Health of the older worker in Ireland - occupational physical and psychosocial factors in the autumn of work life

Introduction: Worldwide, governments are striving to keep people in work to an older age. However, little is known about the effects of work on an older workforce. This thesis aims to investigate the importance of job characteristics to the antecedents and evolution of cardiovascular disease and functional limitations for the older worker (50+ years). Methods: Three studies were used in this thesis. The 5C (Cork Coronary Care Case- Control) Study investigated the association between job strain and a coronary event in males (n=208) 35-74 years old. The Mitchelstown Study examined the association between job characteristics and positive lifestyle behaviours and further, job characteristics and blood pressure for males and females 50-69 years (n=2,047). Finally, the Cork & Kerry Study investigated the physical effects of manual work and reported functional limitations/disabilities in a sample of 60-80 year olds (n=362). Results: Results from the 5C Study show a clear difference between younger (<50 years) and older (≥50 years) workers, with older workers who had a coronary event more likely to have high job strain and low job control. Data from the Mitchelstown Study showed workers with intermediate possibility for development or high quantitative demands (versus low) at work significantly more likely to have co-occurrence of positive lifestyle behaviours. Further, those who had high possibility for development were more likely to have high systolic blood pressure with no indication of recovery from this activation at night. Physically demanding work as reported by the participants of the Cork & Kerry Study was associated with functional limitations and activities of daily living disability for both the paid and unpaid worker. Discussion: The findings from this piece of work highlight the necessity to examine job characteristics and health outcomes in isolation for the over fifties. The challenge is to get this information into the workplace.

A pro-active and dynamic prediction assistance using BaranC framework

Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.