Ignite Creation Date:
2025-12-24 @ 2:38 PM
Ignite Modification Date:
2026-01-07 @ 1:16 PM
Study NCT ID:
NCT07261059
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-03
First Post:
2025-11-21
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-assisted Integrated Care to Promote Colonoscopy Uptake
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D015179', 'term': 'Colorectal Neoplasms'}], 'ancestors': [{'id': 'D007414', 'term': 'Intestinal Neoplasms'}, {'id': 'D005770', 'term': 'Gastrointestinal Neoplasms'}, {'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}, {'id': 'D003108', 'term': 'Colonic Diseases'}, {'id': 'D007410', 'term': 'Intestinal Diseases'}, {'id': 'D012002', 'term': 'Rectal Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'HEALTH_SERVICES_RESEARCH', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 400}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-12-08', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-12', 'completionDateStruct': {'date': '2026-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-02', 'studyFirstSubmitDate': '2025-11-21', 'studyFirstSubmitQcDate': '2025-12-02', 'lastUpdatePostDateStruct': {'date': '2025-12-03', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-12-03', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2026-09', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Uptake of colonoscopy', 'timeFrame': 'Three and six months after recruitment', 'description': 'Whether participants receive colonoscopy for colorectal cancer screening.Data will be collected from information system of hospitals.'}, {'measure': 'Time to completion of colonoscopy', 'timeFrame': 'Six months after recruitment', 'description': 'The interval from intervention initiation to the colonoscopy procedure. Data will be collected from information systems of hospitals.'}], 'secondaryOutcomes': [{'measure': 'CRC screening literacy', 'timeFrame': 'One month after recruitment', 'description': 'CRC screening literacy will be assessed using a "Colorectal Cancer Screening Literacy Questionnaire" developed by the research team to ensure alignment with the study aims and intervention. The questionnaire comprises of 7 items , covering the basic knowledge of CRC, screening methods, benefits, and common misconceptions. Each item is answered with "yes", "no", and "not sure", with one point for a correct answer and no points for "not sure" and incorrect answers. The total score ranges from 0 to 7, and the higher the score, the higher the literacy level of the participants about CRC screening.'}, {'measure': 'CRC screening belief', 'timeFrame': 'One month after recruitment', 'description': 'Participants\' perceptions of CRC risk and their beliefs regarding benefits and barriers of colonoscopy will be assessed using a 5-point Likert scale (from "completely disagree" to "completely agree"). A higher score indicates a stronger belief.'}, {'measure': 'Colonoscopy behavioral intention', 'timeFrame': 'One month after recruitment', 'description': 'Participants\' intention to receive colonoscopy will be measured using a 5-point Likert scale (from "completely disagree" to "completely agree"). Those who agree or completely agree are defined as having colonoscopy intention.'}, {'measure': 'User engagement level with chatbot', 'timeFrame': 'Six months after recruitment', 'description': 'Chatbot engagement level is measured by a binary variable whether users highly engage with chatbot in terms of usage frequency, usage duration, the number of asked questions, and the presence of free-text questions. High-engagement is defined as those using chatbot frequently and longer, and asking more questions and free-text questions; others are low-engagement.'}, {'measure': 'Usability of AI-assisted integrated care intervention', 'timeFrame': 'Six months after recruitment', 'description': 'The usability of the AICC intervention will be evaluated using a series of questions on its acceptability, feasibility, and sustainability. It covers two sections with 9 entries: Evaluation of Clinician-Delivered Health Education (4 entries) and Evaluation of the Chatbot (5 entries). The Likert 5-point scale is used, with scores ranging from 1 to 5 on a scale of "completely disagree" to "completely agree". The total usability score ranges from 9 to 45 points, with higher scores indicating greater perceived usability of the AICC intervention.'}, {'measure': 'Incremental cost-effectiveness ratio (ICER)', 'timeFrame': 'Six months after recruitment', 'description': 'The incremental cost-effectiveness ratio (ICER) of the AICC intervention versus usual specialty care will be assessed from the perspective of the health care system. The ICER is defined as the cost per additional quality-adjusted life year (QALY) gained. Cost data will include screening program costs (from project records), direct medical costs, direct non-medical costs, and indirect costs (sourced from the study in Shandong Province). QALYs will be calculated by applying health utility weights, derived from existing literature, according to patients based on their diagnostic states. The specific ICER value will be reported as the primary economic outcome. Unit of Measure: United States dollar (Dollar).'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['colorectal cancer', 'colonoscopy', 'artificial intelligence', 'integrated care', 'trial'], 'conditions': ['Colorectal Neoplasms', 'Colonoscopy']}, 'referencesModule': {'references': [{'pmid': '11256708', 'type': 'BACKGROUND', 'citation': 'Urbansky ET, Freeman DM, Rubio FJ. Ascorbic acid reduction of residual active chlorine in potable water prior to halocarboxylate determination. J Environ Monit. 2000 Jun;2(3):253-6. doi: 10.1039/b001046o.'}, {'pmid': '38917965', 'type': 'BACKGROUND', 'citation': 'Zhang Q, Wong AKC, Bayuo J. The Role of Chatbots in Enhancing Health Care for Older Adults: A Scoping Review. J Am Med Dir Assoc. 2024 Sep;25(9):105108. doi: 10.1016/j.jamda.2024.105108. Epub 2024 Jun 22.'}, {'pmid': '40725794', 'type': 'BACKGROUND', 'citation': 'Zeng A, Steinke J, Bocse HF, De Pastena M. Dr. LLM Will See You Now: The Ability of ChatGPT to Provide Geographically Tailored Colorectal Cancer Screening and Surveillance Recommendations. J Clin Med. 2025 Jul 18;14(14):5101. doi: 10.3390/jcm14145101.'}, {'pmid': '37984563', 'type': 'BACKGROUND', 'citation': 'Kerbage A, Kassab J, El Dahdah J, Burke CA, Achkar JP, Rouphael C. Accuracy of ChatGPT in Common Gastrointestinal Diseases: Impact for Patients and Providers. Clin Gastroenterol Hepatol. 2024 Jun;22(6):1323-1325.e3. doi: 10.1016/j.cgh.2023.11.008. Epub 2023 Nov 19.'}, {'pmid': '40376022', 'type': 'BACKGROUND', 'citation': 'Maida M, Mori Y, Fuccio L, Sferrazza S, Vitello A, Facciorusso A, Hassan C. Exploring ChatGPT effectiveness in addressing direct patient queries on colorectal cancer screening. Endosc Int Open. 2025 May 12;13:a25689416. doi: 10.1055/a-2568-9416. eCollection 2025.'}, {'pmid': '33168571', 'type': 'BACKGROUND', 'citation': 'Heald B, Keel E, Marquard J, Burke CA, Kalady MF, Church JM, Liska D, Mankaney G, Hurley K, Eng C. Using chatbots to screen for heritable cancer syndromes in patients undergoing routine colonoscopy. J Med Genet. 2021 Dec;58(12):807-814. doi: 10.1136/jmedgenet-2020-107294. Epub 2020 Nov 9.'}, {'pmid': '40285677', 'type': 'BACKGROUND', 'citation': 'Chen D, Avison K, Alnassar S, Huang RS, Raman S. Medical accuracy of artificial intelligence chatbots in oncology: a scoping review. Oncologist. 2025 Apr 4;30(4):oyaf038. doi: 10.1093/oncolo/oyaf038.'}, {'pmid': '27196920', 'type': 'BACKGROUND', 'citation': 'Leung DY, Chow KM, Lo SW, So WK, Chan CW. Contributing Factors to Colorectal Cancer Screening among Chinese People: A Review of Quantitative Studies. Int J Environ Res Public Health. 2016 May 17;13(5):506. doi: 10.3390/ijerph13050506.'}, {'pmid': '29846947', 'type': 'BACKGROUND', 'citation': 'Wolf AMD, Fontham ETH, Church TR, Flowers CR, Guerra CE, LaMonte SJ, Etzioni R, McKenna MT, Oeffinger KC, Shih YT, Walter LC, Andrews KS, Brawley OW, Brooks D, Fedewa SA, Manassaram-Baptiste D, Siegel RL, Wender RC, Smith RA. Colorectal cancer screening for average-risk adults: 2018 guideline update from the American Cancer Society. CA Cancer J Clin. 2018 Jul;68(4):250-281. doi: 10.3322/caac.21457. Epub 2018 May 30.'}, {'pmid': '27687535', 'type': 'BACKGROUND', 'citation': "Peterson EB, Ostroff JS, DuHamel KN, D'Agostino TA, Hernandez M, Canzona MR, Bylund CL. Impact of provider-patient communication on cancer screening adherence: A systematic review. Prev Med. 2016 Dec;93:96-105. doi: 10.1016/j.ypmed.2016.09.034. Epub 2016 Sep 28."}, {'pmid': '32439077', 'type': 'BACKGROUND', 'citation': 'Butterly LF. Proven Strategies for Increasing Adherence to Colorectal Cancer Screening. Gastrointest Endosc Clin N Am. 2020 Jul;30(3):377-392. doi: 10.1016/j.giec.2020.02.003. Epub 2020 Apr 9.'}, {'pmid': '30326005', 'type': 'BACKGROUND', 'citation': 'Dougherty MK, Brenner AT, Crockett SD, Gupta S, Wheeler SB, Coker-Schwimmer M, Cubillos L, Malo T, Reuland DS. Evaluation of Interventions Intended to Increase Colorectal Cancer Screening Rates in the United States: A Systematic Review and Meta-analysis. JAMA Intern Med. 2018 Dec 1;178(12):1645-1658. doi: 10.1001/jamainternmed.2018.4637.'}, {'pmid': '21162896', 'type': 'BACKGROUND', 'citation': 'Deng SX, Cai QC, An W, Gao J, Hong SY, Zhu W, Li ZS. [Factors influencing patient compliance in colorectal cancer screening: qualitative research synthesis]. Zhonghua Yi Xue Za Zhi. 2010 Oct 19;90(38):2679-83. Chinese.'}, {'pmid': '38461798', 'type': 'BACKGROUND', 'citation': 'Yu Z, Li B, Zhao S, Du J, Zhang Y, Liu X, Guo Q, Zhou H, He M. Uptake and detection rate of colorectal cancer screening with colonoscopy in China: A population-based, prospective cohort study. Int J Nurs Stud. 2024 May;153:104728. doi: 10.1016/j.ijnurstu.2024.104728. Epub 2024 Feb 20.'}, {'pmid': '30377193', 'type': 'BACKGROUND', 'citation': 'Chen H, Li N, Ren J, Feng X, Lyu Z, Wei L, Li X, Guo L, Zheng Z, Zou S, Zhang Y, Li J, Zhang K, Chen W, Dai M, He J; group of Cancer Screening Program in Urban China (CanSPUC). Participation and yield of a population-based colorectal cancer screening programme in China. Gut. 2019 Aug;68(8):1450-1457. doi: 10.1136/gutjnl-2018-317124. Epub 2018 Oct 30.'}, {'pmid': '40696392', 'type': 'BACKGROUND', 'citation': 'Chen Y, Zhang Y, Yan Y, Han J, Zhang L, Cheng X, Lu B, Li N, Luo C, Zhou Y, Song K, Iwasaki M, Dai M, Wu D, Chen H. Global colorectal cancer screening programs and coverage rate estimation: an evidence synthesis. J Transl Med. 2025 Jul 22;23(1):811. doi: 10.1186/s12967-025-06887-4.'}, {'pmid': '38129166', 'type': 'BACKGROUND', 'citation': 'Expert Group on Early Diagnosis and Treatment of Cancer, Chinese Society of Oncology, Chinese Medical Association. [Expert consensus on the early diagnosis and treatment of colorectal cancer in China (2023 edition)]. Zhonghua Yi Xue Za Zhi. 2023 Dec 26;103(48):3896-3908. doi: 10.3760/cma.j.cn112137-20230804-00164. Chinese.'}, {'pmid': '38855153', 'type': 'BACKGROUND', 'citation': 'Li J, Li ZP, Ruan WJ, Wang W. Colorectal cancer screening: The value of early detection and modern challenges. World J Gastroenterol. 2024 May 28;30(20):2726-2730. doi: 10.3748/wjg.v30.i20.2726.'}, {'pmid': '33472315', 'type': 'BACKGROUND', 'citation': 'National Cancer Center, China, Expert Group of the Development of China Guideline for the Screening, Early Detection and Early Treatment of Colorectal Cancer. [China guideline for the screening, early detection and early treatment of colorectal cancer (2020, Beijing)]. Zhonghua Zhong Liu Za Zhi. 2021 Jan 23;43(1):16-38. doi: 10.3760/cma.j.cn112152-20210105-00010. Chinese.'}, {'pmid': '36856579', 'type': 'BACKGROUND', 'citation': 'Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. CA Cancer J Clin. 2023 May-Jun;73(3):233-254. doi: 10.3322/caac.21772. Epub 2023 Mar 1.'}]}, 'descriptionModule': {'briefSummary': "Colorectal cancer (CRC) ranks the second most common cancer and the fourth leading cause of cancer-related deaths in China. Early screening of CRC has been proven to reduce the incidence and mortality, with colonoscopy as the gold standard for CRC screening. This trial aims to evaluate the effectiveness of artificial intelligence-assistant integrated care for improving uptake rate of colonoscopy among high-risk individuals aged 40 to 64 in China. It's a two-arm, parallel cluster randomized controlled trial. The main question it aims to answer is whether the AI-assisted integrated care influence participants' screening-related knowledge, health beliefs, behavioral intention, and uptake of colonoscopy.\n\nParticipants will:\n\n1. Be recruited and allocated into one of two groups according to the assigned clusters. Participants in one group will be invited to receive usual specialty care. In addition to usual specialty care, participants in the other group will receive AI-assisted integrated care provided by specialist and general practitioners collaboratively.\n2. Complete a questionnaire survey on their knowledge, health beliefs, behavioral intention on CRC screening.\n3. Have their colonoscopy status checked at the middle and end of trial.", 'detailedDescription': "We will conduct a two-arm, cluster randomized controlled trial to evaluate the effectiveness of an AI-assisted integrated care (AICC) model in improving colonoscopy uptake rate among high-risk individuals aged 40-64. This will be followed by a pragmatic implementation science study to assess user engagement of AICC and identify the facilitators and barriers to its real-world implementation.\n\nSample size calculation, based on detecting an increase in colonoscopy uptake from 15% to 30% with 80% power (α=0.05, two-sided), an ICC of 0.05, and 10 participants per cluster, indicates a need for 18 clusters per arm. Allowing for 10% attrition, the final sample size is determined to be 20 clusters per arm. Thus, a total sample size is 400 participants from 40 clusters.\n\nParticipant recruitment will be conducted across 40 villages/communities in three representative counties/cities in China. An independent biostatistician will randomly allocate these villages/communities within each county/city to the study arms in a 1:1 ratio. The study procedure involves first identifying high-risk individuals for CRC through an initial risk assessment questionnaire and a fecal immunochemical test (FIT). Those who meet the criteria will then receive the intervention corresponding to their village's assigned study group.\n\nParticipants in the intervention group will receive AICC. This includes a colonoscopy recommendation from a county specialist for both participants and their families, followed by an introduction to and guided registration for a CRC education chatbot with an initial 5-minute tutorial. Subsequently, general practitioners will conduct three monthly face-by-face follow-ups, each comprising a brief reminder of colonoscopy and a guided usage of CRC education chatbot. The control group will receive only a colonoscopy recommendation from a county specialist, with access to the chatbot granted only after the end of the 6-month study period. Post-intervention, all participants will complete a questionnaire assessing CRC screening knowledge, health beliefs, and behavioral intention. Colonoscopy uptake will be collected via the hospital information system at the 3- and 6-month follow-up.\n\nThe primary analysis will follow the intention-to-treat (ITT) principle. The primary outcome is the uptake and timing of colonoscopy at 3 and 6 months after intervention. Secondary outcomes encompassed several domains: CRC screening knowledge, beliefs, and intention; chatbot usability and user engagement; and intervention costs. Between-group comparisons for continuous and categorical variables will utilize t-tests and chi-square tests. To account for potential confounders, the generalized estimating equation (GEE) will be employed to derive robust effect estimates. The timing of colonoscopy uptake will be analyzed using Kaplan-Meier survival curves and log-rank tests, and the intervention effects on the time-to-event will be quantified with a Cox proportional hazards model. Subgroup analyses will be conducted to elucidate the effect heterogeneity across populations stratified by baseline characteristics."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '64 Years', 'minimumAge': '40 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Individuals who test positive on either the Colorectal Cancer Risk Assessment Scale or the fecal immunochemical test (FIT);\n* Aged 40 \\~ 64 years;\n* Proficient in smartphone use and able to engage with the intervention;\n* Provided informed consent .\n\nExclusion Criteria:\n\n* History of colorectal cancer;\n* Contraindications to colonoscopy,(e.g. severe cardiac, cerebral, lung diseases, or renal dysfunction).'}, 'identificationModule': {'nctId': 'NCT07261059', 'briefTitle': 'AI-assisted Integrated Care to Promote Colonoscopy Uptake', 'organization': {'class': 'OTHER', 'fullName': 'Fudan University'}, 'officialTitle': 'Artificial Intelligence-assisted Integrated Care to Promote Colonoscopy Uptake in China: a Cluster Randomized Controlled Trial', 'orgStudyIdInfo': {'id': 'Fudan-CRC chatbot'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'AICC intervention group', 'description': 'Participants in the intervention group will receive AICC. This includes a colonoscopy recommendation from a county specialist for both participants and their families, followed by an introduction to and guided registration for a CRC education chatbot with an initial 5-minute tutorial. Subsequently, general practitioners will conduct three monthly face-by-face follow-ups, each comprising a brief reminder of colonoscopy and a guided usage of CRC education chatbot.', 'interventionNames': ['Behavioral: AI-assisted integrated care']}, {'type': 'NO_INTERVENTION', 'label': 'Control group', 'description': 'Participants in this group will receive usual specialty care, only a colonoscopy recommendation from a county specialists. For ethical considerations, participants in this arm will be offered access to the chatbot after the end of the study.'}], 'interventions': [{'name': 'AI-assisted integrated care', 'type': 'BEHAVIORAL', 'description': 'A colorectal cancer screening chatbot delivered via WeChat or a web browser, designed to provide information and health education about the colonoscopy, including essential knowledge, screening rationale, methods, procedural details, and local screening policies,. The chatbot is powered by large language models and is trained on an expert-validated knowledge base derived from authoritative sources such as the China colorectal cancer screening guidelines to ensure accuracy. The knowledge base is validated by colorectal cancer specialists. The chatbot engages users in interactive, conversational dialogue to answer questions and address concerns regarding colorectal cancer and colonoscopy.\n\nIn addition to a colonoscopy recommendation from a county specialist at on-site, general practitioners will also join to provide recommendation and brief reminder of colonoscopy within the follow-up period.', 'armGroupLabels': ['AICC intervention group']}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Zhiyuan Hou, PhD', 'role': 'CONTACT', 'email': 'zyhou@fudan.edu.cn', 'phone': '86+21 54231112'}], 'overallOfficials': [{'name': 'Zhiyuan Hou, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Fudan University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Individual participant data will not be shared due to participant privacy concerns and institutional data protection policies.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Fudan University', 'class': 'OTHER'}, 'collaborators': [{'name': 'Sun Yat-sen University', 'class': 'OTHER'}, {'name': 'Shandong University', 'class': 'OTHER'}, {'name': 'Shandong Cancer Hospital and Institute', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor', 'investigatorFullName': 'Zhiyuan Hou', 'investigatorAffiliation': 'Fudan University'}}}}