Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review

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.