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. 2019 Dec 27;17(1):216.
doi: 10.3390/ijerph17010216.

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

Yuko Caballero  1 , Takafumi J Ando  2 , Satoshi Nakae  3 , Chiyoko Usui  4 , Tomoko Aoyama  2 , Motofumi Nakanishi  5 , Sho Nagayoshi  5 , Yoko Fujiwara  1 , Shigeho Tanaka  2
Affiliations

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

Yuko Caballero et al. Int J Environ Res Public Health. .

Abstract

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).

Keywords: %heart rate reserve; leave-one-out method; physical activity intensity; physical fitness; resting heart rate.

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Conflict of interest statement

Nakanishi M and Nagayoshi S are employees of OMRON Healthcare Co., Ltd. (funder) and these two authors had a role in the design of the study, in the collection, analyses, and interpretation of data, in the writing of the manuscript, and in the decision to publish the results. Tanaka S received a research grant from OMRON Healthcare Co., Ltd. The other authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Selection of independent variables (n = 40). Dependent variable was METs. All possible combinations of variables were computed by using the brute force method. After that, the model with the minimum value of Akaike’s Information Criterion (AIC), which gives the best fit of all models, deleting redundant variables was selected. * r is the correlation coefficient between METs and each variable.
Figure 2
Figure 2
Mean percent error of each equation validated by leave-one-out (n = 40).
Figure 3
Figure 3
Differences between measured and estimated METs by Bland–Altman analysis (N = 40, 673 dots).
Figure 4
Figure 4
Comparison of prediction errors expressed as RMSE with those of previous studies.
Figure 5
Figure 5
Measured and estimated METs of previous studies and present study.
See this image and copyright information in PMC

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