Evaluation of heart rate as a method for
`assessing moderate intensity
`physical activity
`
`SCOTT J. STRATH, ANN M. SWARTZ, DAVID R. BASSETT, JR., WILLIAM L. O’BRIEN, GEORGE A. KING, and
`BARBARA E. AINSWORTH
`
`Department of Exercise Science and Sport Management, University of Tennessee, Knoxville TN 37996, and Department of
`Epidemiology and Biostatistics and Department of Exercise Science, School of Public Health, University of South
`Carolina, Columbia SC 29208
`
`ABSTRACT
`
`STRATH, S. J, A. M. SWARTZ, D. R. BASSETT, JR., W. L. O’BRIEN, G. A. KING, and B. E. AINSWORTH. Evaluation of heart
`rate as a method for assessing moderate intensity physical activity. Med. Sci. Sports Exerc., Vol. 32, No. 9, Suppl., pp. S465–S470,
`2000. To further develop our understanding of the relationship between habitual physical activity and health, research studies require
`a method of assessment that is objective, accurate, and noninvasive. Heart rate (HR) monitoring represents a promising tool for
`measurement because it is a physiological parameter that correlates well with energy expenditure (EE). However, one of the limitations
`of HR monitoring is that training state and individual HR characteristics can affect the HR–V˙ O2 relationship. Purpose: The primary
`purpose of this study was to examine the relationship between HR (beatszmin21) and V˙ O2 (mLzkg21z21min21) during field- and
`laboratory-based moderate-intensity activities. In addition, we examined the validity of estimating EE from HR after adjusting for age
`and fitness. This was done by expressing the data as a percent of heart rate reserve (%HRR) and percent of V˙ O2 reserve (%V˙ O2R).
`Methods: Sixty-one adults (18 –74 yr) performed physical tasks in both a laboratory and field setting. HR and V˙ O2 were measured
`continuously during the 15-min tasks. Mean values over min 5–15 were used to perform linear regression analysis on HR versus V˙ O2.
`HR data were then used to predict EE (METs), using age-predicted HRmax and estimated V˙ O2max. Results: The correlation between
`HR and V˙ O2 was r 5 0.68, with HR accounting for 47% of the variability inV˙ O2. After adjusting for age and fitness level, HR was
`an accurate predictor of EE (r 5 0.87, SEE 5 0.76 METs). Conclusion: This method of analyzing HR data could allow researchers
`to more accurately quantify physical activity in free-living individuals. Key Words: KARVONEN FORMULA, ENERGY EXPEN-
`DITURE, OXYGEN UPTAKE, EXERCISE
`
`Over the last four decades, there has been substantial
`
`the importance of habitual
`evidence to support
`physical activity (PA) in maintaining good health
`and avoiding chronic disease (17). To further develop our
`understanding of the association between habitual PA and
`health, and to define an optimal quantity of PA needed to
`produce improvements in health, accurate methods of PA
`assessment are needed. At present, researchers encounter
`difficulties in measuring habitual PA levels noninvasively
`and accurately (10,16). To further explore the relationship
`between PA and health, a method that would address these
`issues is required.
`Heart rate (HR) has been commonly employed as an
`objective method of assessing PA (6,20,23,26). The use of
`HR as a measure of PA is promising because it is a phys-
`iological parameter known to have a strong positive asso-
`ciation with energy expenditure (EE) during large muscle
`dynamic exercise (7). HR has been shown to be valid
`compared with ECG monitoring in both the laboratory
`
`0195-9131/00/3209-0465/0
`MEDICINE & SCIENCE IN SPORTS & EXERCISE®
`Copyright © 2000 by the International Life Sciences Institute
`
`(12,14,23) and field settings (23). Reproducibility within
`subjects has also been shown to be quite high (25). HR
`recording is a method that is relatively low cost, noninva-
`sive, and able to give information on the pattern of physical
`activity. In addition, technological advancements now en-
`able HR recorders to store information over a period of days
`or weeks, thus providing data on various components of PA,
`including frequency, intensity and duration.
`Various techniques have been presented in the literature
`for using HR data as an estimate of EE. Average pulse rate
`has been used as a predictor of daily EE (7,18). A second
`method uses net HR (activity HR –resting HR), which has
`been shown to be a simple and relatively accurate method
`for assessing EE in the field (26). A third approach was
`single and multiple individual HR–V˙ O2 calibration curves
`performed in the laboratory, which offers the most accurate
`way to predict EE (1,3,15,18). This approach accounts for
`differences in V˙ O2max and HRmax that exist between indi-
`viduals. However, the latter technique cannot be employed
`in large-scale epidemiological studies due to limitations in
`both time and expense.
`The primary purpose of this study was to examine the
`relationship between HR and V˙ O2 during field and
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`laboratory based moderate intensity activities. However,
`factors such as the individual’s age and fitness level can
`affect the HR–V˙ O2 relationship. Thus, a secondary purpose
`was to examine the validity of using HR data to predict EE,
`after adjustment for age and fitness. This was accomplished
`by expressing the data as a percent of heart rate reserve
`(%HRR) and percent of V˙ O2 reserve (%V˙ O2R). The latter
`variables, %HRR and %V˙ O2R, have been shown to be
`tightly coupled and numerically similar over the entire range
`of exercise intensities (21,22). This method allowed us to
`predict EE in METs (1 MET 5 average rate of EE at rest,
`or 3.5 mLzkg21z21min21), based on the activity HR and
`well-established physiological relationships.
`
`METHODS
`
`Eighty-one participants (19 –74 yr) volunteered to take
`part in this study. Twenty participants were excluded due to
`HR data not being collected. Therefore, 61 people (14%
`African American, 3% Asian, 1% Hispanic, and 82% Cau-
`casian), including 31 men (age 41 6 13 yr, BMI 26.2 6 5.7
`kgzm2, mean 6 SD) and 30 women (age 40 6 12 yr, BMI
`27.1 6 6.2 kgzm2, mean 6 SD), were included in this study.
`All participants were recruited from within the university
`and surrounding community through public postings and
`word of mouth. Each participant read and signed an in-
`formed consent approved by the University of Tennessee
`Institutional Review Board. Along with the informed con-
`sent, the participants completed a physical activity readiness
`questionnaire (PAR-Q).
`Before testing, each subject’s height and weight (one
`layer of clothes, no shoes) were measured via a stadiometer
`and a standard physician’s scale respectively. Body density
`and percentage of body fat were estimated from skinfolds
`using the three site equations of Pollock et al. (chest, abdo-
`men and thigh for men; tricep, suprailiac, and thigh for
`women) by means of Lange Calipers (Cambridge, MD)
`(19).
`Procedures. Each participant performed from one to
`seven of the following activities:
`Activities performed at the participants’ homes and at
`local golf and tennis clubs:
`Inside. Vacuuming, sweeping and mopping,
`laundry,
`ironing, washing dishes, cooking, light cleaning, and gro-
`cery shopping with a cart, feeding and grooming animals,
`and caring for small children.
`Outside. Mowing the lawn (manual and power mow-
`ers), raking, trimming, and gardening, playing with children
`in the yard, and playing with animals in the yard, doubles
`tennis, golf-carrying clubs, golf-pulling clubs, and softball.
`Activities performed in the University of Tennessee’s
`Applied Physiology Laboratory and surrounding grounds:
`Inside. Walking at 67 mzmin21 while carrying items of
`6.8 kg, walking at 93.8 mzmin21 while carrying items of 6.8
`kg; loading and unloading boxes of 6.8 kg; stretching and
`light calisthenics.
`Outside. Slow walk (average 78 mzmin21) and fast walk
`(average 100 mzmin21) performed on an outdoor track.
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`Activities were performed for 15 min at the participants’
`own self-selected pace. Before each activity, and between
`activities, the participant was asked to sit quietly for 5 min.
`Indirect calorimetry. Each participant wore
`the
`Cosmed K4b2 (Cosmed S.r.I, Rome, Italy), a portable indi-
`rect calorimetry system, while performing every activity and
`throughout the rest periods. The Cosmed K4b2 unit was
`mounted on the participant via a chest harness. A flexible
`face mask (Hans-Rudolph, Kansas City, MO), with dispos-
`able gel seal, covered the participant’s mouth and nose and
`was attached to a flowmeter. The face mask and adjoining
`flowmeter were secured to the participant via a head strap.
`The flowmeter is a bi-directional digital turbine and uses an
`opto-electric reader. The Cosmed K4b2 oxygen analyzer and
`the carbon dioxide analyzer were calibrated immediately
`before each test session according to manufacturer’s guide-
`lines. After the calibration process was completed, subject
`characteristics (age, gender, height and weight) were en-
`tered into the Cosmed K4b2.
`Heart rate monitoring. The Cosmed K4b2 also re-
`corded HR throughout each activity, via a Polar HR trans-
`mitter (Polar Electro, Tampere, Finland). As previously
`cited, the use of HR recording has been shown to be valid
`in both laboratory (12,14,23) and field settings (23). The
`Cosmed K4b2 uses a Polar HR “detection board” (PCBA
`receiver 380193) to receive HR data from the Polar HR
`transmitter. This is the same technology as that found in
`Polar heart watches, which have previously been shown to
`be valid (13). We decided to further assess its accuracy in a
`validation study among a subgroup of eight volunteers from
`this study. In this validation study, HR was measured during
`the final minute of successive 3-min stages, which included
`seated rest on a Monark 818E cycle ergometer (Varberg,
`Sweden) and pedaling at power outputs of 50, 100, 150, and
`200 W. The correlation between HR, from the Cosmed
`K4b2, and an ECG tracing (Burdick EK10, Milton WI),
`using the number of complete cardiac cycles in a 60’s
`interval (Lead II), was r 5 1.00, SEE 5 0.65 beatszmin21.
`Nonexercise V˙ O2max and HRmax prediction. A
`nonexercise prediction equation estimate of V˙ O2max and
`age-predicted HRmax was employed. V˙ O2max was predicted
`for each participant using the equation of Jackson et al. (9),
`which incorporated physical activity level, age in years,
`percent body fat, and gender. Physical activity status was
`evaluated using a 0 –7 scale, which was developed by
`NASA’s Johnson Space Center and used by Jackson et al.
`(9,21). Body density, and subsequently percent body fat,
`was estimated from skin-fold measures as described previ-
`ously. The Jackson et al. (9) equation follows:
`V˙ O2max (mlzkgz21min21) 5 50.513 1 1.589 (PA[0 –7])
`2 0.289(yrs) 2 0.552(%fat) 1 5.863(F 5 0, M 5 1).
`%V˙ O2R was then calculated using predicted V˙ O2max, and
`the measured resting and activity V˙ O2 values. The use of
`V˙ O2Rwas employed rather than %V˙ O2max, as it has recently
`been shown to more accurately reflect %HRR (21,22).
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`Figure 2—Minute-by-minute tracking of V˙ O2(mLzkg21zmin21) and
`HR (beatszmin21) for the activities of lawn mowing (manual push
`mower), trimming (electric), and gardening (pulling weeds, planting
`flowers).
`
`puted from minutes 5–15 for each activity. Statistical anal-
`yses were performed within SPSS 9.0 for Windows (Chi-
`cago, IL). The mean values for the subjects were then
`pooled and a linear regression analysis was performed to
`demonstrate the relationship between EE and HR. In addi-
`tion, correlational analysis was used to determine the valid-
`ity of estimating EE from activity HR after adjustment for
`individual age and fitness level. A Bland-Altman plot was
`constructed to show the relationship of the error score (mea-
`sured EE – estimated EE) across a wide range of exercise
`intensities.
`
`RESULTS
`The ability of HR to track V˙ O2 during activity is shown
`in the minute-by-minute graph of HR (beatszmin21) and
`V˙ O2 (mLzkg21z21min21) for an activity period that in-
`cluded:
`lawn mowing (manual push mower),
`trimming
`(electric), and gardening (pulling weeds, planting flowers)
`(Fig. 2).
`between HR
`relationship
`the
`shows
`3
`Figure
`(beatszmin21) and oxygen uptake (mLzkg21z21min21) with
`a correlation of r 5 0.68. Heart rate accounted for 47% of
`uptake, SEE 5 18.23
`the
`variability
`in
`oxygen
`mLzkg21z21min21.
`Figure 4 shows the relationship between measured EE
`and estimated EE (using HR data and adjusting for age and
`fitness) with a correlation of r 5 0.87. Estimated EE ac-
`counted for 78% of the variability in measured EE, SEE 5
`0.76 METs.
`Figure 5 highlights the relationship of the error score
`(measured EE – estimated EE) across a wide range of ex-
`ercise intensities, mean error 5 0.04 METs, 95% confidence
`interval (CI) 5 (21.48, 1.56) METs.
`
`Figure 1—Flow diagram demonstrating the use of activity HR to
`calculate EE (METs) via age-predicted %HRR and estimated %V˙ O2R.
`
`Calculations. The oxygen uptake and HR data from the
`Cosmed K4b2 were stored in memory and directly down-
`loaded to a Windows-based laptop PC after the test was
`completed. EE in METs was computed from the partici-
`pants’ activity HR (Fig. 1). Recorded HR values were trans-
`formed into %HRR values by utilizing the formula:
`%HRR 5 [(activity HR 2 resting HR)/
`2 resting HR)]*100%
`
`(est. HRmax
`where HRmax was assumed to equal 220 minus age (yr)
`(11). Taking into consideration that %HRR is approximately
`equal to the %V˙ O2R, as shown by Swain et al. (21,22), the
`relative intensity of the exercise bout was determined.
`%V˙ O2R for each activity was transformed to an absolute
`oxygen consumption (V˙ O2 mLzkg21z21min21) using the
`formula:
`2 resting V˙ O2)/
`5 [(activity V˙ O2
`%V˙ O2R
`(est. V˙ O2max
`2 resting V˙ O2)]*100%
`whereV˙ O2max was obtained from the nonexercise predic-
`tion equation of Jackson et al. (9). V˙ O2 (mLzkg21z21min21)
`was converted to METs by dividing by 3.5.
`Statistical analysis. Minute-by-minute values were
`obtained for HR and V˙ O2. For each subject, the mean HR
`(beatszmin21) and mean V˙ O2 (mLzkg21zmin21) were com-
`
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`Figure 3—Relationship
`(mLzkg21zmin21).
`
`between HR (beatszmin21)
`
`and V˙ O2
`
`DISCUSSION
`This study found that HR (beatszmin21) is moderately
`correlated to V˙ O2 (mLzkg21z21min21) during field and lab-
`oratory activities (r 5 0.68). Rodahl et al. (20) looked at the
`relationship between simultaneously recorded HR and V˙ O2
`in Nordic ocean fishermen. Oxygen uptake was measured
`by the Douglas bag method during specific activities. The
`measured V˙ O2 values were compared with predicted V˙ O2
`values estimated from the HR–V˙ O2 relationship determined
`in the laboratory. The results showed that the predicted V˙ O2
`values deviated from the measured values by no more
`than 6 15% (20).
`Individual variation in gender, age, and training status
`have been shown to affect the HR–V˙ O2 relationship (5). It
`has long been known that trained persons have a lower HR
`at a given V˙ O2 (4). Thus, if one correlates HR versus V˙ O2,
`the correlation can be low because it does not take into
`account that a more highly fit individual has a lower HR at
`
`Figure 4 —Relationship between measured METs and estimated
`METs.
`
`Figure 5—Bland-Altman plot showing the relationship of the error
`score (measured EE – estimated EE) across a wide range of exercise
`intensities.
`
`any given V˙ O2. This factor causes difficulty for the estima-
`tion of EE from raw HR.
`The relationship between markers of relative intensity
`(%HRR and %V˙ O2R) is much tighter than the relationship
`between HR and V˙ O2 (21,22). Therefore, we applied the
`well-established equations for age-predicted HRmax (11) and
`nonexercise estimates of V˙ O2max (9) to allow the relative
`intensity of the activity to be expressed. A limitation of the
`present study was that we did not directly measure maximal
`exercise values. However, this might be impractical and/or
`unfeasible in larger studies, particularly those studies where
`elderly participants are involved. Despite this limitation, our
`findings were in agreement with those of Swain et al.
`(21,22), who demonstrated a strong numerically similar
`relationship between %HRR and %V˙ O2R in the laboratory.
`Had we actually measured HRmax and V˙ O2max, it would
`have most likely improved the estimate of EE.
`An important advantage of using HR over motion sensors
`is that HR monitoring provides an index of both the relative
`(%V˙ O2R), as well as the absolute intensity (METs) of the
`physical activity performed. The importance of relative in-
`tensity can be seen when classifying different individuals on
`the basis of exercise intensity. The recommendation of the
`Centers for Disease Control and Prevention and the Amer-
`ican College of Sports Medicine states that every U.S. adult
`should accumulate 30 min or more of moderate intensity
`physical activity on most, preferably all, days of the week
`(17). Moderate intensity refers to an intensity level of 3– 6
`METs. However, the use of absolute cut points, such as 3
`and 6 METs, holds limited validity when considering pop-
`ulations of different ages and different fitness levels. Six
`METs could be perceived as “light” for a young athlete, but
`“hard” for an 80-yr old person. Figure 4 highlights this fact.
`The activities undertaken in this study were thought to
`represent moderate intensity physical activity; however,
`there were a number of older subjects who were above this
`level of intensity and approached 80 –100% of their esti-
`mated %HRR and %V˙ O2R.
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`To account for this problem, the Surgeon General’s report
`on Physical Activity and Health suggests the use of age-
`adjusted absolute MET cut points (24). However, an alter-
`native approach suggested in the report is the use of five
`relative intensity categories: very light (,25%), light (25–
`44%), moderate (45–59%), hard (60 – 84%), and very hard
`($85%). In fact, it may be preferable to limit the number of
`categories to lower the possibility of misclassification, and
`use relative intensity cut points of less than 30% (light), 30
`to 60% (moderate) and greater than 60% (hard).
`Figure 2 shows the time course of changes in V˙ O2 and
`HR for the activities of mowing, trimming, and gardening.
`From this figure it can be seen that HR takes 2- to 3-min to
`increase to a level representative of the activity being per-
`formed, as does V˙ O2, the gold standard for EE measure-
`ment. Likewise, at the termination of activity, both HR and
`V˙ O2 take a few minutes to decrease to resting levels. This
`is different from the instantaneous response known to occur
`with motion sensors. With regard to motion sensors, other
`papers in this series have reported on their accuracy in
`estimating EE (2,8,27). Such studies have found lower cor-
`relation coefficients (r 5 0.4 – 0.6) between EE and accel-
`erometers during “lifestyle activities,” than the one shown in
`this paper between EE and the HR method (r 5 0.87). In
`addition, the variation of error involved in the HR method is
`less than those seen with motion sensors during “lifestyle
`activities” (2). The 95% CI of the error score was (21.48,
`1.56) METs, as compared with those seen with motion
`sensors, ranging from approximately (22.3, 2.3) to (22.7,
`
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`
`The authors wish to acknowledge Cary Springer (UTK Statistical
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`identification only and does not imply endorsement.
`Address for correspondence: Scott J. Strath, Department of Ex-
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