ASSETs for Aging in Place: Quantitative Evaluation of Technology-Supported Interprofessional Care Coordination After Nursing Home Discharge (Preprint)

BACKGROUND Older and disabled adults face ongoing barriers to aging in place after nursing home discharge, particularly following the end of time-limited transition programs. Sustained care coordination and early detection of health and functional changes remain limited, especially in rural and under-resourced communities. OBJECTIVE To examine changes in quality of life (QoL), function, and cognition over 12 months among older adults enrolled in the ASSETs for Aging in Place (AIP) program and to assess the feasibility of technology-enabled interdisciplinary care coordination. METHODS This quantitative, descriptive study used repeated measures over 12 months among older and disabled adults one-year post-discharge from Missouri’s Show-Me Home program. An interdisciplinary team (registered nurse, occupational therapist, and social worker) provided remote care coordination supported by open-source smart home sensors, consumer-grade wearable devices, and telehealth. Outcomes were assessed quarterly using the interRAI Home Care assessment and the Money Follows the Person Quality of Life survey. RESULTS Nineteen participants were included. Functional status, cognitive performance, and instrumental activities of daily living remained stable over time. Quality of life measures were consistently high across domains, with no significant declines observed. Reports of unmet medical care needs decreased across follow-up. Sensor and wearable data supported remote monitoring, early identification of changes in activity patterns, and timely care coordination. CONCLUSIONS This study demonstrates the feasibility of a digital health–enabled, interdisciplinary care coordination program using cost-feasible open-source and consumer-grade technologies. Preliminary findings suggest maintenance of function and quality of life among participants. Larger-scale implementation is needed to further evaluate effectiveness and refine data-driven care coordination workflows for underserved populations.