Integration of a Trained Language Model to Improve Glycemic Control Through Increased Physical Activity: a Fully Digital My Heart Counts Smartphone App Randomized Trial

Stanford University

Status: Not yet enrolling

Conditions: Type2diabetes

Treatments: Behavioral: Validation of language model prompts in increasing short-term physical activity; Behavioral: Assessment of long-term changes to physical activity and glycemic control

Study type: Interventional

Funder types: Other

Identifiers

NCT06596330

76338

Details and patient eligibility

About

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.

Enrollment: 1,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• 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

Exclusion criteria

• Criteria that fall outside of the inclusion criteria.

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Crossover Assignment

Masking: None (Open label)

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1,000 participants in 2 patient groups

LLM-generated coaching prompt

Experimental group

Treatment:

Behavioral: Assessment of long-term changes to physical activity and glycemic control

Behavioral: Validation of language model prompts in increasing short-term physical activity

10,000 Step Reminder

Active Comparator group

Treatment:

Behavioral: Assessment of long-term changes to physical activity and glycemic control

Behavioral: Validation of language model prompts in increasing short-term physical activity