Definition of gaps and needs on quality and availability of data to answer the relevant questions on determinants of food purchase: D5.5

The aim of deliverable 5.5 was to define gaps and needs and identify potentials and limitations with the tools collected in the WP 5 inventory. The data collection process of the purchase tools were investigated and covered by considering what/who/why/how the toolsmet the food purchase purpose, the method/s used for data collection, contextual influences on the purchase and intentional or actual eating behaviour were also covered in the investigation. The type of data which we found interesting from a RICHFIELDS perspective, potentiallyexplaining consumer purchase behaviour was 1) the purpose of the tool (i.e. the user´s motivation for using the app), 2) the purchase method “how was purchased”, 3) the product characteristics “what unit”, 4) “how much” and 5) possible contextual influences on users’ purchase behaviour. These data have the potential to be used for key research questions (i.e., What/Who/Why/How).Our result shows that the purchase apps in our inventory are a heterogeneous sample of mobile apps supporting the users in different phases of the purchase process. And apps in the same category do not even generate the same kind of data. Generated data can also be intentional purchase data, or actual, or both intentional and actual. This result makes it very difficult to draw any conclusion and characterise a typical app in each of the four categories(i.e. the four purposes). However, an integration of food purchase data with relevant contextual generated data has the potential to give a more reliable picture of consumer purchase and eating behaviour. And moreover, purchase data together with preparation and consumption generated data have a potential to give a more complete picture of consumer behaviour since food activities are complex and is influenced by many factors. Some identified limitations are that the availability of publicly accessible data about the collected tools is limited. There is a lack of documentation about the procedures for data access and insufficient information about the technical infrastructure for data access. The limitations about e.g. the tools´ documentation of options and methods for accessing and extracting data, technical infrastructure for data assess as well as what format the generated data has are connected to large challenges in the continuation of the RICHFIELDS project. The last and final phase [phase 3] of the project aims to design the research infrastructure and its governance, intellectual property rights and ethical aspects. Specific information regarding access strategy, scientific case, business model, governance and ethics are thereby crucial factors for the platform

Inventory of types of consumer data and data collection methodologies for consumer-generated food consumption data: D7.1

The overall aim of RICHFIELDS is to design a research infrastructure for the collection, integration, processing and sharing of consumer generated data as related to food behavior and associated lifestyle activities. An important part of the RICHFIELDS design will center on the evaluation of the scientific, technical, legal and ethical aspects related to integration and governance of consumer-generated data on food behavior. The tasks related to Deliverables5.1 to 7.1 are to implement the provided quality framework and operationalization of Deliverables 5.3-7.3 and to collect the necessary data for the creation of an inventory of data and data collection tools. The aim of the inventory is to provide a list of data collection tools which is representative for the variety of tools used by and accessible to the general public, the methodologies they implement, the health and lifestyle parameters they collect and integrate. The tools and data collected in this inventory provide the basis for the identification of possible scientific, legal, technical and ethical gaps and needs regarding the use and integration of the consumer generated food behavior data and to capture developments to improve or simplify current practices in the collection and integration of food consumption data. The Deliverables 5.1-7.1 share a common framework and tool for data collection, but the tools and scientific data collected for the inventory are specific for the domains purchase (D5.1), preparation (D6.1) and consumption (D7.1)). Also, domain specific search strategies for the generation of their respective part of the inventory have been applied. The present report is based on the inventory of tools related to food consumption and lifestyle data (Deliverable 7.1). The result of this deliverable are 1) the inventory in the form of a database of food consumption tools and methodologies (mainly smart phone apps) including the associated quality information related to the dimensions of scientific relevance, legal governance and data management, which was collected based on the quality framework and operationalizations developed and described in Deliverable 7.3, 2) a description of the methodology underlying the generation of this inventory including the tool selection and data collection process and 3) aggregations of relevant descriptive data about the tools listed in the inventory. Aggregations, analyses and evaluations of the collected information related to the quality criteria developed in Deliverable 7.3 will be part of Deliverable 7.4 and 7.5

Development of a quality evaluation framework for consumer generated domestic food preparation data: D6.3

This deliverable formulates a set of quality criteria for the evaluation of this consumergenerated food preparation data in terms of its scientific relevance and technical and legal governance. These three area were selected as indicators of quality as they allow for the assessment of data in relation to key questions relating to domestic food preparation behaviour (i.e., What/Who/Why/How and Where). This is, in addition to assessing the legal limitations, organizational restrictions, confidentiality and privacy concerns related to collection, integration and dissemination of consumer-generated data and the technical protocols and standards for data access and data processing. Information about these topics is crucial for developing the blueprint of a data platform, such as RICHFIELDS, as well as for its data governance structure.In addition to providing a framework for the evaluation of data quality, the result of this deliverable also provides structure and guidance for the data collection process of deliverable 6.1, which is an inventory of consumer-generated food preparation data tools. Morespecifically, this quality framework provides an operationalised definition for each quality criteria in the form of a set of relevant questions that should be answered for each tool included in the RICHFIELDS Inventory Management System (RIMS). RIMS is an online management system for the storage and assessment of tools that produce consumergenerated data on the purchase, preparation, consumption of food and/or beverages and their associated lifestyle data that could potentially be of use to social science researchers.RIMS comprises two component parts; [1] a typology of the tools stored within the inventory, and [2] a list of quality criteria against which each tool can be evaluated. The typology is a scheduled framework categorizing the food preparation tools according to defined groupings. The current typology for food preparation is a four-level model. The firstlevel is the overall domain - in this instance, domestic food preparation. The second level reflects the goal of underlying motivation of the behaviour captured by the tool. The third level reflects the specific behaviours captured by the tool and the final level is indicative of the recorded behaviour. The identified quality criteria are based on aspects of health and lifestyle specific to food consumption. Preparation behaviours are in some respect quite distinct and different from food intake, as they frequently require a degree of pre-behaviour decision making such as looking up a recipe. In this regard the current quality criteria don’t sufficiently capture ‘intended’ behaviours, only enacted behaviours. The next step for these criteria is to test them with the tools currently in RIMS. However, for these tools it will be challenging to validate them according to current criteria at the level required for the inventory presented in deliverable 6.1. As for many tools, it is not possible to respond to these the criteria, particularly with the feasibility parameters worked to in this exercise. That is to say, it is not possible to easily identify certain aspects of a tool’s quality without either expert knowledge of the fields of ICT and Law, and without the downloading and the downloading and testing of a tool, the examination of a tool’s data structure and/or the examination of a hosting data infrastructure. This is therefore a potentially time consuming and costly process to validate the quality of consumer-generated data produced via a tool

Development of a quality evaluation framework for consumer generated food purchase data: D5.3

The overall aim of RICHFIELDS is to design a Research Infrastructure (RI) and data platform for the collection, integration, processing and sharing of consumer generated data related to food intake activities. In order for the data to be valuable to users of RICHFIELDS it is essential that factors influencing the quality of this data are identified and thereby visualize the potential opportunities, as well as the gaps and needs, with the data as part of the collection, integration and dissemination process.A set of quality criteria was formulated for the evaluation and inventory framework of the consumer generated food intake activities, within the areas of scientific relevance and technical and legal governance. Furthermore, the result of this deliverable should also provide structure and guidance for the data collection and inventory of consumer generated food purchase tools (task 5.1).A literature search has been conducted and existing quality frameworks of eHealth and mHealth applications have been summarized in order to create the quality framework. Quality criteria from that overview were selected based on the significance for the quality dimensions, data management and legal governance. To evaluate the relevance of the selection of quality criteria, experts in the relevant fields of Law and ICT were contacted. Based on the experts’ opinions the selection of quality criteria was adjusted. The work also continued parallel to the actual inventory (task 5.1), adding variables/inputs to the criteria alongside increased knowledge about different tool types and what consumer generated purchase data they potentially generated. However, existing quality frameworks are rather general in nature with respect to scientific relevance and do not focus on specific scientific fields such as those relevant to RICHFIELDS. Thus, it was necessary for the assessment of quality within RICHFIELDS to create a unique set of criteria. The selected quality criteria are thought to be relevant and comprehensive across the needs and requirements of the various disciplines involved in designing the blueprint of the RI and data platform

Inventory of types of purchase data and data collection methodologies for consumer-generated food purchase data: D5.1

The overall aim of Phase 1 within the RICHFIELDS project is to design a Research Infrastructure (RI) for the collection, integration, processing and sharing of consumergenerated data as related to food intake activities and thereby including food behaviour and lifestyle determinants. The Deliverables 5.1, 6.1 and 7.1 share a common framework and tool for the data collection method, where the labels for scientific data collected in the inventory are specific for the domains purchase (D5.1), preparation (D6.1) and consumption (D7.1). 5.1 made an inventory of available mobile applications (apps) for consumergenerated purchase data based on the quality framework developed in task 5.3. The inventory provides a list of available consumer purchase apps with data collection methods that generate data on consumer food intake activities in relation to key questions relating to food purchase behaviour (i.e., What/Who/Why/How/Where). The inventory was made in Mobile application stores; ITunes and Google Play, and by using search engines Google and fnd.io. In addition, apps for inclusion were found in reference lists of searched articles, links found on the internet, etc. Fifty-four mobile applications were identified for inclusion into the RICHFIELDS Inventory Management System (RIMS), an online management system created in response to Task 5.1, 6.1 and 7.1. These apps were assessed in terms of their descriptive, scientific, legal and technical characteristics. This report contains an outline of the methodology used for the identification of the apps and a discussion of the application of the quality criteria. Aggregations, analyses and evaluations of the collected information related to the quality criteria developed in Deliverable 5.3 will be part of Deliverable 5.4 and 5.5

Sustainable food choice motives : The development and cross-country validation of the Sustainable Food Choice Questionnaire (SUS-FCQ)

In view of all kinds of sustainability concerns related to our current diet, it is essential to gain a good understanding of the sustainability motives consumers have for selecting their food. A comprehensive and validated scale to measure sustainability motives within the full range of food choice motives could contribute to this understanding, especially as sustainability is a multi-faceted concept in which the different aspects can sometimes be conflicting. The current paper aims to 1) develop the Sustainable Food Choice Questionnaire (SUS-FCQ) that covers the full concept of sustainability, 2) test which dimensions of sustainable food choice motives can be distinguished and 3) validate the scale as part of the Food Choice Questionnaire in multiple countries. An online survey was completed by 5,116 respondents from five European countries (The Netherlands, Denmark, Czech Republic, France and Italy). The scale was developed with a Dutch sub-sample and validated in all included countries. Exploratory factor analysis followed by confirmatory factor analyses resulted in a two-factor solution. A ‘general sustainability’ dimension (6 items, covering environmental, ethical and animal welfare aspects) and a ‘local & seasonal’ dimension (3 items) were identified. The Sustainable Food Choice Questionnaire shows to be reliable and valid in the five included countries and can be used as an addition to the Food Choice Questionnaire developed by Steptoe and colleagues (1995). The scale is suitable to gain a better understanding of the position of sustainability motives against other motives in consumers food choices and can be used for country comparisons.</p

Development of a quality evaluation framework for consumer generated food consumption data: D7.3

RICHFIELDS is a design project of a research infrastructure (RI) and data platform which aims to collect, integrate, analyse and share food consumption and associated lifestyle data for the better understanding about what people eat and why they make their choices. One important source of data RICHFIELDS is focusing on is data generated by a vast amount of consumers and users of wearables and software applications, which are accessible to the general public. For the design phase of this RI it is crucial to provide an overview and characterization (Deliverable 7.1) and an evaluation (Deliverable 7.5) of consumer generated food consumption and associated lifestyle data. For the creation of these deliverables the current deliverable (Deliverable 7.3) plays a key role. Aim of this deliverable is to define a set of quality criteria which forms the basis of the evaluation of consumer generated food consumption and lifestyle data and which supports the identification of relevant opportunities as well as possible gaps and needs regarding data integration and sharing. In addition, the quality framework created in this deliverable should provide structure and guidance for data collection and characterization, which is needed for the inventory of consumer generated food consumption and lifestyle data collection tools. More specifically the framework will provide operationalisations for each quality criterion in the form of a set of relevant questions that should be answered for each tool included in the inventory of deliverable 7.1. Based on the needs of Phase 3 as they have been identified in the DOA, a quality assessment framework has been created for the evaluation of data in terms of scientific relevance, data management and legal governance. Data quality related to these three dimensions were considered important, because they can provide indications about: 1) what we can learn from such consumer generated data about peoples’ food consumption behavior, 2) the legal limitations, organizational restrictions, confidentiality and privacy concerns related to collection, integration and dissemination of consumer generated data and 3) the technical protocols and standards for data access and data processing. A literature search has been conducted and existing quality frameworks of eHealth and mHealth applications have been summarized. Quality criteria related to the dimensions of scientific relevance, data management and legal governance where characterized and based on expert opinions and overall feasibility included in the final quality assessment framework. Since the summarized existing quality frameworks were found to be too unspecific regarding scientific relevance of food consumption and lifestyledata we additionally relied on the current literature on dietary intake assessments and the determinants of food consumption behaviour for the creation of the quality criteria related to the dimension of scientific relevance. Since a large number of quality criteria have beenexcluded from the quality framework the scope of quality assessment by the current framework will be limited, however, we believe that the selected quality criteria are relevant and comprehensive across the needs and requirements of the various disciplines involved in designing the blueprint of the RI and data platform