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Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range
Accelerometer data can be used to estimate incident oxygen consumption (VO(2)) during physical activity. Relationships between the accelerometer metrics and VO(2) are typically determined using specific walking or running protocols on a track or treadmill. In this study, we compared the predictive p...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255337/ https://www.ncbi.nlm.nih.gov/pubmed/37299803 http://dx.doi.org/10.3390/s23115073 |
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author | Vähä-Ypyä, Henri Bretterhofer, Jakob Husu, Pauliina Windhaber, Jana Vasankari, Tommi Titze, Sylvia Sievänen, Harri |
author_facet | Vähä-Ypyä, Henri Bretterhofer, Jakob Husu, Pauliina Windhaber, Jana Vasankari, Tommi Titze, Sylvia Sievänen, Harri |
author_sort | Vähä-Ypyä, Henri |
collection | PubMed |
description | Accelerometer data can be used to estimate incident oxygen consumption (VO(2)) during physical activity. Relationships between the accelerometer metrics and VO(2) are typically determined using specific walking or running protocols on a track or treadmill. In this study, we compared the predictive performance of three different metrics based on the mean amplitude deviation (MAD) of the raw three-dimensional acceleration signal during maximal tests performed on a track or treadmill. A total of 53 healthy adult volunteers participated in the study, 29 performed the track test and 24 the treadmill test. During the tests, the data were collected using hip-worn triaxial accelerometers and metabolic gas analyzers. Data from both tests were pooled for primary statistical analysis. For typical walking speeds at VO(2) less than 25 mL/kg/min, accelerometer metrics accounted for 71–86% of the variation in VO(2). For typical running speeds starting from VO(2) of 25 mL/kg/min up to over 60 mL/kg/min, 32–69% of the variation in VO(2) could be explained, while the test type had an independent effect on the results, except for the conventional MAD metrics. The MAD metric is the best predictor of VO(2) during walking, but the poorest during running. Depending on the intensity of locomotion, the choice of proper accelerometer metrics and test type may affect the validity of the prediction of incident VO(2). |
format | Online Article Text |
id | pubmed-10255337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102553372023-06-10 Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range Vähä-Ypyä, Henri Bretterhofer, Jakob Husu, Pauliina Windhaber, Jana Vasankari, Tommi Titze, Sylvia Sievänen, Harri Sensors (Basel) Article Accelerometer data can be used to estimate incident oxygen consumption (VO(2)) during physical activity. Relationships between the accelerometer metrics and VO(2) are typically determined using specific walking or running protocols on a track or treadmill. In this study, we compared the predictive performance of three different metrics based on the mean amplitude deviation (MAD) of the raw three-dimensional acceleration signal during maximal tests performed on a track or treadmill. A total of 53 healthy adult volunteers participated in the study, 29 performed the track test and 24 the treadmill test. During the tests, the data were collected using hip-worn triaxial accelerometers and metabolic gas analyzers. Data from both tests were pooled for primary statistical analysis. For typical walking speeds at VO(2) less than 25 mL/kg/min, accelerometer metrics accounted for 71–86% of the variation in VO(2). For typical running speeds starting from VO(2) of 25 mL/kg/min up to over 60 mL/kg/min, 32–69% of the variation in VO(2) could be explained, while the test type had an independent effect on the results, except for the conventional MAD metrics. The MAD metric is the best predictor of VO(2) during walking, but the poorest during running. Depending on the intensity of locomotion, the choice of proper accelerometer metrics and test type may affect the validity of the prediction of incident VO(2). MDPI 2023-05-25 /pmc/articles/PMC10255337/ /pubmed/37299803 http://dx.doi.org/10.3390/s23115073 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Vähä-Ypyä, Henri Bretterhofer, Jakob Husu, Pauliina Windhaber, Jana Vasankari, Tommi Titze, Sylvia Sievänen, Harri Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range |
title | Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range |
title_full | Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range |
title_fullStr | Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range |
title_full_unstemmed | Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range |
title_short | Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range |
title_sort | performance of different accelerometry-based metrics to estimate oxygen consumption during track and treadmill locomotion over a wide intensity range |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255337/ https://www.ncbi.nlm.nih.gov/pubmed/37299803 http://dx.doi.org/10.3390/s23115073 |
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