Thaddeus and Chris at ERISFIT are consummate biohackers. They focus on how they can modify their nutrition, exercise, sleep, movement, and mindset to maximize performance and achieve greater levels of wellbeing. Their goal is to bring these tools and practices into the workplace, helping entrepreneurs maximize performance and effectiveness.
Biohacking and neurohacking are close cousins. Whereas biohacking focuses on the body, neurohacking focuses on the mind/brain interface. What they share is a spirit of ‘empowered responsibility,’ embracing self-experimentation in order to discover what works best for their unique physiology, track their experience with various quantified self tools, and sharing their results. They seemed like the perfect candidates to take Qualia for a test drive.
This summer, we gave them a one month sample. A few weeks ago, they gave their (very thorough!) review of the product.
Though it may be coincidental, the review begins with Thaddeus popping his first dose of Qualia and then breaking the Paleo f(x) deadlift record. We like that. :)
They go on to give an analysis of the choice of ingredients and dosages used in our nootropic stack, as well as mental performance parameters they tracked using Quantified Mind.
We were impressed with the effort put into the review, so we decided to respond in kind. We noticed some lack of clarity around our dosage choices, and realized we could do a better job explaining our scientific approach. Here is one excerpt we sent them and thought worth mentioning here:
One of our key differentiators and special sauce, beyond just big data analysis, is our approach to understanding and modeling complex regulatory processes. This is the theoretical framework within which big data analysis becomes particularly meaningful.
Science is about both data and theory: we need big and good data...and, also good hypothesis generation, identification of mechanisms and causal relationships, etc. Better models and hypotheses end up needing a lot less data to make a lot more meaning. And then with more data, it’s that much more meaning.
We’re focused on developing better understanding of synergy and combinatorics - what things do together differently than they do in isolation, what they do in different ratios, what they do that’s unique to a person’s individual biochemistry, etc. It’s not adequate to just look at what each nutrient or therapy or hack does in isolation, but need to look at what they do in synergy with other mechanisms, factoring timing, personalization, etc. This is where the structure of clinical trials fails us. In personalized medicine, we’re trying to understand how combinations of things work on specific individuals. But the structure of a clinical trial is to select exactly one thing, control for all other factors, compare that one thing to a placebo, and do it on as large a group as possible. But what happens when you you’re trying to understand how multiple compounds work together, in various ratios, for a specific individual? This requires a much more complex model of understanding how human physiology and biochemistry work, which is really at the heart of what we’re working to pioneer: including but beyond big data analysis, and into complex modeling.
We provided responses to all the comments they made in their review, and they have inserted them into their original post (in red). Check out the full review at ERISFIT.
Thanks again to Thaddeus & Chris!
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