107 resultados para health late-life
Resumo:
Objective . To determine longitudinal relationships between body mass index (BMI) and health-related quality of life (HRQoL) in an adolescent population sample.
Design. Data collected in 2000 and 2005 within the Health of Young Victorians longitudinal cohort study.
Setting. Originally a community sample of elementary school students in Victoria, Australia. Follow-up occurred in either secondary schools or individuals homes.
Participants. Cohort recruited in 1997 via a random sampling design from Victorian elementary schools. Originally comprising 1 943 children, 1 569 (80.8%) participated in 2000 (wave 2, 8 – 13 years) and 851 (54%) in 2005 (wave 3, 13 – 19 years).
Main outcome measures. In both waves participants and their parents completed the PedsQL, a 23-item child HRQoL measure, and BMI z-scores and status (non-overweight, overweight or obese) were calculated from measured height and weight. Associations were tested cross-sectionally and longitudinally (linear regression, adjusted for baseline values)
Results. A total of 81.6% remained in the same BMI category, while 11.4% and 7.0% moved to higher and lower categories, respectively. Cross-sectional inverse associations between lower PedsQL and higher BMI categories were similar to those for elementary school children. Wave 2 BMI strongly predicted wave 3 BMI and wave 2 PedsQL strongly predicted wave 3 PedsQL. Only parent-reported Total PedsQL score predicted higher subsequent BMI, though this effect was small. Wave 2 BMI did not predict wave 3 PedsQL.
Conclusions. This novel study confi rmed previous cross-sectional associations, but did not provide convincing evidence that
BMI is causally associated with falling HRQoL or vice versa across the transition from childhood to adolescence.
Resumo:
Objective: To assess the prospective relationship between obesity and health-related quality of life, including a novel assessment of the impact of health-related quality of life on weight gain.
Design and setting: Longitudinal, national, population-based Australian Diabetes, Obesity and Lifestyle (AusDiab) study, with surveys conducted in 1999/2000 and 2004/2005.
Participants: A total of 5985 men and women aged 25 years at study entry.
Main outcome measure(s): At both time points, height, weight and waist circumference were measured and self-report data on health-related quality of life from the SF-36 questionnaire were obtained. Cross-sectional and bi-directional, prospective associations between obesity categories and health-related quality of life were assessed.
Results: Higher body mass index (BMI) at baseline was associated with deterioration in health-related quality of life over 5 years for seven of the eight health-related quality of life domains in women (all P0.01, with the exception of mental health, P>0.05), and six out of eight in men (all P<0.05, with the exception of role-emotional, P=0.055, and mental health, P>0.05). Each of the quality-of-life domains related to mental health as well as the mental component summary were inversely associated with BMI change (all P<0.0001 for women and P0.01 for men), with the exception of vitality, which was significant in women only (P=0.008). For the physical domains, change in BMI was inversely associated with baseline general health in women only (P=0.023).
Conclusions: Obesity was associated with a deterioration in health-related quality of life (including both physical and mental health domains) in this cohort of Australian adults followed over 5 years. Health-related quality of life was also a predictor of weight gain over 5 years, indicating a bi-directional association between obesity and health-related quality of life. The identification of those with poor health-related quality of life may be important in assessing the risk of future weight gain, and a focus on health-related quality of life may be beneficial in weight management strategies.
Resumo:
Purpose Health-related quality of life (HRQOL) can be significantly impaired by the presence of chronic conditions such as cardiovascular disease (CVD) and major depressive disorder (MDD). The aim of this paper was to (1) identify differences in HRQOL between individuals with CVD, MDD, or both, compared to a healthy reference group, (2) establish whether the influence of co-morbid MDD and CVD on HRQOL is additive or synergistic and (3) determine the way in which depression severity interacts with CVD to influence overall HRQOL.
Methods Population-based data from the 2007 Australian National Survey of Mental Health and Well-being (NSMHWB) (n = 8841) were used to compare HRQOL of individuals with MDD and CVD, MDD but not CVD, CVD but not MDD, with a healthy reference group. HRQOL was measured using the Assessment of Quality of Life (AQOL). MDD was identified using the Composite International Diagnostic Interview (CIDI 3.0).
Results Of all four groups, individuals with co-morbid CVD and depression reported the greatest deficits in AQOL utility scores (Coef: −0.32, 95% CI: −0.40, −0.23), after adjusting for covariates. Those with MDD only (Coef: −0.27, 95% CI: −0.30, −0.24) and CVD only (Coef: −0.08, 95% CI: −0.11, −0.05) also reported reduced AQOL utility scores. Second, the influence of MDD and CVD on HRQOL was shown to be additive, rather than synergistic. Third, a significant dose–response relationship was observed between depression severity and HRQOL. However, CVD and depression severity appeared to act independently of each other in impacting HRQOL.
Conclusions HRQOL is greatly impaired in individuals with co-morbid MDD and CVD; these conditions appear to influence HRQOL in an additive fashion. HRQOL alters with depression severity, therefore treating depression and improving HRQOL is of clinical importance.
Resumo:
Background Participation in coronary heart disease secondary prevention programs is low. Innovative programs to meet this treatment gap are required.
Purpose To aim of this study is to describe the effectiveness of a telephone-delivered secondary prevention program for myocardial infarction patients.
Methods Four hundred and thirty adult myocardial infarction patients in Brisbane, Australia were randomised to a 6-month secondary prevention program or usual care. Primary outcomes were health-related quality of life (Short Form-36) and physical activity (Active Australia Survey).
Results Significant intervention effects were observed for health-related quality of life on the mental component summary score (p = 0.02), and the social functioning (p = 0.04) and role-emotional (p = 0.03) subscales, compared with usual care. Intervention participants were also more likely to meet recommended levels of physical activity (p = 0.02), body mass index (p = 0.05), vegetable intake (p = 0.04) and alcohol consumption (p = 0.05).
Conclusions Telephone-delivered secondary prevention programs can significantly improve health outcomes and could meet the treatment gap for myocardial infarction patients.
Resumo:
PURPOSE: To conduct a meta-analysis evaluating the effectiveness of depression treatment on mental and physical health-related quality of life (HRQOL) of cardiac patients.
METHODS: Studies were identified using medical, health, psychiatry, psychology, and social sciences databases. Inclusion criteria were (1) 1 or more control conditions, (2) random assignment to condition after admission for myocardial infarction (MI)/acute coronary syndrome, after recording positive results on a depression screener, (3) documentation of depression symptoms at baseline, (4) depression management as a component of the rehabilitation/intervention, (5) validated measure of HRQOL as an outcome, at minimum 6-month followup. For meta-analysis, mental and physical HRQOL were the end points studied, using standardized mean differences for continuous outcome measures, with 95% confidence intervals. Heterogeneity was explored by calculating I2 statistic.
RESULTS: Five randomized controlled trials included in the analysis represented 2105 participants (1058 intervention vs 1047 comparator). Compared with a comparator group at 6 months, a test for overall effect demonstrated statistically significant improvements in mental HRQOL in favor of the intervention (standardized mean differences = −0.29 [−0.38 to −0.20], [P < .00001]; I2 = 0%). Depression treatment had a modest yet significant impact on physical HRQOL (standardized mean differences = −0.14 [−0.24 to −0.04] [P = .009]; I2 = 15%).
CONCLUSION: While the impact of post-MI depression interventions on physical HRQOL is modest, treatment can improve mental HRQOL in a significant way. Future research is required to develop and evaluate a program that can achieve vital improvements in overall HRQOL, and potentially cardiovascular outcomes, of cardiac patients.
Resumo:
The health and wellbeing of children in lower-income countries is the focus of much international effort, yet there has been very little direct measurement of this. Objective. The current objective was to study the health-related quality of life (HRQoL) in a general population of secondary school children in Fiji, a low middle-income country in the Pacific. Methods. Self-reported HRQoL was measured by the Pediatric Quality of Life Inventory 4.0 in 8947 school children (aged 12–18 years) from 18 secondary schools on Viti Levu, the main island of Fiji. HRQoL in Fiji was compared to that of school-aged children in 13 high- and upper middle-income countries. Results. The school children in Fiji had lower HRQoL than the children in the 13 comparison countries, with consistently lower physical, emotional, social, and school functioning and wellbeing. HRQoL was particularly low amongst girls and Indigenous Fijians. Conclusions. These findings raise concerns about the general functioning and wellbeing of school children in Fiji. The consistently low HRQoL across all core domains suggests pervasive underlying determinants. Investigation of the potential determinants in Fiji and validation of the current results in Fiji and other lower-income countries are important avenues for future research.
Resumo:
Traffic noise causes adverse effects on the health and quality of life of individuals and communities exposed to it, including annoyance, sleep disturbance, decreased performance at school/work, stress, hypertension, and ischemic heart disease. In Australia there are few standards or policies addressing noise in urban environments, with many discrepancies in noise level thresholds when comparing states and regions. Currently Victoria has a day-to-night threshold for noise levels well above accepted levels in Europe, and there is no standard for the late night period. A better understanding of the health impacts of noise in the Australian context is vital for informing development and implementation of policy and legislation for road traffic noise management. This paper reviews the evidence base and policies related to traffic noise in urban areas, and presents a case study of noise mapping and assessing population health impacts (eg. sleep disturbance), in Geelong,Vcitoria,Australia.
Resumo:
Late adolescence and early adulthood are times of major behavioral transition in young women as they become more independent and make choices about lifestyle that will affect their long-term health. We prospectively evaluated nutritional and lifestyle factors in 566 15 30-year-old female twins participating in a mixed longitudinal study of diet and lifestyle.Twins completed 790 visits including questionnaires and measures of anthropometry. Nonparametric tests (chi-square, Mann-Whitney U, and Kruskal-Wallis; SPSS) were used to examine age-related differences in selected variables. Dietary calcium intake by short food frequency questionnaire was relatively low [511 (321,747)] mg/day (median, IQR; 60 % of estimated daily total) and did not vary significantly with age. The number of young women who reported ever consuming alcohol (12+ standard drinks ever) increased from 50 % under 18 years to 93 99 % for the 18+ age groups. Of those who consumed alcohol in the preceding year, monthly intake doubled from under 18 years (5.7, 3.9, 19.0 standard drinks; median, IQR) to 18+ years (12.0, 4.7, 26.0; P < 0.001) with the highest consumers being 21 23 and 27 29 years. At age 15 17 years, 14 % reported ever smoking and by age 27–29, 51 % had smoked (P = 0.002). Under the age of 20 years, average cigarette consumption in smokers was six cigarettes per day, increasing to ten above age 20 (P < 0.001). Participation in sporting activity decreased with age (P < 0.001): 47.5 % of 15–17-year-olds undertook 4 or more hour/week of sport, compared with 23.5 % at age 27–29 years. Conversely, sedentary behavior increased with age: 25.0 % of 15–17-year-olds reported 1 or less hour/week of exercise compared with 50.0 % at age 27–29 years. BMI increased with age (P = 0.011), from 21.3 (19.5, 23.6; median, IQR) in the youngest to 23.1 (21.5, 25.9) in the oldest. These highly significant changes in behavior in young women as they transitioned into independent adult living are predicted to impact adversely on bone and other health outcomes in later life. It is crucial to improve understanding of the determinants of these changes and to develop effective interventions to improve long-term health outcomes in young women.
Resumo:
Purpose:
To identify the demographic factors, impairments and activity limitations that contribute to health-related quality of life (HRQOL) in people with idiopathic Parkinson’s disease (PD).
Method:
Two hundred and ten individuals with idiopathic PD who participated in the baseline assessment of a randomized clinical trial were included. The Parkinson’s Disease Questionnaire-39 summary index was used to quantify HRQOL. In order to provide greater clarity regarding the determinants of HRQOL, path analysis was used to explore the relationships between the various predictors in relation to the functioning and disability framework of the International Classification of Functioning model.
Results:
The two models of HRQOL that were examined in this study had a reasonable fit with the data. Activity limitations were found to be the strongest predictor of HRQOL. Limitations in performing self-care activities contributed the most to HRQOL in Model 1 (β = 0.38; p < 0.05), while limitations in functional mobility had the largest contribution in Model 2 (β = −0.31; p < 0.0005). Self-reported history of falls was also found to have a significant and direct relationship with HRQOL in both models (Model 1 β = −0.11; p < 0.05; Model 2 β = −0.21; p < 0.05).
Conclusions:
Health-related quality of life in PD is associated with self-care limitations, mobility limitations, self-reported history of falls and disease duration. Understanding how these factors are inter-related may assist clinicians focus their assessments and develop strategies that aim to minimize the negative functional and social sequelae of this debilitating disease.
