Colorectal NeoplasmsNCT07612436
AI-empowered Nudge to Improve Colonoscopy Uptake (AINC)
A study testing AI-empowered nudge (AINC) strategy in people with Colorectal Neoplasms and Colonoscopy, run by Fudan University.
Trial summary
Colorectal cancer (CRC) ranks third in both incidence and mortality among all malignant tumors in China. Studies have shown that early screening can significantly reduce its incidence and mortality. Read more about this study
- Who is it for?
- Ages 45 to 74 yearsHealthy volunteers can take part
- Conditions the study is for
- Colorectal Neoplasms, Colonoscopy
- What is being tested?
- AI-empowered nudge (AINC) strategy
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,680 participants
- Where
- No sites listed yet
- Study dates
- 30 May 2026to 31 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 45 to 74 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Aged 45-74 years;
- Test positive on the Colorectal Cancer Risk Assessment Scale and the immunochemical fecal occult blood test;
- In good general health, mentally competent;
- Provide informed consent.
Who cannot join
- History of colorectal resection;
- Previous diagnosis of cancer or currently undergoing any cancer-related treatment;
- Underwent a colonoscopy or sigmoidoscopy within the past 5 years;
- Contraindications to colonoscopy (e.g. severe cardiac, cerebral, lung diseases, or renal dysfunction).
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: AI-empowered Nudge to Improve Colonoscopy Uptake (AINC): A Pragmatic Cluster-Randomized Trial
Colorectal cancer (CRC) ranks third in both incidence and mortality among all malignant tumors in China. Studies have shown that early screening can significantly reduce its incidence and mortality. Colonoscopy is the gold standard for CRC screening; however, compliance with colonoscopy among high-risk groups in China is very low. Artificial intelligence (AI)-assisted tools can provide real-time, personalized health education, and nudge strategies can help translate intent into action. This trial aims to evaluate the effectiveness of AI-empowered nudge for improving colonoscopy uptake among high-risk individuals aged 45 to 74 in China. It's a two-arm, pragmatic cluster randomized controlled trial. The main question it aims to answer is whether the AI-enabled personalized health education and nudge strategies improve colonoscopy adherence.
Participants will:
- Be recruited and allocated into one of two groups according to the assigned clusters. Participants in one group will be invited to receive usual care. In addition to usual care, participants in the other group will receive AI-empowered nudge, featuring an AI chatbot providing real-time personalized responses and a nudge environment with default screening option.
- Have their colonoscopy status checked at the end of trial.
Read the detailed description
We will conduct a two-arm, parallel-group, cluster-randomized controlled trial to evaluate the effectiveness of an AI-empowered nudge model in improving colonoscopy uptake (AINC) among high-risk individuals aged 45 to 74. The AI-empowered nudge model combines default screening nudging with an AI chatbot on colorectal cancer screening. We will also evaluate the feasibility of this AINC model, and identify the facilitators and barriers to its real-world adoption.
The colonoscopy uptake rate is approximately 15% in China, and the proposed intervention is expected to increase this rate by 10%. Sample size calculation, based on detecting an increase in colonoscopy uptake from 15% to 25% with 90% power (α=0.05, two-sided), an ICC of 0.05, and 30 clusters per arm, indicates a need for 24 participants per cluster. There are 720 per arm, and 1440 in total. Allowing for 15% attrition, the final sample size is determined to be 1680 from 70 clusters. As a pragmatic trial in real world, the number of participants each cluster depends on the population size of the respective villages or communities. All eligible participants in the participating villages or communities will be included in the study.
Participant recruitment will be conducted across 70 villages/communities in three representative counties/cities in China, covering urban, suburban, and rural areas. Cluster randomisation will be performed at the level of villages or communities using a stratified block design to ensure balanced allocation across the two trial arms. Stratification factors include geographic access to colonoscopy hospital and the size of individuals aged 45 to 74 for each cluster. Clusters with comparable levels of these factors will be grouped into blocks within each city and then randomly assigned within each block to the AINC or control group. The random allocation sequence will be generated by an independent statistician using a computer-based random number generator in R software and implemented via a secure centralised system.
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 arm. Participants in the intervention group will receive an AI-powered nudge for colonoscopy (AINC), featuring an AI chatbot providing real-time personalized responses and a nudge environment with default screening option, followed by message reminders once per two weeks. The control group will receive usual care. Colonoscopy uptake will be collected via the hospital information system at the 3-month follow-up.
The primary analysis will follow the intention-to-treat (ITT) principle, while the per-protocol (PP) analysis will serve as the secondary analysis. In the ITT analysis, all subjects randomized to each group will be included. Between-group comparisons for continuous and categorical variables will utilize t-tests and chi-square tests. The primary outcome (colonoscopy uptake) will be analyzed using Generalized Estimating Equations (GEE), adjusting for cluster effects and relevant covariates to obtain robust estimates. Covariates include region, age, sex, smoking history, Body Mass Index, history of bowel-related symptoms or diseases, and family history. 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 pre-specified characteristics, including region, age, sex, smoking history, Body Mass Index, history of bowel-related symptoms or diseases, and family history.
- Conditions
- Colorectal Neoplasms, Colonoscopy
- Being tested
- AI-empowered nudge (AINC) strategy, Usual Care
- Keywords
- Colorectal cancer, artificial intelligence, screening adherence, randomized controlled trial, nudge, Behavioral intervention
- Medical subject headings
- Colorectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-empowered nudge (AINC) strategyBehavioural
A digital health education and behavioral nudge intervention. It utilizes an intelligent chatbot to provide real-time, personalized information about colonoscopy and implements a default screening mechanism to facilitate the translation from screening intention to behavior.
Usual CareOther
Usual notification of screening results and opt-in appointment for colonoscopy.
| Study group | Role | What they receive |
|---|---|---|
| AI-empowered nudge group | Experimental | This arm implements a multi-component AI-empowered nudge strategy:
Default Appointment: On-site pre-scheduling of colonoscopies for high-risk individuals, providing an "opt-out" mechanism.
AI Chatbot: Guided on-site use (≥3 mins) of a dedicated chatbot offering personalized responses on CRC questions to facilitate self-learning.
LLM-produced SMS Reminders: For non-adherent participants, ChatGPT-5 generates risk-tailored SMS reminders sent bi-weekly to participants and their families (5 times). Receives: Behavioral: AI-empowered nudge (AINC) strategy |
| Control Group | Active comparator | Usual care: Based on the results of the risk assessment questionnaire and FIT test, village doctors will notify the screening results to colorectal cancer high-risk individuals, and instructs recipients to go to the designated hospital for a colonoscopy. Colonoscopy appointments will be scheduled only for residents who are willing to undergo a colonoscopy. Receives: Other: Usual Care |
What is being measured
The primary outcome is the main question the study is designed to answer.
Uptake of colonoscopy
Defined as whether the participant completes the colonoscopy. Data will be collected through the Hospital Information System (HIS) using participants' identification.
Measured: Three months after recruitment
Secondary outcomes (1)
Time to completion of colonoscopy
The interval from intervention initiation to the colonoscopy procedure. Data will be collected from information systems of hospitals.
Measured: Three months after recruitment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 May 2026
- Study start30 May 2026 (estimated)
- Primary completion1 Oct 2026 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Colorectal NeoplasmsStudies of the same condition, open and under way.
- More trials run by Fudan UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- This study employs a two-arm, parallel, cluster-randomized controlled trial design. To minimize intervention contamination between participants, randomization is conducted at the cluster level (community/village) rather than the individual level. A total of 70 communities/villages will be randomly assigned in a 1:1 ratio to either the control arm (usual care) or the experimental arm (AI-powered nudge strategy). All eligible high-risk residents identified within a specific cluster will receive the identical intervention assigned to that cluster. Both arms will run simultaneously and parallelly throughout the study period.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 1,680 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fudan University
- Sponsor type
- University, hospital or other
- Collaborators
- Nantong Center for Disease Control and Prevention
- Responsible party
- Principal investigator, Zhiyuan Hou, Fudan University
- Investigators
- Zhiyuan Hou, PhD, Principal investigator, Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07612436
- Sponsor's protocol ID
- Fudan-AINC
- Status last verified
- Jun 2026
- First submitted
- 21 May 2026
- Last updated
- 4 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared due to participant privacy concerns and institutional data protection policies
Publications
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.
PubMed 38917965Maida 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.
PubMed 40376022Heald 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.
PubMed 33168571Chen 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.
PubMed 40285677Dougherty 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.
PubMed 30326005Yu 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.
PubMed 38461798Chen 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.
PubMed 30377193Chen 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.
PubMed 40696392
Study at a glance
From the registry record, last updated by the sponsor on 4 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-empowered Nudge to Improve Colonoscopy Uptake (AINC): A Pragmatic Cluster-Randomized Trial
- ClinicalTrials.gov ID
- NCT07612436
- Acronym / sponsor ID
- Fudan-AINC
- Conditions
- Colorectal Neoplasms, Colonoscopy
- Keywords
- Colorectal cancer, artificial intelligence, screening adherence, randomized controlled trial, nudge, Behavioral intervention
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,680 participants (estimated)
- Lead sponsor
- Fudan UniversityUniversity, hospital or other
- Collaborators
- Nantong Center for Disease Control and Prevention
- Study start
- 30 May 2026 (estimated)
- Primary completion
- 1 Oct 2026 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 28 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Colorectal cancer (CRC) ranks third in both incidence and mortality among all malignant tumors in China. Studies have shown that early screening can significantly reduce its incidence and mortality. Colonoscopy is the gold standard for CRC screening; however, compliance with colonoscopy among high-risk groups in China is very low. Artificial intelligence (AI)-assisted tools can provide real-time, personalized health education, and nudge strategies can help translate intent into action. This trial aims to evaluate the effectiveness of AI-empowered nudge for improving colonoscopy uptake among high-risk individuals aged 45 to 74 in China. It's a two-arm, pragmatic cluster randomized controlled trial. The main question it aims to answer is whether the AI-enabled personalized health education and nudge strategies improve colonoscopy adherence.
Participants will:
- Be recruited and allocated into one of two groups according to the assigned clusters. Participants in one group will be invited to receive usual care. In addition to usual care, participants in the other group will receive AI-empowered nudge, featuring an AI chatbot providing real-time personalized responses and a nudge environment with default screening option.
- Have their colonoscopy status checked at the end of trial.
Detailed description
We will conduct a two-arm, parallel-group, cluster-randomized controlled trial to evaluate the effectiveness of an AI-empowered nudge model in improving colonoscopy uptake (AINC) among high-risk individuals aged 45 to 74. The AI-empowered nudge model combines default screening nudging with an AI chatbot on colorectal cancer screening. We will also evaluate the feasibility of this AINC model, and identify the facilitators and barriers to its real-world adoption.
The colonoscopy uptake rate is approximately 15% in China, and the proposed intervention is expected to increase this rate by 10%. Sample size calculation, based on detecting an increase in colonoscopy uptake from 15% to 25% with 90% power (α=0.05, two-sided), an ICC of 0.05, and 30 clusters per arm, indicates a need for 24 participants per cluster. There are 720 per arm, and 1440 in total. Allowing for 15% attrition, the final sample size is determined to be 1680 from 70 clusters. As a pragmatic trial in real world, the number of participants each cluster depends on the population size of the respective villages or communities. All eligible participants in the participating villages or communities will be included in the study.
Participant recruitment will be conducted across 70 villages/communities in three representative counties/cities in China, covering urban, suburban, and rural areas. Cluster randomisation will be performed at the level of villages or communities using a stratified block design to ensure balanced allocation across the two trial arms. Stratification factors include geographic access to colonoscopy hospital and the size of individuals aged 45 to 74 for each cluster. Clusters with comparable levels of these factors will be grouped into blocks within each city and then randomly assigned within each block to the AINC or control group. The random allocation sequence will be generated by an independent statistician using a computer-based random number generator in R software and implemented via a secure centralised system.
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 arm. Participants in the intervention group will receive an AI-powered nudge for colonoscopy (AINC), featuring an AI chatbot providing real-time personalized responses and a nudge environment with default screening option, followed by message reminders once per two weeks. The control group will receive usual care. Colonoscopy uptake will be collected via the hospital information system at the 3-month follow-up.
The primary analysis will follow the intention-to-treat (ITT) principle, while the per-protocol (PP) analysis will serve as the secondary analysis. In the ITT analysis, all subjects randomized to each group will be included. Between-group comparisons for continuous and categorical variables will utilize t-tests and chi-square tests. The primary outcome (colonoscopy uptake) will be analyzed using Generalized Estimating Equations (GEE), adjusting for cluster effects and relevant covariates to obtain robust estimates. Covariates include region, age, sex, smoking history, Body Mass Index, history of bowel-related symptoms or diseases, and family history. 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 pre-specified characteristics, including region, age, sex, smoking history, Body Mass Index, history of bowel-related symptoms or diseases, and family history.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
- Model description
- This study employs a two-arm, parallel, cluster-randomized controlled trial design. To minimize intervention contamination between participants, randomization is conducted at the cluster level (community/village) rather than the individual level. A total of 70 communities/villages will be randomly assigned in a 1:1 ratio to either the control arm (usual care) or the experimental arm (AI-powered nudge strategy). All eligible high-risk residents identified within a specific cluster will receive the identical intervention assigned to that cluster. Both arms will run simultaneously and parallelly throughout the study period.
| Arm | Type | Receives |
|---|---|---|
| AI-empowered nudge group | Experimental | Behavioral: AI-empowered nudge (AINC) strategyThis arm implements a multi-component AI-empowered nudge strategy: Default Appointment: On-site pre-scheduling of colonoscopies for high-risk individuals, providing an "opt-out" mechanism. AI Chatbot: Guided on-site use (≥3 mins) of a dedicated chatbot offering personalized responses on CRC questions to facilitate self-learning. LLM-produced SMS Reminders: For non-adherent participants, ChatGPT-5 generates risk-tailored SMS reminders sent bi-weekly to participants and their families (5 times). |
| Control Group | Active comparator | Other: Usual CareUsual care: Based on the results of the risk assessment questionnaire and FIT test, village doctors will notify the screening results to colorectal cancer high-risk individuals, and instructs recipients to go to the designated hospital for a colonoscopy. Colonoscopy appointments will be scheduled only for residents who are willing to undergo a colonoscopy. |
Who may qualify
- Ages
- 45 to 74 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Aged 45-74 years;
- Test positive on the Colorectal Cancer Risk Assessment Scale and the immunochemical fecal occult blood test;
- In good general health, mentally competent;
- Provide informed consent.
Key exclusion criteria (4 listed)
- History of colorectal resection;
- Previous diagnosis of cancer or currently undergoing any cancer-related treatment;
- Underwent a colonoscopy or sigmoidoscopy within the past 5 years;
- Contraindications to colonoscopy (e.g. severe cardiac, cerebral, lung diseases, or renal dysfunction).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-empowered nudge (AINC) strategyA digital health education and behavioral nudge intervention. It utilizes an intelligent chatbot to provide real-time, personalized information about colonoscopy and implements a default screening mechanism to facilitate the translation from screening intention to behavior. | Behavioural | AI-empowered nudge group |
| Usual CareUsual notification of screening results and opt-in appointment for colonoscopy. | Other | Control Group |
Primary outcome
Uptake of colonoscopy
Time frame: Three months after recruitment
Defined as whether the participant completes the colonoscopy. Data will be collected through the Hospital Information System (HIS) using participants' identification.
1 secondary outcome
Time to completion of colonoscopy
Time frame: Three months after recruitment
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jun 2026.
- Background: PubMed 38917965Zhang 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.
- Background: PubMed 40376022Maida 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.
- Background: PubMed 33168571Heald 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.
- Background: PubMed 40285677Chen 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.
- Background: PubMed 30326005Dougherty 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.
- Background: PubMed 38461798Yu 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.
- Background: PubMed 30377193Chen 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.
- Background: PubMed 40696392Chen 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.
- Individual participant dataSharing planned: No. Individual participant data will not be shared due to participant privacy concerns and institutional data protection policies
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Zhiyuan Hou, PhD principal investigator
Fudan University
Questions about this trial
What is this clinical trial studying?
A study testing AI-empowered nudge (AINC) strategy in people with Colorectal Neoplasms and Colonoscopy, run by Fudan University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 4 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Zhiyuan Hou, PhD (86+21 54231112, zyhou@fudan.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Uptake of colonoscopy (time frame: Three months after recruitment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07612436, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Jun 2026.