My interest in personalized health began at the park with my son, shortly after his first brain surgery to treat a condition called hydrocephalus.
I remember the moment in full color. I’m pushing my five-month-old son in the baby swing, his head in a beanie to hide the semi-circular scar left behind by brain surgery. I am wondering if kids like him who love swings have better medical outcomes because of what centripetal force might do for his condition.
It’s a stupid question. It’s inane. It’s a desperate and — seemingly — an answerable one, if anyone had bothered to collect the data about whether kids with hydrocephalus on swings went longer between brain surgeries.
But of course no one has collected such data, and to even try is alarmingly difficult (I know this because I did actually try).
My son’s condition threatens, at any time, to send him to the hospital for brain surgery, and living with it contains a multitude of lessons. Everyone’s body is painfully, obviously different. Treatments work reasonably well for some people and not for others. The average time between surgeries is meaningless without a standard deviation, and the uncertainty is as bad as the condition itself.
In fact, basically everything we understand about human bodies and health is generalized from a small set of humans. The revolutionary change that we are about to witness is to run this learning engine in reverse: what we understand about our own bodies will be distilled from large sets of humans.
The significance of this is hard to overstate. In some ways it’s obvious that generalizing from small sets presents real challenges. For example, in 2000, the CDC revised the growth charts used to measure the health of infants, because the data they had used since 1977 were drawn from a small sample of white, middle class, formula-fed babies from southern Ohio. How likely is it that this data is very accurate reference material for an Indian-American infant born in San Francisco?
Similarly, lots of studies were only performed on men because it’s much easier to isolate the variables you care about if your study subjects don’t have menstrual cycles or pregnancies muddling the data.
Of course, good enough is often good enough. There is this remarkable property of statistics where if you sample something, you can make good predictions about what is happening with everything you didn’t measure. This is how ratings, political polls, and fitness advice work. We’re so used to it we don’t even question it anymore, but it’s worth remembering that all such techniques are really just very good guesswork. And as a result a lot of the nuance of individuality is lost.
If I trust some poll that tells me Ozark is popular but if I don’t like it, all I lose is time. If I rely on the same kind of data to figure out how much sugar, caffeine, exercise, omeprazole, sleep, or vitamin D to take, I could lose much more.
What’s different about how milk, exercise, or sleep affects me versus everyone else? What can I change about how I eat, how I move, and how I live so that I can feel better and live longer? What would it be worth to me to feel better every day?
Once you begin asking these questions you realize that, much like my ridiculous question about infants in swings, they are all answerable with today’s technology once we stop extrapolating from small data sets, and start distilling from large ones.
Union Square Ventures’ Fred Wilson describes a moment in investing when the opportunity comes together as The Opening:
I like to think of investing in new things a bit like a football running play. Imagine you are the running back. You’ve been handed the football and you are looking for a hole to open up and run through. What you really want is some running room beyond the opening.
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What we have been looking for is the consumer opportunity to emerge. Until you have billions of consumers around the world using a technology, you don’t have a new wave to ride. So like the running back, you wait and hope you don’t get hit.
But in the last few months, the opening is emerging. In slow motion. I can see the left tackle move his man off the line. I can see the left guard move his man off the line. And there is running room. The defensive backs are on the other side of the field.
In that post, Fred was talking about consumer-level crypto experiences and NFTs. But I think this is a wonderful way to think about how things look in personalized health right now as well. There is an opening, and it’s time for someone to make the play.
At Alsop Louie, we have a few companies in our portfolio that are taking advantage of this opening to bring amazingly accurate and personalized health information to normal people: FullPower Technologies is at the forefront of clinically accurate sleep measurement at home. This is a remarkable achievement, and once you start to understand how thoroughly sleep affects every other part of your life, it’s easy to see how better understanding sleep on an individual level could make a huge difference in people’s lives.
Shape Scale is building a 3D body scanner that shows you how your body is changing every day in response to your health choices. Right now, the Shape Scale is most interesting to you if you’re running a workout program or a diet, so you can measure your performance and adjust — but it’s easy to see how the company’s vision extends beyond fitness.
Both of these companies are applying machine learning to previously uncollectible data sets in order to distill health insights that can be accurate and meaningful for individual people.
But the opening isn’t here just because of technology innovation. The opening is here because there is also a strong cultural shift towards paying serious attention to your body. There’s a good list of health outcomes that don’t quite fall under the medical supervision of the government but that people care deeply about. Sleep, energy levels, metabolic health, and physical appearance, to name a few.
Even these adjacent markets are huge. In 2020, the energy drink industry — which we can take as a sort of proxy for the number of people interested in spending money on having better energy levels — was a $57bn market. The beauty industry, which rolls up some combination of skin care, hair care, cosmetics, and the like, is a $483bn market. Pretty soon someone is going to figure out how to pull an “Uber” on the health industry by offering something that is “painkiller-valuable” to people while living in the gray zone just outside of strong health regulation.
Some companies are already dancing around the edge of this business model innovation. Levels* offers a subscription service that includes time with a continuous glucose monitor so that you can experiment with how your body reacts to different foods and adjust your habits as a result. They’ve managed to provide CGMs to people in the US by partnering with doctors who will prescribe CGMs to people who don’t have diabetes. Supersapiens and January.ai offer something very similar, although I’ve used Levels’ product and I haven’t used the others.
Meanwhile, as you learn, so do they: each of these startups are collecting unprecedented amounts of data about how people react individually to a wide variety of foods and activities that can be distilled for more accurate and meaningful advice individually.
People don’t often think of mental health in the same bucket as physical health, but Calm.com asks right on their home screen: are you after more focus? Better sleep? Reduced stress?
Pair Calm’s business with some phone or smart watch sensor data collection and what might we learn about how your mental well being is different from mine?
The opportunities contained in this cultural and technological shift are enormous, not just financially but in terms of how people’s lives will improve. I’m excited for a future where we will know not only whether my son should be on the swing more often, but which foods, sleep habits, exercise, and drugs are best tailored to his temperament, bloodwork, and happiness.
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* I participated in the Levels equity crowdfunding campaign, so technically, I am a small investor in this company as well.
