# Using artificially intelligent text messaging technology to improve American Heart Association’s Life’s Simple 7 Health Behaviors: LS7 Bot + Backup > **NIH NIH UH3** · KAISER FOUNDATION RESEARCH INSTITUTE · 2024 · $1,137,968 ## Abstract PROJECT SUMMARY/ABSTRACT Our goal is to improve control of cardiovascular (CV) disease risk factors using a multilevel intervention leveraging cellphone-based text messages integrated within health systems to improve control of American Heart Association’s Life’s Simple 7 (LS7) lifestyle factors (blood glucose, cholesterol, blood pressure, physical activity, weight, diet, and smoking). When uncontrolled, these lifestyle factors lead to common co-existing chronic conditions (e.g., hypertension, diabetes), morbidity, health care costs and death. Patients who experience health disparities (i.e., ethnic minorities, those with limited English proficiency and those with low- income) are disproportionately affected by CV diseases, have worse disease control and suffer greater sequelae. Self-management is an individual’s role in managing chronic disease and has strong evidence of benefit. It includes self-care, lifestyle changes (e.g., increase physical activity), taking medications as prescribed and managing exacerbations of chronic condition(s). Text messaging interventions have improved health behaviors including physical activity and medication adherence. Incorporating behavioral “nudges,” defined as a small change in choice architecture that “alters people’s behavior in a predictable way” into text messages may further augment its impact. Behavioral nudges are more personalized, resonate better with patients, and have changed health behaviors. However, text message interventions have typically not been delivered to large samples, focused on patients experiencing health disparities, nor leveraged health system electronic health record (EHR) data to personalize content and maximize scale, reach and impact of messages. Using a patient-level randomized pragmatic trial, we will test the comparative effectiveness of 3 text messaging delivery strategies: 1) generic text messages; 2) interactive AI chatbot text messaging leveraging evidenced- based communication strategies with attention to patient context and sociocultural factors influencing self- management; or 3) interactive AI chatbot text messaging plus proactive pharmacist management. We plan to enroll 6,000 patients from clinics within 3 health systems that care for large populations experiencing health disparities: 1) Denver Health and Hospital Authority, 2), Salud Family Health Centers and 3) STRIDE Community Health Center. We will use health system EHR data to identify eligible patients, deliver the intervention, and assess patient-centered outcomes. The study findings will provide evidence regarding the best population-based strategy for universal delivery to engage all patients with health disparities in self- management to improve the AHA’s LS7. The intervention will be delivered in real world settings to augment routine clinical care and improve access to care. We will incorporate lessons learned from one health system into adaptations for the other health systems in the study. ## Key facts - **NIH application ID:** 11239289 - **Project number:** 7UH3HL168504-03 - **Recipient organization:** KAISER FOUNDATION RESEARCH INSTITUTE - **Principal Investigator:** P. MICHAEL HO - **Activity code:** UH3 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $1,137,968 - **Award type:** 7 - **Project period:** 2023-06-01 → 2028-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/11239289 ## Citation > US National Institutes of Health, RePORTER application 11239289, Using artificially intelligent text messaging technology to improve American Heart Association’s Life’s Simple 7 Health Behaviors: LS7 Bot + Backup (7UH3HL168504-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11239289. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*