Exploring lay views on physical activity and their implications for public health policy. A case study from East Belfast

It is now recognised that inactive lifestyles underpin much of the disease burden evident in the richer nations of the world. Indeed, the WHO has identified physical inactivity as a 'global public health problem' and has established minimum physical activity (PA) targets for people at different stages of the life-course. Yet, according to WHO, just under 1/3 of working age adults across the globe meet those targets and it is not at all clear how the disjunction between the recommendations of policy makers and the behaviour of ordinary people might be surmounted. Using an opportunity to examine the impact of an urban regeneration project on community residents in East Belfast (Northern Ireland) this paper examines the views of some 113 people on how to increase rates of PA in an area of multiple deprivation. The results of the analysis suggest that lay people rarely consider PA as a discrete issue, or one that centres on individuals and their motivation, but rather as one component in a complex web of concerns, processes and events that include such things as the actions of neighbours and relatives, material and political environments, vandalism, violence, and the weather. We explore and unravel the nature of those concerns using novel methods of content analysis that generate 'issue webs'. Particular attention is paid to the ways in which lay people conceptualize 'activity' and to the manner in which they point to ways of encouraging activity that are rooted in everyday life rather than in the corpocentric, agent-centred and often sport dominated strategies favoured by local policy makers. Our results support those who argue that interventions to increase rates of PA need to move beyond behavioural approaches that focus on individuals and consider the social, political and material contexts in which 'activity' occurs.

Handling Sequential Observations in Intelligent Surveillance

Demand for intelligent surveillance in public transport systems is growing due to the increased threats of terrorist attack, vandalism and litigation. The aim of intelligent surveillance is in-time reaction to information received from various monitoring devices, especially CCTV systems. However, video analytic algorithms can only provide static assertions, whilst in reality, many related events happen in sequence and hence should be modeled sequentially. Moreover, analytic algorithms are error-prone, hence how to correct the sequential analytic results based on new evidence (external information or later sensing discovery) becomes an interesting issue. In this paper, we introduce a high-level sequential observation modeling framework which can support revision and update on new evidence. This framework adapts the situation calculus to deal with uncertainty from analytic results. The output of the framework can serve as a foundation for event composition. We demonstrate the significance and usefulness of our framework with a case study of a bus surveillance project.