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

Abstract

Objectives: 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. Methods: We measured the MR and heart rate (HR) of both younger and older men at ambient temperatures (T_a) 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_2max). 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_re) were measured during the study. HR, ambient temperature (T_a), 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_res, calculated as 100 × [(HR − resting HR) / (maximal HR − resting HR)]; HR_net, calculated as (HR − resting HR); and HR_i, calculated as (HR / resting HR). Results: The R² value indicated that the models with HR_res or HR_net were more accurate than those with HR_i or HR. T_a 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. Conclusions: HR_res or HR_net enabled more accurate MR prediction than HR. BW and BF would increase the accuracy of MR prediction.

(J Occup Health 2014; 56: 519–525)

Introduction

The Ministry of Health, Labour and Welfare of Japan reported that 47 workers died from employment-related heat stroke in 2010 due to hot temperature; a sharp increase from the average of 20 workers per year over the previous ten years¹⁾. Heat stroke made up 3.9% of all employment-related deaths in 2010, and it has become a critical issue in industrial health in Japan. Risk factors for heat stroke in the workplace include high workload, clothing and high heat stress environments involving high ambient temperatures, high humidity, high radiation levels and low air velocity²⁾. These factors increase heat storage in the body and consequently elevate the deep body temperature. Worker age is also considered to increase the occurrence of heat stroke.

ISO 7243³⁾ provides a simple standard for employees working under heat stress conditions using the wet-bulb globe temperature (WBGT) index. WBGT reference values are listed in ISO 7243³⁾ for acclimatized and non-acclimatized workers, corresponding to metabolic rates (MR) during work. In addition to ISO 7243³⁾, ACGIH-TLVs⁴⁾ on heat stress and heat strain also set a WBGT limit in work places according to the MR level, acclimatization and work-rest ratio. To apply these standards or guidelines to workplaces, information about both the WBGT of the working environment and the MR of the employee during work is needed. The method for estimating MR by job content outlined in ISO 7243³⁾ is ambiguous. Though WBGT can be measured by a natural wet bulb sensor, globe sensor and air temperature sensor, measuring metabolic rate is difficult.

ISO 8996⁵⁾ provides four levels for the determination of MR: screening, observation, analysis and expertise. Screening provides a range of metabolic rates for each type of work, as well as examples of types of work with resting, low, moderate, high and very high MRs. However, the MR estimated by this screening method cannot be assumed to be correct. Observation provides time-averaged MRs for individual tasks. However, it is difficult to apply observation results to accurately calculate MRs. One reason for this is that it is time-consuming to record each task performed by workers. Moreover, the metabolic rate of each task would be different among workers due to different intensities of work during specific tasks⁶⁾. At the analysis level, HR is used to estimate MR as a function of the worker's age, weight and sex, supposing a linear relationship between HR and MR. Some studies have demonstrated a linear relationship between HR and MR within a range of approximately 90–150 beats/min^{7, 8)}. In expertise, MR is determined by measuring oxygen consumption rate. However, direct measurement of oxygen consumption (V̇O₂) in industry is rather cumbersome.

HR monitoring would be one of the most economical and convenient ways of estimating the MR of large groups of people. Much research has been carried out on the prediction of MR using HR in industry⁹⁾, sport¹⁰⁾ and daily life⁸⁾. The HR method has been found to be more reliable than self-report activity diary methods¹¹⁾. Recent advances in handy type heart rate monitors have made it easier to record the interval between an R wave and the next R wave in an electrocardiogram (R-R interval) in the field. One shortcoming of the HR method is that the HR-MR relationship is influenced by weight, body composition, age and fitness level. Because of this, it is generally agreed that HR-MR calibration for each individual increases the accuracy of MR prediction using HR¹²⁾. However, measuring an HR-MR curve for every worker in the field is unfeasible. To decrease the errors in MR prediction by HR without calibration for each individual, some papers have used the generalized MR prediction equation for a large number of people, taking into account age, body weight (BW) and sex¹³⁾. Other papers have used percent of HR reserve (HR_res) or HR index (HR_i) instead of HR to consider the subjects' fitness level¹⁴⁾. However, there are few reports about MR prediction taking into account ambient temperature (T_a). In outdoor work in the summer, heat stress is so high that the effect of T_a on the HR-MR relationship should be considered.

To apply a standard, such as ISO 7243³⁾ or ACGIH-TLVs⁴⁾, with a view to decrease the effect of heat stress on workers, the MR of workers should be measured more accurately than simply using job content. In this study, to predict the MR of outdoor workers in the summer, we studied MR prediction equations for younger and older men using HR variables (HR, HR_res, HR_i, and HR net (HR_net)), age, BW, body fat percentage (BF) and T_a.

Methods

Subjects

Subjects were qualified to participate in this study if they were free from heart disease, pulmonary diseases and hypertension and were able to complete the assigned tasks. Seven younger male university students and 7 older male workers participated in this study. The older male workers were recruited from an employment service center for older people. They were performing a job introduced by the employment service center after retiring from their former jobs at the age of 60. Female subjects were not included, as physical capacity is dependent on sex. The physical characteristics of the younger and older subjects (mean ± SD) were 22.9 ± 0.7 and 61.7 ± 2.2 years old, 64.1 ± 6.0 and 67.7 ± 18.0 kg in weight, 173.9 ± 5.3 and 168.1 ± 8.6 cm in height and 17.1 ± 4.3 and 20.9 ± 6.4% body fat, respectively. None of these parameters were significantly different by the Student's t-test excluding age. The criteria for selection of the subjects followed the 8^th Edition of the Guidelines for Exercise Testing and Prescription¹⁵⁾. Subjects' health conditions were certified by a medical doctor based on a medical examination carried out within a year of the study. All subjects were informed of the procedure and schedule of this study, and signed a written informed consent form before participating. The subjects were paid after the experiments even if they had not completed the scheduled tasks. The study protocol was approved in advance by the local Institutional Ethics Committee in accordance with the recommendations of the Declaration of Helsinki.

Protocol

The subjects underwent the same experiment three times under different room conditions from mid-September to early October 2012 in Tokyo. The subjects reported to the laboratory on three days during the same week. On each day, the subjects cycled in a climate chamber with a T_a of 25.0 ± 0.5°C, 30.0 ± 0.5°C or 35.0 ± 0.5°C in the afternoon at the same time on each day. The tests were conducted in the afternoon and at least 2 hours after a light lunch. Four young subjects and one old subject had a smoking habit, but they were restricted from consuming caffeine or smoking for 12 hours before and during the tests. Water intake was not restricted before or during the tests. In the morning of the first day of the experiment, the BF of each subjects was estimated before the experiment by measuring the skinfold thickness at three points (subscapular, triceps and abdominal sites). Body density was estimated from the sum of the skinfold thickness of the three points and the age of the subject using the prediction equations of Tahara¹⁶⁾. Using the body density, the BF was estimated using the equations of Brožek¹⁷⁾. Each subject performed a cycle ergometry ramp protocol at a maximum HR of (180-age) beats per minute (bpm). The measured workload of the cycle ergometry was used to determine the load used in a submaximal test for each subject.

Figure 1 shows the experimental protocol and measurement methods. The subjects first entered the pre-test chamber, which was controlled at a T_a of 25 ± 0.5°C and a relative humidity (RH) of 40%. Before testing, a thermistor was inserted into the rectum to a depth of 10 cm. The rectal temperature (T_re), was collected on a portable data logger (LT-8A, Gram, Japan). HR was recorded by R-R interval with an RS800CX heart rate monitor (Polar Electro, Kempele, Finland), with the transmitter fitted on a chest band. After attaching this measurement equipment, the weights of the subjects were measured (Sartorius, Göttingen, Germany) with the summer work clothing worn during the experiment. The summer work clothing consisted of trunks, T-shirts, outer work clothes with long sleeves and pants, socks, a helmet and safety boots. The thermal insulation of all the items worn by the subjects was about 1.0 clo. After attaching sensors to the body, the subjects rested in the pre-test chamber for 30 minutes. During the experiment, rectal temperature, HR and body weight loss were measured. Then, the subjects entered the test chamber set at a different T_a on each experimental day, 25.0 ± 0.5°C, 30.0 ± 0.5°C or 35.0 ± 0.5°C, and a RH of 40 ± 3%. After resting for 40 minutes while sitting on a chair, the subjects performed three workload exercises on a bicycle ergometer with successively heavier loads to reach approximately 30, 45 and 60% of V̇O_2max based on the Karvonen Formula. This level of load conformed to the guidelines for exercise testing of the American College of Sports Medicine¹⁵⁾. The exercise lasted five minutes at a fixed load, and one more minute was used for cooling down by linearly decreasing the load. A pedal rate of 50 r/min was used during all of the cycle exercise tests. Between the exercises, the subjects took a rest of 10 minutes each. Ad libitum drinking of water was encouraged during the rest periods between exercises. Each subject was tested at the same time of day to minimize the effects of circadian variation in HR and core temperature.

Fig. 1.

Experiment protocol and measurement items.

Metabolic rate, heart rate and rectal temperature were measured during the time shown in this figure. The workload exercises on a bicycle ergometer in exercises 1, 2 and 3 were performed at approximately 30, 45 and 60% of V̇O_2max based on Karvonen Formula, respectively.

During the test, subjects wore a face mask connected to a Metamax® Metabolic Test System (Cortex Biophysik GmbH, Leipzig, Germany). Before the test, the gas analyzer was calibrated with a 3-l syringe, a reference gas with 5% CO₂ + 15% O₂ + balance gas of N₂ and fresh air. Metamax software was used to calculate breath-by-breath ventilation, oxygen consumption (V̇O₂), rates of carbon dioxide production, and respiratory exchange using the ambient air pressure.

Since subjects over 55-years-old were included, we set mild termination criteria for these trials, including

1. (1) T_re of 38.5°C,
2. (2) HR sustained over 180-age bpm for three minutes,
3. (3) participant wished to stop, and/or
4. (4) experimenter judged that the subject was not able to continue the trials.

Data analysis

Metabolic rate (MR) was calculated from the measured O₂ consumption using the partial method described in ISO 8996⁵⁾. In our protocol, HR and MR recovered to the initial resting level in the rest period between exercises. We averaged the HR and MR from the first 4 to 5 minutes of the exercise. The resting HR was calculated as the average of the last 5 minutes of HR in the pre-test room. The measured MR was compared with the MR predicted by ISO 8996⁵⁾. Generalized linear models were used to calculate the MR prediction equation on a desktop computer using SAS 9.3 (SAS Institute Inc., Cary, NC, USA) for Windows. HR was the main predictor used in the models. The alternative predictors of HR were %HR reserve (HR_res), HR net (HR_net) and HR index (HR_i), whose equations are shown below.

  

The other predictors used in the models were BW, T_a and BF. Age was not included in this model, because younger and older men were examined separately. R² values were calculated as an index to show the goodness of fit of each model.

Results

Heart rate and metabolic rate almost reached a steady state during the 5 minutes of exercise and recovered to the initial level during the rest period (Fig. 2). There were no subjects who exceeded the termination criteria of this study. The average T_re of the young men at start of study were 37.1–37.3°C, and those at the end (mean ± SD) were 37.5 ± 0.3 (max: 38.0°C), 37.3 ± 0.5 (max: 37.9°C) and 37.4 ± 0.3°C (max: 38.0°C) for T_a of 25, 30 and 35°C, respectively. The average T_re of the old men at the start of the study were 37.0–37.1°C, and those at the end (mean ± SD) were 37.2 ± 0.3 (max: 37.6°C), 37.1 ± 0.5 (max: 37.6°C) and 37.2 ± 0.4°C (max: 37.5°C) for T_a of 25, 30 and 35°C, respectively. The HR of each exercise period is shown in Table 1.

Fig. 2.

Example data of heart rate and metabolic rate of younger (A) and older (B) men at room temperature of 30°C.

The subjects cycled the ergometer according to the scheme of Fig. 1. Short term decrease of metabolic rate during rest indicates when the subject drunk water. Experiment time is consistent with Fig. 1.

Table 1. Mean heart rate of each exercise

Younger men
Ta	25°C	30°C	35°C
Exercise 1	110.9 ± 8.1 (127.6)	105.2 ± 6.6 (117.3)	106.8 ± 7.5 (113.3)
Exercise 2	128.7 ± 8.3 (141.0)	126.8 ± 8.5 (135.9)	126.3 ± 5.1 (133.1)
Exercise 3	151.6 ± 6.1 (158.8)	148.9 ± 10.5 (159.0)	149.5 ± 6.8 (157.0)
Older men
Ta	25°C	30°C	35°C
Exercise 1	90.7 ± 4.7 (97.8)	87.7 ± 7.9 (104.4)	88.3 ± 6.7 (99.1)
Exercise 2	103.9 ± 4.6 (109.3)	100.7 ± 7.5 (114.2)	101.4 ± 6.8 (111.1)
Exercise 3	114.7 ± 3.5 (118.7)	108.5 ± 8.4 (117.2)	109.7 ± 6.7 (119.5)

The values of this table indicate mean heart rate and standard deviation (SD). The values in parentheses are maximum heart rate of the subjects.

A linear relationship between MR and HR variables (HR, HR_res, HR_net and HR_i) was found. The coefficients of the MR prediction equation are listed in Table 2 for one of the HR variables (HR, HR_res, HR_net and HR_i) and the other selected predictors (T_a, BW, BF). The R² of the generalized linear model using HR_res or HR_net was larger than those for the other models of HR or HR_i in Table 2. For the models using HR_res and HR_net, BW and BF were significant explanatory variables of MR. T_a was a significant explanatory variable with HR_res and HR_net only for older men. The mean relative errors of the data compared with the prediction equation were 7–9% in the models using HR_res or HR_net.

Table 2. The coefficients of the metabolic rate prediction equation for explanatory variables with coefficients of determination for each heart rate variable

Younger men
Variables	Temperature	Body weight	% Body fat	HR variables	Intercept	R2	MRE (%)
HR	−0.29 (0.80)	1.53 (0.08)	−0.39 (0.68)	3.12** (<.001)	−241.8* (.002)	0.74	13.5
HRres	−0.32 (0.66)	2.43** (<.001)	−1.49* (0.011)	4.03** (<.001)	−44.9 (0.28)	0.90	7.2
HRnet	−0.71 (0.32)	2.94** (<.001)	−2.43** (<.001)	3.11** (<.001)	−34.5 (0.40)	0.90	7.7
HRi	−1.15 (0.26)	3.09** (<.001)	−2.95** (<.001)	184** (<.001)	−172.0* (0.01)	0.79	11.2
Older men
Variables	Temperature	Body weight	% Body fat	HR variables	Intercept	R2	MRE (%)
HR	1.32 (0.80)	0.57 (<.005)	−0.14 (0.79)	3.51** (<.001)	−235.9** (<.001)	0.74	10.0
HRres	1.76** (<.005)	0.54** (<.001)	−0.99* (0.02)	3.30** (<.001)	−10.2 (0.66)	0.84	7.7
HRnet	1.67* (0.02)	0.39* (0.02)	−1.41** (<.005)	3.31** (<.001)	−44.6 (0.09)	0.79	8.8
HRi	1.38 (0.10)	0.21 (0.31)	−1.51** (0.01)	180** (<.001)	−92.3* (0.02)	0.68	11.0

The p-value is shown next to the coefficient. MRE stands for mean relative error.

*  0.01≤p<0.05;

**  p<0.01.

Discussion

We studied the MRs of older men and younger men during three levels of ergometric activity at 25, 30 and 35°C. Multiple regression analysis with the explanatory variables of Ta, BW, BF and HR variables (HR, HR_res, HR_net and HR_i) showed that the R² value of the model constructed using HR_res or HR_net had a lower margin of error (Table 2). BW and BF were significant explanatory predictors of MR for both younger and older men. For older men, T_a was a significant explanatory predictor of MR.

Through the well-known linear relationship¹⁸⁾ between HR and MR, MR can be estimated when HR is known. Previous studies have shown that the HR-MR relationship is affected by factors such as age, gender, weight, body composition and fitness level¹²⁾. Therefore, it is generally accepted that calibration for each individual is needed to correctly predict MR by HR monitoring. However, calibration for each individual is unfeasible for every outdoor worker during the summer. In this study, to decrease the error in MR prediction caused by different fitness levels, we introduced HR_res, HR_i and HR_net as variables, which negate the error caused by the difference in resting HR. To correct the linear HR-MR relationship for different individuals, ISO 8996⁵⁾ includes age, weight and gender as explanatory variables, and other studies have included gender and weight in their multiple regression analyses. In this study, we included temperature in addition to BW, BF and HR variables (HR, HR_res, HR_i and HR_net). Table 2 shows that the model using HR_res has the smallest error and that HR_net is a better parameter than HR for both younger and older men. A previous study¹⁴⁾ also showed that HR_net and HR_i predicted MR more accurately than HR. Another study¹⁹⁾ provided evidence that MR prediction using HR_res and HR_net is more accurate than HR. It is known that the resting HR of physically fit subjects is lower than that of unfit subjects. Since the difference in resting HR accounts for a large part of the MR prediction error, using an HR variable of HR_res or HR_net, which includes the resting HR, for MR prediction could reduce the error from a physiological point of view.

One subjective problem is whether T_a is an explanatory variable of MR. In this study, T_a was a significant explanatory variable of MR for older workers. In acclimatized men, HR does not increase during rest or exercise in hot environments²⁰⁾, though it is generally known that HR increases in a high T_a without an increase in energy expenditure in younger men²¹⁾. The subjects in this study can be assumed to have acclimatized because the experiment was carried out at around the end of the summer season. Therefore, T_a could have an augmentary effect on MR without increasing HR in older men. A high T_a would increase the risk of heat stroke in older men by increasing the MR.

Metabolic rate is composed of three components: resting energy expenditure, physical activity energy expenditure and the thermic effect of food. Many papers have suggested that the fat-free mass is a significant factor for resting energy expenditure^{22, 23)}. From a physiological point of view, the fat-free mass includes the organs that consume high amounts of energy, such as muscle. Then lower BF leads higher energy expenditure. For BW, physical activity energy expenditure is correlated with BW because all physical activities are weight bearing. Kleiber²⁴⁾ suggested that metabolic rate was proportional to 0.75 power of the body mass. The statistical results of this study, that the coefficients of BW were significantly positive and that those of BF were significantly negative (Table 2), coincided with the current physiological knowledge of MR.

Inclusion of smokers in the subjects of this study would not have any effects on the results of this study. A previous report²⁵⁾ showed that there was no chronic metabolic effect of smoking. Although it was reported that metabolic rate increased on the order of 2–3% after smoking, the effect disappeared within half an hour. Concerning the effect of smoking on HR, HR increased to the maximum value within 5 to 10 minutes and returned to the base level after 2 hours²⁶⁾. In this study, subjects were restricted from smoking for 12 hours before the test. The effect of smoking on the results of this study is considered negligible.

In conclusion, this study showed that MR can be predicted more precisely when using HR_res or HR_net than HR, that BW and BF are significant explanatory variables of MR and that T_a was also a significant factor for older men. By utilizing these variables, the accuracy of MR prediction would be raised in field studies.

Acknowledgments: This study was supported by Grant-in-Aid for Scientific Research (KAKENHI) 23590767. The authors wish to thank Tatsuo Oka for help with data collection and Shin-ichi Sawada for supporting the metabolic rate measurement system.

Conflict of interest: The authors have no potential conflicts of interest to disclose.

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