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Quality of life is an important health outcome for older persons. It predicts the adverse outcomes of institutionalization and premature death. The aim of this cross-sectional study was to determine the influence of both disability in activities of daily living (ADL) and instrumental activities of daily living (IADL) on physical and mental dimensions of quality of life. A total of 377 Dutch people aged 75 years and older completed a web-based questionnaire. This questionnaire contained the Groningen Activity Restriction Scale (GARS) for measuring ADL and IADL and the Short-Form Health Survey (SF-12) for measuring quality of life. The SF-12 distinguishes two dimensions of quality of life, a physical and mental dimension. All ADL disability items combined and all IADL disability items combined explained a significant part of the variance of the physical and the mental dimension of quality of life. Only ADL item \"stand up from sitting in a chair\", and IADL items \"do \"heavy\" household activities\" and \"do the shopping\" were negatively associated with both quality of life dimensions after controlling for all the variables in the model. This study showed that disability in ADL and IADL is negatively associated with quality of life in older people. Therefore, it is important for health care professionals to carry out interventions aimed to prevent and diminish disability or the adverse outcomes of disability such as a lower quality of life. In order to be effective these interventions should be inexpensive, feasible, and easy to implement.

Background One of the most challenging issues for the elderly population is the clinical state of frailty. Frailty is defined as a cumulative decline across psychological, physical, and social functioning. Hospitalization is one of the most stressful events for older people who are becoming frail. The aim of the present study was to determine the effectiveness of interventions focused on management of frailty in hospitalized frail older adults. Methods A systematic review and meta-analysis of research was conducted using the Medline, Embase, Cochrane, ProQuest, CINAHL, SCOPUS and Web of Science electronic databases for papers published between 2000 and 2019. Randomized controlled studies were included that were aimed at the management of frailty in hospitalized older adults. The outcomes which were examined included frailty; physical, psychological, and social domains; length of stay in hospital; re-hospitalization; mortality; patient satisfaction; and the need for post discharge placement. Results After screening 7976 records and 243 full-text articles, seven studies (3 interventions) were included, involving 1009 hospitalized older patients. The quality of these studies was fair to poor and the risk of publication bias in the studies was low. Meta-analysis of the studies showed statistically significant differences between the intervention and control groups for the management of frailty in hospitalized older adults (ES = 0.35; 95% CI: 0. 067–0.632; z = 2.43; P  < 0.015). However, none of the included studies evaluated social status, only a few of the studies evaluated other secondary outcomes. The analysis also showed that a Comprehensive Geriatric Assessment unit intervention was effective in addressing physical and psychological frailty, re-hospitalization, mortality, and patient satisfaction. Conclusions Interventions for hospitalized frail older adults are effective in management of frailty. Multidimensional interventions conducted by a multidisciplinary specialist team in geriatric settings are likely to be effective in the care of hospitalized frail elderly. Due to the low number of RCTs carried out in a hospital setting and the low quality of existing studies, there is a need for new RCTs to be carried out to generate a protocol appropriate for frail older people.

Abstract Objective. To examine the relationship between frailty and pain, particularly to analyze whether pain predicts physical, psychological and social frailty, after controlling for the effects of life-course determinants and comorbidity. Design. Cross-sectional. Methods. A nonprobabilistic sample of 252 community dwelling elderly was recruited. Frailty and determinants of frailty were assessed with the Tilburg Frailty Indicator and pain was measured with the Pain Impact Questionnaire. Hierarchical and logistic regression analyses were conducted. Results. In this study, 52.4% of the participants were aged 80 years and over, and 75.8% were women. Pain and frailty were higher in women, and physical frailty was higher in those aged ≥80 years. After controlling for the effects of the determinants and comorbidity, pain predicted 5.8% of the variance of frailty, 5.9% of the variance of physical frailty, and 4.0% of the variance of psychological frailty, while the prediction of social frailty was nonsignificant. Overall, a greater pain impact score was associated with the presence of frailty (odds ratio 1.06; 95% CI 1.03–1.10; P < 0.001). Conclusion. Frailty was independently predicted by pain, emphasizing the importance of its treatment, potentially contributing to the prevention of vulnerability, dependency, and mortality. Nonetheless, longitudinal studies are required to better understand the possible association between pain and frailty.

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 community-dwelling 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 community-dwelling older people have the possibility to aging in place accompanied by a good quality of life.

Purpose Although frailty was originally a medial concept, nowadays more and more researchers are convinced of its multidimensional nature, including a psychological and social domain of frailty as well as a physical domain. The objective of this study was to test the hypothesis that the prediction of quality of life by physical frailty components is improved by adding psychological and social frailty components. Methods This cross-sectional study was carried out with a sample of Dutch citizens. A total of 1,031 people aged 65 years and older completed a Web-based questionnaire containing the Tilburg Frailty Indicator for measuring physical, psychological, and social frailty, and the WHOQOLBREF for measuring four quality of life domains (physical health, psychological, social relations, environmental). Results The findings show that the prediction of all quality of life domains by eight physical components of frailty was improved after adding four psychological and three social frailty components. The psychological frailty component 'feeling down' significantly improved the prediction of all four quality of life domains, after controlling for the effects of background characteristics and all other frailty components. Conclusion This study emphasizes the importance of a multidimensional assessment of frailty in the prediction of quality of life in older people.

Elderly people constitute over 80% of the population of patients with heart failure (HF). Frailty is a distinct biological syndrome that reflects decreased physiologic reserve and resistance to stressors. Moreover, frailty can serve as an independent predictor of visits to the emergency department, hospitalizations, and mortality. The purpose of this paper was to assess the relationship between frailty, anxiety and depression, and the health-related quality of life (HRQoL) of elderly patients with HF. The study included 100 patients (53 men and 47 women) with a diagnosis of HF. Frailty was measured using the Tilburg Frailty Indicator (TFI) scale. HRQoL was measured using the 36-Item Short Form Medical Outcomes Study Survey. To determine the prevalence of anxiety and depression, the Hospital Anxiety and Depression Scale was used. Frailty was found in 89% of the studied population. The study showed significant inverse correlations between the values of the physical component scale (PCS) domain results and TFI score, and a significant inverse correlation between the values of the mental component scale (MCS) domain and TFI score. When participants showed increased levels of frailty as measured by the TFI scale, there was also an increase in the levels of anxiety and depression. With increased anxiety and depression, there was deterioration in the quality of life of patients with HF. Frailty has a negative impact on the HRQoL results of elderly patients with HF. The assessment of frailty syndrome, and anxiety and depression should be taken into account when estimating risk and making therapeutic decisions for cardiovascular disease treatment and care.

The prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients’ responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the non-physical origins of HF.

To predict mortality by disability in a sample of 479 Dutch community-dwelling people aged 75 years or older. A longitudinal study was carried out using a follow-up of seven years. The Groningen Activity Restriction Scale (GARS), a self-reported questionnaire with good psychometric properties, was used for data collection about total disability, disability in activities in daily living (ADL) and disability in instrumental activities in daily living (IADL). The mortality dates were provided by the municipality of Roosendaal (a city in the Netherlands). For analyses of survival, we used Kaplan-Meier analyses and Cox regression analyses to calculate hazard ratios (HR) with 95% confidence intervals (CI). All three disability variables (total, ADL and IADL) predicted mortality, unadjusted and adjusted for age and gender. The unadjusted HRs for total, ADL and IADL disability were 1.054 (95%-CI: [1.039;1.069]), 1.091 (95%-CI: [1.062;1.121]) and 1.106 (95%-CI: [1.077;1.135]) with p-values <0.001, respectively. The AUCs were <0.7, ranging from 0.630 (ADL) to 0.668 (IADL). Multivariate analyses including all 18 disability items revealed that only \"Do the shopping\" predicted mortality. In addition, multivariate analyses focusing on 11 ADL items and 7 IADL items separately showed that only the ADL item \"Get around in the house\" and the IADL item \"Do the shopping\" significantly predicted mortality. Disability predicted mortality in a seven years follow-up among Dutch community-dwelling older people. It is important that healthcare professionals are aware of disability at early stages, so they can intervene swiftly, efficiently and effectively, to maintain or enhance the quality of life of older people.

Background Frail older adults who are hospitalized, are more likely to experience missed nursing care (MNC) due to high care needs, communication problems, and complexity of nursing care. We conducted a qualitative study to examine the factors affecting MNC among hospitalized frail older adults in the medical units. Methods This qualitative study was carried using the conventional content analysis approach in three teaching hospitals. Semi-structured interviews were conducted with 17 nurses through purposive and snowball sampling. The inclusion criteria for the nurses were: at least two years of clinical work experience on a medical ward, caring for frail older people in hospital and willingness to participate. Data were analyzed in accordance with the process described by Graneheim and Lundman. In addition, trustworthiness of the study was assessed using the criteria proposed by Lincoln and Guba. Results In general, 20 interviews were conducted with nurses. A total of 1320 primary codes were extracted, which were classified into two main categories: MNC aggravating and moderating factors. Factors such as “age-unfriendly structure,” “inefficient care,” and “frailty of older adults” could increase the risk of MNC. In addition, factors such as “support capabilities” and “ethical and legal requirements” will moderate MNC. Conclusions Hospitalized frail older adults are more at risk of MNC due to high care needs, communication problems, and nursing care complexity. Nursing managers can take practical steps to improve the quality of care by addressing the aggravating and moderating factors of MNC. In addition, nurses with a humanistic perspective who understand the multidimensional problems of frail older adults and pay attention to their weakness in expressing needs, can create a better experience for them in the hospital and improve patient safety.

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.