Hiraguchi, H.; Perone, P.; Toet, A.; Camps, G.; Brouwer, A.-M. Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review. Sensors2023, 23, 7757.
Hiraguchi, H.; Perone, P.; Toet, A.; Camps, G.; Brouwer, A.-M. Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review. Sensors 2023, 23, 7757.
Hiraguchi, H.; Perone, P.; Toet, A.; Camps, G.; Brouwer, A.-M. Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review. Sensors2023, 23, 7757.
Hiraguchi, H.; Perone, P.; Toet, A.; Camps, G.; Brouwer, A.-M. Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review. Sensors 2023, 23, 7757.
Abstract
To monitor adherence to diets and to design and evaluate nutritional interventions it is essential to obtain objective knowledge about eating behavior. In most research, measures of eating behavior are based on self-reporting, such as 24-h recalls, food records (food diaries), and food frequency questionnaires. Self-reporting is prone to inaccuracies due to inaccurate and subjective recall and other biases. Recording behavior using non-obtrusive technology in daily life would overcome this. We here provide an up-to-date systematic overview encompassing all (close-to) publicly or commercially available technologies to automatically record eating behavior in real-life settings. 1328 studies were screened and after applying defined inclusion and exclusion criteria, 122 studies were included for in-depth evaluation. Technologies in these studies were categorized by what type of eating behavior they measure and which type of sensor technology they use. In general, we found that relatively simple sensors are often used. Depending on the purpose, these are mainly motion sensors, microphones, weight sensors, and photo cameras. While several of these technologies are commercially available, there is still a lack of publicly available algorithms that are needed to process and interpret the resulting data. We argue that future work should focus on developing robust algorithms and validating these technologies in real-life settings. Combining technologies (e.g., prompting individuals for self-reports at sensed, opportune moments) is a promising route toward ecologically valid studies of eating behavior.
Keywords
eating; drinking; daily life; real life; sensors; technology; behavior
Subject
Social Sciences, Behavior Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
