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

TL;DR

This survey reviews 136 studies on behavioral closed-loop systems for ingestion health, highlighting sensing, reasoning, and intervention strategies, and identifying gaps and design insights for future adaptive interventions.

Contribution

It introduces a behavioral closed-loop paradigm based on context-aware computing, providing a comprehensive taxonomy and analysis of sensing and intervention modalities in ingestion health.

Findings

Identification of prevalent sensing and intervention modalities

Analysis of evaluation methods and design trends

Highlighting critical gaps and future directions

Abstract

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.