How many steps did you take today?
- Don’t know?
- Don’t care?
Maybe you would if you found out that you only took 2,000, when the daily recommended number from the NHS is to take 10,000. A seemingly trivial improvement to your life could be made if you knew your step count was low, and you took measures improve it. It might even help you live longer.
To do this, all you need is a pedometer. You can get one for just 53p on Amazon. You attach it to your belt and step away. Start walking to the shops rather than driving, take the stairs not the lift, get up and get yourself a drink of water once every hour.
Of course, with smart phones, pedometers are all but obsolete. There are now a wide range of apps that can provide self-tracking, and the number of steps you’re taking is one of the more trivial measurements possible.
Logging the commute and more
I hunted around for an app that would suit me. It needed to be simple, with minimal setting up. I didn’t want to have to put much effort into my tracking.
I found Endomondo and used it to track my cycling commute to work. I thought the journey took about 35 minutes. Using Endomondo, I found that it took an average of 32.34 over a five day period. It also tracked my speed.
On lazy days I was pacing around 14 km/h. On the faster days I paced at a much higher 18 km/h. As I logged my times and speeds I began to set myself challenges to beat myself, constantly shaving off minutes on the way to work. In the next week I had got my average to 31.25.
This self-competition might seem trivial or even banal, but when pooled with the data from other people, potentially who you have never met, things get rather more interesting.
Strava is a widely used self tracking app (fast approaching 10m users) amongst the cycling community. On a premium subscription, it allows you to track progress in particular age and weight classes, competing against people on particular routes. Take a look at the ‘typical Saturday’ activity map below:
When we look at something as seemingly complex as the above, we’re entering the realms of ‘big data’ – where machines begin to spot patterns and relationships between datasets which humans wouldn’t notice.
In cycling, this might mean recommending particular training methods or particular routes that might improve certain aspects of technique, like hill climbing.
Beyond health and fitness data
But there is much more to track than your physical activity. In a quite remarkable blog post entitled ‘The Personal Analytics of My Life’ British Mathematician Stephen Wolfram published graphs of his electronic communication dating back to 1990!
While ‘life logging’ in this much detail certainly won’t be for everyone, monitoring personal data such as email communication could be useful for productivity. If we measured email usage and personal browsing (particularly social media usage) during work hours, we would almost certainly be able to spot patterns that could help us improve our productivity and concentration levels.
We can also think of the measurement of our social lives through social media. Wolfram Alpha allows people to look up their own Facebook Report, a kind of quantification of our social lives.
From it I can see details about who has interacted with me the most – the problem is, it’s those who are active on social media, rather than my closest friends. The same sort of quantification can be deemed from social scoring applications like Klout or PeerIndex.
These effectively take a social ‘data exhaust’ and use it to give people a score for how ‘influential’ they are online. While people might criticise the nature of these scores, they can be useful in finding active and influential people online. But still, they are completely reliant social media usage, so they are difficult to take seriously in a consideration of real life ‘influence’.
My Facebook Report is not a realistic interpretation of who my closest friends are, only those who have interacted with me the most on the platform.
But with our mobile phones, there is rather more data to mine. Going beyond a singular app or your social media activity, you could log the following:
- Where you’re going.
- Who you’ve interacted with.
- How long you’ve spoken to friends.
- The affinity of connections.
- How long it takes to get to work.
- The tone of your messages.
- The amount you text, tweet or update.
- How much exercise you’re getting.
- How much you get distracted.
In an episode of BBC Horizon called ‘Monitor Me‘, Dr Kevin Fong interviewed Professor Sandy Pentland from the University of California, who was able to build an app using information such as that above to determine whether or not people were showing signs of depression.
By mashing certain streams of data, such as browsing history or geolocation, the app determines scores, such as ‘Focus’ or ‘Activity’ which contribute to an overall score for determining depression.
BBC Horizon – Monitor Me (2013)
While it might seem remarkable that a mobile phone app could determine whether or not a person may be showing signs of depression, trials of the app demonstrated its success when compared to the opinions of a doctor.
It points towards amazing possibilities for monitoring human health. Just through search behaviour, Google is now able to determine flu outbreaks in real time (an example of ‘Big Data’ in action). It is also possible through self-measurement and monitoring to detect early signs of life threatening diseases.
If you regularly took a blood sample, it would be possible to monitor the number of particular cells in your blood – an important step for spotting early stage cancers and other life threatening illnesses.
Too many apps
One of the key problems with the quantified self currently, is that there are a range of different apps that do a range of different things. Strava might track your run or bike ride well, but it doesn’t check your nutrition.
While it’s possible to combine data points by using a tool like Tic-Trac (a self-measurement dashboard), we’re all in danger of data fatigue. Maintaining more than one app may be too time consuming for busy people, and this might mean they are forced to pick one. At this point, one company effectively ‘owns’ a part of your life.
A conversation with a friend about Nike+ really made this plain to me. Because he had logged so many miles on the app, he felt like he couldn’t move to another. There was no way to export his achievements and import to another app. He was locked into Nike+ even though he felt another app would be more suitable.
With this in mind, it could be construed that there could be a serious encroachment on the individual’s privacy. Will the health companies of the future effectively ‘own’ your body through the mass of data that they’re logging on you?
The big web players like Facebook and Google already have masses of data on people who have signed up to accounts. If you have an Android handset, Google likely knows where your work and home are without you ever telling it or knowingly opting in for that information. For some people, it’s all getting quite creepy.
There’s a trade-off to be made. You will likely be signing away a lot of personal data along with a subscription if you are to use health care apps or any kind of life logging in the future. It’s the price we will have to pay to ‘opt in’ to human quantification.
But while Generation X and older get unnerved about threats to data, it’s quite clear that Generation Y are less cautious. CTO for the Obama Re-election Campaign Harper Reed has said, ‘Anyone under 25, for the most part, has the same views on privacy on data and trust, which is that they don’t really care.’
A change in generational attitudes is likely to open the floodgates for self-monitoring, from fringe sporting interest, to a social norm.
The future is irresistable
And while the quantified self might seem uncomfortable to many, it may also force a spectacular revolution in human health. 75bn devices will be on the web by 2020, constantly monitoring and optimising their own performance, and probably optimising our lives.
Larry Smarr, a Californian based computer scientist has stated, ‘We’re almost at day zero in a whole new world of medicine.’ With personal data input and algorithms spotting patterns previously hidden from us, he may well be right.
Big data pattern spotting via machine learning may be the key to dramatically increase the length of the human lifespan, or even put conciousness into a state of virtual reality. Jeanne Calment currently holds the official record for the longest human lifespan, at 121 years and 164 days.
While that might seem extraordinary to us right now, for people under 20, it might not be nearly as old as they will become. We’re just at the beginning.
Too much hyperbole? Then watch this Ted Talk 🙂