[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100631826":3},{"organization":4,"outcomesModule":7,"designInfo":53,"detailedDescription":52,"studyPopulation":52,"armGroups":61,"interventions":73,"overallOfficials":52,"centralContacts":82,"locations":52,"responsibleParty":88,"collaborators":92,"id":99,"slug":52,"hasResults":100,"nctId":101,"briefTitle":102,"officialTitle":103,"acronym":52,"eligibilityCriteria":104,"healthyVolunteers":100,"sex":105,"minAge":106,"maxAge":52,"enrollmentInfo":107,"targetDuration":52,"studyType":110,"phases":111,"briefSummary":113,"conditions":114,"keywords":116,"overallStatus":124,"whyStopped":52,"lastUpdateSubmitDate":125,"lastUpdatePostDateStruct":126,"startDateStruct":129,"completionDateStruct":131,"leadSponsor":133,"locationsCount":52},{"fullName":5,"class":6},"The Hong Kong Polytechnic University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":39,"otherOutcomes":52},[9,13,16,19,22,26,29,32,35],{"measure":10,"description":11,"timeFrame":12},"Screening rate","Calculated by dividing the number of sarcoma patients screened by the research team by the number of sarcoma patients.","During the enrollment period of this project.",{"measure":14,"description":15,"timeFrame":12},"Eligibility rate","Calculated by dividing the number of participants who are eligible by the number who are screened.",{"measure":17,"description":18,"timeFrame":12},"Consent rate","Calculated by dividing the number of participants who were randomized into intervention and control groups by those provide consent.",{"measure":20,"description":21,"timeFrame":12},"Randomization rate","Calculated by dividing the number of participants who consent to join the study by the number who are eligible.",{"measure":23,"description":24,"timeFrame":25},"Attendance rate","Calculated by dividing the number of participants who complete the intervention by those who are randomised","Baseline and immediately after intervention (T1)",{"measure":27,"description":28,"timeFrame":25},"Retention rate","Calculated by dividing the number of participants who remain in the study by those who are randomized. This will be calculated by groups.",{"measure":30,"description":31,"timeFrame":25},"Completion rate","Calculated by dividing the number of participants who returned questionnaires by the number of questionnaires distributed. Calculated by dividing the number of participants who remain in the study by those who are randomized. This will be calculated by groups.",{"measure":33,"description":34,"timeFrame":25},"Proportion of missing data","Calculated as the percentage of missing values in the dataset. Unknown or blank values will be considered missing values.",{"measure":36,"description":37,"timeFrame":38},"Adverse events","Adverse events are defined as unfavourable and unintended events that are not present, or appear to have worsened during the study.","immediately after intervention (T1)",[40,43,46,49],{"measure":41,"description":42,"timeFrame":25},"Information satisfaction","The EORTC QLQ-INFO25 will be used to evaluate participants' satisfaction with the information received. A four-point Likert scale is used to score 25 items ('nothing' = 1 to 'a lot' = 4), with higher scores indicating greater information satisfaction.",{"measure":44,"description":45,"timeFrame":25},"Anxiety and stress","The Depression Anxiety Stress Scale-21 (DASS-21) will be used to assess anxiety and stress.(36) 21 items are scored using a four-point Likert scale, with higher score indicating more severe symptoms.",{"measure":47,"description":48,"timeFrame":25},"Self-efficacy","Self-efficacy will be assessed using the Strategies Used by People to Promote Health (SUPPH) with good reliability and validity. This 28-item likert scale measure self-care self-efficacy, with higher scores indicating more positive perceptions of self-efficacy.",{"measure":50,"description":51,"timeFrame":38},"System Usability Scale","System Usability Scale (SUS) will be used to measure the usability of digital health solutions by 10 items rated on a 5-point Likert scale from 0-100. The chatbot will be considered highly usable if the mean score exceeds 68.",null,{"allocation":54,"interventionModel":55,"interventionModelDescription":52,"primaryPurpose":56,"observationalModel":52,"timePerspective":52,"maskingInfo":57},"RANDOMIZED","PARALLEL","SUPPORTIVE_CARE",{"masking":58,"maskingDescription":52,"whoMasked":59},"SINGLE",[60],"OUTCOMES_ASSESSOR",[62,68],{"label":63,"type":64,"description":65,"interventionNames":66},"ASCC","EXPERIMENTAL","An AI-based sarcoma chatbot building on ChatGPT (ASCC) using a co-design approach.",[67],"Other: ASCC (sarcoma-specific AI chatbot)",{"label":69,"type":6,"description":70,"interventionNames":71},"Control","The standard information from doctor-patient consultations and hospital",[72],"Other: Routine Clinical Care",[74,78],{"type":6,"name":75,"description":76,"armGroupLabels":77,"otherNames":52},"ASCC (sarcoma-specific AI chatbot)","The participants in the experimental group will access the ASCC via a webpage alongside standard information from health professionals for one month (15 minutes weekly).",[63],{"type":6,"name":79,"description":80,"armGroupLabels":81,"otherNames":52},"Routine Clinical Care","Participants in the control group will receive routine clinical care, including standard information from doctor-patient consultations (e.g., diagnosis, treatment, medical tests) and hospital provided sarcoma and treatment leaflet.",[69],[83],{"name":84,"role":85,"phone":86,"phoneExt":52,"email":87},"Qi LIU, PhD","CONTACT","85255741349","charlotte-qi.liu@polyu.edu.hk",{"type":89,"investigatorFullName":90,"investigatorTitle":91,"investigatorAffiliation":5,"oldNameTitle":52,"oldOrganization":52},"PRINCIPAL_INVESTIGATOR","LIU Qi Charlotte","Postdoc fellow",[93,95,97],{"name":94,"class":6},"Queen Mary Hospital, Hong Kong",{"name":96,"class":6},"Prince of Wales Hospital, Shatin, Hong Kong",{"name":98,"class":6},"Queen Elizabeth Hospital, Hong Kong","100631826",false,"NCT07505732","Information-providing Chatbot","The Development and Evaluation of a Co-designed, AI-based Information-providing Chatbot for Sarcoma Patients: A Pilot Randomised Controlled Trial","Inclusion Criteria:\n\n* (1) diagnosed with soft tissue sarcoma;\n* (2) receiving active treatment at any stage;\n* (3) aged≥18 years;\n* (4) able to speak Cantonese, Mandarin and English;\n* (5) access to an electronic device enabling ASCC use\n\nExclusion Criteria:\n\n* (1) patients with unstable physical or mental conditions;\n* (2) inability to communicate logically;\n* (3) using chatbots (e.g., ChatGPT, Deepseek) for sarcoma information in the last month;\n* (4) mental health disorders documented by the medical system","ALL","18 Years",{"count":108,"type":109},78,"ESTIMATED","INTERVENTIONAL",[112],"NA","This study aims to develop and examine the feasibility and preliminary effectiveness of an AI-based sarcoma chatbot building on ChatGPT (ASCC) to address the information needs of sarcoma patients. We hypothesized: 1) the ASCC will demonstrate good usability; 2) the study will be feasible in terms of all feasibility indicators; 3) the experimental group will report improved satisfaction and self-efficacy, decreased anxiety and stress than the control groups upon completion of the intervention. The ASCC will be developed using a co-design approach. A pilot randomized controlled trial will then be conducted in the three oncology wards of collaborative hospitals. Seventy-eight sarcoma patients will be recruited and randomized to the experimental group (n=39) and the control group (n=39). The experimental group will use the ASCC available 24\u002F7 via voice or text for disease-related questions for one month while the control group will receive usual care.",[115],"Sarcoma",[117,118,119,120,121,122,123],"sarcoma","health information","artificial intelligence","ChatGPT","chatbot","unmeet needs","cancer","NOT_YET_RECRUITING","2026-03-26",{"date":127,"type":128},"2026-04-01","ACTUAL",{"date":130,"type":109},"2027-04-01",{"date":132,"type":109},"2028-07-31",{"name":5,"class":6}]