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THE SURPRISING HISTORY OF THE "HRmax=220 -age" EQUATION. Robert A. Robergs, Roberto Landwehr. JEPonline. 2002;5(2):1-10. The estimation of maximal heart rate (HRmax) has been a feature of exercise physiology and related applied sciences since the late 1930's. The estimation of HRmax has been largely based on the formula; HRmax=220-age. This equation is often presented in textbooks without explanation or citation to original research. In addition, the formula and related concepts are included in most certification exams within sports medicine, exercise physiology, and fitness. Despite the acceptance of this formula, research spanning more than two decades reveals the large error inherent in the estimation of HRmax (Sxy=7-11 b/min). Ironically, inquiry into the history of this formula reveals that it was not developed from original research, but resulted from observation based on data from approximately 11 references consisting of published research or unpublished scientific compilations. Consequently, the formula HRmax=220 -age has no scientific merit for use in exercise physiology and related fields. A brief review of alternate HRmax prediction formula reveals that the majority of age -based univariate prediction equations also have large prediction errors (>10 b/min). Clearly, more research of HRmax needs to be done using a multivariate model, and equations may need to be developed that are population (fitness, health status, age, exercise mode) specific.
Prediction of Maximal Heart Rate
Journal of Exercise Physiologyonline
Official Journal of The American
Society of Exercise Physiologists (ASEP)
ISSN 1097-9751
An International Electronic Journal
Volume 5 Number 2 May 2002
Exercise Physiology Laboratories, The University of New Mexico, Albuquerque, NM
THE SURPRISING HISTORY OF THE “HRmax=220-age” EQUATION. Robert A. Robergs, Roberto
Landwehr. JEPonline. 2002;5(2):1-10. The estimation of maximal heart rate (HRmax) has been a feature of
exercise physiology and related applied sciences since the late 1930’s. The estimation of HRmax has been
largely based on the formula; HRmax=220-age. This equation is often presented in textbooks without
explanation or citation to original research. In addition, the formula and related concepts are included in most
certification exams within sports medicine, exercise physiology, and fitness. Despite the acceptance of this
formula, research spanning more than two decades reveals the large error inherent in the estimation of HRmax
(Sxy=7-11 b/min). Ironically, inquiry into the history of this formula reveals that it was not developed from
original research, but resulted from observation based on data from approximately 11 references consisting of
published research or unpublished scientific compilations. Consequently, the formula HRmax=220-age has no
scientific merit for use in exercise physiology and related fields. A brief review of alternate HRmax prediction
formula reveals that the majority of age-based univariate prediction equations also have large prediction errors
(>10 b/min). Clearly, more research of HRmax needs to be done using a multivariate model, and equations may
need to be developed that are population (fitness, health status, age, exercise mode) specific.
Key Words: Cardiovascular function, Estimation, Error, Exercise prescription, Fitness.
This short manuscript has been written to provide insight into the history of the maximal heart rate (HRmax)
prediction equation; HRmax=220age. Surprisingly, there is no published record of research for this equation.
As will be explained, the origin of the formula is a superficial estimate, based on observation, of a linear best fit
to a series of raw and mean data compiled in 1971 (1). However, evidence of the physiological study of
maximal heart rate prediction dates back to at least 1938 from the research of Sid Robinson (2).
Research since 1971 has revealed the error in HRmax estimation, and there remains no formula that provides
acceptable accuracy of HRmax prediction. We present the majority of the formulae that currently exist to
Prediction of Maximal Heart Rate
estimate HRmax, and provide recommendations on which formula to use, and when. We also provide
recommendations for research to improve our knowledge of the between subjects variability in HRmax.
Heart rate is arguably a very easy cardiovascular measurement, especially in comparison to the invasive or
noninvasive procedures used to estimate stroke volume and cardiac output. Consequently, measurement of
heart rate is routinely used to assess the response of the heart to exercise, or the recovery from exercise, as well
as to prescribe exercise intensities (3). Given that the increase in heart rate during incremental exercise mirrors
the increase in cardiac output, maximal heart rate is often interpreted as the upper ceiling for an increase in
central cardiovascular function. Indeed, research for the last 100 years has demonstrated that heart rate does in
fact have a maximal value (4); one that cannot be surpassed despite continued increases in exercise intensity or
training adaptations.
Perhaps the most important application of the heart
rate response to exercise has been the use of
submaximal heart rate, in combination with resting
and maximal heart rate, to estimate VO2max. In
many instances, maximal heart rate estimation is
recommended by using the formula HRmax=220-
age. Based on this application, heart rate responses
to exercise have been used to calculate exercise
intensities, such as a percent of maximal heart rate
(%HRmax) or a percent of the heart rate reserve
(%HRR) (Table 1).
Due to our interest in improving the accuracy of maximal heart rate estimation, we have tried to research the
origin of the formula HRmax=220-age (Tables 2 and 3). As far as we could determine from books and
research, the first equation to predict maximal heart rate was developed by Robinson in 1938 (2). His data
produced the equation HRmax=212-0.77(age), which obviously differs from the widely accepted formula of
HRmax=220-age. As we will explain below, there are numerous HRmax prediction equations (Table 3), yet it
is the history of the HRmax=220-age equation that is most interesting.
The Formula: “HRmax=220-Age”
Within textbooks, failure to cite the original research regarding the formula HRmax=220-age indirectly affirms
a connection to Karvonen. This association exists due to the textbook presentation of HRmax prediction with
the concept of a heart rate reserve, which was devised by Karvonen (3). Ironically, the study of Karvonen was
not of maximal heart rate. To clarify, Dr. Karvonen was contacted in August of 2000 and subsequent
discussion indicated that he never published original research of this formula, and he recommended that we
research the work of Dr. Åstrand to find the original research.
Another citation for the formula is Åstrand (7). Once again, this study was not concerned with HRmax
prediction. We were able to discuss this topic with Dr. Åstrand in September 2000 while he was in
Albuquerque to receive his Lifetime Achievement Award in Exercise Physiology from the American Society of
Exercise Physiologists. Dr. Åstrand stated that he did not publish any data that derived this formula. However,
Table 1: Use of heart rate to estimate
exercise intensities that coincide with
%VO2max % HRmax %HRR*^
40 63 40
50 69 50
60 76 60
70 82 70
80 89 80
90 95 90
*based on Karvonen method (HR=HRrest +
((intended fraction) * (HRmax - HRrest)));
^%HRR equals the intended fraction expressed as %
Adapted from Heyward V. (5) and Swain et al. (6)
Prediction of Maximal Heart Rate
he did comment that in past presentations he had stated that such a formula appears close to research findings,
and would be a convenient method to
Interestingly, Åstrand published
original HRmax data for 225 subjects
(115 male, 110 female) for ages 4 to 33
years in one of his earlier texts (8). The
data are from either treadmill or cycle
ergometer exercise tests to VO2max,
with no knowledge of protocol
characteristics. This data is presented
in Figure 1a and b. When data for ages
>10 years are used (Figure 1b), there is
a significant correlation (r=0.43), yet
considerable error (Sxy=11 b/min).
The resulting formula is; HRmax =
216.60.84(age). Despite the similarity
of the prediction equation to
HRmax=220age, the notable feature
of this data set is the large error of
prediction. Interestingly, in two other
studies, Åstrand found that the average
decrease in HRmax for women was 12
beats in 21 years (9) and 19 beats in 33
years (10). For men, the decrease in
HRmax was 9 beats in 21 years (9) and
~26 in 33 years (10). If the formula
HRmax=220-age is correct, the slope
for HR decrement with increasing age
would be 1. In addition, Åstrand’s data
Table 2: The research and textbooks, and the citations used or
not used, in crediting the source of the HRmax=220-age
Publication Year Citation
Engels et al. 1998 Fox & Haskell, 1971
O’Toole et al. 1998 ACSM. 1995
Tanaka 2001 Fox & Haskell, 1971
Vandewalle & Havette 1987 Astrand, 1986
Whaley et al. 1992 Froelicher,1987
ACSM 2001 ACSM, 2000
Baechle & Earle 2000 No Citation
Baumgartner & Jackson 1995 No Citation
Brooks et al. 2000 No Citation
Fox et al. 1989 No Citation
Garret & Kirkendall 2000 No Citation
Heyward 1997 No Citation
McArdle, Katch & Katch 1996 Londeree, 1982
McArdle, Katch & Katch 2000 No Citation
Nieman 1999 No citation
Plowman & Smith 1997 Miller et al. 1993
Powers & Howley 1996 No Citation
Robergs & Roberts 1997 Hagberg et al, 1985
Robergs & Roberts 2000 No Citation
Roberts et al. 1997 Asmussen, 1959
Rowland 1996 No Citation
Wasserman et al. 1994 No Citation
Wilmore & Costill 1999 No Citation
2 6 10 14 18 22 26 30 34
Age (years)
HRmax (b/min)
Figure 1: Data of HRmax for a) 225
subjects, 4 to 33 years, and b) a subset of
the subjects, ages 11 to 33 years, n=196.
Prediction of Maximal Heart Rate
indicates that HRmax prediction from such
formula should not be used on children 10 years
or younger, as HRmax follows a different age
associated change for children. In addition, the
likelihood that children attain a true HRmax
during exercise testing can be questioned.
It appears that the correct citation for the origin of
HRmax=220-age is Fox et al. (1). However, and
as explained by Tanaka et al. (11), Fox did not
derive this equation from original research. We
evaluated the original manuscript of Fox et al. (1),
which was a large review of research pertaining to
physical activity and heart disease. In a section
subtitled “Intensity”, a figure is presented that
contains the data at question, and consists of
approximately 35 data points. No regression
analysis was performed on this data, and in the
figure legend the authors stated that;
“….no single line will adequately represent the
data on the apparent decline of maximal heart
rate with age. The formula maximum heart
rate=220age in years defines a line not far
from many of the data points..”
We decided to replicate the approach used by
Fox et al (1), using the original data presented in
their manuscript. As we could not find all
manuscripts due to inaccurate citations, we
reproduced the data from the figure and
presented it in Figure 2. We fit a linear
regression to the data set and derived the following equation; HRmax=215.4 0.9147(age), r=0.51, Sxy=21
b/min. Thus, even the original data from which observation established the HRmax=220-age formula does no
support this equation.
We retrieved as much of the research on HRmax as is possible. This was a daunting task, as many of the
original research and review studies on this topic did not provide complete references, or citations of the
original research of this topic. We collated 43 formulae from different studies, and these are presented in Table
3, along with pertinent statistics when possible.
To verify if there was a trend towards the equation HRmax=220-age, we selected 30 equations from the ones
presented in Table 3 (excluded equations derived from non-healthy subjects). The equations were used to re-
calculate HRmax for ages 20 to 100 years of age, and a new regression equation was calculated from the data
(Figure 3). The regression equation yielded a prediction formula; HRmax=208.754-0.734(age), r=0.93 and
Sxy=7.2, which is very close to that derived by Tanaka et al. (11) (Table 3).
10 14 18 22 26 30 34
240 HRmax = (-0.8421*age) + 216.6
r2 = 0.1859 ; Sy.x = 11 b/min
Age (years)
HRmax (b/min)
0 10 20 30 40 50 60 70
220 Robinson
HRmax = 215.4 - (0.9147*Age)
Age (yr)
HRmax (b/min)
Figure 2. A reproduced figure from the data of Fox et al. (1)
which was used to derive the original HRmax=220-age formula.
Blue line represents line of best fit. Red line represents 220-age.
Prediction of Maximal Heart Rate
Table 3. The known univariate prediction equations for maximal heart rate.
Study N Population Mean Age
(range) Regression
(HRmax=) r2 Sxy
Univariate Equations
Astrand, in
Froelicher (2) 100
Healthy Men cycle
ergometer 50 (20 - 69) 211-0.922a N/A
Brick, in Froelicher
(2) ?
Women N/A 226-age N/A
Bruce (12) 1295
CHD 52±8 204-1.07a 0.13
Bruce (12) 2091
Healthy Men 44±8 210-0.662a 0.19
Bruce (12) 1295
Hypertension 52±8 204-1.07a 0.24
Bruce (12) 2091
Hypertension + CHD 44±8 210-0.662a 0.10
Cooper in
Froelicher (2) 2535
Healthy Men 43(11 - 79) 217-0.845a N/A
Ellestad in
Froelicher (2) 2583
Healthy Men 42(10-60) 197-0.556a N/A
Fernhall (13) 276
Mental Retardation 9-46 189-0.56a 0.09
Fernhall (13) 296
Healthy W & M N/A 205-0.64a 0.27
Froelicher (2) 1317
Healthy Men 38.8(28-54) 207-0.64a 0.18
Graettinger (14) 114
Healthy Men (19-73) 199-0.63a 0.22
Hammond (15) 156
Heart Disease 53.9 209-age 0.09
Hossack (16) 104
Healthy Women (20-70) 206-0.597a 0.21
Hossack (16) 98
Healthy Men (20-73) 227-1.067a 0.40
Inbar (17) 1424
Healthy W & M 46.7(20-70) 205.8-.685a 0.45
Jones (18) 100
Healthy W & M cycle
ergometer (15 71) 202-0.72a 0.52
Jones N/A ?
Healthy W &M 210-0.65a 0.04
Jones (18) 60
Healthy Women (20-49) 201-0.63a
Lester (19) 48
W & M Trained 205-0.41a 0.34
Lester (19) 148
W & M Untrained 43(15 75) 198-0.41a N/A
Londeree (20) ?
National Level Athletes N/A 206.3-0.711a 0.72
Miller (21) 89
W & M Obese 42 200-0.48a 0.12
Morris, in
Froelicher (2) 1388
Heart Disease 57(21 89) 196-0.9a 0.00
Morris, in
Froelicher (2) 244
Healthy Men 45(20 72) 200 -0.72a 0.30
Ricard (22) 193
Treadmill W&M 209 -0.587a 0.38
Ricard (22) 193
W & M - cycle
ergometer 200 -0.687a 0.44
Robinson 1938 in
Froelicher (2) 92
Healthy Men 30(6 - 76) 212 -0.775a 0.00
Rodeheffer (23) 61
Healthy Men 25 - 79 214-1.02a 0.45
Schiller 24) 53
Women Hispanic 46(20-75) 213.7-0.75a 0.56
Schiller (24) 93
Women Caucasian 42(20-75) 207 -0.62a 0.44
Sheffield (25) 95
Women 39(19 - 69) 216 -0.88a 0.58
Tanaka (11) ?
Sedentary W&M 211 -0.8a 0.81
Tanaka (11) ?
Active W&M 207 -0.7a 0.81
Tanaka (11) ?
Endurance trained W&M
206 -0.7a 0.81
Prediction of Maximal Heart Rate
Study N Population Mean Age
(range) Regression
(HRmax=) r2 Sxy
Univariate Equations
Tanaka (11)
Women & Men 208-0.7a 0.81
Whaley (26) 754
Women 41.3(14-77) 209-0.7a 0.37
Whaley (26) 1256
Men 42.1(14-77) 214-0.8a 0.36
W=women, M=men
Table 4. The known multivariate prediction equations for maximal heart rate.
Study and Equations r2
Londeree (20)
PMHR = 196.7+1.986xC2+5.361xE+1.490xF4+3.730xF3+4.036xF2-00006xA4-0.542xA2 0.77
PMHRI = 199.1+0.119xAEF4+0.112xAE+6.280xEF3+2.468xC2+3.485xF2-.00006xA4-0.591xA
PMHRC = 205-3.574xT1+8.316xE-7.624xF5-.00004xA4-0.624xA2 0.85
PMHRCI = 205-0.116xAEF3-0.223xAF5+0.210xAE+6.876xEF3+2.091xC2-3.310xT1-
0.0005xA4-0.654xA 0.86
PMHR (National Collegiate Athletes) = 202.8-0.533xA-00006xA4 0.73
PMHR=predicted maximal heart rate, C=Cross Sectional, I=interaction; a=A=age; A2=age; A4= (age4)/1000; C#=continent ( if
European, then C2=1, otherwise C2=0); E=ergometer (if treadmill, then E=1, if bicycle then E=0); F#=fitness level (if sedentary,
F2=1, otherwise F2=0; if active then F3=1, otherwise F3=0, if endurance trained, then F4=1, otherwise F4=0; Type # =type of
exercise protocol (if continuous and incremental, then T1=1, otherwise T1=0). Multiple letters interaction terms which should be
multiplied together.
Interestingly, Londeree (20) developed a
multivariate equation using the variables age, age2,
age4/1000, ethnicity, mode of exercise, activity
levels, and type of protocol used to assess HR
(Table 4). However, no statistical results pertaining
to significant increases in the explanation of
variance in HRmax using a mutivariate model was
provided by the authors. The same criticism
applies to the study of Tanaka et al. (11). As
Zavorsky (27) showed that endurance training
lowers HRmax, and others have shown the exercise
mode specificity of HRmax (28,29,30), an original
study of HRmax using multiple independent
variables is long overdue.
The data from research of HRmax are clear in
showing the large error of HRmax prediction using
just a y-intercept and slope when age is the sole independent variable. Furthermore, the results and regression
equations need to be recognized as being modespecific (28,29,30). It is unfortunate that the mode-specificity
of HRmax prediction equations is not clearly addressed in textbooks of exercise physiology and exercise
prescription. Finally, even a multivariate model of HRmax prediction and variance explanation does not reduce
the error of HRmax prediction.
20 30 40 50 60 70 80
200 Compiled studies average
Londeree meta-analysis
Tanaka meta-analysis
Age (yrs)
Maximal Heart Rate
Regression Lines
Figure 3. Regression lines from data obtained from 220-
the mean of 30 studies from Table 3, and the meta analyses
of Londeree (28) and Tanaka (47).
Prediction of Maximal Heart Rate
What is an Acceptable Error of HRmax Prediction?
Given the precision of HR measurement, the measurement error of HRmax is small and attributable to the
exercise protocol and subject motivation. Consequently, HRmax measurement is likely to be accurate to within
±2 b/min, if the subject truly attains maximal exertion. Nevertheless, another factor to consider is the impact of
prediction error on the application of HRmax. For the estimation of two exercise intensities (Table 5), HRmax
prediction errors (HRmaxpredicted=error) of 2, 4, 6 and 8 b/min cause negligible error. For example, a HR of
150 b/min, which lies in the center of the “true” heart rate prescription range, remains within the recommended
heart rate ranges for all error examples. However, as revealed in Table 3, errors in HRmax estimation can be in
excess of 11 b/min. Consequently, it is likely that current equations used to estimate HRmax are not accurate
enough for prescribing exercise training heart rate ranges for a large number of individuals.
Table 5. Estimations of error in submaximal exercise intensities and VO2max when using HRmax
estimated with errors of 2, 4, 6, and 8 b/min (underestimated prediction of HRmax).
HR values For Given HRmax Error (True-Estimated, b/min (%))
Intensity True 2 (1) 4 (2.1) 6 (3.1) 8 (4.2)
Submaximal exercise intensities
60-80% HRR 135-164 134-162 133-160 132-159 130-157
YMCA* (mL/min) 4200 4083 6967 3850 3733
Error (mL/min)
0 117 233 350 467
Error (%)
0 2.8 5.6 8.3 11.11
Calculations are based on assuming a resting heart rate of 50 b/min, for a 25 year old person with a HRmax=192 b/min ; HRR=heart
rate reserve ; for YMCA protocol, heart rates and workloads were assumed to be (HR:kgm/min) 90:150, 125:750, 153:1200,
When the prediction of HRmax is used in the estimation of VO2max, as it is in the YMCA method, there can be
considerable errors in estimated VO2max (Table 5). For example, when HRmax is underestimated by 6 b/min,
there is a resulting error in estimated VO2max of 350 mL/min. This equates to an error of -8.3%, or -4.7
mL/kg/min for a 75 kg person.
The data of Table 5 help in selecting a suitable error in HRmax estimation. The error can be larger for purposes
of prescribing training heart rate ranges than in the estimation of VO2max. For purposes of prescribing training
heart rate ranges, errors 8 b/min are likely to be acceptable. However, for VO2max, it can be argued that
prediction errors in HRmax need to be <±3 b/min.
Based on this review of research and application of HRmax prediction, the following recommendations can be
1. Currently, there is no acceptable method to estimate HRmax.
2. If HRmax needs to be estimated, then population specific formulae should be used. However, the most
accurate general equation is that of Inbar (17) (Table 3); HRmax=205.8-0.685(age). Nevertheless, the error
(Sxy=6.4 b/min) is still unacceptably large.
3. An acceptable prediction error for HRmax for application to estimation of VO2max is <±3 b/min. Thus, for
a person with a HRmax of 200 b/min, error equals ±1.5%. If this precision is not possible, then there is no
justification for using methods of VO2max estimation that rely on HRmax prediction formulae.
Prediction of Maximal Heart Rate
4. Additional research needs to be performed that develops multivariate regression equations that improve the
accuracy of HRmax prediction for specific populations, and modes of exercise.
5. The use of HRmax is most prevalent in the fitness industry, and the people who work in these facilities
mainly have a terminal undergraduate degree in exercise science or related fields. These students/graduates
need to be better educated in statistics to recognize and understand the concept of prediction error, and the
practical consequences of relying on an equation with a large standard error of estimate (Sxy).
6. Textbooks in exercise physiology and exercise prescription should contain content that is more critical of the
HRmax=220-age or similar formulae. Authors need to stress the mode-specificity of HRmax, provide alternate,
research substantiated formula, and express all content of items 1-5, above. Similarly, academic coverage of
HRmax needs to explain how this error detracts from using HRmax estimation in many field tests of physical
fitness and in exercise prescription.
Address for correspondence: Robert A. Robergs, Ph.D., FASEP, EPC, Director-Exercise Physiology
Laboratories, Exercise Science Program, Department of Physical Performance and Development, Johnson
Center, Room B143, The University of New Mexico, Albuquerque, NM 87131-1258, Phone: (505) 277-2658,
FAX: (505) 277-9742; Email:
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... Age-based formulas have been developed from a variety of studies [11][12][13][14][15][16], but perhaps the most commonly used equations are the Fox equation (HR max = 220 − age) [17] and Tanaka equation (HR max = 208 − 0.7 × age) [18] among the suggested equations. Fox equation has developed in 1971 and has been extensively investigated within the specific population (i.e., healthy, obese, and athlete) for adults [4,19,20], and especially, the HR max prediction equation devised by Fox was widely utilized for physical activity and heart diseases study [21]. Later in 2001, the Tanaka equation was developed using a meta-analysis of 351 studies and showed high accuracy (r = −0.90) ...
... The reason is that since Fox s HR max prediction equation often has been applied to non-athletic males of a wide range of ages [35], females may have been underrepresented to predict HR max based on the formula. Furthermore, the majority of research regarding Fox s equation has reported that the equation had a standard deviation of about 7-13 bpm [2,20,21], which is consistent with the outcome overestimated approximately 9 bpm in the current study. This may not be suitable for predicting HR max of the general population because Fox s equation was determined based on a review of 10 studies without proper regression analysis and developed in older adults (over 60 years of age) with cardiovascular diseases. ...
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The primary purpose of the present study was to re-visit HRmax prediction by two commonly used equations (i.e., Fox′s and Tanaka′s equation) compared to the direct measured HRmax using the large sample size of Asians. The second aim of the study was to focus on suggesting new equations for the Asian population by separating gender and specific age groups. A total of 672 participants aged from 7 to 55 years were recruited for the study (male: 280 and female: 392), and the maximal graded exercise test with Bruce protocol was used to measure HRmax. All data obtained from the study were analyzed by SPSS 25.0. Additionally, three statistical analysis methods (i.e., Mean Absolute Percent Errors (MAPE), Bland–Altman plots, and equivalence testing) were utilized to confirm the consistency between the measured HRmax and the two prediction equations. The main finding was that two equations showed significant differences in predicting the HRmax of Korean aged from 7 to 55 years. The outcome of children aged from 7 to 14 was a different fit in the agreement compared to other age groups. Fox′s equation had the best fit in the average of the difference closer to zero and completely included within the equivalence zone, but females over 15 years old revealed higher errors than males in the values calculated by the two equations compared to the direct measured HRmax. Consequently, the study demonstrated that both equations tended to overestimate the HRmax for males and females over 15 years old, and the two universal equations were not suitable to predict the HRmax of Koreans except for children aged from 7 to 14 years. The new HRmax prediction equations suggested in this study will more accurately predict the HRmax of Asians, and additional analyses should be examined the cross-validity of the developed HRmax equation by age and gender in the future study.
... HRrest was the minimum heart rate estimated within the last minute of the rest period [26]. Also, individual maximum heart rate (HRmax) was determined as 220 -age [27]. Blood samples were collected after an overnight fasting for 12 hours (drinking water only) for lipid profile evaluation before the study and at least 48 hours after the last exercise session in the study. ...
... The maximum HR was calculated using Roberg's and Landwehr's formula (HRmax = 205.8 -0.685 × age) [37]. The threshold values have been established for pulse rate or heart rate of 130 bpm, CBT temperature of 37°C, and environmental temperature and humidity of 33°C and 70%, respectively. ...
Agricultural workers are often exposed to high temperatures in the field to do their jobs and they are among the most vulnerable to heat-related illnesses. Heatstroke is a severe case of hyperthermia in which the body temperature significantly increased due to excessive external heat or the physical effort of workers. Heatstroke can potentially be harmful to agricultural workers while working in hot environments. To avert this potentially fatal condition, a Wearable Heatstroke Alert System (WHSAS) was developed with early notification ability to avoid heatstroke while performing various agricultural activities in the field. The WHSAS is a Bluetooth module based android application. The simulation of the system is based on non-invasively real-time data such as body temperature, pulse, ambient temperature and humidity. The device was tested and compared with clinical standards for performance benchmarking. The developed device provides new opportunities to manage heat stress in open fields when agricultural workers are subjected to high temperatures and humidity. If an alarming situation is detected, then the device will activate the alert function to remind the user to act suitably to prevent heatstroke. The device also can be used as a research tool to study physiological responses under various environmental conditions, such as extreme heat, humidity etc., and can be customized to incorporate new sensors to explore other lines of inquiry.
... Several investigations have also demonstrated high validity and reliability evidences of smart watch including the Garmin Fenix 6 pro or earlier models in accessing PA, step count HR, RR, calories (Navalta et al., 2020) and sleep parameters (Chinoy et al., 2021). A detailed description of PA (i.e.; PA, step count), physiological (i.e.; HR, RR, calories) and sleep (i.e.; sleep time, sleep latency, REMS time) parameters selected to be analyzed in the current study, is presented in Table 2. HR was expressed in bpm (Souissi et al., 2021), and as a percentage of the predicted maximal HR (PMHR (bpm) =220age (year)) (Robergs and Landwehr, 2002). ...
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The COVID-19 outbreak resulted in the shutdown of athletic training facilities. Although the effects of these restrictions on daily activity and sleep patterns have been widely analyzed, the employed tools often lacked accuracy, and were based on subjective measures. This study assessed the effects of home confinement on objective physical activity (PA), physiological and sleep parameters in active individuals. Sixteen male elite fitness coaches (age: 29±3 years; height: 183±6 cm; body mass: 82±5 kg, body mass index: 24.7±1.8 kg/m 2) participated in this retrospective study. One-way analysis of variance was conducted to analyze selected PA, physiological and sleep parameters collected by smartwatch (Garmin Fenix 6 pro, USA) data during four consecutive months [i.e., pre-confinement, 1 st and 2 nd months of confinement, and post-confinement, year 2020]. Ramadan intermittent fasting (RIF) month occurred during the 2 nd month of confinement. Compared to pre-confinement, significant changes were registered for almost all parameters during the 1 st and/or the 2 nd month of confinements (p<0.001), with (i) higher values for resting heart rate, sleep latency, and total, light and rapid eye movements sleep times (% change=7-523 %), and (ii) lower values for PA parameters, calories/day spent, average and highest respiratory rates, and deep sleep time during the home confinement period (% change=5-36 %). During the post-confinement month, all parameters regained pre-confinement values. In conclusion, home confinement-induced detraining negatively influenced the objective measurements of cardiorespiratory and sleep parameters among fitness coaches with a deeper effect during the 2 nd month of home confinement, possibly due to the effect of RIF.
... This formula was developed based on 11 references with small sample sizes. A recent study also questioned the limitations of this formula [10]. Therefore, many researchers presented an alternative formula to calculate HRmax using data of large sample size: 206.3-0.711×age ...
Objective: To compare the predicted and actual maximal heart rate (HRmax) values in the cardiopulmonary exercise test (CPET). Methods: We retrospectively investigated 1,060 patients who underwent a CPET between January 2016 and April 2020 at our institution's cardiopulmonary rehabilitation center. The following patients were included: those aged >20 years, those tested with a treadmill, and those who underwent symptom-limited maximum exercise testing- reaching ≥85% of the predicted HRmax (62% if taking beta-blockers) and highest respiratory exchange ratio ≥1.1. Ultimately, 827 patients were included in this study. Data on diagnosis, history of taking beta-blockers, age, body mass index (BMI), and CPET parameters were collected. Subgroup analysis was performed according to age, betablockers, BMI (low <18.5 kg/m2, normal, and high ≥25 kg/m2), and risk classification. Results: There was a significant difference between the actual HRmax and the predicted value (p<0.001). Betablocker administration resulted in a significant difference in the actual HRmax (p<0.001). There were significant differences in the moderate-to-high-risk and low-risk groups and the normal BMI and high BMI groups (p<0.001). There was no significant difference between the elderly and younger groups. We suggest new formulae for HRmax of cardiopulmonary patients: estimated HRmax=183-0.76×age (the beta-blocker group) and etimated HRmax=210-0.91×age (the non-beta-blocker group). Conclusion: Age-predicted HRmax was significantly different from the actual HRmax of patients with cardiopulmonary disease, especially in the beta-blocker group. For participants with high BMI and moderate-tosevere risk, the actual HRmax was significantly lower than the predicted HRmax.
... Although there is a discussion about this formula, it has been described as one of the most used due to its practical application and simplicity [17,18]. On the other hand, studies based on meta-analysis reported that "HRmax = 208 − 0.7 × age" is a more reliable option to predict HRmax [19,20]. However, people with FM show reduced cardiorespiratory fitness [6,12,21] as well as autonomic dysfunction (dysautonomia) [13,22,23]. ...
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Objectives: This article aims to verify the agreement between the standard method to determine the heart rate achieved in the ventilatory threshold 1 in the cardiopulmonary exercise testing (VT1) and the mathematical models with exercise intensities suggested by the literature in order to check the most precise for fibromyalgia (FM) patients. Methods: Seventeen women with FM were included in this study. The VT1 was used as the standard method to compare four mathematical models applied in the literature to calculate the exercise intensity in FM patients: the well-known "220 - age" at 76%, Tanaka predictive equation "208 - 0.7 × age" at 76%, the FM model HRMax "209 - 0.85 × age" at 76%, and Karvonen Formula at 60%. Bland-Altman analysis and correlation analyses were used to explore agreement and correlation between the standard method and the mathematical models. Results: Significant correlations between the heart rate at the VT1 and the four mathematical estimation models were observed. However, the Bland-Altman analysis only showed agreement between VT1 and "220 - age" (bias = -114.83 + 0.868 × x; 95% LOA = -114.83 + 0.868 × x + 1.96 × 7.46 to -114.83 + 0.868 × x - 1.96 × 7.46, where x is the average between the heart rate obtained in the CPET at VT1 and "220 - age", in this case 129.15; p = 0.519) and "209 - 0.85 × age"(bias = -129.58 + 1.024 × x; 95% LOA = -129.58 + 1.024 × x + 1.96 × 6.619 to -129.58 + 1.024 × x - 1.96 × 6.619, where x is the average between the heart rate obtained in the CPET at VT1 and "209 - 0.85 × age", in this case 127.30; p = 0.403). Conclusions: The well-known predictive equation "220 - age" and the FM model HRMax ("209 - 0.85 × age") showed agreement with the standard method (VT1), revealing that it is a precise model to calculate the exercise intensity in sedentary FM patients. However, proportional bias has been detected in all the mathematical models, with a higher heart rate obtained in CPET than obtained in the mathematical model. The chronotropic incompetence observed in people with FM (inability to increase heart rate with increasing exercise intensities) could explain why methods that tend to underestimate the HRmax in the general population fit better in this population.
Objectives Secreted by white adipose tissue, asprosin is a newly recognized adipokine whose physiological function is not well comprehended. This study intended to determine the effect of spinning and stationary cycling on serum asprosin levels in overweight women. Methods Forty-five overweight women with BMI>25 kg/m ² in the age range of 30–40 years were assigned randomly to three groups of 15 participants: control, spinning (group cycling with music), and stationary bike (individual pedaling on a stationary bike). The participants performed the exercises three sessions per week for six weeks. Lipid profile and asprosin levels were measured by enzymatic and ELISA methods, respectively. Moreover, the paired t-test and one-way ANOVA were employed to make within-group and between-group comparisons, respectively. Results The stationary cycling and spinning exercise groups experienced significant reductions in weight, BMI, serum triglyceride, and asprosin levels from the pretest to the posttest. The control group showed no statistically significant differences. Serum concentrations of total cholesterol and low-density lipoprotein only declined in the spinning group. In this regard, neither the control group nor the stationary bicycle exhibited no significant change over time. The spinning group demonstrated a significant rise in high-density lipoprotein levels, which was not observed in the control group. In addition, there was no significant difference in WHR index between the intervention groups. Conclusions By lowering the serum asprosin level, a spinning exercise program appears to be effective in reducing disorders linked to metabolic diseases in overweight women.
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We examined the effect of physiological workload on gaze behaviour during defensive performance in 2 vs. 1 +goalkeeper game situations in football. Twenty-two players were assigned to either a high- or low-performing group based on a validated measure of tactical performance. A total of 12 game sequences (trials) were presented under high- and low-workload conditions. At the end of each sequence, participants were asked to indicate their perceived exertion using the Rating Scale of Mental Effort and the Borg Scale. The low- and high-workload conditions were defined when the players achieved 60 and 90% of their maximal heart rate, respectively, as per their performance in the Yo- Yo Intermittent Recovery Test. Visual search behaviours were recorded using Tobii Pro eye-movement registration glasses. Players reported higher rates of perceived exertion on the high- compared to low-workload condition. Participants in the low-performing group increased their average fixation duration and decreased the number of fixations and number of fixation locations from the low- to high-workload conditions. The low- and high-performing groups displayed different visual search strategies with regards the areas of interest fixated upon. Participants in the high-performing group focused on the SpaceFrontPlayer, followed by Ball, and AnotherOpponent. The low-performing group spent more time focusing on the SpaceFrontPlayer and SpacePlayer than Ball and AnotherOpponent. It appears that physiological workload and tactical expertise interact in constraining visual search behaviours in football players. Coaches and practitioners should consider ways to manipulate individual and task constraints while attending to the close interplay between physiological workload, visual behaviour, and tactical performance during practise.
In an attempt to reduce the confusion regarding reported effects of age upon maximal exercise heart rate (HR max), a comprehensive review of the English literature was conducted to obtain descriptive statistical data representing over 23,000 independent subjects from 5 to 81 years old. The data were split randomly into two data sets for independent regression analyses. HR max was the dependent variable while independent variables include: age, age2, age3, age4, sex, level of fitness, type of ergometer, exercise protocol, continent of residence, and race. After cross validation the data were pooled and reanalyzed. Additional validation was accomplished on identifiable subsets of the data, e.g., cross sectional, longitudinal, training, comparative ergometry, and comparative sex studies. Results identified negative linear and non-linear age factors, an ergometry factor, a fitness factor and a continent factor. Age accounted for about 70–75% of the variability. Generalized equations were proposed. Even with all factors accounted for, the 95% confidence interval of individual HR max was about 45 beats/min. Tables of HR max derived from the equations are included.
The purpose of this study was to compare oxygen consumption (VO 2 ) and energy expenditure alter 20 min of self-selected submaximal exercise for four modes of exercise. Eighteen subjects (9 male and 9 female) first completed a test of VO 2max during treadmill running. On separate days. subjects then completed 20 min submaximal treadmill running (TR), simulated cross-country skiing (XC). cycle ergometry (CE), and aerobic riding (AR) exercise. Total VO 2 , and energy expenditure were significantly higher for TR than all other modes for both males and females (43,6 ± 10.4, 39.1 ± 9.7, 36.1 ± 7.6. 28.4 ± 6.1 LO 2 , for TR, XC, CE. and AR, respectively. P < 0.0001). For males and females, heart rate was similar during TR and XC and lower during CE and AR (154,8 ± 14.2. 152.6 ± 13.1, 143.4 ± 14.9, and 126.2 ± 12.0 beatsmin for TR. XC. CE, and AR, respectively. P < 0.0001). Compared with females, males had significantly greater vO 2 (P < 0.005) and energy expenditure (P < 0.004), while females had higher heart rates (P < 0.003). Ratings of perceived exertion (RPE) were not different between TR. XC, and CE, but were significantly lower during AR (13.4 ± 1.3. 13.6 ± 0.8), 13.2 ± 0.9. and 12.6 ± 1.0 for TR, XC, CE, and AR. respectively, P < 0.003). TR elicited the greatest vO 2 and energy expenditure during self-selected exercise despite an RPE similar to XC and CE. Therefore, treadmill exercise may be the modality of choice for individuals seeking to improve cardiorespiratory endurance and expend a larger numher of kjoules.