# Implementing and Scaling the STEADI Fall Prevention Algorithm Using a Conversational Relational Agent for Community-Dwelling Older Adults with and without Mild Cognitive Impairment (MCI). > **NIH NIH R44** · CARE.COACH CORPORATION · 2023 · $544,163 ## Abstract ABSTRACT Falls are a serious threat to the health and well-being of older Americans, especially those with mild cognitive impairment (MCI) and dementia due to the role cognition plays in gait. Falls are the leading cause of injury- related deaths among Americans ≥ 65 years of age (older adults), and the age-adjusted rate of deaths from falls is increasing. One in three older adults falls each year, and over 3 million are treated in hospitals for fall injuries every year. Those who fall once are two to three times more likely to fall again, and older adults with MCI/dementia fall two to three times more than cognitive healthy older adults. Effective fall prevention could substantially reduce disability, hospitalizations, and loss of independence in older adults, and improve their quality of life. The STEADI (Stopping Elderly Accidents, Deaths & Injuries) Initiative by the Centers for Disease Control and Prevention (CDC) combines fall risk screening, assessments and intervention based on the clinical practice guidelines (2010) by the American and British Geriatrics Societies. As a comprehensive, multifactorial intervention, STEADI can reduce the risk and rate of falling, but is challenging to implement using traditional dissemination models (pen and ink, in-person visits, human adherence monitoring and care coordination) that require extensive infrastructure and human resources to significantly impact the population at risk. The proposed Fast-Track SBIR by care.coach corporation (Millbrae, CA) in partnership with clinical, academic, and community partners, aims to test the feasibility and efficacy of a digital STEADI intervention including an evidence-based exercise program (Otago) to offer older adults with and without MCI strength and balance training in the comfort of their home, and remotely monitor their gait as an early clinical indicator of fall risk and decline. To deliver STEADI remotely, care.coach will adapt its already successful conversational technology platform with virtual health assistant (avatar) to screen community-dwelling older adults for fall risk and modifia- ble risk factors, and to intervene using an effective, personalized exercise regimen and coaching program that learns and adapts over time. The digital intervention is executed with the help of artificial intelligence (AI) and is overseen by a 24x7 team of human staff who manage the avatars remotely, offer companionship, and escalate as needed to care providers. The deliverable is a rigorously tested, scalable digital STEADI intervention opti- mized for ongoing fall risk monitoring at home, customized intervention and exercise, and timely care coordina- tion with health professionals. Upon completion of this work, the program will be ready for national implementa- tion and commercialization via existing and new customers. ## Key facts - **NIH application ID:** 10822816 - **Project number:** 1R44AG085913-01 - **Recipient organization:** CARE.COACH CORPORATION - **Principal Investigator:** Chantal M Kerssens - **Activity code:** R44 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $544,163 - **Award type:** 1 - **Project period:** 2023-09-26 → 2025-02-28 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10822816 ## Citation > US National Institutes of Health, RePORTER application 10822816, Implementing and Scaling the STEADI Fall Prevention Algorithm Using a Conversational Relational Agent for Community-Dwelling Older Adults with and without Mild Cognitive Impairment (MCI). (1R44AG085913-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10822816. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*