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.

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.

Background: Stomach fullness is a determinant of satiety. Although both the viscosity and energy content have been shown to delay gastric emptying, th

Background. More people than ever seek nutrition information from online sources. The chatbot ChatGPT has seen staggering popularity since its inception and may become a resource for information in nutrition. However, the adequacy of ChatGPT to answer questions in the field of nutrition has not been investigated. Thus, the aim of this research was to investigate the competency of ChatGPT in answering common nutrition questions. Methods. Dieticians were asked to provide their most commonly asked nutrition questions and their own answers to them. We then asked the same questions to ChatGPT and sent both sets of answers to other dieticians (N=18) or nutritionists and experts in the domain of each question (N=9) to be graded based on scientific correctness, actionability, and comprehensibility. The grades were also averaged to give an overall score, and group means of the answers to each question were compared using permutation tests. Results. The overall grades for ChatGPT were higher than those from the dieticians for the overall scores in five of the eight questions we received. ChatGPT also had higher grades on five occasions for scientific correctness, four for actionability, and five for comprehensibility. In contrast, none of the answers from the dieticians had a higher average score than ChatGPT for any of the questions, both overall and for each of the grading components. Conclusions. Our results suggest that ChatGPT can be used to answer nutrition questions that are frequently asked to dieticians and provide encouraging support for the role of chatbots in offering nutrition support.

Current methods to detect eating behavior events (i.e., bites, chews, and swallows) lack objective measurements, standard procedures, and automation. The video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we reviewed the current methods to automatically detect eating behavior events from video recordings. According to PRISMA guidelines, publications from 2010–2021 in PubMed, Scopus, ScienceDirect, and Google Scholar were screened through title and abstract, leading to the identification of 277 publications. We screened the full text of 52 publications and included 13 for analysis. We classified the methods in five distinct categories based on their similarities and analyzed their accuracy. Facial landmarks can count bites, chews, and food liking automatically (accuracy: 90%, 60%, 25%). Deep neural networks can detect bites and gesture intake (accuracy: 91%, 86%). The active appearance model can detect chewing (accuracy: 93%), and optical flow can count chews (accuracy: 88%). Video fluoroscopy can track swallows but is currently not suitable beyond clinical settings. The optimal method for automated counts of bites and chews is facial landmarks, although further improvements are required. Future methods should accurately predict bites, chews, and swallows using inexpensive hardware and limited computational capacity. Automatic eating behavior analysis will allow the study of eating behavior and real-time interventions to promote healthy eating behaviors.

Unhealthy dietary habits are considered as the primary cause of various chronic diseases, including obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoL) of people with diet-related diseases through dietary assessment. In this work, we propose a novel contactless radar-based approach for food intake monitoring. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movements from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network with self-attention (3D-TCN-Att) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions (more realistic scenarios). We create a public dataset comprising 70 meal sessions (4,132 eating gestures and 893 drinking gestures) from 70 participants with a total duration of 1,155 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, seven-fold cross-validation method is applied. The 3D-TCN-Att model achieves a segmental F1-score of 0.896 and 0.868 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.