Longitudinal changes of frailty in 8 years: comparisons between physical frailty and frailty index

Abstract Background Few studies have made longitudinal comparisons between frailty phenotype (FP) and frailty index (FI) changes. We aimed to investigate frailty status changes defined by FP and FI concurrently, and to compare the associated factors and incident disability among different combination of FI and FP trajectory groups. Methods Data on respondents aged over 50 who completed the 1999, 2003 and 2007 Taiwan Longitudinal Study on Aging (TLSA) surveys (n = 2807) were excerpted. Changes of FI, FP and major time-dependent variables were constructed by group-based trajectory modeling. Logistic regression was used to investigate the associated factors and relationships with incident disability among different frailty trajectories. Results We identified four FP trajectories – stably robust, worsened frailty, improved frailty, and stably frail and three FI trajectories – stable FI, moderate increase FI and rapid increase FI. Lower self-rated health, mobility impairment, and depressed mood were associated with unfavorable FP and FI changes (all p < 0.001). Regardless of FP trajectory groups, the moderate and rapid increase FI group had significantly more comorbidities than the stable FI group, and more visual, hearing, oral intake impairment, more difficulty in meeting living expenses, and poorer cognitive function in ≥65-year-olds (all p < 0.05). In addition, the worsened frailty, improved frailty, and stably frail groups had ORs for incident disability of 10.5, 3.0, and 13.4, respectively, compared with the stably robust group (all p < 0.01); the moderate and rapid increase FI groups had 8.4-fold and 77.5-fold higher risk than the stable FI group (both p < 0.001). When combining FI and FP trajectories, risk increased with FI trajectory steepness, independent of FP change (all p < 0.01 in rapid increase FI vs stable FI). Conclusions Four FP trajectories (stably robust, worsened frailty, improved frailty, and stably frail) and three FI trajectories (stable FI, moderate increase FI and rapid increase FI) were identified. Lower self-rated health, mobility impairment, and depressed mood were associated with both unfavorable FP and FI trajectories. Nevertheless, even for individuals in stably robust or improved frailty FP groups, moderate or rapid increase in FI, either due to comorbidities, sensory impairment, cognitive deficits, or financial challenges, may still increase the risk of incident disability.

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Toward Using a Smartwatch to Monitor Frailty in a Hospital Setting: Using a Single Wrist-Wearable Sensor to Assess Frailty in Bedbound Inpatients

Gerontology ◽

10.1159/000484241 ◽

2017 ◽

Vol 64 (4) ◽

pp. 389-400 ◽

Cited By ~ 8

Author(s):

Hyoki Lee ◽

Bellal Joseph ◽

Ana Enriquez ◽

Bijan Najafi

Keyword(s):

Frailty Index ◽

Hospital Setting ◽

Area Under The Curve ◽

Screening Tools ◽

Recursive Feature Elimination ◽

Physical Frailty ◽

Frailty Phenotype ◽

Frailty Status ◽

Flexion Extension ◽

Sensor Configuration

Background: While various objective tools have been validated for assessing physical frailty in the geriatric population, these are often unsuitable for busy clinics and mobility-impaired patients. Recently, we have developed a frailty meter (FM) using two wearable sensors, which allows capturing key frailty phenotypes (weakness, slowness, and exhaustion), by testing 20-s rapid elbow flexion-extension test. Objective: In this study, we proposed an enhanced automated algorithm to identify frailty using a single wrist-worn sensor. Methods: The data collected from 100 geriatric inpatients (age: 78.9 ± 9.1 years, 49% frail) were reanalyzed to validate the new algorithm. The frailty status of the participants was determined using a validated modified frailty index. Different FM phenotypes (31 features) including velocity of elbow rotation, decline in velocity of elbow rotation over 20 s, range of motion, etc. were extracted. A regression model, bootstrap with 2,000 iterations, and recursive feature elimination technique were used for optimizing the FM parameters and identifying frailty using a single wrist-worn sensor. Results: A strong agreement was observed between two-sensor and wrist-worn sensor configuration (r = 0.87, p < 0.001). Results suggest that the wrist-worn FM with no demographic information still yields a high accuracy of 80.0% (95% CI: 79.7-80.3%) and an area under the curve of 87.7% (95% CI: 87.4-87.9%) to identify frailty status. Results are comparable with two-sensor configuration, where the observed accuracy and area under the curve were 80.6% (95% CI: 80.4-80.9%) and 87.4% (95% CI: 87.1-87.6%), respectively. Conclusion: The simplicity of FM may open new avenues to integrate wearable technology and mobile health to capture frailty status in a busy hospital setting. Furthermore, the reduction of needed sensors to a single wrist-worn sensor allows deployment of the proposed algorithm in the form of a smartwatch application. From the application standpoint, the proposed FM is superior to traditional physical frailty-screening tools in which the walking test is a key frailty phenotype, and thus they cannot be used for bedbound patients or in busy clinics where administration of gait test as a part of routine assessment is impractical.

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Letter to the Editor: Exploring Associations Between Oral Health and Frailty in Community-Dwelling Older People

Journal of Frailty & Aging ◽

10.14283/jfa.2021.10 ◽

2021 ◽

pp. 1-1

Author(s):

R.C. Castrejón-Pérez

Keyword(s):

Oral Health ◽

Older People ◽

Medical Record ◽

Frailty Index ◽

Community Dwelling ◽

Frailty Phenotype ◽

Cross Sectional ◽

Frailty Status ◽

Community Dwelling Older People ◽

Oral Conditions

The studies exploring the association between oral conditions and Frailty status are increasing in number, and many manuscripts have been published during the last couple of years. Even when Everaars et al. (1) manuscript is cross-sectional, it contributes to the knowledge by confirming the association between oral conditions and Frailty despite the selected strategy for measuring Frailty since authors added the interview Groningen Frailty Index and the Frailty Index (computed with data extracted from the Electronic Medical Record) to the most frequently used Frailty Phenotype and Kihon checklist (2).

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Reproducibility and responsiveness of the Frailty Index and Frailty Phenotype in older hospitalized patients

BMC Geriatrics ◽

10.1186/s12877-021-02444-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Marlies Feenstra ◽

Frederike M.M. Oud ◽

Carolien J. Jansen ◽

Nynke Smidt ◽

Barbara C. van Munster ◽

...

Keyword(s):

Characteristic Curve ◽

Frailty Index ◽

Correlation Coefficients ◽

Medical Patients ◽

Frailty Phenotype ◽

Intra Class Correlation ◽

Frailty Status ◽

Smallest Detectable Change ◽

Standard Error Of Measurement

Abstract Background There is growing interest for interventions aiming at preventing frailty progression or even to reverse frailty in older people, yet it is still unclear which frailty instrument is most appropriate for measuring change scores over time to determine the effectiveness of interventions. The aim of this prospective cohort study was to determine reproducibility and responsiveness properties of the Frailty Index (FI) and Frailty Phenotype (FP) in acutely hospitalized medical patients aged 70 years and older. Methods Reproducibility was assessed by Intra-Class Correlation Coefficients (ICC), standard error of measurement (SEM) and smallest detectable change (SDC); Responsiveness was assessed by the standardized response mean (SRM), and area under the receiver operating characteristic curve (AUC). Results At baseline, 243 patients were included with a median age of 76 years (range 70–98). The analytic samples included 192 and 187 patients in the three and twelve months follow-up analyses, respectively. ICC of the FI were 0.85 (95 % confidence interval [CI]: 0.76; 0.91) and 0.84 (95% CI: 0.77; 0.90), and 0.65 (95% CI: 0.49; 0.77) and 0.77 (95% CI: 0.65; 0.84) for the FP. SEM ranged from 5 to 13 %; SDC from 13 to 37 %. SRMs were good in patients with unchanged frailty status (< 0.50), and doubtful to good for deteriorated and improved patients (0.43–1.00). AUC’s over three months were 0.77 (95% CI: 0.69; 0.86) and 0.71 (95% CI: 0.62; 0.79) for the FI, and 0.68 (95% CI: 0.58; 0.77) and 0.65 (95% CI: 0.55; 0.74) for the FP. Over twelve months, AUCs were 0.78 (95% CI: 0.69; 0.87) and 0.82 (95% CI: 0.73; 0.90) for the FI, and 0.78 (95% CI: 0.69; 0.87) and 0.75 (95% CI: 0.67; 0.84) for the FP. Conclusions The Frailty Index showed better reproducibility and responsiveness properties compared to the Frailty Phenotype among acutely hospitalized older patients.

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Prevalence of Physical Frailty: Results from the DO-HEALTH Study

Journal of Frailty & Aging ◽

10.14283/jfa.2021.18 ◽

2021 ◽

pp. 1-8

Author(s):

M. Gagesch ◽

P.O. Chocano-Bedoya ◽

L.A. Abderhalden ◽

G. Freystaetter ◽

A. Sadlon ◽

...

Keyword(s):

Controlled Trial ◽

Frailty Index ◽

Female Gender ◽

Community Dwelling ◽

Health Study ◽

Geriatric Syndrome ◽

Physical Frailty ◽

Frailty Phenotype ◽

Cross Sectional ◽

Frailty Status

Background: Frailty is a geriatric syndrome associated with multiple negative health outcomes. However, its prevalence varies by population and instrument used. We investigated frailty and pre-frailty prevalence by 5 instruments in community-dwelling older adults enrolled to a randomized-controlled trial in 5 European countries. METHODS: Cross-sectional baseline analysis in 2,144 DO-HEALTH participants recruited from Switzerland, Austria, France, Germany, and Portugal with complete data for frailty. Frailty status was assessed by the Physical Frailty Phenotype [PFP], SOF-Frailty Index [SOF-FI], FRAIL-Scale, SHARE-Frailty Instrument [SHARE-FI], and a modified SHARE-FI, and compared by country, age, and gender. Logistic regression was used to determine relevant factors associated with frailty and pre-frailty. RESULTS: Mean age was 74.9 (±4.4) years, 61.6% were women. Based on the PFP, overall frailty and pre-frailty prevalence was 3.0% and 43.0%. By country, frailty prevalence was highest in Portugal (13.7%) and lowest in Austria (0%), and pre-frailty prevalence was highest in Portugal (57.3%) and lowest in Germany (37.1%). By instrument and overall, frailty and pre-frailty prevalence was highest based on SHARE-FI (7.0% / 43.7%) and lowest based on SOF-FI (1.0% / 25.9%). Frailty associated factors were residing in Coimbra (Portugal) [OR 12.0, CI 5.30-27.21], age above 75 years [OR 2.0, CI 1.17-3.45], and female gender [OR 2.8, CI 1.48-5.44]. The same three factors predicted pre-frailty. CONCLUSIONS: Among relatively healthy adults age 70 and older enroled to DO-HEALTH, prevalence of frailty and pre-frailty differed significantly by instrument, country, gender, and age. Among instruments, the highest prevalence of frailty and pre-frailty was documented by the SHARE-FI and the lowest by the SOF-FI.

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CONSTRUCT VALIDITY OF FOUR FRAILTY MEASURES IN AN OLDER AUSTRALIAN POPULATION: A RASCH ANALYSIS

Journal of Frailty & Aging ◽

10.14283/jfa.2016.83 ◽

2016 ◽

pp. 1-4

Author(s):

I.S. WIDAGDO ◽

N. PRATT ◽

M. RUSSELL ◽

E.E. ROUGHEAD

Keyword(s):

Construct Validity ◽

Rasch Analysis ◽

Frailty Index ◽

Principal Component ◽

Older Population ◽

Frailty Phenotype ◽

Frailty Status ◽

Increased Risk ◽

Explained Variance ◽

Unexplained Variance

Individuals identified as frail have been shown to be at an increased risk of adverse health outcomes. However, there is no gold standard frailty measure and frailty status can vary depending on the measure used, suggesting the measures perform differently. Construct validity can be used to assess a measure’s performance. This study aimed to examine the construct validity of four frailty measures in an Australian older population using Rasch analysis. Frailty status among the 2087 participants aged 65 years and above from the Australian Longitudinal Study of Ageing (ALSA) was assessed using: frailty phenotype - FP, simplified frailty phenotype - SFP, frailty index - FI, and prognostic frailty score – PFS. Rasch analysis was used to assess the unidimensionality of the measures, which is the extent to which the underlying characteristic of frailty is assessed. The criteria for unidimensionality from principal component analysis of the residuals was when 50% or more of the raw variance was explained by the measures, and less than 5% was unexplained variance. Only FI meet the unidimensionality criteria with 74% of explained variance and 2.1% of unexplained variance. SFP did not show a unidimensional construct with 13.3% of explained variance and 47.1% of unexplained variance. FP and PFS had 39.6%, 18.1% and 46.5%, 8.7% of explained and unexplained variance, respectively. Our findings showed that FI has better construct validity than the other three measures in assessing frailty among the Australian older population.

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Frailty state utility and minimally important difference: findings from the North West Adelaide Health Study

Age and Ageing ◽

10.1093/ageing/afaa166 ◽

2020 ◽

Author(s):

Mark Q Thompson ◽

Olga Theou ◽

Julie Ratcliffe ◽

Graeme R Tucker ◽

Robert J Adams ◽

...

Keyword(s):

Dynamic Condition ◽

Short Form ◽

Frailty Index ◽

Community Dwelling ◽

Health Study ◽

Economic Evaluations ◽

Minimally Important Difference ◽

Health State ◽

Frailty Phenotype ◽

Frailty Status

Abstract Background frailty is a dynamic condition for which a range of interventions are available. Health state utilities are values that represent the strength of an individual’s preference for specific health states, and are used in economic evaluation. This is a topic yet to be examined in detail for frailty. Likewise, little has been reported on minimally important difference (MID), the extent of change in frailty status that individuals consider to be important. Objectives to examine the relationship between frailty status, for both the frailty phenotype (FP) and frailty index (FI), and utility (preference-based health state), and to determine a MID for both frailty measures. Design and setting population-based cohort of community-dwelling Australians. Participant in total, 874 adults aged ≥65 years (54% female), mean age 74.4 (6.2) years. Measurements frailty was measured using the FP and FI. Utilities were calculated using the short-form 6D health survey, with Australian and UK weighting applied. MID was calculated cross-sectionally. Results for both the FP and FI, frailty was significantly statistically associated (P < 0.001) with lower utility in an adjusted analysis using both Australian and UK weighting. Between-person MID for the FP was identified as 0.59 [standard deviation (SD) 0.31] (anchor-based) and 0.59 (distribution-based), whereas for the FI, MID was 0.11 (SD 0.05) (anchor-based) and 0.07 (distribution-based). Conclusions frailty is significantly associated with lower preference-based health state utility. Frailty MID can be used to inform design of clinical trials and economic evaluations, as well as providing useful clinical information on frailty differences that patients consider important.

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Frailty and Prodromal Parkinson’s Disease: Results From the HELIAD Study

The Journals of Gerontology Series A ◽

10.1093/gerona/glaa191 ◽

2020 ◽

Author(s):

Eva Ntanasi ◽

Maria Maraki ◽

Mary Yannakoulia ◽

Maria Stamelou ◽

Georgia Xiromerisiou ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Frailty Index ◽

Disease Diagnosis ◽

Community Dwelling ◽

Older Individuals ◽

Linear Regression Models ◽

Frailty Phenotype ◽

Frailty Status ◽

Underlying Mechanisms

Abstract Background To investigate the association between frailty, Parkinson’s disease (PD), and the probability of prodromal Parkinson’s disease (prodromal PD) in Greek community-dwelling older individuals. Methods Parkinson’s disease diagnosis was reached through standard clinical research procedures. Probability of prodromal PD was calculated according to the International Parkinson and Movement Disorder Society’s research criteria for PD-free participants. Frailty was evaluated according to definitions of the phenotypic and multidomain approach. Logistic and linear regression models were performed to investigate associations between frailty (predictor) and the probability of prodromal PD, either continuous or dichotomous (≥30% probability score), or PD (outcome). Results Data from 1765 participants aged 65 and older were included in the present analysis. Parkinson’s disease and prodromal PD prevalence were 1.9% and 3.0%, respectively. Compared to nonfrail participants, those who were frail, as identified with either the Fried frailty phenotype or Frailty Index had approximately 4 (odds ratio [OR] 4.09, 95% confidence interval [CI] 1.54–10.89) and 12 times (OR 12.16, 95% CI 5.46–27.09) higher odds of having a PD diagnosis, respectively. Moreover, compared to the nonfrail, frail participants as identified with either the Fried frailty phenotype or Frailty Index had 2.8 (OR 2.83, 95% CI 1.09–7.37) and 8.3 times (OR 8.39, 95% CI 4.56–15.42) higher odds of having possible/probable prodromal PD, respectively. Conclusions Frailty status was associated with prodromal PD and PD, suggesting common characteristics or underlying mechanisms of these conditions. Although prospective studies are warranted, acknowledging the possible association of frailty, PD, and prodromal PD may improve their clinical management.

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Frailty Prevalence in the COMPASS-ND Study of Neurodegenerative Disorders

Canadian Geriatrics Journal ◽

10.5770/cgj.22.392 ◽

2019 ◽

Vol 22 (4) ◽

pp. 205-212 ◽

Cited By ~ 2

Author(s):

Jacqueline R. Burt ◽

Judith Godin ◽

Josée Filion ◽

Manuel Montero-Odasso ◽

Kenneth Rockwood ◽

...

Keyword(s):

Cohort Study ◽

Health Status ◽

Health Outcomes ◽

Neurodegenerative Disorders ◽

Frailty Index ◽

Frailty Phenotype ◽

Frailty Status ◽

Adverse Health ◽

Adverse Health Outcomes ◽

The Difference

BackgroundFrailty is characterized by increased vulnerability to adverse health outcomes. The prevalence of frailty across neurodegenerative disorders (NDD) is largely unknown. Symptoms of frailty and NDD overlap, calling into question a tautology in some frailty instruments. Our objectives were 1) to construct a Frailty Index (FI) independent of NDD symptoms, and 2) to estimate frailty prevalence in a broad NDD cohort using both the Frailty Phenotype (FP) and the constructed FI as measures.MethodsData from the Canadian COMPASS-ND cohort study were assessed for applicability to FI construction. Frailty status accord-ing to FI and FP criteria were ascertained for each participant. Results81 items were selected for the FI. In the cohort (150 participants; 46% women; mean age 73.6±7.0; 10 NDD subgroups), frailty was identified in 11% and 14% of participants according to the FI and FP, respectively. The difference between estimates was not significant. The FP classified most participants (84%) as pre-frail. ConclusionThe presence of frailty elements, regardless of whether they are part of NDD, is likely to influence health status. Given the FP identified a large proportion of the cohort as pre-frail or frail, it is likely worthwhile to identify frailty in the context of NDD.

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The Framingham risk score is associated with incident frailty, or is it?

BMC Geriatrics ◽

10.1186/s12877-021-02387-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Hui Shi ◽

Mei-Ling Ge ◽

Birong Dong ◽

Qian-Li Xue

Keyword(s):

Older Adults ◽

Risk Score ◽

Cox Regression ◽

Activity Level ◽

Cvd Risk ◽

Frailty Phenotype ◽

Frailty Status ◽

Adverse Health Outcomes ◽

Comorbidity Burden

Abstract Backgrounds Cardiovascular disease (CVD) risk factors are individually associated with frailty. This study examined whether Framingham CVD risk score (FRS) as an aggregate measure of CVD risk is associated with incident frailty among Chinese older adults. Methods This study used data from the China Health and Retirement Longitudinal Study. A sample of 3,618 participants aged 60 to 95 years and without CVD at baseline were followed for four years. FRS was calculated at baseline. Frailty status was defined as not-frail (0–2 criteria) or frail (3–5 criteria) based on the physical frailty phenotype consisting of five binary criteria (weakness, slowness, exhaustion, low activity level, and weight loss). After excluding subjects who were frail (n = 248) at baseline, discrete-time Cox regression was used to evaluate the relationship between FRS and incident frailty. Results During a median follow-up of 4.0 years, 323 (8 %) participants developed CVD and 318 (11 %) subjects had frailty onset. Higher FRS was associated with greater risk of incident frailty (HR: 1.03, 95 % CI: 1.00 to 1.06) after adjusting for education, marital status, obesity, comorbidity burden, and cognitive function. This association however was no longer significant (HR: 1.00, 95 % CI: 0.97 to 1.03) after additionally adjusting for age. These findings remained essentially unchanged after excluding subjects with depression (n = 590) at baseline or incident CVD (n = 323) during the 4-year follow-up. Conclusions The FRS was not independently associated with incident frailty after adjusting for chronological age. More research is needed to assess the clinical utility of the FRS in predicting adverse health outcomes other than CVD in older adults.

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Frailty Status and All-Cause Mortality in the Community-Dwelling Older People: An Umbrella Review

Innovation in Aging ◽

10.1093/geroni/igaa057.1580 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 488-489

Author(s):

A R M Saifuddin Ekram ◽

Joanne Ryan ◽

Carlene Britt ◽

Sara Espinoza ◽

Robyn Woods

Keyword(s):

Older People ◽

Systematic Reviews ◽

Community Dwelling ◽

Umbrella Review ◽

Frailty Phenotype ◽

Frailty Status ◽

Increased Risk ◽

All Cause Mortality ◽

Community Dwelling Older People ◽

Meta Analyses

Abstract Frailty is increasingly recognised for its association with adverse health outcomes including mortality. However, various measures are used to assess frailty, and the strength of association could vary depending on the specific definition used. This umbrella review aimed to map which frailty scale could best predict the relationship between frailty and all-cause mortality among community-dwelling older people. According to the PRISMA guidelines, Medline, Embase, EBSCOhost and Web of Science databases were searched to identify eligible systematic reviews and meta-analyses which examined the association between frailty and all-cause mortality in the community-dwelling older people. Relevant data were extracted and summarised qualitatively. Methodological quality was assessed by AMSTAR-2 checklist. Five moderate-quality systematic reviews with a total of 374,529 participants were identified. Of these, two examined the frailty phenotype and its derivatives, two examined the cumulative deficit models and the other predominantly included studies assessing frailty with the FRAIL scale. All of the reviews found a significant association between frailty status and all-cause mortality. The magnitude of association varied between individual studies, with no consistent pattern related to the frailty measures that were used. In conclusion, regardless of the measure used to assess frailty status, it is associated with an increased risk of all-cause mortality.

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