Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study.

BACKGROUND: Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE: This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS: We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS: Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS: ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.

Participation in Cost-offset Community Supported Agriculture by Low-income Households in the U.S. is Associated with Community Characteristics and Operational Practices.

OBJECTIVE: Subsidized or cost-offset community supported agriculture (CO-CSA) connects farms directly to low-income households and can improve fruit and vegetable intake. This analysis identifies factors associated with participation in CO-CSA. DESIGN: Farm Fresh Foods for Healthy Kids (F3HK) provided a half-price, summer CO-CSA plus healthy eating classes to low-income households with children. Community characteristics (population, socio-demographics, health statistics) and CO-CSA operational practices (share sizes, pick-up sites, payment options, produce selection) are described and associations with participation levels examined. SETTING: Ten communities in New York (NY), North Carolina (NC), Vermont, and Washington states in USA. PARTICIPANTS: Caregiver-child dyads enrolled in spring 2016 or 2017. RESULTS: Residents of micropolitan communities had more education and less poverty than in small towns. The one rural location (NC2) had the fewest college graduates (10%) and most poverty (23%), and poor health statistics. Most F3HK participants were white, except in NC where 45.2% were African American. CO-CSA participation varied significantly across communities from 33% (NC2) to 89% (NY1) of weeks picked-up. Most CO-CSAs offered multiple share sizes (69.2%) and participation was higher than when not offered (76.8% vs. 57.7% of weeks); whereas 53.8% offered a community pick-up location, and participation in these communities was lower than elsewhere (64.7% vs. 78.2% of weeks). CONCLUSION: CO-CSAs should consider offering choice of share size and innovate to address potential barriers such as rural location and limited education and income among residents. Future research is needed to better understand barriers to participation, particularly among participants utilizing community pick-up locations.