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

Gerontology ◽  
2017 ◽  
Vol 64 (4) ◽  
pp. 389-400 ◽  
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.

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

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.

scholarly journals 20 Comparison of Six Frailty Screening Tools in Patients Aged 65+ with An Arm Fragility Fracture

2021 ◽  
Vol 50 (Supplement_1) ◽  
pp. i1-i6
Author(s):  
K Ibrahim ◽  
T Lim ◽  
M A Mullee ◽  
G L Yao ◽  
S Zhu ◽  
...  
Keyword(s):  
Fragility Fracture ◽  
Frailty Index ◽  
Screening Tools ◽  
Frailty Phenotype ◽  
Fracture Clinic ◽  
Increased Risk ◽  
Sensitivity Range ◽  
Practical Tool ◽  
Acute Trust ◽  
Medication Changes

Abstract Introduction Frailty is associated with an increased risk of falling and fracture, but not routinely assessed in fracture clinic. Early identification and management of frailty among older people with arm fragility fracture could help avoid further falls and fractures, especially of the hip. We evaluated the feasibility of assessing frailty in a busy fracture clinic. Methods People aged 65+ years with an arm fracture in one acute trust were recruited. Frailty was assessed in fracture clinics using six tools: Fried Frailty Phenotype (FFP), FRAIL scale, PRISMA-7, electronic Frailty Index (e-FI), Clinical Frailty Score (CFS), and Study of Osteoporotic Fracture (SOF). The sensitivity and specificity of each tool was compared against FFP as a reference. Participants identified as frail by 2+ tools were referred for Comprehensive Geriatric Assessment (CGA). Results 100 patients (mean age 75 years±7.2; 20 men) were recruited. Frailty prevalence was 9% (FRAIL scale), 13% (SOF), 14% (CFS &gt; 6), 15% (FFP; e-FI &gt; 0.25), and 25% (PRISMA-7). Men were more likely to be frail than women. Data were complete for all assessments and completion time ranged from one minute (PRISMA-7; CFS) to six minutes for the FFP which required most equipment. Comparing with FFP, the most accurate instrument for stratifying frail from non-frail was the PRISMA-7 (sensitivity = 93%, specificity = 87%) while the remaining tools had good specificity (range 93%–100%) but average sensitivity (range 40%–60%). Twenty patients were eligible for CGA. Five had recently had CGA and 11/15 referred were assessed. CGA led to 3–6 interventions per participant including medication changes, life-style advice, investigations, and onward referrals. Conclusion It was feasible to assess frailty in fracture clinic and to identify patients who benefitted from CGA. Frailty prevalence was 9%—25% depending on the tool used and was higher among men. PRISMA-7 could be a practical tool for routine use in fracture clinics.

scholarly journals Spousal Concordance of Physical Frailty in Older Korean Couples

2020 ◽  
Vol 17 (12) ◽  
pp. 4574 ◽  
Author(s):  
Suah Kang ◽  
Miji Kim ◽  
Chang Won Won
Keyword(s):  
Risk Factor ◽  
Married Couples ◽  
Limited Data ◽  
Contributing Factors ◽  
Physical Frailty ◽  
Frailty Phenotype ◽  
Frailty Status ◽  
Spousal Concordance ◽  
Logistic Regressions ◽  
Association Data

Marital status is an important risk factor for physical frailty. However, there are limited data on spousal concordance of physical frailty among married couples. Here, we evaluate the spousal concordance of frailty as defined by the Fried frailty phenotype and specific phenotype components that contribute to this association. Data on 315 married couples (630 individuals) aged between 70 and 84 years were obtained from the Korean Frailty and Aging Cohort Study (KFACS). Multivariate logistic regressions were used for the analysis. After adjusting for covariates (age, body mass index, education, house ownership, comorbidity, cognition, depressive symptoms, cohabitation with adult children for both partners), a husband’s frailty was positively associated with his wife’s frailty (odds ratio (OR) 3.34, 95% confidence interval (CI) 1.04–10.73, p < 0.05), and a wife’s frailty was significantly associated with her husband’s frailty (OR 4.62, 95% CI 1.31–16.33, p < 0.05), indicating a greater effect of the frailty status of the spouse among women than among men. Among the five components of the Fried frailty phenotype, weight loss, slowness, and exhaustion were the main contributing factors to the spousal association for frailty. In conclusion, having a frail spouse is a strong and independent risk factor for frailty among community-living older adults.

scholarly journals Toward Using Wearables to Remotely Monitor Cognitive Frailty in Community-Living Older Adults: An Observational Study

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2218 ◽  
Author(s):  
Javad Razjouyan ◽  
Bijan Najafi ◽  
Molly Horstman ◽  
Amir Sharafkhaneh ◽  
Mona Amirmazaheri ◽  
...  
Keyword(s):  
Decision Tree ◽  
Area Under The Curve ◽  
Sleep Time ◽  
Accelerometer Data ◽  
Decision Tree Classifier ◽  
Physical Frailty ◽  
Frailty Phenotype ◽  
Cognitive Frailty ◽  
Time In Bed ◽  
Tree Classifier

Physical frailty together with cognitive impairment (Cog), known as cognitive frailty, is emerging as a strong and independent predictor of cognitive decline over time. We examined whether remote physical activity (PA) monitoring could be used to identify those with cognitive frailty. A validated algorithm was used to quantify PA behaviors, PA patterns, and nocturnal sleep using accelerometer data collected by a chest-worn sensor for 48-h. Participants (N = 163, 75 ± 10 years, 79% female) were classified into four groups based on presence or absence of physical frailty and Cog: PR-Cog-, PR+Cog-, PR-Cog+, and PR+Cog+. Presence of physical frailty (PR-) was defined as underperformance in any of the five frailty phenotype criteria based on Fried criteria. Presence of Cog (Cog-) was defined as a Mini-Mental State Examination (MMSE) score of less than 27. A decision tree classifier was used to identify the PR-Cog- individuals. In a univariate model, sleep (time-in-bed, total sleep time, percentage of sleeping on prone, supine, or sides), PA behavior (sedentary and light activities), and PA pattern (percentage of walk and step counts) were significant metrics for identifying PR-Cog- (p < 0.050). The decision tree classifier reached an area under the curve of 0.75 to identify PR-Cog-. Results support remote patient monitoring using wearables to determine cognitive frailty.

scholarly journals The prevalence of frailty, measured with different diagnostic tools, and autonomy decline: Results of the Crystal study

2021 ◽  
Vol 25 (1) ◽  
pp. 35-43
Author(s):  
Anna V. Turusheva ◽  
Elena V. Frolova ◽  
Tatiana A. Bogdanova
Keyword(s):  
Vision Loss ◽  
Factor Model ◽  
Frailty Index ◽  
Screening Tools ◽  
Diagnostic Tools ◽  
Geriatric Syndromes ◽  
Frailty Phenotype ◽  
Groningen Frailty Indicator ◽  
Blocking Factor ◽  
Frailty Indicator

INTRODUCTION: Frailty prevalence differs across different population depending on the models used to assess, age, economic situation, social status, and the proportion of men and women in the study. The diagnostic value of different models of frailty varies from population to population. OBJECTIVES: To assess the prevalence of frailty using 4 different diagnostic models and their sensitivity for identifying persons with autonomy decline. MATERIAL AND METHODS: A random sample of 611 people aged 65 and over. Models used: the Age is not a blocking factor model, the SOF Frailty Index, the Groningen Frailty Indicator, L. Fried model. Covariates: nutritional status, anemia, functional status, depression, dementia, chronic diseases, grip strength, physical function. RESULTS: The prevalence of the Frailty Phenotype ranged from 16.6 to 20.4% and the Frailty Index was 32.6%. Frailty, regardless of the used models was associated with an increase in the prevalence of the geriatric syndromes: urinary incontinence, hearing and vision loss, physical decline, malnutrition and the risk of malnutrition, low cognitive functions and autonomy decline (p 0.05). The negative predictive value (NPV) of the Age is not a blocking factor model, the SOF Frailty Index, the Groningen Frailty Indicator for identifying individuals with autonomy decline was 8690%. CONCLUSION: The prevalence of frailty depended on the operational definition and varied from 16.6 to 32.6%. The Age is not a blocking factor model, the SOF Frailty Index, the Groningen Frailty Indicator, L. Fried model can be used as screening tools to identify older patient with autonomy decline. Regardless of the model used, frailty is closely associated with an increase in the prevalence of major geriatric syndromes.

scholarly journals Polypharmacy and lack of joy predict physical frailty among northern Japanese community-dwellers from the ORANGE cohort study

2020 ◽  
Author(s):  
Yu Kume ◽  
Tomoko Takahashi ◽  
Yuki Itakura ◽  
Sangyoon Lee ◽  
Hyuma Makizako ◽  
...  
Keyword(s):  
Older Adults ◽  
Rural Areas ◽  
Assessment Tool ◽  
Frailty Index ◽  
Limited Information ◽  
Physical Frailty ◽  
Cognitive Domains ◽  
Frailty Status ◽  
Critical Issues ◽  
Community Dwellers

Abstract Background: A gradually increasing prevalence of frailty is recognized in the super-aging society that Japan faces, and early detection and intervention of frailty in community-dwellers are critical issues to prevent frailty. Although previous studies have well documented the characteristics of physical disability, there is limited information on frail state differences among older adults in Japanese rural areas. The aim of this study was to clarify the association and predictors of frail status in northen Japan community-dwellers aged 65 or more.Methods: The investigation was conducted from 2018 to 2020. After obtaining informed consent from each participant, assessments and outcomes were evaluated according to the ORANGE protocol. Participants were recruited from Akita community-dwellers in northern Japan. We applied the frailty index of Gerontology - the Study of Geriatric Syndromes (NCGG-SGS) to classify frailty status, collecting data of demographics and psycho-social status using the Kihon checklist and cognitive domains including the National Center for Geriatrics and Gerontology Functional Assessment Tool (NCGG-FAT).Results: Our subjects included 313 older adults divided into 138 robust, 163 prefrail and 12 frail. For statistical analysis, physical frailty and cognitive decline were related, and polypharmacy and a lack of joy in daily life were the main predictors of frail status.Conclusions: Reducing medications and finding fun in your life are important to prevent frailty.

scholarly journals Polypharmacy Is Significantly and Positively Associated with the Frailty Status Assessed Using the 5-Item FRAIL Scale, Cardiovascular Health Phenotypic Classification of Frailty Index, and Study of Osteoporotic Fractures Scale

2021 ◽  
Vol 10 (19) ◽  
pp. 4413
Author(s):  
Chi-Di Hung ◽  
Chen-Cheng Yang ◽  
Chun-Ying Lee ◽  
Stephen Chu-Sung Hu ◽  
Szu-Chia Chen ◽  
...  
Keyword(s):  
Cardiovascular Health ◽  
Frailty Index ◽  
Osteoporotic Fractures ◽  
Cross Sectional Study ◽  
Screening Tools ◽  
Future Research ◽  
Frailty Phenotype ◽  
Cross Sectional ◽  
Phenotypic Classification

The aim of this study was to investigate the association between frailty and polypharmacy using three different frailty screening tools. This was a cross-sectional study of people aged ≥65 years. Participants were included and interviewed using questionnaires. Polypharmacy was defined as the daily use of eight or more pills. Frailty was assessed using a screening tool, including (1) the Fatigue, Resistance, Ambulation, Illness and Loss of Weight Index (5-item FRAIL scale), (2) the Cardiovascular Health Phenotypic Classification of Frailty (CHS_PCF) index (Fried’s Frailty Phenotype), and (3) the Study of Osteoporotic Fracture (SOF) scale. A total of 205 participants (mean age: 71.1 years; 53.7% female) fulfilled our inclusion criteria. The proportion of patients with polypharmacy was 14.1%. After adjustments were made for comorbidity or potential confounders, polypharmacy was associated with frailty on the 5-item FRAIL scale (adjusted odds ratio [aOR]: 9.12; 95% confidence interval [CI]: 3.6–23.16), CHS_PCF index (aOR: 8.98; 95% CI: 2.51–32.11), and SOF scale (aOR: 6.10; 95% CI: 1.47–25.3). Polypharmacy was associated with frailty using three frailty screening tools. Future research is required to further enhance our understanding of the risk of frailty among older adults.

scholarly journals DETECTING PRE-FRAILTY STATUS: COMPARISON OF CLINICAL JUDGMENTS AND THE PAULSON LICHTENBERG FRAILTY INDEX.

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S897-S898
Author(s):  
Yi-Ling Hu ◽  
Heather A Fritz
Keyword(s):  
Clinical Judgment ◽  
Medical Records ◽  
Frailty Index ◽  
Primary Care Clinic ◽  
Descriptive Statistics ◽  
Screening Tools ◽  
Clinical Judgments ◽  
Care Clinic ◽  
Frailty Status ◽  
Level Of Agreement

Abstract Nearly 50% of U.S. elders are prefrail and at risk for frailty. Identifying prefrail elders and escalating care could attenuate frailty progression. Screening tools are seldom used in practice. Thus, clinical judgment may be a realistic way to ensure widespread frailty screening. No studies, however, have assessed the validity of clinicians’ judgment in identifying prefrail elders. This study explored the level of agreement between clinical judgments of frailty status and status categorizations made using the validated Paulson Lichtenberg Frailty Index (PLFI). Older Blacks (n = 202) recruited from a primary care clinic were first categorized as healthy, pre-frail, or frail using the PLFI. Next, geriatric physicians and nurses categorized participants into one of the same categories based on clinical judgment. Clinicians could use medical records to make determinations. We used Cohen’s Kappa to determine the level of agreement of both approaches. We used descriptive statistics to explore if any of the 5 PLFI indicators explained discordant categorizations. Of the 202 participants (mean age: 76.7 8.6), 52 (26%) were prefrail and 57 (28%) were frail based on the PLFI. Physicians’ judgments aligned with the PLFI in 43% of prefrail and 65.7% of frail cases. Nurse judgments aligned with the PLFI in 43.9% of prefrail and 17% of frail cases. There was slight to fair agreement between clinical judgments and PLFI (physicians Cohen’s κ = .23; Nurses Cohen’s κ = .59). No specific PLFI indicators independently explained discordant categorizations. Findings suggest that clinical judgments did not align well with PLFI categorizations.

Polypharmacy and Lack of Joy Are Related to Physical Frailty among Northern Japanese Community-Dwellers from the ORANGE Cohort Study

Gerontology ◽  
2021 ◽  
pp. 1-10
Author(s):  
Yu Kume ◽  
Tomoko Takahashi ◽  
Yuki Itakura ◽  
Sangyoon Lee ◽  
Hyuma Makizako ◽  
...  
Keyword(s):  
Older Adults ◽  
Rural Areas ◽  
Assessment Tool ◽  
Frailty Index ◽  
Limited Information ◽  
Physical Frailty ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
Frailty Status ◽  
Community Dwellers

<b><i>Introduction:</i></b> A prevalence of frailty is gradually increasing with the progress of aging in Japan, and critical challenges regarding early diagnosis and prevention of frailty were necessary in community. Although previous studies have well documented the characteristics of physical disability, there is limited information on frail state differences among older adults in Japanese rural areas. The aim of our cross-sectional observational study was to clarify the association of frail status in northern Japanese community-dwellers aged 65 or more. <b><i>Methods:</i></b> 345 participants were recruited from 2018 to 2020, and after getting informed consent from each participant, assessments and outcomes were evaluated according to the ORANGE protocol. We applied the frailty index of Gerontology-the Study of Geriatric Syndromes (NCGG-SGS) to classify frailty status by collecting data of demographics and psychosocial status using the Kihon checklist (KCL) and cognitive domains used by the National Center for Geriatrics and Gerontology-Functional Assessment Tool (NCGG-FAT). <b><i>Results:</i></b> Our subjects included 313 older adults divided into 138 robust, 163 prefrail, and 12 frail. For statistical analysis, we found that the frail group had a lower educational duration, worsened KCL items, lower cognitive functions, and a tendency toward depression compared to the other groups. Moreover, physical frailty and cognitive decline were related, and polypharmacy and a lack of joy in daily life were explanatory variables of frail status. <b><i>Conclusions:</i></b> We suggest that KCL is important for frail discrimination, and in order to prevent physical frailty, our community should take care of not only exercise and nutrition but also cognitive functioning and depressive tendencies. In particular, polypharmacy and the presence of fun in your life are possible to be related to frailty.

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

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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