PROJECT SUMMARY/ABSTRACT Innovative medical technologies can improve health and increase longevity for older adults with chronic disease and multimorbidity, yet new data are needed to promote their adoption and effective use in real-world settings. For example, nearly a quarter of all adults ≥65 years old in the US have diabetes. Technology-based approaches to diabetes management, such as continuous glucose monitoring (CGM), can improve clinical outcomes and quality of life in this age group, in addition to preventing dangerous episodes of hypoglycemia. Despite the potential benefits, CGM remains underutilized among older adults compared to younger adults. My career objective is to generate the scientific evidence needed to expand and improve the delivery of guidelines-aligned care for the expanding population of older adults with diabetes, and particularly with respect to rapidly emerging technology such as CGM. To this end, analyzing healthcare data (e.g., medical record data, insurance claims) can directly complement the existing evidence, and particularly randomized clinical trial (RCT) data, by revealing how therapies work in routine care, over longer durations of time, and in populations that are typically underrepresented in those RCTs (e.g., those ≥65 years and subgroups with multimorbidity, frailty, and cognitive impairment). Economic evaluation can further elucidate the broader implications of scaling uptake and sustained use of evidence-based, technologic therapies in older adult populations. My goal for this K01 award is thus to acquire rigorous training under interdisciplinary mentorship to develop (1) an understanding of the strengths and limitations of healthcare data sources and linkage solutions, (2) the capacity to apply advanced causal methods to analyze linked healthcare data, and (3) experience with economic evaluation. My proposal has exciting, high-impact training opportunities to address three fundamental gaps in the literature, including sparse research characterizing (1) patterns of CGM use among older adults and (2) the clinical effects of long-term use in such real-world settings, as well as a dearth of data on the (3) economic impacts of scaling CGM in this age group. I will (1) use a linked, real-world healthcare database to characterize subgroups of older adults with insulin-requiring diabetes who do and do not use CGM; (2) model the clinical effects of long-term CGM use on outcomes that are relevant for both endocrinology and geriatric medicine, and across key subgroups (e.g., ≥80 years, with cognitive impairment, multimorbidity, and recurrent hypoglycemia); and (3) evaluate the cost-effectiveness of scaling CGM use in this population. Completion of my aims will generate new evidence for CGM use in older adults that is relevant to patients, providers, and payers— a critical step to increase widespread uptake. By the end of the award, I will be positioned as a future leader in aging research with expertise to evalua...