Are Machine Learning Algorithms More Accurate in Predicting Vegetable and Fruit Consumption Than Traditional Statistical Models? An Exploratory Analysis.

Machine learning (ML) algorithms may help better understand the complex interactions among factors that influence dietary choices and behaviors. The aim of this study was to explore whether ML algorithms are more accurate than traditional statistical models in predicting vegetable and fruit (VF) consumption. A large array of features (2,452 features from 525 variables) encompassing individual and environmental information related to dietary habits and food choices in a sample of 1,147 French-speaking adult men and women was used for the purpose of this study.

Artificial intelligence in nutrition research: perspectives on current and future applications.

Artificial intelligence (AI) is a rapidly evolving area that offers unparalleled opportunities of progress and applications in many healthcare fields. In this review, we provide an overview of the main and latest applications of AI in nutrition research and identify gaps to address to potentialize this emerging field. AI algorithms may help better understand and predict the complex and non-linear interactions between nutrition-related data and health outcomes, particularly when large amounts of data need to be structured and integrated, such as in metabolomics.

Changes in diet quality and food security among adults during the COVID-19-related early lockdown: results from NutriQuébec.

Background: The impact that the coronavirus disease 2019 (COVID-19)-related early lockdown has had on dietary habits of the population and on food insecurity is unknown.

Objective: The aim of this study was to document the change in diet quality and in food insecurity observed during the COVID-19-related early lockdown. We hypothesized that the lockdown was associated with a deterioration in overall diet quality and an increase in food insecurity.

A cross-sectional survey examining motivation and beliefs to participating in a web-based prospective cohort study on nutrition and health among individuals with a low socioeconomic status.

Background: Prospective cohort studies may support public health efforts in reducing health inequalities. However, individuals with a low socioeconomic status (SES) are generally underrepresented in health research. This study aimed to examine the intention and determinants of intention of individuals with a low SES towards participation in a Web-based prospective project on nutrition and health (NutriQuébec) in order to develop recruitment and retention strategies.

Beliefs Related to Participation in a Large Web-Based Prospective Survey on Diet and Health Among Individuals With a Low Socioeconomic Status: Qualitative Study.

Background: NutriQuébec is a Web-based prospective study on the relationship between diet and health as well as the impact of food-related health policies in the adult population of Québec, Canada. Recruitment and retention of individuals with a low socioeconomic status (SES) in such a study are known to be challenging, yet critical for achieving representativeness of the entire population.

Objective: This study aimed to identify the behavioral, normative, and control beliefs of individuals with a low SES regarding participation in the NutriQuébec project and to identify their preferences regarding recruitment methods.