Diabetes Mellitus, Type 2, Prediabetes, Prediabetes / Type 2 Diabetes
Conditions
Keywords
PIPE-AI, DiabetesGPT, Prediabetes, Type 2 diabetes, Diabetes self-management, Patient empowerment, Digital health, Artificial intelligence, Continuous glucose monitoring, Primary healthcare, Hong Kong, Pilot randomized controlled trial
Brief summary
This pilot randomized controlled trial evaluates the feasibility, effectiveness, and acceptance of the Personalized Interactive Patient Empowerment Artificial Intelligence Platform (PIPE-AI) enhanced by DiabetesGPT among adults with prediabetes or diabetes in Hong Kong primary healthcare settings. Participants will be randomized in a 1:1 ratio to an intervention group receiving PIPE-AI or to a waitlist control group receiving usual care during the first 3 months. Both groups will complete baseline and 3-month assessments, including patient-reported outcome measures, clinical outcomes, healthcare service utilization, and intermittent continuous glucose monitoring using Abbott FreeStyle Libre 2 Plus sensors.
Detailed description
This study is a pilot randomized controlled trial designed to assess the feasibility, effectiveness, and acceptance of the PIPE-AI platform enhanced by DiabetesGPT before broader implementation in primary healthcare settings. The PIPE-AI platform integrates individualized risk assessments and a locally fine-tuned large language model, DiabetesGPT, to generate personalized health advice and patient empowerment support for people with prediabetes or diabetes. Approximately 50 participants will be recruited and randomized in a 1:1 ratio to the intervention group or waitlist control group. Eligibility will be assessed by nurses and doctors in participating primary healthcare settings. After written informed consent, trained research assistants will conduct baseline assessments, including demographic and socioeconomic data, lifestyle behaviours, medical history, current medications, patient-reported outcome measures, and venous blood sampling for fasting glucose and HbA1c. Randomization will be performed before recruitment by a statistician using R software. After baseline assessment, participants will receive an opaque letter containing their assigned group and follow-up instructions. Research assistants involved in subject recruitment and baseline assessment will be blinded to grouping to reduce measurement bias where operationally feasible. Participants in the intervention group will install the PIPE-AI app and receive instructions on how to use it. An individualized patient empowerment programme will be provided during the 3-month follow-up period. App login frequency will be monitored on the server, and reminders may be sent by SMS, WhatsApp, or WeChat if a participant does not log in within one week after recruitment or stops logging in for more than one month. Participants in the control group will follow a waitlist approach. During the first 3 months, they will receive usual care, including group-based patient empowerment programmes where available through the District Health Centre, general guidance from the Hong Kong Reference Framework for Diabetes Care for Adults in Primary Care Settings, or routine diabetes education and management services in their respective clinics. After the 3-month follow-up assessment, control group participants will be given access to the app. All RCT participants in both groups will use Abbott FreeStyle Libre 2 Plus continuous glucose monitoring sensors intermittently during the 3-month trial. After successful enrolment and baseline assessments, participants receive two CGM sensors for use during Weeks 1-4. If CGM data are successfully collected in the study system, the research team will contact the participant near the end of the trial to collect a third CGM sensor for use during Weeks 11-12. CGM data will be downloaded or exported for research analysis using pseudonymized study IDs. Participants in both groups will complete a 3-month follow-up assessment, including patient-reported outcome measures, clinical outcomes, healthcare service utilization, and CGM-related data where applicable. Main analysis will adopt an intention-to-treat principle, with per-protocol analysis as sensitivity analysis. As this is a pilot RCT, CGM analyses will be exploratory and will assess feasibility, adherence, completeness of CGM data collection, glycaemic profiles, and signal of change for future definitive trials.
Interventions
BEHAVIORALPIPE-AI digital patient empowerment intervention
PIPE-AI is a personalized interactive patient empowerment artificial intelligence platform enhanced by DiabetesGPT. It integrates individualized risk assessment and a locally fine-tuned large language model to provide personalized health advice, diabetes self-management support, and patient empowerment during the 3-month follow-up period.
OTHERWaitlist usual care control
Participants receive usual care during the first 3 months and are given access to the PIPE-AI app after completion of the 3-month follow-up assessment.
Sponsors
The Hong Kong Polytechnic University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SUPPORTIVE_CARE
Masking
SINGLE (Outcomes Assessor)
Masking description
Participants and intervention staff cannot be blinded because the intervention group receives access to the PIPE-AI app. Participants will be randomized in a 1:1 ratio using sequentially numbered, opaque, sealed envelopes prepared before recruitment according to a pre-generated randomization list. After eligibility confirmation, written informed consent, and baseline assessment, the next available envelope will be opened according to the study SOP to reveal the assigned group and follow-up instructions. The envelope system is used for allocation concealment before assignment and to reduce selection bias.
Intervention model description
Randomization will be performed before recruitment by a statistician. After baseline assessment, each participant will receive an opaque letter containing the assigned group and follow-up instructions. Research assistants involved in subject recruitment and baseline assessment will be blinded to group allocation to reduce measurement bias where operationally feasible. Participants cannot be blinded to PIPE-AI access.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adults aged 18 years or older. * Diagnosed with prediabetes or diabetes by their family doctors. * Able to understand Chinese. * Possess a mobile phone that can install the app developed for this project.
Exclusion criteria
* Under legal guardianship. * Comorbid schizophrenia. * Intellectual disability. * Incompetent in giving consent. * Other significant cognitive disorders.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in Diabetes Self-Management Questionnaire total score from baseline to 3 months | Baseline and 3-month follow-up | Diabetes self-management is assessed using the 16-item Diabetes Self-Management Questionnaire (DSMQ). After reverse scoring the applicable items, the DSMQ total score is transformed to a scale from 0 to 10 points; higher scores indicate better diabetes self-management. The reported outcome is the change score calculated as the 3-month total score minus the baseline total score, with a possible range from -10 to 10 points. A positive change indicates improvement. |
| Change in Michigan Diabetes Knowledge Test 2 general knowledge score from baseline to 3 months | Baseline and 3-month follow-up | Diabetes knowledge is assessed using the 14-item general knowledge section of the Michigan Diabetes Knowledge Test 2 (DKT2). Each correct answer receives 1 point and an incorrect or missing answer receives 0 points. The general knowledge score ranges from 0 to 14 points; higher scores indicate greater diabetes knowledge. The reported outcome is the change score calculated as the 3-month score minus the baseline score, with a possible range from -14 to 14 points. A positive change indicates improvement. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in HbA1c from baseline to 3 months | Baseline and 3-month follow-up | HbA1c will be tested by the study team or obtained through study procedures. The endpoint is the change in HbA1c from baseline to the 3-month follow-up, using the same unit at both time points. |
| Change in fasting glucose from baseline to 3 months | Baseline and 3-month follow-upFasting glucose will be measured by the study team or obtained through study procedures. The endpoint is the change in fasting glucose from baseline to the 3-month follow-up, using the same unit at both time points. | — |
| User satisfaction with the PIPE-AI programme | 3-month follow-up | User satisfaction with the PIPE-AI programme will be assessed at follow-up using study questionnaires among participants with access to the intervention. Scores or item responses will be summarized descriptively according to the final questionnaire scoring rule. |
Contacts
CONTACTShuya Lu
CONTACTYang Lin, Doctor
PRINCIPAL_INVESTIGATORLin Yang, Doctor
The Hong Kong Polytechnic University
Outcome results
None listed