A Review of Behavioral Closed-Loop Paradigm from Sensing to Intervention for Ingestion Health
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912524347113472 |
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| author | Fang, Jun Zhou, Yanuo Chan, Ka I Li, Jiajin Sun, Zeyi Li, Zhengnan Fu, Zicong Piao, Hongjing Xu, Haodong Shi, Yuanchun Wang, Yuntao |
| author_facet | Fang, Jun Zhou, Yanuo Chan, Ka I Li, Jiajin Sun, Zeyi Li, Zhengnan Fu, Zicong Piao, Hongjing Xu, Haodong Shi, Yuanchun Wang, Yuntao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Review of Behavioral Closed-Loop Paradigm from Sensing to Intervention for Ingestion Health Fang, Jun Zhou, Yanuo Chan, Ka I Li, Jiajin Sun, Zeyi Li, Zhengnan Fu, Zicong Piao, Hongjing Xu, Haodong Shi, Yuanchun Wang, Yuntao Human-Computer Interaction 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. |
| title | A Review of Behavioral Closed-Loop Paradigm from Sensing to Intervention for Ingestion Health |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.03185 |