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

Yuko Caballero, Takafumi J. Ando, Satoshi Nakae, Chiyoko Usui, Tomoko Aoyama, Motofumi Nakanishi, Sho Nagayoshi, Yoko Fujiwara and Shigeho Tanaka
Additional contact information
Yuko Caballero: Department of Food and Nutritional Science, Ochanomizu University, Tokyo 112-8610, Japan
Takafumi J. Ando: Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo 162-8636, Japan
Satoshi Nakae: Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan
Chiyoko Usui: Faculty of Sport Sciences, Waseda University, Saitama 359-1192, Japan
Tomoko Aoyama: Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo 162-8636, Japan
Motofumi Nakanishi: Omron Healthcare Co., Ltd., Kyoto 617-0002, Japan
Sho Nagayoshi: Omron Healthcare Co., Ltd., Kyoto 617-0002, Japan
Yoko Fujiwara: Department of Food and Nutritional Science, Ochanomizu University, Tokyo 112-8610, Japan
Shigeho Tanaka: Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo 162-8636, Japan

IJERPH, 2019, vol. 17, issue 1, 1-15

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: physical activity intensity; physical fitness; %heart rate reserve; resting heart rate; leave-one-out method (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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