Description:

This presentation will describe the application of metabolomics to the identification of robust biomarkers of food intake (BFIs) and the development of resources (databases, kits, software) that should allow the application of BFIs toward precision nutrition.

Learning Objectives:

1. Attendees will learn about the basic principles of metabolomics and a typical metabolomics workflow 
2. Attendees will learn about different classes of food biomarkers and the consensus requirements for biomarkers to be useful in assessing food intake (the BFI definition)
3. Attendees will learn about a number of new and useful BFIs and about data resources that contain detailed molecular information about food constituents, food metabolites and food biomarkers
4. Attendees will learn about a variety of low-cost kits that have been developed or are being developed to assess specific food intake in a variety of matrices

Description:

This presentation will describe the development and validation steps of novel indicators of dietary intake and status with the use of targeted metabolomics, in the example of riboflavin (vitamin B2).

Learning Objectives:

1. To describe key steps in the development and validation of nutritional biomarkers for the assessment of dietary intake.
2. To explain how biomarker characteristics and biospecimen processing techniques  impact the applicability and validity of biomarkers as reliable indicators.
3. To discuss strategies for the implementation of international harmonization strategies in the quantitation and interpretation of nutritional biomarkers

Description:

Dietary assessment is crucial in human nutrition research. Findings based on dietary self-reported data are often criticized due to measurement error. Current methods used are either burdensome to participants or administrator leading to poor compliance, incomplete data and/or high costs. To palliate these limitations, the objective was to develop a mobile application (Keenoa©) for patients and participants to capture pictures of their meals and specify dietary intake, connected to their dietitian or researcher in real-time. Artificial intelligence (AI) algorithms of food recognition, a restructured Canadian Nutrient File and dynamic portion size pickers were integrated to minimize burden on the participant; an average of 5 minutes is required to complete one diary day. The >15,000 users of the Keenoa mobile app are aged between ~10-75 y suggesting it may be adapted to different age groups. Validation of the Keenoa application is currently under study.  

Learning Objectives:

1. To uncover the challenges related to the development of an AI-enhanced mobile app aimed at tracking diet. 
2. To illustrate the use of a novel AI-enhanced mobile app for dietary assessment in various settings including dietetics research, private practice and clinical. 
3. To demonstrate the potential of technology in addressing limitations of self-reported dietary assessment methods. 

Description:

Food labelling (e.g. the back-of-pack Nutrition Facts table (NFt)) is a common intervention to improve diets but many consumers find it difficult and time-consuming to use. It is essential that consumers have the tools to allow for easily accessible nutrition information. The objective was to develop a smartphone app with interpretative nutrition rating systems (INRS), FoodFlip©, to address these limitations. The FoodFlip© app contains healthfulness ratings (as front-of-pack labels) and healthfulness comparison feature for ~18,000 packaged foods and beverages. Healthfulness ratings are based on nutrient profiling scores for three INRS: traffic light, ‘high-in’ warning and health star ratings symbols.

Learning Objectives:

1. To illustrate the underlying mechanisms, novel evidence and specific components of nutrition education smartphone apps on facilitating behaviour change 
2. To illustrate the use of a smartphone app in assessing the impact of Front-of-Pack Labels (traffic light, health star rating and ‘high in’ labels) on consumers’ food choices and perceptions of healthfulness in comparison to the current food labelling (i.e. Nutrition Facts Table) 
3. To facilitate a discussion on effectiveness of the food and nutrition smartphone applications in promoting healthy dietary decision making among consumers/individuals 
4. To demonstrate the potential of smartphone apps in encouraging healthier decision making in real-time and address nutrition-related public health policies aimed at creating supportive food environments  

Description:

There is increasing recognition of the need to account for the complexity of dietary patterns rather than focusing on particular foods and beverages. Indeed, it is likely the combinations of foods and beverages we consume rather than specific nutrients or foods that influence our long-term health. Canada's new Food Guide, released in January 2019, uses a plate to depict a pattern of eating. This approach is consistent with international trends toward a focus on proportionality and recognizes the complexity of eating patterns, while simplifying guidance to the public by eliminating prescriptive food group targets. Potential strategies for examining the alignment of dietary patterns with the plate and associated guidelines will be examined, drawing from experience with the US Healthy Eating Index and other approaches to characterize the multidimensionality and dynamism of dietary patterns. Considerations regarding the types of dietary intake data needed to support assessment of alignment with dietary guidance will also be discussed.

Learning Objectives:

1. Describe the motivation for focusing on dietary patterns rather than isolated dietary constituents, such as nutrients or food groups.
2. Discuss possible approaches to assessing dietary patterns, including their alignment with dietary guidelines.

Description:

Assessing adherence to recommendations in the latest edition of CFG (2019) requires crafting a new tool made for that purpose. This task comes with specific challenges including ascribing foods into appropriate components and determining valid scoring standards, while being fully consistent with CFG. This talk aims at presenting these challenges and considerations along with specific examples.

Learning Objectives:

1. To learn about challenges related to measuring adherence to recommendations in CFG 2019
2. To learn about challenges related to ascribing foods into appropriate components
3. To learn about challenges related to the determination of scoring standards
4.  To understand the importance of considering the recommendations found in CFG 2019 as the basis of decision-making

Description:

For the first time, the 2019 Canada’s Food Guide not only provided guidance on what Canadians should eat, but also how we should eat. The 2019 Food Guide recommends that we eat meals with others, enjoy our food, cook more often, and be mindful of what we eat. This presentation will discuss the challenges associated with measuring Canadians’ eating habits and identify potential assessment tools that could inform metrics to assess compliance to Canada’s Food Guide recommendations.  

Learning Objectives: 

1.  Identify key challenges associated with assessing eating habits; 
2.  Identify potential tools to assess eating habits among Canadians.