OBJECTIVE: To examine how a healthy lifestyle is related to life expectancy that is free from major chronic diseases.DESIGN: Prospective cohort study.SETTING AND PARTICIPANTS: The Nurses' Health Study (1980-2014; n=73 196) and the Health Professionals Follow-Up Study (1986-2014; n=38 366).MAIN EXPOSURES: Five low risk lifestyle factors: never smoking, body mass index 18.5-24.9, moderate to vigorous physical activity (≥30 minutes/day), moderate alcohol intake (women: 5-15 g/day; men 5-30 g/day), and a higher diet quality score (upper 40%).MAIN OUTCOME: Life expectancy free of diabetes, cardiovascular diseases, and cancer.RESULTS: The life expectancy free of diabetes, cardiovascular diseases, and cancer at age 50 was 23.7 years (95% confidence interval 22.6 to 24.7) for women who adopted no low risk lifestyle factors, in contrast to 34.4 years (33.1 to 35.5) for women who adopted four or five low risk factors. At age 50, the life expectancy free of any of these chronic diseases was 23.5 (22.3 to 24.7) years among men who adopted no low risk lifestyle factors and 31.1 (29.5 to 32.5) years in men who adopted four or five low risk lifestyle factors. For current male smokers who smoked heavily (≥15 cigarettes/day) or obese men and women (body mass index ≥30), their disease-free life expectancies accounted for the lowest proportion (≤75%) of total life expectancy at age 50.CONCLUSION: Adherence to a healthy lifestyle at mid-life is associated with a longer life expectancy free of major chronic diseases.
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In light of current worldwide developments, the conference theme “Value Diversity” explicitly refers to the changes we need to see.This contribution is about Life expectancy of people with a severe or profound intellectual disability. Their life expectancy increases, which contributes to the risk of developing dementia. However, early detection and diagnosing dementia is complex, because of their low-level baseline functioning. Therefore, the aim is to identify observable dementia symptoms in adults with severe or profound intellectual disability in available literature.
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Advanced technology is a primary solution for the shortage of care professionals and increasing demand for care, and thus acceptance of such technology is paramount. This study investigates factors that increase use of advanced technology during elderly care, focusing on current use of advanced technology, factors that influence its use, and care professionals’ experiences with the use. This study uses a mixed-method design. Logfiles were used (longitudinal design) to determine current use of advanced technology, questionnaires assessed which factors increase such use, and in-depth interviews were administered to retrieve care professionals’ experiences. Findings suggest that 73% of care professionals use advanced technology, such as camera monitoring, and consult clients’ records electronically. Six of nine hypotheses tested in this study were supported, with correlations strongest between performance expectancy and attitudes toward use, attitudes toward use and satisfaction, and effort expectancy and performance expectancy. Suggested improvements for advanced technology include expanding client information, adding report functionality, solving log-in problems, and increasing speed. Moreover, the quickest way to increase acceptance is by improving performance expectancy. Care professionals scored performance expectancy of advanced technology lowest, though it had the strongest effect on attitudes toward the technology.
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AimsKnowledge of patient preferences is vital for delivering optimal healthcare. This study uses utility measurement to assess the preferences of heart failure (HF) patients regarding quality of life or longevity. The utility approach represents the perspective of a patient; facilitates the combination of mortality, morbidity, and treatment regimen into a single score; and makes it possible to compare the effects of different interventions in healthcare.Methods and resultsPatient preferences of 100 patients with HF were assessed in interviews using the time trade-off (TTO) approach. Health-related quality of life (HR-QoL) was assessed with the EQ-5D and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Patients' own estimation of life expectancy was assessed with a visual analogue scale (VAS). Of the 100 patients (mean age 70 ± 9 years; 71% male), 61% attach more weight to quality of life over longevity; while 9% and 14% were willing to trade 6 and 12 months, respectively, for perfect health and attach more weight to quality of life. Patients willing to trade time had a significantly higher level of NT-proBNP and reported significantly more dyspnoea during exertion. Predictors of willingness to trade time were higher NT-proBNP and lower EQ VAS.ConclusionThe majority of HF patients attach more weight to quality of life over longevity. There was no difference between both groups with respect to life expectancy described by the patients. These insights enable open and personalized discussions of patients' preferences in treatment and care decisions, and could guide the future development of more patient-centred care. © 2013 Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2013. For permissions please email: journals.permissions@oup.com.
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Background: The increase in life expectancy has brought about a higher prevalence of chronic illnesses among older people. Objectives: To identify common chronic illnesses among older adults, to examine the influence of such conditions on their Health-Related Quality of Life (HRQoL), and to determine factors predicting their HRQoL. Method: A population-based cross-sectional study was conducted involving 377 individuals aged 60 years and above who were selected using multi-stage sampling techniques in Olorunda Local Government, Osun State, Nigeria. Data were collected using an interviewer-administered questionnaire comprising socio-demographic characteristics, chronic illnesses, and the World Health Organization quality of life instrument (WHOQOL-BREF) containing physical health, psychological, social relationships, and environmental domains. Results: About half (51.5%) of the respondents reported at least one chronic illness which has lasted for 1–5 years (43.3%). The prevalence of hypertension was 36.1%, diabetes 13.9% and arthritis 13.4%. Respondents with chronic illness had significantly lower HRQoL overall and in the physical health, social relationships and the environmental domains (all p<0.05) compared to those without a chronic illness. Factors that predicted HRQoL include age, marital status, level of education, the presence of chronic illness and prognosis of the condition. Conclusion: This study concluded that chronic illness is prevalent in Nigerian older people and significantly influence their HRQoL. Age, marital status, and level of education were associated with HRQoL in this group.
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Background: Increasing life expectancy in high-income countries has been linked to a rise in fall mortality. In the Netherlands, mortality rates from falls have increased gradually from the 1950s, with some indication of stabilisation in the 1990s. For population health and clinical practice, it is important to foresee the future fall mortality trajectories. Methods: A graphical approach was used to explore trends in mortality by age, calendar period and cohorts born in the periods of 1915–1945. Population data and the numbers of people with accidental fall fatality as underlying cause of death from 1990 to 2021 were derived from Statistics Netherlands. Age-standardised mortality rates of unintentional falls per 100 000 population were calculated by year and sex. A log-linear model was used to examine the separate effects of age, period and cohort on the trend in mortality and to produce estimates of future numbers of fall deaths until 2045. Results: While the total population increased by 17% between 1990 and 2021, absolute numbers of fall-related deaths rose by 230% (from 1584 to 5234), which was 251% (an increase of 576 deaths in 1990 to 2021 deaths in 2020) for men and 219% (from 1008 to 3213) for women. Age-standardised figures were higher for women than men and increased more over time. In 2020, 79% of those with death due to falls were over the age of 80, and 35% were 90 years or older. From 2020 to 2045, the observed and projected numbers of fall deaths were 2021 and 7073 for men (250% increase) and 3213 and 12 575 for women (291% increase). Conclusion: Mortality due to falls has increased in the past decades and will continue to rise sharply, mainly caused by growing numbers of older adults, especially those in their 80s and 90s. Contributing risk factors are well known, implementation of preventive measures is a much needed next step. An effective approach to managing elderly people after falls is warranted to reduce crowding in the emergency care and reduce unnecessary long hospital stays.
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Abstract Purpose To determine the predictive value of quality of life for mortality at the domain and item levels. Methods This longitudinal study was carried out in a sample of 479 Dutch people aged 75 years or older living independently, using a follow-up of 7 years. Participants completed a self-report questionnaire. Quality of life was assessed with the WHOQOL-BREF, including four domains: physical health, psychological, social relationships, and environment. The municipality of Roosendaal (a town in the Netherlands) indicated the dates of death of the individuals. Results Based on mean, all quality of life domains predicted mortality adjusted for gender, age, marital status, education, and income. The hazard ratios ranged from 0.811 (psychological) to 0.933 (social relationships). The areas under the curve (AUCs) of the four domains were 0.730 (physical health), 0.723 (psychological), 0.693 (social relationships), and 0.700 (environment). In all quality of life domains, at least one item predicted mortality (adjusted). Conclusion Our study showed that all four quality of life domains belonging to the WHOQOL-BREF predict mortality in a sample of Dutch community-dwelling older people using a follow-up period of 7 years. Two AUCs were above threshold (psychological, physical health). The findings offer health care and welfare professionals evidence for conducting interventions to reduce the risk of premature death.
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Introduction Many health care interventions have been developed that aim to improve or maintain the quality of life for frail elderly. A clear overview of these health care interventions for frail elderly and their effects on quality of life is missing. Purpose To provide a systematic overview of the effect of health care interventions on quality of life of frail elderly. Methods A systematic search was conducted in Embase, Medline (OvidSP), Cochrane Central, Cinahl, PsycInfo and Web of Science, up to and including November 2017. Studies describing health care interventions for frail elderly were included if the effect of the intervention on quality of life was described. The effects of the interventions on quality of life were described in an overview of the included studies. Results In total 4,853 potentially relevant articles were screened for relevance, of which 19 intervention studies met the inclusion criteria. The studies were very heterogeneous in the design: measurement of frailty, health care intervention and outcome measurement differ. Health care interventions described were: multidisciplinary treatment, exercise programs, testosterone gel, nurse home visits and acupuncture. Seven of the nineteen intervention studies, describing different health care interventions, reported a statistically significant effect on subdomains of quality of life, two studies reported a statistically significant effect of the intervention on the overall quality of life score. Ten studies reported no statistically significant difference between the intervention and control groups. Conclusion Reported effects of health care interventions on frail elderly persons’ quality of life are inconsistent, with most of the studies reporting no differences between the intervention and control groups. As the number of frail elderly persons in the population will continue to grow, it will be important to continue the search for effective health care interventions. Alignment of studies in design and outcome measurements is needed.
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At the beginning of May 2020 all Inholland-students received an invitation to participate in a large international study on the corona crisis impact on student life and studies. This poster, presented by the Study Success Research Group, covers relevant results divided in four themes. These themes are student wellbeing, student engagement, satisfaction and the coronavirus. To determine student wellbeing we asked students about their feelings and contacts. Student engagement is phrased in time allocation and engagement. We also wanted to find out how satisfied students were with things like ICT facilities, quality of education and provision of information. Of course we asked students about (not) having corona and adhering to the measures.
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Objective: The effects of sociodemographic factors on quality of life in older people differ strongly, possibly due to the fact that different measurement instruments have been used. The main aim of this cross-sectional study is to compare the associations of sex, age, marital status, education, and income with quality of life assessed with the Short-Form Health Survey (SF-12), the World Health Organization Quality of Life Questionnaire-BREF (WHOQOL-BREF), and the World Health Organization Quality of Life Questionnaire-Older Adults Module (WHOQOL-OLD). Methods: The associations between sociodemographic factors and eleven quality of life domains were examined using a sample of 1,492 Dutch people aged $50 years. Participants completed the “Senioren Barometer”, a web-based questionnaire including sociodemographic factors, the SF-12, the WHOQOL-BREF, and the WHOQOL-OLD. Results: All the sociodemographic factors together explained a significant part of the variance of all the quality of life domains’ scores, ranging from 5% to 17% for the WHOQOL-BREF, 5.8% to 6.7% for the SF-12, and 1.4% to 26% for the WHOQOL-OLD. Being a woman and being older were negatively associated with two and four quality of life domains, respectively. Being a woman, being married or cohabiting, and having higher education and a higher income were positively associated with six, six, one, and eleven quality of life domains, respectively. Conclusion: Our study showed that the associations of sociodemographic factors and quality of life in middle-aged and older people depend on the instruments used to assess quality of life. We recommend that health care and welfare professionals focus particularly on people with a low income and carry out interventions aimed at improving their quality of life.
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