Creating intelligent environments to monitor and manipulate physical activity and sedentary behavior in public health and clinical settings

It is generally agreed, that an active lifestyle promotes healthy living across different age groups. It helps to combat obesity, reduce the risk of diabetes and heart disease, and support 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 in contrast to days, weeks or months seeing the effects activity and sedentary behaviour has 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, adults, older adults, obese, infirm, disabled, and depressed). There is a need to provide effective mechanisms to monitor and manipulate physical activity and sedentary behaviour. Whilst several commercially available products exist to achieve this, compliance is poor. The challenge is to use new and novel technologies that are unobtrusive and natural adjunct to a persons day-to-day activity. This paper builds on existing ideas and explores how activity and sedentary behaviour information can be collected from different environments. We have developed an initial working prototype to evaluate the applicability of our approach.