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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In deze literatuurstudie werden vier databanken doorzocht met behulp van trefwoorden zoals chronic disease, e-health, factors en suggested interventions. Kwalitatieve, kwantitatieve en mixed methods-studies werden meegenomen. Uit de data van de 22 artikelen die werden geïncludeerd in de studie, blijken leeftijd, geslacht, inkomen, opleidingsniveau, etnische achtergrond en woonplaats (stad of platteland) in meer of mindere mate van invloed te zijn op het gebruik van e-health. Het artikel is een Nederlandstalige samenvatting van het artikel: Reiners, Sturm, Bouw & Wouters (2019) uit Int J Environ Res Public Health 2019;16(4)
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Alongside the growing number of older persons, the prevalence of chronic diseases is increasing, leading to higher pressure on health care services. eHealth is considered a solution for better and more efficient health care. However, not every patient is able to use eHealth, for several reasons. This study aims to provide an overview of: (1) sociodemographic factors that influence the use of eHealth; and (2) suggest directions for interventions that will improve the use of eHealth in patients with chronic disease. A structured literature review of PubMed, ScienceDirect, Association for Computing Machinery Digital Library (ACMDL), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was conducted using four sets of keywords: “chronic disease”, “eHealth”, “factors”, and “suggested interventions”. Qualitative, quantitative, and mixed-method studies were included. Four researchers each assessed quality and extracted data. Twenty-two out of 1639 articles were included. Higher age and lower income, lower education, living alone, and living in rural areas were found to be associated with lower eHealth use. Ethnicity revealed mixed outcomes. Suggested solutions were personalized support, social support, use of different types of Internet devices to deliver eHealth, and involvement of patients in the development of eHealth interventions. It is concluded that eHealth is least used by persons who need it most. Tailored delivery of eHealth is recommended
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Abstract Background: Multidimensional frailty, including physical, psychological, and social components, is associated to disability, lower quality of life, increased healthcare utilization, and mortality. In order to prevent or delay frailty, more knowledge of its determinants is necessary; one of these determinants is lifestyle. The aim of this study is to determine the association between lifestyle factors smoking, alcohol use, nutrition, physical activity, and multidimensional frailty. Methods: This cross-sectional study was conducted in two samples comprising in total 45,336 Dutch communitydwelling individuals aged 65 years or older. These samples completed a questionnaire including questions about smoking, alcohol use, physical activity, sociodemographic factors (both samples), and nutrition (one sample). Multidimensional frailty was assessed with the Tilburg Frailty Indicator (TFI). Results: Higher alcohol consumption, physical activity, healthy nutrition, and less smoking were associated with less total, physical, psychological and social frailty after controlling for effects of other lifestyle factors and sociodemographic characteristics of the participants (age, gender, marital status, education, income). Effects of physical activity on total and physical frailty were up to considerable, whereas the effects of other lifestyle factors on frailty were small. Conclusions: The four lifestyle factors were not only associated with physical frailty but also with psychological and social frailty. The different associations of frailty domains with lifestyle factors emphasize the importance of assessing frailty broadly and thus to pay attention to the multidimensional nature of this concept. The findings offer healthcare professionals starting points for interventions with the purpose to prevent or delay the onset of frailty, so communitydwelling older people have the possibility to aging in place accompanied by a good quality of life.
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As the two prime examples of sport light, running and walking have become very popular sports activities in the past decades. There are references in the literature of similarities between both sports, however these parallels have never been studied. In addition, the current digitalisation of society can have important influences on the further diversification of profiles. Data of a large-scale population survey among runners and walkers (n = 4913) in Flanders (Belgium) were used to study their sociodemographic, sports related and attitudinal characteristics, and wearable usage. The results showed that walkers are more often female, older, lower educated, and less often use wearables. To predict wearable usage, sports-related and attitudinal characteristics are important among runners but not among walkers. Motivational variables to use wearables are important to predict wearable usage among both runners and walkers. Additionally, whether or not the runner or walker registers the heart rate is the most important predictor. The present study highlights similarities and differences between runners and walkers. By adding attitudinal characteristics and including walkers this article provides new insights to the literature, which can be used by policymakers and professionals in the field of sport, exercise and health, and technology developers to shape their services accordingly.
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Abstract Aim: To gain insight into the relationship between self-management abilities (taking initiatives, investment behaviour, variety, multifunctionality, self-efficacy, positive frame of mind) and physical, psychological and social frailty. Design: A cross-sectional study. Methods: 145 community-dwelling older people receiving home-care completed a questionnaire on sociodemographic factors, the Self-Management-Ability-Scale and the Tilburg Frailty Indicator. After determining correlations, sequential multiple linear regression analyses were executed. Results: All self-management abilities are negatively associated with physical frailty; five (except multifunctionality) are negatively associated with psychological frailty. Variety in resources and positive frame of mind are negatively associated with social frailty. Sociodemographic characteristics, chronic diseases and self-management abilities together significantly explain participants’physical (34.9%), psychological (21.4%) and social (43.9%) frailty. After controlling for sociodemographic characteristics and chronic diseases, the self-management abilities together significantly explain 11 per cent of psychological and 6.8 per cent of social frailty. Having a positive frame of mind significantly negatively influences social frailty.
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What does this paper add to existing knowledge? • This study provides insight into the severity of the problem. It demonstrates the differences in risk factors and OHRQoL between patients diagnosed with a psychotic disorder (first-episode) and the general population. • A negative impact on OHRQoL is more prevalent in patients diagnosed with a psychotic disorder (first-episode) (14.8%) compared to the general population (1.8%). • Patients diagnosed with a psychotic disorder (first-episode) have a considerable increase in odds for low OHRQoL compared to the general population, as demonstrated by the odds ratio of 9.45, which supports the importance of preventive oral health interventions in this group. What are the implications for practice? • The findings highlight the need for oral health interventions in patients diagnosed with a psychotic disorder (first-episode). Mental health nurses, as one of the main health professionals supporting the health of patients diagnosed with a mental health disorder, can support oral health (e.g. assess oral health in somatic screening, motivate patients, provide oral health education to increase awareness of risk factors, integration of oral healthcare services) all in order to improve the OHRQoL.
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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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Introduction: Cancer survivors face physical, lifestyle, psychological, and psychosocial challenges. Despite the availability of aftercare services, survivors still have unmet needs. Digital aftercare programs may offer support, but their use is limited. This study aimed to examine what is needed to improve uptake and adoption of these programs. Additionally, it explored sociodemographic and clinical variables that may influence these needs. Methods: A mixed-methods approach was used, involving qualitative interviews and a questionnaire. The research was guided by the COM-B model of behaviour, which considers capability, opportunity, and motivation crucial for behaviour. Qualitative analysis was performed using the framework method. Statistical analyses involved descriptive statistics and regression analysis. Results: Fourteen cancer survivors were interviewed, and 213 participants completed the questionnaire. Findings indicated that most respondents had a positive or neutral attitude towards digital aftercare programs, believing these could address their cancer-related challenges. Still, only a small percentage had experience with them, and most were unaware of their existence. Many expressed a desire to be informed about them. Some were uncertain about their effectiveness. Others were concerned about a lack of reimbursement. No significant influence of the sociodemographic and clinical variables was found. Conclusion: Cancer survivors are generally positive about digital aftercare programs but are often unaware of their availability. Raising awareness, clarifying their value, and providing support and reimbursement could enhance uptake and adoption. Implications for Cancer Survivors: The current insights can help improve participation in digital aftercare programs, ultimately fostering health, well-being, and quality of life of cancer survivors.
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Purpose: To examine the development of multidimensional frailty, including physical, psychological and socialcomponents, over a period of seven years. To determine the effects of sociodemographic factors (gender, age, marital status, education, income) on the development of frailty. Methods: : This longitudinal study was conducted in sample of 479 community-dwelling people aged ≥ 75 years living in the municipality of Roosendaal, the Netherlands. The Tilburg Frailty Indicator (TFI), a self-report questionnaire, was used to collect data about frailty. Frailty was assessed annually. Results: : Frailty increased significantly over seven years among the people who completed the entire TFI all years (n = 121), the average score was 3.75 (SD 2.80) at baseline and 5.05 (SD 3.18) after seven years. Regarding frailty transitions, most participants remained unchanged from their baseline status. The transition from non-frail to frail was present in 8.3% to 12.6% of the participants and 5.1% to 10.7% made a transition from frail to nonfrail. Gender (woman), age (≥80 years), marital status (not married/cohabiting), high level of education, and incomes from €601-€1800 were significantly associated with a higher frailty score. Conclusion: : This study showed that multidimensional frailty, assessed with the TFI, increased among Dutch community-dwelling people aged ≥75 years using a follow-up of seven years. Gender, age, marital status, education, and income were associated with frailty transitions. These findings provide healthcare professionals clues to identify people at increased risk of frailty, and target interventions which aim to prevent or delay frailty and its adverse outcomes, such as disability and mortality.
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