Obesity in People with Learning Disabilities: the impact of nurse led health screenings and health promotion activities

Background. Obesity appears to be more common among people with intellectual disabilities, with few studies focusing on achieving weight reduction. Aim. Firstly, to follow up people identified as overweight and obese following special health screening clinics and to determine the actions taken. Secondly, to evaluate the impact of health promotion classes on participants' weight loss. Methods. A clinic led by two learning disbaility nurses was held for all people aged 10 years and over (n=464) who attended special services within the area of one Health and Social Services Trust in Northern Ireland. In a second study, the nurses organised health promotion classes for 20 people over a 6 - 8 week period. Findings. The health screen identified 64% of adults and 26% of 10 - 19 year olds as being overweight or obese. Moreover, those aged 40 - 49 years who were obese had significantly higher levels of blood pressure. However, information obtained from a follow up questionnaire sent after 3 months suggested that of the 122 people identified for wiehgt reduciton, action had been taken for only 34% of them and only three were reported to have lost weight. The health promotion classes, however, led to a significant reduction in weight and body mass index scores. Conclusion. Health screening per se has limited impact on reducing obesity levels in this client group. Rather, health personnel such as general practitioners, nurses and health promotion staff need to work in partnership with service staff, carers and people with intellectual disabiltieis to create more active lifestyles.

A Hybrid Evolutionary Approach to the Nurse Rostering Problem

Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimisation benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better in finding feasible solutions but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridise it with a recently proposed simulated annealing hyper-heuristic within a local search and genetic algorithm framework. The hybrid algorithm shows significant improvement over both the genetic algorithm with stochastic ranking and the simulated annealing hyper-heuristic alone. The hybrid algorithm also considerably outperforms the methods in the literature which have the previously best known results.