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http://hdl.handle.net/1942/37449
Title: | A Mobile Application to Perform the Six-Minute Walk Test (6MWT) at Home: A Random Walk in the Park Is as Accurate as a Standardized 6MWT | Authors: | SCHERRENBERG, Martijn BONNEUX, Cindel MAHMOOD, Deeman HANSEN, Dominique DENDALE, Paul CONINX, Karin |
Issue Date: | 2022 | Publisher: | MDPI | Source: | SENSORS, 22 (11) (Art N° 4277) | Abstract: | The six-minute walk test (6MWT) provides an objective measurement of a person's functional exercise capacity. In this study, we developed a smartphone application that allows cardiac patients to do a self-administered 6MWT at home on a random trajectory. In a prospective study with 102 cardiovascular disease patients, we aimed to identify the optimal circumstances to perform a smartphone-measured 6MWT, i.e., the best algorithm and the best position to wear the smartphone during the test. Furthermore, we investigated if a random walk is as accurate as a standardized 6MWT. When considering both the reliability and accuracy of the distance walked, the best circumstances to perform a standardized smartphone-measured 6MWT are wearing the smartphone in a strap around the patient's arm and using an algorithm that relies on the processed step count data acquired from Google Fit. Furthermore, we demonstrated that a smartphone-measured walk along a random trajectory is as accurate to determine a cardiac patient's functional exercise capacity as a standardized (smartphone-measured) 6MWT. We conclude this paper by presenting how our 6MWT application can be used in a home setting to remotely follow up on cardiac patients' functional exercise capacity. | Keywords: | six-minute walk test;6MWT;six-minute walk distance;6MWD;walking;cardiovascular disease;smartphone;digital health | Document URI: | http://hdl.handle.net/1942/37449 | e-ISSN: | 1424-8220 | DOI: | 10.3390/s22114277 | ISI #: | 000808783300001 | Rights: | Copyright: © 2022 by the authors. Li‐ censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con‐ ditions of the Creative Commons At‐ tribution (CC BY) license (https://cre‐ ativecommons.org/licenses/by/4.0/). | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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Scherrenberg et al. 2022 Sensors.pdf | Published version | 1.7 MB | Adobe PDF | View/Open |
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