Prediction of Mortality by the Tilburg Frailty Indicator (TFI)

Longitudinal Relationship Between Baseline Social Frailty and Cognitive Impairment in Older Adults: 14-Year Follow-Up Results From the Korean Longitudinal Study of Ageing

OBJECTIVES

This study aimed to investigate the longitudinal relationship between social frailty and cognitive impairment among community-dwelling older adults.

DESIGN

This retrospective cohort study is based on the first to eighth waves of the Korean Longitudinal Study of Ageing (2006-2020).

SETTING AND PARTICIPANTS

The participants were 2106 community-dwelling older adults aged 65 years or older and without cognitive impairment in 2006.

METHODS

Social frailty was assessed with 5 items including social support, social activity, social network, loneliness, and living alone (0 = social nonfrailty, 1 = social prefrailty, 2 or more = social frailty). Cognitive function was assessed using the Korean Mini-Mental State Examination, and scores below 24 indicated cognitive impairment. We used the generalized estimating equation to assess the longitudinal relationship between social frailty and cognitive impairment.

RESULTS

Of the 2106 participants, 515 (24.4%) had social frailty, 669 (31.8%) had social prefrailty, and 922 (43.8%) were social nonfrailty based on the baseline assessments. Relative to the social nonfrailty group, the odds ratios of the social prefrailty and social frailty groups for cognitive impairment were 1.30 (95% CI 1.10-1.54) and 1.41 (95% CI 1.16-1.71), respectively, during the follow-up. Subgroup analysis showed that social inactivity and loneliness were significantly associated with cognitive impairment.

CONCLUSIONS AND IMPLICATIONS

These findings highlight the need for health care providers to introduce and use available social resources for older adults with social frailty to increase the relationships between individual and social context. Social inactivity and loneliness were the major domains associated with cognitive impairment, and loneliness can be resolved by participating in social activities. Therefore, health care providers especially provide opportunities for social activities, such as group-based programs in the community, to reduce social frailty and cognitive impairment.

Frailty Assessment and Its Impact on Loneliness among Older Adults Receiving Home-Based Healthcare during the COVID-19 Pandemic

The purpose of this study was to identify the effect of frailty on loneliness among older adults receiving home care, in correlation to their socioeconomic and homebound statuses. This cross-sectional study recruited 218 individuals aged ≥65 years receiving home-based health services from the "Help at Home" program in the Region of Evrytania, Greece through an open invitation from the municipality authorities from March to June 2022. The Tilburg Frailty Indicator (TFI) was used for the evaluation of frailty, the UCLA Loneliness Scale version 3 was used for loneliness, and social isolation was accessed through five questions (living alone, frequency of interaction with children, relatives, friends, and participation in social organizations). The mean age of the participants was 81.48 ± 9.06, 61.9% were female, 54.1% experienced high levels of loneliness (UCLA-3 mean 45.76 ± 11.10 [range 20-68]), and 46.3% of the participants were found to be socially isolated. Also, 58.3% of the individuals were identified as frail (TFI mean 5.95 ± 3.07) [TFI range 0-13], with 57.3% experiencing physical frailty, 43.6% experiencing psychological frailty, and 27.1% experiencing social frailty. An analysis of covariance (ANCOVA) using UCLA-3 as the dependent variable revealed that loneliness across all domains of TFI was significantly higher in participants with frailty (total frailty [Yes] 49.27 vs. [No] 40,87 p < 0.001) (physical frailty [Yes] = 48.99 vs. [No] = 41.42, p < 0.001, psychological: 48.60 vs. 43.57 p < 0.001, and social: 53.38 vs. 42.94 p < 0.001), particularly compared to non-frail individuals, even after adjusting for potential confounding effects (covariates: gender, age, marital status, family status, living status, educational level, annual income, chronic diseases, homebound status, and social isolation). Our findings indicate that frail older adults experienced higher levels of loneliness, suggesting that frailty and loneliness are independently associated among older adults who receive home-based healthcare.

Prevalence of multidimensional frailty among community-dwelling older adults: A systematic review and meta-analysis

OBJECTIVE

The aims of this systematic review were to explore the pooled prevalence of multidimensional frailty assessed by the Tilburg Frailty Indicator among community-dwelling older adults.

DESIGN

A systematic review and meta-analysis.

METHODS

A comprehensive literature search was conducted across multiple databases, including PubMed, Web of Science, Embase, the Cochrane Library, CINAHL and three Chinese databases. Two independent researchers selected the literatures, extracted the data and evaluated the quality. All statistical analyses were performed using STATA version 16.0.

RESULTS

There were 66 studies with a total of 40,597 individuals that were eligible for the meta-analysis. Data from the meta-analysis revealed the pooled prevalence of 42 % for multidimensional frailty (95 % CI: 38 %-45 %, I2 = 98.9 %, T2 = 0.024, p < 0.001). Among the six studies that provided data for different age groups, the results demonstrated an increasing trend in the prevalence of multidimensional frailty with advancing age. The results of gender-stratified analysis proved that the pooled prevalence of multidimensional frailty in women (45 %, 95 % CI: 39 %-51 %, p < 0.001) was higher than that in men (33 %, 95 % CI: 28 %-39 %, p < 0.001). Based on different education levels, the prevalence of multidimensional frailty is highest in the primary elementary or illiterate group (41 %, 95 % CI: 30 %-52 %, p < 0.001). According to different marital status types, the pooled prevalence of multidimensional frailty in the married group was significantly lower (36 %, 95 % CI: 28 %-43 %) than that in the unmarried, divorced or widowed group (51 %, 95 % CI: 37 %-65 %).

CONCLUSIONS

Through a comprehensive review, we identified that 42 % of elderly individuals living in communities exhibit multidimensional frailty, indicating that multidimensional frailty is relatively common in this population. Stratified analysis revealed that advanced age, female gender, lower education level and unmarried status were associated with higher rates of multidimensional frailty.

The Prediction of Quality of Life by Frailty and Disability among Dutch Community-Dwelling People Aged 75 Years or Older

The present study aimed to examine the prediction of quality of life by frailty and disability in a baseline sample of 479 Dutch community-dwelling people aged 75 years or older using a follow-up period of 8 years. Regarding frailty, we distinguish between physical, psychological, and social frailty. Concerning physical disability, we distinguish between limitations in performing activities in daily living (ADL) and instrumental activities in daily living (IADL). The Tilburg Frailty Indicator (TFI) and the Groningen Activity Restriction Scale (GARS) were used to assess frailty domains and types of disability, respectively. Quality of life was determined by the WHOQOL-BREF containing physical, psychological, social, and environmental domains. In our study, 53.9% of participants were woman, and the mean age was 80.3 years (range 75-93). The study showed that psychological frailty predicted four domains of quality of life and physical frailty three. Social frailty was only found to be a significant predictor of social quality of life and environmental quality of life. ADL and IADL disability proved to be the worst predictors. It is recommended that primary healthcare professionals (e.g., general practitioners, district nurses) focus their interventions primarily on factors that can prevent or delay psychological and physical frailty, thereby ensuring that people's quality of life does not deteriorate.

The effect of social frailty on mental health and quality of life in older people: a cross-sectional study

PURPOSE

This study aims to evaluate anxiety, depression, loneliness, death anxiety, and quality of life and investigate their relationship with social frailty in the geriatric population. Additionally, it aimed to identify social frailty predictors.

METHODS

The study included 136 participants admitted to the geriatric outpatient clinic. The 15-item Geriatric Depression Scale (GDS-15), the Multidimensional Scale of Perceived Social Support (MSPSS), the Cumulative Illness Rating Scale for Geriatrics (CIRS-G), the Templer Death Anxiety Scale (T-DAS), the Loneliness Scale for the Elderly (LSE), the Quality of Life Scale (CASP-19), the Generalized Anxiety Disorder-7 Test (GAD-7), the Tilburg Frailty Indicator (TFI), the FRAIL Scale, and the Clinical Frailty Scale (CFS) were performed. The TFI was used to collect data about social frailty.

RESULTS

There were 61.8% females, and the median age (min-max) was 72.2 (65.3-90.3) years. The prevalence rate of social frailty was 26.7%. The rates of depression, loneliness, anxiety, death anxiety, the burden of chronic disease, and frailty were higher in the social frailty group. Furthermore, logistic regression analysis revealed a strong relationship between social frailty status and widowhood (odds ratio (OR) 6.86; 95% confidence interval (95% CI), 2.42-19.37; p < 0.001), moderate to severe anxiety symptoms (OR 4.37; 95% CI 1.08-17.68; p = 0.038), and a TFI-physical frailty score (OR 1.40; 95% CI 1.12-1.73; p = 0.002).

CONCLUSION

In older adults, the social dimension of frailty is associated with quality of life and psychological state. Physical frailty and sociodemographic characteristics may affect the development of social frailty.

Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk

Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.

Exploring the concept of psychological frailty in older adults: a systematic scoping review

OBJECTIVES

We reviewed the existing definitions of psychological frailty and provided a comprehensive overview of the concept and associated measurements.

STUDY DESIGN AND SETTING

We followed the PRISMA guidelines for scoping reviews and the Joanna Briggs Institute Manual for Evidence Synthesis. The eligibility criteria for including studies were developed based on the participants-concept-context framework. We searched the Cumulative Index to Nursing and Allied Health Literature, Scopus, PubMed, Web of Science and PsycINFO databases, and other sources for relevant studies published between January 2003 and March 2022.

RESULTS

The final scoping review included 58 studies. Of these, 40 defined psychological frailty, seven provided a novel definition, and 11 focused on the components defining psychological frailty. We proposed four groups of components to better characterize psychological frailty: mood, cognitive, other mental health, and fatigue-related problems. We identified 28 measuring tools across studies, and the Tilburg Frailty Indicator was the most frequently used (46.6%).

CONCLUSION

Psychological frailty is a complex concept whose definition seems to lack consensus. It could include both psychological and physical features. Depression and anxiety are commonly used to define it. This scoping review outlined future research directions for refining the concept of psychological frailty.

Relationship between occlusal force and psychological frailty in Japanese community-dwelling older adults: The Septuagenarians, Octogenarians, Nonagenarians Investigation with Centenarians study

BACKGROUND

Frailty increases the risk of negative health-related events, such as falls, disability, hospitalizations, and death. Although the association between oral health and physical frailty is well established, the relationship between oral health and psychological frailty has not yet been investigated. Therefore, we conducted a cross-sectional study to examine the association between maximal occlusal force and psychological frailty in Japanese community-dwelling older adults.

METHODS

Psychological frailty was defined as a World Health Organization-5 scale (WHO-5) score of <13, cognitive and functional status was defined as a Japanese version of the Montreal Cognitive Assessment (MoCA-J) score of <23, and psychological robustness was defined as a WHO-5 score of ≥13 and a MoCA-J score of ≥23. We used a cross-sectional study design to measure maximal occlusal force in 1810 participants, and examined the following factors relevant to psychological frailty: educational level, financial status, living situation, history of chronic diseases, handgrip strength, and instrumental activities of daily living. We used propensity score matching to match the psychological frailty and psychological robustness groups according to demographic and confounding factors. This process, resulted in 344 participants, of whom 172 were in the psychological frailty group and 172 were in the psychological robustness group. In the matched cohort, differences between groups with and without psychological frailty were compared using generalized estimating equations for maximal occlusal force after adjusting for the number of teeth.

RESULTS

After controlling for potential confounding factors of frailty, the psychological frailty group showed lower maximal occlusal force compared with the psychological robustness group (unstandardized regression coefficients = -72.7, 95% confidence interval: -126.3 to -19.1).

CONCLUSIONS

Maximal occlusal force was associated with a reduced prevalence of psychological frailty among Japanese community-dwelling older adults participating in our study.

Deepening the understanding of the structural validity of the Tilburg Frailty Indicator

BACKGROUND

Psychometric properties of the Tilburg Frailty Indicator (TFI) have shown low internal consistency for psychological and social domains, and evidence for its structure validity is controversial. Moreover, research on TFI is frequently limited to community dwellings.

AIMS

To evaluate structural validity, reliability, and convergent and divergent validity of the Spanish version of the Tilburg Frailty Indicator (TFI) in both community-dwelling and institutionalized older people.

MATERIALS AND METHODS

A cross-sectional study was conducted on Spanish older adults (n = 457) recruited from both community settings (n = 322) and nursing homes (n = 135). Participants completed the TFI and other frailty instruments: Fried's Frailty Phenotype, Edmonton Frailty Scale, FRAIL Scale, and Kihon Checklist (KCL). Confirmatory Factor Analysis (CFA), and reliability and validity coefficients were estimated.

RESULTS AND DISCUSSION

Some items from physical and social domains showed low factor loadings (< 0.40). The three-factor CFA model showed better fit indices after depurating these items. Reliability estimates were good (CRI ≥ 0.70) for physical and psychological domains in the institutionalized sample, while in the community dwellings, only physical domain reliability was adequate. Convergent and divergent validity of physical and psychological domains was good, except for some alternative psychological measures highly correlating with the TFI physical component (KCL-depressive mood and Edmonton mood). However, the social domain showed low correlations with some social indicators.

CONCLUSION

The findings of this study clarify some of the controversial validation results of the TFI structure and provide evidence to improve its use in psychometric terms.

CLINICAL TRIAL REGISTRATION NUMBER

NCT03832608.

Predicting Disability Using a Nomogram of the Tilburg Frailty Indicator (TFI)

Disability is associated with lower quality of life and premature death in older people. Therefore, prevention and intervention targeting older people living with a disability is important. Frailty can be considered a major predictor of disability. In this study, we aimed to develop nomograms with items of the Tilburg Frailty Indicator (TFI) as predictors by using cross-sectional and longitudinal data (follow-up of five and nine years), focusing on the prediction of total disability, disability in activities of daily living (ADL), and disability in instrumental activities of daily living (IADL). At baseline, 479 Dutch community-dwelling people aged ≥75 years participated. They completed a questionnaire that included the TFI and the Groningen Activity Restriction Scale to assess the three disability variables. We showed that the TFI items scored different points, especially over time. Therefore, not every item was equally important in predicting disability. 'Difficulty in walking' and 'unexplained weight loss' appeared to be important predictors of disability. Healthcare professionals need to focus on these two items to prevent disability. We also conclude that the points given to frailty items differed between total, ADL, and IADL disability and also differed regarding years of follow-up. Creating one monogram that does justice to this seems impossible.

Association of Depressive Mood and Frailty With Mortality and Health Care Utilization: Korean National Cohort Study

OBJECTIVES

To investigate the association of depressive mood and frailty with mortality and health care utilization (HCU) and identify the coexisting effect of depressive mood and frailty in older adults.

DESIGN

A retrospective study using nationwide longitudinal cohort data.

SETTING AND PARTICIPANTS

A total of 27,818 older adults age 66 years from the National Screening Program for Transitional Ages between 2007 and 2008, part of the National Health Insurance Service-Senior cohort.

METHODS

Depressive mood and frailty were measured by the Geriatric Depression Scale and Timed Up and Go test, respectively. Outcomes were mortality and HCU, including long-term care services (LTCS), hospital admissions, and total length of stay (LOS) from the index date to December 31, 2015. Cox proportional hazards regression and zero-inflated negative binomial regression were performed to identify differences in outcomes by depressive mood and frailty.

RESULTS

Participants with depressive mood and frailty represented 50.9% and 2.4%, respectively. The prevalence of mortality and LTCS use in the overall participants was 7.1% and 3.0%, respectively. More than 3 hospital admissions (36.7%) and total LOS above 15 days (53.2%) were the most common. Depressive mood was associated with LTCS use [hazard ratio (HR) 1.22, 95% confidence interval (CI) 1.05-1.42] and hospital admissions [incidence rate ratio (IRR) 1.05, 95% CI 1.02-1.08]. Frailty had associations with mortality risk (HR 1.96, 95% CI 1.44-2.68), LTCS use (HR 4.86, 95% CI 3.45-6.84), and LOS (IRR 1.30, 95% CI 1.06-1.60). The coexistence of depressive mood and frailty was associated with increased LOS (IRR 1.55, 95% CI 1.16-2.07).

CONCLUSIONS AND IMPLICATIONS

Our findings highlight the need to focus on depressive mood and frailty to reduce mortality and HCU. Identifying combined problems early in older adults may contribute to healthy aging by reducing adverse health outcomes and the burden of health care costs.

Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands

Background Frailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. Objective We compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. Methods We used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome "frail". The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). Results The ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs >0.700. Evaluating the importance of the predictors in each model, "diseases or chronic disorders" was the most important predictor in all models (10 times). The predictors "lifestyle" and "monthly income" were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1800 euro increased the likelihood of frailty. Conclusions Although the ten experts all made different graphs, the predictive performance was always satisfying (AUCs >0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of "diseases or chronic disorders", "lifestyle" and "monthly income". All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.

Frailty Viewed From a Nursing Perspective

Introduction

More and more researchers are convinced that frailty should refer not only to physical limitations but also to psychological and social limitations that older people may have. Such a broad, or multidimensional, definition of frailty fits better with nursing, in which a holistic view of human beings, and thus their total functioning, is the starting point.

Purpose

In this article, which should be considered a Practice Update, we aim at emphasizing the importance of the inclusion of other domains of human functioning in the definition and measurement of frailty. In addition, we provide a description of how district nurses view frailty in older people. Finally, we present interventions that nurses can perform to prevent or delay frailty or its adverse outcomes. We present, in particular, results from studies in which the Tilburg Frailty Indicator, a multidimensional frailty instrument, was used.

Conclusion

The importance of a multidimensional assessment of frailty was demonstrated by usually satisfactory results concerning adverse outcomes of mortality, disability, an increase in healthcare utilization, and lower quality of life. Not many studies have been performed on nurses' opinions about frailty. Starting from a multidimensional definition of frailty, encompassing physical, psychological, and social domains, nurses are able to assess and diagnose frailty and conduct a variety of interventions to prevent or reduce frailty and its adverse effects. Because nurses come into frequent contact with frail older people, we recommend future studies on opinions of nurses about frailty (e.g., screening, prevention, and addressing).

Predictive validity of the Short Functional Geriatric Evaluation for mortality, hospitalization and institutionalization in older adults: A retrospective cohort survey

Objectives

Bio-psycho-social frailty is related to increased risk of death and utilization of health services. This paper reports the predictive validity of a 10-min multidimensional questionnaire on the risk of death, hospitalization and institutionalization.

Methods

A retrospective cohort study was performed based on data from the "Long Live the Elderly!" program, involving 8,561 community-dwelling Italian people >75, followed for an average of 516.6 days (Median = 448, P ₂₅-P ₇₅: 309-692). Mortality, hospitalization, and institutionalization rates according to frailty levels assessed by the Short Functional Geriatric Evaluation (SFGE) have been calculated.

Results

Compared with the robust, the pre-frail, frail, and very frail faced a statistically significant increase in the risk of mortality (RR = 1.40, 2.78 and 5.41), hospitalization (OR = 1.31, 1.67, and 2.08) and institutionalization (OR = 3.63, 9.52, and 10.62). Similar results were obtained in the sub-sample of those with only socio-economic issues. Frailty predicted mortality with an area under the ROC curve of 0.70 (95% CI 0.68-0.72) with sensitivity and specificity of 83.2% and 40.4%. Analyses of single determinants of these negative outcomes showed a multivariable pattern of determinants for all the events.

Conclusions

The SFGE predicts death, hospitalization and institutionalization by stratifying older people according to the levels of frailty. The short administration time, the socio-economic variables and the characteristics of personnel administering the questionnaire make it suitable for being used in public health as a screening tool for a large population, to put frailty at the core of the care for community-dwelling older adults. The difficulty in capturing the complexity of the frailty is witnessed by the moderate sensitivity and specificity of the questionnaire.

Nurses' Opinions on Frailty

Nurses come into frequent contact with frail older people in all healthcare settings. However, few studies have specifically asked nurses about their views on frailty. The main aim of this study was to explore the opinions of nurses working with older people on the concept of frailty, regardless of the care setting. In addition, the associations between the background characteristics of nurses and their opinions about frailty were examined. In 2021, members of professional association of nurses and nursing assistants in the Netherlands (V&VN) received a digital questionnaire asking their opinions on frailty, and 251 individuals completed the questionnaire (response rate of 32.1%). The questionnaire contained seven topics: keywords of frailty, frailty domains, causes of frailty, consequences of frailty, reversing frailty, the prevention of frailty, and addressing frailty. Regarding frailty, nurses especially thought of physical deterioration and dementia. However, other domains of human functioning, such as the social and psychological domains, were often mentioned, pointing to a holistic approach to frailty. It also appears that nurses can identify many causes and consequences of frailty. They see opportunities to reverse frailty and an important role for themselves in this process.

The Tilburg Frailty Indicator: A psychometric systematic review

BACKGROUND

The Tilburg Frailty Indicator (TFI) is one of the most prominent multidimensional frailty assessment instruments. This review aimed to critically appraise and summarise its measurement properties.

METHODS

Reports were eligible if they included results of studies aimed at developing the TFI or evaluating its measurement properties. We performed a literature search in MEDLINE, CINAHL, and PsycINFO databases from their inception until December 8, 2021. We also searched grey literature databases. We assessed the methodological quality of the included studies using the "COSMIN Risk of Bias". The measurement properties were evaluated using specific criteria. We graded the quality of the evidence using a GRADE approach.

RESULTS

Sixty-three studies were included. We found moderate sufficient evidence for TFI content validity, although it is still insufficient for the comprehensiveness of its items. TFI construct validity was based on sufficient evidence from two studies of its structural validity as well as multiple hypothesis-testing for construct validity studies with inconsistent results. We did not find any studies that assessed cross-cultural validity. Only one of TFI's three dimensions showed sufficient evidence for the internal consistency of its scores, and results in test-retest reliability were inconsistent. The TFI showed high sufficient concurrent validity with the comprehensive geriatric assessment. We identified several studies assessing its predictive validity for adverse frailty-related outcomes, although most of the evidence from these studies was insufficient. We did not find any studies that assessed the responsiveness of TFI scores.

CONCLUSIONS

The TFI had evidence gaps in several relevant measurement properties. Further research is needed to strengthen its usefulness as a clinical decision-making tool.

Self-reported quality of life as a predictor of mortality in renal cell carcinoma

BACKGROUND

This study evaluated the utility of self-reported quality of life (QOL) metrics in predicting mortality among all-comers with renal cell carcinoma (RCC) and externally tested the findings in a registry of patients with small renal masses.

METHODS

The Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey (SEER-MHOS) captured QOL metrics composed of mental component summary (MCS) and physical component summary (PCS) scores. Regression models assessed associations of MCS and PCS with all-cause, RCC-specific, and non-RCC-specific mortality. Harrell's concordance statistic (the C-index) and the Akaike information criterion (AIC) determined predictive accuracy and parsimony, respectively. Findings were tested in the prospective Delayed Intervention and Surveillance for Small Renal Masses (DISSRM) registry.

RESULTS

In SEER-MHOS, 1494 patients had a median age of 73.4 years and a median follow-up time of 5.6 years. Each additional MCS and PCS point reduced the hazard of all-cause mortality by 1.3% (95% CI, 0.981-0.993; P < .001) and 2.3% (95% CI, 0.971-0.984; P < .001), respectively. Models with QOL metrics demonstrated higher predictive accuracy (C-index, 72.3% vs 70.1%) and parsimony (AIC, 9376.5 vs 9454.5) than models without QOL metrics. QOL metrics exerted a greater effect on non-RCC-specific mortality than RCC-specific mortality. External testing in the DISSRM registry confirmed these findings with similar results for all-cause mortality.

CONCLUSIONS

Models with self-reported QOL metrics predicted all-cause mortality in patients with RCC with higher accuracy and parsimony than those without QOL metrics. Physical health was a stronger predictor of mortality than mental health. The findings support the incorporation of QOL metrics into prognostic models and patient counseling for RCC.

FRAGILIDADE EM PESSOAS IDOSAS RESIDENTES NO DOMICÍLIO INSCRITAS NUMA UNIDADE DE SAÚDE DO NORTE DE PORTUGAL

Introdução: Com o aumento da longevidade e declínio da função física, psicológica e social dos idosos é essencial perceber as condições sociodemográficas e de saúde que concorrem para a fragilidade. Objetivos: Analisar o perfil de fragilidade dos idosos de uma unidade de saúde do norte de Portugal. Metodologia: Estudo descritivo, transversal com 173 idosos a residir no domicílio e inscritos numa Unidade de Saúde. Como instrumento de recolha de dados usou-se um inquérito, realizado por telefone, contendo dados sociodemográficos, de saúde e o Índice de fragilidade de Tilburg (TFI). Resultados: Amostra predominantemente feminina com idade média de 81,11 anos, maioritariamente casados, com diversas comorbilidades e polimedicados. A representação da fragilidade foi de 60,7%, estando essa condição significativamente associada ao género, estado civil, número de doenças crónicas, polimedicação e autoperceção da saúde. Para a maioria dos idosos (83,8%), a condição de fragilidade é influenciada cumulativamente pelas dimensões físicas, psicológicas e sociais. Conclusão: A fragilidade é uma condição prevalente e o perfil está associado a um conjunto de caraterísticas nas quais é possível intervir retardando a progressão da fragilidade que ocorre com o envelhecimento.

Exercise interventions for older people at risk for frailty: A protocol for systematic review and meta-analysis

BACKGROUND

Frailty is a state of age-related reduced physiological reserve characterized by an increased risk of adverse clinical outcomes. Studies have shown that exercise can improve frailty in older people. However, it remains to be seen which exercises will most improve the fitness of older people with frailty or those at the risk for frailty.Objective: This protocol aims to determine whether physical exercise can improve frailty in older people, and if which methods are most effective.

METHODS

We searched the following databases for relevant articles published between January 1, 2012 and January 1, 2021: PubMed, EMBASE, the Cochrane Library, Wanfang, the China National Knowledge Infrastructure, Clinical Trials Database, and the Science Network. Two independent reviewers will carry out data extraction, discuss and resolve differences, and obtain consensus from the third author. We will select randomized control trials (RCTs) according to the preformulated inclusion criteria. The main outcomes in this study are scores from Fried Frailty Phenotype Criteria; the Frailty Trait Scale-short form; the SHARE Frailty Instrument; the FRAIL scale; the Gérontopôle Frailty Screening Tool; the Clinical Frailty Scale, the Rockwood and Mitnitsky Frailty Index; the Study of Osteoporotic Fractures Index; the Edmonton Frailty Scale; the Fatigue, Resistance, Ambulation, Illness and Loss of Weight Index; the Multidimensional Prognostic Index; the Tilburg Frailty Indicator; PRISMA-7; the Groningen Frailty Indicator; the Sherbrooke Postal Questionnaire; and the Kihon Checklist. Secondary outcomes are muscle strength, gait velocity, stair-climbing power, and level of spontaneous physical activity. If the heterogeneity test shows slight or no statistical heterogeneity, a fixed effects model will be used for data synthesis; otherwise, a random effects model will be used. We will develop a unified data extraction table that includes a number of parameters. The Cochrane Cooperative Bias Risk Tool will be used to evaluate the methodological quality of the selected RCTs. RevMan Manager 5.3 and STATA 14.0 will be used for data analysis if enough RCTs (more than 10) are identified and selected.

RESULT

The final results will provide information on the effectiveness of intervention programs for frail older adul and further demonstrate which exercise programs are more effective and which methods can significantly improve frailty.

CONCLUSION

This protocol will contribute to the development of more effective interventions for elderly individuals with frailty.

ETHICS AND DISSEMINATION

This study applies existing literature references; therefore, ethical approval is not required.

INPLASY REGISTRATION NUMBER

INPLASY202130107.

The prediction of mortality by quality of life assessed with the WHOQOL-BREF: a longitudinal analysis at the domain and item levels using a seven-year follow-up period

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.

Social Frailty in the COVID-19 Pandemic Era

Special attention and efforts to protect from or reduce health-related outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus triggering coronavirus disease 2019 (COVID-19), should be applied in susceptible populations, including frail older people. In particular, the early death cases occurred primarily in older people with a frailty status, possibly due to a weaker immune system fostering faster progression of the viral infection. Frailty is an age-related multidimensional clinical condition defined as a non-specific state of vulnerability, identifying older people at increased risk of falls, institutionalization, hospitalization, disability, dementia, and death. Among frailty phenotypes, social frailty has been least studied. It considers the role of socioeconomic context as a vulnerability status later in life. COVID-19 does not affect all populations equally, and social inequalities contribute to drive the spread of infections. It was known that the perception of social isolation, e.g., loneliness, affects mental and physical health, but the implicated molecular mechanisms, also related to the immune system, and its associated cognitive and health-related sequelae, are poorly understood. The increasing psychological distress derived by prolonged exposure to stress due to the lockdown scenario, and the reduced sources of support, contributed to making heavy demands on personal resources, i.e., self-efficacy and interpersonal variables. So, perceived loneliness may be a factor associated with psychological distress and an outcome in itself. In the COVID-19 pandemic era, a correct assessment of social frailty may be essential in terms of the prevention of late-life neuropsychiatric disorders.