Sina Masoud-Ansari1, James Diprose2, Nick Young3, Mark Gahegan4, Richard Hosking5, Arron Mclaughlin6, Stefanie Vandevjvere7, Cliona Ni Mhurchu8, Andrew Jull9
1University of Auckland, Auckland, New Zealand, email@example.com
2University of Auckland, Auckland, New Zealand, firstname.lastname@example.org
3University of Auckland, Auckland, New Zealand, email@example.com
4University of Auckland, Auckland, New Zealand, firstname.lastname@example.org
5University of Auckland, Auckland, New Zealand, email@example.com
6University of Auckland, Auckland, New Zealand, firstname.lastname@example.org
7University of Auckland, Auckland, New Zealand, email@example.com
8University of Auckland, Auckland, New Zealand, firstname.lastname@example.org
9University of Auckland, Auckland, New Zealand, email@example.com
The University of Auckland’s Centre for eResearch has been collaborating with researchers from the School of Population Health on a number of projects over the past two years; highlighting the changing landscape of research in this discipline and the need for technical support offering deeper engagement. Beyond providing access to computing and data infrastructure, we see demand for software development expertise, in some cases bringing production ready services to public as well as expertise in data analysis and machine learning. We present our recent projects in this area highlighting challenges and preliminary outcomes.
Kids’Cam  is a novel dataset developed to understand the complex system of factors affecting childhood obesity. With over 1.3 million images and corresponding GPS records taken from wearable sensors, Kids’Cam provides a unique insight into the health environment of 169 school children in the Wellington Region, New Zealand. Researchers annotated these images by hand to understand the exposure to a range of health related factors such as food and food marketing. The Centre for eResearch explored the feasibility of using machine learning to automate the annotation process and developed workflows for processing the associated spatial data. The longer term vision for this work is to use Kids’Cam along with other datasets to create virtual labs for simulating the effects of policy, environment, behaviour and other factors in preventing obesity.
Foodback is a mobile app that aims to empower people to create healthier community food places, through crowdsourcing of foods advertised and sold in and around local community settings, including: schools, medical centers, hospitals, supermarkets, takeaways and sport and recreation centers. Example screenshots of Foodback are shown in Figure 1. Foodback was developed through an iterative design process with key stakeholders where user workflows were prototyped and refined on paper, implemented, and then tested by the research group to gather feedback and improve the design. Data gathered with Foodback will be used to better understand the healthiness of the food landscape in New Zealand, and to help encourage and support local ‘change agents’ to make positive, healthy changes to foods advertised and sold in their settings.
Figure 1: The Foodback app
Unhealthy diets contribute to obesity and diet-related non-communicable diseases (NCDs). The cost of food is a major determinant of food choices. The International Network for Food and Obesity/NCDs Research, Monitoring and Action Support (INFORMAS), coordinated at the University of Auckland School of Population Health, is developing methods to monitor the cost differential between healthy and less healthy, current diets in New Zealand and globally. The variation of the cost of diets is important but currently unknown. Many diet scenarios can be constructed using a list of commonly consumed foods to meet nutrient and food-based dietary guidelines (for ‘healthy’ diets) or specified nutrient and food intakes (for ‘current’ diets). This research involves developing a novel program to model the cost of the range of healthy and current diets using all different combinations of a selection of commonly consumed foods, determined by a set of constraints for each, and specified food and nutrient intakes. Through taking into account the variation of the cost of diets, the program will allow answering the question whether a healthy diet is significantly cheaper or more expensive than the current, less healthy diet. If successful, this program can be franchised to other countries.
Our collaborations show that there are exciting opportunities for eResearch professionals to support health researchers in novel methods for gathering and processing crowd-sourced data as well as supporting them to extract meaning from disparate data sources. We have found that working closely with researchers at the research design stage to prove and test workflows is crucial for providing effective software and analytics support after data has been collected. While demands for specialised IT are not new in traditionally computational disciplines, we see increasing need for app development and analytics support in health research and a gap between industry focused IT and research that eResearch units in universities can be effective in.
1. Signal LN, Smith MB, Barr M, Stanley J, Chambers TJ, Zhou J, Duane A, Jenkin GLS, Pearson AL, Gurrin C, Smeaton AF, Hoek J, Ni Mhurchu C. Kids’Cam: An objective methodology to study the world in which children live. American Journal of Preventive Medicine 2017; published online April 25, 2017: http://doi.org/10.1016/j.amepre.2017.02.016.
I work as a Research IT Specialist in the University of Auckland’s Centre for eResearch.