Big data against childhood Obesity
Children and adolescents with obesity have a higher risk of developing various diseases later on, compared to children without obesity. The reasons why some children become obese are complex, and behaviour, the living environment, and public health policies all play a role. BigO collects and analyses anonymous data on children's behavioural patterns and their living environment. With advanced analytics BigO extracts data-driven evidence on which local factors are involved, and how these factors influence childhood obesity in Europe.
With this information BigO will be able to advice clinics and public health authorities on how to develop and plan effective programs and policies in an attempt to reduce childhood obesity.
This year, the annual international conference of the IEEE Engineering in Medicine and Biology Society is hosting their conference online. The conference is in conjunction with the annual Conference of the Canadian Medical and Biological Engineering Society. This new EMBS Virtual Academy takes place on 20-24 July. The conference consist of several live webcast sessions.…Read more >
On July 8th 2020, a new scientific paper from BigO was published in the journal JMIR mHealth and uHealth. It is titled: ‘Mobile Health Apps in Pediatric Obesity Treatment: Process Outcomes From a Feasibility Study of a Multicomponent Intervention’. The paper describes the process methods for applying an mHealth intervention to reduce the rate of…Read more >
This academic year, for the first time, Masters students in Nutrition and Health at Wageningen University had the opportunity to attend a course on data science tailored to their domain expertise. Interest in the topic has been growing within the field. With the rise of projects like BigO, the need for domain experts with data science skills is also growing. Project scientists participating in the…Read more >