I have been a bit of a runner for most of my life. Where I grew up in the Highlands of Scotland there was plenty of cross country running tracks to be explored. Of course that was mixed in with mountain biking, fly fishing, kayaking, sailing, football, hiking, rock-climbing… you get the picture.
I have decided to run a year of events for a cancer charity. You may have seen the social media posts floating around (link here). Whilst I’m out on my many various training sessions I often get time to chat to other runners or get to contemplate and consolidate my thoughts.
Recently, I was engaged in a lengthy conversation with another runner about training methods. Let me set the scene. We had just finished a swim of 1000m and a 5km run. We were cooling down with a short jog along the beach. The conversation went something like this:
Other runner: You’re certainly improving, I can see that you are climbing positions in the field.
Me: Thanks! I’m feeling a lot fitter and keen to keep progressing.
Other runner: Yeah, you’ll get there. Actually, we should get together some time and train if you like?
Me: That would be great! I am always keen to learn off others to improve my own performance.
Other runner: Well, if you are keen to improve your performance you might be interested in MAF training. I tried it and I feel considerably fitter since doing it.
Me: (For a split second my mind skipped to Married At First Sight training and then thought he must have made a mistake) Oh you mean MAP training? (not actually realising that I then would have to explain what my made up abbreviation actually meant!) What does it involve?
Other Runner: No no, MAF training (Phew humiliation of bullshitting averted!) Well, you minus your age from 180 (set as your maximum heart rate) and then aim to maintain the heart rate whilst gradually increasing your running speed. It’s really worked for me. I’ve been able to gradually increase my pace whilst maintaining a steady heart rate.
Me: Oh right, wow! How long did it take you to get to an optimum level?
Other runner: About a year…
After this we kind of chewed the fat about future events and meeting up for training together. So, MAF training eh? Now my usual go to thought for any ideas I get when running or when someone suggests something to me is, “How does it relate to pain management?” In any case, I decided to find out more about MAF training.
Off I went to scour the research search engines. Unfortunately, there is limited empirical evidence on the MAF method and upon reading Dr Maffetone’s white paper (Maffetone, 2016) the only supporting evidence is a poster he and his co-researcher delivered in 2015 (Hoeg & Maffetone, 2015). The notion behind the MAF method is to use the ‘180 Formula’ and adopt an optimal running gait… uh…whatever that is??
What is MAF?
It’s the abbreviation for Maximum Aerobic Function. The founder of the methodology, Dr. Phillip Maffetone, has worked in the field of ‘human biology, kinesiology, physiotherapy and Chinese medicine… is a pioneer in the field of biofeedback’.
Dr. Maffetone explains that the MAF method harnesses the body’s true aerobic or energy system which is the fat-burning system. He continues to explain that any impairment in the aerobic system can lead to global ill health problems in the body including diabetes, cancer, chronic heart disease or joint disease. This also qualifies for the trained athlete leading to injuries, lack of improvement and overtraining.
Dr Maffetone continues by recommending ‘a period of low-intensity training and natural movement (again, I have to ask, “what does he mean by natural movement?”) to improve aerobic function and health before embarking on higher-intensity training.’ His justification for this being that high intensity, short rest, high volume fitness programs may result in fitness gains but do not provide adequate health gains (referring to increases in metabolic and oxidative stress, decrease immune function, release of pro-inflammatory mediators etc).
So, the method of MAF focuses on improving the function of the aerobic system. To do this Dr Maffetone has developed a training formula based upon what he claims to be years of research and utilising the measurement of heart rate (HR). The 180 formula has been selected based upon the utilisation of correct substrates in this case fatty acids.
Why a 180 formula?
Dr Maffetone argues that once heart rate begins to exceed 150bpm this tips the body into anaerobic threshold meaning that the body starts to burn sugar rather than fat. Anaerobic function creates higher levels of biochemical stress, decreases immune function, increases inflammation and impairs fat burning. I suspect what he is referring to is prolonged anaerobic function…?
What I find particularly interesting is the sources of energy he refers to under an umbrella term ‘sugar’ all have an important role to play providing fuel for exercise. This includes, glycogen, lactate and blood sugar. Whilst they may all be related to ‘sugar’ each of these substrates are important. Take lactate for example, sprint training is part of a long distance runners exercise repertoire. Lactate can actually be metabolised to produce ATP (the bodies energy molecule). Sure, there appears to be agreement that fat is the bodies richest fuel source. However, anyone reading may not be surprised when I say the body is highly adaptable and has been known to shift energy utilisation based upon the most abundant fuel source (Volek, Noakes, & Phinney, 2015).
As Dr Maffetone claims, a heart rate above 150bpm appears to purely utilise the process of anaerobic glycolysis (sugar based fuel utilisation) rather than fat. The formula is displayed below:
180 – (persons age) = (target HR)
Dr Maffetone acknowledges the idiosyncrasies in individuals fitness and health and so provided additional instructions for HR monitoring:
a. If you have or are recovering from a major illness (heart disease, any operation or hospital stay, etc.) or are on any regular medication, subtract an additional 10.
b. If you are injured, have regressed in training or competition, get more than two colds or bouts of flu or other infection per year, have seasonal allergies or asthma, or if you have been inconsistent or are just getting back into training, subtract an additional 5.
c. If you have been training consistently (at least four times weekly) for up to two years without any of the problems just mentioned, keep the number (180-age) as maximum.
d. If you have been training for more than two years without any of the problems listed above, and have made progress in athletic competition without injury, add 5.
The MAF assessment and additional instructions are claimed to have been devised following “years of clinical assessments.” Although, Dr Maffetone’s reasoning for why specific training at low intensity has empirical evidence his 180 formula lacks efficacy. I want to briefly highlight my issues with such a stringent method that seems to demonstrate a large margin of error.
We know very little of Dr Maffetone’s work except for the fact that he has conducted “years of clinical assessments.” Questions that immediately spring to mind include:
- Do we assume that all his clinical assessments were in laboratory setting? He does state that a running test should be done on a 400m track. I don’t know many that have access to this facility.
- How did he set up his clinical assessments?
- Did he accommodate for huge variation comparing running terrain, footwear, temperature, the elements (wind, rain, heat, cold), competition vs recreational, body type, genetics, previous exercise history, sleep, diet, resilience, pessimism vs optimism, behaviour, culture? The list can go on as I’m sure you understand.
- Further to point 3, how can we continue to think that a univariate formula will accommodate multivariate differences such as gender, development, health, socio-economics etc.
- He discusses biomechanics as if there should be an optimal way to run but how can you accommodate for that particularly with reference to the above factors and that much of the evidence has refuted the theories of biomechanics.
- What did he compare his formula too? He states that his 180 formula is not a replacement for executed laboratory tests… although it usually corresponds with them.
- There has been a significant amount of research published supporting the effects of resistance training and high intensity exercise on chronic disease such as diabetes, metabolic syndrome, cardiac related problems. This type of training utilises fast twitch fibres and primarily the process of anaerobic glycolysis where lactate and glycogen are utilised as a fuel source (Sullivan & Baker, 2017). So, does this refute Dr Maffetone’s proposal that low intensity exercise is a better option to utilise fat as a fuel source?
Overall, there are a number of problems with Dr Maffetone’s reasoning. In particular, and with all heart rate equations, the yardstick he uses is univariate. Human beings are not univariate. Multiples systems are working at once and we have to consider context in all circumstances. He uses heart rate to justify a method, yet individual heart rates in chronic disease or exercise with this method is so arbitrary it can be difficult to broadly apply the method to individuals that may be of the same age or health/fitness level etc. As mentioned above, there are too many variables to consider. I respect that it did work for the guy I chatted too, however I did not probe enough into how he performed his training and I suspect that he trained for one particular sport. Due to the lack of evidence and what I have highlighted above, I remain sceptical.
Following this investigation of the 180 formula I decided to continue reading around other maximum heart rate (MHR) formulas, in particular the Karvonen method and to discuss its utility in pain management strategies such as exercise induced hypoalgesia (EIH). I admit this is a rather large and complex area of research and the evidence base is extensive. I do my best to cover what I believe is derived from current evidence. However if anyone reading this wants to share their knowledge then please leave a comment.
Maximum Heart Rate Theory?
The most frequently used formula for identifying maximum heart rate is the age predicted method of HR max (220bpm – age). Typically, training zones specific to the fuel source are then calculated using a percentage of heart rate. (see table 1).
The familiar heart rate HR max formula of 220 – age is ubiquitous throughout healthcare. After a bit of digging around, I found that the 220 formula has been subject to quite a bit of scrutiny. Predominantly, its utility has been in the 60+ age group because of the prevalence of heart disease in this population.
Yet, the MHR formula, despite being a cornerstone, is contentious and may not be appropriate to population groups not living with heart disease. Like a lot of things we are identifying (posture, lifting, sitting, movement), context has a profound effect on our physiology.
It turns out MHR was not based on original research and was more an observation derived from 11 references by one of its founding physicians – Fox and Haskell (Robergs & Landwehr, 2002). Robergs & Landwehr, (2002) explain in their paper that most MHR formulas have no scientific merit due to large prediction errors. This is because MHR formulas represent an age-based univariate prediction equation. Studies have shown the 220 – age equation fails to provide adequate measurement of heart rate training zones with variation of up to 12 beats per minute (Black et al., 2017; Sluka., 2016). The interactions within humans and with context are not univariate as we are well aware from research in causation (Evans, Lucas, & Kerry, 2017). Heart rate itself does not remain at a steady state it has multiple fluctuations based on context.
This is crucial information particularly if the physiotherapy profession’s stance is firmly within exercise. I have been witness to, and engaged in, many conversations regarding exercise erroneously represented as the panacea for chronic disease, injury and even pain management. Commonly, we use aerobic exercise prescription as a management approach. There is a high consensus that the axiomatic formula for prescribing aerobic exercise is to use HRmax = 220-age. Please note this is not universally so much as significantly used.
The Karvonen method
Named after the Finnish physiologist and also known as Heart Rate Reserve, the Karvonen method calculates the lower- and upper-threshold HR levels at a percentage of the difference between resting and maximum HR.
The formula is expressed below:
- Calculate predicted HRmax
HRmax = 208 – 0.7 x Age
- Calculate LLthr:
LLthr = [(HRmax – HRrest) x 0.50} + HRrest
- Calculate ULthr:
ULthr = {(HRmax – HRrest) x 0.85) + HRrest
The Karvonen method was developed to consider the additional variable of HRrest as well as age, which is the main variable in the 220 equation. It also aims to optimise heart rate range between 50 and 85% of maximum heart rate. You may have noticed that the HRmax is 208bpm. The Karvonen method was traditionally used with the popular 220 – age equation. A paper by She, Nakamura, Makino, Ohyama, & Hashimoto, (2015) selected a series of heart rate formulas for use with the Karvonen method to calculate exercise intensity in male university students. They author found the traditional 220-age equation and the formula proposed by Miller et al., (1993) (HRmax = 217 − 0.85 × age) to be most suitable for 20 year old males.
Also included in She et al’s paper, researchers Tanaka, Monahan, & Seals, (2001) conducted a meta-analysis investigating where gender or habitual physical activity status had an influence on HRmax – age relation. The results of their study obtained the regression equation of 208 -0.7 x age to predict HRmax. Interestingly, they found that ‘HRmax was strongly and inversely related to age in both men and women and when all subjects (sedentary, gender, active and endurance subjects) were combined the regression equation was 208 – 0.7 x age.’ They also compared the traditional 220 -age equation with the regression equation and identified that ‘the traditional equation overestimates HRmax in young adults, intersects with the present equation at age 40 years and then increasingly underestimates HRmax with further increases in age.’ So, use of 220 – age in younger and older age populations would appear to create issues around heart rate training zones. However, what with the fluctuations in heart rate in relation to context, using heart rate may be futile. Thus the use of subjective ratings of exertion (RPE) such as tiredness, difficulty, out of breath may be a better measure as eloquently explained by Gunnar Borg back in 1982 (Borg, 1982).
Exercise Induced Hypoalgesia
Does this mean that heart rate measurement (such as the equation from Tanaka et al) can be used as a means to promote EIH?
EIH has been shown to occur with resistance, isometric and aerobic exercise. Regardless of the type of exercise, pain relief following exercise is systemic. This has been repeatedly shown in healthy subjects (Black et al., 2017; Sluka, 2016). Studies have shown VO2max as the preferred means to determine appropriate percentages of exercise intensity for promoting EIH. Naugle, Fillingim, & Iii, (2012) conducted a meta-analytical (MA) review of the effects EIH in aerobic and isometric exercise. They examined the literature in “healthy” subjects and individuals living with chronic pain. The studies selected in the MA review identified a percentage of 75% of VO2max (moderate to high intensity) whilst exercising for 30 minutes (healthy subjects) was effective in promoting EIH. This is also supported by Sluka, (2016). In chronic pain subjects percentage of VO2max range from 50-70% (low to moderate) with exercise duration ranging from around 10 – 30 minutes. The caveat here is that EIH did not occur in individuals with chronic widespread pain and only in those with regional chronic pain conditions.
Whist Naugle’s MA review did highlight a percentage of VO2 max and duration of exercise at varying intensities, it was still difficult to determine the appropriate dose of exercise to produce hypoalgesia.
We can see that dosing exercise to produce EIH is a challenge. Measuring VO2max is also a challenge as it requires specialised laboratory equipment which is not readily available clinically.
What of using HR as a means of producing EIH? Is there really any point? It is still worth exploring how we might be able to promote EIH clinically using HR equations. Heart rate monitors are readily available with the explosion of wearable biofeedback technology.
A number of methods have been derived from VO2max and HRmax regression equations including that of Tanaka et al (2001). Non-exercise equations and heart rate predictions have been the basis of studies in an attempt to identify appropriate exercise intensity levels for a range of population sub-groups (Barboza, dos Santos Nogueira, & Pompeu, 2017; Rexhepi & Brestovci, 2014; Uth, Sørensen, Overgaard, & Pedersen, 2004). However, the VO2 estimation equations are incredibly complicated and if you’re like me who has a fear of maths equations it’s likely something that you won’t see the point of doing clinically. For your interest I’ve attached one of the equations below
So it would seem, the variability in heart rate, the challenge of reliably measuring heart rate, the multifactorial nature of pain, the individual differences of populations and the effect of context make producing EIH clinically kinda difficult. That doesn’t mean we shouldn’t try. Clinicians need to consider a broader context of factors when implementing exercise principles.
A reflection I can take away from my reading is that using the traditional HRmax equation (220 – age) may only be effective for specific population sub-groups and that using the HRmax regression equations, developed by Tanaka et al may provide more reliable heart rate training zones in various age populations. As for individuals living with persistent pain, perhaps identifying what their HRrest is prior to commencing exercise and using the adapted Karvonen method could prove useful. In addition, determining what their exercise levels are, what psychosocial barriers are present and what is of value in terms of activity in their lives may provide a more appropriate means of determining an adequate heart rate training zone and may facilitate some apprehension towards the dread flare up that frequently occurs following activity.
Summary
So, to go back to the 180formula. I remain sceptical. Dr Maffetone in his wisdom highlights some additional messages that I feel are nothing short of plain common sense.
- Working at a lower intensity is easier for the body and utilises the aerobic fat burning system.
- The message that health is not the same as fitness.
My own thoughts are as follows:
- Due to the variability of heart rate, the challenges of adequately measuring heart rate, the individual differences of humans, the varying environments that people train, changes in health status, sleep issues makes monitoring heart rate training adequately incredibly challenging.
- The impact of psychosocial barriers needs to be factored in to Dr Maffetone’s work if he is to provide any form of reliable HR measurement for adequate training.
- Personally, I think listening to your body and the use of RPE is altogether a better method.
A final thought would be the all too familiar message of ‘one size does not fit all’ is highly appropriate. As for the 180formula, I will remain sceptical.
Thanks for having a read.
TNP.
References
Barboza, J. A., dos Santos Nogueira, F., & Pompeu, F. A. M. S. (2017). A New Accurate Model to Predict Maximal Heart Rate. Journal of Exercise Physiologyonline, 20(5), 23–28.
Black, C. D., Huber, J. K., Ellingson, L. D., Ade, C. J., Taylor, E. L., Griffeth, E. M., … Sutterfield, S. L. (2017). Exercise-Induced Hypoalgesia Is Not Influenced by Physical Activity Type and Amount, (9), 975–982. https://doi.org/10.1249/MSS.0000000000001186
Borg, G. A. (1982). Psychophysical bases of perceived exertion. Medicine and Science in Sports and Exercise, 14(5), 377–381. https://doi.org/10.1249/00005768-198205000-00012
Evans, D. W., Lucas, N., & Kerry, R. (2017). The form of causation in health, disease and intervention: biopsychosocial dispositionalism, conserved quantity transfers and dualist mechanistic chains. Medicine, Health Care, and Philosophy, 0(0), 0. https://doi.org/10.1007/s11019-017-9753-6
Hoeg, T. B., & Maffetone, P. B. (2015). The Development and Initial Assessment of a Novel Heart Rate Training Formula. Wilderness & Environmental Medicine, 26(4), e5. https://doi.org/10.1016/j.wem.2015.03.016
Katch, V. L., McArdle, W. D., & Katch, F. I. (2011). Training the Anaerobic and Aerobic Energy Systems. In Essentials of Exercise Physiology(4th Editio, pp. 409–442). Lippincott Williams & Wilkins.
Linda S Pescatello. (2014). ACSM’s guidelines for exercise testing and prescription 9th ed. 2014. Philadelphia : Wolters Kluwer/Lippincott Williams & Wilkins Health, ©2014.https://doi.org/10.1017/CBO9781107415324.004
Maffetone, P. (2016). White Paper: MAF Exercise Heart Rate. How it can help improve health and sports performance.
Naugle, K. M., Fillingim, R. B., & Iii, J. L. R. (2012). A meta-analytic review of the hypoalgesic effects of exercise. The Journal of Pain, 13(12), 1139–1150. https://doi.org/10.1016/j.jpain.2012.09.006.A
Rexhepi, A. M., & Brestovci, B. (2014). Prediction of VO 2 max based on age , body mass , and resting heart rate, 15(1), 56–59. https://doi.org/10.2478/humo-2014-0003
Robergs, R. A., & Landwehr, R. (2002). THE SURPRISING HISTORY OF THE “HRmax=220-age” EQUATION ROBERT. Journal of Exercise Physiology Online, 5(2), 1–10.
She, J., Nakamura, H., Makino, K., Ohyama, Y., & Hashimoto, H. (2015). Selection of suitable maximum-heart-rate formulas for use with Karvonen formula to calculate exercise intensity. International Journal of Automation and Computing, 12(1), 62–69. https://doi.org/10.1007/s11633-014-0824-3
Sluka, K. A. (2016). Mechanisms and Management of Pain for the Physical Therapist(Second Edi). Philadelphia: IASP Press.
Sullivan, J. M., & Baker, A. (2017). The Barbell Prescription: Strength Training for Life After 40. The Aasgaard Company.
Tanaka, H., Monahan, K. D., & Seals, D. R. (2001). Age-predicted maximal heart rate revisited. Journal of the American College of Cardiology, 37(1), 153–156. https://doi.org/10.1016/S0735-1097(00)01054-8
Uth, N., Sørensen, H., Overgaard, K., & Pedersen, P. K. (2004). Estimation of VO2max from the ratio between HRmax and HRrest – The heart rate ratio method. European Journal of Applied Physiology, 91(1), 111–115. https://doi.org/10.1007/s00421-003-0988-y
Volek, J. S., Noakes, T., & Phinney, S. D. (2015). Rethinking fat as a fuel for endurance exercise. European Journal of Sport Science, 15(1), 13–20. https://doi.org/10.1080/17461391.2014.959564
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