Combining contingency management with continuous glucose monitoring to support type 2 diabetes prevention and care

Abstract

Diabetes prevention and care require recurring self-management behaviors whose benefits are often delayed and uncertain. Digital contingency management (CM), which arranges incentives contingent on objective evidence of behavior or outcomes, offers a scalable way to strengthen these behaviors. This commentary argues that continuous glucose monitoring (CGM) can transform CM for diabetes by enabling frequent, remote reinforcement of clinically meaningful proximal targets, including sensor wear, time in range, and other glucose-derived metrics. Emerging artificial intelligence tools may further personalize treatment during risk periods and tailor reinforcement schedules. Although cost-effectiveness data in diabetes are needed, the high clinical and economic burden of diabetes provides a strong rationale for evaluating CGM-linked CM as a scalable behavioral intervention. We discuss previous CM research, implementation readiness, and future research priorities.