[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Dr. Mark Mulder\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":87},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,47],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":28,"overallStatus":35,"whyStopped":4,"lastUpdateSubmitDate":36,"lastUpdatePostDateStruct":37,"startDateStruct":40,"completionDateStruct":42,"leadSponsor":44,"locationsCount":4},"100648121","easy-to-use-live-interactive-support-avatar-100648121",false,"NCT07715981","Easy-to-use, Live, Interactive Support Avatar","ELISA: Easy-to-use, Live, Interactive Support Avatar","ELISA","Inclusion Criteria:\n\n* Aged 18 years or older;\n* Indication to start a new line of anti-cancer treatment at Erasmus MC Medical Oncology;\n* Proficient in the Dutch language;\n* Has access to the internet;\n* Is capable using a digital device such as a computer, smartphone, or tablet;\n* Able and willing to provide informed consent.\n\nExclusion Criteria:\n\n* Has participated in this study for a previous line of anti-cancer treatment education.\n* Significant cognitive impairment.\n* Uncorrected visual or hearing impairment.","ALL","18 Years",{"count":20,"type":21},100,"ESTIMATED","INTERVENTIONAL",[24],"NA","Patients starting new cancer treatment must absorb complex information (treatment schedule, medication, side effects, safety instructions), often while coping with a new diagnosis or progression. Recall averages around 60%, with lower rates after bad-news consultations, so information often needs repeating before treatment starts, adding to nurse workload.\n\nDutch cancer incidence is projected to rise from 118,000 (2019) to \\~156,000 diagnoses\u002Fyear by 2032, with \\~1.4 million cancer survivors by then, compounding existing staff shortages. Existing digital tools (websites, videos) offer only static, non-personalized information, whereas LLM-based systems allow natural-language, personalized interaction.\n\nELISA pairs AI-generated educational videos with an interactive avatar restricted to physician-approved content, referring patients to their care team otherwise. This trial tests whether ELISA plus shortened nurse-led education yields non-inferior recall versus standard education, alongside patient experience measures.\n\nObjective(s) The primary objective of this study is to determine whether AI-supported education consisting of AI-generated videos and an interactive, life-like avatar, in addition to shortened nurse-led education, is non-inferior to standard nurse-led education alone with respect to patients' recall of treatment information when starting a new line of anti-cancer treatment. The secondary objective is to assess patients' experience with and usage of the AI-supported patient education.\n\nStudy type A prospective, single-center, randomized controlled non-inferiority trial Study population Adult patients, 18 years or older, who have an indication to start a new line of anti-cancer therapy at Erasmus MC Medical Oncology Methods Eligible patients, after giving eConsent, are randomized 1:1 to the control or study group. The study group gets access to ELISA. Both groups will receive an educational session with an oncology nurse. Two days after the educational session both groups receive a digital version of the Netherlands Patient Information Recall Questionnaire (NPIRQ) to test information retention. The study group will receive an additional online Chatbot Usability Questionnaire (CUQ) one month after getting access to ELISA.\n\nBurden and risks The additional burden for participants in participating in this study is minimal and consists of watching an informational video of less than 10 minutes and completing brief questionnaires, which will take no more than 40 minutes in total. The AI-generated educational videos are static and its content is based on the existing informational brochures for oncologic treatments, which are thoroughly reviewed by oncologists and nurses. The interactive, life-like avatar is based on a large language model (LLM). As with all LLM's, there is a small chance it may occasionally hallucinate, giving inaccurate information. To minimize this risks, information to answer participants' questions is limited to information from the existing informational brochures for oncologic treatments. The interactive, life-like avatar cannot independently generate or retrieve new information beyond the scope of this information. Furthermore, the interactive life-like avatar is explicitly prompted to not answer questions beyond the information provided, to never give medical advice and to not ask for personal information.\n\nRecruitment and consent Eligible patients are identified and informed by their oncologists during routine clinic visits. Eligible patients, after giving eConsent are randomized 1:1 to the control or study group.",[27],"Cancer",[29,30,31,32,33,34],"Education","Treatment","Side effects","Artificial Intelligence","Avatar","Video","NOT_YET_RECRUITING","2026-07-20",{"date":38,"type":39},"2026-07-21","ACTUAL",{"date":41,"type":21},"2026-09-01",{"date":43,"type":21},"2027-12-31",{"name":45,"class":46},"Dr. Mark Mulder","OTHER",{"id":48,"slug":49,"hasResults":11,"nctId":50,"briefTitle":51,"officialTitle":52,"acronym":53,"eligibilityCriteria":54,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":55,"targetDuration":57,"studyType":58,"phases":4,"briefSummary":59,"conditions":60,"keywords":69,"overallStatus":78,"whyStopped":4,"lastUpdateSubmitDate":79,"lastUpdatePostDateStruct":80,"startDateStruct":82,"completionDateStruct":84,"leadSponsor":85,"locationsCount":86},"100613134","developing-a-multimodal-cancer-pain-database-to-support-ai-based-automatic-pain-assessment-100613134","NCT07262632","Developing a Multimodal Cancer Pain Database to Support AI-Based Automatic Pain Assessment","SENSAI: Seeing, hEaring, seNsing: Smart, Effortless and Objective Pain Assessment With Mobile AI Technology - DataBase Development","SENSAI-DBD","Inclusion Criteria:\n\n* Adult patients (≥18 years)\n* Diagnosed with cancer, active\n* Able to communicate verbally in Dutch or English\n* Able to provide written informed consent.\n* Pain group specific:\n* Experiencing pain related to cancer\n* Admitted to the hospital due to pain\n* Control group specific:\n* Not experiencing pain (NRS = 0)\n* Admitted to the hospital for chemotherapy\n\nExclusion Criteria:\n\n* Cognitive, physical, or medical limitations that prevent participation in the audiovisual recording sessions or affect facial expressions or voice (e.g. facial paralysis, tracheostomy, severe speech impairment).\n* Critical illness or end-of-life care where participation would impose an additional burden.\n* Experiencing pain not associated with cancer\n* Infectious isolation precautions that prevent safe data collection",{"count":56,"type":21},200,"3 Weeks","OBSERVATIONAL","The goal of this observational study is to collect short video and sound recordings of people with cancer to create a secure database that can be used in future research to develop an artificial intelligence (AI) tool for pain assessment. The main aim is to build a large, high-quality collection of audiovisual data showing how people with cancer express themselves when they do and do not have pain.\n\nParticipants will include adults with cancer who are admitted to the oncology ward for pain treatment and a control group admitted for chemotherapy who have no pain. After giving consent, participants will:\n\n* Be recorded on video (from the shoulders up) for up to 60 seconds while reading a short sentence and describing their pain or daily experience.\n* Complete a short questionnaire about their mood and pain expression.\n* Allow researchers to collect some information from their medical record, such as their pain score, medications, and cancer type.\n\nThese recordings will be securely stored and used to create a database for future AI research. No medical tests, new treatments, or extra hospital visits are involved. This study will provide the foundation for developing future AI-based tools that could support doctors and patients in monitoring and managing pain more accurately and easily.",[61,62,63,64,65,66,67,68,27],"Cancer-related Pain","Pain Assessment","Artificial Intelligence (AI)","Oncology","Oncology Pain","Database","Facial Expression","Voice",[61,70,63,64,65,66,71,72,73,74,75,76,77],"Pain assessment","Facial Action Unit","Vocalizations","Acoustic","Automatic assessment","Machine learning","Audiovisual data","Paralinguistics","RECRUITING","2026-03-10",{"date":81,"type":39},"2026-03-11",{"date":83,"type":39},"2025-12-01",{"date":43,"type":21},{"name":45,"class":46},1,""]