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Related Concept Videos

Regulation of Food Intake01:30

Regulation of Food Intake

Short-term regulation of food intake primarily involves neural signals from the gastrointestinal (GI) tract, blood nutrient levels, and GI tract hormones. Communication between the gut and brain via vagal nerve fibers plays a significant role in evaluating the contents of the gut. Clinical studies have shown that protein ingestion produces a more prolonged response in these nerve fibers compared to an equivalent amount of glucose. Additionally, the activation of stretch receptors caused by GI...

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Related Experiment Video

Updated: Jun 9, 2026

Control of Eating Behavior Using a Novel Feedback System
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Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.

Haruka Hiraguchi1,2, Paola Perone1, Alexander Toet1

  • 1TNO Human Factors, Netherlands Organization for Applied Scientific Research, Kampweg 55, 3769 DE Soesterberg, The Netherlands.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

Objective monitoring of eating behavior is crucial for diet adherence and nutritional interventions. This review explores technologies for automatically recording eating habits in real-life settings, moving beyond self-reporting inaccuracies.

Keywords:
behaviordaily lifedrinkingeatingreal lifesensorstechnology

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Area of Science:

  • Nutrition science
  • Biomedical engineering
  • Behavioral science

Background:

  • Accurate monitoring of eating behavior is essential for diet adherence and nutritional interventions.
  • Current methods rely on self-reporting (e.g., food diaries), which are prone to inaccuracies and biases.
  • Objective, nonobtrusive recording of eating behavior in daily life is needed to overcome self-reporting limitations.

Purpose of the Study:

  • To provide a systematic overview of available technologies for automatic, real-life recording of eating behavior.
  • To categorize these technologies based on the type of eating behavior measured and sensor technology used.
  • To identify gaps and future directions in automated eating behavior monitoring.

Main Methods:

  • Systematic review of published and commercially available technologies for automatic eating behavior recording.
  • Screening of 1328 studies, with 122 included for in-depth evaluation.
  • Categorization of technologies by measured eating behavior and sensor type (motion, microphones, weight, cameras).

Main Results:

  • A wide range of technologies, often using simple sensors like motion detectors, microphones, weight sensors, and cameras, are available.
  • While many technologies are commercially available, there is a significant lack of publicly accessible algorithms for data processing and interpretation.
  • The review identified limitations in current technologies and highlighted areas for future development.

Conclusions:

  • Future research should prioritize the development of robust algorithms for processing sensor data related to eating behavior.
  • Validation of these automated technologies in real-life settings is critical for their widespread adoption.
  • Combining sensor technologies with opportune self-reporting prompts offers a promising approach for ecologically valid eating behavior studies.