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Longitudinal Examination of Age-Predicted Symptom-Limited Exercise Maximum HR

ZHU, NA1; SUAREZ-LOPEZ, J. RICARDO1; SIDNEY, STEPHEN2; STERNFELD, BARBARA2; SCHREINER, PAMELA J.1; CARNETHON, MERCEDES R.3; LEWIS, CORA E.4; CROW, RICHARD S.1; BOUCHARD, CLAUDE5; HASKELL, WILLIAM L.6; JACOBS, DAVID R. JR.1,7

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Medicine & Science in Sports & Exercise: August 2010 - Volume 42 - Issue 8 - p 1519-1527
doi: 10.1249/MSS.0b013e3181cf8242
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Abstract

Fitness testing is used in a variety of circumstances, often with a goal of estimating physiologic maximum HR (MHR). MHR is commonly used in the medical field for preventive or diagnostic purposes. For example, the recommendation for maintenance or improvement of cardiorespiratory fitness is based on functions of MHR, resulting in a target HR, calculated either as a percentage of MHR or as a percentage of MHR reserve (9,23). Additionally, MHR minus HR 2 min later defines HR recovery, an assessment of vagal tone that is associated with risk of death beyond the risk predicted by Framingham and European risk scores (1,14). Because of the potential risks of undergoing true maximal treadmill testing (e.g., in a patient with heart disease being tested for a cardiac rehabilitation prescription) and the discomfort of reaching true maximal exertion, cardiovascular stress tests commonly use symptom-limited maximal testing or end points associated with cutoff percentages of age-predicted MHR (e.g., achieving 85% of predicted MHR) (9).

The inverse association of age and MHR was initially described in the 1970s by the formula generated by Fox et al. (6) that MHR = 220 − age. Tanaka et al. (22) later refined the formula to 208 − 0.7 × age. Gellish et al. (8) recommended an association similar to that of Tanaka in a longitudinal study of 132 adults who completed a symptom-limited exercise test, between the ages of 27 and 78 yr, but reported that a quadratic association of age with MHR fit the data better.

In this article, we studied the association of MHR with age and other correlates, on the basis of one to three symptom-limited maximal exercise tests completed over 20 yr in a large population-based sample. Our primary objective was to provide a formula that linked MHR to age on the basis of the repeated testing in this large cohort and to compare our formula with existing formulae (8,22). Our hypothesis was that our findings would be similar to those of Tanaka et al. (22), which were based on a large number of true maximal tests studied in cross-sectional designs. We examined whether the change in MHR with age within a person is concordant with cross-sectional associations of MHR regressed on age. Therefore, a larger goal was to characterize how the information about MHR in symptom-limited maximal testing compares with that in true maximal testing (22), considering that not every participant in a symptom-limited test performs at full effort.

METHODS

Study design.

Coronary Artery Risk Development in Young Adults (CARDIA) is a multicenter longitudinal cohort study established to understand the precursors and development of CHD risk among adults initially between 18 and 30 yr. CARDIA examined 5115 participants with balanced subgroups of race (black and white), gender, education, and age (range = 18-24 yr and 25-30 yr) at baseline (year 0) in 1985-1986. Recruitment was at random from the general population in Birmingham, AL, Chicago, IL, and Minneapolis, MN, and from members of the Kaiser Permanente Medical Care Plan in Oakland, CA (7,11). Exclusions from the baseline CARDIA examination were done for the following reasons: blindness, deafness, muteness, inability to communicate, and permanent inability to walk. Six follow-up examinations were conducted at years 2, 5, 7, 10, 15, and 20, with 91%, 86%, 81%, 79%, 74%, and 72% of the surviving cohort returning, respectively. This article focuses on the symptom-limited maximal graded treadmill exercise tests performed at years 0, 7, and 20. The year 20 exercise tests were obtained as part of an ancillary study, the CARDIA Fitness Study. The institutional review boards for the protection of human subjects for the participating study sites provided approval for the study, and written informed consent was obtained from all participants.

Data collection.

Age, race, and sex were self-reported. At each examination, height and weight were measured to the nearest 0.5 cm and 0.09 kg, respectively. After 5 min of rest, seated blood pressure was measured three times with the average of the last two measurements used in analysis. Smoking status was self-reported; current smoking or nonsmoking was used as a covariate in these analyses. Concurrent use of systemic β-blocker medications was obtained from self-reports, with examination of medication bottles when available, at each period. Diagnosis of asthma was made if the subject was receiving asthma medication (medicine containers examined) or if there was a self-reported doctor's or nurse's diagnosis of asthma (3).

Physical activity was measured at each examination by an interviewer-administered questionnaire regarding the frequency of 13 different activities during the past 12 months (12,19). Because participants were not asked specifically about the duration of physical activity, the exact energy expenditure could not be estimated; however, the activity level was estimated by exercise units (EU). Duration of TV viewing was assessed by the answers to the related questions on a self-administered questionnaire at years 5, 10, and 20 (20). Lung function was measured at years 0, 10, and 20 using a Collins Survey 8-L water-sealed spirometer and an Eagle II Microprocessor (Warren E. Collins, Inc., Braintree, MA) and following the standard procedures of the American Thoracic Society (2). Daily checks for leaks and volume calibration with a 3-L syringe and weekly calibration in the 4- to 7-L range were undertaken to minimize methodological artifacts between examinations.

Graded exercise testing.

The graded symptom-limited maximal exercise test used at years 0, 7, and 20 followed a modified Balke protocol, which consisted of up to nine 2-min stages of gradually increasing difficulty (18). Stage 1 was 2% grade at 3 miles·h−1; stages 2-6 were 6%, 10%, 14%, 18%, and 22% grade at 3.4 miles·h−1; stages 7 and 8 were 22% and 25% grade at 4.2 miles·h−1; and stage 9 was 25% grade at 5.6 miles·h−1. HR (bpm) was measured by ECG at the end of each stage and on cessation of the test. At each stage, participants were asked their RPE using a 15-point Borg scale (range = 6-20) (4). Participants were determined to be ineligible for the test primarily for the following reasons: history of heart disease, elevated resting blood pressure, abnormal ECG results, and acute illness with a fever (and not possible to reschedule for a later date) (see table, Supplemental Digital Content 1, which shows the exclusion criteria for taking treadmill tests, http://links.lww.com/MSS/A21). Overall, test termination was due to fatigue or shortness of breath in approximately 98% of participants at all years.

The exercise test was performed by 4968, 3560, and 2870 participants (97%, 70%, and 56% of the CARDIA cohort) at years 0, 7, and 20, respectively. A systematic protocol violation of the year 7 tests was discovered in the Minnesota clinic after the examination period ended (21), which prompted the exclusion of 977 year 7 tests, leaving 2583 tests at year 7. Participants who took the tests in the Minnesota clinic tended to have longer maximal treadmill test duration (711.8 vs 545.7 s) with lower MHR (174.4 vs 177.9 bpm) than those who took tests at other clinics at year 7 examination. Although the difference in MHR between these 977 Minnesota participants and the remaining participants from other clinics was similar at baseline (177.3 vs 180.7 bpm) as at year 7, other characteristics differed. At baseline, the 977 participants who were subject to the Minnesota protocol violation were more likely to be current smokers (36% vs 25%) and had lower maximal treadmill test duration (583.4 vs 600.8 s) than the 2583 people from other clinics who were included at year 7. In addition, we excluded 266 tests of participants with concurrent β-blocker medication use and 572 tests that were terminated for medical reasons including abnormal ECG manifestations (n = 111 tests), abnormal blood pressure changes (n = 45 tests), and several other medical reasons such as back pain, nausea, or dizziness. We also excluded all 109 tests of participants who were younger than 18 yr or older than 30 yr at the time of baseline testing. Some participants met more than one exclusion criteria.

Statistical analysis.

We first examined characteristics of participants according to treadmill test completion status. Because the focus of Table 1 was on the willingness and ability to be tested during a longitudinal epidemiologic study, we included the year 7 tests in Minnesota in the categorization of treadmill test attendance. This analysis included 4969 participants, excluding 72 who never took the treadmill test and 74 participants who took the tests only during follow-up. The remaining analyses in this article included 9622 tests in 4844 participants. To characterize relationship of age with MHR, we plotted mean, SD, and fifth percentile of MHR at each year of age (Fig. 1). To account for correlations between repeated tests within person, we analyzed the association of MHR with age using a repeated-measures regression analysis, with Toeplitz covariance structure and random intercept (PROC MIXED; SAS, Cary, NC). The predictor variable was current age; the quadratic form was used when testing for curvature. In accord with the hypothesis that the association of MHR with age is nonlinear, we have strong power to predict a difference in the change in MHR by age. The minimal detectable difference (5% type I error (two-tailed) with 85% power) was 0.14 bpm·yr−1 in the difference of the MHR slope per year starting at an average age of 25 yr and followed for 7 yr minus the difference of the MHR slope per year starting at an average age of 32 yr and followed for 13 yr.

TABLE 1
TABLE 1:
Baseline characteristics (mean ± SD or %) of the participants according to treadmill test attendance status.
FIGURE 1-Di
FIGURE 1-Di:
stribution of MHR (1 SDerror bars and age-specific fifth percentiles) of eligible tests across a 20-yr follow-up by age at examination (9622 tests in 4844 participants).

In true maximal testing, achievement of MHR can be objectively verified (e.g., by verifying lack of change of HR despite increasing workload). However, most people do not voluntarily perform at maximal level of exertion, indicated by a plateauing of HR or oxygen consumption. Symptom-limited maximal tests vary in how close they come to maximal effort, but the percent of maximum achieved cannot be measured. Therefore, restricting to tests that most likely represented full effort could help interpret the association of age and observed MHR in CARDIA. Our investigation of this topic concluded that such restriction was not helpful (see text, Supplemental Digital Content 2, which shows the findings of this methodological investigation, http://links.lww.com/MSS/A22).

The MHR-age relationship was further analyzed according to the quartile of baseline body mass index (BMI), smoking status, forced vital capacity, physical activity, television watching, and baseline treadmill duration. In addition, general linear models were used to examine the relationship of MHR at each visit and the changes of MHR between consecutive visits, as dependent variables, with several predictors at baseline or changes between visits, as shown in the tables.

RESULTS

Participant characteristics and selection bias.

During the 20-yr follow-up, 928 underwent exercise treadmill testing only at baseline, 1480 at baseline and one follow-up visit (year 7 or 20), and 2182 at baseline and both follow-up visits (years 7 and 20; Table 1). The baseline treadmill exercise test duration was (mean ± SD) 589 ± 171 s, and MHR achieved was 179 ± 15 bpm. Several baseline characteristics suggest selection bias in those who were willing or able to undergo multiple treadmill tests during the study. Participants who had higher MHR or longer treadmill duration at baseline were more likely to undergo follow-up testing. Other baseline characteristics associated with completing more than one treadmill test were older age, white race, lower BMI, lower systolic blood pressure, higher forced vital capacity (FVC) and forced expiratory volume in 1 s, higher self-reported activity, less TV watching, and not smoking.

Distribution of MHR.

Mean MHR declined with age, with age-specific SD ranging from 14 to 16 bpm (Fig. 1). As illustrated by age-specific fifth percentiles, observed MHR for 5% of participants was <155 bpm at age 18 yr, declining to below 140 bpm at age 50 yr. The age-specific first percentile of MHR ranged from 113 to 142 bpm without any age pattern (data not shown). The age-specific mean levels in Figure 1 display a quadratic pattern for estimated MHR (eMHR). Observed MHR tracked over examinations, with an intraclass correlation between 0.4 and 0.55 in all analyses.

MHR and age.

Among the 4844 participants who completed one to three eligible tests, the relationship between age and MHR was significantly quadratic (eMHR = 179 + 0.29 × age − 0.011 × age2). While restricting to those who completed all three tests during follow-up, the association maintained its quadratic shape (eMHR = 184 + 0.16 × age − 0.010 × age2). The overall MHR observed in CARDIA are substantially lower than the Tanaka-, Gellish-, and Fox-predicted MHR (Fig. 2). However, the association was linear over restricted age ranges. For the 2216 participants who completed both years 0 and 7 treadmill tests (age range = 18-37 yr), the age-MHR relationship was linear (eMHR = 190 − 0.37 × age). Also, for the 1574 participants who participated in both of the treadmill tests at years 7 and 20 (age range = 25-30 yr), this association was linear (eMHR = 199 − 0.63 × age). The mean MHR in people who completed tests at both years 0 and 7 was 181.0 ± 14.8 and 178.3 ± 15.1 bpm, and for people who completed tests at both years 7 and 20, mean MHR was 179.4 ± 14.6 and 171.0 ± 14.7 bpm, at the respective examinations. In addition, in restriction sets, obtained by excluding less than full effort tests based on RPE, stage achieved, certain percentage of the eMHR of Tanaka et al., or any other rule, the relationship between age and MHR was quadratic (see table, Supplemental Digital Content 3, which demonstrates prediction of MHR from age in all available data and in some restriction sets obtained by different trimming methods, http://links.lww.com/MSS/A23; see figure, Supplemental Digital Content 4, which shows distribution of MHR by age after excluding those with values below the 85% eMHR of Tanaka et al., http://links.lww.com/MSS/A24).

FIGURE 2-Di
FIGURE 2-Di:
stribution of MHR of treadmill tests predicted by existing formulae using CARDIA data. †Constant cohort represents 1479 participants who took treadmill tests at years 0, 7, and 20.

MHR and age across BMI quartiles.

The age-MHR relationship was analyzed further across baseline BMI quartiles (Table 2). The rate of decline of MHR was significantly different across baseline BMI quartiles (P for interaction < 0.0001). In quartile 1, the pattern of the relationship between age and MHR was quadratic but with shallower rate of decline (0.24 bpm·yr−1 in years 0-7 and 0.51 bpm·yr−1 in years 7-20) than the results for all participants (0.35 bpm·yr−1 in years 0-7 and 0.63 bpm·yr−1 in years 7-20). The rate of decline of MHR with age became steeper as BMI increased, particularly in the younger ages, so that in the highest baseline BMI quartile, there was a linear rate of decline of 0.6-0.7 bpm·yr−1 during the full age range of 18-50 yr. Similar analysis conducted among those who completed all three tests yielded a similar pattern of decline of MHR with age across BMI quartiles (data not shown).

TABLE 2
TABLE 2:
MHR-age formula and number of participants according to quartile of baseline BMI.

Figure 3 shows the BMI quartile-specific eMHR. The overall quadratic solution predicts lower MHR than the estimates of lower BMI quartiles at a younger age and overlaps with the estimates of lower BMI quartile at an older age. People in the highest BMI quartile had the lowest predicted MHR. Those who completed all three tests during 20 yr of follow-up had relatively higher eMHR than the overall cohort did.

FIGURE 3-Di
FIGURE 3-Di:
stribution of MHR of treadmill tests by age across quartiles of baseline BMI in CARDIA.

Differences in rate of decline of MHR according to other baseline characteristics were much less than those shown in Table 2. The rate of decline of MHR with age was somewhat steeper in those with initially lower FVC than in those with initially higher FVC (data not shown, P for interaction < 0.0001). Those with shorter versus longer year 0 treadmill duration tended to have less decrease in MHR with age (data not shown, P for interaction < 0.0001), suggesting regression toward the mean or a learning effect, i.e., those who reached a lower proportion of true MHR at year 0 tended to reach a somewhat higher proportion of true MHR on repeat testing). There was no age interaction with baseline physical activity, TV watching, or smoking status.

Other correlates of MHR.

Besides age, several variables were related to MHR either cross-sectionally or longitudinally (Table 3; see table, Supplemental Digital Content 5, which showed the equation for tests at year 0, years 0 and 7, and then years 0, 7, and 20, http://links.lww.com/MSS/A25). Women had lower MHR than men, although mean MHR increased in women relative to men between years 7 and 20. Blacks had lower MHR than whites, but this difference was attenuated between years 7 and 20. BMI was consistently inversely associated with MHR in all analyses. Taller people tended to have lower MHR cross-sectionally, but the change in MHR was not predicted by height. Those with higher preexercise HR achieved higher mean MHR than those with lower preexercise HRs. However, higher preexercise HR at baseline predicted a decrease in MHR over time. Similar to preexercise HR, higher systolic blood pressure, cross-sectionally, and increases in systolic blood pressure over time were associated with higher MHR and change in MHR, respectively, yet those with higher baseline blood pressure had a faster decline in MHR over time. Participants whose FVC was higher had higher MHR. Increasing FVC between years 0 and 7 was related to increasing MHR over time. Neither physical activity nor TV watching showed strong relationships with MHR or its changes. Current smokers (consistent across visits) and those who recently started smoking had lower MHR. The slope of MHR on age was substantially attenuated after adjustment for these factors. Nevertheless, relatively little of the variance of MHR was predictable in any of the models in Table 3, the maximum r2 being 0.22 for the baseline cross-sectional model.

TABLE 3
TABLE 3:
Linear regression analysis of MHR (bpm) and its changes during 20 yr (dependent variables) in relation to baseline (year 0 or 7) variables and their changes.

DISCUSSION

The primary finding in this study was that, in repeat exercise treadmill testing between ages 18 and 50 yr, the decline of MHR with age was less at younger ages than at older ones. The fitted rates of loss of MHR according to a quadratic age function, 179 + 0.29 × age − 0.011 × age2, are similar to the observed rates of loss between years 0 and 7 (0.37 bpm·yr−1) and between years 7 and 20 (0.63 bpm·yr−1), respectively. Another important observation is that the rate of decline of MHR with age was slower in people who were thinner at baseline than in those who had higher BMI.

Our findings are consistent with those of two other longitudinal studies. Plowman et al. (15) reported on 36 healthy women retested after an interval of 6.1 yr. MHR in symptom-limited testing remained relatively steady during ages in the 20s and 30s, then declined at a faster rate in the 50- and 60-yr-old age groups. Gellish et al. (8) studied 908 symptom-limited maximal exercise treadmill tests in 132 men and women attending a university fitness center for several years. Although they concluded that eMHR = 207 − 0.7 × age fit their data well, eMHR = 191.5 − 0.007 × age2 fit the data better. There is a negligible difference between the curvature of our formula and that of Gellish among ages 27-50 yr that occurs in both studies. In their study, the rate of decline in MHR was close to zero through about age 40 yr and was about 0.5 bpm·yr−1 between ages 40 and 50 yr. We showed a reduced rate of decline in CARDIA participants who were thinner or fitter at baseline, at ages 18-30 yr. Consistent with this finding, it is likely that the participants in the study of Gellish et al. were thinner and more fit than were members of the general population studied in CARDIA, which may account for the slower rate of loss of MHR in the study of the Gellish et al. than in CARDIA.

On the other hand, our findings conflict with those of Tanaka et al., who found that eMHR = 208 − 0.7 × age in a summary of 492 groups that included true maximal exercise tests in 18,712 persons, confirmed in their own specific study of true maximal tests in 514 individuals. There was no sign of curvature in the MHR-and-age relationship in this monumental study. The finding of Tanaka et al. (22) differs from ours in important ways. The results of Tanaka et al. were based on cross-sectional study designs, so these could not capture individual changes over time, whereas the longitudinal study designs used by Plowman et al. (15), Gellish et al. (8), and CARDIA were able to assess individual changes. In individual changes, the slope of MHR over age was consistently lower in persons initially younger than at older ages. Thus, in this respect, the CARDIA study, which included tests in 97% of the general population sample recruited into the study, suggests curvature in the MHR-and-age relationship that is missed in cross-sectional study designs.

The true maximal exercise test is a superior measure of cardiorespiratory capacity compared with the symptom-limited test that CARDIA, Plowman et al. (15), and Gellish et al. (8) used, but it may restrict the sample to those who are willing and able to do the test and, in this way, skew the estimate of the relation of MHR to age in the general population. A characteristic of the symptom-limited maximal exercise protocol is that it does not, in most cases, achieve true maximum, as reflected in Figure 2 by the higher age-specific eMHR using the formula of Tanaka et al. or Fox et al. than using any of the CARDIA findings. Furthermore, in a symptom-limited test, the extent of full effort is a differential proportion of true maximum that varies by person. A common strategy is to eliminate from statistical analysis all tests deemed to be "less than full effort" (e.g., using 85% of the prediction of Tanaka et al.). As shown in the Supplement, all restriction methods considered, whether on the basis of RPE, stage achieved, excluding tests with MHR less than 85% of the eMHR of Tanaka et al., or any other rule for excluding less than full effort tests, yielded a quadratic relationship between MHR and age. However, viewed from a population perspective, we think that the volunteers in the article of Tanaka et al. (22) almost certainly were highly selected, excluding those for whom a true maximal test would be uncomfortable. Because CARDIA was able to test 97% of its participants at baseline, we were able to assess selection bias in future testing. Among important baseline characteristics of those least likely to complete one or two more tests during 20 yr were reduced fitness and lung capacity, adiposity, smoking, and sedentary lifestyle. Consequently, participants who completed three tests, who tended to be the healthiest at baseline, had relatively higher eMHR than the overall CARDIA cohort did. Therefore, the formula derived from these participants was less generalizable than the formula derived from the whole CARDIA cohort. Moreover, the CARDIA formula may also be more realistic for the general population than the formula of Gellish et al. (8). CARDIA showed a slightly steeper rate of decline in MHR with age at each age than did Gellish et al., but the sample of Gellish et al. was considerably selected compared with the population-based CARDIA sample as is visually apparent in Table 2.

The decrease of MHR with advancing age is a reasonable expectation for physiologic change. Rodeheffer et al. (16) performed serial gated blood pool scans at rest and during exercise treadmill tests for 61 participants aged 25-79 yr. They found an age-related increase in end-diastolic volume and stroke volume and an age-related decrease in HR during treadmill tests, such that cardiac output both at rest and during exercise was maintained across ages. The changes in the cardiovascular system associated with aging (5) and adiposity (13,17) tend to decrease HR and HR response to exercise. These changes include apoptosis of sinoatrial node pacemaker cells, decreased responsiveness to β-adrenergic receptor stimulation, and decreased reactivity to baroreceptors and chemoreceptors (5,16,17).

Furthermore, the quadratic component of the age-related MHR decrease, implying more rapid MHR decrease in older than in younger ages, might be partly explained by physiologic change. Cheitlin (5) pointed out that apoptosis of sinoatrial node pacemaker cells leads to a loss of 50%-75% of cells by age 50 yr, resulting in slower intrinsic HR. Because the exact rate of the loss of cells is not available and has not been proven to be linear, it is possible that this rate of loss is quadratic, leading to greater loss of intrinsic HR at older than at younger ages and thus contributing to greater loss of MHR at older than at younger ages. In addition, Cheitlin (5) pointed to decreased responsiveness to β-adrenergic receptor stimuli with aging, partly compensated by an increase in circulating catecholamines. However, whether this compensation is at a constant rate with aging or not was not assessed. Therefore, further cardiovascular physiological studies would be helpful, related to the change, especially the change rate of atrial pacemaker cells, responsiveness to β-adrenergic receptor stimuli, and compensation by circulating catecholamines with aging.

We previously reported a finding that is consistent with a slower loss of cardiorespiratory capacity in thinner than in heavier people, namely, that the people in the lowest quartile of baseline BMI maintained their lung function through their mid 30s (24). Thus, the structure and function of cardiovascular and pulmonary tissue were maintained in the smaller, leaner participants. The steeper rate of decline in the participants with higher BMI was not due to the reduced physical activity because physical activity itself was not a strong correlate of MHR. Another possible explanation of the more rapid rate of decline in people with higher BMI is that they did not work as hard on the treadmill as thinner people did, especially as they aged, as would be evidenced by stopping the test at lower RPE. However, adjustment for RPE did not much alter the pattern of the decline of MHR with age across baseline BMI quartiles. Furthermore, the rate of decline of the MHR at older ages was likely underestimated because the people with higher BMI had a tendency not to return for retesting (Table 1).

Apart from age and BMI, several variables were related to MHR. The underlying mechanisms are not well studied. For example, smoking was related to a reduced MHR, but smokers did not have a greater rate of decline of MHR than nonsmokers did. It is known that nicotine increases HR, myocardial contractility, and blood pressure by stimulating sympathetic neurotransmission. However, the norepinephrine-releasing effect of nicotine cannot explain the smoking-MHR association in this study (10). We hypothesize that, during exercise, nicotine acts as a β-blocker, which lowers MHR acutely (21) but is not associated with MHR decreases over time.

The large, population-based sample of adults and the 20-yr follow-up of participants with three tests in many cases are the strengths of this study. Our results may be more generalizable than previous studies owing to the population-based sampling of participants, the substantial proportion of women and black participants, and the inclusion of smokers and obese persons. Although the repeat testing is a strength, a limitation for determining repeatability is that testing was completed only three times by each participant spread out for 20 yr. Because of the symptom-limited maximal test protocol that was used, it is difficult in these data to distinguish differences among participants in true MHR from differences in proportion of true maximum achieved. However, use of the symptom-limited test allowed us to perform testing in nearly everyone in the study.

In conclusion, for those using exercise testing for providing a target HR during exercise, one implication from this study is that people with lower BMI lose MHR very slowly through their 20s and 30s, then start to lose more quickly in the 40s, yet at a slower rate than was observed in the equation of Tanaka et al. People with higher BMI lose MHR throughout ages 18-50 yr at about the rate of 0.7 bpm·yr−1 as specified by Tanaka et al. (22), although no precise alteration to the formula 208 − 0.7 × age is possible from these CARDIA data, partly because such an alteration would only apply to ages 18-50 yr. Clinicians making exercise prescriptions should be aware that the loss of MHR is quadratic in most people and is very slow in the smallest and younger individuals. For those with higher BMI, the clinician should be aware that the rate of loss of MHR is steep, even at younger ages. Future study involving participants at older ages (>50 yr) is needed. Further characterization of the distribution of the percent of true MHR achieved in submaximal testing would be helpful.

This study is supported by the CARDIA contract (N01-HC-48047-N01-HC-48050 and N01-HC-95095) and a grant for the CARDIA fitness study R01 HL 078972.

This work also received funding from the National Institutes of Health.

Na Zhu and J. Ricardo Suarez-Lopez contributed equally as first authors.

The authors do not have a professional relationship with companies or manufacturers that may benefit from the results of this study.

The results of the present study do not constitute endorsement by the American College of Sports Medicine.

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Keywords:

PREDICTION EQUATIONS; GRADED EXERCISE TEST; MIXED MODELS; EPIDEMIOLOGIC STUDY

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