710 resultados para Aging health

em Queensland University of Technology - ePrints Archive


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We investigated the temporal relationship between lifestyle and mental health among 564 midlife women. The mental health measured included anxiety, depression, and mental well-being; the lifestyle measures included body mass index (BMI), exercise, smoking, alcohol use, and caffeine consumption. We found that BMI was positively related with mental well-being (r = .316, p = .009); smokers had lower mental well-being than nonsmokers (β = 6.725, p = .006), and noncaffeine drinkers had higher mental well-being (β = 5, p = .023). Past alcohol-drinkers had less anxiety than nondrinkers (β = 1.135, p = .04). Therefore, lifestyle is predictive of mental health among midlife and older women.

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Objective: To examine the extent to which socio-demographics, modifiable lifestyle, and physical health status influence the mental health of post-menopausal Australian women. Methods: Cross-sectional data on health status, chronic disease and modifiable lifestyle factors were collected from a random cross-section of 340 women aged 60-70 years, residing in Queensland, Australia. Structural equation modelling (SEM) was used to measure the effect of a range of socio-demographic characteristics, modifiable lifestyle factors, and health markers (self-reported physical health, history of chronic illness) on the latent construct of mental health status. Mental health was evaluated using the Medical Outcomes Study Short Form 12 (SF-12®) which examined and Center for Epidemiologic Studies Depression Scale (CES-D). Results: The model was a good fit for the data (χ2=4.582, df=3, p=0.205) suggesting that mental health is negatively correlated with sleep disturbance (β = -0.612, p <0.001), and a history of depression (β = -0.141, p = 0.024).While mental health was associated with poor sleep, it was not correlated with most lifestyle factors (BMI, alcohol consumption, or cigarette smoking) or socio-demographics like age, income or employment category and they were removed from the final model. Conclusion: Research suggests that it is important to engage in a range of health promoting behaviours to preserve good health. We found that predictors of current mental health status included sleep disturbance, and past mental health problems, while socio-demographics and modifiable lifestyle had little impact. It may be however, that these factors influenced other variables associated with the mental health of post-menopausal women, and these relationships warrant further investigation.

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BACKGROUND: Despite advancements in our understanding of the importance of stress reduction in achieving good health, we still only have limited insight into the impact of stress on cellular function. Recent studies have suggested that exposure to prolonged psychological stress may alter an individual's physiological responses, and contribute to morbidity and mortality. This paper presents an overview of the study protocol we are using to examine the impact of life stressors on lifestyle factors, health-related quality of life and novel and established biomarkers of stress in midlife and older Australian women.The primary aim of this study is to explore the links between chronic psychological stress on both subjective and objective health markers in midlife and older Australian women. The study examines the extent to which exposure frightening, upsetting or stressful events such as natural disasters, illness or death of a relative, miscarriage and relationship conflict is correlated with a variety of objective and subjective health markers.Methods/design: This study is embedded within the longitudinal Healthy Aging of Women's study which has collected data from midlife and older Australian women at 5 yearly intervals since 2001, and uses the Allostastic model of women's health by Groer and colleagues in 2010. The current study expands the focus of the HOW study and will assess the impact of life stressors on quality of life and clinical biomarkers in midlife and older Australian women to explain the impact of chronic psychological stress in women. DISCUSSION: The proposed study hypothesizes that women are at increased risk of exposure to multiple or repeated stressors, some being unique to women, and the frequency and chronicity of stressors increases women's risk of adverse health outcomes. This study aims to further our understanding of the relationships between stressful life experiences, perceived quality of life, stress biomarkers, chronic illness, and health status in women.

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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.

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Objectives: Previous research has linked unhealthy lifestyle with a range of negative health outcomes in women. As women age however, they may have fewer performance expectations, but may view their health more positively. Clearly, the experiences of midlife and older women in relation to health and wellbeing need further exploration. The purpose of this study is to examine the factors associated with poor health-related quality of life in midlife (HRQoL) and older Australian women. Methods: The Australian longitudinal Healthy Aging of Women (HOW) study prospectively examines HRQoL, chronic disease and modifiable lifestyle factors midlife and older women as they age. Random sampling was used to select rural and urban based women from South-East Queensland, Australia. Data were collected from 386 women at three time points over the last decade (2001, 2004 and 2011). Results: The average age of women in this study was 65 years (SD = 2.82). Almost three-quarters (73%, n = 248) of the sample were married or living as though married, nine per cent (n = 30) were separated or divorced and a small proportion were had never married (n = 13). Most (86%, n = 291) of the women sample reported being Australian born, around one quarter (34%, n = 114) had completed additional study since leaving school (university degree or diploma). Over half (55%, n = 186) of participants were retired, one quarter (25%, n = 85) were in paid employment and the remained were unemployed (1%, n = 4), unable to work because of illness (2%, n = 6) or worked within the home (17%, n = 56). Using data collected over time we examined the relationship between a range of modifiable lifestyle factors and mental health using structural equation modelling. The overall model exhibited a good fit with the data. Poor sleep quality was associated with reduced mental health while better mental health was reported in women who exercised regularly and satisfied with their currently weight. As hypothesized, past mental health was a significant mediator of current mental health. Conclusions: These findings demonstrate that the mental health of women is complex and needs to be understood not only in terms of current lifestyle but also in relation to previously reported health status.

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There is increased recognition that determinants of health should be investigated in a life-course perspective. Retirement is a major transition in the life course and offers opportunities for changes in physical activity that may improve health in the aging population. The authors examined the effect of retirement on changes in physical activity in the GLOBE Study, a prospective cohort study known by the Dutch acronym for "Health and Living Conditions of the Population of Eindhoven and surroundings," 1991–2004. They followed respondents (n = 971) by postal questionnaire who were employed and aged 40–65 years in 1991 for 13 years, after which they were still employed (n = 287) or had retired (n = 684). Physical activity included 1) work-related transportation, 2) sports participation, and 3) nonsports leisure-time physical activity. Multinomial logistic regression analyses indicated that retirement was associated with a significantly higher odds for a decline in physical activity from work-related transportation (odds ratio (OR) = 3.03, 95% confidence interval (CI): 1.97, 4.65), adjusted for sex, age, marital status, chronic diseases, and education, compared with remaining employed. Retirement was not associated with an increase in sports participation (OR = 1.12, 95% CI: 0.71, 1.75) or nonsports leisure-time physical activity (OR = 0.80, 95% CI: 0.54, 1.19). In conclusion, retirement introduces a reduction in physical activity from work-related transportation that is not compensated for by an increase in sports participation or an increase in nonsports leisure-time physical activity.

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PURPOSE: To explore the effects of glaucoma and aging on low-spatial-frequency contrast sensitivity by using tests designed to assess performance of either the magnocellular (M) or parvocellular (P) visual pathways. METHODS: Contrast sensitivity was measured for spatial frequencies of 0.25 to 2 cyc/deg by using a published steady- and pulsed-pedestal approach. Sixteen patients with glaucoma and 16 approximately age-matched control subjects participated. Patients with glaucoma were tested foveally and at two midperipheral locations: (1) an area of early visual field loss, and (2) an area of normal visual field. Control subjects were assessed in matched locations. An additional group of 12 younger control subjects (aged 20-35 years) were also tested. RESULTS: Older control subjects demonstrated reduced sensitivity relative to the younger group for the steady (presumed M)- and pulsed (presumed P)-pedestal conditions. Sensitivity was reduced foveally and in the midperiphery across the spatial frequency range. In the area of early visual field loss, the glaucoma group demonstrated further sensitivity reduction relative to older control subjects across the spatial frequency range for both the steady- and pulsed-pedestal tasks. Sensitivity was also reduced in the midperipheral location of "normal" visual field for the pulsed condition. CONCLUSIONS: Normal aging results in a reduction of contrast sensitivity for the low-spatial-frequency-sensitive components of both the M and P pathways. Glaucoma results in a further reduction of sensitivity that is not selective for M or P function. The low-spatial-frequency-sensitive channels of both pathways, which are presumably mediated by cells with larger receptive fields, are approximately equivalently impaired in early glaucoma.

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This paper describes an experiment undertaken to investigate intuitive interaction, particularly in older adults. Previous work has shown that intuitive interaction relies on past experience, and has also suggested that older people demonstrate less intuitive uses and slower times when completing set tasks with various devices. Similarly, this experiment showed that past experience with relevant products allowed people to use the interfaces of two different microwaves more quickly, although there were no significant differences between the different microwaves. It also revealed that certain aspects of cognitive decline related to aging, such as central executive function, have more impact on time, correct uses and intuitive uses than chronological age. Implications of these results and further work in this area are discussed.

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Aging in humans is associated with a loss in neuromuscular function and performance. This is related, in part, to the reduction in muscular strength and power caused by a loss of skeletal muscle mass (sarcopenia) and changes in muscle architecture. Due to these changes, the force-velocity (f-v) relationship of human muscles alters with age. This change has functional implications such as slower walking speeds. Different methods to reverse these changes have been investigated, including traditional resistance training, power training and eccentric (or eccentrically-biased) resistance training. This review will summarise the changes of the f-v relationship with age, the functional implications of these changes and the various methods to reverse or at least partly ameliorate these changes.

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College students (N = 3,435) in 26 cultures reported their perceptions of age-related changes in physical, cognitive, and socioemotional areas of functioning and rated societal views of aging within their culture. There was widespread cross-cultural consensus regarding the expected direction of aging trajectories with (1) perceived declines in societal views of aging, physical attractiveness, the ability to perform everyday tasks, and new learning, (2) perceived increases in wisdom, knowledge, and received respect, and (3) perceived stability in family authority and life satisfaction. Cross-cultural variations in aging perceptions were associated with culture-level indicators of population aging, education levels, values, and national character stereotypes. These associations were stronger for societal views on aging and perceptions of socioemotional changes than for perceptions of physical and cognitive changes. A consideration of culture-level variables also suggested that previously reported differences in aging perceptions between Asian and Western countries may be related to differences in population structure.

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The objective of this study was to investigate the factors that influence midlife women to make positive exercise and dietary changes. In late 2005 questionnaires were mailed to 866 women aged 51–66 years from rural and urban locations in Queensland, Australia and participating in Stage 2 of the Healthy Aging of Women Study. The questionnaires sought data on socio-demographics, body mass index (BMI), chronic health conditions, self-efficacy, exercise and dietary behavior change since age 40, and health-related quality of life. Five hundred and sixty four (69%) were completed and returned by early 2006. Data analysis comprised descriptive and bivariate statistics and structural equation modeling. The results showed that midlife is a significant time for women to make positive health behavior changes. Approximately one-third of the sample (34.6%) indicated that they had increased their exercise and around 60% had made an effort to eat more healthily since age 40. Modeling showed self-efficacy to be important in making both exercise and dietary changes. Although education appeared to influence self-efficacy in relation to exercise change, this was not the case for dietary change. The study has application for programs promoting healthy aging among women, and implies that those with low education, high BMI and poor mental health may need considerable support to improve their lifestyles.

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Adherence to medicines is a major determinant of the effectiveness of medicines. However, estimates of non-adherence in the older-aged with chronic conditions vary from 40 to 75%. The problems caused by non-adherence in the older-aged include residential care and hospital admissions, progression of the disease, and increased costs to society. The reasons for non-adherence in the older-aged include items related to the medicine (e.g. cost, number of medicines, adverse effects) and those related to person (e.g. cognition, vision, depression). It is also known that there are many ways adherence can be increased (e.g. use of blister packs, cues). It is assumed that interventions by allied health professions, including a discussion of adherence, will improve adherence to medicines in the older aged but the evidence for this has not been reviewed. There is some evidence that telephone counselling about adherence by a nurse or pharmacist does improve adherence, short- and long-term. However, face-to-face intervention counselling at the pharmacy, or during a home visit by a pharmacist, has shown variable results with some studies showing improved adherence and some not. Education programs during hospital stays have not been shown to improve adherence on discharge, but education programs for subjects with hypertension have been shown to improve adherence. In combination with an education program, both counselling and a medicine review program have been shown to improve adherence short-term in the older-aged. Thus, there are many unanswered questions about the most effective interventions to promote adherence. More studies are needed to determine the most appropriate interventions by allied health professions, and these need to consider the disease state, demographics, and socio-economic status of the older-aged subject, and the intensity and duration of intervention needed.

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The menopausal transition is a marker of aging for women and a time when health professionals urge women to prevent disease. In this research we adopted a constructivist, inductive approach in exploring how and why midlife women think about health in general, about being healthy, and about factors that influence engaging in healthy behaviors. The sample constituted 23 women who had participated in a women’s wellness program intervention trial and subsequent interviews. The women described lives of healthy eating and exercise, yet, their perceptions of health and healthy behavior at midlife contradicted that history. Midlife was associated with risk and guilt at not doing enough to be healthy. Health professionals provided a very limited frame within which to judge what is healthy. Mostly this was left up to individual women. Those who were successful framed health as “being able to do what you want to do when you want to do it.” In this article we present study findings of how meanings attached to health and being healthy were constructed through social expectations, family relationships, and life experiences.

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Abstract OBJECTIVE: To assess the psychometric properties and health correlates of the Geriatric Anxiety Inventory (GAI) in a cohort of Australian community-residing older women. METHOD: Cross-sectional study of a population-based cohort of women aged 60 years and over (N = 286). RESULTS: The GAI exhibited sound internal consistency and demonstrated good concurrent validity against the state half of the Spielberger State Trait Anxiety Inventory and the neuroticism domain of the NEO five-factor inventory. GAI score was significantly associated with self-reported sleep difficulties and perceived memory impairment, but not with age or cognitive function. Women with current DSM-IV Generalized Anxiety Disorder (GAD) had significantly higher GAI scores than women without such a history. In this cohort, the optimal cut-point to detect current GAD was 8/9. Although the GAI was designed to have few somatic items, women with a greater number of general medical problems or who rated their general health as worse had higher GAI scores. CONCLUSION: The GAI is a new scale designed specifically to measure anxiety in older people. In this Australian cohort of older women, the instrument had sound psychometric properties.