Predicting wellness among rural older Australians : A cross sectional study

Introduction: Prior research on older people’s wellbeing and quality of life has lacked clarity and consistency. Research examining older people’s health has tended to use these different terms and measurement tools interchangeably, which might explain why the evidence is somewhat mixed. There is a paucity of research that uses the multi-dimensional construct of wellness in rural older people. Addressing both limitations, this study seeks to make a unique contribution to knowledge testing an ecological model of wellness that includes intrapersonal factors, interpersonal processes, institutional factors, community factors and public policy.

Methods: Six rural case study sites were chosen across two Australian sites, the states of Queensland and Victoria. A community saturation recruitment strategy was utilised. Telephone surveys were conducted with community-dwelling rural older people (n=266) aged ≥65 years across the sites. The central variable of the study was wellness as measured by the Perceived Wellness Survey. The ecological model developed included the following intrapersonal factors: physical and mental health, loneliness and social demographic characteristics (age, sex, marital status and financial capability). Interpersonal factors included a measure of social and community group participation, social network size and support provided. Institutional factors were measured by series of questions devised around the resource base environment and access to amenities and services.

Results: A hierarchical regression analysis was conducted to determine which variables in the model predict wellness. The results showed that a combination of intrapersonal factors (physical health, mental health, loneliness and financial capability) and interpersonal factors (size of social network and community participation) predicted wellness. However, institutional factors, the resource base environment, and access to amenities and services, contributed only marginally to the model. Community factors, including the personal and physical characteristics of community, also only made a marginal contribution.

Conclusions: The study identified the usefulness of using an integrated model of measurement in wellness. This model recognised the interrelated physical, social and economic influences that impact on rural older people throughout their life course. The study found that physical health made the greatest contribution to perceived wellness, followed by mental health. These findings support a body of research that has found that rural older people experience poorer health outcomes than those in urban areas. Lower levels of loneliness were also a strong predictor of perceived wellness, thus supporting research that has examined the impact of loneliness on physical and mental health. The presence of social capital, as measured by social network size, and the degree of community participation, were also predictors of perceived wellness. Overall, the findings of the present study implications for policy as well as subsequent strategies designed to increase the capacity of wellness in rural older people. Such strategies need to consider the contribution of a range of factors.