Monitoring and measuring physical activity and sedentary behaviour. (1st January 2012)

• Record Type: Journal Article
• Authors: Fergus, Paul; Attwood, Andrew; Dobbins, Chelsea; Stratton, Gareth; Hussain, Abir; Al-Jumeily, Dhiya; Randles, Martin
• Is Part Of: International journal of healthcare technology and management. Volume 13:Number 5/6(2012)
• Page Start: 283
• Page End: 303
• Publication Date: 2012-01-01
• Subjects: intelligent environments -- physical activity -- sedentary behaviour -- sensors -- sensor networks -- monitoring -- measurement; Medical technology -- Periodicals; Medical technology -- Management -- Periodicals; 610.28
• Languages: English
• ISSNs: 1368-2156

Abstract

It is generally agreed that an active lifestyle promotes healthy living across different age groups. It helps to combat obesity, reduces the risk of diabetes and heart disease, and supports independent living, as we age. However, it is difficult to quantify a direct correlation between physical activity and health outcomes. Given that obesity and lifestyle-related illnesses occur over years, rather than days, weeks or months, seeing the effects that activity and sedentary behaviour have on individuals, in the short term, is not always possible. The ubiquitous nature of physical activity makes it extremely difficult to capture as people go about their lives. Consequently, there has been a great deal of debate on the frequency intensity time, and the type of physical activity required by different groups (pre-schoolers, children and adults, older adults, obese, infirm, disabled and depressed). There is a need to provide effective mechanisms to monitor and measure physical activity and sedentary behaviour. While several commercially available products exist to achieve this, they are expensive, proprietary, and compliance is poor. The challenge is to use new and novel technologies that are unobtrusive and natural to use, adjunct to a person's day-to-day activities. This paper builds on this idea and explores how physical activity and sedentary behaviour information can be collected, from different environments. We have successfully developed an open and extensible working prototype to evaluate the applicability of our applied computing approach.