Getting Ready to Know Everything about Yourself

By Arko Olesk

There are close to a hundred locations around the world where you can find a local group of a movement called Quantified Self. These groups regularly organize meetings for people to share their life–logging experiences – recording and measuring variable aspects of their daily lives and activities.

In a way, this is something that all of us engage in more or less knowingly but these groups are taking it to the next level. With the help of an increasing selection of gadgets, apps and websites, they produce data sets which provide a radical improvement in understanding our behaviour and lead to insights which could make us change ourselves to live better and healthier lives.

This trend, however, presents new challenges to scientists, doctors and other specialists who so far have had privileged access to the accumulation and interpretation of data. Meaningful results and wise decisions can only be achieved with the help of shared expertise and communication between experts and the public. As tools of quantifying one’s health and activities can be expected to become widely used in coming years and decades, the most valuable skill will become seeing what’s behind the numbers.

The so–called Show & Tell meetings around the world are modelled on the San Francisco Bay Area Quantified Self that was started by Gary Wolf and Kevin Kelly in 2008. The aim of the Quantified Self movement, as Wolf explains in his 2010 TED–talk is to improve knowledge about ourselves and use it for self–improvement. “If we want to act more knowingly in the world we have to know ourselves better,“ he says in the talk.

The themes discussed at local groups and global conferences range from self–tracking to self–experimentation, self–diagnostics and psychological self–assessment.

People record their sleep patterns, physical activities, food intake, computer usage, mood, even blood readings and other health data. While some find a nerdy pleasure in logging all possible aspects of their lives, others report how the collected data has helped them to lose weight, get enough physical activity or get rid of harmful habits. “You can’t manage what you don’t measure,” as is the slogan of InsideTracker, one of such web health tracking services.

The roots of this revolution are obvious. Smartphones and other technological advances make data collection almost or fully automatic. You can track the dynamics not just the snapshots that single measurements provide. Apps and web services allow entering data and make it easy to analyse and spot trends. A lot of it was possible before, in the analog era, but required a lot more effort. Now all you need to do is push a few buttons. Once it is simple, people readily adopt the technology: I bet we have all seen people posting on Facebook the routes and results of their latest jogging or cycling effort, enabled by smartphone apps. Just recently, a friend was trying to convince me that anyone doing any physical exercise should start by getting oneself a heart rate monitor accompanied by a software program that tracks your activities and will make recommendations about intensity of training.

Considering the pace of scientific and technological advances the future possibilities for quantifying oneself seem almost unlimited. Much of what is now confined to sophisticated laboratories will become available to the average consumer and new technologies will allow access to measure things with precision and from quantities currently out of reach. What is now a hobby will become deeply ingrained to our daily life. The potential is especially remarkable concerning health: personal genomics and other –omics can guide us to right lifestyle choices and tailor–made treatments. Routine blood tests (or implanted sensors) will look for biomarkers that can discover possible problems at a very early stage.

One attempt to imagine what the future could hold in store for us by 2100 was made by physicist and science writer Michio Kaku in his recent book “Physics of the Future” He writes: “While washing your face, hundreds of hidden DNA and protein sensors in the mirror, toilet, and sink silently spring into action, analyzing the molecules you emit in your breath and bodily fluids, checking for the slightest hint of any disease at the molecular level.”

Technologically Kaku’s vision is probably not very far–fetched; he emphasizes that everything he describes in the book is already being developed in labs around the world. However, there are more than just technological hurdles to overcome before realizing the vision of omnipresent tracking technologies guiding our lives.

The advances in personal genomics have already laid out some of the potential fields of discussion that will gain importance as technology starts to follow more and more aspects of our private lives. We can expect the issue of privacy to be under constant re–negotiation, accompanied by ethical questions such as a person’s right to know or not know about his or her results. The solutions to these questions will determine the real rate of adoption and use.

The early experiences with Quantified Self, personal genomics and other similar services indicate that many people seem eager to adopt such tools. And there is plenty to choose from. Even in my native Estonia, several start–ups are aiming to go with this trend, for example an app that measures your water intake and reminds you to have a sip once in a while. Or another that analyses your gut flora and gives dietary recommendations based on the results. And of course there is the Estonian Biobank (link: that hopes for government funding to provide every Estonian with a personal gene chip, vying for Estonia to become the first nation where gene data is routinely used in clinical practice.

One of the main arguments for many of these services is the one already mentioned: knowledge provides incentive to change. Just as many of us book a time in the gym when the scales reveal a few extra pounds, experts hope for people to change their habits towards a healthier lifestyle when they become aware of their behaviour or genetic risks. It is not just the data that will make the difference. The personal involvement in collecting and analysing the data should also increase motivation to act based on the data. Traditionally, we have relied on experts to make the measurements and then give us the results and guidance about behaviour. Now, being part of the process could enhance understanding about the mechanisms involved and lead to a more active and committed behaviour.

However, these optimistic scenarios rely on a few major assumptions. First, that data accurately reflects the measured object; second, that we interpret data in a way that it makes sense.

We tend to trust numbers. They have a sense of unambiguity in them and carry the authority of science. Of course, anyone more familiar with the insides of science knows not to take numbers by the face value. Still, being able to quantify things does have a general appeal in it.

Yet, even many of the widely used numbers are just approximations. Take, for example, the number of calories in a meal or calories burned during exercise. These are impossible to measure precisely so we operate with rough estimations. Although often equated (e.g. ‘it takes 30 minutes of cycling to burn off one chocolate bar‘), these numbers hardly reflect the true energy balance in the organism.

Similarly, many measurements rely on proxies of the actual objects or processes. A well–known example is functional MRI, a brain imaging tool (Kaku believes that handheld MRI–devices will be available by the end of the century so they also might be used for Quantified Self). Widely used by neuroscientists to investigate the activity of brain during different tasks, fMRI actually measures blood flow in the brain. The principle is based on the (very logical) assumption that increased blood flow reflects more activity in that region of the brain since working neurons require energy. Yet there are critics who claim that since fMRI measures brain activity indirectly many of the widely publicised results may in fact be misguided.

There is no problem when we acknowledge the fact that we are dealing with estimations or proxies and value the results accordingly. But how often is this the case? As different aspect of quantified self will use different types of measurement, it will be crucial for the users to differentiate between them and be aware which is being used. Especially when decisions about health are being made, the value and validity of any results must be known.

Even if we know we are measuring something precisely the question remains how to interpret the data. Personal genomics is a case in point. There are very few gene variants which single–handedly determine a trait or condition. Most traits or diseases are complex, influenced by dozens, sometimes even hundreds of gene variants. Each of them contributes a little so knowing about which variant you possess can only tell that the risk level is somewhat higher or lower. Often the levels are so marginal that the information has no practical value.

There are other cases where the results might be hard to interpret not only because of variation between individuals but because of inherent ambiguity. For example, critics claim that in case of the PSA (prostate–specific antigen) test used for prostate cancer screening the risks of unnecessary treatment might outweigh the benefits of early detection. That is, the abnormal levels of the antigen do not necessarily indicate a malignant nature of the tumour leading to some people receiving unnecessary interventions.

The limitations to these and other tests are well known to specialists. But there is no reason not to expect that many of these tests will be available and used by general public for purposes of self–tracking and self–diagnosis. The trend of Quantified Self is shifting the collection and interpretation of data – whether officially endorsed or not – from the specialists to the general users. As it happens, the expertise about interpreting the data needs to be transferred as well.

No doubt that all of the tests and services will come with thorough manuals and warnings and include sophisticated algorithms that will analyse data and make recommendations. But as the decisions and choices based on the results will start carrying more weight we need more than that. We need a better general understanding about testing and statistics.

The education system should pay more attention to teaching about basics of statistics and their use in real–life situations. The skills needed are the understanding of scientific method and ability to critically analyse claims and results. Scientific and statistical literacy will guide us to better decisions – even if the data is self–collected.

In all of this, the skills of experts are still needed. No algorithm can perfectly substitute human expertise. Rather, AI, personal experience and expert knowledge will be complementary in getting the best results.

But as people themselves are starting to perform functions that used to be the exclusive area of specialists, experts need to take on a more active role in communication. This means providing the public with context for results, helping to put things in a bigger picture. Science will still provide the tools, methods and limit values used for the measurements. But taking into account that the public now might have the possibility to actively use them not just take notice of their existence, they need to be communicated in a substantially different way.

The Quantified Self approach has the promise of adding a valuable dimension to data about people which can benefit people themselves as well as science. As a result, people will get more power over their life, able to make more informed choices. But this can only be achieved if we become aware of the potential pitfalls – ethical, scientific and social – and communicate wisely to avoid them.

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Arko Olesk is the head of Science and Innovation Communication Centre at Tallinn University, Estonia.

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