Gerontology

Regenerative and Technological Section / Original Paper

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

Lee H.a · Joseph B.b · Enriquez A.a · Najafi B.a

Author affiliations

aInterdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, and bDivision of Trauma, Critical Care, Burns, and Acute Care Surgery, Department of Surgery, University of Arizona, Tucson, AZ, USA

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Gerontology 2018;64:389–400

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Article / Publication Details

First-Page Preview
Abstract of Regenerative and Technological Section / Original Paper

Received: July 21, 2017
Accepted: October 16, 2017
Published online: November 25, 2017
Issue release date: June 2018

Number of Print Pages: 12
Number of Figures: 5
Number of Tables: 3

ISSN: 0304-324X (Print)
eISSN: 1423-0003 (Online)

For additional information: https://beta.karger.com/GER

Abstract

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.

© 2017 S. Karger AG, Basel




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Article / Publication Details

First-Page Preview
Abstract of Regenerative and Technological Section / Original Paper

Received: July 21, 2017
Accepted: October 16, 2017
Published online: November 25, 2017
Issue release date: June 2018

Number of Print Pages: 12
Number of Figures: 5
Number of Tables: 3

ISSN: 0304-324X (Print)
eISSN: 1423-0003 (Online)

For additional information: https://beta.karger.com/GER


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