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M. Canevelli1,2,*, F.S. Bersani1,*, F. Sciancalepore1, M. Salzillo1, M. Cesari3,4, L. Tarsitani1, M. Pasquini1, S. Ferracuti1, M. Biondi1, G. Bruno1


1. Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy; 2. National Center for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy; 3. Geriatric Unit, IRCCS Istituti Clinici Scientifici Maugeri, Milan, Italy; 4. Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy; *These authors contributed equally to the work.

Corresponding Author: Marco Canevelli, Francesco Saverio Bersani, Department of Human Neurosciences, Sapienza University of Rome, Viale dell’Università 30, 00185, Rome, Italy, marco.canevelli@uniroma1.it, francescosaverio.bersani@uniroma1.it

J Frailty Aging 2021;in press
Published online June 25, 2021, http://dx.doi.org/10.14283/jfa.2021.29



Background: Studies increasingly suggest that chronic exposure to psychological stress can lead to health deterioration and accelerated ageing, thus possibly contributing to the development of frailty. Recent approaches based on the deficit accumulation model measure frailty on a continuous grading through the “Frailty Index” (FI), i.e. a macroscopic indicator of biological senescence and functional status.
OBJECTIVES: The study aimed at testing the relationship of FI with caregiving, psychological stress, and psychological resilience.
DESIGN: Cross-sectional study, with case-control and correlational analyses.
PARTICIPANTS: Caregivers of patients with dementia (n=64), i.e. individuals a priori considered to be exposed to prolonged psychosocial stressors, and matched controls (n=64) were enrolled.
MEASUREMENTS: The two groups were compared using a 38-item FI condensing biological, clinical, and functional assessments. Within caregivers, the association of FI with Perceived Stress Scale (PSS) and Brief Resilience Scale (BRS) was tested.
RESULTS: Caregivers had higher FI than controls (F=8.308, p=0.005). FI was associated directly with PSS (r=0.660, p<0.001) and inversely with BRS (r=-0.637, p<0.001). Findings remained significant after adjusting for certain confounding variables, after excluding from the FI the conditions directly related to psychological stress, and when the analyses were performed separately among participants older and younger than 65 years.
CONCLUSIONS: The results provide insight on the relationship of frailty with caregiving, psychological stress, and resilience, with potential implications for the clinical management of individuals exposed to chronic emotional strain.

Key words: Frailty, stress, resilience, caregiving, psychopathology, comorbidity.



Frailty has been originally conceptualized in the context of geriatric research to describe a condition of reduced homeostatic reserves and increased vulnerability to exogenous and endogenous stimuli often characterizing older adults (1). Although a definition of frailty has long been debated, it has recently been defined by the World Health Organization as “a progressive age-related decline in physiological systems that results in decreased reserves of intrinsic capacity, which confers extreme vulnerability to stressors and increases the risk of a range of adverse health outcomes” (2), and by an international consensus group as “a medical syndrome with multiple causes and contributors that is characterized by diminished strength, endurance, and reduced physiologic function that increases an individual’s vulnerability for developing increased dependency and/or death” (3).
Overall, frailty is considered to represent an estimate of organism’s biological age and, as such, it is increasingly explored in several medical areas to account for the interindividual variability in health trajectories and outcomes (4-6). Recent approaches measure frailty on a continuous grading according to the model of deficit accumulation, which postulates that the individual’s degree of frailty is related to the amount of health deficits accumulated with aging; accordingly, one’s biological and clinical complexity can be estimated by condensing such negative attributes in a single continuous variable, the Frailty Index (FI) (5, 7-11).
The concept of psychological stress is nowadays increasingly explored in virtually all fields of health-related research. It has been shown that exposure to psychological and social stress in childhood and adulthood, as well as the cumulation of psychosocial stressors over time, can have a significant negative impact on health mediated by a range of molecular mechanisms including, but not limited to, alterations in hypothalamic–pituitary–adrenal axis functioning and inflammatory responses (12).
Exposure to chronic psychological stress has been associated with increased vulnerability and worse outcomes related to cardiovascular, mental, metabolic, oncological, and infectious diseases, as well as to disability and earlier mortality (12). As a consequence, it seems possible that chronic psychological stress represents a factor contributing to the development of frailty. Such possibility is supported by the evidence linking psychological stress to the molecular mediators of biological age: while the condition of frailty has been proposed to represent a product of senescent biological age (4-6, 9, 13), studies have suggested that chronic exposure to psychological stress play a role in accelerating biological aging, i.e. leading to premature senescence, as documented, for example, by the widely replicated association of perceived psychological stress and stress-related psychiatric disorders with shorter telomere length (TL) in leukocytes (14-16).
Relatively few research has been performed so far to test the association between psychological stress and frailty. Further, to the best of our knowledge, no studies have explored such relationship among caregivers of patients with dementia, i.e. individuals exposed to severe and prolonged psychological burden (17), and operationalizing frailty through the FI as deficit accumulation. It can be hypothesized that caregiving, intended as a condition of chronic psychological stress exposure, is associated with accelerated senescence and higher accrual of health deficits, and that, among caregivers, frailty levels are directly related to the intensity of perceived psychological stress and inversely related to the individual’s capacity of psychological resilience, i.e. the capacity of maintaining positive emotional responses in the presence of psychosocial stressors. Therefore, the aim of the present study was (i) to compare biological age and functional status assessed through the FI in caregivers and matched controls, and (ii) within caregivers, to test the association of FI with measures of perceived psychological stress and resilience.




A total of 128 individuals were enrolled in the study: 64 caregivers of patients with dementia (i.e. individuals a priori considered as exposed to prolonged psychosocial stressors) and 64 non-caregiver matched controls. Such amount of participants was selected as a sample size calculation performed with the G*Power 3.1 software (18) indicated that 64 subjects in each group are needed to achieve an effect size of 0.5 with power=0.80 and alpha=0.05 (two-tailed) in between-group comparisons.
Caregivers were consecutively recruited through their carereceiver’s healthcare professionals at the Memory Clinic of Policlinico Umberto I University Hospital of Rome (Italy). Non-caregiver controls were recruited among volunteers in order to be age- and sex-matched with caregivers. Inclusion criteria for caregivers were (i) being spouse, child, sibling, or parent of a patient with a major neurocognitive disorder diagnosed according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM5) (19), (ii) living with the carereceiver, and (iii) having the role of principal caregiver from at least two years. Caregivers and controls were not included if they had a clinically unstable and disabling medical condition at the time of the evaluation.
Participants received a complete explanation of the procedures of the study and provided consent for allowing the utilization of the collected data for research purposes. Participants were not compensated for the participation to the study.

Procedures and assessments

The frailty status of all participants (caregivers and controls, n=128) was evaluated using a 38-item FI designed according to the model of Rockwood and Mitniski, following a standard procedure (5, 9-11). As mentioned above, the FI is a health-state measure, condensing information from multiple physiological systems; it is thought to reflect individual’s biological age and vulnerability to adverse outcomes. The FI is defined as the ratio between the deficits presented by the subject and the number of deficits explored in the context of a comprehensive clinical assessment. Candidate variables include symptoms, signs, comorbidities, and disabilities meeting standardized criteria (11). Each composing item is coded as “0” or “1” depending on whether the corresponding health deficit is absent or present, respectively. The FI thus provides a continuous measure of frailty potentially ranging between 0 and 1 for each individual. A cut-off of 0.25 has been adopted to identify frail and non-frail subjects (20-22). The deficits considered for the computation of the FI used in the present study emerged from biological (e.g. oxygen saturation, systolic blood pressure, body mass index [BMI], heart rate), clinical (e.g. current and past pathologies, chronic conditions, comorbidities) and functional (e.g. difficulties in transportations, money management, household keeping) evaluations, and they are listed in Table 1. In the present study the Cronbach’s α for the overall sample (caregivers and controls) was 0.76.

Table 1. Deficits considered in the computation of the Frailty Index


In addition to the FI, 62 of the caregivers were also evaluated with the 10 Items Perceived Stress Scale (PSS) (23-24), and with the Brief Resilience Scale (BRS) (25-27). In the PSS, respondents are asked to rate how often they experienced psychological stress in the past month on a 5-point Likert-type scale ranging from “Never = 0” to “Very Often = 4”, and a total score is calculated such that higher score reflects higher perceived psychological stress (23-24). In the present study, the Italian version of the scale was used (24), and the Cronbach’s α was 0.85. In the BRS, respondents are asked to rate their ability to recover from psychosocial stress by answering to 6 questions on a 5-point Likert-type scale ranging from “strongly disagree=1” to “strongly agree= 5”, and a total score is calculated such that higher score reflects higher resilience (25-27). Further, the authors of BRS proposed the following scores to qualitatively differentiate levels of resilience: below 3.00=low resilience, above 4.30=high resilience (27). In the present study, the Italian version of the scale was used (25), and the Cronbach’s α was 0.89.

Statistical methods

The Statistical Package for the Social Sciences (SPSS) was used for statistical calculations. All tests were 2-tailed with alpha=0.05. Quantitative data are expressed as means ± standard deviations (SD).
Parametric tests were performed as data were normally distributed (skewness and kurtsosis between -2 and 2). Analysis of variance (ANOVA) was used to test inter-group differences in FI between caregivers and controls, while analysis of covariance (ANCOVA) was used to control for confounding variables. Pearson correlations and partial correlations were performed to test the association of PSS and BRS with FI within caregivers.
ANOVA, Pearson correlation, and chi-square (Χ²) tests were also used to perform the following sensitivity analyses: (i) analyses performed using a modified FI computed after the exclusion of variables directly related to psychological stress; (ii) analyses performed separately among participants older and younger than 65 years; and (iii) analyses performed to further stratify/investigate the characteristics of the enrolled sample).



Almost all caregivers were spouses/partners (n=42, 65.6%) or children (n=20, 31.2%) of their carereceivers. The duration of their role as caregivers ranged between six and ten years in almost a half of cases (48.3%) and was lower than five years in 33.3% of cases. There were no significant differences between caregivers and non-caregiver controls by age, BMI, sex, and use of medications (Table 2).

Table 2. Characteristics of caregivers and controls

Abbreviations: BMI=Body Mass Index; ANOVA= Analysis of variance; X²=Chi squared; BMI data were available for 54 caregivers and 51 controls, information on medications was available for 33 caregivers and 34 controls.


Consistently with previous studies, the distribution of the adopted 38-item FI showed right skewness with a maximal score below 0.7 (scores ranged between 0 and 0.58).

In the overall study population, the adopted 38-item FI had a characteristic (11) right-skewed distribution with scores ranging between 0 and 0.58 (Figure 1). The mean FI value was 0.19±0.12, the median value was 0.16 (interquartile range [IQR] 0.11-0.26), and the 99th percentile was 0.56. The FI scores were higher in women than in men (0.20±0.12 vs. 0.16±0.09; F=5.708, p=0.018). Across all participants, FI scores were positively correlated with chronological age (r=0.419, p<0.001) (Figure 2). Based on the above-mentioned qualitative FI cut-off of 0.25, 28.1% (n=36) of participants could be classified as frail.

Figure 1. Distribution of FI values in caregivers and controls. Data are shown as %. FI scores are on the X-axis

Consistently with previous studies, the distribution of the adopted 38-item FI showed right skewness with a maximal score below 0.7 (scores ranged between 0 and 0.58).


The mean FI value was 0.21±0.12 in caregivers and 0.16±0.11 in controls. One-way ANOVA determined highly significant group differences between caregivers and controls (F[1, 127] = 8.308, p = 0.005) (Figure 3), and an extended model using age and sex as covariates did not alter the significance of this result (F[1, 124] = 11.247, p = 0.001). Using the above-mentioned FI cut-off, higher frailty prevalence was observed among caregivers compared with controls (24 vs 12, i.e. 37.5% vs. 18.8%; Χ²=5.565; p=0.018). Resilient caregivers (n=17), i.e. those with high resilience according to the above-mentioned BRS cut-off, had mean FI similar (non significantly lower) than controls (0.11±0.06 vs 0.16±0.11, F=2.247; p=0.138).

Figure 2. Scatterplot showing the significant (r=0.419, p<0.001) association of FI with age across all participants (caregivers are in black, controls are in white)

Figure 3. Graph showing the significant difference in FI between caregivers and controls (F=8.308, p=0.005). Bars indicate standard error


Within caregivers, mean scores of PSS and BRS were 17.94±8.90 and 3.36±1.07, respectively. FI was significantly positively associated with PSS (r=0.660, p<0.001), and it was significantly negatively associated with BRS (r=-0.637, p<0.001) (Figure 4). These correlations remained statistically significant (both p≤0.001) when age, sex, education, BMI, years of caregiving, and type of relationship with the carereceiver (i.e. being spouses/partners, children, siblings, or parents of carereceivers) were included as covariates. The FI was not significantly different between caregivers who were spouses/partners of the carereceivers (n=42) and caregivers who were children of the carereceivers (n=20) (F=0.866; p=0.356).

Figure 4. Scatterplots showing the significant association of FI with perceived psychological stress (PSS, r=0.660, p<0.001) and resilience (BRS, r=-0.637, p<0.001) within caregivers


As four of the 38 variables included in the FI are directly related to psychological stress (i.e. irritability, anhedonia, fatigue, sleep disorders), we created a second modified FI (defined as 34-item FI) excluding such variables from the computation of the score to be tested in sensitivity analyses. Performing between-group comparisons (caregivers vs controls) on 34-item FI and correlation analyses between 34-item FI, PSS and BRS within caregivers still gave significant findings (data not shown).
For further sensitivity, we performed between-group and within-group analyses (i) only including participants with an age higher than 64 years (caregivers n=38, controls n=39), and (ii) only including participants with an age in the range of 18-64 years (caregivers n=26, controls n=25). The mean FI values were again significantly higher in caregivers than in controls (older adults: 0.25±0.12 vs 0.19±0.11, F=4.090, p=0.047; adults: 0.17±0.10 vs 0.10±0.07, F=7.216, p=0.010), and, within caregivers, they were again significantly directly associated with PSS and inversely associated BRS in both groups (all p ≤0.003 by zero-order correlations).



Consistently with the hypotheses, in the present research we observed that caregivers had more pronounced frailty, as assessed by the FI, than controls (Figure 3), and that within caregivers the degree of frailty was directly associated with the degree of perceived psychological stress and inversely associated with the degree of resilience capacity (Figure 4). The statistical significance of such findings remained when certain potentially confounding variables were controlled for, when certain conditions directly related to psychological stress (irritability, anhedonia, fatigue, sleep disorders) were excluded from the computation of FI, and when the analyses were performed separately among participants older and younger than 65 years.
Previous evidence suggested that caregivers can show more pronounced health deficits and biological senescence than non-caregivers as a consequence of the prolonged psychological burden of caregiving (17, 28), that psychological stress has a deteriorating effect on health (12), that resilience skills can contribute to reduce the negative impact of psychological stress on health (17, 29), and that frail older adults have more stress-related psychological symptoms than non-frail older adults (30). Further, studies suggested that psychological stress is associated with accelerated/premature biological senescence measured through molecular indicators of ageing, i.e. molecular systems which are thought to reflect, mediate or promote cellular ageing and biological senescence (e.g. TL, mitochondrial DNA copy number, epigenetic signatures), while psychological resilience can have a protective role (9, 14, 16, 31-33). The findings of the present research are consistent with such data, and add novelty and specificity to the field as (i) the enrolled sample had a mean age of 67.71±11.57 with ages ranging between 34 and 89, while previous studies linking FI with psychopathology were mainly focused on older adults (i.e. individuals with age ≥65) (34-37); (ii) the health and biological measures were conceptualized within the multidimensional construct of frailty evaluated on a continuous grading (FI) rather that dichotomously (i.e. frail vs non frail subjects); (iii) information was collected multimodally, i.e. by integrating senescence- and health-related dimensions emerging from biological (e.g. oxygen saturation, systolic blood pressure, BMI, heart rate), clinical (e.g. current and past pathologies, chronic conditions, comorbidities) and functional (e.g. difficulties in transportations, money management, household keeping) evaluations (Table 1).
In relation to frailty, the findings of the present study are in line with previous observations emerging from studies based on FI in which different health deficits were considered: FI scores were significantly associated with chronological age (Figure 2) (7); significantly higher FI values were observed in women than in men, consistently with previous observations of sex-specific differences in FI scores, which are often defined in the literature as the “male-female health-survival paradox” (38); the FI exhibited a right-skewed distribution with a maximal value of 0.58 (Figure 1), consistently with previous data showing that FI had an upper limit of 0.7, which have been explained by the fact that people cannot tolerate and survive health deficits above a certain threshold (39).
In relation to the health status of caregivers, our results support the relevance of resilience to psychosocial stress for such at-risk individuals: (i) among caregivers, higher levels of frailty were significantly associated with higher perceived psychological stress and with lower capacity of psychological resilience; (ii) across all subjects, while the FI of caregivers was significantly higher than that of controls, the FI of those caregivers with high levels of resilience was similar (non significantly lower) than that of controls. Such data can contribute to extend to psychological resilience the evidence suggesting that physical resilience (which has been defined as the capacity of function maintenance or recovery following biomedical or pathological challenges) plays a protecting role towards the development of frailty (40). Further, consistently with previous studies (17, 41), these findings support the possibility that being the caregiver of a chronically ill person does not inevitably lead to a more deteriorated health profile, and that the way caregivers respond to adversities play a relevant role in determining how psychosocial stressors affect health. From a clinical perspective, it is thus possible that interventions on caregivers focused on decreasing perceived psychological stress and increasing psychological resilience skills can play a role in the improvement of their frailty and general health status. Of relevance, as this was a cross-sectional study, longitudinal and causal relationships between such variables cannot be established, and both directions of causality are potentially possible.
Overall, while the study of the relationship between psychological stress, health and ageing through the analysis of molecular markers of ageing is closely related to pathophysiology, the study of such link through a FI can be more closely related to clinical and functional state, thus possibly expanding the knowledge originating from such area of investigation (9). Relatedly, recent studies in older adults have observed FI to be significantly associated with TL and epigenetic clocks in leukocytes (9, 42-44), and preliminary studies have integrated various mlecular indicators of ageing within a biomarker-based FI (45). Among the molecular indicators of ageing, increasing attentions is nowadays given to the so-called “inflammaging”, i.e. chronically increased levels of inflammatory cytokines which are thought to underlie the progression of senescence-related processes (9, 46). While the FI adopted in the current study did not incude the assessment of cytokine levels, it did consider certain conditions which are tightly related to increased inflammation, such as cancer, osteoporosis, diabetes, and previous episodes of transient ischemic attack or stroke; subsequently, the adopted deficit accumulation approach may capture certain aspects of inflammation even in absence of molecular measures of inflammatory processes.
The present study has several limitations, among which: (i) the cross sectional nature of the research does not allow to establish causal relationships , if any, between correlated variables and the potential direction of causality; (ii) some pieces of information were not fully collected for all participants: complete BMI data were available for 54 caregivers and 51 controls, data on medication use were available for 33 caregivers and 34 controls, information on PSS, BRS, and education was collected in 62 of the 64 caregivers and not in controls, information on years of caregiving was collected in 60 of the 64 caregivers; (iii) the reliability of self-report measures (such as PSS and BRS) can be affected by several biases (e.g. social desirability bias, response bias) (47); (iv) mean age of caregivers and controls was 67.72 ± 11.59 and 67.70 ± 11.63, so the findings may not be applicable to cohorts of different ages, although when the analyses were performed separately among participants older and younger than 65 years the significance of the main results did not change; (v) the study sample size (n = 128) was adequate to test between-group differences on FI (as indicated by an a priori power analysis), but not to test within group correlations, although the observed significant associations of FI with PSS and BRS had large effect sizes (r=0.660 and -0.637 respectively); (vi) a selection bias may have occurred as recruiting caregivers through their carereceiver’s healthcare professionals may have favoured the inclusion of caregivers with higher levels of psychological stress; (vii) we performed a 1:1 match between cases and controls, while epidemiological studies suggest to enrol more than one control for every case to obtain the best methodology (48); (viii) although we statistically controlled for certain potential confounders, other residual confounding factors may have influenced the results of the study. Among the strengths, (i) participants of both groups were in good general health at the moment of the evaluation, i.e. they were not included if they currently had a clinically unstable and disabling medical condition; (ii) the FI was developed according to a well established procedure (11), it showed adequate reliability (Cronbach’s α=0.76), and it comprehensively assessed information arising from biological, clinical, and functional evaluations (the participants were thus assessed multimodally); (iii) we used PSS and BRS, which are extensively used and validated assessment instruments for psychological stress and resilience, and showed satisfactory Cronbach’s α was in the current sample (0.85 and 0.89 respectively).
In conclusions, our findings provide pieces of insight on the complex relationships of frailty, conceptualized as a measure of deficit accumulation and an indicator of functional status and biological age, with caregiving, psychological stress and resilience, with potential implications for the psychological and medical management of individuals exposed to chronic emotional strain.


Conflicts of Interest: The authors do not have conflicts of interest to declare.

Ethical standards: Participants provided consent for allowing the utilization of the collected data for research purposes.



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J.P. Lim1,2, J. Chew1,2, N.H. Ismail2,3, W.S. Lim1,2


1. Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore; 2. Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore; 3. Department of Continuing and Community Care, Tan Tock Seng Hospital, Singapore

Corresponding Author: Jun Pei Lim, Department of Geriatric Medicine, Institute of Geriatrics and Active Aging, Tan Tock Seng Hospital, Annex 2 Level 3, 11 Jalan Tan Tock Seng, Singapore 308433, Email: Jun_Pei_LIM@ttsh.com.sg, Telephone: +65-6359 6474, Fax: +65-6359 6294

J Frailty Aging 2021;in press
Published online June 23, 2021, http://dx.doi.org/10.14283/jfa.2021.28


Dear Editor,

Sarcopenic obesity (SO) is defined as the concomitant presence of sarcopenia and obesity (1). Studies have employed different obesity definitions to understand the relation of SO to cardiometabolic outcomes, with more recent studies examining muscle-related outcomes (2, 3). The leading candidates amongst obesity definitions are waist circumference (WC), percentage fat mass (FM%), and fat mass index (FMI). The last two measures are derived from dual energy X-ray absorptiometry (DXA) or bioelectrical impedance (BIA), and adjust for fat quantity irrespective of distribution (4).
The emergence of abdominal adiposity as significant in the pathogenesis of SO reinforces the importance of incorporating fat distribution when considering obesity definitions (5). Abdominal adiposity is associated with adipose tissue inflammation, which in turn predisposes to the systemic pro-inflammatory milieu implicated in ectopic fat accumulation in skeletal muscle. Using WC as a surrogate of abdominal adiposity to define obesity, we previously demonstrated in obese and SO subjects elevated levels of Monocyte Chemoattractant Protein-1 (MCP-1), a pro-inflammatory cytokine secreted by adipocytes and adipose tissue leukocytes, to support the theory of chronic inflammation underpinned by abdominal adiposity (6, 7). Moreover, WC-defined SO was associated with intermuscular adipose tissue (8) and performed the worst amongst body composition phenotypes in handgrip strength, gait speed and Short Physical Performance Battery (SPPB) (9). In a study that compared ten different anatomical locations for measuring waist circumference (including six using bony landmarks and four involving the umbilicus), WC measured at 2.5cm above umbilicus was found to have the highest correlate with abdominal adiposity in older adults (8), suggesting that the different measurement protocols are not equivalent. This is an important consideration, as the umbilicus provides a useful landmark for optimal differentiation of the putative deep subcutaneous adipose tissue (vis-à-vis the protective superficial subcutaneous adipose tissue) in the abdominal wall (10).
Using an alternative WC protocol that measures narrowest point of the waist, the Yishun study in Singapore reported that SO was associated with Short Physical Performance Battery (SPPB) only with FMI but not WC or FM% definitions for obesity (3). Moreover, this WC protocol yielded prevalence of obesity and SO at 64.5% and 16.1% respectively, which was higher than the 52% and 10.5% prevalence observed using 2.5cm above umbilicus landmark (9). There was also a lack of biomarkers to explicate the findings. To ascertain if the observed lack of utility of WC obesity definition of SO can be attributed to the measurement protocol, we conducted the current study using 2.5cm above umbilicus landmark. We used WC, FM% and FMI definitions to compare prevalence of obese and SO; and the association with SPPB and blood inflammatory biomarkers across body composition phenotypes.
We studied 200 community-dwelling older adults (mean age 67.4, Supplementary table 1) from the GERILABS study.9 WC was measured 2.5cm above the umbilicus (Cut-offs: ≥90 cm men, ≥80 cm women, as per the International Diabetes Federation 2006 (IDF 2006) cut-offs for South Asians) (11). Body composition measurements were obtained using DXA. To facilitate comparisons, cut-offs for FM% and FMI [calculated as fat mass(kg) divided by height(m) squared] were referenced to the Yishun study – 31.0% and 7.63 kg/m2 for men, and 41.4% and 9.93 kg/m2 for women. Using the Asian Working Group for Sarcopenia 2014 consensus for sarcopenia, and WC, FM% or FMI definitions for obesity, we classified participants as normal, obese, sarcopenic and sarcopenic obesity (SO). We assessed overall physical performance using SPPB, and measured C-reactive protein (CRP), interlukin-6 (IL-1), tumour necrosis factor-α (TNF-α) and MCP-1 as inflammatory biomarkers. Using the 3 obesity definitions, we compared prevalence and inflammatory biomarkers across body composition phenotypes. We performed linear regression to examine the association with SPPB, adjusting for age, gender, history of diabetes mellitus and stroke disease.
We found that WC definition identified the highest prevalence of obese and SO, followed by FM% and FMI; our figures for WC were, however, lower than the Yishun study (53% vs 64.5%, and 9.5% vs 16,1%, respectively) (3). Moreover, WC definition of SO was most predictive for poor SPPB scores (coefficient -2.169, p <.0005) and had the highest model fit compared to FMI or FM% (table 1). For blood biomarkers, significant trends in both CRP and MCP-1 levels across body composition were observed for WC, FMI and FM% (p<.01), with MCP-1 levels highest in SO defined by WC. Interestingly, using WC definition, SO had higher levels of IL-6 than sarcopenia, whereas SO had higher levels of TNF-α than obese using FMI definition.

Table 1. Prevalence, Regression Coefficients, Blood Biomarkers across different SO definitions

* p values for regression coefficients <0.05; †Adjusted for age, gender, history of diabetes mellitus and stroke disease; ‡Adjusted R-squared 0.246; §Adjusted R-squared 0.163; ||Adjusted R-squared 0.168; { Difference between SO and N (p<0.05); #Difference between SO and O (p<0.05); **Difference between SO and S (p<0.05); †† Difference between S and N (p<0.05); ‡‡ Difference between S and O (p<0.05); §§ Difference between O and N (p<0.05)


Using the 2.5cm above umbilicus landmark, our results support the superiority of the WC definition in the associations of body compositions with physical performance. Other strengths vis-à-vis FMI/FM% definitions include ease of performance without requirement for specialized equipment; higher case detection; and selection for MCP-1 mediated inflammatory pathways associated with abdominal adiposity. Blood biomarker results implicate different pro-inflammatory pathways depending on obesity definitions. IL-6 is secondarily elevated due to MCP-1 induction of mononuclear cells and endothelial cells to express adhesion molecules with release of IL-6.12 In contrast, for FMI definition, CRP and TNF-alpha levels are elevated consistent with an overall increase in adjusted total adiposity. Although MCP-1 is also elevated for FMI/FM% definitions, the difference between SO and obese is lower, suggesting a greater contribution of overall as opposed to abdominal adiposity.
Taken together, our study corroborates the utility of WC obesity definition for muscle outcomes in SO, provided we employ standardized measurements protocols which correlate with abdominal adiposity. Study limitations include the cross-sectional design limiting conclusions of causality and use of a single WC measurement protocol precluding direct comparisons with other protocols. Usage of abdominal callipers by trained personnel may have provided more reliable estimates of abdominal visceral adiposity instead of WC. In addition, the WC cut-offs in our study are from established guidelines for Asian populations and have not been locally validated. Future outcome studies specifically evaluating different WC protocols in relation to SO may be useful to ascertain the optimal protocol.


Acknowledgement: This study was approved by the National Healthcare Group Institutional Research Board and funded by the Lee Foundation Grant 2013. We would like to extend our thanks to Ms Suzanne Yew, Ms Tan Cai Ning and Ms Audrey Yeo for their assistance in data collection.

Conflict of interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest

Author contribution statement: JPL drafted the manuscript. All authors critically appraised and contributed to manuscript revision, approved the final version of the letter, and agree to be accountable for all aspects of the work.





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3. Pang BWJ, Wee S-L, Lau LK, et al. Obesity Measures and Definitions of Sarcopenic Obesity in Singaporean Adults — The Yishun Study. J Frailty Aging. Published online 2020. doi:10.14283/jfa.2020.65
4. Donini LM, Busetto L, Bauer JM, et al. Critical appraisal of definitions and diagnostic criteria for sarcopenic obesity based on a systematic review. Clin Nutr. 2020;39(8):2368-2388. doi:10.1016/j.clnu.2019.11.024
5. Zoico E, Rossi A, Di Francesco V, et al. Adipose tissue infiltration in skeletal muscle of healthy elderly men: Relationships with body composition, insulin resistance, and inflammation at the systemic and tissue level. Journals Gerontol – Ser A Biol Sci Med Sci. 2010;65 A(3):295-299. doi:10.1093/gerona/glp155
6. Lim JP, Leung BP, Ding YY, et al. Monocyte chemoattractant protein-1: A proinflammatory cytokine elevated in sarcopenic obesity. Clin Interv Aging. 2015;10(March):605-609. doi:10.2147/CIA.S78901
7. Afandy NO, Lock HS, Tay L, et al. Association of Monocyte Chemotactic Protein-1 and Dickkopf-1 with Body Composition and Physical Performance in Community-Dwelling Older Adults in Singapore. J Frailty, Sarcopenia Falls. 2021;06(01):25-31. doi:10.22540/jfsf-06-025
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N. Ward1, A. Menta1, S. Peach1, S.A. White2, S. Jaffe2, C. Kowaleski3, K. Grandjean da Costa1, J. Verghese4,5, K.F. Reid2


1. Tufts University, Department of Psychology, Medford, MA, USA; 2. Nutrition, Exercise Physiology and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research on Aging at Tufts University, Boston, MA, USA; 3. City of Somerville Council on Aging, Health and Human Services Department, Somerville, MA, USA; 4. Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA; 5. Institute of Aging Research, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA

Corresponding Author: Nathan Ward, PhD. Department of Psychology, Tufts University, Boston, MA, 02155. Telephone: +1-617-627-2645; Fax: +1-617-627-3181;
E-mail: nathan.ward@tufts.edu

J Frailty Aging 2021;in press
Published online June 21, 2021, http://dx.doi.org/10.14283/jfa.2021.27



The purpose of this study was to characterize Cognitive Motor Dual Task (CMDT) costs for a community-based sample of older adults with Motoric Cognitive Risk Syndrome (MCR), as well as investigate associations between CMDT costs and cognitive performance. Twenty-five community-dwelling older adults (ages 60-89 years) with MCR performed single and dual task complex walking scenarios, as well as a computerized cognitive testing battery. Participants with lower CMDT costs had higher scores on composite measures of Working Memory, Processing Speed, and Shifting, as well as an overall cognitive composite measure. In addition, participants with faster single task gait velocity had higher scores on composite measures of Working Memory, Processing Speed, and overall cognition. Taken together, these results suggest that CMDT paradigms can help to elucidate the interplay between cognitive and motor abilities for older adults with MCR.

Key words: Motoric cognitive risk syndrome, cognitive motor dual task costs, cognition, mobility.


Cognition and mobility are intimately linked, such that motor tasks like walking require increased amounts of cognitive processing, and this linkage is especially important for older adults (1). Studies using Cognitive Motor Dual Task (CMDT) paradigms (i.e., gait paired with a cognitive task) find greater impairments for older adults compared to younger adults. These costs suggest overlapping mechanisms or at least competition for resources between cognition and gait (2). Understanding this interplay between cognition and gait is especially important for certain aging populations, such as older adults with dementia and even pre-dementia classifications who might be at an increased risk of fall (3).
Motoric Cognitive Risk Syndrome (MCR) is a classification in which older adults, despite having preserved activities of daily living, exhibit both subjective memory complaints and slow gait in addition to an elevated risk factor for dementia (4–6). While an MCR diagnosis shares features with a diagnosis of Mild Cognitive Impairment (MCI), MCR predicts risk of dementia even after accounting for overlap with MCI subtypes (7).
MCR has also been associated with reduced gray matter in prefrontal areas, as well as in supplementary motor areas [8,9]. In addition, MCR has been associated with worse cognitive performance in domains that rely on prefrontal and motor planning areas, such as attention and executive function [10], which are also crucial for dual tasking. In short, there may be an impaired executive system in MCR, and CMDT may further reveal these impairments; however, to date no studies have investigated or characterized this.
The purpose of this study was to investigate, for the first time, the association between cognitive performance and CMDT costs for a community-based sample of older adults with MCR. By leveraging a CMDT paradigm, we sought to further understand the mechanisms of cognitive and motor control important for navigating complex environments in which attention may be divided. Furthermore, CMDT costs have been associated with the risk of developing dementia (11, 12) and can provide us with additional insights into MCR and potential compensatory mechanisms involved in cognitive-motor interference.



Study Design

This study used baseline data from a clinical trial examining the real-world effects of a community-based physical activity intervention in a group of older adults with MCR (NCT03750682). Data were collected at a community-based urban senior center in Greater Boston, MA, USA.


Participants were considered to have MCR if they met all of the following criteria: 1. Self-reported memory complaint as assessed using the Geriatric Depression Scale (13); 2. Objectively defined slow gait, defined as gait speed below previously described age-appropriate mean values (age 60-74 yrs: < 0.70 meters per second and age 75+: < 0.60 meters per second; (4); 3. Absence of mobility-disability (inability to ambulate even with assistance or walking aids); 4. Absence of dementia diagnosis. The recruitment of participants resulted from targeted community outreach conducted by the study investigators in close collaboration with the senior center’s Health and Wellness Coordinator. Participants who were interested in the study were pre-screened via telephone or in person and were considered eligible for a screening visit if they were 60-89 years, community-dwelling, sedentary, and reported a subjective memory complaint. Eligible participants were invited to participate in additional MCR screening procedures that consisted of a medical history questionnaire and an objective 4-meter assessment of gait speed as part of the short physical performance battery (SPPB) test (14). Participants were excluded if they had an acute or terminal illness, myocardial infarction or upper and lower extremity fracture in the previous 6 months, symptomatic coronary artery disease, uncontrolled hypertension (>180/100 mmHg), or significant cognitive impairment (Modified Mini-Mental State Examination Score (3MSE) <80; [15]). In addition, participants’ primary care physicians confirmed the absence of a diagnosis of dementia.

Ethical Considerations

A signed informed consent was obtained from all study participants. This study was approved by the Tufts University Health Sciences Institutional Review Board.

Measurements and Procedures

Cognitive testing took place in a quiet room with a research assistant, and participants were offered breaks throughout the testing. The cognitive testing battery was conducted on a tablet device using the mobile application BrainBaseline. BrainBaseline is a scientifically-validated research tool (16), and in the current study, a testing battery of eight standard cognitive tasks was used. Cognitive tasks included the Digit Symbol Substitution Task (DSST), the Digit Span Task, the N-Back Task, the Speed Task, the Erikson Flanker Task, the Stroop Task, the Task Switching Task, and the Trail Making Task, which were combined into several different composite scores (17). Specifically, the Digit Span Task and N-Back Task comprised a Working Memory composite measure; the Flanker Task and Stroop Task comprised an Inhibitory Control composite measure; the Task Switching Task and Trail Making Task comprised a Shifting composite measure; and the Speed Task and DSST comprised a Processing Speed composite measure. In addition, an overall cognition composite was created by combining all of the individual measures from the computerized testing battery as well as the 3MSE measure (17). In order to create the cognitive composite scores, z-scores were calculated for the cognitive variables. Next the Stroop, Flanker, Speed, Task Switch, and Trails were multiplied by -1 so that for all measures, larger values indicate better performance whereas smaller values indicate poorer performance.
To assess dual task function in older adults, complex walking tasks were used (18). The Gait Speed Test was first used to measure speed of walking 7 meters at normal pace, without any other simultaneous task (i.e., Single Task). Next participants were given a letter (e.g., S, T, or M depending on the day of the month they were born) and asked to name as many animals as they could think of whose name started with that letter while walking the same 7-meter distance at their normal walking pace (i.e., Dual Task).

Statistical Analysis

The primary outcome of interest for the current study was CMDT interference and its relationship to our computerized cognitive test battery. We created the CMDT cost metric using the following equation: (single gait speed – dual gait speed)/single gait speed (2). Next, we assessed the relationships between CMDT cost and cognitive composite scores using Spearman’s rank correlation coefficients. All statistical analyses were conducted in Jamovi version 1.1.9 (www.jamovi.org).



Descriptive statistics are detailed in Table 1. Our total sample size included 25 participants with MCR. Participants were predominately white (72%), and female (80%). Fifty-six percent reported having some amount of college education, and the average 3MSE score was 92 out of 100, indicating global cognitive deficits. Six participants reported having at least one fall in the past 12 months. The overall SPPB gait speed sub-scores were indicative of severe mobility and gait speed impairments. In addition, our participants with MCR consistently demonstrated reduced cognitive performance on the computerized cognitive tasks compared to a healthy older adult population (16).

Table 1. Sample characteristics and cognitive test battery scores

Abbreviations: BMI, Body Mass Index; kg, kilograms; m/s, meters per second; 3MSE, Modified Mini Mental Status Exam; DSST, Digit Symbol Substitution Task, one-minute version; n, number of; %, percent correct; ms, milliseconds


Associations Between CMDT Costs and Cognitive Composite Scores

Figure 1 represents linear relationships between CMDT costs and cognitive composite scores that were significant. Specifically, we found that participants with lower CMDT costs performed higher on Working Memory (r = -0.36, p = 0.04), Processing Speed (r = -0.39, p = 0.03), Shifting (r = -0.39, p = 0.03), as well as overall cognition (r = -0.38, p = 0.03).

Figure 1. Associations between cognitive motor dual task cost and cognitive composite z-scores


Associations Between Single Task Gait Speed and Cognitive Composite Scores

Given the exceptionally low average 4-meter gait speed of our sample (i.e., 0.52 m/s), we also wanted to test for associations between single task gait speed and cognitive composite scores. We found that participants with faster gait speed performed higher on Working Memory (r = 0.48, p = 0.01), Processing Speed (r = 0.57, p = 0.001), and overall cognition (r = 0.46, p = 0.01) (Figure 2).

Figure 2. Associations between single task gait speed and cognitive composite z-scores



This study is the first to investigate associations between CMDT costs and cognitive performance for a community-based sample of older adults with MCR. We found that participants with lower CMDT costs performed higher on three individual cognitive composite measures (i.e., Working Memory, Processing Speed, and Shifting), as well as higher on an overall cognitive composite metric. Previous research has found that compared to participants without MCR, participants with MCR had lower performance on a multitude of cognitive measures, including similar measures to those used in the current study (e.g., Digit Span, DSST, Trails) (10, 19–21). Building on this prior research, we found that lower cognitive performance for a sample of older adults with MCR was associated with higher cognitive motor dual task costs. Our study is the first to report on cognitive motor dual task costs for older adults with MCR (22). Furthermore, our results with an older adult MCR sample build on prior, non-MCR research that found that dual task gait speed was related to cognitive decline, which further emphasizes the potential for using cognitive motor dual task paradigms in clinically-meaningful settings (23).
To reiterate, we found that lower cognitive performance was associated with higher cognitive motor dual task costs for a sample of older adults with MCR. This could be due to reduced gray matter in prefrontal areas (8) that are important for dividing (24) or shifting (25) attentional resources between cognitive and motor demands, which might indicate an impaired executive system in MCR (26), although future studies with cognitive neuroscientific measures, such as fMRI or fNIRS, should verify this.
In addition to CMDT costs, we investigated associations between single task walking and cognitive performance as greater understanding of link between walking performance, cognitive frailty and the development of cognitive disorders remains a research area of significant clinical and gerontological importance (27–30). We found that participants with faster gait speed performed higher on two individual cognitive composite measures (i.e., Working Memory and Processing Speed), as well as higher on an overall cognitive composite metric. This aligns with previous research that has found associations between an MCR subtype based on single task gait velocity and global cognition (31), as well as studies that have found relationships between single task gait speed and cognitive decline (32). Furthermore, this reiterates the importance of even single task walking as a possible window into the mind.
As with any study, there are both strengths and limitations of the current investigations. By conducting all study procedures at a senior center, we were able to reach a community-based sample of older adults with MCR that would not likely be well-represented in clinical trials. That said, future research with larger sample sizes and healthy control groups without MCR are required to further understand how much our results generalize beyond our specific community-based sample. Future work should also consider using wearable sensors during single and dual task walking conditions, which would allow for a richer set of gait kinematics to explore how CMDT costs might differ across different MCR subtypes (31).
In conclusion, our results suggest that the interplay between cognitive and motor abilities is an important avenue to explore for older adults with MCR. CMDT paradigms like the one used in the current study could be useful in clinical settings for early diagnosis of cognitive decline (12, 33), as well as an intervention modality (34). Indeed, others have found promising results when using CMDT training for older adults with cognitive impairments, such as MCI or dementias (35), and future work should investigate whether this extends to MCR.


Funding: This research was supported by the Boston Claude D. Pepper Older Americans Independence Center (1P30AG031679), the National Center for Advancing Translational Sciences, National Institutes of Health (NIH) (UL1TR001064) and is based on the work supported by the U.S. Department of Agriculture, under agreement No. 58-8050-9-004. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.

Acknowledgements: We thank the following individuals for supporting this project: Joan Severson, Digital Artefacts LLC; Joseph A. Curtatone, Mayor of Somerville, MA.

Conflicts of Interest: The authors state no conflicts of interest.



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30. Borges MK, Canevelli M, Cesari M, Aprahamian I. Frailty as a Predictor of Cognitive Disorders: A Systematic Review and Meta-Analysis. Front Med. 2019;6. doi:10.3389/fmed.2019.00026
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32. Merchant RA, Goh J, Chan YH, Lim JY, Vellas B. Slow Gait, Subjective Cognitive Decline and Motoric Cognitive Risk Syndrome: Prevalence and Associated Factors in Community Dwelling Older Adults. J Nutr Health Aging. 2021;25: 48–56. doi:10.1007/s12603-020-1525-y
33. Mancioppi G, Fiorini L, Rovini E, Cavallo F. The use of Motor and Cognitive Dual-Task quantitative assessment on subjects with mild cognitive impairment: A systematic review. Mech Ageing Dev. 2021;193: 111393. doi:10.1016/j.mad.2020.111393
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R. O’Caoimh1,2, M. O’Donovan2, K. McGrath1, E. Moloney1,2


1. Department of Geriatric Medicine, Mercy University Hospital, Cork, Ireland; 2. Health Research Board Clinical Research Facility, University College Cork, Mercy University Hospital, Cork City, Ireland; 3. Department of Medicine, University College Cork, Cork City, Ireland.

Corresponding Author: Rónán O’Caoimh, Department of Geriatric Medicine, Mercy University Hospital, Grenville Place, Cork City, T12 WE28, Ireland, Email: rocaoimh@hotmail.com or rocaoimh@muh.ie, Orcid ID: 0000-0002-1499-673X, Tel: 00353 21 420 5976

J Frailty Aging 2021;
Published online June 19, 2021, http://dx.doi.org/10.14283/jfa.2021.26


Dear Editor,

During this unprecedented and ongoing pandemic, the need for healthcare professionals to prognosticate has arguably never been greater (1). Early risk-stratification of older adults admitted with COVID-19 is particularly important to mobilise healthcare professionals to manage their often complex care needs (2). Although frail older adults have higher mortality with COVID-19, given their inherent heterogeneity, predicting outcomes in this population is challenging (3). Multiple scales are available to risk-stratify patients with COVID-19(4). Despite this, their predictive validity among those who are frail and hence, at highest risk of mortality and critical illness, is not yet established.
To examine this, we compared the accuracy of two recently validated risk-prediction instruments, the ISARIC-4C (5) and COVID-GRAM (6), in a sample of older adults admitted to three Irish hospitals (two acute and one rehabilitation). Consecutive patients aged >70 with laboratory-confirmed COVID-19 admitted between February 27th-April 24th 2020 were included. Frailty was determined by comprehensive geriatric assessment and stratified using the Clinical Frailty Scale (CFS). The ISARIC-4C is a new risk-prediction instrument incorporating eight variables scored on admission including age, sex at birth, respiratory rate, oxygen saturation, Glasgow Coma Scale score, blood urea, c-reactive protein value, obesity and number of co-morbidities based on the Charlson Co-morbidity Index (CCI). The COVID-GRAM includes 10 predictors including age, X-ray abnormalities, presence of haemoptysis, dyspnoea, level of consciousness, co-morbidities, history of cancer, neutrophil-lymphocyte ratio, serum lactate dehydrogenase level and direct serum bilirubin. The area under the receiver operating characteristic curve (AUROC) measured predictive accuracy for inpatient mortality, intensive care unit (ICU) admission and the composite outcome (critical illness). Logistic regression provided odds ratios (OR) per standard deviation (SD) increase with 95% confidence intervals (CI) exploring relationships between variables. Ethics approval was obtained (reference:ECM4(e) 05/05/2020).
In all, 69 patients were included, median age 70±10 years; 42% were female. The median CFS score was 5±2 and most (64%) were frail. Their demographic and clinical characteristics are presented elsewhere (7) but in summary, 15.9% (n=16%) had dementia, 58% (n=40) received polypharmacy (>5 prescription medication) and most had high levels of co-morbidity; median CCI score 6±3. The majority (75%) had chest x-ray changes associated with COVID-19. In total, 16 (23%) died and 9 (13%) accessed ICU during their admission while 20 (29%) developed the composite outcome, critical illness. There was no significant difference in the proportion of frail and non-frail patients that died (22% versus 26%, p=0.77) or developed critical illness (24% versus 39%, p=0.26).
At initial diagnosis of COVID-19, 64% were scored as high-risk on the COVID-GRAM. The remainder as medium-risk. None were scored low-risk. Median COVID-GRAM scores were higher in those admitted to ICU (156 versus 210, p=0.01), who died (147 versus 198, p=0.002), or became critically ill (142 versus 198, p<0.001).Those scored at high-risk on the COVID-GRAM were significantly more likely to develop critical illness (p<0.001) or die during their admission (p=0.01). There was no difference in the proportion admitted to ICU (p=0.08). Results were similar for the ISARIC-4C; 8.5% were classed as intermediate-risk, 83% as high-risk and 8.5% as very high-risk, while no patients were scored low-risk. Median ISARIC-4C scores were significantly higher among those who died (10 versus 13, p<0.001) or developed critical illness (10 versus 13, p<0.001). There was no statistically significant difference between the COVID-GRAM (AUROC 0.76, 95% CI:0.65-0.88) and ISARIC-4C (AUROC 0.86, 95% CI:0.76-0.96) in predicting inpatient mortality (p=0.134). The ISARIC-4C was however, significantly more accurate than the COVID-GRAM in predicting those who developed critical illness (AUROC 0.90, 95% CI:0.82-0.97 versus 0.78, 95% CI:0.68-0.89, respectively, p=0.0486). ROC curves are presented in Figure 1. Diagnostic accuracy was similar when non-frail patients were excluded, although the difference in accuracy in predicting critical care was no longer significant (p=0.07). Adjusting for age, co-morbidity (CCI), sex, frailty, dementia and obesity, the COVID-GRAM (OR per SD increase 3.81, 95% CI:1.38-10.55, p=0.01) and ISARIC-4C (OR per SD increase 41.09, 95% CI:5.49-307.39, p<0.001), remained independent predictors of mortality. Similarly, after adjustment, both the COVID-GRAM (OR per SD increase 3.55, 95% CI: 1.39-9.07, p=0.008) and ISARIC-4C (OR 165.06, 95% CI: 9.94-2740.71, p<0.001), independently predicted those who would develop critical illness.

Figure 1. Receiver operating characteristic curves comparing the predictive accuracy of the ISARIC-4C and COVID-GRAM for (a) death and (b) critical illness


In-keeping with the pattern of COVID-19 illness noted in other countries, frailty was common (8) and mortality high in this multi-morbid cohort of older inpatients. Those with higher COVID-GRAM and ISARIC-4C scores were more likely to die or develop critical illness during admission, even after adjustment for potential confounders. Although, both instruments had fair-good predictive validity, the ISARIC-4C had excellent and statistically greater accuracy in predicting critical illness among all but not frail patients, despite requiring fewer and arguably easier-to-obtain variables. Recent external validation of the COVID-GRAM also highlights that it overestimates risk in the highest-risk patients and has only fair accuracy for death (AUROC 0.79) (4) and critical illness (AUROC 0.72) (6), similar to our findings. However, clear ceiling effects were evident for both instruments such that no individuals were identified as low-risk, which may limit their utility in clinical practice. Although, this study is limited by the small sample size and risk of referral (admission rate) bias, it suggests that until these instruments are evaluated in large representative samples of older inpatients, they should only be used with caution to prognosticate and predict risk of critical illness in those who arguably are most likely to require it on admission, older patients. As Europe and other parts of the world face further waves of COVID-19 and until vaccination reduces risk, ongoing studies of prognostication models in frail older patients are needed.


Author contributions: ROC concieved of the work and drafted and revised the paper. MOD assisted with data analysis. KMcG, EM and MOD revised the draft of the paper.

Conflict of interest: The authors report no conflicts of interest.

Funding: No funding was received for this article.



1. Truog RD, Mitchell C, Daley GQ. The toughest triage—allocating ventilators in a pandemic. N Engl J Med. 2020, May 382:1973-1975. doi.org/10.1056/NEJMp2005689.
2. Aprahamian I, Cesari M. Geriatric Syndromes and SARS-COV-2: More than Just Being Old. J Frailty Aging. 2020 Apr 14 : 1–3. doi: 10.14283/jfa.2020.17
3. O’Caoimh R, Kennelly S, Ahern E, O’Keeffe S, Ortuño RR. COVID-19 and the challenges of frailty screening in older adults. J Frailty Aging. 2020 Jun;9:185-6.
4. Covino M, De Matteis G, Burzo ML, et al. Predicting in-hospital mortality in COVID-19 older patients with specifically developed scores. J American Geriatrics Society. 2021 Jan;69(1):37-43. doi.org/10.1111/jgs.16956
5. Knight SR, Ho A, Pius R, et al. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. BMJ. 2020 Sep 9;370. doi.org/10.1136/bmj.m3339
6. Moreno-Pérez Ó, Andrés M, León-Ramirez JM, et al. The COVID-GRAM Tool for Patients Hospitalized With COVID-19 in Europe. JAMA Intern Med. 2021. doi:10.1001/jamainternmed.2021.0491 [Epub ahead of print]
7. Moloney E, Eustace J, O’Caoimh R, et al. Frailty, COVID-19 Disease Severity and Outcome Among Hospitalised Older Adults. Irish Medical Journal. 2020 Nov 1;113(10):208.
8. Kow CS, Hasan SS. Prevalence of Frailty in Patients with COVID-19: A Meta-Analysis. J Frailty Aging. 2021 Feb;10(2):189-90. DOI: 10.14283/jfa.2020.70




D. Tavoian1, D.W. Russ1,2, T.D. Law1, J.E. Simon1,3, P.J. Chase3, E.H. Guseman4,5, B.C. Clark1,6,7


1. Ohio Musculoskeletal and Neurological Institute (OMNI), Ohio University, Athens, USA; 2. School of Physical Therapy and Rehabilitation Sciences, University of South Florida, Tampa, FL, USA; 3. School of Applied Health Sciences and Wellness, USA; 4. Diabetes Institute, USA; 5. Department of Primary Care, USA; 6. Department of Biomedical Sciences, USA; 7. Division of Geriatric Medicine at Ohio University, Athens, OH, USA

Corresponding Author: Dallin Tavoian, Ohio Musculoskeletal and Neurological Institute (OMNI), Ohio University, Athens, USA, dt114412@ohio.edu

J Frailty Aging 2021;in press
Published online May 14, 2021, http://dx.doi.org/10.14283/jfa.2021.21



This Brief Report describes a pilot study of the effect of 12 weeks of stationary bicycle high-intensity interval training, stationary bicycle moderate-intensity continuous training, and resistance training on cardiorespiratory, muscular, and physical function measures in insufficiently-active older adults (N=14; 66.4±3.9 years; 3 male, 11 female). After baseline testing, participants were randomly assigned to one of the exercise groups. High-intensity interval training and moderate-intensity continuous training had small-to-large effect sizes on cardiorespiratory/endurance and physical function measures, but very small effect sizes on muscular measures. Resistance training had small-to-large effect sizes on cardiorespiratory, muscular, and physical function measures. This pilot study should be interpreted cautiously, but findings suggest that resistance exercise may be the most effective of the three studied exercise strategies for older adults as it can induce beneficial adaptations across multiple domains. These effect sizes can be used to determine optimal sample sizes for future investigations.

Key words: High-intensity interval training, exercise, aging, physical function, muscle.

Abbreviations: 4SST: four-square step test; 6MW: six-minute walk; ES: effect size; HIIT: high-intensity interval training; KE: knee extensor; MICT: moderate-intensity continuous training; RT: resistance training; VO2max: maximal oxygen consumption.



Despite well-documented muscular and cardiorespiratory health benefits that accompany regular exercise participation, most older adults are not engaging in exercise with the volume and/or intensity sufficient for maintaining physical function (1, 2). In fact, fewer than 13% of older adults meet the aerobic (150 minutes moderate intensity/week; e.g., walking, stationary bicycling) and muscle strengthening (2 days/week; e.g., weight lifting) guidelines concurrently, while only 31% meet one of the two (3). A more pragmatic approach that emphasizes a single exercise strategy with the greatest effect on overall health may be a reasonable solution to optimize outcomes and improve adherence (4).
High-intensity interval training (HIIT) is an exercise strategy consisting of short periods (10 seconds to 4 minutes) of vigorous exercise interspersed with low-intensity rest periods. It can improve cardiorespiratory fitness and lower cardiovascular disease risk equal to, or greater than, traditional aerobic training (5), and has also been shown to improve muscle strength in young adults (6). However, the potential for HIIT to induce muscular benefits in older adults has not been adequately explored. The aim of this study was to examine whether stationary bicycle HIIT was a more efficient standalone exercise strategy to improve cardiovascular and lower extremity muscular function than established muscle strengthening (resistance training; RT) or aerobic (moderate-intensity continuous training; MICT) programs in older adults.



An in-depth protocol for this study has been published previously (7), and only essential information is provided in this section. It should be noted that a sample size of 24 (n=8/group) was initially planned for this pilot study. However, restrictions on human subjects research associated with the COVID-19 pandemic prevented attainment of the recruitment goal. Thus, we only present descriptive statistics and effect size estimates in this Brief Report.

Participant characteristics

Twenty-two generally healthy but insufficiently active (i.e., not meeting either aerobic or muscle strengthening guidelines (7)) participants aged 60-75 years were recruited, enrolled, and randomized, with 14 (66.4 ± 3.9 years; 3 male, 11 female) completing the study. One was removed for starting a new blood pressure medication while on the study protocol, and seven others were interrupted prior to completion due to the COVID-19 pandemic and unable to resume the study. Written informed consent was obtained from each participant in accordance with the Declaration of Helsinki. Ethical Approval for this study has been obtained from the Ohio University Institutional Review Board. Baseline characteristics are shown in Table 1.

Table 1. Baseline and post-intervention characteristics

Data are means ± SD. 4SST, four-square step test; 6MW, six-minute walk; BMI, body mass index; ES, effect size; HIIT, high-intensity interval training; MICT, moderate-intensity continuous training; RT, resistance training; VO2max, maximal oxygen consumption. Effect sizes are classified as very small (0.01-0.19), small (0.20-0.49), moderate (0.5-0.79), large (0.8-1.19), and very large (>1.20)

Study Design

This study had a screening/baseline assessment period of three sessions, randomization into one of the three exercise groups, a 12-week exercise training period, and a post-intervention assessment period of two sessions (7). All exercises were performed on site three days per week and supervised by an exercise professional. Below we provide a brief description of the experimental procedures and training programs. We refer the reader to the Supplement as well as our previously published detailed protocol (7) for additional information.


Primary Outcomes

• Isokinetic Strength: Obtained at 60°/second from the non-dominant knee extensors.
• Maximal oxygen consumption (VO2max): Obtained during a graded cycle ergometry exercise test.
• Quadriceps muscle volume: Assessed from magnetic resonance imaging scans of the non-dominant leg.

Secondary Outcomes

• Isometric Strength: Obtained from the non-dominant knee extensors at 90° of knee flexion.
• Fatigue Resistance: Assessed through a series of 120 isokinetic leg extensions at 120°/second.
• Total Body Fat Mass: Obtained via whole-body dual-energy X-ray absorptiometry scans.

Physical Function Outcomes

• Six-Minute Walk (6MW): Completed on a 30-meter course.
• Four-Square Step Test (4SST): Performed in a four-foot by four-foot square split into quadrants.
• Grip Strength: Obtained with a Jamar hydraulic grip strength dynamometer at position II.
• Five-Time Chair Rise: Performed on a chair with the seat 18 inches from the ground.

Exercise Intervention

Each participant performed their prescribed exercise 3x/week for 12 weeks. Adherence was defined as an attendance rate ≥80% (i.e., attended 29 of 36 exercise sessions), which all participants achieved. Participants in the HIIT group performed all exercises on a stationary bicycle (Peloton Interactive, Inc. New York City, NY, USA). The duration of the HIIT sessions were half the duration of the MICT sessions. Participants in the MICT group used the same stationary bicycle setup as in the HIIT group. Participants in the RT group performed all exercises using free weights, machines, or body weight.

Statistical analysis

The planned analysis for this study was a one-way ANOVA to compare group means. However, because we could not complete the study due to COVID-19 our sample size is not adequately powered for this type of analysis. Therefore, descriptive statistics, percent change from baseline (primary and secondary outcomes), absolute change from baseline (physical function outcomes), and corrected Hedge’s g effect sizes for small samples are reported. Effect sizes were classified as very small (0.01-0.19), small (0.20-0.49), moderate (0.5-0.79), large (0.8-1.19), and very large (>1.20) (8). 95% confidence intervals for descriptive statistics can be found in the Supplemental Table S1.



High-intensity interval training had very small effects on muscular strength and mass (ES=-0.17 to 0.19), small-to-large effects on cardiorespiratory/endurance measures (ES=0.44 to 1.13), and moderate-to-large effects on most physical function measures (ES=0.50 to 1.08). MICT had very small-to-small effects on muscular strength and mass (ES=-0.04 to 0.21), very small-to-large effects on cardiorespiratory/endurance measures (ES=0.16 to 0.90), and very small-to-very large effects on physical function (ES=0.17 to 1.21). RT had small-to-large effects on muscular strength and mass (ES=0.28 to 0.99), small effects on cardiorespiratory/endurance measures (ES=0.39 to 0.41), and very small-to-large effects on physical function (ES=0.12 to 1.07). All results can be found in Table 1 and Figure 1. See Supplement for detailed adverse event and adherence outcomes.

Figure 1. Changes in primary (A-C), secondary (D-F) and physical function outcomes (G-I) after 12 weeks of HIIT, MICT, or RT

Open symbols are values for individual subjects and solid bars indicate group means. A) knee extensor isokinetic strength; B) absolute VO2max; C) muscle volume; D) knee extensor isometric strength; E) knee extensor fatigue resistance; F) total body fat mass; G) six-minute walk (6MW) distance; H) four-square step test (4SST) time; I) non-dominant hand grip strength; J) five-time chair rise time.



The purpose of this study was to compare the effect of stationary bicycle HIIT on cardiorespiratory/endurance and muscular strength and size measures, as well as physical function adaptations, to MICT or RT in generally healthy but insufficiently active older adults. Though terminated early due to COVID-19 restrictions, the diverse data that were collected allowed us to calculate effect sizes to power future investigations. First, HIIT had a greater effect on VO2max than MICT (ES=0.44 and 0.16, respectively), and a similar large effect on fatigue resistance (ES=1.13 and 0.90, respectively). MICT has long been promoted as an essential element in healthy aging (9), and it is becoming more and more clear that HIIT is also a safe aerobic exercise regimen that is highly effective at improving cardiac, respiratory, and metabolic function in an older adult population (10). A somewhat unexpected finding of this study, however, was the effect of RT on VO2max. The benefits of aerobic and resistance training have historically been considered independent of each other, and as such there has been relatively little attention given to the effects of RT on cardiorespiratory variables (4).
Stationary bicycling is an ideal form of aerobic exercise for older adults due to its effectiveness at inducing cardiorespiratory adaptations and the relative low risk of injury (11), and has also been shown to elicit strength improvements in older adults when used for MICT (12) or HIIT (13). We expected a similar response to our cycling protocols, however, our low-volume bicycle HIIT protocol had a very small effect on muscular strength and size at the group level. There was a diverse response to HIIT at the individual level– some participants showed substantial increases while others demonstrated substantial declines in muscle strength and size (Figure 1). It is unclear why our cycling protocols did not consistently result in improved strength, as has been reported previously (12, 13), although there are several methodological factors that may affect muscular adaptations (e.g., resistance, cadence).
Due to the relatively recent interest in HIIT for older adults there are few studies reporting effects on physical function measures, though those that do appear to indicate beneficial effects (13-15). This proof-of-concept pilot study demonstrates that HIIT had a large effect on 6MW distance and a moderate effect on grip strength and chair rise time, indicating that HIIT can improve physical functional capacity in older adults without overt physical function limitations. This may translate into substantial improvements in physical function capacity in mobility-limited older adults, and future work should investigate this possibility. In this study we chose a pragmatic approach wherein our participants followed national exercise guidelines; however, we should note that nuanced differences in training paradigms (e.g., different intensities or controlling for total volume, duration, or caloric expenditure) could have yielded different results.



HIIT is a time-efficient exercise strategy that has the potential to produce both cardiorespiratory and muscular improvements, but few groups have investigated this potential. Our low-volume HIIT protocol did not consistently induce muscular adaptations but did elicit effects on cardiorespiratory/endurance and physical function measures comparable to MICT with half of the time commitment. Additionally, RT had small-to-moderate effects on cardiovascular/endurance measures along with the expected larger effects on strength. Future work should include strength and physical function measures to better characterize the adaptations to HIIT in order to determine if it is an effective and efficient exercise strategy for healthy and mobility-limited older adults.


Funding: This work was supported, in part, by a pre-doctoral fellowship grant to D Tavoian from the American Heart Association (19PRE34380496). The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Acknowledgements: The authors would like to thank Rachel Clift, Lynn Petrik, Cammie Starner, Simon Moskowitz, Caleb Moore, Erica Baker, and Sam McGee for their assistance with data collection and exercise supervision. This study is registered with clinicaltrials.gov (NCT03978572).

Conflicts of Interest: In the past 5-years, BC has received research funding from NMD Pharma, Regeneron Pharmaceuticals, Astellas Pharma Global Development, Inc., and RTI Health Solutions for contracted studies that involved aging and muscle related research. In the past 5-years, BC has received consulting fees from Regeneron Pharmaceuticals, Zev Industries, and the Gerson Lehrman Group for consultation specific to age-related muscle weakness. BC is a co-founder with equity of OsteoDx Inc. The other authors declare there are no conflicts of interest.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.





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D. Sanchez-Rodriguez1,2,*, S. Piccard3,*, N. Dardenne4, D. Giet5, C. Annweiler6,7, S. Gillain3


1. WHO Collaborating Centre for Public Health aspects of musculo-skeletal health and aging, Division of Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium; 2. Geriatrics Department, Rehabilitation Research Group, Hospital Del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Barcelona, Spain; 3. Geriatrics Department, Liège University Hospital, University of Liège, Liège, Belgium; 4. Public Health Department, Biostatistics, University of Liège, Liège, Belgium; 5. General Medicine Department, University of Liège, Liège, Belgium; 6. Department of Neurosciences and Aging, Division of Geriatric Medicine, Angers University Hospital; Angers University Memory Clinic; Research Center on Autonomy and Longevity; UPRES EA 4638, University of Angers, UNAM, Angers, France; 7. Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada; *Contributed equally.
Corresponding author: Dolores Sanchez-Rodriguez, MD PhD. WHO Collaborating Centre for Public Health aspects of musculo-skeletal health and aging, Division of Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium, Tel: +32 493 43 27 50; Emails: dolores.sanchez@uliege.be

J Frailty Aging 2021;10(3)290-296
Published online May 10, 2021, http://dx.doi.org/10.14283/jfa.2021.22



Introduction: The World Health Organization (WHO) has recently launched the term “intrinsic capacity”, defined as “the composite of all the physical and mental capacities of an individual”. Intrinsic capacity has a positive value towards healthy aging, and is constructed by five domains: cognition, vitality/nutrition, sensory, psychology, and mobility. ICOPE App and ICOPE Monitor are applications for the assessment (screening) of intrinsic capacity. Hypothesis: Intrinsic capacity assessed by the ICOPE Apps at baseline could be associated with the incidence of frailty, functional decline, and health outcomes during 1-year follow-up. Objectives: To assess the association between intrinsic capacity measured by the ICOPE Apps at baseline and the incidence of frailty in community-dwelling older adults during 1-year follow-up. Secondarily, to assess the association of intrinsic capacity and functional decline, mortality, pre-frailty, falls, institutionalization, and quality of life. Methods: Protocol for a cohort study of community-dwelling adults ≥65-year-old, with no other exclusion criteria than the inability to use the Apps or communicate by telephone/video-call for any reason (cognitive or limited access to telephone/video-call) OR being considered frail at baseline (defined as having a Rockwood’s clinical frailty scale, CFS score ≥4). Intrinsic capacity measured by the ICOPE Apps and CFS will be assessed at baseline, 4-, 8- and 12-month follow-up by telephone/video-call. Assuming a prevalence of frailty of 10.7%, and incidence of 13% (alpha-risk=0.05), 400 participants at 12-month end-point (relative precision=0.10) and 600 participants at baseline will be required. Results: Associations among the decrease in intrinsic capacity and higher risk of frailty, functional decline, and health adverse outcomes during 1-year follow-up are expected. Conclusions: ICOPE Apps might identify individuals at higher risk of frailty, functional decline, and health adverse outcomes. The implementation of the ICOPE Apps into clinical practice might help to deliver efficient person-centered care-plans, and benefit the healthcare systems.

Key words: ICOPE, intrinsic capacity, App, functional decline, older people, study protocol.



The World Health Organization aims at promoting initiatives focused on the preservation of individuals’ physical and mental capacities to achieve older ages in a good health status (healthy aging, defined as “the process of developing and maintaining the functional ability that enables well-being”) (1). Intrinsic capacity is a new term launched in the WHO in the plan of action 2016-2020 (1). The operational definition of “intrinsic capacity” is “the composite of all the physical and mental capacities of an individual” and is constructed by 5 domains: locomotion, vitality, sensory (vision and hearing), cognition, and psychological domain (2). Intrinsic capacity has a positive value, is focused on function, and switches the viewpoint from a negative paradigm of ageing (diseases, disability, frailty, etc.) towards the positive focus of a “healthy aging” (2,3).
The Integrated Care for Older People (ICOPE) program was launched by the WHO n. Global strategy and action plan on ageing and health in 2016 (4) and is focused on individuals’ comprehensive assessment and potential interventions on the 5 domains of intrinsic capacity (5, 6). In a second step, the strategy the WHO is aimed on providing evidence about trajectories of life (Normograms for Healthy Ageing Standards). The WHO plan of action scheduled for 2020-2030 would provide the continuity of this line of research (https://www.who.int/ageing/en/). The WHO guidelines on community-level interventions in integrated care have been recently launched, aimed at “Redesigning care for older people to preserve physical and mental capacity”, and involve the comprehensive assessment of the domains of intrinsic capacity (5, 6).
Two new technologies for the assessment of intrinsic capacity are available and capable to screen for individual’s intrinsic capacity (7, 8): The ICOPE application (App) has been developed by WHO and the ICOPE Monitor, has been developed as part of the INSPIRE program, an initiative from the Gérontopôle of Toulouse, which is a WHO Collaborating Center, in collaboration with the WHO and several partners from Toulouse (7, 8). The two Apps are already available free-of-charge in Apple or Android Store.
Frailty, defined as a “syndrome characterized by a clinical state in which there is an increase in an individual’s vulnerability for developing an increased dependency and/or mortality when exposed to a stressor” (9), is also a construct of several domains (10, 11). Despite frailty is a different construct, it would be expected that a decrease in intrinsic capacity, measured by the Apps is associated to the occurrence of frailty and functional decline, but these associations remain unexplored (3).
We hypothesize that intrinsic capacity, assessed by the ICOPE App and ICOPE Monitor at baseline, could identify individuals at higher risk of developing frailty, functional decline, and health adverse outcomes during 1-year follow-up. If these hypotheses are confirmed, ICOPE Apps could be incorporated into clinical practice in community-dwelling older people.
Objectives: Our primary objective is to assess the relationship between intrinsic capacity assessed with the ICOPE Apps (ICOPE App and ICOPE Monitor) at baseline and the incidence of frailty in community-dwelling older adults during 1-year follow up. Secondarily, to assess the association between intrinsic capacity measured by the ICOPE App and the ICOPE Monitor at baseline and the risk of functional decline, mortality, incidence of pre-frailty, falls, institutionalization, and loss of quality of life in this population during 1-year follow-up will be assessed.




Protocol for a prospective cohort study, designed to determine the incidence of frailty in community-dwelling older people during 1-year follow-up. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (12) will be followed. Subjects who volunteer and meet eligibility criteria will be consecutively included.


The study will be conducted in Liège, a city located in the French-speaking region of Belgium. Volunteers will be recruited from 1) the Geriatric Department of the university hospital, 2) Outpatient clinics of primary care, and 3) Local press, television, and radio advertisements. In the first two options, the clinicians in the Geriatrics or primary care department that collaborate with the study, would identify a potential candidate and send an electronic mail with contact details to the personal from the study. In the third option, volunteers will receive instructions to proactively contact by telephone or electronic mail (e-mail) with the personal from the study. After receiving the e-mails, a telephone or video-call between the volunteer and the personal from the study will be scheduled in a maximum of 1 week. During the first interview, subjects will be assessed for eligibility, receive detailed information about the study, and sign electronically the informed consent by drawing their own signature in the screen of the mobile and forwarding it as an image. A printed copy of the informed consent with the signature in paper will be sent to volunteers’ homes by post mail afterward. Technical assistance in downloading the two Apps, signing the informed consent, the baseline assessment, and schedule next follow-up will be given during the first interview. The 4-, 8-, and 12-month follow-up will be conducted in a similar way. The Geriatric and primary care Department count with the technical devices and facilities to conduct the proposed test, and the Apps are free-of-charge (Apple /Android Store https://www.youtube.com/watch?v=gLva4ReV9KA). Data will be analysed at the Research Unit in Biostatistics, Public Health Department, University of Liege, Belgium. Table 1 summarizes study settings, variables, and timeline of the study.

Table 1
Variables, study settings, and timeline

a. Statistical analysis will be conducted at the Research Unit in Biostatistics, Public Health Department, University of Liege, Belgium; The sign “x” means test or probe performed and registered; the sign “-“means test not performed



Prospective cohort study of community-dwelling adults ≥65-year-old living at home, with no other exclusion criteria than the inability to use the ICOPE Apps or communicate by telephone/video-call for any reason (cognitive or limited access to technologies like telephone/video-call) OR being considered frail at baseline (defined as having a Rockwood’s clinical frailty scale, CFS score ≥4).
Intrinsic capacity assessment: Will be administered with the two Apps, the ICOPE App and the ICOPE Monitor.
ICOPE App: The screening by ICOPE App includes polar questions (yes/no) about the 5 domains of intrinsic capacity. Two results are possible: positive (probable decrease in intrinsic capacity) or negative (intrinsic capacity not decreased), as a total binary result of the 5 domains together. The App includes possibility to record the summary of the screening, download it in pdf, or send it by mail.
ICOPE Monitor: For the moment, ICOPE Monitor is equipped with ICOPE Step 1 (i.e. screening tool). ICOPE Monitor includes identification and informed consent of both the professional and the participant, detailed intrinsic capacity assessment of the 5 domains and reports the results obtained in each one of the 5 domains separately afterwards. The results obtained in the 5 domains are provided as a checklist: two results are possible for each one of the 5 domains: “right” or “wrong” in each domain. Updated advice for each domain and a link for further information is also provided. The App automatically schedules the date for the next 4-month follow-up and sends the informed consent by mail to the volunteer.
The results in the two Apps provide different approaches to IC. First, a global binary decision (IC decline or not) is directly provided by the ICOPE App (IC decline or not). Second, a binary decision of each one of the 5 domains per separate is provided directly by the ICOPE Monitor (each domain decreased or not); third, the binary decision obtained in the 5-domains could be combined as a score which might be just a count of IC declines, e.g. if we have 3 domains with decline among the 5 domains, the score would be 3/5. For purpose of this analysis, the second approach, a binary decision of each one of the 5 domains per separate provided directly by the ICOPE will be selected; the other two options would be studied as part of further secondary analysis.

Outcome measures

Primary outcome measure: Incidence of frailty assessed by Rockwood’s clinical frailty scale (CFS) will be assessed at baseline, 4-, 8- and 12-month follow-up by telephone/video-call. An score <4 will indicate robustness, 4-6 pre-frailty status, and ≥ 6 frailty (10). Secondary outcome measures: Incidence of functional decline (assessed by Chair stand test (14, 15) and the loss of ≥20 points in Barthel index (16, 17), death (date), incidence of pre-frailty (assessed by CFS) (10), falls (number and date), institutionalization (date), and quality of life (EuroQol) (18) will be assessed at 4-, 8-, and 12-month follow-up by a telephone/video-call with the volunteer or a contact person.
Functional decline: Incidence of functional decline will be defined as 1) The loss of the capacity to raise from a chair within 14 seconds, measured by the chair stand test in the ICOPE Monitor (Chair stand test) (14, 15); or 2) The loss of at least one activity of daily living, defined as loss of ≥20 points in Barthel index (16, 17), administered by phone (19) at baseline, 4-, 8- and 12-month follow-up (16-20).
Death: Date of death will be recorded, reported by the contact person if the participant did not respond to the scheduled follow-up visit at 4-, 8-, and 12-month follow-up.
Pre-frailty: Incidence of pre-frailty will be assessed by CFS at baseline, 4-, 8- and 12-month follow-up by telephone/video-call. An score <4 will indicate robustness, 4-6 pre-frailty status, and ≥6 frailty (10).
Falls (number and date) will be recorded by the volunteers on a personal dairy book.
Institutionalization: Date of institutionalization will be reported by the contact person if the participant did not respond to the scheduled follow-up.
Quality of life will be measured by self-reported EuroQol (ranging from 0 -the worst possible health status- to 1 -the best possible health status-) (18) by telephone/video-call at baseline, 4-, 8-, and 12-month follow-up.
Covariate data collection: Demographic and clinical data will be collected during the telephone/video calls and used as covariates. Instrumental activities of daily living (IADL) (maximum score 8 points) will be recorded using the Lawton scale (21). The ratio of the total score obtained / total score of the applicable items will be used to avoid any discrimination based on usual housework distribution among couple as in Gillain et al., 2017. E.g. if one of the members of a couple does not usually perform an activity, that item will not account for that individual, then, the total score of the applicable items will be 7 instead of 8 (22).
Feasibility of the ICOPE App and ICOPE Monitor: Feasibility will be defined as “the state or degree of being easily or conveniently done” and explored by the Technological, Economical, Legal, Operational, Schedule (TELOS)-feasibility score, which assesses the potential of implementation of new systems, and considers them feasible if TELOS-feasibility score ≥3) (23). It will be administered to the geriatric and primary care practitioners who collaborate with the study.

Sample size calculation

Sample size has been calculated in terms of the ICOPE study primary objective: to identify incidence of frailty in community-dwelling older patients. Table 2 shows the sample size calculation determined by a power calculation based on the width of the confidence interval for a proportion and on the estimation of an incidence rate (24). Assuming a prevalence of frailty of 10.7% (25), an incidence of 13% (26) with an alpha risk of 0.05, we estimated that 400 participants at the 12-month end-point of the study would be necessary to meet a degree of certainness regarding the incidence (relative precision of 0.10) meaning a sample of 600 volunteers at baseline should be cover a potential large dropout regarding the characteristics of this study (observational, 1-year follow-up, potential frail people, unknown adherence rate to the follow-up).

Table 2
Sample size calculation according to the prevalence and expected incidence of frailty


Statistical plan

Descriptive analysis will be performed for each variable of the study. Qualitative variables will be described by absolute numbers and relative frequencies (%). Quantitative variables will be summarized by the use of means and standard deviation (SD) for symmetrical distribution or median and the interquartile deviation for asymmetrical distributions. Normality of variables will be checked graphically with histograms and quantile-quantile plots, and tested by the Shapiro-Wilk test. A transformation of the data might be performed, if needed.
The evolution of intrinsic capacity during the 1-year follow-up will be analyzed by Generalized Linear Mixte Model (GLMM). These models will be also used to study the evolution of outcomes measured every 4 months and the impact of covariates on these evolutions. Kaplan-Meier methods will be use to represent graphically the notion of time occurrence of frailty and mortality (4-month follow-up time will be used as notion of time).
For the outcomes with a precise date of event (frailty, mortality, falls, institutionalization), their association with intrinsic capacity will be analyzed by a joint model for longitudinal and time-to-event data (27); the same analysis might be performed for other variables of interest, if required. Indeed, on one hand, we want to analyze the evolution over time of covariates and on the other hand the effect of these covariates on the outcome. Multiple imputations methods would be applied to deal with missing data. The data will be processed using SAS 9.4 (©SAS Institute Inc., Cary, NC, USA) and R (version 3.5) (R Core Team) software packages. The level of statistical significance will be set as α = 5% (p < 0.05).


National and International research Ethics guidelines will be followed, including the 1964 Declaration of Helsinki and its further amendments, and the Committee on Publication Ethics (COPE) guidelines (28)(29). Data will be treated according to the law of data protection in Belgium and the Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data (30). The approval of the Ethics Committee (Comité d’Ethique Hospitalo-Facultaire Universitaire de Liège) will be obtained. Written and oral information will be provided and informed consent will be signed electronically by all participants. The study has been registered at www.clinicaltrials.gov (NCT04413877 on June 2, 2020).



Our study will determine the associations among decrease in intrinsic capacity and the incidence of frailty, functional decline, and occurrence of health adverse outcomes in community-dwelling older people during 1-year follow-up. If these associations are observed in the study, the measurement of intrinsic capacity by new technologies would be ready to be implemented in clinical practice.
Several aspects of the study should be highlighted. First, it will include intrinsic capacity assessment (2). Indeed, even if the association of intrinsic capacity with mortality, functional decline, and falls was recently shown in older people in nursing homes (31), there is still very few data in community-dwelling population. The rationale to select intrinsic capacity, instead of using other syndromes or diseases, for example, sarcopenia, malnutrition, or dementia, is due to the multidimensional characteristics of older people, which require a global assessment. A single point of view might not be complete enough to take complex decisions in clinical practice.
Second, the study will provide evidence about the relationship between intrinsic capacity and frailty, which is a different multidimensional construct. Wide differences among frailty and intrinsic capacity have been pointed out, i.e. frailty is driven by deficits, while intrinsic capacity is driven by reserves; frailty is an approach towards treatment usually measured in clinical settings, while intrinsic capacity is an approach focused on function maintenance in the community setting; frailty has wide evidence about its prognosis capacity, while intrinsic capacity is quite new. It would be expected that intrinsic capacity, measured by the Apps is associated to the incidence of frailty, but this association remains unexplored and requires further studies (3).
Three different approaches of IC will be explored, in order to provide novel insight about how to deal with IC as variable, which is still an uncharted territory; for purpose of analysis, the approach that uses a binary decision of each one of the 5 domains per separate provided directly by the ICOPE will be selected, as it is effortless, obtained directly by the ICOPE Monitor, and provide insight to deliver potential therapeutic interventions. The other two options would be studied as part of further secondary analysis.
Mortality has been selected as clinically meaningful outcome of the study, as recommended by the Common data elements (CDE) and core outcome measures (COMs) in Frailty research consensus (32) and the Physical Frailty: ICFSR International Clinical Practice Guidelines for Identification and Management (33). Frailty has shown association with 2-fold higher all-cause mortality (HR 2.17, 95%CI 1.90-2.48) (34), including infectious diseases (HR 1.79, 95%CI 1.03-3.11) (34). Sample size calculation was based on a prevalence of frailty of 10.7% (25) and incidence 13%, (26)(35). Despite lower incidence rates (3.9%, 8%)(35–37), have been shown in other large cohorts, the highest one was selected for our sample size calculation to ensure the detection of frailty (11). Providing data about the relationship between intrinsic capacity and frailty will pave the path towards the change in the negative paradigm of aging, and a person-centred model focused on enhancing the 5 domains towards healthy aging (3).
Third, our study will include the use of new technologies by older people, which is an emerging field with promising results. Several projects on the use of technologies to support health status of older people are currently ongoing, e.g. the J48 supervised machine learning algorithm is identifies future fallers among otherwise healthy, independent older adults (38); eMIND is a randomized controlled trial that includes web-based multidomain interventions (39); and the ALLEGRO living lab is an experimental hospital-based room for the testing of devices by frail hospitalized older people (40). The International Network of Agencies for Health Technology Assessment (INAHTA) provides high quality evidence about new technologies to help health care suppliers and policy makers in their decisions. The Belgian Health Care Knowledge Centre (KCE) has recently joined the INAHTA in 2020 (41), which might be promising for this line of research.
Finally, some limitations related to the cohort design should be acknowledged. The inclusion of healthy community-dwelling older volunteers will be considered as a selection bias, as it has been previously reported in other cohorts of community-dwelling older people (42). The characteristics of voluntary older participants (motivation, involvement in self-care management, etc.) might differ from those who refused to get involved in a research study. Moreover, volunteers who are able to use online resources might be relatively younger at baseline, and their health status might be better than the population of the same age.
In summary, this study will apply the “Action-research philosophy” (43) to bridge the gap between research and clinical practice. It will provide evidence to implement the ICOPE App and ICOPE Monitor, deliver efficient person-centered care-plans, and benefit older adults, professionals, and the healthcare systems.


Funding: No funding has received to conduct this research
Conflicts of interest: Authors declare no conflict of interest
Authors’ contribution: DSR and SG conceived the manuscript; DSR, SP, ND, and SG wrote the manuscript; DSR and SG did literature review; ND calculated the sample size and wrote the statistical plan; DG, and CA corrected the manuscript. All co-authors read and approved the final version of the manuscript.



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A.R.M.S. Ekram1, R.L. Woods1, C. Britt1, S. Espinoza2,3, M.E. Ernst4, J. Ryan1


1. School of Public Health and Preventive Medicine, Monash University, Alfred Campus, Melbourne, Victoria, Australia; 2. Sam and Ann Barshop Institute for Longevity and Aging Studies, UT Health San Antonio Texas Research Park Campus, San Antonio, Texas, USA; 3. Geriatrics Research, Education and Clinical Center, South Texas Veterans Health Care System, San Antonio, Texas, USA; 4. Department of Pharmacy Practice and Science, College of Pharmacy and Department of Family Medicine, Carver College of Medicine, The University of Iowa, Iowa City, USA.

Corresponding Author: ARM Saifuddin Ekram, School of Public Health and Preventive Medicine, Monash University, Alfred Campus, Melbourne, Victoria-3004, Australia; E-mail: saifuddin.ekram@monash.edu

J Frailty Aging 2021;in press
Published online May 5, 2021, http://dx.doi.org/10.14283/jfa.2021.20



Frailty is associated with multiple adverse health outcomes, including mortality. Several methods have been used to characterize frailty, each based on different frailty scales. These include scales based on phenotype, multidomain, and deficit accumulations. Several systematic reviews have examined the association between frailty and mortality; however, it is unclear whether these different frailty scales similarly predict mortality. This umbrella review aims to examine the association between frailty assessed by different frailty scales and all-cause mortality among community-dwelling older adults. A protocol was registered at PROSPERO, and it was conducted following the PRISMA statement. MEDLINE, Embase, PubMed, Cochrane Database of Systematic Reviews, Joanna Briggs Institute (JBI) EBP database, and Web of Science database was searched. Methodological quality was assessed using the JBI critical appraisal checklist and online AMSTAR-2 critical appraisal checklist. For eligible studies, essential information was extracted and synthesized qualitatively. Five systematic reviews were included, with a total of 434,115 participants. Three systematic reviews focused on single frailty scales; one evaluated Fried’s physical frailty phenotype and its modifications; another focused on the deficit accumulation frailty index. The third evaluated the FRAIL (Fatigue, Resistance, Ambulation, Illness, and Loss of weight) scale. The two other systematic reviews determined the association between frailty and mortality using different frailty scales. All of the systematic reviews found that frailty was significantly associated with all-cause mortality. This umbrella review demonstrates that frailty is a significant predictor of all-cause mortality, irrespective of the specific frailty scale.

Key words: All-cause mortality, FRAIL, Frailty deficit accumulation index, Fried frailty phenotype.

Abbreviations: AMSTAR-2: A MeaSurement Tool to Assess systematic Reviews Version 2; CDSR: Cochrane Database of Systematic Reviews; FRAIL: Fatigue, Resistance, Ambulation, Illness, and Loss of weight; HR: Hazard Ratio; JBI: Joanna Briggs Institute; OR: Odds Ratio: PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PROSPERO: International Prospective Register of Systematic Reviews; RR: Relative Risk.



There is increasing attention toward frailty as a clinically meaningful measure of geriatric health (1). Contemporary research has defined frailty’s clinical and physiological characteristics and highlights the vulnerability of frail, older adults to poor health outcomes (2). Accordingly, the number of publications on frailty has increased exponentially over the last few decades (3) as the determination of frailty status is emerging as a significant predictor of outcomes in other fields, including cardiology (4, 5), neurology (6, 7), oncology (8), orthopaedics (9), surgery (10), in addition to geriatrics in general. Consequently, the association between frailty and all-cause mortality has been investigated across different settings and populations.
While the concept of frailty is widely recognized, there is no single explicit criterion to define frailty. In 2013, a consensus statement by six major international scientific societies defined frailty as a medical syndrome with multiple causes and contributors that is characterized by diminished strength, endurance, and reduced physiologic function and increases an individual’s vulnerability for developing disability, dependency, or death (11, 12). In this definition, frailty is viewed as firstly, a clinical entity distinct from disability, sarcopenia, or multimorbidity; secondly, it affects a person’s physical or cognitive domains; and finally, it is considered as a dynamic state, which can improve or deteriorate over time (11). An intermediate or ‘prefrail’ stage has also been recognized (13-15). Several frailty scales have been developed to characterize frailty in older adults, described in three broad categories. The first category includes focused physical scales, which most notably contain the Fried physical frailty phenotype from the Cardiovascular Health Study and adaptations derived from this original scale (13). It consists of five components: unintentional weight loss, muscle weakness, exhaustion or low energy level, slowness or slow gait, and low physical activity.
Persons are frail if three or more of the five criteria are met. The second category of frailty scales is a multidomain scale (16), which describes multidimensional characteristics of frailty containing more than one medical, physical, cognitive, or environmental factor. The third type of scale is a deficit accumulation frailty index (17). It consists of an inventory of various deficits covering multiple domains or body systems and the percentage of deficits calculated. These three types of scales capture different aspects of the frailty syndrome and, therefore, there may be differences in their association with health outcomes. Understanding these differences is important because it could inform how the various measures are best applied. All three categories of frailty assessment scales have some limitations. For example, the phenotype scale does not cover all frailty dimensions, such as cognition or affect (13). The multidomain scale and the deficit accumulation model-based scales are comprehensive but time-consuming. Previously, systematic collection of clinical information was not feasible in many settings, challenging the integration of this scale into regular healthcare practice (18). However, the growing popularity and implementation of electronic health records and automated frailty indexes are increasingly being developed in different countries, e.g., in the USA (19, 20), Australia (21) and various European countries (22). Furthermore, findings from the UK have shown that routine implementation of the electronic frailty index enabled the delivery of evidence-based interventions to improve outcomes in the older population (23).
Few systematic reviews have explored different frailty scales and determined whether frailty assessed by these scales is predictive of all-cause mortality. Furthermore, it remains unclear whether a particular frailty scale is a better predictor of mortality of community-dwelling older adults (13, 24). Therefore, the objective of this umbrella review is to qualitatively synthesize and evaluate the association between frailty determined by different frailty scales and all-cause mortality in community-dwelling older people.



A protocol was developed, and the review was conducted following the Preferred Reporting Items for Systematic reviews and meta-analyses (PRISMA ) statement (25). The protocol was registered at the International Prospective Register of Systematic Reviews or PROSPERO (ID: CRD 42020155407).

Data sources and search strategy

The search strategy aimed to find published systematic reviews and meta-analyses that evaluated the association between frailty and all-cause mortality in community-dwelling older populations. Systematic and comprehensive searches were conducted in electronic databases: MEDLINE, Embase, PubMed, Cochrane Database of Systematic Reviews (CDSR), Joanna Briggs Institute Evidence-Based Practice (JBI EBP) Database, and Web of Science. The search was conducted in October 2019 and updated in July 2020. The search strategy and search terms are provided in Appendix I. Studies conducted on humans and articles published in English were considered eligible for this review, and duplicates were excluded. The searches were independently performed by two authors (ARMSE and CB). Any discrepancies were resolved by discussion.

Inclusion and exclusion criteria

We included systematic reviews and meta-analyses that have reported the association between frailty and mortality among community-dwelling older adults aged 65 years or above using any frailty scales, e.g., Fried physical frailty phenotype or modifications, deficit accumulation frailty index, and multidomain frailty index. We excluded systematic reviews and meta-analyses that included only hospitalized and institutionalized older adults or examined disease-specific outcomes (i.e., falls, fractures, heart failure, etc.) rather than mortality. However, we included two systematic reviews where few studies had participants less than 65 years of age as those were considered in their meta-analyses (26, 27).

Study selection and data extraction

Two reviewers (ARMSE and CB) independently searched titles. They screened abstracts before retrieving the full texts, assessed eligibility for the type of participants, study design, and outcomes. Data were extracted using a standardized form including author and year of publication, location, population characteristic, sample size, the proportion of female participants, age range, frailty scales used, number of deficits used to create the frailty scales, and follow-up period. We also noted the quality and bias assessment, effect sizes, and measure of variance, most commonly hazard ratios with 95% confidence intervals and heterogeneity assessments.

Methodological quality assessment

Manuscripts were assessed for methodological quality before inclusion in the review. The quality assessment of the included five systematic reviews were performed by ARMSE and CB. We used JBI Critical Appraisal Checklist for Systematic Reviews and Research Syntheses (28) (Table 1) and the online ‘A MeaSurement Tool to Assess systematic Reviews Version 2’ (AMSTAR 2) checklist (29).

Table 1. Critical appraisal checklist for systematic reviews and research syntheses

Note: 1 Few included studies had participants with age less than 65 years


Data synthesis and analysis

The studies were combined using qualitative best evidence synthesis, as statistical pooling could not be done due to the high heterogeneity of the included studies’ meta-analyses. We extracted and reported the pooled effect sizes of the outcomes meta-analyzed within the reviews (Table 2).

Table 2. Summary characteristics of the included systematic reviews and meta-analyses

Abbreviations: 95% CI: 95% confidence interval; AHRQ: Agency for Healthcare Research and Quality; AMSTAR 2: A MeaSurement Tool to Assess systematic Reviews 2; AUC: Area under the ROC Curve; DAFI: Deficit Accumulation Frailty Index; ED: Emergency Department; F: Frail; HR: Hazard ratio; NICE: National Institute for Health and Care Excellence; NOS: Newcastle-Ottawa Scale; OR: Odds ratio; PAR: Population Attributable Risk; PF: Prefrail; QUADAS-2: Quality Assessment Tool for Diagnostic Accuracy Studies; R: Robust; ROC: Receiver operating characteristic curve; RR: Relative risk



Search results

A total of 969 records were identified from the six databases, and after removing the duplicates, 686 were screened for eligibility based on title and abstract. Twenty-three full-text articles were then reviewed for relevance, out of which 18 were excluded because aspects similar to, but not defined explicitly as, frailty were assessed, e.g., gait speed (30, 31), sarcopenia (32), various health indicators (33) or geriatric syndromes (34); outcomes other than mortality were examined, e.g., trauma (35), fractures (9, 36), falls (37), high blood pressure and cardiovascular outcomes (38) or heart failure (4); study population included were from clinical practice (39), nursing home (40) or critical care (41) but not from a community setting; the study involved interventions, e.g., treatment modalities (42); or the article was a systematic review protocol or an umbrella review which evaluated frailty scales for clinical outcomes from community, residential care and hospital settings (43-45). This left five eligible systematic reviews and meta-analyses in the umbrella review (Figure 1). All five reviews were of moderate to high quality, as assessed by the JBI critical appraisal checklist (Table 1) and online AMSTAR-2 checklist. The reviews included 93 studies (some of which were included in multiple systematic reviews), and they assessed a range of outcomes. Of these studies, 77 examined the association between frailty and all-cause mortality over one to sixteen years of follow-up and were the focus of this review. Of the five systematic reviews, one review focused only on studies that used the frailty scale exclusively based on the Fried phenotype and its modifications (11 scales in a total of which four were original and seven modified) (46); one examined the FRAIL scale which is a questionnaire-based phenotype scale with five components, i.e., fatigue, resistance, ambulation, illness, and loss of weight (27); one review included studies assessing the deficit accumulation frailty index, with between 23 and 70 deficit items (26); one review included studies assessing either the Fried phenotype (7 studies) or the deficit accumulation frailty index (8 studies) (47); while the fifth review included 25 different scales of which five were Fried phenotype-based scales, 14 multidomain scales and six were deficit accumulation frailty index containing 23 to 83 deficits (16).

Figure 1. PRISMA 2009 Flow Diagram(50): Frailty Status and All-Cause Mortality in Community-Dwelling Older Individuals: An Umbrella Review


Overall, the participants were predominantly over 65 years of age, with a minimum age for inclusion varying from 50 to 75. However, one study included participants with a minimum age of 15 years (48). The maximum age recorded in one study was 108 years (26). The participants included were community-dwelling individuals from Australia, Canada, China, Israel, Mexico, the United Kingdom (UK), the United States of America (USA), and multiple European countries. Female participants represented 42% to 74% of the sample in most studies (Table 2). Individual study’s frailty outcome was adjusted for a range of two to ten covariates (e.g., age, gender, education, smoking, alcohol intake, socioeconomic conditions) in their analysis.

Overall findings for the association between frailty and all-cause mortality

All five systematic reviews reported a significant association between frailty and an increased risk of mortality; however, the effect size between frailty and mortality varied across the included systematic reviews. For example, the meta-analysis that included 24 studies using three types of scales (i.e., Fried physical frailty phenotype, deficit accumulation frailty index, and multidomain frailty index) estimated an overall hazard ratio of 2.34 (95% CI:1.77, 3.09) between frailty and all-cause mortality (16). The estimated overall relative risk was 1.83 (95% CI: 1.68, 1.98). In their analysis, comparing the non-frail to frail groups, the risk associated with mortality varied depending on the frailty scales used. The Fried physical frailty phenotype was associated with a 2.6-fold increased risk of mortality (HR: 2.58; 95% CI: 1.83, 3.64; I2=89%, P <0.001); the multidomain frailty index with a 2.1-fold increased risk (HR: 2.13; 95% CI: 1.38, 3.29; I2=96%, P <0.001); and the deficit accumulation frailty index a 1.85-fold (HR:1.85; 95%CI: 1.30, 2.63; I2 = not available, P = not available) (16). Similar effect sizes were reported from the systematic review that included only the phenotype-based frailty index and found that frailty was associated with a two-fold increased risk of mortality than robust or non-frail persons (HR: 2.00; 95% CI: 1.73, 2.32) (46). Direct comparison of effect sizes from the other systematic review was not possible, given they considered the association between a one-unit increase in frailty score using the deficit accumulation frailty index and mortality (random effect model: HR:1.04; 95% CI: 1.03, 1.04; fixed effect model: HR: 1.28; 95% CI: 1.26, 1.31 per 0.1 increase in frailty index) (26). Only one systematic review included a questionnaire-based FRAIL scale to assess the relationship between frailty and mortality (27). From the eight studies included in this review, it was found that individuals classified as frail or prefrail, compared to non-frail individuals, had a 3.5-fold and 1.8-fold increased risk of mortality, respectively, over 2.4 years to 4.3 years of follow-up. The predictive value of mortality remained similar across definitions of frailty, ranging from 54% to 70% in the receiver operating characteristic curve areas using a questionnaire-based FRAIL scale (27) and remained around 70% if the Fried physical frailty phenotype or the deficit accumulation frailty index were used (47).

Gender differences

Three of the five reviews examined potential gender differences in the association between frailty and mortality and yielded some conflicting results (26, 46, 47). For example, one review using the Fried physical frailty phenotype and another utilizing the deficit accumulation frailty scale showed that older men with frailty had a higher risk of mortality than older women with frailty (26, 46). However, the third review (47) found mixed results depending on the individual study, with some reporting that men had an increased risk of mortality (49-52). Still, others found that women had an increased risk (51-53). One study reported a dose-response association between a more significant number of deficits and increased mortality in women across all age categories (51). However, this review (47) did not directly compare the risk between gender.


Age did not appear to be an effect modifier of the relationship between frailty assessed using the Fried physical frailty phenotype or deficit accumulation index and mortality. Two of the five systematic reviews examined the association between frailty and mortality according to age groups (26, 46). The pooled estimates showed that the association between the deficit accumulation index and mortality did not vary between those aged below 65 years (HR:1.05; 95% CI: 1.03, 1.07) and those above 65 years (HR:1.04; 95% CI:1.03, 1.05) (26) per unit increase in a frailty index. Likewise, mortality risk was similar for those aged below 80 years (HR:1.62; 95% CI:1.39, 1.89) and above 80 years (HR:1.41; 95% CI:1.17, 1.70) estimated by the Fried physical frailty phenotype (46). Three other systematic reviews did not compare mortality based on age stratification (16, 27, 47).

Follow-up duration

The association between frailty and mortality varied according to follow-up duration. The risk of mortality was the lowest when the follow-up period was less than 12 months (HR:1.33; 95% CI:1.11, 1.60) and was the highest when the follow-up period was between two years to five years (HR: 3.25; 95% CI: 2.14, 4.94) (16). However, another review using the deficit accumulation frailty index found that the risk of mortality was higher when a shorter follow-up time was examined than a more extended follow-up, but effect sizes are not mentioned (26). Likewise, one systematic review compared a follow-up time of 4 years versus 11 years and observed that the strongest association between frailty and mortality was in the shorter follow-up group. However, individual values were not provided (47).



This umbrella review synthesized evidence from five large systematic reviews (16, 26, 27, 46, 47) that examined major categories of frailty scales and the association of frailty identified by those scales their association with all-cause mortality in community-dwelling older individuals. A wide-ranging literature search identified five moderate to high-quality systematic reviews that included 93 primary studies comprising 434,115 participants from different countries. These primary studies used eighty different frailty scales, including Fried physical frailty phenotype, and various modifications of this scale, to multidomain scales. All the systematic reviews found that frailty is a predictor of mortality irrespective of the frailty scale used. These results will inform researchers and clinicians that frailty assessment is vital to predicting mortality.
Though all five systematic reviews reported a significant association between frailty and an increased risk of mortality, the effect size between frailty and mortality varied across the included systematic reviews. That means a person may be frail on one scale but not frail on another scale. Thus, the challenge remains which scale is to be used to predict frailty for researchers and clinicians. Only two of the five systematic reviews included in this umbrella review examined the predictability of frailty scales (27, 47). One review (47) compared the survival estimates based on age and adjusted relative risk using both the Fried physical frailty phenotype and the deficit accumulation frailty index. They found a 50% increased risk of mortality in frail older adults than non-frail older adults using the Fried phenotype. On the other hand, there was about a 15% increase in the risk of mortality per unit increase using the deficit accumulation index in frail older adults compared to those who were not frail (26). The variation in prediction values across the different frailty scales emphasizes the need for standardization across frailty scales for research purposes; however, clinically, it is essential that frailty be assessed and identified early such that appropriate preventive measures can be considered.
The included systematic reviews in this umbrella review examined gender differences (26, 46, 47), the role of age (26, 46) and follow-up duration (16, 26, 47) on frailty and mortality. Nevertheless, heterogeneity due to different population groups, diverse frailty scales and different follow-up periods made it challenging to draw definitive conclusions. However, age did not appear to be an effect modifier of the relationship between frailty assessed using the Fried phenotype or deficit accumulation index and mortality between those aged above or below 65 or those aged above or below 80 years. Gender differences were observed. The association between frailty and mortality also varied according to follow-up duration. These issues require further exploration in future longitudinal studies exploring and comparing different frailty scales’ ability to predict the development of frailty and mortality. Furthermore, most scales primarily focused on frailty’s physical and physiological aspects, although frailty’s social, cognitive and psychological elements are essential and merit future research.

Strengths and limitations

The current umbrella review has multiple strengths. The protocol was registered at PROSPERO, and the PRISMA guidelines were followed in completing this review. The review’s search strategy was robust and reproducible and utilized comprehensive search terms in multiple electronic databases. We evaluated five moderate to high-quality systematic reviews, which examined many participants from different parts of the world. Therefore, the generalizability of the results is high. We included systematic reviews that measured frailty using the commonly available scales, i.e., Fried physical frailty phenotype, multidomain frailty scale (including the questionnaire-based FRAIL scale), and deficit accumulation frailty index, meaning the findings will be relevant more broadly.
However, there are some limitations. This umbrella review did not include intervention studies, or systematic reviews of frail participants from hospitals or nursing homes were excluded. Thus, the findings apply to community-dwelling older individuals only. For researchers, this umbrella review shows that any category of frailty scale has utility for predicting mortality. Finally, this umbrella review focussed on the utility of frailty assessment to predict mortality though it could be considered that delaying mortality is not the only or best objective for the geriatric population. Improving the quality of life before death or extending life free of disability could be considered a critical outcome for assessing risk in frail old persons.



This umbrella review’s findings provide evidence that frailty is associated with mortality risk and highlight the importance of assessing frailty in primary community settings. This review has demonstrated that frailty is a significant predictor of all-cause mortality regardless of the specific frailty scale. For example, frailty assessed using five components that exclude cognition and affect Fried phenotype predicted mortality to a similar extent as did more comprehensive deficit accumulation frailty indices that included 83 items. As such, this implies that researchers and clinicians can use the most appropriate frailty scales given their circumstances, resources, and access to information. Together these findings emphasize that the assessment of frailty status itself may be more important than the choice of which type of scale is used. However, future longitudinal studies exploring the potential predictors for the development of frailty and its association with mortality using different frailty scales to determine the predictability would be beneficial.


Ethics approval and consent to participate: It was not requested being a review of already published literature.

Consent for publication: Not applicable.

Availability of data and materials: The datasets generated during the current study are available from the corresponding author.

Competing interests: The authors declare that they have no competing interests.

Funding: Not funded.

Authors’ contributions: ARMSE developed the idea, searched the literature, reviewed articles, extracted data, and contributed to writing. CB searched and reviewed the literature. RLW, SEE, ME, and JR reviewed, edited, and contributed to writing. All authors read and approved the final manuscript.

Acknowledgments: Not applicable.






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48. Rockwood K, Song X, Mitnitski A. Changes in relative fitness and frailty across the adult lifespan: evidence from the Canadian National Population Health Survey. Can Med Assoc J. 2011;183(8):E487-94.
49. Cawthon PM, Marshall LM, Michael Y, Dam TT, Ensrud KE, Barrett-Connor E, et al. Frailty in older men: prevalence, progression, and relationship with mortality. J Am Geriatr Soc. 2007;55(8):1216-23.
50. Ensrud KE, Ewing SK, Cawthon PM, Fink HA, Taylor BC, Cauley JA, et al. A comparison of frailty indexes for the prediction of falls, disability, fractures, and mortality in older men. J Am Geriatr Soc. 2009;57(3):492-8.
51. Gu D, Dupre ME, Sautter J, Zhu H, Liu Y, Yi Z. Frailty and Mortality Among Chinese at Advanced Ages. J Gerontol B Psychol Sci Soc Sci 2009;64B(2):279-89.
52. Puts MTE, Lips P, Deeg DJH. Sex Differences in the Risk of Frailty for Mortality Independent of Disability and Chronic Diseases. J Am Geriatr Soc. 2005;53(1):40-7.
53. Bandeen-Roche K, Xue Q-L, Ferrucci L, Walston J, Guralnik JM, Chaves P, et al. Phenotype of Frailty: Characterization in the Women’s Health and Aging Studies. J Gerontol A Biol Sci Med Sci. 2006;61(3):262-6.



S. Boucher, G. Duval, C. Annweiler


CHU Anger, Centre Hospitalier Universitaire d’Angers, Angers, France

Corresponding Author: Sophie Boucher, CHU Angers, Centre Hospitalier Universitaire d’Angers, Angers; Mitolab unit, Institut MitoVasc, UMR CNRS 6215 INSERM 1083, Université d’Angers, Angers, France sophie.boucher@chu-angers.fr

J Frailty Aging 2021;in press
Published online May 4, 2021, http://dx.doi.org/10.14283/jfa.2021.19


Dear Editor,

«Healthy aging», defined by the World Health Organization (WHO) as « the process of developing and maintaining the functional ability that enables well-being » (1), needs the involvement of various physicians. The WHO-ICOPE (Integrated Care for Older People) approach was developed to early identify, diagnose and treat older adults’ frailties defined as impairments of at least one of the six intrinsic capacities (i.e., hearing, vision, mood, cognition, mobility and nutrition), which could precipitate functional loss and alter the quality of life (2). Among these intrinsic abilities, hearing is the sense of communication and socialization, and may influence the quality and accuracy of the medical examination and interview. Hearing impairment usually develop insidiously and is unrecognized or insufficiently self-reported by older patients. However, by 2050, the number of people with hearing loss will increase to over 900 million worldwide, mainly due to aging population.
During lifespan, hearing is exposed to a variety of aggressions, including occupational and recreational noise, solvents, ototoxic drugs, not to mention the deleterious effects of the metabolic syndrome, leading to age-related hearing loss known as presbycusis. Presbycusis starts with difficulty following conversations in noisy environments that can now be unmasked by speech in noise recognition tests. Hearing impairment then forces people to mobilize working memory in a listening-related effort (corresponding to the mental effort needed to understand the partially percept auditory message), which leads to mental fatigue and increases cognitive load. Over time, people gradually reduce social interactions, with subsequent risks of depression and cognitive decline.
Considering that hearing impairment occurring in midlife is the main modifiable risk factor for dementia (3), and given the frequency of this intrinsic frailty and the adverse impact on quality of life and autonomy, its early detection is essential. WHO-ICOPE recommends using the whisper test (Figure 1). To confirm the possible deficiency detected with this test, suffering inter examiner variation, clinicians may further complete the screening addressing the ability to understand speech in noise at the lowest possible signal-to-noise ratio (4) using the 2-minutes digits-in-noise test, available on smartphones or tablets (hearWHO) in different languages. This test detects presbycusis at its very first stage within a strong correlation (R = 0.80, p < 0.001) between the speech reception threshold and the audiometric pure tone average (from 0,5kHz to 4kHz). Its main limitation is the need for a very calm environment. An alternative for clinical evaluation is the widely used Hearing Handicap Inventory for the Elderly Screening (HHIE-S) questionnaire. This 10-questions assessment is sensitive (73.2%) and very specific (73.8%) for detecting mild-to-severe hearing loss if the score is ≥6 (5).
To confirm the diagnosis, specify the etiology and propose compensating device, clinicians can then refer the patient to an ENT (ears, nose and throat) physician, who will quantify the degree of hearing impairment, eliminate any ear pathology and look for any vestibular dysfunction promoting falls. After an audiometric test determining the auditory pure tone threshold at each frequency from 0,125 to 8kHz, in bone and air conduction (with vibrator or headphones respectively), and also the speech reception threshold in silence and noise, the specialist will determine the pure tone average to classify the severity of hearing impairment and recommend the use (or not) of hearing aid(s), possibly with speech therapy. In severe-to-profound deafness without satisfactory benefit from hearing aids, the ENT physician may propose cochlear implantation (after a multidisciplinary assessment) allowing the implanted cochlear electrodes to directly stimulate the auditory nerve fibers.
The last key role is those of caregivers, essential to support the patient in accepting and getting used to their hearing aids as early as possible during the hearing impairment. Indeed, auditory rehabilitation promotes brain plasticity, which is easier to mobilize in the event of a short period of hearing deprivation. Further, hearing aids improve quality of life and preserve cognitive functions (3) as do cochlear implants. 80% of older people with severe hearing loss and mild cognitive impairment (MCI) improved their cognition one year after cochlear implantation and only 6% developed dementia at 7 years, while this proportion is usually rather 50% in the MCI population at 5 years. Technological advances and rehabilitation improvements strengthened these results. By implementing progressive hearing correction, the hearing care professionals help with the acceptance of hearing aids. The speech therapists offer auditory perceptual rehabilitation, auditory memory and lip reading trainings, which promote progressive habituation and brain plasticity improving speech understanding skills and communication appeal.
In conclusion, identifying and treating hearing decline in older adults within the WHO-ICOPE approach requires strong inter-professional collaboration between family physicians, geriatricians, ENT specialists, hearing professionals and speech therapists, without forgetting the active participation of the elderly themselves and the support of their relatives. These efforts will allow acting on this intrinsic auditory frailty in older adults and maintaining function late in life.

Figure 1. The process of hearing function screening (in orange and green) to hearing impairment confirmation (in blue) and rehabilitation accuracy control


Conflicts of Interest: None declared by the authors.




1. World Health Organization World report on ageing and health. 2015. Available from: https://apps.who.int/iris/handle/10665/186463 [accessed 2021 Feb 26]
2. World Health Organization Integrated care for older people: guidelines on community-level interventions to manage declines in intrinsic capacity. 2017. Available from: https://apps.who.int/iris/handle/10665/258981[accessed 2021 Feb 26].
3. Livingston G, Huntley J, Sommerlad A, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet Lond Engl. 2020 Aug 8;396(10248):413–46.
4. World Health Organization. WHO launches the hearWHO app for mobile devices to help detect hearing loss. Available from: https://www.who.int/deafness/news/hearWHOApp-news/en/ [accessed 2021 Feb 26].
5. Cassarly C, Matthews LJ, Simpson AN, Dubno JR. The Revised Hearing Handicap Inventory and Screening Tool Based on Psychometric Reevaluation of the Hearing Handicap Inventories for the Elderly and Adults. Ear Hear. 2020 Feb;41(1):95–105.



P. Poupin1, D. N’Diaye2, F. Chaumier3,4, A. Lemaignen2, L. Bernard2, B. Fougère1,5


1. Division of Geriatric Medicine, Tours University Medical Center, Tours, France; 2. Division of Infectious Diseases, Tours University Medical Center, Tours, France; 3. Palliative Care Team, Tours University Medical Center, Tours, France; 4. UMR INSERM U1246 SPHERE, Tours University, Tours, France; 5. Education, Ethics, Health (EA 7505), Tours University, Tours, France.

Corresponding Author: Pierre Poupin, MD, Division of Geriatric Medicine, Tours University Medical Center, Tours, France, E-mail: poupinpierre@yahoo.fr, Phone: +33-643-166-637

J Frailty Aging 2021;in press
Published online April 26, 2021, http://dx.doi.org/10.14283/jfa.2021.16



Background: Long-term residential care facilities and nursing homes are known to be particularly vulnerable to viral respiratory diseases and have expressed the need for multidisciplinary collaboration to help manage outbreaks when they occur.
Method: In April 2020, Tours University Medical Center created a multidisciplinary mobile team to help local nursing homes deal with outbreaks of coronavirus disease 2019 (COVID-19). The team included a geriatrician, infectious disease experts, and palliative care specialists.
Results: On April 8th, 2020, the first intervention took place in a 100 residents nursing home with a total of 18 confirmed cases among 26 symptomatic residents and five deaths. The nursing home staffs’ main requests were a multidisciplinary approach, consensus decision-making, and the dissemination of information on disease management.
Conclusion: Three lessons emerged from this collaboration: (i) intensify collaborations between hospitals and nursing homes, (ii) limit disease transmission through the use of appropriate hygiene measures, broad screening, and the isolation of sick residents and sick employees, and (iii) provide sufficient human resources.

Key words: Viral respiratory disease, outbreak, nursing home, multidisciplinary collaboration.



In December 2019, a previously unknown type of severe acute respiratory syndrome emerged in the city of Wuhan (China) (1). In January 2020, the pathogen was isolated and described as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (1). The corresponding disease (coronavirus disease 2019, COVID-19) spread rapidly around the world and prompted the World Health Organization to declare a global pandemic on March 11th, 2020 (2). In France, the outbreak was declared officially on March 15th, 2020.
Long-term residential care facilities and nursing homes (NHs) are known to be particularly vulnerable to viral respiratory diseases (e.g., influenza) (3, 4). Due to frailty and comorbidities, older adults are more likely to experience severe and/or complicated forms of COVID-19, with a higher mortality rate (5). In France, a total of 142,852 confirmed cases had been reported by May 28th, 2020 (6). Of these, 33,646 were NH residents (leading to 13,806 deaths (7)) and 16,215 were NH staff members.
An NH is primarily a place to live and secondarily a place to receive medical care. Faced with this unprecedented health crisis, Tours University Medical Center (Tours, France) created a mobile multidisciplinary team (MMT) to help affected NHs deal with COVID-19 in a coordinated manner. To the best of our knowledge, MMTs have rarely been used to deal with COVID-19 outbreaks among older adults in our country.
The objective of the present report is to share our experience of this novel collaboration between an NH and a university medical center’s department of geriatric medicine.



Over the last decade, the concept of a mobile geriatric team has emerged in France and other countries in response to the constant requirement for cost-effective medical care that optimizes resources. During the pandemic, our mobile geriatric team was redeployed for (i) nasopharyngeal swab testing of NH residents with suspicious symptoms, and (ii) operation of a phone hotline (from 9am to 6pm, seven days a week) for NH medical staff.
Although the French government provided guidance on preventing SARS-CoV-2 from spreading within NHs (the prohibition of visits by family members, the closure of communal dining rooms, the serving of meals in the residents’ bedrooms, the suspension of group activities, etc.) and provided additional resources for NHs (8), it did not issue specific guidelines on how to manage an outbreak.
University medical centers have a role in coordinating and disseminating good practice in geriatric medicine within local NHs. In order to answer the most frequent requests from NH medical staff when an outbreak occurred (mainly information on disease management and on consensus decision-making for sick residents), the Geriatrics Department at Tours University Medical Center created a new entity: a multidisciplinary mobile team (MMT) comprising a geriatrician, two infectious disease experts, a palliative care nurse, and a palliative care physician. Each member’s role is summarized in Figure 1; the objective was to provide appropriate care for each resident by drawing on the MMT members’ expertise.

Figure 1. The MMT: composition and roles



On April 8th, 2020, the MMT’s first intervention took place in a 100-resident NH in the city of Tours. It was one of the 4 seriously affected NHs in the local Indre et Loire area, which has about 60 NHs in total. The MMT members and the NH’s physician, head nurse and director had an initial 5-hour meeting, during which questions from the NH staff members alternated with instructive presentations by the MMT members (Figure 2).

Figure 2. Structure of the initial meeting between the NH staff members and the MMT members


Each of the three floors in the NH was divided into 2 units, and there were 60 care staff. The COVID-19 outbreak in the NH had started about 2 weeks previously, and the first confirmed case in a resident was recorded on March 23rd. The disease spread rapidly to all units, giving a total of 18 confirmed cases among 26 symptomatic residents and 6 among the staff. Five of the 18 SARS-CoV-2-positive residents had died at that point. The change in the number of cases of COVID-19 during the two weeks before the MMT’s intervention is shown in Figure 3.

Figure 3. Cases of COVID-19 among NH residents during the 2 weeks before the start of the MMT’s intervention


Although around half of the staff members had developed symptoms of COVID-19, a shortage of tests prevented us from confirming these suspected cases. It appeared that the first case of COVID-19 in the NH was a staff member who subsequently tested positive for SARS-CoV-2. The staff member had come to work with respiratory symptoms, had not used personal protective equipment (PPE), and had been in close contact with the first of the residents to fall ill. Furthermore, the lack of knowledge about the risk of SARS-CoV-2 transmission by asymptomatic carriers and the sometimes contradictory guidance on PPE use (the use of a face mask, primarily) had resulted in confusion and inadequate behavior among the care staff.
The MMT’s infectious disease experts first outlined the procedures for outbreak management. The NH staff members were given detailed information on the mode of viral transmission and the main strategies for preventing further spreading: cohorting staff (to limit mixing and thus opportunities for transmission), limiting staff meetings or seminars, applying social distancing during staff meal breaks, and wearing face masks at all times. To preserve supplies, the use of PPE was optimized (2 disposable surgical face masks per day and per person). The MMT’s main recommendations are summarized in Table 1.

Table 1. Key recommendations by the MMT


With regard to specific medical care, the palliative care physician, the palliative care nurse, an infectious disease expert and the geriatrician were helped the NH’s medical staff to discuss medical decisions for each resident. This included asking whether the residents or their legal guardian or family had provided with advanced directives, and deciding whether the NH was able to meet residents’ medical needs. Even though most health facilities and hospitals were under pressure or even saturated, we considered that older patients should not be excluded from hospitalization on the basis of their age alone. We considered that hospital admission was relevant for patients with few comorbidities or a low level of dependence when clinical or laboratory criteria for severity were met or when other diagnoses had to be ruled out. If the patient agreed, he/she was admitted to hospital.
The French Geriatric and Gerontology Society (9) had developed a decision tree for COVID-19. Although the MMT considered this decision tree, decisions on hospitalization or the level of care also took account of clinical common sense and discussions between the MMT, the NH’s physician, and the head nurse. The patients and/or their legal guardian or family were kept informed about these discussions. In fact, most of the residents clearly expressed the wish to stay in their usual living environment (i.e. the NH). In other cases, patients appeared to be too frail and too severely ill to benefit from hospitalization.
With regard to treatment, the MMT considered whether or not antibiotic treatment was necessary and emphasized the importance of preventing dehydration, undernutrition, and loss of functional autonomy. The MMT reviewed the NH’s ability to provide oxygen therapy, palliative care, and end-of-life care. These procedures increased the burden of care and prompted the creation of COVID-19-only units.



Four key issues emerged from the MMT’s initial assessment: NH staff members (i) must know how to recognize the signs and symptoms of COVID-19, which are not the same in older adults as in younger adults, (ii) must be aware of how COVID-19 is transmitted and must use PPE appropriately; (iii) require information on patient management and a specific organizational structure for dealing with the COVID-19 outbreak, and (iv) require help with discussing medical decisions and the level of care.
Following our intervention, three NH residents were immediately hospitalized. Local health administrations were asked to reinforce the NHs’ staff. For example, scheduled surgical procedures in local hospitals and clinics were suspended and only emergency operations were carried out; this reduction in the level of activity freed up staff for temporary redeployment to NHs. Home hospital units also provided staff reinforcements for patients requiring a high level of care. The NH’s stocks of PPE, drugs and medical equipment were considered to be sufficient.
Collaboration between healthcare professionals appears to be crucial for developing guidance on the management of COVID-19: it combines the NH staff members’ knowledge of their residents and expertise in allocating resources within their own facilities, the geriatrician’s approach to caring for frail, older adults, the palliative care specialist’s expertise in end-of-life care, and the infectious disease specialist’s expertise on the management of infections. When an outbreak occurs, this emergency situation disrupts the NH’s organization. The NH staff member must then focus on acute medical care – a situation for which they are not prepared and which requires collaborative, adaptive strategies. The collaboration also improved our expertise in outbreak management. For example, screening for SARS-CoV-2 with a reverse transcriptase polymerase chain reaction (RT-PCR) assay (using nasopharyngeal swabs) became much more widely available about a week after our intervention in the NH. The NH’s coordinating physician and director asked all the residents and staff members to be tested. The screening detected a number of asymptomatic carriers among the residents and employees.
The residents who tested positive were isolated in a dedicated unit for two weeks or until they tested negative. The staff members who tested positive were asked to stay at home for two weeks. This broad nasopharyngeal swab screening program appeared to be very useful for controlling the outbreak. However, sensitive but less painful tests would be needed for regular testing, the detection of asymptomatic employees or residents, and thus the prevention of viral transmission. Approximately one month after all the NH staff and residents had been screened, the outbreak had been stabilized and no new cases were recorded.



Nursing homes are extremely vulnerable to contagious viral respiratory diseases such as COVID-19. Outbreaks can be dramatic, and preventing the virus from spreading is a priority (10). The COVID-19 pandemic has highlighted the need for collaboration between NHs and other health care facilities (11). The lessons that emerged from this initial collaboration can be summarized as followed:
1) Guidelines may help with consensus decision-making, the dissemination of information, and multidisciplinary collaboration.
2) Transmission of the virus must be limited by adopting appropriate hygiene measures (e.g. protective face masks), screening all NH residents and employees with an RT-PCR assay, and isolating all confirmed cases.
3) Sufficient human resources must be deployed quickly in these exceptional circumstances.

We hope that this feedback will help the authorities to provide useful, precise, specific guidelines on all aspects of managing COVID-19 outbreaks in NHs. A multidisciplinary MMT approach may help to develop appropriate strategies in NHs.


Acknowledgements: The authors thank the staff of the nursing home in which the intervention took place for their incredible dedication to the residents’ care. We thank Dr. David Fraser (Biotech Communication SARL, Ploudalmézeau, France) for copy-editing assistance and Eliane Sabourin for proofreading the manuscript.

Funding sources: This research did not receive any specific funding from agencies in the public, commercial, or not-for-profit sectors.

Conflicts of Interest: Dr. Adrien Lemaignen reports other from Gilead, non-financial support from Pfizer, personal fees from MSD, outside the submitted work. Pr Louis Bernard, Pr Bertrand Fougère, Dr Diama N’Diaye, Dr Chaumier and Dr Pierre Poupin declared no conflicts of interest.



1. Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020; 382:727-733.
2. World Health Organization. Rolling updates on coronavirus disease (COVID-19). 2020 (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen. opens in new tab). Accessed May 30, 2020.
3. Hand J, Rose EB, Salinas A, et al. Severe respiratory illness outbreak associated with human coronavirus NL63 in a long-term care facility. Emerg Infect Dis 2018; 24:1964-6.
4. McMichael TM, Currie DW, Clark S, et al. Epidemiology of Covid-19 in a Long- Term Care Facility in King County, Washington. N Engl J Med. 2020;382:2005-2011.
5. Wang L, He W, Yu X. Coronavirus disease 2019 in elderly patients: characteristics and prognostic factors based on 4-week follow-up. J Infect. 2020 Jun;80(6):639-345.
6. World Health Organization. Coronavirus disease 2019 (COVID-19): situation report — 111. May 28, 2020. (https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200528-covid-19-sitrep-129.pdf?sfvrsn=5b154880_2). Accessed May 30, 2020.
7. French Public Health Agency. COVID19. Weekly epidemiological record, 29 may 2020. (French). (https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/documents/bulletin-national/covid-19-point-epidemiologique-du-29-mai-2020). Accessed May 30, 2020.
8. French ministry of solidarity and health. Strategy for the care of the older adults in nursing homes and at home in the context of COVID-19 epidemic. https://solidarites-sante.gouv.fr/IMG/pdf/strategie-prise-en-charge-personnes-agees-covid-19.pdf Accessed May 30, 2020.
9. French Geriatric and Gerontology Society. Collegial decision support. https://sfgg.org/actualites-covid-19/prise-en-charge-en-ehpad/ Accessed April 8, 2020.
10. Tan LF, Seetharaman S. Preventing the Spread of COVID-19 to Nursing Homes: Experience from a Singapore Geriatric Centre. J Am Geriatr Soc. 2020;68(5):942.
11. Stall NM, Farquharson C, Fan-Lun C et al. A Hospital Partnership with a Nursing Home Experiencing a COVID-19 Outbreak: Description of a Multi-Phase Emergency Response in Toronto, Canada. J Am Geriatr Soc. 2020, 2020 Jul;68(7):1376-1381.



C. Loecker, M. Schmaderer, L. Zimmerman


University of Nebraska Medical Center, College of Nursing, Omaha, NE, USA

Corresponding Author: Courtney Loecker, MSN, APRN-NP, AGACNP-BC, 985330 Nebraska Medical Center, Omaha, NE, USA, courtneyn.loecker@unmc.edu, 402-559-6571 phone, 402-472-7345 fax

J Frailty Aging 2021;in press
Published online April 14, 2021, http://dx.doi.org/10.14283/jfa.2021.14



Background: Frailty is a public health priority resulting in poor health outcomes and early mortality in older adults. Early identification, management, and prevention of frailty may reduce frailty trajectory into later life. However, little is known about frailty in younger adults.
Objective: Describe frailty prevalence, definitions, study designs, and components contributing to multidimensional frailty in 18 to 65-year-olds and impart guidance for future research, practice, and policies with potential to positively impact frail individuals.
Methods: Integrative review approach was selected to explore frailty allowing for inclusion of diverse methodologies and varied persepectives while maintaining rigor and applicability to evidence-based practice initiatives. CINAHL, Embase, PsycInfo, PubMed databases were searched for studies describing frailty in adults age 18 to 65. Articles were excluded if published prior to 2010, not in English, lacked frailty focus, or non-Western culture.
Results: Twelve descriptive correlational studies were included. No intervention or qualitative studies were identified. No standard conceptual definition of frailty was discovered. Studied in participants with health disparities (n=3) and chronic conditions (n=8); HIV was most common (n=4). Frailty prevalence ranged from 3.9% (313 of 8095) to 63% (24 of 38). Many factors associated with frailty were identified among physical (18) and social (14), and fewer among psychological (11) domains.
Conclusions: Universal frailty definition and multidimensional assessment tool is needed to generate generalizable results in future studies describing frailty in young and middle-aged adults. Early frailty identification by clinicians has potential to facilitate development and implementation of targeted interventions to prevent or mitigate frailty progression, but additional research is needed because risk factors in younger populations may be different than older adults.

Key words: Frail, medical frailty, disability, middle age, young adult.



Frailty, a physiologic decline that heightens vulneraiblity to stressors, is a public health priority according to the World Health Organization (1, 2). Frailty doubles the seven-year mortality rate in older adults, and up to 5% of deaths could be delayed if frailty was prevented (3, 4). Associated with increased risk for falls, disability, hospitalizations, increased costs, and early mortality (2, 4, 5), frailty is traditionally described in the elderly emphasizing functional decline seen with aging (4, 6). However, recent literature describes frailty as multifactorial inclusive not only of physical, but also social and psychological constructs, occurring on a continuum regardless of age (7, 9). A life-course approach has prompted researchers to explore frailty in younger adults with potentially modifiable risk factors that persist into older age (10, 12). Younger adults with comorbidities and lower psychosocial health demonstrate high frailty trajectory into older age (10, 11), but frailty and associated risk factors in earlier life are not well understood because most frailty research targets older adults (4, 8)..
Frailty identification is predominantly based on a physical phenotype (2) or accumulation of deficits approach (6), although both have been criticized for clinical impracticality (13, 14). Inconsistent definitions and myriad frailty tools complicate the arduous but seemingly beneficial task of frailty identification (8, 13-14). Helping to guide clinical decision-making or care planning around elective surgeries or procedures, frailty assessments have utility in clinical settings where biologic age can be a poor prognostic indicator (5, 15-16).
Adult Medicaid expansion represents an arena in which frailty assessment is especially important among younger adults (19 to 64-year-olds whose income is at or below 138% of the federal poverty level) (17). Individual states are required to define medical frailty intended to protect benefits for those with complex health care needs who do not qualify based on a disability. Improper determination could have dire consequences in a group of socioeconomically disadvantaged adults likely to have higher than average frailty rates (18). Failure to identify medical frailty could result in unmet needs or deprivation of key benefits succumbing to worse outcomes and higher costs. On the other hand, over-identification may result in wasted resources and excess expenditures.
Once identified, earlier targeted interventions have potential to prevent or mitigate frailty progression. Earlier health promotion or targeted population approaches in younger vulnerable adults may confer greater impact than directing strategies toward frailer older adults (12, 19-20). In a study that aimed to quantify frailty risk factors in over 6,000 middle-aged adults, unhealthy behaviors accounted for 30% of the socioeconomic gradient. Smoking, alcohol, activity, and diet have been identified as potentially modifiable frailty risk factors, but how and why risk factors develop in early adulthood remains unknown (10, 11). Another large cohort study observed age 65 as the turning point; frailty increased twice as fast after age 65 suggesting interventions may be more effective before old age (21). Evidence points toward opportunity to intervene early, but we need a better understanding of frailty in younger adults to help explain risk factors and their relationships. Therefore, the purpose of this integrative review is to synthesize literature to describe frailty prevalence, definitions, study designs, and components contributing to multidimensional frailty (physical, psychological, social) in 18 to 65-year-olds and impart guidance for future research, practice, and policies that has potential to positively impact frail individuals. This exploratory work also intends to support future inquiries surrounding medical frailty among Medicaid adult expansion beneficiaries in pursuit of a more cohesive characterization of frail young and middle-age adults.



Search Strategy

An integrative review approach was selected to explore the phenomenon of frailty in lieu of other review types (e.g., systematic, meta-analysis) because it allows for inclusion of diverse methodologies (e.g., experimental and non-experimental, quantitative and qualitative) while employing rigorous methods to explore a broad topic from many viewpoints rather than focusing on a specific clinical question (22). Findings from integrative reviews enhance holistic understanding of complex topics like frailty and can be applied to clinical practice and health care policy (22). Whittemore & Knafl’s (22) integrative review methodology was thus utilized to conduct a comprehensive search following steps of the PRISMA checklist (23). A sentinel frailty model expanding the concept to include tripartite domains of frailty and determinants of health was published in 2010 (7), followed by acknowledgement of frailty as a public health priority (1) thus prompting a search of articles from inception (February 2020) dating back to January 2010.
Databases CINAHL, Embase, PsycInfo, and PubMed were searched using subject search terms “frail*” or «medically frail» or «medical frailty,» and full text search terms «young old» or «young adult» or «middle old» or «middle age.» In addition, “psychological frailty» or «social frailty” or “physical frailty” as described in the theoretical Frailty Framework among Vulnerable Populations (FFVP) (9) were used as full text search terms individually and combined with aforementioned terms. The FFVP is a theoretical framework derived from extant frailty and vulnerability frameworks, empirical literature, and expert consultation recognizing multidimensional constructs of frailty (physical, psychological, social) among vulnerable populations regardless of age [9]. Adult Medicaid expansion beneficiaries represent young and middle-aged adults who may lack resources rendering them vulnerable, or at increased risk for frailty, and similar frailty domains may be evident among that population. Bibliographies of relevant articles were further hand searched. Efforts were thus made to include all articles addressing frailty specifically in 18 to 65-year-olds.

Selection Criteria

Articles were included in the review if they were published in English dating back to 2010 and described frailty in adults age 18 to 65 years. Age range representing Medicaid adult expansion beneficiaries (19 to 64 years) was expanded to include 18 and 65-year-olds because multiple studies would have been otherwise excluded. Qualitative and quantitative research was included as part of the integrative methodology. Western cultures were a criterion based on the knowledge that frail Medicaid adult expansion beneficiaries are recipients of traditional Western medicine favored in the United States and reflective of Western culture (e.g., evidence-based diagnosis and guideline-driven treatment recommendations based on symptom recognition, physical exam, and diagnostic confirmation) (24). Articles were excluded if they were dissertations, theses, abstracts, editorials, lacked a frailty focus (e.g., if frailty was not a variable or outcome but merely mentioned in text), if the study included “frail elderly” with “elderly” defined as greater than 65 years, or if the aim of this review was not addressed. Studies inclusive of those > 65 were intentionally omitted because it was felt including the elderly would misdirect the purpose of the review.

Data Abstraction

Study design, setting, country, sample size, baseline/defining participant characteristics, and physical, psychological, and social factors associated with frailty were abstracted. Frailty definitions, measurement tools, and prevalence of frailty and/or prefrailty were also gleaned from each study.



Study Selection

A total of 569 records were identified, 42 duplicates were removed, and 527 records were screened for eligibility through title and abstract review. Of those screened, 137 records, plus one record identified from a hand search of relevant articles’ bibliographies, totaling 138 underwent full-text review (see Figure 1). Of those 138 studies, 12 met criteria and were included in the review. A flowchart of the search strategy and selection criteria is depicted in Figure 1. Studies included in the review are summarized in Appendix A.

Figure 1. PRISMA diagram (23) depicts a flowchart of search strategy and selection criteria


Appraisal of Study Quality

Study appraisal was conducted using Joanna Briggs Institute (JBI) critical appraisal checklist (25) independently and agreed upon by a second author. The 8-item checklist was utilized for critical appraisal across all studies with a uniform approach to allow for comparison. The tool was felt to be appropriate because all studies contained primarily cross-sectional descriptive data. Initially designed for appraising cross-sectional analytical studies in systematic reviews, the tool is also used for appraising more broad topics such as those described in an integrative review (25). The appraisal results and JBI checklist are detailed in Appendix B and C, respectively. Percentage “yes” responses were calculated, omitting any “non-applicable” responses, and results ranged from 42% (26) to 100% (27). Criteria deemed “unclear” were similar among studies and may be attributed to heterogeneity of frailty tools and lack of ‘gold standard.’ Most studies did not clearly report reliability and validity for tool(s) (n=10) data (27- 36). In general, studies were considered of moderate to high quality evidence; all meeting nearly half (at least 42%), and majority meeting more than half (57%) of JBI criteria (27-33, 35-37). All articles were thus included in the review.

Defining Characteristics of Studies

Studies were primarily descriptive correlational (n=12) (26-37). Variations among these study designs included a prospective cohort of participants that attended a maximum of eight study visits every six months (29). Another included a prospective subset of participants that attended a follow-up visit approximately 3.5 years after baseline (36). Two authors described longitudinal associations of participants at baseline and one time point, six years and nine years, respectively (26, 32). A descriptive pilot study was included (36). Matched cohorts were compared in four descriptive correlational studies (26, 28-29, 32), and the remainder were single cohort cross-sectional (27, 30-31, 33-37).
Half of studies (n=6) included participants that were part of larger cohort studies, (28, 32-35, 37), and two samples were part of the same larger study involving adults infected with human immunodeficiency virus (HIV) (28, 34). Most studies were conducted in the continental United States (n=9) located in urban areas of the Midwest (26, 32, 36), Maryland (37) and California (27-29, 34). International study settings were the United Kingdom (35), Austria (31), and Turkey [30]. Sample sizes ranged widely from 38 to 8,095 participants. Age of participants ranged from 18 to 65 years at the time of baseline data collection. Mean age was reported by nine authors and ranged from 38.9 to 58.7 years. A study sample comprised only of women had the youngest mean age (38.9 years) of all studies (27). Follow-up periods in the two studies reporting prospective and longitudinal outcomes ranged from six months to nine years, respectively. Loss to follow-up was reported in studies that assessed mortality in relation to frailty; seven of 222 and 42 of 2541 participants (26, 37).

Frailty Definitions

A standard conceptualization of frailty was not recognized, but similarities suggested frailty is a multisystem (29, 34-36) age-related (29, 32-35, 37) syndrome (27, 30, 33, 37) characterized by vulnerability (28-29,32,34) to stressors (26, 28) that increases risk for adverse health outcomes (26-28, 30, 34, 37) and mortality (27, 32, 37). Physical attributes of Fried’s criteria are described as characterizing frailty by two authors (32-33). Accumulation of health deficits was an alternate approach to defining frailty (36). Physical, psychological, and social domains were specifically named by two authors (27, 36), and each domain was defined separately in one of the two articles (27). Prefrailty is simply described as “an early stage of frailty” (31) or “prodromal frailty” (37).

Frailty Operationalized

Fried’s criteria was utilized most often (n=9) (28-30, 32, 33, 35, 36). Also referred to as Fried’s Frailty Phenotype or Fried’s Frailty Index, Fried’s criteria defines frailty as the presence of at least three of the following criteria; weakness, slowness, shrinking (unintended weight loss), low activity level, and exhaustion. Prefrailty is the presence of at least two of the five criteria (2). Of the nine studies that operationalized frailty citing Fried’s criteria, five adapted criteria to meet the purpose or needs of the study or population (28, 29, 32, 33, 35). For example, “low lean muscle mass” was calculated using x-ray absorptiometry in childhood cancer survivors and a benchmark served as “unintended weight loss,” defined by Fried (2) as self-reported weight loss of 10 pounds or more in the past 12 months (32). Another study involving men with and without HIV categorized participants as frail if any one of Fried’s criteria was met (28). In a sample of English general practice patients, Fried’s criteria was adapted into a questionnaire and data was collected via mailed correspondence (35). The Frailty Instrument for Primary Care of the Survey of Health, Ageing and Retirement in Europe (SHARE-FI), a tool based on Fried’s criteria plus a sex-specific calculation, was used to operationalize frailty in a sample of patients with rheumatoid arthritis (31).
The second most common tools to measure frailty were the Frailty Index, a calculation of accumulated health deficits (n=2) (26, 36), and the FRAIL scale (n=2) (26, 37) which consists of self-reported fatigue, resistance (ability to climb 10 stairs), ambulation (ability to walk a quarter mile), number of illnesses, and loss of weight. One author adapted ‘loss of weight’ criteria to an inquiry about appetite (37).
Another study operationalized frailty using the 15-item Tilburg Frailty Indicator to assess specific domains of physical, psychological, and social frailty (27). The Study of Osteoporotic Fractures (SOF) scale (26) and the Comprehensive Frailty Assessment Instrument (CFAI) were also used to measure frailty (36).
Most studies measured frailty using only one tool (n=10) (27-29, 30, 31-34, 37); however, another study measured frailty using four tools (FRAIL, SOF, Fried’s criteria, and Frailty Index) (25). One study used two tools (CFAI and Fried’s Criteria) and created seven evidence-based questions (36). Measuring frailty using different tools in the same study sample of adults seeking care at free clinics yielded different results; 24 of 38 participants were determined frail using the CFAI versus 4 of 38 according to Fried’s criteria (36).
To operationalize prefrailty, Fried’s criteria was used most often (n=3) (28, 32, 35), but the CFAI (n=1) [36] and SHARE-FI (n=1) (31) were also utilized. Measuring prefrailty using different tools in a single sample also yielded different results; the CFAI determined only eight of 38 participants prefrail, but Fried’s criteria determined 21 of 38 participants prefrail (32).
Measurement data were gleaned from medical records and collected during study visits, mailed questionnaires (35), and home-based assessments (25).

Prevalence of Frailty

Frailty prevalence varied depending on the population, tool(s), and criteria used. Of those studies that reported frailty and prefrailty, prevalence ranged from 3.9% (313 of 8095) to 63% (24 of 38) (n=8) and 11% (125 of 1122) to 55% (21 of 38) (n=7), respectively (28, 30-33, 35-37). The table in Appendix D outlines each study’s author, publication year, purpose, frailty and prefrailty prevalence (if reported), and tool(s) used to measure frailty.
Some authors alternatively compared frailty among matched cohorts (n=3) (26,29,34). The proportion of “men who have sex with men” that converted to a positive frailty phenotype was 12% of HIV infected men versus 9% of HIV non-infected men (29). Mean frailty index scores were higher in a cohort of middle-aged African American diabetics compared to non- diabetics (26). A stepwise pattern of frailty index scores from more frail to less frail was described among three cohorts of comorbid HIV positive methamphetamine users, non-users, and a control group (34). One author quantified frailty with subscales of physical, psychological, and social frailty in homeless, formerly incarcerated women (27).

Factors Associated with Frailty

Factors associated with frailty were identified among nearly all studies and divided among physical, psychological, and social frailty domains (see Appendix A) guided by the FFVP (9). One descriptive study did not perform correlational statistics, so the strength or direction of variables were not described (36).
Physical domain. The most common factor identified was age (n=6) (27, 29, 30, 32, 33, 37), followed by HIV infection (n=3) (28, 29, 34), pain (n=2) (27, 31), diabetes (n=2) (26, 29), and higher BMI (n=2) (32, 36). Other factors were polypharmacy (37), functional limitations (26), comorbidities (34), kidney disease (29), hepatitis C infection (29), higher rheumatoid arthritis disease activity and longer duration (31), female gender (37), lower BMI (32), and prior radiation (32). Elevated cytokines (26) and laboratory abnormalities including decreased serum vitamin D, hemoglobin, and albumin levels in the setting of chronic kidney disease (26).
Psychological domain. Depressive symptoms (n=3) (27-29), illicit drug use (n=2) (27, 34), and smoking (n=2) (29, 32) were most commonly associated with frailty. Higher perceived stress (28), lower self-rated health (37), lower personal mastery, lower grit, lower optimism (28], emotional regulation difficulty, witnessed violence, and post-traumatic stress disorder symptoms (27] were also described as contributors to frailty.
Social domain. Unemployment (n=2) (31, 35) and lower education (n=2) (29,3 7) were most commonly associated with frailty in the social domain. Many other factors were reported; poverty (37), lower social support (28), black race (29), more likely to disclose HIV status to family (33), adverse employment outcomes, not coping at work, sick leave, health related job loss, homelessness, incarceration (27), negative interactions (28), and prior violence (27) were also associated with frailty.



This review aimed to synthesize literature to gain a better understanding of the current state of the science of frailty in young and middle-aged adults. We identified 12 studies that examined frailty in adults age 18 to 65 years. We expected to find frailty examined in vulnerable younger adult populations with comorbidities or disabilities, but frailty was also described in adults with health disparities (27, 36-37) underscoring the importance of considering socioeconomic contributions to frailty development in younger adults. Frailty prevalence ranged from 3.9% (313 of 8095) (35) to 63% (24 of 38) (36), proportions similarly reported in community-dwelling older adults (4% to 59%) depending on criteria and tool(s) utilized [38]. One explanation for this may be the lack of a uniform frailty definition, measurement tool, and criteria being adapted to meet the needs of a study or population.
The large variation of prefrailty prevalence described in the same sample using different tools (55% using CFAI versus 11% using Fried’s criteria) (36) may be explained by literature confirming unidimensional versus multidimensional tools captures different components of frailty (39). These findings further support the need for a uniform frailty measurement to enable relative comparisons. Of the six studies that described prefrailty, the proportions of prefrail participants were described as higher than frail participants with the exception of a study examining frailty in hemodialysis patients (53% frail, 18% prefrail) (30). Younger adults with advanced kidney disease may especially benefit from early frailty identification and intervention.
No universal frailty definition was recognized, but similar themes suggested frailty is a multidimensional state of reduced adaptability associated with age resulting in health-related adverse outcomes. This finding is consistent with recent literature highlighting the absence of a universal frailty definition and emerging evidence to support multiple overlapping domains of frailty (8, 9). Most authors used Fried’s physical phenotype to operationalize frailty despite discovering a number of social and psychological factors associated with frailty. Frailty in young and middle-aged adults compared to elderly may conceivably look different and potentially require an alternative operational definition. For example, grip strength as a single frailty measure (40) may be of less utility in younger adults compared to elderly. However, authors of the original investigations included in this review frequently adapted measurement tools to suit their population which is also a routine practice among studies inclusive of older adults (13, 14, 41). Our findings suggest a comprehensive standardized tool may capture additional frailty attributes specific to younger adults and allow for comparison across studies. Modifications for specific clinical needs or settings may also be beneficial based on the proportion of studies that modified existing frailty measures. The lack of reported reliability and validity of tools used by authors suggest validation of frailty tools in younger populations is also needed.
Prefrailty was measured most often using the physical phenotype, and consistently lacked explanation or definition. Implications of prefrailty were difficult to extrapolate without a conceptual meaning behind the reason for measurement. Although logical interpretation of prefrailty suggests the concept is a worthy focus of future frailty prevention and/or progression to a frail state, particularly given its prevalence described in this review (29, 30, 32, 34, 36, 37). All authors examined some aspect what contributed to frailty, but few described traditional outcomes (2, 42) like falls (26), functional status (26), and mortality (26, 32, 37). We recommend additional longitudinal studies to examine outcomes of frailty in young adults.
Factors associated with frailty in physical, psychological, and social domains were identified similar to those described in the FFVP (9). HIV infection, diabetes, and chronic kidney disease are described as health-related risk factors owing to frailty (9) and may represent valid health concerns for non-geriatric adults in the form of opportunistic infections, neuropathy, heart disease, or poor bone health. Early identification of health-related risk factors can allow for self-management interventions. Chronic disease can parallel biological mechanisms thought to contribute to frailty and accelerated aging in the form of chronic inflammation or hormone dysregulation reflective of HIV infection (43) or type 2 diabetes (44). Keeping viral loads undetectable through medication adherence or optimal blood sugar control with lifestyle changes may prove beneficial earlier in life. Unemployment (31, 35) is a situational frailty risk factor in the FFVP (9) that has potential to affect physical and/or mental health. Depressive symptoms and behaviors including illicit drug use and smoking are described as frailty risk factors in both the FFVP and this review. Depressive symptoms may influence all three frailty domains (9). Additional research is needed to untangle relationships among frailty risk factors and discover opportunities to favorably intervene.
A literature review examining frailty in older adults reported the most common components across physical, psychological, and social frailty domains were mobility, balance, nutrition, and cognitive function (39). In this review, the most common factors associated with frailty across all three domains, aside from older age, an expected finding (27, 29, 30, 32, 33, 37), were; unemployment (31, 35) lower education (29, 37), depressive symptoms (26, 27, 28), HIV (28, 29, 34), pain (27, 31), diabetes (24, 25), and abnormal BMI (32, 37). As young adults age with diabetes or HIV, unemployment may contribute to the inability to pay for preventative care or treatment of acute illness. Living with a chronic disease and/or unemployment may trigger depressive symptoms and result in further detriment, disability, or frailty. Early detection of frailty risk factors in an individual who is likely to experience frailty progression into older age thus presents an opportunity to intervene.
Existing literature clearly demonstrates patterns of increased health care costs and utilization associated with frailty in aging adults (45-47). This review offers new insight into frailty prevalence and factors associated with frailty among adults age 18 to 65 years. Based on these results, we suggest consideration of early frailty screening in younger adults with health disparities or chronic conditions, especially those with advanced kidney disease, HIV, diabetes, depressive symptoms, chronic pain, or obesity. Our findings are complimented by existing literature suggesting earlier frailty identification may be beneficial to develop targeted interventions (3, 48-51).
Intervention and qualitative studies were not identified suggesting there is much work to be done. Exercise and nutrition interventions to slow or reverse frailty have been described in older adults with some success (20, 52). Frail older adults have relayed the importance of social support and spirituality to ward off frailty (53, 54), but few studies incorporate experiences according to frail individuals. Adult Medicaid expansion beneficiaries represent a population of vulnerable younger adults in which attention to frailty is especially needed. Determination of medically frail individuals is important to preserve benefits but overidentification could waste resources. Informing policy makers about frailty in this population could thus support guidelines for accurate determination. Development and testing of tailored interventions are also important to consider given the increasing population of aging adults with comorbid conditions, but additional research is needed.


There were limitations to the present review. An existing theoretical framework (9) was utilized to help provide key search terms which may have introduced bias. Use of extant literature may pose a challenge to realizing aspects of frailty outside the framework, and potential findings may not have been elucidated in this review. A theoretical framework can also link findings to existing literature when studying a broad and comprehensive concept like frailty (55). Few studies have examined frailty in non-geriatric populations suggesting this area of research is in early stages, so including only peer-reviewed articles may have omitted unpublished data currently in development. Limiting the age range of study participants may have omitted studies inclusive of both frail younger and older adults; however, our intent was to explore frailty in those consistent in age with Medicaid adult expansion beneficiaries. Excluding studies published prior to 2010 may have eliminated literature that could have possibly contributed additional facets of frailty recognized among younger adults. Finally, one-third of articles examined frailty in adults with HIV. Generalization may be limited owing to the disproportionate number of studies involving HIV positive adults.



Frailty prevalence in young and middle-aged adults was similar to community-dwelling older adults, although factors associated with frailty across domains may differ. Presence of many physical and social, and fewer psychological factors associated with frailty suggest a multidimensional problem, but frailty was most often measured using a physical phenotype. Heterogeneity of frailty definitions, criteria, and tools used to measure frailty among samples with various health conditions and disparities created challenges in making relative comparisons across studies.
A universal frailty definition and multidimensional assessment tool that can be feasibly implemented in a variety of young and middle-aged populations is needed to conduct studies that can generate generalizable results. A robust understanding of factors associated with frailty in young and middle-aged adults is needed to assist with early detection, proper determination of medical frailty, and development of targeted interventions to prevent and/or mitigate frailty progression.


Disclosure statement: We have no disclosures.

Ethical Standards: This article does not contain studies with human participants performed by any of the authors.





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