Type2diabetesNCT06596330
Integration of a Trained Language Model to Improve Glycemic Control With the My Heart Counts Smartphone App
A study testing Validation of language model prompts in increasing short-term physical activity in people with Type2diabetes, run by Stanford University.
Trial summary
Type 2 diabetes (T2D) is one of the most common and fastest growing diseases, affecting 1 in 8 adults (nearly 800 million) worldwide by 2045. Sedentary behavior and increased adiposity are major risk factors for T2D. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Type2diabetes
- What is being tested?
- Validation of language model prompts in increasing short-term physical activity
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 0 participants
- Where
- No sites listed yet
- Study dates
- Jul 2025to Jul 2029
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Individuals aged ≥18 years old, with a clinical diagnosis of T2D, able to read and understand English, and who are physically able to walk, will be included in our study
Who cannot join
- Criteria that fall outside of the inclusion criteria.
About this study
Official title: Integration of a Trained Language Model to Improve Glycemic Control Through Increased Physical Activity: a Fully Digital My Heart Counts Smartphone App Randomized Trial
Type 2 diabetes (T2D) is one of the most common and fastest growing diseases, affecting 1 in 8 adults (nearly 800 million) worldwide by 2045. Sedentary behavior and increased adiposity are major risk factors for T2D. Cardiovascular disease is the leading cause of death in those with T2D, while diabetic microvascular disease, causing kidney disease, neuropathy, and retinopathy, contributes to T2D morbidity.
Physical activity is one of the most potent therapies in preventing/treating T2D and its complications. Mean daily steps is a proxy for physical activity, with even modest improvements in step count (i.e., +500 steps) associated with decreased T2D and mortality. However, adherence to regular physical activity remains low in T2D patients, with short-term decreases in daily step count associated with impaired glycemic control and T2D recurrence.
The investigators have developed an artificial intelligence (AI) language model (similar to ChatGPT), which can automatically generate coaching prompts to encourage physical activity by incorporating an individual's stage of change. The investigators will extend our research using the My Heart Counts (MHC) smartphone app to 1) validate the efficacy of the AI-generated prompts in patients with T2D and 2) perform a longer-term randomized crossover trial using the language model as a social accountability chatbot - encouraging participants to maintain their physical activity changes over months. The investigators hypothesize that my AI-assisted coaching prompts will significantly increase 1) mean daily step count by 500 steps in 1,000 adults recruited nationwide over a 7-day period, and 2) improve HbA1c and weight via long-term behavior change over a 24-week intervention period.
- Conditions
- Type2diabetes
- Being tested
- Validation of language model prompts in increasing short-term physical activity, Assessment of long-term changes to physical activity and glycemic control
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Validation of language model prompts in increasing short-term physical activityBehavioural
Aim 1: In preliminary data, the investigators have pre-trained an open-source language model, LLAMA, with expert-created coaching prompts based on the stages of change model for physical activity. Seven different prompts (for each day of an intervention "week") will be generated, accounting for race/ethnicity, age, gender, and stage of change, to improve personalization. Using the existing MHC app, the investigators will perform a randomized crossover trial on mean daily steps across each intervention. The investigators will compare the interventions of a daily reminder to reach 10,000 steps (a neutral control) and AI-personalized interventions based on an individual's stage of change.
Assessment of long-term changes to physical activity and glycemic controlBehavioural
Aim 2: Using social accountability and the trained language model generating personalized coaching interventions, the investigators will conduct a long-term follow-up randomized, unblinded trial. Over a 24-week intervention period, participants will receive either a generic daily reminder to reach 10,000 steps or an AI-generated coaching prompt, with the AI group also being able to "chat" with the language model to ask for advice on maintaining their physical activity. The outcomes of this long-term trial will be change in: 1) daily steps over the intervention period, 2) weight (via HealthKit link to MHC), and 3) HbA1c (as derived from EMR records linked to the HIPAA-compliant MHC app).
| Study group | Role | What they receive |
|---|---|---|
| LLM-generated coaching prompt | Experimental | Not described Receives: Behavioral: Validation of language model prompts in increasing short-term physical activity; Behavioral: Assessment of long-term changes to physical activity and glycemic control |
| 10,000 Step Reminder | Active comparator | Not described Receives: Behavioral: Validation of language model prompts in increasing short-term physical activity; Behavioral: Assessment of long-term changes to physical activity and glycemic control |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mean daily steps
Mean daily steps over the course of an intervention week (aim 1) and 24 week period (aim 2).
Measured: 7 days and 24 weeks
Secondary outcomes (3)
Change in weight
Change in weight over the long term intervention (Aim 2)
Measured: 24 Weeks
Change in HbA1c
Change in HbA1c over the long term intervention (Aim 2)
Measured: 24 Weeks
Weekly Active Minutes
Total weekly active minutes over the course of an intervention week (aim 1) and 24 week period (aim 2).
Measured: 7 days and 24 Weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study startJul 2025 (estimated)
- Primary completionJul 2029 (estimated)
- Study completionJul 2029 (estimated)
Why it stopped: Lack of resources
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Type2diabetesStudies of the same condition, open and under way.
- More trials run by Stanford 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
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Stanford University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06596330
- Sponsor's protocol ID
- 76338
- Status last verified
- Aug 2026
- First submitted
- 26 Aug 2024
- Last updated
- 20 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Integration of a Trained Language Model to Improve Glycemic Control Through Increased Physical Activity: a Fully Digital My Heart Counts Smartphone App Randomized Trial
- ClinicalTrials.gov ID
- NCT06596330
- Acronym / sponsor ID
- 76338
- Conditions
- Type2diabetes
- Study type
- Interventional
- Recruitment status
- WithdrawnReason stopped: Lack of resources
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Stanford UniversityUniversity, hospital or other
- Study start
- Jul 2025 (estimated)
- Primary completion
- Jul 2029 (estimated)
- Study completion
- Jul 2029 (estimated)
- First posted
- 19 Sep 2024 (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.
Type 2 diabetes (T2D) is one of the most common and fastest growing diseases, affecting 1 in 8 adults (nearly 800 million) worldwide by 2045. Sedentary behavior and increased adiposity are major risk factors for T2D. Cardiovascular disease is the leading cause of death in those with T2D, while diabetic microvascular disease, causing kidney disease, neuropathy, and retinopathy, contributes to T2D morbidity.
Physical activity is one of the most potent therapies in preventing/treating T2D and its complications. Mean daily steps is a proxy for physical activity, with even modest improvements in step count (i.e., +500 steps) associated with decreased T2D and mortality. However, adherence to regular physical activity remains low in T2D patients, with short-term decreases in daily step count associated with impaired glycemic control and T2D recurrence.
The investigators have developed an artificial intelligence (AI) language model (similar to ChatGPT), which can automatically generate coaching prompts to encourage physical activity by incorporating an individual's stage of change. The investigators will extend our research using the My Heart Counts (MHC) smartphone app to 1) validate the efficacy of the AI-generated prompts in patients with T2D and 2) perform a longer-term randomized crossover trial using the language model as a social accountability chatbot - encouraging participants to maintain their physical activity changes over months. The investigators hypothesize that my AI-assisted coaching prompts will significantly increase 1) mean daily step count by 500 steps in 1,000 adults recruited nationwide over a 7-day period, and 2) improve HbA1c and weight via long-term behavior change over a 24-week intervention period.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| LLM-generated coaching prompt | Experimental | Behavioral: Validation of language model prompts in increasing short-term physical activity, Behavioral: Assessment of long-term changes to physical activity and glycemic control |
| 10,000 Step Reminder | Active comparator | Behavioral: Validation of language model prompts in increasing short-term physical activity, Behavioral: Assessment of long-term changes to physical activity and glycemic control |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Individuals aged ≥18 years old, with a clinical diagnosis of T2D, able to read and understand English, and who are physically able to walk, will be included in our study
Key exclusion criteria (1 listed)
- Criteria that fall outside of the inclusion criteria.
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 |
|---|---|---|
| Validation of language model prompts in increasing short-term physical activityAim 1: In preliminary data, the investigators have pre-trained an open-source language model, LLAMA, with expert-created coaching prompts based on the stages of change model for physical activity. Seven different prompts (for each day of an intervention "week") will be generated, accounting for race/ethnicity, age, gender, and stage of change, to improve personalization. Using the existing MHC app, the investigators will perform a randomized crossover trial on mean daily steps across each intervention. The investigators will compare the interventions of a daily reminder to reach 10,000 steps (a neutral control) and AI-personalized interventions based on an individual's stage of change. | Behavioural | 10,000 Step Reminder, LLM-generated coaching prompt |
| Assessment of long-term changes to physical activity and glycemic controlAim 2: Using social accountability and the trained language model generating personalized coaching interventions, the investigators will conduct a long-term follow-up randomized, unblinded trial. Over a 24-week intervention period, participants will receive either a generic daily reminder to reach 10,000 steps or an AI-generated coaching prompt, with the AI group also being able to "chat" with the language model to ask for advice on maintaining their physical activity. The outcomes of this long-term trial will be change in: 1) daily steps over the intervention period, 2) weight (via HealthKit link to MHC), and 3) HbA1c (as derived from EMR records linked to the HIPAA-compliant MHC app). | Behavioural | 10,000 Step Reminder, LLM-generated coaching prompt |
Primary outcome
Mean daily steps
Time frame: 7 days and 24 weeks
Mean daily steps over the course of an intervention week (aim 1) and 24 week period (aim 2).
3 secondary outcomes
Change in weight
Time frame: 24 Weeks
Change in HbA1c
Time frame: 24 Weeks
Weekly Active Minutes
Time frame: 7 days and 24 Weeks
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Aug 2026.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing Validation of language model prompts in increasing short-term physical activity in people with Type2diabetes, run by Stanford 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 1 inclusion and 1 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: withdrawn, as of the sponsor's last update on 20 Aug 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?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mean daily steps (time frame: 7 days and 24 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 20 Aug 2026.