This page holds a collection of examples, links and videos that relate to the topics that will be discussed at the event. We are looking for your ideas and suggestions. If you have a good article, video or case study that should be listed here, please let us know by email, or on Twitter with the hashtag #nestahottopics.
Measuring our wellbeing
An introduction into this fast moving area is provided in this short talk by journalist and contributing editor at Wired Gary Wolf delivered at [email protected] in 2010.
Gary Wolf also introduces the state of play in this excellent piece in the New York Times. Talking about how difficult it is for us to judge and modify our behaviour in the absence of objective facts, he tells the story of quantified self through a series of narratives of people that have chosen a different route - one which is driven by numbers.
This article in the Guardian highlights specific example where increased self-awareness through self-tracking has helped people.
Similarly, this article in the FT and this piece in the Economist introduce some more hackers, highlighting how they have been helped through self-tracking, and why the field appears to be at a tipping point.
Although the idea of collecting physical data to improve performance is not new and is often used in professional sports for example, recent developments in the ubiquitous use of increasingly cheaper sensors such as in smartphones and in social media are making the process of tracking yourself easier. But what might future mainstream devices of the future measure?
For a clue, we can turn to the many examples of what current self-hackers are tracking. A great resource here is the Quantified Self website. Through a series of meetings that currently take place in 56 cities from around the world from Rio de Janeiro to Beirut, the website holds a great series of 'show and tell' videos.
See for example this video by Kiel Gilleade - Lessons from a year of heart rate data.
James Stout who is a professional cyclist and because of his diabetes he measures his blood glucose level.
Other examples includes: tracking to deal with insomnia, films seen over a 10 year period, a brain scanner that can be connected to your smartphone, measurements on posture using accelerometers attached to your shirt, data collected on muscle activity, different ways to sample stress, measuring productivity, and tracking your mood.
As highlighted in this Economist piece, measurement alone can act as a powerful motivator for behaviour change. One example is found in this interesting show and tell by Robby MacDonell who tracked his mode of transport and concluded that it was time to buy a bicycle. In this video, Nick Crocker talks about his experience to implement change.
Devices here and now
Today, the average smartphone contains useful sensors that can be exploited by quantified self tools such as a camera, gyroscope, GPS, and an accelerometer, so it is no surprise that many self tracking tools are in the form of apps.
There are too many to mention here, but for example, this app called 'Vital Signs' from Philips uses advanced image processing to log and record your heart rate and breathing rate using a smartphone camera.
There are also several apps that track happiness and mood. For example, Mappiness by researchers at LSE aims asks you to rate your happiness. It simultaneously collects data about your location and the current noise level and it maps aggregate data in order to help researchers to learn how happiness is affected by air pollution, noise and green spaces.
New hardware is also continually being developed, and here is a nice gallery summary of current consumer devices from the MIT magazine Technology Review, which includes movement trackers such as Fitbit and sleep monitors such as Zeo,
Many of these commercial devices rely on the accelerometer. In this article in the New York Times, Gary Wolf tells the story of Ken Fyfe - an early pioneer of using algorithms to convert the data from such sensors into meaningful tracking data.
Although a grassroots movement, large technology firms are taking note - not surprising seeing as many of the early adopters of quantified self are employees of large tech companies such as Google and Intel.
Philips, for example, has a device that tracks movement called DirectLife, as explained in this video by Lukas Kreutzer. Here is an interesting project by Intel described as having a therapist in your pocket.
One of the most successful companies in this space is Nike. The Nike+ system has millions of users and the new FuelBand is likely to increase this user base.
Another interesting development in hardware is from the start-up GreenGoose. Based in San Francisco, the company has developed tiny motion sensors that can be attached to everyday items, such as a toothbrush or a watering can. The use of such objects can then be tracked using wireless signal which is sent to a base-station. More details in this article in the Economist.
Collaboration and personalised medicine
One of the most intriguing things about the quantified self movement is the ability to share your data using social media.
This has lead to some interesting data sets that have been crowdsourced, such as on sleeping patterns by Zeo who are using the data for original research.
Similarly, Asthmapolis, a small start-up based in Wisconsin, tracks the geographic location of asthma attacks through a small device that fits onto an inhaler. Every time the inhaler is used, the geographic location is tagged. Harnessing these large datasets will allow researchers to link asthma attacks to environmental conditions such as air quality.
As highlighted in this article in the Guardian, CureTogether's data has lead to some interesting findings, such as revealing "that people who experienced vertigo in conjunction with migraines were four times as likely to have painful negative reactions when using the migraine drug Imitrex as those who did not have vertigo."
Another compelling example comes from PatientsLikeMe. In 2008, a research group in Italy reported a beneficial effect of taking lithium in a small group of 16 patients suffering from amyotrophic lateral sclerosis - a fatal neurodegenerative disease. Acknowledging that many users with ALS were involved in self-experimentation with lithium, PatientsLikeMe built a lithium-specific tool and were able to show that it was in fact not an effective treatment for ALS. This was subsequently supported by randomised trials. This research was published in Nature Biotechnology last year.
How will self-tracking sit within the impending rise of personalised medicine (also known as 'Healthcare 2.0')? One vision is best painted in this article in the Technology Review, which tells the compelling story of Prof. Larry Smarr, who directs the California Institute for Telecommunications and Information Technology. Write some about the case, with additional links?
Finally, Healthcare 2.0 as it is often called will likely demand more self-management and self-monitoring from us, and here quantified self can make a big impact. For example, many self-trackers are combining their tracking data with traditional medical data, or even to their genomes through services such as 23andme - a company that has commercialised DNA sequencing for the consumer market with investment from Google and Genentech.
For a more extensive discussion see this video from a meeting organised at the California Institute for Telecommunications and Information Technology.