AI-Assisted Blood Glucose Management Study

NCT ID: NCT07160985

Last Updated: 2025-09-30

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

123 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-09-07

Study Completion Date

2025-10-26

Brief Summary

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AI-driven health management tools can leverage continuous glucose monitoring (CGM), physical activity, and dietary data to provide real-time, individualized feedback, improving self-management and adherence. The X Life model integrates AI algorithms with wearable devices to dynamically adjust dietary and exercise recommendations. Preliminary user studies suggest good usability and user experience, with potential to promote positive behavior change.

This trial aims to preliminarily evaluate whether the X Life AI system combined with CGM management can improve glucose tolerance (measured by oral glucose tolerance test \[OGTT\] incremental area under the curve \[iAUC\]) in adults with prediabetes, providing effect size and protocol design reference for a future confirmatory randomized controlled trial.

Detailed Description

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Conditions

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Pre-diabetes

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

TREATMENT

Blinding Strategy

NONE

Study Groups

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X Life Lifestyle Guidance

Group Type EXPERIMENTAL

AI-based Lifestyle Management

Intervention Type OTHER

Participants will use the X Life model via smartphone/tablet for 28 days, receiving real-time, personalized dietary and exercise recommendations triggered by CGM and activity tracker data. Participants can interact with the system by uploading meal images, physical activity data, and wearable-derived metrics.

Standard Care

Group Type ACTIVE_COMPARATOR

Lifestyle Management

Intervention Type OTHER

Participants will receive guideline-based lifestyle counseling according to national prediabetes prevention guidelines, delivered via mobile terminal, without AI-generated recommendations.

Interventions

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AI-based Lifestyle Management

Participants will use the X Life model via smartphone/tablet for 28 days, receiving real-time, personalized dietary and exercise recommendations triggered by CGM and activity tracker data. Participants can interact with the system by uploading meal images, physical activity data, and wearable-derived metrics.

Intervention Type OTHER

Lifestyle Management

Participants will receive guideline-based lifestyle counseling according to national prediabetes prevention guidelines, delivered via mobile terminal, without AI-generated recommendations.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Age 18-70 years
* Prediabetes (per ADA or WHO criteria)
* Stable lifestyle and body weight (±5%) for ≥3 months
* Owns and can operate a smartphone
* Able to understand and sign informed consent

Exclusion Criteria

* Current glucose-lowering medication use
* Severe cardiovascular disease, liver/kidney dysfunction, or active malignancy
* Psychiatric or cognitive disorders affecting participation
* Planned major surgery or long-distance travel during the study
* Allergy/intolerance to CGM sensor materials
Minimum Eligible Age

18 Years

Maximum Eligible Age

70 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Li Huating

OTHER

Sponsor Role lead

Responsible Party

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Li Huating

Professor

Responsibility Role SPONSOR_INVESTIGATOR

Locations

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Shanghai Sixth People's Hospital

Shanghai, , China

Site Status RECRUITING

Countries

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China

Facility Contacts

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Huating Li, Professor

Role: primary

+86-17749716891

Other Identifiers

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2025-06

Identifier Type: -

Identifier Source: org_study_id

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