Predictors of Mental Health in Midlife and Older Women: Results from the Australian Healthy Aging of Women Study

Friday, 26 July 2013: 8:50 AM

Charrlotte Seib, PhD, MN, RN
Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia
Debra J. Anderson, RN, BA, GDNS (ed), MN, PhD
School of Nursing and Midwifery, Queensland University of Technology, Brisbane, Queensland, Australia

Learning Objective 1: The learner will have a better understanding of the factors which may predict poor mental health in women as they age

Learning Objective 2: The learner will be able to better response to potential risk factors associated with poor mental health in midlife and older women

Purpose: To examine the extent to which socio-demographic characteristics, modifiable lifestyle factors and health status influence the mental health of midlife and older Australian women from the Australian Healthy Aging of Women (HOW) study.

Methods: Data on health status, chronic disease and modifiable lifestyle factors were collected from a random sample of 340 women aged 40-65 years, residing in Queensland, Australia in 2011. Structural equation modelling (SEM) was used to measure the effect of a range of socio-demographic characteristics (marital status, age, income), modifiable lifestyle factors (caffeine intake, alcohol consumption, exercise, physical activity, sleep), and health markers (self-reported physical health, history of chronic illness) on the latent construct, mental health. Mental health was evaluated using the Medical Outcomes Study Short Form 12 (SF-12®) and the Center for Epidemiologic Studies Depression Scale (CES-D).

Results: The model was a good fit for the data (χ2 = 40.166, df =312, p 0.125, CFI = 0.976, TLI = 0.950, RMSEA = 0.030, 90% CI = 0.000-0.053); the model suggested mental health was negatively influenced by sleep disturbance (β = -0.628), sedentary lifestyle (β = -0.137), having been diagnosed with one or more chronic illnesses (β = -0.203), and poor self-reported physical health (β = - 0.161). While mental health was associated with sleep, it was not correlated with many other lifestyle factors (BMI (β = -0.050), alcohol consumption (β = 0.079), or cigarette smoking (β = 0.008)) or background socio-demographic characteristics (age (β = 0.078), or income (β = -0.039)).

Conclusion: While research suggests that it is important to engage in a range health promoting behaviours to preserve good health, we found that only sleep disturbance, physical health, chronic illness and level of physical activity predicted current mental health. However, while socio-demographic characteristics and modifiable lifestyle factors seemed to have little direct impact on mental health, they probably had an indirect effect.