A Review of Behavioral Closed-Loop Paradigm from Sensing to Intervention for Ingestion Health

Ingestive behavior plays a critical role in health, yet many existing interventions remain limited to static guidance or manual self-tracking. With the increasing integration of sensors, context-aware computing, and perceptual computing, recent systems have begun to support closed-loop interventions that dynamically sense user behavior and provide feedback during or around ingestion episodes. In this survey, we review 136 studies that leverage sensor-enabled or interaction-mediated approaches to influence ingestive behavior. We propose a behavioral closed-loop paradigm rooted in context-aware computing and inspired by HCI behavior change frameworks, comprising four components: target behaviors, sensing modalities, reasoning and intervention strategies. A taxonomy of sensing and intervention modalities is presented, organized along human- and environment-based dimensions. Our analysis also examines evaluation methods and design trends across different modality-behavior pairings. This review reveals prevailing patterns and critical gaps, offering design insights for future adaptive and context-aware ingestion health interventions.