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Say Goodbye to 220-Age. Discover the Game-Changing Alternatives for Your Best Fitness Results Yet:

Updated: Mar 5, 2023

Part One of Our Exclusive Five-Part Series!



Maximum heart rate (MHR) is a fundamental measure of cardiovascular function, providing insight into a person's fitness level and indicating the intensity of exercise that would be most beneficial for them. Several formulas have been proposed to estimate MHR, each with strengths and weaknesses. Here we will examine the creation, development, and use of different MHR formulas and discuss their potential applications and limitations.


Creation of MHR Formulas


The concept of MHR has been around for over a century, with early studies attempting to establish the maximum heart rate based on age alone. One of the earliest known studies was conducted by Hill and Lupton in 1923, who defined MHR as the highest heart rate that an individual could achieve under any circumstances (Hill & Lupton, 1923). However, it was not until the 1970s that a widely used formula was introduced by Fox and Haskell in 1970, called the age-predicted maximum heart rate (APMHR) formula. The APMHR formula estimates MHR based on age alone, and is calculated as 220 minus the person's age (Fox & Haskell, 1970).


Development of MHR Formulas


Over time, researchers have recognized the limitations of the APMHR formula, particularly its inability to take into account individual differences in fitness level, sex, and other factors that can influence MHR. As a result, several alternative formulas have been proposed, each incorporating additional variables to enhance the accuracy of MHR estimation. One such formula is the Tanaka formula, introduced in 2001, which estimates MHR based on age and sex, and is calculated as 208 minus 0.7 times the person's age (Tanaka et al., 2001). Another example is the Gellish formula, introduced in 2007, which estimates MHR based on age, sex, body weight, and height, and is calculated as 207 minus 0.7 times the person's age minus 0.1 times their weight plus 0.1 times their height (Gellish et al., 2007).


In addition to these formulas, other studies have proposed alternative methods for estimating MHR, including the use of heart rate variability (HRV) (Plews et al., 2013), or the incorporation of submaximal exercise data, such as the heart rate at rest or during submaximal exercise (Robergs et al., 2002).



Use of MHR Formulas


MHR formulas are used for several purposes, including exercise prescription, fitness assessment, and cardiac rehabilitation. In exercise prescription, MHR determines the target heart rate range for aerobic exercise, typically 60-85% of MHR, depending on the individual's fitness level and goals (American College of Sports Medicine, 2018). This target range ensures that the person exercises at an appropriate intensity to achieve cardiovascular benefits without causing undue strain or injury.


In fitness assessment, MHR is used as a measure of cardiovascular fitness, with a higher MHR indicating better fitness levels. MHR is also used as a marker of health status, with a low MHR potentially indicating an increased risk of cardiovascular disease (Fleg et al., 2015).


In cardiac rehabilitation, MHR is used to monitor the response to exercise and to adjust the exercise prescription as needed. By tracking changes in MHR over time, healthcare providers, coaches, and trainers can assess the effectiveness of the rehabilitation program and make adjustments to optimize outcomes.


Limitations and Future Directions


Despite the widespread use of MHR formulas, several limitations must be acknowledged. One limitation is that MHR formulas are based on population averages and do not account for individual variability in genetics, fitness level, or a linear decline in heart rate with age, which may not hold true for all individuals. Some studies have suggested that the decline in MHR with age may be more complex, with nonlinear patterns of decline (Fleg et al., 2005). This may suggest that age-based formulas may not be the most accurate way to estimate MHR for all individuals.


Another limitation of MHR formulas is that they do not account for the influence of certain factors on MHR, such as medication use, sleep patterns, or hydration status. These factors can influence heart rate and may affect the accuracy of MHR estimates based on formulas alone.


Furthermore, MHR formulas may not be appropriate for all populations, particularly for those with underlying medical conditions that may affect heart rate, such as arrhythmias or heart failure. In such cases, other methods, such as exercise stress testing, may be more appropriate for estimating MHR and determining the appropriate exercise prescription.


Conclusion



MHR formulas are widely used for exercise prescription, fitness assessment, and cardiac rehabilitation. While age-based formulas, such as the APMHR formula, have been widely used for several decades, alternative formulas, such as the Tanaka and Gellish formulas, have been proposed to account for individual differences in fitness level, sex, body weight, and height. However, limitations in MHR formulas must be considered, including the assumption of a linear decline in heart rate with age and the need for more consideration for individual variability in heart rate response to exercise. Further research is needed to refine MHR formulas and explore alternative methods for estimating MHR to optimize exercise prescription and cardiac rehabilitation outcomes.


Get Ready to Unlock the Secrets of Five Incredible MHR Formulas! This Upcoming Series Will Take You on a Journey Through Each Formula (Tanaka, and Gellish). Plus, I'll Reveal A Bonus Formula You Need to Know About!"


References:

American College of Sports Medicine. (2018). ACSM's Guidelines for Exercise Testing and Prescription (10th ed.). Wolters Kluwer.

Fleg, J. L., Morrell, C. H., Bos, A. G., Brant, L. J., Talbot, L. A., & Wright, J. G. (2005). Accelerated longitudinal decline of aerobic capacity in healthy older adults. Circulation, 112(5), 674-682.

Fleg, J. L., Forman, D. E., Berra, K., Bittner, V., Blumenthal, J. A., Chen, M. A., … & Hayman, L. L. (2015). Secondary prevention of atherosclerotic cardiovascular disease in older adults: a scientific statement from the American Heart Association. Circulation, 131(4), e368-e472.

Gellish, R. L., Goslin, B. R., Olson, R. E., McDonald, A., Russi, G. D., & Moudgil, V. K. (2007). Longitudinal modeling of the relationship between age and maximal heart rate. Medicine and Science in Sports and Exercise, 39(5), 822-829.

Hill, A. V., & Lupton, H. (1923). Muscular exercise, lactic acid, and the supply and utilization of oxygen. Quarterly Journal of Medicine, 16, 135-171.

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