45 research outputs found
Inventory of types of consumer data and data collection methodologies for consumer-generated food consumption data: D7.1
No full textThe 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
Report on the synthesis of findings for WP5-7 phase 1: D4.2
No full textIn order to support the design of the Research Infrastructure (RI) Phase 1 was tasked with exploring the range of consumer-generated data currently collected, mainly via smart phone applications and tools (APPS) in terms of the type and quality of consumer-generated data collected and consumers’ perspectives on willingness to share their data with researchers. The purpose of this deliverable, is to provide input to the final design of the proposed RICHFIELDS RI by Phase 3 and insight for the development of the wider roadmap proposal for the FNH-RI by synthesising the findings across the three domains within Phase 1 (purchase, preparation and consumption); highlighting opportunities and potential limitations for the scientific use of consumer-generated data; identifying potential opportunities/issues that are relevant for the final design of the RICHFIELDS RI/data platform (Phase 3), but which may not be covered specifically in the Phase 1 deliverables
Inventory of types of consumer-generated food preparation data and data collection methodologies: D6.1
No full textThe RICHFIELDS project aims to design a Research Infrastructure (RICHFIELDS RI) for the collection, integration, processing and sharing of consumer-generated data as it relates to food behaviour and lifestyle determinants. In order to achieve this, it is necessary to explore the range of consumer-generated data currently available, in terms of its type and quality.This document reports a scoping exercise to examine the breadth of domestic food preparation apps currently available in the marketplace that collect consumer-generated data related to food preparation and create an inventory of prototypical examples of these applications. It additionally aimed to test the feasibility of applying the Quality Criteria set outin Deliverable 6.3 for the classification of apps, and other related ICT, namely descriptive, scientific, legal and technical characteristics. To this end, a search was made of UK based retailers of mobile applications, or apps. Apps arising from this search were then classified according to a set definition of domestic food preparation and a typology of available apps was created.This report highlights the breadth of domestic food preparation apps that collect consumergenerated data currently available in the marketplace and the range of data collected by these apps. The search protocol identified a multitude of available apps. These search results were narrowed to a final list of 54 prototypical apps that represent those available in the current marketplace. These prototypical apps can be said to fulfil three main user motivations. That is, to gain ‘knowledge and understanding’, gain assistance with ‘meal preparation and cooking’, and the ‘planning and organisation’ of meals, foods and meal plans. Within the category of ‘knowledge and understanding’, the primary user behaviour was that of ‘searching for information’ and/or the ‘sharing of knowledge and experience’ with others. Many domestic food preparation apps – such as a recipe database app (e.g., Paprika Recipe manager) - provide the consumer with the ability to search for information by either within pre-determined categories (e.g., breakfast, lunch, dinner) or by entering a search term. A common feature of this category of apps is also to allow the consumer with the ability to share information with others, such as by posting to social media or emailing the information to another. The feasibility for the application of the quality criteria set out in Deliverable 6.3 was tested, and in many cases, the level of detail necessary to fulfil these criteria was not publically available. In many cases, the specificity of these quality criteria did not afford the flexibility necessary for the sufficient categorisation of these apps according to these criteria. It is recommended that these quality criteria are reviewed in line with the finding of this exercise and the classification of consumer-generated data at an app level be reconsidered.Legal and technical quality criteria fields were completed for apps where information was available. However, as stated above this information was not publically available in all cases. This raises an interesting ethical issue as regards the inclusion of apps and/or data into the RICHFIELDS RI where consumers do not have ready access to the terms and conditions of use. When the information was publically available, the terms and conditions were often difficult to interpret by researchers without a legal and technical background
Report on gaps and needs - WP6: report on the potentials and limitations for the use of user-generated domestic food preparation data to answer questions regarding determinants of nutrition and eating: D6.5
No full textThe overarching aim of RICHFIELD’s Phase 1 is to explore the available consumer-related data on food purchase, preparation and consumption, in terms of its type and quality. This Phase consists of three Workpackages(WP5-7) which study food purchase (WP5), preparation (WP6) and consumption (WP7). This report aims to identify the potential and limitation of present and future data to answer key question on the determinants of domestic food preparation. An inventory of types of domestic food preparation data and data collection methodologies was conducted. A scoping exercise was performed with the aim of identifying the range of available domestic food preparation applications (apps) that collected user-generated data. The results of this exercise were evaluated and from this, 54 prototypical examples of domestic food preparation apps were identified and classified. For 48 (89%) of the apps, the motivation for use was classified as ‘Knowledge and Understanding’ with 33 (61%) allowing the user to ‘Search for information’, and 15 (28%) for the user to ‘Share knowledge and experience’. A further 53 (98%) were classified as having the ‘Planning and organisation’ as their primary motivation for use, of these 18 (33%) allowed the user to perform ‘Recipe management’, ten (18%), to perform ‘Meal/menu planning’ and 25 (46%) to carry out ‘Documenting/recording of food’. A further 18 (33%) apps fell into the category of ‘Meal preparation and cooking’, within this classification, nine (17%) apps were classified as ‘Interacting with sensors’, and nine (17%) apps ‘classified as using apps as cooking aids.’Users’ primary motivation for using domestic food preparation apps is to develop personal food knowledge, skills and/or abilities. This opens up the potential to answer research questions relating to Individual Psychological determinants, such as food beliefs, habits and self-regulation in relation to food. However, the limited availability of contextual data, such as that at the ‘Individual/Situation’, and ‘Interpersonal/Social’ levels, means that much of this data is detached from the user. Researchers intending to use this data will have to carefully consider the degree to which additional contextual information is required to draw conclusions. The interconnectedness of the apps presents new opportunities to further enrich the collected data from external sources. There is the potential to create ‘links’ between multiple app usages from a single user. For example, it may be useful to gain domestic food preparation specific information from dedicated apps, and enrich this with demographic, situational and social context data collected through apps such as Facebook, Twitter and Instagram. However, the degree to which users would find this interlinkage acceptable still needs to be investigated. A further point to consider with user-generated domestic food preparation data, is the degree to which it can act as a ‘proxy’ for intake. The data collected via app usage reflects the motivation to gain knowledge and to develop skills. The degree to which this is translated into intake cannot be directly drawn from the data in its current form. At best, it describes an ‘intention’ to intake certain foods and/or meals. Again, it is possible to link data from the consumption apps identified in WP7 and map food choice and eating behaviour from preparation through to consumption. Although, a protocol for performing such exercises still needs to be developed. Finally, the availability and accessibility of the user-generated data for use in the RICHFIELDS RI still needs to be established. It is essential that legal and technical experts work with the RI to ensure easy and cost effective access to multiple big-data sets for the RICHFIELD end user
Development of a quality evaluation framework for consumer generated domestic food preparation data: D6.3
No full textThis 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
Definition of gaps and needs on quality and availability of data to answer the relevant questions on determinants of food purchase: D5.5
No full textThe 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
Development of a quality evaluation framework for consumer generated food consumption data: D7.3
No full textRICHFIELDS 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
Report on gaps and needs: potentials and limitations for the use of consumer generated food consumption data in nutrition research: D7.5
No full textAim of the present deliverable 7.5 was to identify the potentials and limitations of the tools collected in the inventory of deliverable 7.1, for getting a better understanding of the determinants of food consumption behavior. For that purpose, we investigated the data collection process of food consumption data by these tools, including its purpose, the applied dietary assessment methodology, the types of nutrients calculated and the possible contextual influences on users’ dietary behavior. In addition, in order to get an overview ofthe data associated with the collected dietary assessment data, we investigated the types of contextual data collected by the tools and the sources for exchanging and integrating contextual data from external sources such as wearables, partner apps and aggregators. We found that the vast majority of tools in the investigated sample collected consumer generated food consumption data using food diaries allowing for the input of a large variety of food consumption data from various sources. The quality of the compilation process of the underlying pre-compiled as well as user-generated food databases remains undocumented for the vast majority of investigated tools. Contextual data collected by the investigated tool, in addition to food consumption data, seems to bear interesting opportunities for a better understanding of the determinants of food consumption behavior. The type and variability of this data, however, appears to emphasize contextual data related to weight management, which has been identified as purpose for the majority of tools. Similarly, the large amount of potential influences aimed at changing users’ food consumption behaviors (e.g., reminders, social support) and the low level of detail regarding food composition estimations might also be a consequence of the numerous tools aiming at weight management in the inventory. Considering the lack of information provided by the investigated dietary assessment tools regarding the procedures and protocols for data access, the emerging networks of consumer generated data might provide a more efficient opportunity for researchers who want to access and integrate food consumption data with relevant contextual data. Further research is needed, however, in order to better understand the nature of this data networks, their access points and the types and structures of data they exchange. Supporting the compilation of food composition databases, the harmonization of consumer generated data, and the reflection on and interpretation of collected users data, might offer potentially important value propositions RICHFIELDS could provide for its various stakeholders. Application vendors, users as well as researchers could benefit from such services
Development of a quality evaluation framework for consumer generated food purchase data: D5.3
No full textThe 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
No full textThe 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
