Frailty assessment in older adults using upper-extremity function: index development

Abstract

Background: Numerous multidimensional assessment tools have been developed to measure frailty; however, the clinical feasibility of these tools is limited. We previously developed and validated an upper-extremity function (UEF) assessment method that incorporates wearable motion sensors. The purpose of the current study was to: 1) crosssectionally validate the UEF method in a larger sample in comparison with the Fried index; 2) develop a UEF frailty index to predict frailty categories including non-frail, pre-frail, and frail based on UEF parameters and demographic information, using the Fried index as the gold standard; and 3) develop a UEF continuous score (points scores for each UEF parameter and a total frailty score) based on UEF parameters and demographic information, using the Fried index as the gold standard. Methods: We performed a cross-sectional validation and index development study within the Banner Medical Center, Tucson, and Banner Sun Health Research Institute, Sun City, Arizona. Community-dwelling and outpatient older adults (≥60 years; n = 352; 132 non-frail, 175 pre-frail, and 45 frail based on Fried criteria) were recruited. For the UEF test, each participant performed a 20-s elbow flexion, within which they repetitively and rapidly flexed and extended their dominant elbow. Using elbow motion outcomes two UEF indexes were developed (categorical and score). The Fried index was measured as the gold standard. Results: For the categorical index, speed of elbow flexion, elbow range of motion, elbow moment, number of flexion, speed variability and reduction within 20 s, as well as body mass index (BMI) were included as the pre-frailty/frailty predictor parameters. Results from 10-fold cross-validation showed receiver operator characteristic area under the curve of 0.77 ± 0.07 and 0.80 ± 0.12 for predicting Fried pre-frailty and frailty, respectively. UEF score (0.1 to 1.0) was developed using similar UEF parameters. Conclusions: We present an objective, sensor-based frailty assessment tool based on physical frailty features including slowness, weakness, exhaustion (muscle fatigue), and flexibility of upper-extremity movements. Within the current study, the method was validated cross-sectionally using the Fried index as the gold standard and the UEF categorical index and UEF frailty score were developed for research purposes and potentially for future clinical use.

Functional Capacity and Levels of Physical Activity in Aging: A 3-Year Follow-up

“…This topic should be object of more studies since this association may be a determinant of a frailty phenotype ( 44 – 46 ). In addition, we noticed that 39.5% at M1 and 35.7% of participants at M2 walked less than 400 m on 6MWT, which is a cut-off point to classify sarcopenia with limited mobility; on average, diabetic participants walked way below of this cut-off latter, representing their high susceptibility to be classified as sarcopenic obese and higher risk of frailty ( 47 , 48 ). Regarding handgrip data, values split by hypercholesterolemia group were paradoxical since hypercholesterolemia group had higher values of strength, nevertheless the proportion of males and body weight values in the group of hypercholesterolemia were higher than in the group without hypercholesterolemia (35.7 vs 24.9%)., which may explain our results.…”

A Microservices e-Health System for Ecological Frailty Assessment Using Wearables

“…Schwenk et al [15] and Kumar et al [16] applied Multinomial Logistic Regression (MLR) model to discriminate between the three frailty status, by measuring the gait, balance, and physical activity (PA). Toosizadeh et al [3] used Ordinal Logistic Regression (OLR) for a frailty detection in 20 s elbow flexion. Greene et al [17,52] designed a digital assessment based on a Logistic Regression (LR) model for detecting falls, frailty and mobility impairment.…”

“…MLR: Accuracy: 77.7% Sensibilty: 76.8% Specificity: 80% [3] To assess frailty by a wearable during the flexibility of upper-extremity movements. No Gyroscope 1 None Three 2 OLR: Accuracy: 69% [17,52] To design a digital assessment protocol and algorithm for prediction of falls, frailty and mobility impairment.…”

“…2 Considered Fried 3 classes: non-frail, pre-frail, frail status. 3 Considered Fried 2 classes: identification of frail participants against non-frail and prefrail participants together as a single class. 4 Considered Fried 2 classes: identification of non-frail participants against pre-frail and frail participants together as a single class.…”

“…In a global ageing society, it is important to detect the frailty in the early stages to be able to slow down the decline of elderly people and to keep them active and healthy for as long as possible. Frailty is a syndrome of older adults that increases the risk of falls, hospitalizations and even death [1,2]; affecting 11% of the home-dwelling elderly population without hospitalization and increasing drastically to 30-70% of the surgical patients over 65 years old [3]. The early detection of frailty has been proved to increase the independence of the older adults and the decrease on health care costs [4,5].…”