5 years, 5 lessons
Quantiﬁed Self: The next frontier in mobile healthcare
IEEE Communications Society SCV + TiE SV 19 Sep 2013
Rajiv Mehta • email@example.com • 650/823-3274
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September 10, 2008
The ﬁrst meeting of QS was at the home of Kevin Kelly, on September 10, 2008. About 30 of us were there.
• The very ﬁrst speaker showed us something unexpected. He had been recording his activities, some as brief as 5 minutes, for the past 3 years! He showed us color-coded charts of this
data. And then, astoundingly, he asked us what he might learn from it. And that started the conversation.
• The speaker in the photo is Seth Roberts, a Berkeley professor, who has been doing self-tracking and self-experimentation for decades.
• The guy in the middle with a yellow t-shirt with red letters is Tim Ferris, who has since gone on to write the bestselling book “The 4-Hour Body” based on his own self-experimentation.
• I’m the guy up front on the left of the picture.
That ﬁrst meeting was almost exactly 5 years ago.
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1. Market size
3. Feedback loops
From these 5 years of experiences, I’m going to highlight 5 things that seem especially pertinent to pursing business opportunities: the market size; the key topics of sensors, feedback
loops, and engagement; and the question of what value users are ﬁnding.
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#1 Market Size
Niche or mainstream?
First the issue of market size. Ironically, for a topic that’s all about data, it’s hard to get clarity on the size of the market. Is it still a niche, just early adopters, or is this already mainstream? It’s
confusing ... there’s a mess of indicators.
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Salt Lake City
Rio de Janeiro
Let’s look at QS itself. From that modest beginning, QS has grown rapidly. When I last looked, we had 96 meetup groups around the world — including new ones in Mumbai and Bangalore.
We’ve had 4 conferences, 2 in the Valley and 2 in Amsterdam, and the 5th one is coming up in San Francisco next month.
Speakers at QS events generally talk about their own experiences. We ask them: What did you do? How did you do it? What did you learn?
For the past couple of years we’ve also asked for a show of hands: “How many of you are at a QS event for the ﬁrst time?” Usually about half the people raise their hands. That’s amazing.
This is obviously growing fast. Still, the total number of people who have attended at QS event is admittedly a very small percentage of the population.
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Gartner’s Hype Cycle for Emerging Technologies 2013
This is Gartner’s famous “hype cycle” chart, where technologies proceed from some innovation trigger, through the peak of inﬂated expectations, crashing down into the trough of
disillusionment, along the way to the plateau of productivity.
Regarding QS, Gartner seems to want it both ways. As you can see, QS is placed on the left side of the chart reﬂecting its newness. But, they’ve noted it as a light-blue dot indicating they
think it will reach the plateau stage in just 2-5 years. So, it’s supposed to go from something new to something mainstream practically overnight. On the other hand, they’ve put “Mobile
Health Monitoring” technologies further along the chart, but with a dark blue dot indicating it’ll take 5-10 years before that is mainstream. Gartner seems to be saying that QS will become
mainstream sooner than the technologies that drive it.
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Pew Internet “Tracking for Health” 2013
For some more confusion, a recent Pew Research survey found that 70% of American adults say that they track at least one health indicator. That’s pretty darn mainstream. However, half of
those people that track say they just track “in their head”. Overall, they found that only about 11% of American adults use apps or devices for tracking.
But, is this an overcount? Even 11% seems much beyond an early-adopter, niche market. Some argue that this is in fact an undercount, because the sales of home glucometers, scales,
blood pressure cuffs, etc. is higher than this 11%.
Pew has deep experience doing such surveys, so the numbers must be correct. But they don’t feel right, and so there has been lot of conversation online and off about how to reconcile
these results with common experience.
One issue that comes up, is that of perceptions: not everyone who is currently tracking thinks of what they’re doing as “tracking” — they’re managing their diabetes, not tracking their glucose
Rajiv MehtaTiE & IEEE: QS the next frontier in mobile healthcare, 19 Sep 2013
ABI Research, Feb 2013
100 Million (2014)
300 Million (2019)
annual wearable device shipments
61% of wearables
are sports / activity trackers 2013
Business Intelligence, Apr 2013
Some industry forecasters are quite bullish. Here’s one middle-of-the road forecast. It would seem this is already a signiﬁcant business.
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Finally, many crowdfunding campaigns for personal trackers have had success.
* Instabeat, based in Beirut, is a gadget that attaches to your swim goggles, tracking your performance while you’re in the water.
* Lumoback is a local company focused on posture. We’ll be hearing more about it from CEO Monisha Parkesh in a few minutes.
* Beddit, from Finland, provides a thin ﬁlm strip that you place underneath your sheets for sleep sensing. It’s very accurate and unobtrusive.
* Emotiv, based in San Francisco, has head gear for measuring brain waves.
* And Scandu, also based locally, is developing the Star Trek tricorder.
Clearly there are many that want such products.
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... not a new idea
It’s also worth mentioning that this idea of self-reﬂection, self-analysis is nothing new. The Buddha, and many other philosophers before and after him have been teaching us the value of
paying attention to one’s self. Something that’s a few thousand years old can hardly be thought of as new.
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#1 Most everybody’s
doing it ... but they
don’t call it tracking,
and they may not use
Bottom line ... self-tracking is commonplace. If you can tap into what people are already doing, the market could be huge.
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Next, let’s talk about sensors. The availability of cheap, small sensors sparked the Quantiﬁed Self movement.
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Shiny new toys
There’s lots of excitement about these new technologies — after all, the technical possibilities explored by the ﬁrst panel is partly what lured you here today.
And it feels like we’re just barely scratching the surface, that there is a lot of innovation to come in this space.
The shininess of the new devices, their gadgety novelty, often is what attracts people to self-tracking in the ﬁrst place. This is not to criticize these gadgets. If they inspire people to consider
their wellbeing, that’s fantastic.
But it’s more than just the excitement of a new toy ...
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“The real voyage of
discovery consists not in
seeking new landscapes,
but in having new eyes”
Marcel Proust said “The real voyage ... eyes”
These new sensors, just like the telescope and the microscope before them, open new worlds to us. Each new sensor allows us to see something new about our universe, something that’s
always been right there in front of us, but that we weren’t able to see before. Sensors, and the new data they provide, are absolutely essential to this space.
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However, interestingly and unexpectedly, these new sensors have reminded us that we all possess some in-built sensors, that work quite well.
Many of those who have done self-tracking have discovered that, with practice, they get very good at knowing their numbers.
People using pedometers pretty soon just know about how many steps they’ve walked that day.
People using sleep measurement devices soon know how they’ve slept and what their “sleep score” is just from how groggy they feel that morning, and so don’t need to look at the sensor
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And if you look around, you see that such perception skills are commonplace.
Any good mechanic is able to grab the right wrench or the right screw without getting out his calipers.
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Artists differentiate between many subtle color differences just using their eyes, and piano tuners just use their ears.
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teaching us to see
Basically we’ve found that while the new sensors do open our eyes to things we had not perceived before, that we often learn to continue perceiving without the crutch of the sensors.
So, are these new sensors giving us new eyes, or simply teaching us how to see?
And do we measure the success of a new sensor by how often it is worn, or rather by how much more accurately the user perceives his world whether or not he continues to use the sensor?
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Making sense of sensors
Another key issue about sensors is making sense of the numbers.
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“What we discover in
nature is not really a
matter of what exists;
what we ﬁnd is
determined by our ability
Another novelist, Dane Peter Hoeg, had this to say “What we discover ... understand.” (In “Smilla’s Sense of Snow”)
Can you understand what the data is showing?
Figuring out what the data means, what you should do, whether or not you should even pay attention to it ... these are not trivial issues.
Many people looked through telescopes when they were ﬁrst invented. Only a few, like Galileo, understood what they were seeing.
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This challenge -- making sense of the data -- is a challenge in many, many ﬁelds.
Nate Silver made his name initially by brilliant baseball analysis, then got even more famous by predicting elections much more accurately than all those pundits.
In this great book, he covers many ﬁelds.
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#2 Sensors ... are
So, these exciting new sensors are indeed very valuable ... but not every single one, and not always ... and don’t underestimate the value of those real people that the sensors are attached
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#3 Feedback Loops
A hot meme in this space is feedback loops
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Wired magazine captured the zeitgeist in a June 2011 article. Thomas Goetz, such a good writer, captured the essence of why so many are excited about the potential of feedback loops. It
seems so straightforward: measure what’s important, compare to your goal, make a change, measure again ... and you should quickly reach your goal.
And you’ll ﬁnd many success stories in the QS community. People who have been able to make signiﬁcant improvements in their lives by being able to get and respond to data about
You’ll hear later from Sky Christopherson, who will share with you some amazing stories of his own achievements and those he’s worked with using such techniques.
But also true is that the experience of many QS experiments is that often the goal is not reached, despite the measurement and tinkering.
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As anyone who is an IEEE member knows very well, feedback loops are not a new discovery.
This great poster by Dubberly Design shows that the basic idea is used in many ﬁelds.
So, there is deep knowledge about feedback loops that helps explain the lack of success.
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On key point is that your actuator, the thing you can control, has to have a meaningful impact on the variable you’re trying to control.
Otherwise the feedback loop isn’t going to work out as planned.
You can set your home thermostat to whatever you want ... it won’t make any difference to the temperature outside.
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“like applying sunscreen
and claiming it protected
you from a nuclear
meltdown ... wholly
inadequate to the scale
of the problem”
Nate Silver, The Signal and The Noise
Sometimes it seems the controls at your disposal just aren't good enough.
As Nate Silver says it's “like applying ... problem”.
You can track your weight, and eat more broccoli and walk 10,000 steps, and still not have much impact on your weight. If your body’s weight set-point is responding to other, more powerful
stimuli, your body will just adjust itself to compensate for all that broccoli and walking.
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Simple feedback loops are often no match for complex systems.
And one major complexity, when it comes to our bodies, is the time lag between a change in diet or environment and whatever it is that you’re tracking.
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Tighter feedback loops?
There’s a tendency to think that more information, faster information, and more responsiveness is always better. One wants to tighten the feedback loop.
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“When there are long delays in feedback
loops ... foresight is essential.
... If a decision point in a system is
responding to delayed information the
decisions will be off-target
... if action is taken too fast, it may
nervously amplify short-term variation and
create unnecessary instability.
... Overshoots, oscillations, and collapses
are always caused by delays.”
Donella Meadows, Thinking in Systems: A Primer
Recipe for disaster
But in fact, that’s often a recipe for disaster.
Donella Meadows notes that “If a decision point ... unnecessary instability.”
Time lags play havoc with feedback loops.
Many experienced self-trackers have learned not to react too quickly.
The difﬁculty is knowing when to wait and when to react. ...
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#3 Feedback Loops
— triggers for
reﬂection, not instant
So, if you’re going to build a business around some smart sensors that are going to help people know what to do, proceed with caution.
You’re safer promising feedback for the person’s own reﬂection, rather than promising to tell them the “right action”.
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Another hot meme is engagement ... motivation, behavior change.
The idea that these new tools are going to get you to do what you should be doing.
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$$ or “points”
This is a very popular idea. Many in the QS community have struggled with changing their own behaviors, and so have developed tools to help themselves. They want to hack their own
behaviors, and help others do the same. There have been lots of experiments. • People have tried money or points, that you win some for doing the right thing or you lose some for missing
your target. • People have tried public commitments, motivated by either the support that comes from people cheering you on, or the shame of failing publicly. • People have tried social
support ranging from very social sites, to small groups, to very private.
Interestingly, all this QS experimentation mainly seems to have conﬁrmed what the experts have been saying all along ...
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B. J. Fogg
BJ Fogg is a Stanford professor, a well known and popular lecturer and consultant on technologies for behavior change.
One of things he stresses is that motivation is most likely not the problem.
He advises: Don’t build tools to increase motivation. Build tools that makes things easier to do.
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B. J. Fogg
BJ emphasizes that for a behavior to happen, both motivation and ability are required. And, in practice, the problem usually is that something is simply too hard to do. BJ also emphasizes
that a trigger is necessary, something has to remind you at-the-right-moment that you should do something. Too much energy is being focused on motivation, when ability and triggers are
often what’s really lacking.
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Decades of behavior research conclusion
small motivational boost
for routine, boring tasks
requiring no creativity
There’s also decades of research that external motivation hardly ever works.
External rewards — carrots & sticks — only work, a little, in very speciﬁc situations.
At other times, they are not just useless, but often counter-productive.
Daniel Pink's book "Drive" is a wonderful lay summary of this ﬁeld.
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Decades of behavior research recommendation
Make it easier
for people to do
what they want to do
Things that have been successful in the QS world so far — such as the original Nike + system, or the MyFitnessPal or RunKeeper apps — have made it easier for people to do what they
already wanted to do.
Just note that in this context the idea of “easier” also includes making things more pleasing, fun, beautiful, etc.
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#4 Engagement —
So, based on the experience of successful QS products and of decades of behavior science, you’re better of building a business supporting what people want to do, than on nagging them to
do what you think they’re supposed to do.
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The ﬁnal lesson I’ll share with you from the QS experience is what value people are getting from self-tracking.
It may surprise you.
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Many of the QS presentations you’ll hear are of successful experiments — people tracked something, made some adjustments in their lives that resulted in a positive change.
People like presenting successes.
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Goals not achieved
Only when you talk with people informally do you learn that in fact most of the time people aren’t very successful in achieving the change they seek: they haven’t lost a lot of weight,
signiﬁcantly improved their sleep, etc. Speciﬁc improvements have been hard to come by.
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Why? It’s hard!
Why? Partly because its just plain hard ... I’ve just described some of the complications.
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Why? Bad science
Poor experiment design
Not based on evidence
And partly because many people seem to have a very poor grasp on the basics of science, on the basics of doing experiments. Almost all of the QS experiments I’ve come across have been
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And yet ...
Learn something valuable
And yet, almost all the people who did those ﬂawed experiments seem to have found the experience worthwhile, and have learned something valuable. They’ve come to this learning from
doing the experiment. They get satisfaction from doing something, from paying attention, from learning something about themselves.
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Finding the ﬁnding
We talk about “ﬁnding the ﬁnding”, the unexpected discoveries. I tracked my own sleep only to discover I didn’t have a sleep problem. But in the process came to better appreciate the
normal ups & downs of life. Another QSer tracking energy expenses couldn’t make much impact on her budget, but came to learn how much she enjoyed the meditative rhythms of line-
drying her clothes. The meta-learning is often far more valuable than the actual data, the experiment results.
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#5 Value is in the doing,
in the engagement
A possible lesson to be drawn is to make sure you design products or services such that don’t preclude this possibility.
If your solution is too paternalistic, you may make such learning impossible.
Pay attention to what Steve Jobs said.
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Next local meetup: Sep 25
Conference: Oct 10-11
firstname.lastname@example.org • 650/823-3274
If you want to tap into these early adopters yourself, you’re welcome to come to QS activities.
The next local meetup is next week in San Francisco.
And the next conference is just a month away.
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