Handgrip Strength Time Profile and Frailty: An Exploratory Study (original) (raw)
Related papers
eFurniture for home-based frailty detection using artificial neural networks and wireless sensors
Medical Engineering & Physics, 2013
The purpose of this study is to integrate wireless sensor technologies and artificial neural networks to develop a system to manage personal frailty information automatically. The system consists of five parts: (1) an eScale to measure the subject's reaction time; (2) an eChair to detect slowness in movement, weakness and weight loss; (3) an ePad to measure the subject's balancing ability; (4) an eReach to measure body extension; and (5) a Home-based Information Gateway, which collects all the data and predicts the subject's frailty. Using a furniture-based measuring device to provide home-based measurement means that health checks are not confined to health institutions. We designed two experiments to obtain optimum frailty prediction model and test overall system performance: (1) We developed a three-step process to adjust different parameters to obtain an optimized neural identification network whose parameters include initialization, L.R. dec and L.R. inc. The post-process identification rate increased from 77.85% to 83.22%. (2) We used 149 cases to evaluate the sensitivity and specificity of our frailty prediction algorithm. The sensitivity and specificity of this system are 79.71% and 86.25% respectively. These results show that our system is a high specificity prediction tool that can be used to assess frailty.
Sensors (Basel, Switzerland), 2021
Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal...
An Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population
International Journal of Environmental Research and Public Health
Frailty, one of the major public health problems in the elderly, can result from multiple etiologic factors including biological and physical changes in the body which contribute to the reduction in the function of multiple bodily systems. A diagnosis of frailty can be reached using a variety of frailty assessment tools. In this study, general characteristics and health data were assessed using modified versions of Fried’s Frailty Phenotype (mFFP) and the Frail Non-Disabled (FiND) questionnaire (mFiND) to construct a Self-Organizing Map (SOM). Trained data, composed of the component planes of each variable, were visualized using 2-dimentional hexagonal grid maps. The relationship between the variables and the final SOM was then investigated. The SOM model using the modified FiND questionnaire showed a correct classification rate (%CC) of about 66% rather than the model responded to mFFP models. The SOM Discrimination Index (SOMDI) identified cataracts/glaucoma, age, sex, stroke, pol...
Prototype sensor system for analyzing frailty in older people: a pilot study
Research, Society and Development
Introduction: Frailty syndrome is characterized by reduced physical and cognitive reserves, making older people vulnerable to adverse events. This study describes a prototype sensor system developed for assessing frailty through physiological parameters and frailty markers. Methods: A prototype combining four sensors in network and a software package was developed and tested in four long-term care facility senior residents of both sexes, aged 60 and older, showing no locomotive syndrome or severe cognitive impairment. Three of them were frail and able to walk without aid (P1), holding onto the wall (P2) or with a cane (P3), and a non-frail participant (P4) walked without aid. Results: Regarding mean acceleration, P1 and P4 showed the lowest and highest values, respectively, on the antero-posterior axis; P4 had the lowest value on the medio-lateral axis; and P3 presented the highest value on the vertical axis. All participants showed similar roll angular velocity; P4 presented the lo...
Frailty status can be accurately assessed using inertial sensors and the TUG test
Age and Ageing, 2013
Background: frailty is an important geriatric syndrome linked to increased mortality, morbidity and falls risk. Methods: a total of 399 community-dwelling older adults were assessed using Fried's frailty phenotype and the timed up and go (TUG) test. Tests were quantified using shank-mounted inertial sensors. We report a regression-based method for assessment of frailty using inertial sensor data obtained during TUG. For comparison, frailty was also assessed using the same method based on grip strength and manual TUG time.
BMC Geriatrics
Background: Frailty is a clinical condition among older adults defined as the loss of resources in one or more domains (i.e., physical, psychological and social domains) of individual functioning. In frail subjects emergency situations and mobility levels need to be carefully monitored. This study aimed to: i) evaluate differences in the mobility index (MI) provided by ADAMO system, an innovative remote monitoring device for older adults; ii) compare the association of the MI and a traditional physical measure with frailty. Methods: Twenty-five community-dwelling older adults (71 ± 6 years; 60% women) wore ADAMO continuously for a week. The time percentage spent in Low, Moderate and Vigorous Activities was assessed using ADAMO system. Walking ability and frailty were measured using the 400 m walk test and the Tilburg Frailty Indicator, respectively. Results: Controlling for age and gender, the ANCOVA showed that frail and robust participants were different for Low (frail = 58.8%, robust = 42.0%, p < 0.001), Moderate (frail = 25.5%, robust = 33.8%, p = 0.008), and Vigorous Activity (frail = 15.7%, robust = 24.2%, p = 0.035). Using cluster analysis, participants were divided into two groups, one with higher and one with lower mobility. Controlling for age and gender, linear regression showed that the MI clusters were associated with total (β = 0.571, p = 0.002), physical (β = 0.381, p = 0.031) and social (β = 0.652, p < 0. 001) frailty; and the 400 m walk test was just associated with total (β = 0.404, p = 0.043) and physical frailty (β = 0. 668, p = 0.002). Conclusion: ADAMO system seems to be a suitable time tracking that allows to measure mobility levels in a nonintrusive way providing wider information on individual health status and specifically on frailty. For the frail individuals with an important loss of resources in physical domain, this innovative device may represent a considerable help in preventing physical consequences and in monitoring functional status.
Data-Driven Continuous Assessment of Frailty in Older People
Frontiers in Digital Humanities, 2018
The process of aging affects an individual's potential in several dimensions, encompassing the physical, cognitive, psychological, economic, and social domains. The assessment of frailty in elderly patients is key to estimate their overall well-being and to predict mortality risk. In the clinical practice, frailty is usually estimated through medical tests and questionnaires performed sporadically. Continuous automatic assessment may help physicians in evaluating frailty by complementing their assessments with quantitative and non sporadic measurements. In this paper, we present the state-of-the-art in frailty evaluation, we summarize recent research achievements that could lead to an improved assessment, and we illustrate a case study we are conducting in our institution. Finally, based on our experience and results, we comment on the open challenges of automatic assessment of frailty.
Frailty assessment in older adults using upper-extremity function: index development
BMC Geriatrics, 2017
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
Development of a Monitoring System for Physical Frailty in Independent Elderly
— Frailty is of increasing concern due to the associated decrease in independence of elderly who suffer from the condition. An innovative system was designed in order to objectively quantify the level of frailty based on a series of remote tests, each of which used objects similar to those found in peoples' homes. A modified ball, known as the Grip-ball was used to evaluate maximal grip force and exhaustion during an entirely remote assessment. A smartphone equipped with a tri-axial accelerometer was used to estimate gait velocity and physical activity level. Finally, a bathroom scale was used to assess involuntary weight loss. The smart phone processes all of the data generated, before it is transferred to a remote server where the user, their entourage, and any medical professionals with authorization can access the data. This innovative system could enable the onset of frailty to be detected early, thus giving sufficient time for a targeted intervention program to be implemented, thereby increasing independence for elderly users.
Screening for frailty in primary care: Accuracy of gait speed and hand-grip strength
Canadian family physician Medecin de famille canadien, 2017
To examine the accuracy of individual Fried frailty phenotype measures in identifying the Fried frailty phenotype in primary care. Retrospective chart review. A community-based primary care practice in Kitchener, Ont. A total of 516 patients 75 years of age and older who underwent frailty screening. Using modified Fried frailty phenotype measures, frailty criteria included gait speed, hand-grip strength as measured by a dynamometer, and self-reported exhaustion, low physical activity, and unintended weight loss. Sensitivity, specificity, accuracy, and precision were calculated for single-trait and dual-trait markers. Complete frailty screening data were available for 383 patients. The overall prevalence of frailty based on the presence of 3 or more frailty criteria was 6.5%. The overall prevalence of individual Fried frailty phenotype markers ranged from 2.1% to 19.6%. The individual criteria all showed sensitivity and specificity of more than 80%, with the exception of weight loss ...