AI-empowered Nudge to Improve Colonoscopy Uptake (AINC)

May 21, 2026 updated by: Zhiyuan Hou, Fudan University

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:

  1. 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.
  2. Have their colonoscopy status checked at the end of trial.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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), and the secondary outcome (colonoscopy duration) will be analyzed using Generalized Linear Mixed Models (GLMM); both approaches adjust 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 Type

Interventional

Enrollment (Estimated)

1680

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • 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 ④ Provide informed consent.

Exclusion Criteria:

  • History of colorectal resection; ② Previous diagnosis of cancer or currently undergoing any cancer-related treatment;

    • Underwent a colonoscopy or sigmoidoscopy within the past 5 years; ④ Contraindications to colonoscopy,(e.g. severe cardiac, cerebral, lung diseases, or renal dysfunction).

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-empowered nudge group

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).
Behavioral: AI-empowered nudge (AINC) strategy
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.
Active Comparator: Control Group
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.
Other: Usual Care
Usual notification of screening results and opt-in appointment for colonoscopy.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Three months after recruitment

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Time to completion of colonoscopy
Time Frame: Three months after recruitment
The interval from intervention initiation to the colonoscopy procedure. Data will be collected from information systems of hospitals.
Three months after recruitment

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
User engagement level with intervention
Time Frame: Three months after recruitment
Assessed by the issuing number of appointment card and chatbot usage metrics, including usage frequency, interaction duration, and the number of questions asked. Data will be obtained through backend system logs.
Three months after recruitment
Usability of AI-empowered Nudge Intervention
Time Frame: Three months after recruitment
The usability of the intervention will be evaluated using a series of questions on its feasibility, acceptability, and sustainability, as well as the facilitators and barriers of its implementation. Data will be collected via semi-structured interviews.
Three months after recruitment
Intervention Cost
Time Frame: Three months after recruitment
The costs associated with both study arms obtained through work logs, including expenses for doctor manpower, chatbot development, and usage. Unit of Measure: Chinese Yuan (CNY).
Three months after recruitment

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fudan University

Collaborators

Nantong Center for Disease Control and Prevention

Investigators

  • Principal Investigator: Zhiyuan Hou, PhD, Fudan University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

May 30, 2026

Primary Completion (Estimated)

October 1, 2026

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

May 21, 2026

First Submitted That Met QC Criteria

May 21, 2026

First Posted (Actual)

May 28, 2026

Study Record Updates

Last Update Posted (Actual)

May 28, 2026

Last Update Submitted That Met QC Criteria

May 21, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Fudan-AINC

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data will not be shared due to participant privacy concerns and institutional data protection policies

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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