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Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals

Background: Heart rate (HR) during physical activity is strongly affected by the level of physical fitness. Therefore, to assess the effects of fitness, we developed predictive equations to estimate the metabolic equivalent (MET) of daily activities, which includes low intensity activities, by % HR...

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Autores principales: Caballero, Yuko, Ando, Takafumi J., Nakae, Satoshi, Usui, Chiyoko, Aoyama, Tomoko, Nakanishi, Motofumi, Nagayoshi, Sho, Fujiwara, Yoko, Tanaka, Shigeho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6981897/
https://www.ncbi.nlm.nih.gov/pubmed/31892255
http://dx.doi.org/10.3390/ijerph17010216
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author Caballero, Yuko
Ando, Takafumi J.
Nakae, Satoshi
Usui, Chiyoko
Aoyama, Tomoko
Nakanishi, Motofumi
Nagayoshi, Sho
Fujiwara, Yoko
Tanaka, Shigeho
author_facet Caballero, Yuko
Ando, Takafumi J.
Nakae, Satoshi
Usui, Chiyoko
Aoyama, Tomoko
Nakanishi, Motofumi
Nagayoshi, Sho
Fujiwara, Yoko
Tanaka, Shigeho
author_sort Caballero, Yuko
collection PubMed
description Background: Heart rate (HR) during physical activity is strongly affected by the level of physical fitness. Therefore, to assess the effects of fitness, we developed predictive equations to estimate the metabolic equivalent (MET) of daily activities, which includes low intensity activities, by % HR reserve (%HRR), resting HR, and multiple physical characteristics. Methods: Forty volunteers between the ages of 21 and 55 performed 20 types of daily activities while recording HR and sampling expired gas to evaluate METs values. Multiple regression analysis was performed to develop prediction models of METs with seven potential predictors, such as %HRR, resting HR, and sex. The contributing parameters were selected based on the brute force method. Additionally, leave-one-out method was performed to validate the prediction models. Results: %HRR, resting HR, sex, and height were selected as the independent variables. %HRR showed the highest contribution in the model, while the other variables exhibited small variances. METs were estimated within a 17.3% difference for each activity, with large differences in document arrangement while sitting (+17%), ascending stairs (−8%), and descending stairs (+8%). Conclusions: The results showed that %HRR is a strong predictor for estimating the METs of daily activities. Resting HR and other variables were mild contributors. (201 words)
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spelling pubmed-69818972020-02-07 Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals Caballero, Yuko Ando, Takafumi J. Nakae, Satoshi Usui, Chiyoko Aoyama, Tomoko Nakanishi, Motofumi Nagayoshi, Sho Fujiwara, Yoko Tanaka, Shigeho Int J Environ Res Public Health Article Background: Heart rate (HR) during physical activity is strongly affected by the level of physical fitness. Therefore, to assess the effects of fitness, we developed predictive equations to estimate the metabolic equivalent (MET) of daily activities, which includes low intensity activities, by % HR reserve (%HRR), resting HR, and multiple physical characteristics. Methods: Forty volunteers between the ages of 21 and 55 performed 20 types of daily activities while recording HR and sampling expired gas to evaluate METs values. Multiple regression analysis was performed to develop prediction models of METs with seven potential predictors, such as %HRR, resting HR, and sex. The contributing parameters were selected based on the brute force method. Additionally, leave-one-out method was performed to validate the prediction models. Results: %HRR, resting HR, sex, and height were selected as the independent variables. %HRR showed the highest contribution in the model, while the other variables exhibited small variances. METs were estimated within a 17.3% difference for each activity, with large differences in document arrangement while sitting (+17%), ascending stairs (−8%), and descending stairs (+8%). Conclusions: The results showed that %HRR is a strong predictor for estimating the METs of daily activities. Resting HR and other variables were mild contributors. (201 words) MDPI 2019-12-27 2020-01 /pmc/articles/PMC6981897/ /pubmed/31892255 http://dx.doi.org/10.3390/ijerph17010216 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Caballero, Yuko
Ando, Takafumi J.
Nakae, Satoshi
Usui, Chiyoko
Aoyama, Tomoko
Nakanishi, Motofumi
Nagayoshi, Sho
Fujiwara, Yoko
Tanaka, Shigeho
Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals
title Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals
title_full Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals
title_fullStr Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals
title_full_unstemmed Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals
title_short Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals
title_sort simple prediction of metabolic equivalents of daily activities using heart rate monitor without calibration of individuals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6981897/
https://www.ncbi.nlm.nih.gov/pubmed/31892255
http://dx.doi.org/10.3390/ijerph17010216
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