Tag Archives: qseu14

QSEU14 Breakout: Measuring with Muppets

Today’s post comes to us from June Lee and Jennifer Kotler. June and Jennifer are researchers at Sesame Workshop, where they are conducting work exploring children’s media use. Below you can read their description of a breakout session they led on the topic at the 2014 Quantified Self Europe Conference. If you have ideas about measuring media use or want to continue this conversation we invite you to join the discussion in the forum.

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Measuring with Muppets
by June Lee & Jennifer Kotler

The goal of the session was to exchange ideas for ways to measure and track children’s media use across contexts (which include physical spaces such as work vs. school, and social contexts such as with whom they are using media). An ideal device would be a wearable device that’s a “Shazam meets LENA,” which would identify the media content being used, as well as capture the conversation taking place around media use.

Currently, different technologies approximate what we would like to do. For instance, iBeacon is used in shopping malls to track and deliver messages to shoppers; smartwatches could be good for capturing audio; Bluetooth recognition could identify devices that are nearby and partly capture the social context. Different apps, however, don’t use the same system and are difficult to integrate. The main takeaway from the session is that nothing exists yet that does what we would like to do. We would need different apps and systems.

The session generated other useful ideas, such as the asking what parents would like to track in terms of media and their child, and what parents currently track (if they do). Another suggestion was to look at the rare disease or health care community, which is ahead of the curve in terms of tracking and managing child health; Human-Computer Interaction departments or Interaction Design departments at universities could be another good resource. Many agreed that we could start with simple, low-tech approaches: observations and/or manual paper recording. Or do the research in stages, using technology that does exist. In short, we needed to narrow our research questions because the tool we’re looking for does not (yet) exist.

Editor’s note: While doing some research around measurement and children we stumbled upon this great Sesame Street video. Enjoy Elmo singing about the power of measuring!

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QSEU14 Breakout: The Future of Behavior Change

Today’s post come to us from Lukasz Piwek. Lukasz is a behavioral science researcher at the Bristol Business School, University of West England. We were happy to welcome Lukasz, who led an well attended breakout session at the 2014 Quantified Self Europe Conference where conference attendees discussed current issues and new dimensions of behavior change. We encourage you to read his description below (which first appeared on his cyberjournal, Geek on Acid) and join the conversation in our forum

The Future of Behavior Change
by Lukasz Piwek

I gave a short talk, and moderated a breakout discussion, on the future of behaviour change in the context of quantified self approach. It was an inspiring session for me so I summarised my slides here with the discussion that followed.

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First, I highlighted that behaviour change interventions require multidisciplinary approach in order to target a broad range of behaviours related to health (e.g. healthy eating, alcohol & drug use, stress management), sustainability (e.g. travel habits change, energy saving, recycling) or education.

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Health interventions are good example where behaviour change can enormously benefit from smart technology. Currently we have what we call a “sick care” model: when we notice a specific symptoms of illness we share it with our GP, and we get prescription, or we’re referred for more detailed diagnosis. This classic and dominant “sick care” model focuses on relatively passive way to manage illness “after” it occurs.

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However, in the future we can envision ourselves being empowered by smart devices that track various variables in our daily life (such as heart rate, body temperature, activity levels, mood, diet). This variables will get combined in sophisticated analysis merged with our illness history and DNA screening. This continuously provides us with information about “risk factors” for illnesses, which enables us in turn to act and change our behaviour before the onset of a disease. This is what we call a real “preventive care” model of healthcare. Clearly we’re not there yet.

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The key question we discussed was: “what critical features or solutions we are missing to make a breakthrough in behaviour change interventions with quantified self approach?” I started the discussion with giving two possible answers.

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First, we lack long-term user engagement for smart wearables and self-tracking solutions. A recent study showed that 32% of users stop using wearables after 6 months, and 50% – after just over a year. Similarly, there is a high drop rate amongst smartphone apps users: 26% of apps being used only once and 74% of apps are not used more than 10 times (although discussion pointed out that we might not need long-term engagement for many interventions).

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Second, existing devices for self-tracking lack data validity and reliability. Proprietary closed platforms and limited access APIs make it difficult for us scientists to validate how well self-tracking devices measure what they intend to measure. This is a major problem from the perspective of methodology for behaviour change interventions in clinical context.

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In the discussion that followed my presentation, the major reoccurring theme was a lack of robust and reliable feedback provided to users/clients. We agreed that new model of feedback would incorporate such concepts as: narratives, actionable advices on specific consequences of behaviour, and personalised, rapid, relevant data visualisation.

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Another problem highlighted was related to psychological resistance towards smart technologies in our lives, especially in the groups that are not motivated to use wearables/self-monitoring.

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Finally, it seems clear that we’re currently focusing on “exploratory” side of quantified self, and its important we start moving towards more “explanatory” and predictive approach, like in the healthcare example described above. This requires a development of new methodology for n=1 research and creation of data bank of personal analytics. Such bank would enable better generalisation and evaluation of results for larger-scale interventions.

I’m totally on it.

If you’re interested in the intersection of Quantified Self and behavior change we invite you to join the conversation in our forum.

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Debbie Chaves: A Librarian in Numbers

Debbie Chaves is a science and research librarian at Wilfred Laurier University and was interested in understanding her job and the various demands placed on her time. Using methods she’d employed previously she set about tracking different aspects of her work. The data she gathered allowed her to advocate for new changes and policies within her library. In this video, presented at the 2014 Quantified Self Europe Conference, Debbie explains her tracking, what she found, and what she was able to accomplish.

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QSEU14 Breakout: Best Practices in QS APIs

Today’s post comes to use from Anne Wright and Eric Blue. Both Anne and Eric are longtime contributors to many different QS projects, most recently Anne has been involved with Fluxtream and Eric with Traqs.me. In our work we’ve constantly run into more technical questions and both Anne and Eric has proven to be invaluable resources of knowledge and information about how data flows in and out of the self-tracking systems we all enjoy using. We were happy to have them both at the 2014 Quantified Self Europe Conference where they co-led a breakout session on Best Practices in QS APIs. This discussion is highly important to us and the wider QS community and we invite you to participate on the QS Forum.

Best Practices in QS APIs
Anne Wright 

Before the breakout Eric and I sorted through the existing API forum discussion threads for what issues we should highlight. We found the following three major issues:

  • Account binding/Authorization: OAuth2
  • Time handling: unambiguous, UTC or localtime + TZ for each point
  • Incremental sync support

We started the session by introducing ourselves and having everyone introduce themselves briefly and say if their interest was as an API consumer, producer, or both. We had a good mix of people with interests in each sphere.

After introductions, Eric and I talked a bit about the three main topics: why they’re important, and where we see the current situation. Then we started taking questions and comments from the group. During the discussion we added two more things to the board:

  • The suggestion of encouraging the use of the ISO 8601 with TZ time format
  • The importance of API producers having a good way to notify partners about API changes, and being transparent and consistent in its use

One attendee expressed the desire that the same type of measure from different sources, such as steps, should be comparable via some scaling factor and that we should be told enough to compute that scaling factor. This topic always seems to come up in discussions of APIs and multiple data sources. Eric and I expressed the opinion that that type of expectation is a trap, and there are too many qualitative differences in the behavior of different implementations to pretend they’re comparable. Eric gave the example of a site letting people compare and compete for who walks more in a given group, if this site wants to pretend different data sources are comparable, they would need to consider their own value system in deciding how to weight measures from different devices. I also stressed the importance of maintaining the provenance of where and when data came from when its moved from place to place or compared.

On the topic of maintaining data provenance, which I’d also mentioned in the aggregation breakout: a participant from DLR, the German space agency, came up afterwards and told me that there’s actually a formal community with conferences that cares about this issues. It might be good to get better connections between them and our QS API community.

The topic of background logging on smartphones came up. A attendee from SenseOS said that they’d figured out how to get an app that logs ambient sound levels and other sensor data on iOS through the app store on the second try.

At some point, after it seemed there weren’t any major objections to the main topics written on the board, I asked everyone to raise their right hand, put their left over their heart, and vow that if they’re involved in creating APIs that they’d try hard to do those right, as discussed during the session. They did so vow. :)

After the conference, one of the attendees even contacted me, said he went right to his development team to “spread the religion about UTC, oAuth2 and syncing.” He said they were ok with most of it, but that there was some pushback about OAuth2 based on this post. I told him what I saw happening with OAuth2 and a link to a good rebuttal I found to that post. So, at least our efforts are yielding fruit with at least one of the attendees.

We are thankful to Anne and Eric for leading such a great session at the conference. If you’re interested in taking part in and advancing our discussion around QS APIs and Data Flows we invite you to participate: 

You can sign up for the QS Toolmakers List
You can take part in ongoing discussions in the API Forum Thread .
And lastly, you can comment on this particular breakout discussion here

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Justin Timmer: A Lazy Workout

Justin Timmer is a student in human movement science and a fitness instructor. He was interested in exploring what he could do to increase his strength. Rather then starting with a typical strength training program Justin wanted to test if isometric muscle contraction alone could increase his strength. This type of exercise involves just squeezing the muscles without using any weight. He even went so far as to only target one side of his body so that he could test against his non-squeezing muscle groups. In this talk, presented at the 2014 Quantified Self Europe Conference, Justin explains his process and the results of this 4-week experiment.

What did you do?
For four week, I was “squeezing” (isometric contractions) my muscles four times a day. I trained my right leg, abdominals, and right chest and arm.

How did you do it?
During every quiet moment during the day I contracted my muscles as long and hard as possible. I quantified my progress by completing maximum repetitions on a fitness machine every week.

What did I learn?
I learned that in four weeks I almost doubled my force on the right side of my body. But I also learned that this training was going too fast, I got a lot of issues with little unexplained pains in my legs, and rising fluids whenever I contracted my abdominals. Overall I learnt this was a very effective training that was very easy to implement in my daily life.

You can also view Justin’s slides here.

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Alberto Frigo: A 36-year Tracking Project

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“I’ve been systematically tracking my life since the 24th of September, 2003.”

A little over 10 years ago Alberto Frigo embarked on an ambitious project, 2004-2040, to understand himself. Starting with tracking everything his right (dominant) hand has used, he’s slowly added on different tracking and documentation projects. Keeping the focus on himself and his surrounding has helped him connect to himself and the world around him.

Beside the more technical challenges ahead, I have learned how much we can engage in tracking and quantifying ourselves. I have learned that I am what I track and in this respect what I track shapes my life. I believe that every individual can activate him or herself to record his or her life and create a playful engagement with an otherwise dull surrounding. In my opinion then, it is a very healthy commitment as it makes us more aware as well as more engaged with our everyday life.

At the 2014 Quantified Self Conference Alberto led our second day opening plenary, which focused on “Tracking Over Time.” We invite you to watch his talk and read the transcript below to learn about his plans for the next 26 years and what hear what he’s learned through this process.

Transcript
Good morning everyone and thank you for inviting me to this inspiring and well organized event! My name is Alberto Frigo, I am 34 years old, and I was born in a small village in the Italian Alps but I have spent most of my life abroad doing media research and living in Northern Europe, North America and China. Currently I live in Sweden where I have been systematically tracking my life since the 24th of September 2003. I clearly remember that day: it was very sunny in Stockholm, but I felt very frustrated since I was about to start a residency the following day and the wearable computer I had for two years designed to record my life was not working. I was walking through the city with my frustrations when I passed by a tiny store selling dusty photographic equipment. In the middle of it there was a brand new and tiny digital camera. I immediately gave up all my frustrations with the clumsy wearable I had so far been constructing, and just got that very camera to start photographing every object my right hand use.

It has been more then 10 years since I have started that project, to be precise today the 11th of May 2014, is my 3.882nd day I have been photographing every object my right hand uses. With this project, my idea is to track all my daily activities that I do through the very objects I utilize to accomplish them. In total, I have been photographing 295.032 activities, an average of 76 objects a day and one picture every 15 minutes, depending on how busy that day has been. I have also discovered that, if I keep up the project until I turn 60, I will have photographed 1.000.000 objects and could thus claim to have some kind of DNA code of my life, or at least of the core of my life as a mature individual. I like to see this code as made of a continuous sequence of repeating elements, the objects I use as the letters of an alphabet which can give rise to different patterns and understandings.

In this respect, I have embarked on a 36 years long project, from 2004 to 2040, and new kinds of self-tracking projects, or life-codes have naturally come about, mostly in order to compensate the photographic tracking of activities. After a few years, in 2005, I have also started to consider to keep track of myself looking at other perspectives and using different other media such as recording my dreams through writings and recordings the songs I listen to through musical notations. Additionally, I also started to keep track of my social surrounding and the weather. I thus ended up, for example, filming every public space in which I seat and keeping track of the wind when I am outdoor. At this point of time I am conducting 36 different projects to record my life… as many as the years I mean to undertake the project. 18 of these projects are actual tracking of either myself, or the surrounding or the weather and 18 of them are elaborations, like books, gadgets or exhibitions I make about them… not the least this very speech and other meta projects like a virtual memory cathedral I will present in the office hour section after lunch.

To give you an idea of what I am up to these days, I can tell you what I have accomplished so far. Beside recording 295.032 of my activities by photographing the objects my right hand has used with this camera, I have been tracking 12.360 dreams, 5.440 songs that I have heard and recognized using this phone to keep track of them, 620 portraits of new acquaintances using this camera, 285 square meters of discarded objects picked from the sidewalk using this pouch to collect them, 1.512 news of casualties, 15.660 films of public spaces where I seat using this video-camera, 7.560 drawings of ideas, 2.760 recordings of thoughts while walking alone using this recorder, 1.704 shapes of clouds and so forth. By the way this the USB where all my work is stored, always on me.

It was not immediate to be able to track so many things at once; I have learned that one has to start with something basic and simple and then add up to new perspectives and tracking technologies, preferably crafting his or her framework. “From one thing comes another” then but I am also interested to keep up all the projects, more as some sort of a challenge in addition to simply a tool to later make sense of my life. I feel in this respect like the character of a computer game, with a mission to accomplish and this is really my drive in life, conduct these 36 tracking projects till I am 60, in 2040 then, 26 years ahead of me. 26 years in which new challenges arise such as the fact that the technology I have been deploying, like my camera, is already out of production. Well, one ought to act providently and myself, I have got a box full of these refurbished cameras… so lets just hope the operating system now won’t change too drastically in the coming years!!

Beside the more technical challenges ahead, I have learned how much we can engage in tracking and quantifying ourselves. I have learned that I am what I track and in this respect what I track shapes my life. I believe that every individual can activate him or herself to record his or her life and create a playful engagement with an otherwise dull surrounding. In my opinion then, it is a very healthy commitment as it makes us more aware as well as more engaged with our everyday life.

Saying this however, I have to warn you that it is important to give priorities also in what we track. I mostly give priorities to the tracking relating to myself, giving less priority to other forms of tracking, particularly to those forms that are more dependent to the social surrounding and I cannot control. With these priorities in mind I am rather positive that I can succeed in my self-exploration and the exploration of the world through myself, sharing my experience as time passes by and new insights are gained. I really hope my ten years old experience and commitment can be of inspiration of you. Please feel free to approach me so that I can photograph you as my 621st new acquaintance. Thank you!

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QSEU14 Breakout: Open Privacy

Today’s post comes to us from Laurie Frick. Laurie led a breakout session at the 2014 Quantified Self Europe Conference that opened up a discussion about what it would mean to be able to access all the data being gathered about yourself and then open that up for full transparency. In the summary below, Laurie describes that discussion and her ideas around the idea of living an open and transparent life. If you’re interested in these ideas and what it might mean to live an open and transparent life we invite you to join the conversation on our forum.  

LFrick

Open Privacy
by Laurie Frick

Fear of surveillance is high, but what if societies with the most openness develop faster culturally, creatively and technically?

Open-privacy turns out to an incredibly loaded term, something closer to data transparency seems to create less consternation. We opened the discussion with the idea, “What if in the future we had access to all the data collected about us, and sharing that data openly was the norm?”

Would that level of transparency gain an advantage for that society or that country? What would it take to get to there? For me personally, I want access to ALL the data gathered about me, and would be willing to share lots of it; especially to enable new apps, new insights, new research, and new ideas.

In our breakout, with an international group of about 21 progressive self-trackers in the Quantified Selfc community, I was curious to hear how this conversation would go. In the US, data privacy always gets hung-up on the paranoia for denial of health-care coverage, and with a heavy EU group all covered with socialized-medicine, would the health issue fall away?

Turns out in our discussion, health coverage was barely mentioned, but paranoia over ‘big-brother’ remained. The shift seemed to focus the fear toward not-to-be-trusted corporations instead of government. The conversation was about 18 against and 3 for transparency. An attorney from Denmark suggested that the only way to manage that amount of personal data was to open everything, and simply enforce penalizing misuse. All the schemes for authorizing use of data one-at-a-time are non-starters.

“Wasn’t it time for fear of privacy to flip?” I asked everyone, and recalled the famous Warren Buffet line “…be fearful when others are greedy and greedy when others are fearful”. It’s just about to tip the other way, I suggested. Some very progressive scientists like John Wilbanks at the non-profit Sage Bionetworks are activists for open sharing of health data for research. Respected researchers like Dana Boyd, and the smart folks at the Berkman Center for Internet and Society at Harvard are pushing on this topic, and the Futures Company consultancy writes “it’s time to rebalance the one-sided handshake” and describes the risk of hardening of public attitudes as a result of the imbalance.

Once you start listing the types of personal data that are realistically gathered and known about each of us TODAY, the topic of open transparency gets very tricky.

  • Time online
  • Online clicks, search
  • Physical location, where have you been
  • Money spent on anything, anywhere
  • Credit history
  • Net-worth
  • Do you exercise
  • What you eat
  • Sex partners
  • Bio markers, biometrics
  • Health history
  • DNA
  • School grades/IQ
  • Driving patterns, citations
  • Criminal behavior

For those at the forefront of open privacy and data transparency it’s better to frame it as a social construct rather than a ‘right’. It’s not something that can be legislated, but rather an exchange between people and organizations with agreed upon rules. It’s also not the raw data that’s valuable – but the analysis of patterns of human data.

I’m imagining one country or society will lead the way, and it will be evident that an ecosystem of researchers and apps can innovate given access to pools of cheap data. I don’t expect this research will lessen the value to the big-corporate data gatherers, and companies will continue to invest. A place to start is to have individuals the right to access, download, view, correct and delete data about them. In the meantime I’m sticking with my motto: “Don’t hide, get more”.

If you’re interested in the idea of open privacy, data access, and transparency please  join the conversation on our forum or here in the comments. 

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QSEU14 Breakout: Mapping Data Access

Today’s post comes to us from Dawn Nafus and Robin Barooah. Together they led an amazing breakout session at the 2014 Quantified Self Europe Conference on the topic of understanding and mapping data access. We have a longstanding interest in observing and communicating how data moves in and out of the self-tracking systems we use every day. That interest, and support from partners like Intel and the Robert Wood Johnson Foundation, has helped us start to explore different methods of describing how data flows. We’re grateful to Dawn and Robin for taking this important topic on at the conference, and to all the breakout attendees who contributed their thoughts and ideas. If mapping data access is of interest to you we suggest you join the conversation on the forum or get in touch with us directly.

Mapping Data Access
By Dawn Nafus and Robin Barooah

One of the great pleasures of the QS community is that there is no shortage of smart, engaged self-trackers who have plenty to say. The Mapping Data Access session was no different, but before we can tell you about what actually happened, we need to explain a little about how the session came to being.

Within QS, there has been a longstanding conversation about open data. Self-trackers have not been shy to raise complaints about closed systems! Some conversations take the form of “how can I get a download of my own data?” while other conversations ask us to imagine what could be done with more data interoperability, and clear ownership over one’s own data, so that people (and not just companies) can make use of it. One of the things we noticed about these conversations is that when they start from a notion of openness as a Generally Good Thing, they sometimes become constrained by their own generality. It becomes impossible not to imagine a big pot of data in the sky. It becomes impossible not to wonder about where the one single unifying standard is going to come from that would glue all this data together in a sensible way. If only the world looked something like this…

data_access

We don’t have a big pot of data in the sky, and yet data does, more or less, move around one way or another. If you ask where data comes from, the answer is “it depends.” Some data come to us via just a few noise-reducing hops away from the sensors from which they came, while others are shipped around through multiple services, making their provenance more difficult to track. Some points of data access come with terms and conditions attached, and others less so. The system we have looks less like a lot and more like this…

mapping_access

… a heterogeneous system where some things connect, but others don’t. Before the breakout session, QS Labs had already begun a project [1] to map the current system of data access through APIs and data downloads. It was an experiment to see if having a more concrete sense of where data actually comes from could help improve data flows. These maps were drawn from what information was publicly available, and our own sense of the systems that self-trackers are likely to encounter.

Any map has to make choices about what to represent and what to leave out, and this was no different. The more we pursued them, there more it became clear that one map was not going to be able to answer every single question about the data ecosystem, and that the choices about what to keep in, and what to edit out, would have to reflect how people in the community would want to use the map. Hence, the breakout session: what we wanted to know was, what questions did self-trackers and toolmakers have that could be answered with a map of data access points? Given those questions, what kind of a map should it be?

Participants in the breakout session were very clear about the questions they needed answers to. Here are some of the main issues that participants thought a mapping exercise could tackle:

Tool development: If a tool developer is planning to build an app, and that app cannot generate all the data it needs on its own, it is a non-trivial task to find out where to get what kind of data, and whether the frequency of data collection suits the purposes, whether the API is stable enough, etc.. A map can ease this process.

Making good choices as consumers: Many people thought they could use a map to better understand whether the services they currently used cohered with their own sense of ‘fair dealings.’ This took a variety of forms. Some people wanted to know the difference between what a company might be capable of knowing about them versus the data they actually get back from the service. Others wanted a map that would explicitly highlight where companies were charging for data export, or the differences between what you can get as a developer working through an API and what you can get as an end user downloading his or her own data. Others still would have the map clustered around which services are easy/difficult to get data out of at all, for the reason that (to paraphrase one participant) “you don’t want to end up in a data roach motel. People often don’t know beforehand whether they can export their own data, or even that that’s something they should care about, and then they commit to a service. Then they find they need the export function, but can’t leave.” People also wanted the ability to see clearly the business relationships in the ecosystem so they could identify the opposite of the ‘roach motel’—“I want a list of all the third party apps that rely on a particular data source, because I want to see the range of possible places it could go.”

Locating where data is processed: Many participants care deeply about the quality of the data they rely on, and need a way of interpreting the kinds of signals they are actually getting. What does the data look like when it comes off the sensor, as opposed to what you see on the service’s dashboard, as opposed to what you see when you access it through an API or export feature? Some participants have had frustrating conversations with companies about what data could fairly be treated as ‘raw’ versus where the company had cleaned it, filtered it, or even created its own metric that they found difficult to interpret without knowing what, exactly, goes into it. While some participants did indeed want a universally-applicable ‘quality assessment,’ as conveners, we would point out that ‘quality’ is never absolute—noisy data at a high sample rate can be more useful for some purposes than, say, less noisy but infrequently collected data. We interpreted the discussion to be, at minimum, a call for greater transparency in how data is processed, so that self-trackers can have a basis on which to draw their own conclusions about what it means.

Supporting policymaking: Some participants had a sense that maps which highlighted the legal terms of data access, including the privacy policies of service use, could support the analysis of how the technology industry is handling digital rights in practice, and that such an analysis could have public policy implications. Sometimes this idea didn’t take the form of a map, but rather a chart that would make the various features of the terms of service comparable. The list mentioned earlier of which devices and services rely on which other services was important not just to be able to assess the extent of data portability, but also to assess what systems represent more risk of data leaking from one company to another without the person’s knowledge or consent. As part of the breakout, the group drew their own maps—maps that either they would like to exist in the world even if they didn’t have all the details, or maps of what they thought happened to their own data. One person, who drew a map of where she thought her own data goes, commented (again, a paraphrase) “All I found on this map was question marks, as I tried to imagine how data moves from one place to the next. And each of those question marks appeared to me to be an opportunity for surveillance.”

What next for mapping?

If you are a participant, and you drew a map, it would help continue the discussion if you talked a little more about what you drew on the breakout forum page. If you would like to get involved in the effort, please do chime in on the forum, too.

Clearly, these ecosystems are liable to change more rapidly than they can be mapped. But given the decentralized nature of the current system (which many of us see as a good thing) we left the breakout with the sense that some significant social and commercial challenges could in fact be solved with a better sense of the contours and tendencies of the data ecosystem as it works in practice.

[1] This work was supported by Intel Labs and the Robert Wood Johnson Foundation. One of us (Dawn) was involved in organizing support for this work, and the other (Robin) worked on the project. We are biased accordingly.

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Alex Tarling on Tracking and Changing Happiness

Alex Tarling starting using the Mappiness app to track his happiness along with other contextual data. Over time the ritual of having to ask himself, “How happy am I?” three times a day started to get him thinking about how he thought about his own happiness and what that meant to him. In this talk, presented at the 2014 Quantified Self Europe Conference, Alex talks about his experience, some of the data he gathered, and how a slight change in attitude has increased his self-rating of happiness over time.


You can also view the slides here.

What did you do?
I tracked my own experience of happiness several times per day, along with location, what I was doing and who I was with.

How did you do it?
I used the Mappiness app to track my rating of happiness and other contextual data such as what I was doing and who I was with. –

What did you learn?
I can’t measure my own happiness without affecting it, one way or another. Happiness is a conscious cognitive assessment of feelings, beliefs and behaviours that tends to be a habitual pattern of thinking.
Given that it’s a mental habit, it is possible to make an intentional choice to change it. As Abraham Lincoln said, “Most folks are as happy as they make up their minds to be.”

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Morris Villarroel: A Four-Year Journal

Four years ago Morris Villarroel was inspired to start writing things down. He started with a simple Muji notebook and begun adding some structure such as daily logs, life events, and review of books and articles he had read. In the process of filling out over 130 journals his process has evolved to include journaling about other important aspects of his life. In this talk, presented at the 2014 Quantified Self Europe Conference, Morris explains his journaling in detail, gives a few examples of how he’s able to analyze the data he’s tracking in his journals, and explains how this process has improved his reflection and preparation for future events.


You can also view the slides here.

What did you do?
Kept a log book of daily events over the past four years in muji notebooks, including work, personal life and readings.

How did you do it?
The writing evolved into different sections, including an agenda, food page, idea page, book index and readings written from back to front. I titled each page with the main events and included all pages and events in an excel spreadsheet for easy access and analysis.

What did you learn?
That most events in my life can be classified as work (57%), personal (32%) and writings (11%) and were not very correlated with steps (Fitbit data), and a little more with floors. The whole process also inculcated more reflection on the preparation of events, their intensity and reviewing and reusing results, to then improve preparation in the future.

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