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Heart Rate Zone Methodology – What You Should Know

Despite growing popularity and adoption of programs incorporating heart rate (HR) zone methodology, the fitness industry in general appears to lack a solid understanding of the scientific facts and limitations to this programming concept. The goal therefore is to present relevant information so that fitness practitioners and fitness enthusiasts can better understand the pros and cons to using HR zones.

Maximal Heart Rate (MHR)

First and foremost, many models utilize a mathematical formula to determine maximal heart rate (MHR) based upon one’s age, assuming all people of the same age have the same MHR – this cannot be further from the truth (1). Maximal HR is determined primarily by genetics and individualized to each person rather than generalized by a formula – it is influenced by a myriad of events that include:

  • Age – although humans do witness age-related decreases in MHR as we age, it does not necessarily decrease linearly, nor is it one beat per minute (bpm) per year. Data exists to demonstrate how fit individuals can maintain the same MHR for periods of 15-20 years – unfit individuals however, do show a more linear decrease over time (2).
  • Conditioning level – as the cardiopulmonary system adapts to training (predominantly aerobic training), MHR may decrease as maximal cardiac output (Q) is attained by an increase in stroke volume (SV). Cardiac output represents the work capacity of the cardiovascular system (HR x SV) whereas SV represents the volume of blood pumped out of the left ventricle with each beat (3).
  • Stress, stimulants, recovery status and blood volume (hydration) can all impact resting, exercising and MHR – as they can vary from day-to-day, so too can a person’s MHR change.

While more than 30 different MHR formulas exist, the Fox and Haskell model (220-age) is perhaps the most popular given its simplicity of use, although it also has one of the largest margins of error – this error is estimated to be approximately 12 bpm (one standard deviation) (4, 5). What this means as an example, is that for a group of 100, 20-year old individuals, 68% would have a true MHR ranging between 188-212 bpm (220-age ± 12 bpm) (Figure 1). Expanding to 2 standard deviations or approximately 95% of the group, the error doubles to 24 bpm (i.e., MHR = 176-224 bpm); 3 standard deviations represents approximately 99% of the group with the error expanding to 36 bpm on either side of the estimate (i.e., MHR = 164-236 bpm). Furthermore, this formula tends to over-estimate in younger individuals (e.g., a 25-year old may never attain 195 bpm) and tends to under-estimate in older adults (e.g., a 60-year old can exceed 160 bpm). You can now see the probability of error. A takeaway here is that if a MHR formula is to be used (which shouldn’t), consider one with smaller errors (e.g., Tanaka = 208 – {0.7 x age}).

Figure 1: Error example of the 220-age formula for estimating MHR in a 20-year old


Zone Methodology

Most models incorporate percentiles to distinguish their zones (e.g., 60-70% MHR), selecting values often based on arbitrary ideas, simplicity or mathematical convenience rather than evidence-based science, with some subscribing to an assumption that each zone range optimally evokes some specific metabolic response (e.g., 60-75% MHR = fat-burning zone). Unfortunately, no consistent evidence exits to support the notion that a specific intensity evokes that same adaptation in all people. For example, in some individuals their optimal fat-burning zone may occur at 55-60% of MHR, whereas in others, it may as high as 80-85% MHR. The truth is that our metabolism is as unique as our fingerprint and is influenced by gender (women typically burn more fat than men at rest and at sub-maximal intensities), hormonal levels (e.g., estrogen, human growth hormone), diet (very influential – high fat diets can increase fat utilization), adaptations to exercise specificity (e.g., high-intensity exercise can promote greater carbohydrate storage and utilization 24-7), genetics (e.g., ectomorphs – tall and slim favor carbohydrates over endomorphs – heavier who favor fats), medications and more. So then, how can we define a zone by some generic range? Take MyZone as an example which incorporates a 5-zone model – Grey, Blue, Green, Yellow and Red with each representing a specific intensity range (e.g., 50-59% MHR; 60-69% MHR, etc.). Does each zone represent a specific physiological or metabolic event? Doubtful when using a one-size fits all approach. Granted, they, like some others use percentile ranges for other purposes – while they do not represent physiological or metabolic zones, they can be used to represent intensities from which accomplishments, recognitions, badges or rewards are attained (i.e., gameification). Using this data, progress and adherence is supposed to be measured. Here to, we need to understand the limitations to exercising heart rate which also influenced by many events that include:

  • Blood volume – a dehydrated body generally decreases blood volume, subsequently increasing HR responses above normal – does that mean you are rewarded with more points for being dehydrated?
  • Simulants (e.g., caffeine) – they can activate the sympathetic nervous system which accelerates HR responses above normal – again, is one rewarded with more points for taking a stimulant?
  • Stress and lack of recovery – a body not afforded adequate recovery from exercise or life stress may demonstrate elevated HR responses above normal.
  • Fitness improvements – As mentioned previously, training adaptations lower HR responses due to improved cardiorespiratory efficiency – attaining pre-set intensity ranges can become more difficult over time (i.e., a person finds it harder to earn zone points due to improved fitness). Why should they be penalized for becoming more fit?

We should therefore ask – if tracking time or attainment in zones is so inconsistent when derived from %MHR, is it really a valid indicator of adherence, progress or improvement?

Anaerobic Training and HR

Any time exercise intensity changes, the body’s cardiopulmonary system adapts to meet the new demands, but unfortunately this take time – anywhere from 30-45 seconds up to several minutes, depending upon the intensity challenge (6,7). The ability of the body to meet current energy demands is known as attaining steady-state (SS), often referred to as getting the ‘second wind,’ – it essentially represents HR responses matching work demands. Why do we care about SS-intensity exercise? SS-HR responses during sub-maximal work (i.e., outside of resting HR or MHR) correlate decently with oxygen consumption (VO2), from which calories can be estimated consistently. But, this correlation only applies to SS-HR responses during exercise and not to non-SS exercise (3,6). Considering the popularity of many of today’s workouts where work intervals are generally performed for less than 3-minutes (e.g., HIIT-type training, resistance training sets), they mostly involve non-SS HR response, rather than SS-HR responses. Subsequently, the HR response measured does not necessarily reflect the actual work performed by the body. As proof of concept of this HR-response lag or to demonstrate the non-SS nature of interval-type training, conduct the following simple test:

  • Perform a SS, light-to-moderate intensity bout of exercise for 4-minutes while monitoring and recording HR response (by 4-min, you HR should ideally level off, attaining a SS-response). Next, perform an all-out 60-75 sec bout of high-intensity exercise before returning to an easy pace to recover – complete the following tasks:
  1. How long did it take for the HR response to start climbing? Had it increased much by 10-sec? How about at the 30-sec mark? In fact, it may still be climbing by the end of the work interval.
  2. Monitor HR response during the first 30-seconds of recovery. Did HR continue to climb higher during the early phase of recovery or did it being to drop immediately following the end of the exercise bout?

What this translates to is that HR measured during interval-type training cannot be used to estimate true work performed by the body or calories, given its delayed response time – therefore it is only a number. But, as a number it can still hold some value if the same work intensity becomes performed consistently at lower non-SS HR responses or if HR during the recovery phase decreases more rapidly (i.e., both imply improved cardiopulmonary efficiency).

What conclusions do we take from this? We agree that mathematical formulas, especially 220-age are flawed, as are zones using arbitrarily defined ranges. Aiming to score or aggregate time spent in zones based off exercising HR is too inconsistent and influenced by far too many variables unrelated to exercise effort. Additionally, some models fail to adjust their zones to accommodate for training adaptations. Furthermore, during non-SS, interval-type exercise, HR responses do not reflect actual physiological work performed by the body (i.e., time delay), nor do the HR responses accurately estimate calories.

So then, should we discard zone methodology? Absolutely not… Zone methodology offers the potential to systematically compartmentalize adaptions much like we witness in resistance training (i.e., endurance, hypertrophy, strength). However, we need to rely upon more accurate methodologies that impose the appropriate demands upon the body’s systems to evoke the desired adaptations (e.g., fat-burning efficiency, anaerobic capacity).

The most logical solution lies with utilizing more individualized zone methodology derived from a person’s unique metabolic markers (i.e., Ventilatory Threshold One – VT1; Ventilatory Threshold Two – VT2) rather than from generic formulas of MHR (3,7).

  • VT1 is a metabolic marker of aerobic efficiency and provides great insight to what we burn as fuel (e.g., fats, carbohydrates) – aka caloric quality. Relevance here is that it will greatly influence what we burn during SS-exercise and more importantly, what we burn as fuels throughout the day.
  • VT2 is a metabolic marker of anaerobic capacity and provides insight into caloric quantity (i.e., the number of calories which hold relevance in performance and perhaps with weight loss).

The beauty of using individualized zone programs derived from unique metabolic markers is that they can be continuously adjusted as the body undergoes adaptation, providing more realistic means to monitor progress and achievements. Keep in mind however, even with this model, there is no solution for accurately measuring non-SS HR responses – we need to simply accept that inevitable truth.

Fortunately, today’s technology and wearable devices are advancing very quickly and it is just a matter of time before the innovators within the fitness industry transition away from MHR-based zone methodology and adopt better models based upon metabolism (Ambiotex™ is one innovative tech company who has developed a wearable shirt that measures these metabolic biomarkers). Given the sheer size of the larger commercial users of HR Zones such as Orange Theory Fitness™ and MyZone, I believe they may be the catalyst to shift the entire fitness industry forward. In closing, if you’re one who thrives on being innovative and evidence-based, perhaps it is time for you to start considering how and when you’ll make the transition to these cutting-edge ideas and applications.


  1. Robergs R, and Landwehr R, (2002). The surprising history of the HRmax= 220-age equation. Journal of Exercise Physiology Online, 5(2), ISSN 1097-9751.
  2. Bryant CX, Merrill S, and Green DJ, (editors), (2014). Personal trainer manual (5th edition). San Diego, CA. American Council on Exercise.
  3. Kenney WL, Wilmore JH, and Costill DL, (2012). Physiology of sport and exercise (5th edition). Champaign, Il., Human Kinetics.
  4. Skinner JS, Bryant CX, Merrill S, and Green DJ, (editors), (2015). Medical exercise specialist manual. San Diego, CA. American Council on Exercise.
  5. Riebe D (editor) (2018). ACSM’s guidelines for exercise testing and prescription (10th edition). Riverwoods, Il, Wolters Kluwer Health.
  6. Kraemer WJ, Fleck SJ and Deschenes MR (2012). Exercise Physiology: Integrating theory and application. Baltimore, MD. Lippincott Williams and Wilkins.
  7. Pocari J, Bryant CX and Comana F, (2015). Exercise Phsyiology. Philadelphia, PA. F.A. Davis and Company.
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The Author

Fabio Comana

Fabio Comana

Fabio Comana, M.A., M.S., is a faculty instructor at San Diego State University, and University of California, San Diego and the National Academy of Sports Medicine (NASM), and president of Genesis Wellness Group. Previously as an American Council on Exercise (ACE) exercise physiologist, he was the original creator of ACE’s IFT™ model and ACE’s live Personal Trainer educational workshops. Prior experiences include collegiate head coaching, university strength and conditioning coaching; and opening/managing clubs for Club One. An international presenter at multiple health and fitness events, he is also a spokesperson featured in multiple media outlets and an accomplished chapter and book author.