849 resultados para lifestyle factors


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We evaluated sustainability of an intervention to reduce women’s cardiovascular risk factors, determined the influence of self-efficacy, and described women’s current health. We used a mixed method approach that utilized forced choice and open-ended questionnaire items about health status, habits, and self-efficacy. Sixty women, average age 61, returned questionnaires. Women in the original intervention group continued health behaviors intended to reduce cardiovascular disease (CVD) at a higher rate than the control group, supporting the feasibility of a targeted intervention built around women’s individual goals. The role of self-efficacy in behavior change is unclear. The original intervention group reported higher self-reported health.

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Aims To assess self-reported lifetime prevalence of cardiovascular disease (CVD) among colorectal cancer survivors, and examine the cross-sectional and prospective associations of lifestyle factors with co-morbid CVD. Methods Colorectal cancer survivors were recruited (n = 1966). Data were collected at approximately 5, 12, 24 and 36 months post-diagnosis. Cross-sectional findings included six CVD categories (hypercholesterolaemia, hypertension, diabetes, heart failure, kidney disease and ischaemic heart disease (IHD)) at 5 months post-diagnosis. Longitudinal outcomes included the probability of developing (de novo) co-morbid CVD by 36 months post-diagnosis. Lifestyle factors included body mass index, physical activity, television (TV) viewing, alcohol consumption and smoking. Results Co-morbid CVD prevalence at 5 months post-diagnosis was 59%, and 16% of participants with no known CVD at the baseline reported de novo CVD by 36 months. Obesity at the baseline predicted de novo hypertension (odds ratio [OR] = 2.20, 95% confidence intervals [CI] = 1.09, 4.45) and de novo diabetes (OR = 6.55, 95% CI = 2.19, 19.53). Participants watching >4 h of TV/d at the baseline (compared with <2 h/d) were more likely to develop ischaemic heart disease by 36 months (OR = 5.51, 95% CI = 1.86, 16.34). Conclusion Overweight colorectal cancer survivors were more likely to suffer from co-morbid CVD. Interventions focusing on weight management and other modifiable lifestyle factors may reduce functional decline and improve survival.

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The purpose was to determine intake of phytoestrogens in a sample of older Australian women, and to investigate associated lifestyle factors. Subjects were an age-stratified sample of 511 women aged 40-80 y, randomly selected from the electoral roll and participating in the Longitudinal Assessment of Ageing in Women at the Royal Brisbane and Women’s Hospital. A cross-sectional study was conducted to assess isoflavone and lignan intake over the past month from food and supplements using a 112-item phytoestrogen frequency questionnaire. Data were also collected on nutrient intakes, physical activity, smoking, alcohol, non-prescription supplements, hormone therapy, education and occupation. Logistic regression was used to evaluate associations between demographic and lifestyle variables and soy/linseed consumption while controlling for age. Isoflavone intakes were significantly higher in the younger compared to older age groups (p<0.001); there were no age-related differences in lignan intake. Forty-five percent of women consumed at least one serve of a soy and/or linseed item and were defined as a soy/linseed consumer. Median (range) intakes by consumers for isoflavones and lignans (3.9 (0-172) mg/d and 2.4 (0.1-33) mg/d) were higher than intakes by non-consumers (0.004 (0-2.6) mg/d and 1.57 (0.44-4.7) mg/d), respectively (p<0.001). Consumers had higher intakes of dietary fibre (p=0.003), energy (p=0.04) and polyunsaturated fat (p=0.004), and higher levels of physical activity (p=0.006), socio-economic position (p<0.001), education (p<0.001) and supplement use (p<0.001). Women who consumed soy or linseed foods differed in lifestyle and demographic characteristics suggesting these factors should be considered when investigating associations with chronic disease outcomes.

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Background It is well known that lifestyle factors including overweight/obesity, physical inactivity, smoking and alcohol use are largely related with morbidity and mortality of chronic diseases including diabetes and cardiovascular diseases. The effect of lifestyle factors on people’s mental health who have a chronic disease is less defined in the research. The World Health Organisation has defined health as “a state of complete physical, mental and social well-being”. It is important, therefore to develop an understanding of the relationships between lifestyle and mental health as this may have implications for maximising the efficacy of health promotion in people with chronic diseases. Objectives The overall aim of the research was to examine the relationships between lifestyle factors and mental health among Australian midlife and older women. Methodology The current research measured four lifestyle factors including weight status, physical activity, smoking and alcohol use. Three interconnecting studies were undertaken to develop a comprehensive understanding of the relationships between lifestyle factors and mental health. Study 1 investigated the longitudinal effect of lifestyle factors on mental health by using midlife and older women randomly selected from the community. Study 2 adopted a cross-sectional design, and compared the effect of lifestyle factors on mental health between midlife and older women with and without diabetes. Study 3 examined the mediating effect of self-efficacy in the relationships between lifestyle factors and mental health among midlife and older women with diabetes. A questionnaire survey was chosen as the means to gather information, and multiple linear regression analysis was conducted as the primary statistical approach. Results The research showed that the four lifestyle factors including weight status, physical activity, smoking and alcohol use did impact on mental health among Australian midlife and older women. First, women with a higher BMI had lower levels of mental health than women with normal weight, but as women age, the mental health of women who were overweight and obese becomes better than that of women with normal weight. Second, women who were physically active had higher levels of mental health than those who were not. Third, smoking adversely impacted on women’s mental health. Finally, those who were past-drinkers had less anxiety symptoms than women who were non-drinkers as they age. Women with diabetes appeared to have lower levels of mental health compared to women without. However, the disparities of mental health between two groups were confounded by low levels of physical activity and co-morbidities. This finding underlines the effect of physical activity on women’s mental health, and highlights the potential of reducing the gap of mental health by promoting physical activity. In addition, self-efficacy was shown to be the mediator of the relationships between BMI, physical activity and depression, suggesting that enhancing people’s self-efficacy may be useful for mental health improvement. Conclusions In conclusion, Australian midlife and older women who live with a healthier lifestyle have higher levels of mental health. It is suggested that strategies aiming to improve people’s mental health may be more effective if they focus on enhancing people’s self-efficacy levels. This study has implications to both health education and policy development. It indicates that health professionals may need to consider clients’ mental health as an integrated part of lifestyle changing process. Furthermore, given that lifestyle factors impact on both physical and mental health, lifestyle modification should continue to be the focus of policy development.

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Aim: The associations between perceived wellness and health-related quality of life, comorbidities and modifiable lifestyle factors in older adults were explored. Methods: Self-administered questionnaires including the Perceived Wellness Survey and the 36-Item Short Form of the Medical Outcomes Study version two were distributed to 328 community-living adults aged 65 years and over. Results: Results showed positive associations between perception of wellness and health-related quality of life. General health (r(249) = 0.66, P < 0.01), vitality (r(249) = 0.59, P < 0.01) and mental health (r(249) = 0.52, P < 0.01) had the strongest association; and social functioning (r(249) = 0.3, P < 0.01) and pain (r(249) = 0.36, P < 0.01) the lowest. Perceived wellness was influenced by hearing, mobility, memory, chronic disease, exercise, gambling and single status. Conclusion: The study identified that perceived wellness in older adults is a multidimensional construct.

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The aims of this study were to examine: (1) the association between sociodemographic and lifestyle factors and sleep quality in a population-based cohort of Australian women and (2) possible influence of reproductive status and mental and physical health factors on these associations. Data on 3,655 women (mean age046.6 years, range 34.3–67.4) were obtained from the Mater Hospital University of Queensland Study of Pregnancy for this cross-sectional study. Self-rated sleep quality was assessed using the Pittsburgh Sleep Quality Index. For the purpose of this study, two cutoff points (scores 5 and 10) were used to divide women into three categories: normal (65.2 %), moderately poor (26.4 %), and very poor sleep quality (8.5 %). Other covariates were measured at 21-year follow-up as well. After adjusting for reproductive status, mental and physical health, there were significant associations between moderately poor sleep quality and education and between very poor sleep quality and unemployment, both measures of socioeconomic status. In addition, work-related exertion was associated with increased rates of moderately poor sleep quality, whereas those women undertaking moderate exercise were less likely to experience very poor sleep quality. Independent associations between sociodemographic factors and exercise with moderately poor and very poor sleep quality were identified. These findings demonstrate the dynamic nature of the association between exercise/exertion, socioeconomic status, and sleep quality and highlight the importance of taking these into consideration when dealing with issues of poor sleep quality in women.

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Background Poor mental health is a significant cause of morbidity and mortality, yet debate continues about factors most likely to predict poor mental health outcomes. Objective This cohort study examines the influence of modifiable lifestyle factors, menopausal symptoms, and physical health on the mental health of midlife and older Australian women. Methods: Random sampling was used to recruit women aged 40-55, from rural and urban areas of Queensland, Australia. Overall, 340 women completed mailed surveys on socio-demographic characteristics, midlife symptoms (Greene Climacteric Scale©), modifiable lifestyle factors, and mental health (SF-12©) in 2001, 2004 and 2011. Hierarchical repeated-measure models were used to explore the correlates of poor mental health over time. Results The mean age [SD] at baseline was 55 [2.7] years, most were married (73%, n=248) and 18% were pre-menopausal. The model suggested that variance in mental health widened and showed a non-linear increase with age. Decrements in mental health were associated with an increase in midlife symptoms (Greene psychological scale, P <0.01; Greene somatic scale, P <0.05), time (P <0.01), poor physical health (P <0.01) and individual variance (P <0.01). Socio-demographics and lifestyle factors had little influence on mental health over time. Conclusion Findings suggest that while women’s mental health may decline during midlife, the effect is temporary; in older women, physical health and individual factors seem to be increasingly significant. This research highlights the importance of active health promotion as a means of enhancing both physical and mental health in midlife women.

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Background: There is a current lack of consensus on defining metabolically healthy obesity (MHO). Limited data on dietary and lifestyle factors and MHO exist. The aim of this study is to compare the prevalence, dietary factors and lifestyle behaviours of metabolically healthy and unhealthy obese and non-obese subjects according to different metabolic health criteria. Method: Cross-sectional sample of 1,008 men and 1,039 women aged 45-74 years participated in the study. Participants were classified as obese (BMI ≥30kg/m2) and non-obese (BMI <30kg/m2). Metabolic health status was defined using five existing MH definitions based on a range of cardiometabolic abnormalities. Dietary composition and quality, food pyramid servings, physical activity, alcohol and smoking behaviours were examined. Results: The prevalence of MHO varied considerably between definitions (2.2% to 11.9%), was higher among females and generally increased with age. Agreement between MHO classifications was poor. Among the obese, prevalence of MH was 6.8% to 36.6%. Among the non-obese, prevalence of metabolically unhealthy subjects was 21.8% to 87%. Calorie intake, dietary macronutrient composition, physical activity, alcohol and smoking behaviours were similar between the metabolically healthy and unhealthy regardless of BMI. Greater compliance with food pyramid recommendations and higher dietary quality were positively associated with metabolic health in obese (OR 1.45-1.53 unadjusted model) and non-obese subjects (OR 1.37-1.39 unadjusted model), respectively. Physical activity was associated with MHO defined by insulin resistance (OR 1.87, 95% CI 1.19-2.92, p = 0.006).

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Background: Although the incidence of small intestinal adenocarcinoma (SIA) is low, rates are increasing and little information regarding modifiable lifestyle risk factors is available.

Aim: To provide a systematic review of lifestyle factors and SIA risk.

Methods: Ovid MEDLINE, EMBASE and WEB OF SCIENCE were searched from inception to Week 1 October 2013. Nine publications that reported on SIA risk in relation to alcohol intake (n=6), tobacco smoking (n=6), diet (n=5), body mass (n=3), physical activity (n=1), hormone use (n=1) and/or socio-economic status (n=3) were retrieved. Results for alcohol, smoking and SIA risk were pooled using random-effects meta-analyses to produce relative risks (RR) and 95% confidence intervals (CI).

Results: The summary RR for individuals consuming the highest versus lowest category of alcohol intake was 1.51 (95% CI 0.83-2.75; n=5 studies) with significant increased risks emerging in sensitivity analysis with reduced heterogeneity (RR: 1.82, 95% CI: 1.05-3.15; n=4 studies). The pooled SIA RR for individuals in the highest versus lowest category of smoking was 1.24 (95% CI 0.71-2.17; n=5 studies). In relation to dietary factors, high fibre intakes and normal body weight may be protective, while high intakes of red/processed meat and sugary drinks may increase SIA risk. Evidence on socio-economic status and SIA risk was equivocal. Data on other factors were too sparse to draw any conclusions.

Conclusions: Alcohol may be associated with an increased risk of SIA. Further investigation of lifestyle factors, particularly alcohol, smoking and diet, in the aetiology of this cancer is warranted in large consortial studies.

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AIM: To evaluate the association between various lifestyle factors and achalasia risk.

METHODS: A population-based case-control study was conducted in Northern Ireland, including n= 151 achalasia cases and n = 117 age- and sex-matched controls. Lifestyle factors were assessed via a face-to-face structured interview. The association between achalasia and lifestyle factors was assessed by unconditional logistic regression, to produce odds ratios (OR) and 95% confidence interval (CI).

RESULTS: Individuals who had low-class occupations were at the highest risk of achalasia (OR = 1.88, 95%CI: 1.02-3.45), inferring that high-class occupation holders have a reduced risk of achalasia. A history of foreign travel, a lifestyle factor linked to upper socio-economic class, was also associated with a reduced risk of achalasia (OR = 0.59, 95%CI: 0.35-0.99). Smoking and alcohol consumption carried significantly reduced risks of achalasia, even after adjustment for socio-economic status. The presence of pets in the house was associated with a two-fold increased risk of achalasia (OR = 2.00, 95%CI: 1.17-3.42). No childhood household factors were associated with achalasia risk.

CONCLUSION: Achalasia is a disease of inequality, and individuals from low socio-economic backgrounds are at highest risk. This does not appear to be due to corresponding alcohol and smoking behaviours. An observed positive association between pet ownership and achalasia risk suggests an interaction between endotoxin and viral infection exposure in achalasia aetiology.

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We examined the relationship between blood antioxidant enzyme activities, indices of inflammatory status and a number of lifestyle factors in the Caerphilly prospective cohort study of ischaemic heart disease. The study began in 1979 and is based on a representative male population sample. Initially 2512 men were seen in phase I, and followed-up every 5 years in phases II and III; they have recently been seen in phase IV. Data on social class, smoking habit, alcohol consumption were obtained by questionnaire, and body mass index was measured. Antioxidant enzyme activities and indices of inflammatory status were estimated by standard techniques. Significant associations were observed for: age with α-1-antichymotrypsin (p<0.0001) and with caeruloplasmin, both protein and oxidase (p<0.0001); smoking habit with α-1-antichymotrypsin (p<0.0001), with caeruloplasmin, both protein and oxidase (p<0.0001) and with glutathione peroxidose (GPX) (p<0.0001); social class with α-1-antichymotrypsin (p<0.0001), with caeruloplasmin both protein (p<0.001) and oxidase (p<0.01) and with GPX (p<0.0001); body mass index with α-1-antichymotrypsin (p<0.0001) and with caeruloplasmin protein (p<0.001). There was no significant association between alcohol consumption and any of the blood enzymes measured. Factor analysis produced a three-factor model (explaining 65.9% of the variation in the data set) which appeared to indicate close inter-relationships among antioxidants.