Designers of visual communication material want their material to attract and retain attention. In marketing research, heat maps, dwell time, and time to AOI first hit are often used as evaluation parameters. Here we present two additional measures (1) “scan path entropy” to quantify gaze guidance and (2) the “arrow plot” to visualize the average scan path. Both are based on string representations of scan paths. The latter also incorporates transition matrices and time required for 50% of the observers to first hit AOIs (T50). The new measures were tested in an eye tracking study (48 observers, 39 advertisements). Scan path entropy is a sensible measure for gaze guidance and the new visualization method reveals aspects of the average scan path and gives a better indication in what order global scanning takes place.

Our results show that increasing the viscosity is less effective than increasing the energy density in slowing gastric emptying. However, the viscosity is more important to increase the perceived fullness. These results underscore the lack of the satiating efficiency of empty calories in quickly ingested drinks such as sodas. The increase in perceived fullness that is due solely to the increased viscosity, which is a phenomenon that we refer to as phantom fullness, may be useful in lowering energy intake. This trial was registered at www.trialregister.nl as NTR4573.

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Data currently generated in the field of nutrition is becoming increasingly complex and high-dimensional, bringing with it new methods of data analysis. The characteristics of machine learning make it suitable for such analysis and thus lends itself as an alternative tool to deal with data of this nature. Machine learning has already been applied in important problem areas in nutrition such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of machine learning, which limits its application and therefore potential to solve currently open questions. Thus, the current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. Machine learning is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which machine learning is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is first outlined to guide the interested researcher in integrating machine learning into their work. By acting as a resource to which researchers can refer, we hope to support the integration of machine learning in the field of nutrition to facilitate modern research.

SummaryBackground: Fast food patronage is a frequent reality for many children and their parents. Although there