Factors influencing health promotion activities in midlife and older Australian women with a chronic disease

Background: Chronic diseases including type 2 diabetes are a leading cause of morbidity and mortality in midlife and older Australian women. There are a number of modifiable risk factors for type 2 diabetes and other chronic diseases including smoking, nutrition, physical activity and overweight and obesity. Little research has been conducted in the Australian context to explore the perceived barriers to health promotion activities in midlife and older Australian women with a chronic disease. Aims: The primary aim of this study was to explore women’s perceived barriers to health promotion activities to reduce modifiable risk factors, and the relationship of perceived barriers to smoking behaviour, fruit and vegetable intake, physical activity and body mass index. A secondary aim of this study was to investigate nurses’ perceptions of the barriers to action for women with a chronic disease, and to compare those perceptions with those of the women. Methods: The study was divided into two phases where Phase 1 was a cross sectional survey of women, aged over 45 years with type 2 diabetes who were attending Diabetes clinics in the Primary and Community Health Service of the Metro North Health Service District of Queensland Health (N = 22). The women were a subsample of women participating in a multi-model lifestyle intervention, the ‘Reducing Chronic Disease among Adult Australian Women’ project. Phase 2 of the study was a cross sectional online survey of nurses working in Primary and Community Health Service in the Metro North Health Service District of Queensland Health (N = 46). Pender’s health promotion model was used as the theoretical framework for this study. Results: Women in this study had an average total barriers score of 32.18 (SD = 9.52) which was similar to average scores reported in the literature for women with a range of physical disabilities and illnesses. The leading five barriers for this group of women were: concern about safety; too tired; not interested; lack of information about what to do; with lack of time and feeling I can’t do things correctly the equal fifth ranked barriers. In this study there was no statistically significant difference in average total barriers scores between women in the intervention group and those is the usual care group of the parent study. There was also no significant relationship between the women’s socio-demographic variables and lifestyle risk factors and their level of perceived barriers. Nurses in the study had an average total barriers score of 44.48 (SD = 6.24) which was higher than all other average scores reported in the literature. The leading five barriers that nurses perceived were an issue for women with a chronic disease were: lack of time and interferes with other responsibilities the leading barriers; embarrassment about appearance; lack of money; too tired and lack of support from family and friends. There was no significant relationship between the nurses’ sociodemographic and nursing variables and the level of perceived barriers. When comparing the results of women and nurses in the study there was a statistically significant difference in the median total barriers score between the groups (p < 0.001), where the nurses perceived the barriers to be higher (Md = 43) than the women (Md = 33). There was also a significant difference in the responses to the individual barriers items in fifteen of the eighteen items (p < 0.002). Conclusion: Although this study is limited by a small sample size, it contributes to understanding the perception of midlife and older women with a chronic disease and also the perception of nurses, about the barriers to healthy lifestyle activities that women face. The study provides some evidence that the perceptions of women and nurses may differ and argues that these differences may have significant implications for clinical practice. The study recommends a greater emphasis on assessing and managing perceived barriers to health promotion activities in health education and policy development and proposes a conceptual model for understanding perceived barriers to action.

Prevalence of malnutrition in Parkinson's disease : a systematic review

Parkinson's disease (PD) patients may be at higher risk of malnutrition because of the symptoms associated with the disease and the side effects of the medication used to manage it. A decline in nutritional status is associated with many adverse outcomes related to health and quality of life. It is not clear, however, to what extent this population is currently affected by malnutrition. The objective of this review was to systematically assess the methodology and outcomes of studies reporting the prevalence of malnutrition in PD patients. Studies that attempted to classify participants with PD into nutritional risk and/or malnutrition categories using body mass index, weight change, anthropometric measures, and nutritional screening and assessment scores were included. The prevalence of malnutrition ranged from 0% to 24% in PD patients, while 3–60% of PD patients were reported to be at risk of malnutrition. There was a large degree of variation among studies in the methods chosen, the definition of malnutrition using those methods, and the detail in which the methodological protocols were reported. The true extent of malnutrition in the PD population has yet to be accurately quantified. It is important, however, to screen for malnutrition at the time of PD diagnosis.

The Metapopulation Dynamics of an Infectious Disease: Tuberculosis in Possums

An SEI metapopulation model is developed for the spread of an infectious agent by migration. The model portrays two age classes on a number of patches connected by migration routes which are used as host animals mature. A feature of this model is that the basic reproduction ratio may be computed directly, using a scheme that separates topography, demography, and epidemiology. We also provide formulas for individual patch basic reproduction numbers and discuss their connection with the basic reproduction ratio for the system. The model is applied to the problem of spatial spread of bovine tuberculosis in a possum population. The temporal dynamics of infection are investigated for some generic networks of migration links, and the basic reproduction ratio is computed—its value is not greatly different from that for a homogeneous model. Three scenarios are considered for the control of bovine tuberculosis in possums where the spatial aspect is shown to be crucial for the design of disease management operations

Falls in Parkinson’s disease : evidence for altered stepping strategies on compliant surfaces

Background: Real-world environments comprise surfaces of different textures, densities and gradients, which can threaten postural stability and increase falls risk. However, there has been limited research that has examined how walking on compliant surfaces influences gait and postural stability in older people and PD patients. Methods: PD patients (n = 49) and age-matched controls (n = 32) were assessed using three dimensional motion analysis during self-paced walking on both firm and foam walkways. Falls were recorded prospectively over 12 months using daily falls calendars. Results: Walking on a foam surface influenced the temporospatial characteristics for all groups, but PD fallers adopted very different joint kinematics compared with controls. PD fallers also demonstrated reduced toe clearance and had increased mediolateral head motion(relative to walking velocity) compared with control participants. Conclusions: Postural control deficits in PD fallers may impair their capacity to attenuate surface-related perturbations and control head motion. The risk of falling for PD patients may be increased on less stable surfaces.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.