Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review

NutriColoring: designing a doodling toolkit to support daily self-reported dietary assessment among office workers

This study was motivated by a desire to help working-age individuals gain a better understanding of their daily nutritional intakes with a new self-reported dietary assessment method because an unhealthy eating behavior increases the risks of developing chronic diseases. In this study, we present the design and evaluation of NutriColoring, a food diary that leverages doodling on sketches to report and reflect on everyday diet in the working context. Through a 2-week field study involving 18 participants, the usefulness of NutriColoring in facilitating dietary assessment was tested by making comparisons with the typical bullet diary method. Our quantitative results showed that NutriColoring provided users with improved dietary assessment experience and intrinsic motivations, with significantly low task frustration and high enjoyment. Because of the freedom and playfulness in reporting intakes at work, the interview findings showed a high acceptance of employing NutriColoring at work. This article is concluded with a set of implications for the design and development of a Doodling toolkit to support healthy eating behaviors among office workers.

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

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 nonobtrusive technology in daily life would overcome this. Here, we 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. A total of 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.