Are we Over Complicating Health?

“If we could measure the precise position and trajectory of every particle in the universe, could we predict the future?” – Laplace

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Like our universe, the body is a complex system that is influenced by random inputs, and subject to the constraints of chaos theory. As we race down the path of reductionism by measuring exponentially more biomarkers at increasingly smaller scale, are we losing sight of the possibility that there may be a far more effective network of metrics available? Could simply re-discovering what it means to be healthy and coming up with a set of heuristics for good health be the key to vastly improved health outcomes? Or put another way, are we focusing too much on trying to understand predictive metrics at the micro level and spending too little time rethinking the basics?

This nation is currently faced with a number of pandemics that are driven, for the most part, by two factors – immobility and poor food choice. Could we not just find a way to motivate mobility and good food choice. As creatures of evolution, we have not adapted quickly enough to changes in lifestyle, and studies have shown that by simply sitting less we could vastly impact our health outcomes. Instead of measuring how many calories we are burning, could we not start simply measuring our sitting to standing ratio throughout the day?

Instead of measuring calories consumed, which, for the most part, motivates reduction of fat content and dangerously skews our diet, could we simply measure the ratio of high to low quality food in any given meal. A metric like this would naturally motivate a far more effective mechanism for optimizing our diet – under optimal nutrition conditions, our bodies naturally regulate food consumption. Could we not derive new metrics for improving health from the constraints that our bodies and minds evolved with for millions of years – building motivation based on these factors could have a far greater impact on health than from any one biomarker.

Motivating mobility and nutrition are the quick wins in health, however, we could apply this thinking to other areas. Instead of measuring relative levels of stress by checking cortisol levels and understanding heart rate variability, could we not simply measure minutes of meditation, laugh frequency or minutes smiled per day in a way that positively influences these behaviors? Proponents of the upcoming consumer health revolution have proven that at a basic level, quantifying yourself seems to be a good motivator for improving behavior and is becoming increasingly easy to do.

On the flip-side, there is likely value in measuring ourselves at the particle level because biomarkers are becoming increasingly predictive of heath outcomes and there is no doubt that they are a very powerful tool. Nonetheless, without first getting the basics right and establishing a basic set of heuristics for good health would be like trying to cram big rocks into a glass full of sand. Predicting the future by understanding the parts is difficult (or impossible) however, observing the parts is a powerful way of reinforcing good behaviors. So, perhaps there is a middle ground, a meeting point where the real metrics we establish for good health are focused primarily on simply being healthy in every way possible, and the biomarkers are the reinforcing feedback mechanism. Or another way to think about it, is that a new network of behavioral focused metrics are the big rocks, the 90%, and the biometrics, the sand, are designed for further optimization and positive reinforcement.

Assuming we evolve towards this new paradigm, is there a way of building these types of feedback mechanisms in to our lives so that they are seamless? The quantified self and consumer health movements are rapidly evolving towards the more is better paradigm – more markers, more accuracy, more data points and along with these advancements, we are learning that seamless metrics for health are possible. The increase in granularity is probably a good thing, but do we need to sit back and re-evaluate our basic assumptions for good health and re-design the foundation metrics that we use to drive good behavior.

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