The effectiveness of personalised food choice advice tailored to an individual’s socio-demographic, cognitive characteristics, and sensory preferences

Personalised dietary advice has become increasingly popular, currently however most approaches are based on an individual’s genetic and phenotypic profile whilst largely ignoring other determinants such as socio economic and cognitive variables. This paper provides novel insights by testing the effectiveness of personalised healthy eating advice concurrently tailored to an individual’s socio-demographic group, cognitive characteristics, and sensory preferences. We first used existing data to build a synthetic dataset based on information from 3654 households (Study 1a), and then developed a cluster model to identify individuals characterised by similar socio- demographic, cognitive, and sensory aspects (Study 1b). Finally, in Study 2 we used the characteristics of 8 clusters to build 8 separate personalised food choice advice and assess their ability to motivate the increased consumption of fruit and vegetables and decreased intakes of saturated fat and sugar. We presented 218 par- ticipants with either generic UK Government “EatWell” advice, advice that was tailored to their allocated cluster (matched personalised), or advice tailored to a different cluster (unmatched personalised). Results showed that, when compared to generic advice, participants that received matched personalised advice were significantly more likely to indicate they would change their diet. Participants were similarly motivated to increase vegetable consumption and decrease saturated fat intake when they received unmatched personalised advice, potentially highlighting the power of providing alternative food choices. Overall, this study demonstrated that the power of personalizing food choice advice, based on a combination of individual characteristics, can be more effective than current approaches in motivating dietary change. Our study also emphasizes the viability of addressing population health through automatically delivered web-based personalised advice

Design Space Exploration for Efficient Data Intensive Computing on SoCs

International audienceFinding efficient implementations of data intensive applications, such as radar/sonar signal and image processing, on a system-on-chip is a very challenging problem due to increasing complexity and performance requirements of such applications. One major issue is the optimization of data transfer and storage microarchitecture, which is crucial in this context. In this chapter, we propose a comprehensive method to explore the mapping of high-level representations of applications into a customizable hardware accelerator. The high-level representation is given in a language named Array-OL. The customizable architecture uses FIFO queues and a double buffering mechanism to mask the latency of data transfers and external memory access. The mapping of a high-level representation onto a given architecture is achieved by applying loop transformations in Array-OL. A method based on integer partition is used to reduce the space of explored solutions. Our proposition aims at facilitating the inference of adequate hardware realizations for data intensive applications. It is illustrated on a case study consisting in implementing a hydrophone monitoring application

System architecture of a European platform for health policy decision making::MIDAS

Abstract Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. Methods: The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. Results: A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. Conclusions: The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions