Exercise Responders vs. Non-Responders: Just a Matter of Time?

This post was inspired by a recent “Hypothesis and Theory” article I recently read >>> Anabolic Heterogeneity Following Resistance Training: A Role for Circadian Rhythm? (Camera, 2018).

Circadian rhythms are a hot topic right now. Fields such as medicine, human health and performance, in particular, are coming to the realization that things we once thought were “black and white” — such as a “one size fits all” approach for drug prescription or that exercise training is uniformly beneficial for all — are not so.

The above confusion, why do some people respond favorably to a certain treatment or intervention while other don’t, was once thought (and still is, to some degree) to be a problem of interindividual variability, maybe even inadequacy. This concept, in a nutshell, proposes that some people might just not have “it” — “it” perhaps being the “right” genetic makeup, hormonal mileu, fiber composition, or internal motivation required to respond to the provided treatment. The concept also proposes that there isn’t much we can do about it.

This led to the concept of “responders” and “non-responders” in the research literature, to where we now have a full continuum of “non”, “low”, “moderate”, “high”, and “extreme” responders characterized in multiple different exercise training studies.

There are many problems with the “non-responder” concept.

Problem #1: inherent in the concept of “responder” vs. “non-responder” is the vagueness of these names. In the article cited above, Camera indeed states that “such terminology is subjective and depending on the statistical procedures (if any) used to classify/segregate the participants, can have no physiological or mechanistic basis” Who determines (besides statistical software) whether or not a 2–3% increase in VO2 max qualifies as a “response” or a “non-response” or furthermore, whether this response is “low”, “moderate”, or “extreme?” Only in relation to other individuals in the study does one get classified into a certain class of responder.An individual’s classification is largely at the discretion of the researcher conducting the study, and the cut off points they choose to set.

Problem #2: sourced from an article by Booth and Laye (2010), this issue seems intuitive and points to a problem I’ll discuss next. By using the term “non-responder” — we imply that “no exercise-induced adaptations occur.” This seems absurd; given the multiplicity of genomic, molecular, cardiovascular, and muscular adaptations that are up, down, and sideways regulated in response to exercise. I hate to use absolutes — but when you exercise, something WILL change.

Problem #3: If we conduct a training study, provide a supplement or medication for x amount of time, or implement some sort of diet or lifestyle intervention and fail to see an improvement — then perhaps we just aren’t measuring the right thing! Many studies are powered to detect a certain change in n participants with a certain power, and the biomarkers they choose to study are just that, the ONLY ones they have chosen to study. Even the most comprehensive of panels will leave out literally thousands of other potentially changed factors. You can only measure so much.

It might just be that we design a fantastic exercise training regimen, delicately refine the dose of a particular medicine to the patient’s exact need, only to find ZERO statistically significant change in one or more participants upon analysis — because we looked at the wrong thing

Or perhaps…just at the wrong time.

Can the time at which you exercise influence the training adaptations that you receive?

Camera, in the same article, makes the case that this is so. Circadian rhythms — the molecular clocks that control our physiological cycles such as sleep-wake, body temperature, hormonal secretion, activity, protein expression and enzyme activity — have the role of “optimizing” every event that occurs within us.

Skeletal muscle (what contracts during exercise) expresses circadian clocks, which rhythmically regulate gene expression involved in muscle function and of particular importance to exercise, growth and metabolism.

Timing exercise when cortisol is lowest and anabolic hormones are highest might lead to the greatest exercise-adaptations. Source: Camera (2018)

Our skeletal muscles aren’t constantly primed to produce maximum power, infinitely plastic, or consistently flexible (you can easily demonstrate this by trying a simple toe-touch in the morning vs. the evening…to illustrate the point). No — just as there are peak times for mental activity, there are peak times for cardiovascular and anaerobic output, peak times during the day when we can perform at our best — and these show large individual variability.

One recent and influential study demonstrated this (neat video abstract below!) Highlighting that peak performance time in athletes varied substantially between “larks” and “owls” (a.k.a “morning/evening types”). Classifying individuals based on their “chronotype” was able to accurately predict when they would perform their best on a test of cardiovascular endurance.

(VIDEO) The Impact of Circadian Phenotype and Time since Awakening on Diurnal Performance in Athletes

Interestingly, rather than “time of day”, the internal or biological time (i.e. the time since they were accustomed to awakening) predicted peak performance. Even within the span of one day, individuals could vary in performance as much as 26%!

Exercise performance as a function of time of day in early [B], intermediate [C] and late [D] chronotypes. Source: Facer-childs et al. (2015)

Early and intermediate chronotypes = peak performance around 5.5–6 hours after awakening

Late chronotypes = peak performance around 11 hours after biological start of the day (wake time)

“…results leave no doubt that the correct determination of an athlete’s personal best performance requires consideration of circadian phenotype, performance evaluation at different times of day, and analysis of performance as a function of time since entrained awakening.”

What application does this have to training and research, and to our concept of exercise “responders.”

It turns out that, due to the rhythmic fluctuations in things such as growth hormones (Insulin like growth factor-1), regulators of muscle protein transcription (MyoD), and our stress-hormone cortisol, certain pathways respond in a different manner to training at different times of day, and therefore the acute anabolic (growth) response to exercise is highly influenced by TIME OF DAY. If this is the case, then time of day similarly influences the adaptations — muscle hypertrophy, body composition, strength and power changes — that occur with training.

This might turn out to be just as important as how much you sleep before and after training, and what you eat surrounding your workout.

Additionally, the potential for an “optimal” time to perform resistance exercise in conjunction with circadian clock pathways and hormonal regulation raises the prospect of an “anabolic periodicity” such that certain times of the day may provide a greater overall cellular and systemic environment to maximize resistance exercise adaptation responses.

If an individual isn’t performing training at their “optimal time” — perhaps a “night owl” is forced to show up for training at 6 a.m— then it seems to reason that the adaptations they receive (or don’t) will be greatly attenuated. Put them in the same study, exercise them with another individual at the same time, and should they have different sleep-wake propensities and preferences, these two may respond entirely different.

Our “non-responder”, in this case might simply be a victim of study design. Rather than someone who fails to respond to exercise, she is someone who “fails to respond to “x” strength training regimen performed for “x” number of weeks at “x” time of day.”

The fatal flaw with the theory of exercise “non-responders” lies in that it places total dependence on the individual’s internal predisposition. Rather than the interplay of the Universe, failure to improve upon training or treatment rests on the individual — it’s “your fault” that the treatment didn’t work.

There are too many factors involved to just write off “no response” as an intrinsic individual factor, it’s absurd. Perhaps researchers, clinicians, and scientists, should blame themselves — for not paying enough attention to uniqueness, or rather, to assuming heterogeneity. Not giving thought to the fact that, like most things, a simple solution won’t suffice.

“One size fits all” needs to change to “one size fits one”. The age of precision medicine, while still in its infancy, will demand this.

Practical applications for this are hard to infer — the data just aren’t there yet. Studies showing identical responses for morning vs. evening exercise can’t offer any conclusions. Studies showing “periodicity” in a variety of hormones related to muscle growth have failed to document actual training adaptations— and only propose underlying possible mechanisms.

What I can infer is that when YOU LIKE to exercise is possibly the best time to exercise 1. because you’ll actually do it and 2. because our body has a fantastic way of telling us when it is primed to take on certain challenges. That is what the circadian rhythm evolved for — as an “ancitipatory mechanism” that would up regulate functions such as metabolic and stress hormones to “prepare” us for food, fight, or flight.

If you take to exercising the morning because you feel the strongest, the most alert, the most motivated — then your circadian signature is trying to tell you something. The optimal exercise adaptations are going to happen when you’re involved in a regimen you consistently do, and consistently advance through.

Technology allowed us to shy away from the sun as our giver of time and direction.

Paradoxically, technology now allows us to recognize the solar heritage of our inner selves. Let’s use it to our advantage and optimize medicine, fitness, and intellectual progress.

PhD candidate at the University of Florida — Science writing with a particular focus on exercise and nutrition interventions, aging, health, and disease.