Optimizing Continuous Glucose Monitoring for Older Adults with Diabetes: From Clinical Evidence to a Practical...

Older adults represent a growing proportion of people with diabetes and often experience hypoglycemia, glycemic variability (GV), multimorbidity, frailty, and treatment-related vulnerabilities that are not fully captured by glycated hemoglobin A1c (HbA1c) alone. Continuous glucose monitoring (CGM) provides time-resolved glucose profiles, trend information, alerts, and remote data sharing, making it well suited to safety-focused geriatric diabetes care.In this narrative review, we synthesize evidence on CGM features, randomized and real-world outcomes, patient and caregiver experience, barriers to sustained use, and practical implementation in older adults. Across studies, the principal benefits of CGM vary by diabetes type and treatment context. In older adults with type 1 diabetes, randomized evidence most consistently supports reduced hypoglycemia exposure and time below range, whereas in basal-insulin-treated type 2 diabetes, CGM primarily improves time in range and reduces hyperglycemia. CGM may also support individualized treatment adjustment by revealing nocturnal hypoglycemia, GV, and postprandial patterns that are missed by intermittent testing.Real-world studies associate CGM use with fewer acute diabetes events and hospitalizations in selected insulin-treated populations, but residual confounding limits causal interpretation. Implementation evidence shows that benefit depends on more than device performance. Usability, skin tolerability, education, alert burden, caregiver workflows, digital literacy, cost, and equitable access strongly influence sustained use.We propose a pragmatic geriatric CGM pathway with an explicit triage algorithm that emphasizes risk-based candidate selection, functional assessment, tailored device and support models, individualized alerts and glycemic goals, focused review of CGM metrics, structured education, and early follow-up. Current evidence supports CGM primarily as a safety and decision-support technology for selected older adults, particularly those using insulin or at elevated hypoglycemia risk. Future studies should include frail and cognitively impaired populations and prioritize severe hypoglycemia, falls, treatment burden, caregiver burden, acute care use, quality of life, and scalable delivery models.