Quantified Self Redux?

David Shaywitz

The first iteration of the “Quantified Self” movement largely fizzled out about five years ago. Avid self-trackers, at the time, started to worry they were drowning in data, but lacking in insight. 

Today, we seem to be entering Quantified Self 2.0. Once again, an expanding assortment of consumer devices promises to measure every parameter of our health and well-being. 

The obvious question: “has anything changed?” 

Let’s start with some context.

The “Quantified Self” movement was born in 2007, the brainchild of Wired magazine editors Gary Wolff and Kevin Kelly. The term was used to describe “a collaboration of users and tool makers who share an interest in self-knowledge through self-tracking.” 

This initiative was propelled by powerful, emerging consumer technologies, as Lindsay Rothfeld captured in Mashable in 2014:

“Before things like smartphones [note the iPhone debuted in 2007] or wearables, we’d have to consult doctors or data technicians or manually log activities to determine how many calories we consume and burn. But now, with Fitbits, Fuelbands, Jawbones, and Whistles (even our dogs are tracking activity!), we can capture this data in a snap, see it updated in real time and use it to make better, more healthy decisions.”

Observing the evolution of this ecosystem back in 2011, I wrote,

“It will also be important to ensure that even as we recognize — and seek to capture, leverage, and ultimately monetize — the value associated with the collection of an ever-increasing amount of data, we also recognize that most people don’t want to be perpetually monitored (at least not intrusively).  While there’s a much-discussed movement called “Quantified Self,” focused on capturing and sharing vast quantities of physiological data using sensors and other devices, this sort of excessive monitoring is almost certainly not something most of us want.  One challenge will be figuring out how to capture useful physiological information in a way that offers benefit while also remaining unobtrusive and respecting privacy concerns.”

Putting a finer point on it in 2014, I noted the disconnect between the promise of digital health and the demonstrated impact. “The goal,” I wrote, “is to find solid evidence that a proposed innovation actually leads to measurably improved outcomes, or to a material reduction in cost. Not that it could or should, but that it does.”

I was not alone. After nearly a decade of escalating hype, many users started to take stock, and asked what they learned from such obsessive monitoring. Frequently, the answer turned out to be, “not much.”

Wired editor Chris Anderson, a previous acolyte of the Quantified Self movement, seemed to put the nail in the coffin, tweeting in 2016:

“After many years of self-tracking everything (activity, work, sleep) I’ve decided it’s ~pointless. No non-obvious lessons or incentives 🙁 “

It seemed like this was the end. 

But instead, it may have proved to be only a short Quantified Self winter.

Today, everywhere you look, there are companies promising to quantify nearly every aspect of your behavior and habits, your physiology and activity, your physical performance and your mental health. 

Consumers are now offered continuous glucose monitoring, heart rate variability assessment, and even “brainwave feedback,” via devices which are claimed, respectively, to enable improvements in metabolic health (for non-diabetics), exercise recovery, and “mental strengths and weakness” (to enhance performance on videogames).

Less clear is whether anything has substantively changed. Are we measuring parameters in a fashion that’s now more accurate? More useful? Or are we just essentially repackaging old approaches with a fancier user interface and the promise of AI, yet selling consumers the same dubious message that more data inevitably equates to better insight into how individuals can improve their day-to-day state of health and wellness?

Prior experience (to be Baysean about this) suggests we should remain skeptical. The fact that a device can generate a number and ascribe it to a particular parameter doesn’t mean that the measurement is either accurate or meaningful. We tend to measure what we can, which isn’t necessarily what we should. It’s also challenging to translate even reliable data into relevant insights, and notoriously difficult to translate actionable insight into durable behavior change.

At the same time, science evolves, technologies improve, and more importantly, entrepreneurs adapt.  Sometimes — as venture capitalist Ali Tamasub highlights in Super Founders — it takes a number of tries to get it right. Google was hardly the first search engine; Facebook was not the first social network. 

At a minimum, we should remain open to the possibility that on occasion, someone will crack this difficult nut, and turn the promise of data abundance into durable evidence of meaningful impact.

I embrace this hope – and look forward to the evidence.

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