Metabolic Rate Prediction in Young and Old Men by Heart Rate, Ambient Temperature, Weight and Body Fat Percentage

Access this Article

Search this Article



<b>Objectives:</b> An estimation of metabolic rate (MR) is needed to determine wet-bulb globe temperature (WBGT) reference values in order to reduce heat strain in physical workers. The aim of this study was to develop MR prediction equation for younger and older men in hot working environments. <b>Methods:</b> We measured the MR and heart rate (HR) of both younger and older men at ambient temperatures (T<sub>a</sub>) of 25, 30 and 35°C while they cycled on a bicycle ergometer at a workload of 30, 45 and 60% of maximal oxygen uptake (V̇O<sub>2max</sub>). Seven younger male university students aged 22.9 ± 0.7 (mean ± SD) years and seven older male workers aged 61.7 ± 2.2 (mean ± SD) years participated in this study. MR, HR and rectal temperature (T<sub>re</sub>) were measured during the study. HR, ambient temperature (T<sub>a</sub>), body weight (BW) and body fat percentage (BF) served as predictors of MR using multivariate analysis. To increase the MR prediction accuracy, the following three alternative predictors of HR were used: HR<sub>res</sub>, calculated as 100 × [(HR − resting HR) / (maximal HR − resting HR)]; HR<sub>net</sub>, calculated as (HR − resting HR); and HR<sub>i</sub>, calculated as (HR / resting HR). <b>Results:</b> The R<sup>2</sup> value indicated that the models with HR<sub>res</sub> or HR<sub>net</sub> were more accurate than those with HR<sub>i</sub> or HR. T<sub>a</sub> had a significantly positive correlation with MR in older men. BW had a significantly positive correlation with MR in both younger and older men, and BF had a significantly negative correlation with MR in both younger and older men. <b>Conclusions:</b> HR<sub>res</sub> or HR<sub>net</sub> enabled more accurate MR prediction than HR. BW and BF would increase the accuracy of MR prediction.(J Occup Health 2014; 56: 519–525)


  • Journal of Occupational Health

    Journal of Occupational Health 56(6), 519-525, 2014

    Japan Society for Occupational Health


  • NII Article ID (NAID)
  • Text Lang
  • ISSN
  • NDL Article ID
  • NDL Call No.
  • Data Source
Page Top