In my work, I'm very much fascinated by computational approach to
understand human behaviour and which shaped the entire direction
of my recent academic work, so far.
I've been really fascinated by the way we can understand
individuals and population behaviour by studying the patterns in
the digital data that we generate through the use of
smartphones, wearable technology, and social networks.
And really, I wanted to, in this talk, to give you a bit of a
flavour of the very unique times that we are living in right now
that give us this enormous capability to understand the reality
and humans through the digital technology that they are
connected to. I perceive this global adoption of smartphones and
associated digital devices and services as one of the most
transformative processes in our reality.
All of this is about the connectivity, about the fact that we
use the point in the technological development where we can
really, truly take advantage of powerful, fast, ubiquitous
digital devices, and the connectivity capability that they
bring. And let's make no mistake here.
We really are in the world where a lot of us are right now,
heavily connected individuals.
There are four point sixty six billion active Internet users,
which represents around 60 percent of the world population.
Also, there are about three point five billion people with
smartphones globally.
That's about 45 percent of world population.
What this enables us to do is to almost create like a digital
layer, which in turn makes the mechanics of our physical world
more efficient. We live in times where our education, travel,
shopping, work, entertainment, social communication and content
creation is massively empowered by the digital devices that we
own and connect to.
And we have also an unprecedented era of choices and options.
Now, what this digital connectivity also brings is the ability
to collect and use data.
Data itself, of course, is just a tool.
But this collective ability to produce and exchange it is really
a game changer. And it's a cliche to say that everyone in the
society benefits from the digital data.
Governments can make better measurements of success of their
programs, hopefully, and media and other non-government
organisations can use data to support their work and check
facts. A particular case I wanted to highlight is related to a
health innovation that are driven by digital technology.
Of course, that's just one of the cases where things are really
transforming rapidly and in a very much science fiction
direction. One of the experiments from Clayton and team that
they done a couple of years ago and looking at this phenomena of
the smartphone separation.
So what they did, they invited a group of participants for an
experiment, and they asked the group to take part in a simple
set of cognitive tests, you know, IQ tests and so on.
They told participants, well, when we'll be doing those tests
we want to measure your heart rate.
So we're going to attach this heart rate monitor to to your
chest. But unfortunately, this heart rate monitor is quite
sensitive to your smartphone signal.
So could you just take your smartphone and leave it there on the
shelf, on the side so it won't interfere with the signal of the
recorder, of the heart rate recorder.
So when participants then were taking part in this task, they
were filling in the tests and, they weren't aware that actually
that wasn't a real goal of this experiment.
During this process, the researchers actually called the
participants so they could see that their phones started
vibrating there in the shelf when they were still participating
in experiments trapped with all this gear to measure their heart
rate and some other biophysical characteristics.
And what happened then?
Well, their average heart rate actually increased by 10 beats
per minute when they were prevented from answering the iPhones,
when it was just lying there on the shelf.
And at the very end of the experiment, when they were finally
reunited with their smartphones, their heart rate actually
dropped by 13 beats per minute when they got their phones back.
So I think that there is probably no better testament how much
we are actually attached to our smartphones, that this small
anecdotal experiment done by Clayton and colleagues in 2015.
Smartphones and wearables are very sophisticated technologies
packed with sensors, multifunctional and connected, as I said,
with almost every aspect of our life.
And when we add to this the sheer number of social networks and
digital services that connect, that we are connected to with
these particular platforms, it is really an enormous amount of
data that's getting generated, something that we refer to as
digital traces and tiny footprints of data that we as
individuals leave in the daily usage of those different digital
extensions that we have.
Now, we know already that we can use those digital traces to
properly understand behaviour of individual groups, but how
specifically? Let's look at a couple of examples here.
A very basic sensor, accelerometer, the one that's one of the
most kind of traditionally occurring in smartphones and wearable
technology that responds to the level of movement and was
originally designed so that we could, when we invert our phone,
the screen would react and got inverted as well, later expanded
for gaming. But recently it's been in the center of any self
tracking technology in terms of tracking performance and cycling
activity. And in fact, it's been widely used to predict whether
you are sitting, walking, jogging, cycling or driving or even
what kind of public transport you are using with up to 95
percent accuracy.
The same sensor, for example, can be also used to evaluate your
quality of the sleep from just the body movement.
So it's sensitive enough that if it's left, somewhere on the bed
and the right applications are activated, they can actually
detect a movement of your body that is changing during the
different stages of sleep that you're going through.
From other different metadata and data that's available g
enerally, simple things like call logs, text message logs, and
Bluetooth scans and application usage profiles can be used to
predict personality traits with up to 70 percent accuracy.
And further, the length of your text messages or other messages
that you write and how fast you type or erase it can be used to
identify your mood or a global emotional state.
Stress levels can be also detected with up to 80 percent
accuracy by capturing only a few words with a microphone.
And even, this is not about the meaning of the words.
It's about the sound of your voice, about actually just the
characteristics, the vocal characteristics of your voice, how
fast you are talking, how loud, whether there are particular
frequencies, higher or lower present in your voice can really
strongly indicate stress and other emotional levels.
In one study by a Stanford Group, they show that heart rate,
skin temperature and blood oxygen level that were recorded with
simple wearable device that can be purchased for a low cost on
the consumer market, could be used to identify early signs of
Lyme disease, inflammatory responses, so some sort of infections
and physiological differences between insulin sensitive and
insulin resistant individuals.
So in this case, the researchers compared some of the signals
that they got that I mentioned here from, from the wearables
that the participants were wearing and also from some clinical
markers that they used.
And they found that actually they could only need the wearable
data to make an indication that something is happening.
And this is quite a powerful point here, because it opens the
door for what we can call preventive, predictive diagnostics
that will be able to capture that if something is changing in
our body and or some illness or infection is developing before
we actually feel that this is happening.
In a similar manner, wearable activity data in another study from
a group in Stanford show that wearable activity data can be used
for cardiac imaging to determine how the size of the heart is
influenced by the physical activity.
And for this area called Lipidomics to identify species of
particular proteins that are associated with obesity, diabetes
or heart diseases. Again, in this case, the wearable activity
data was compared to those cardiac imaging and tests that were
done with the clinical grade devices.
But these showed those really clear associations that were
between those two sources of the data.
So the predictive preventive diagnostics, this emerging promise
that we will be able very soon or we already have the potential
to actually take this data and use it in a constructive way to
make a prediction about health conditions.
And, of course, another domain here is the potential for
facilitating behavioural change that's closely related in some
sense, to the diagnostic capabilities of the consumer devices.
Almost 50 percent, 45 percent of risk that are attributed to the
higher mortality are risks such as high blood pressure, high
cholesterol level, physical inactivity or obesity.
And when you look at those particular conditions, it's quite
clear that a lot of them could be addressed with the right
behavioural change interventions.
And wouldn't it be great if the same technology that allegedly
put us in the couch, as you know, that really stimulated the
sedentary lifestyle could be used to actually target such
issues? Almost all of those, if they are used in the right way
with the wearable technology, could be somehow addressed in this
way. And the principle behind this, how this could be addressed,
is coming from quite old theories around the self tracking
feedback. But because of this emergence of personal digital
technology and the fact that the smartphone is with us
continuously during the day and by our bedside during the night
and is one of the first things we grab in the morning and the
fact that they are personal devices on our wrists and other
parts of the body that also take those measurements.
Those theories suddenly became more real, more instant in terms
of the feedback that they can provide, more personalised and
also the fact that this process can be continuous, intelligent,
much more detailed than some of the solutions that have been
used in the past, such as self report, surveys or questionnaires
that, of course, require you to introspect, understand the
question and make some answer.
While in here, there is just a stream of data that's getting
evaluated by the algorithm.
And in first, whether there is a point where it could suggest
something to you, there might be some change or that you maybe
should consider going to see a GP because your heart rate is
showing some abnormal patterns, or that your levels of activity
drop below the levels that you would like to have.
You might remember a very large phenomena in 2016, and that
refers to Pokémon Go when it had almost half billion downloads
and it still has about 20, 30 million active users, over three
point six billion in lifetime revenue that, that manufactures or
the developers of this solution created.
You might remember that the idea with Pokémon go, with this
smartphone game was that you were going around the real world,
the actual physical world with your smartphone in front of you.
And you are basically just hunting and throwing those pokeballs
into small virtual monsters that were overlaid on the actual
real world. So they, it was based on the geolocation algorithms.
The gamers were just running around the cities and trying to
catch those those Pokemon.
But what Microsoft Research has actually noticed in this period
that there was overall a 25 percent average increase in that
physical activity for users who played Pokemon Go across
genders, ages, weight status, and prior research activity level.
So those factors weren't actually influencing this.
So this is really kind of a nice example of almost an accidental
effect of digital technology on physical activity that, through
gamification, elevated a large number of people to actually move
more, be more physically active and in, and of course, as a
result, contributing to their health.
The fun part of my title was The Future of of the Analytics.
I wanted to say that the future is actually already here, it's
just not evenly distributed yet, by paraphrasing or quoting one
of my favourite science fiction authors, William Gibson, who
said it. And really I couldn't agree more.
Ladies and gentlemen, we live in a very unique time when
technologically we have enormous capabilities, enormous amount
of choices, and there are so many potential directions in how we
can use this to make our and other lives better, to improve the
functioning of organisations, Governments, almost every aspect
of our life can, could be addressed and improved with the right
application and elevating this data.
So we are already living in this future in a sense, because all
those future science, those science fiction scenarios from the
70s and 80s are now floating away.
And we can clearly see in which direction we are heading with
some of those phenomenons.
Only a tiny percentage of the data that we generate is actually
ever analysed and used.
And this brings this enormous potential for all of us.