u002For eating disorders.\n* Current or known history of coronary artery disease that is not stable with medical management, including unstable angina, or angina that prevents moderate exercise despite medical management, or a history of myocardial infarction, percutaneous coronary intervention, or coronary artery bypass grafting within the previous 12- months\n* Any planned surgery during the study which could be considered major in the opinion of the investigator\n* Blood disorder or dyscrasia within 3 months before screening, or the use of hydroxyurea, which, in the investigator's opinion, could interfere with the determination of HbA1c\n* Has taken oral or injectable steroids within the past 8 weeks or plans to take oral or injectable steroids during the study, as they may interfere with the determination of HbA1c.\n* Planning to move from Colorado within 3 months\n* Current Pregnancy or planning on pregnancy in the next 3 months\n* Unable to safely comply with study procedures and reporting requirements (e.g. impairment of vision that impacts ability to see FitBit, impaired memory)\n* Unable to speak English as this is a small feasibility study that does not have the resources to adapt the intervention for Spanish\n* Current participation in another diabetes-related clinical trial","ALL","18 Years","80 Years",{"count":21,"type":22},36,"ESTIMATED","OBSERVATIONAL","The goal of this observational study is to evaluate the feasibility and acceptability of a 12-week intervention utilizing a Fitbit and artificial intelligence (AI)-delivered diabetes self-management education and support (DSMES) with tailored text messages.\n\nThe main question it aims to answer is:\n\nDoes providing a wearable fitness and activity tracker plus AI-tailored and DSMES improve clinical outcomes for patients with type 2 diabetes?\n\nParticipants will complete a baseline visit, wear a Fitbit and answer text messages for 12-weeks, and complete by a final visit.",[26,27,28,29,30,31,32],"Type 2 Diabetes","Type 2 Diabetes Mellitus (T2DM)","T2DM (Type 2 Diabetes Mellitus)","T2D","T2DM","Remote Patient Monitoring","Artificial Intelegence",[34,35,36,37,38,39],"type 2 diabetes","artificial intelligence","fitbit","primary care","remote patient monitoring","physical activity","RECRUITING","2026-05-15",{"date":43,"type":44},"2026-05-19","ACTUAL",{"date":46,"type":44},"2025-09-03",{"date":48,"type":22},"2026-08-30",{"name":50,"class":51},"University of Colorado, Denver","OTHER",1,{"id":54,"slug":55,"hasResults":11,"nctId":56,"briefTitle":57,"officialTitle":58,"acronym":4,"eligibilityCriteria":59,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":60,"enrollmentInfo":61,"targetDuration":63,"studyType":23,"phases":4,"briefSummary":64,"conditions":65,"keywords":66,"overallStatus":40,"whyStopped":4,"lastUpdateSubmitDate":67,"lastUpdatePostDateStruct":68,"startDateStruct":70,"completionDateStruct":72,"leadSponsor":74,"locationsCount":52},"100613042","performance-comparison-of-large-language-models-in-tap-block-ultrasound-interpretation-100613042","NCT07261436","Performance Comparison of Large Language Models in TAP Block Ultrasound Interpretation","Performance Comparison of Large Language Models in TAP Block Ultrasound Interpretation: A Double-Blind Prospective Study","Inclusion Criteria:\n\n* Adults aged 18-85 years\n* ASA I-III physical status\n* Undergoing elective surgery with a lateral TAP block performed as part of routine anesthesia care\n* Complete ultrasound-guided block procedure recorded on video\n* Able to provide written informed consent\n\nExclusion Criteria:\n\n* Unsuccessful or incomplete TAP block procedure\n* Poor-quality ultrasound video (needle tip or anesthetic spread not visible)\n* Missing demographic or clinical data\n* Withdrawal of consent at any time","85 Years",{"count":62,"type":22},40,"1 Day","The goal of this study is to learn how accurately two artificial intelligence (AI) models, Gemini 2.5 Pro and ChatGPT-5.1, can interpret ultrasound videos of the Transversus Abdominis Plane (TAP) block, a regional anesthesia technique used for pain control after surgery.\n\nThe main questions this study aims to answer are:\n\nHow accurately can each AI model identify anatomical structures on TAP block ultrasound videos? Can the AI models correctly evaluate the spread of local anesthetic and determine whether the block is successful? How closely do the AI models' answers match the evaluations of expert anesthesiologists? No additional procedures will be performed on patients. TAP blocks will be done as part of routine clinical care, and the ultrasound videos will be recorded and de-identified.\n\nParticipants will not need to do anything extra for the study. Experienced anesthesiologists will review the videos and provide expert answers. The AI models will be given the same videos and asked the same questions. A second expert, who does not know which answers came from humans or AI, will compare all responses.\n\nThe results will help researchers understand whether advanced AI systems can safely support clinicians in interpreting ultrasound-guided regional anesthesia procedures and improve education and decision-making in anesthesia practice.",[32],[35],"2026-02-02",{"date":69,"type":44},"2026-02-04",{"date":71,"type":44},"2026-01-15",{"date":73,"type":22},"2026-05-01",{"name":75,"class":51},"Kanuni Sultan Suleyman Training and Research Hospital"]