Tag Archives: sharing
This morning President Barack Obama announced a new Precision Medicine Initiative, a key $215 million piece of the proposed 2016 budget. Much has been written since last week’s State of the Union, when this initiative was first mentioned by President Obama. In brief, the initiative is an investment in new programs and funding initiatives at major government bodies that influence the current and future health of all Americans, including the National Institutes of Health (NIH), the Food and Drug Administration (FDA), and the Office of the National Coordinator for Health Information Technology (ONC). These programs will focus on developing “a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.”
There is a lot of information being circulated about this new initiative, and we’ve collected some links below, but we’d like to highlight something directly related to our interests in self-tracking data, personal data access, and new models of participatory research. In this morning’s announcement President Obama mentioned a long-term goal of creating a participatory research cohort comprised of 1 million volunteers who will be called upon to share personal medical record data, genetic samples, biological samples, and diet and lifestyle information. This is truly an ambitious goal and we are happy to see the President take care to mention the importance of including patients and the individuals who collect this data in the decision making and research process. For example, here is the description of this specific program from the NIH Precision Medicine Infographic
Here at QS Labs, we’re dedicated to helping create and grow a culture that enables everyone to generate personal meaning from their personal data. Sharing, participation, and exploring new models of discovery are a core themes we’re exploring as part of our QS Access work. We’ll be following this initiative as it moves from today’s announcement to tomorrow’s reality. Be sure to stay tuned to our QS Access Channel for more updates as we learn more.
Learn more about the Precision Medicine Initiative
NIH mini site describing the initiative
White House Blog: The Precision Medicine Initiative: Data-Driven Treatments as Unique as Your Own Body
FACT SHEET: President Obama’s Precision Medicine Initiative
A New Initiative on Precision Medicine by Francis Collins and Harold Varmus (New England Journal of Medicine).
Welcome to part 5 of the QS book on mood tracking that Robin Barooah and I wrote. This chapter has some tips that we’ve found helpful for getting started with mood tracking. Enjoy!
Once you’ve been tracking mood for a while, and have a good baseline established, it’s time to play. What if you could influence the factors that shape your mood? What if you had a trusted buddy to confide in, to make your tracking more robust? If we know ourselves better, we can make choices that help us to make the most of our lives. We’ll explore how and why to experiment with and share your mood in this chapter.
There’s a concept called heutagogy that applies nicely to self-tracking activities. Heutagogy is basically the idea that people direct their own learning, using personal experiences to update their models of themselves and the world around them. Stewart Hase and Chris Kenyon, who came up the term, write that “people only change in response to a very clear need… involving confusion, dissonance, fear, or intense desire.”
At Quantified Self, we usually see intense desire as a motivator, but fear creeps in too, often for health concerns. If you do want to change your mood, it’s helpful to know how others with similar motivations have gone about doing it, to get some ideas and approaches to adapt to your needs.
If you’ve been reading our posts for a while you’ve most likely noticed that we typically reference the Quantified Self community. If you’re not familiar with our structure and how to get involved you might be asking yourself, ”How do I get involved with the QS community?”
On Tuesday March 12, Nick Dawson asked if there was a Quantified Self Twitter Chat. Lots of interest followed so we decided to make it happen. We had the very first #qschat twitter chat last Thursday and it was a lot of fun. I learned a lot about what people are thinking and doing in the Quantified Self space and in their daily lives. I’ve included some highlights from the chat below, but you can always just search Twitter for #qschat for the full list of tweets.
We’re going to try our best to make this happen every Thursday night at 6PM PST. Just follow the Quantified Self Twitter account to join in!
Our three questions for tonight (3/22/12) will be:
- What is one thing you’ve learned from your self-tracking and Quantified Self practice?
- Have you ever shared your experiments and results with anyone? If so how did it go. If not, why not?
- Health is an obvious Quantified Self area of interest. What other areas have you applied, or want to apply, Quantified Self to?
Again, join us at 6 PM PST to talk about these three questions and more!
Read more to see some selected tweets from our first chat last Thursday!
Ewart de Visser had a friend who “didn’t like the whole work thing” and started speculating on foreign currencies. When Ewart asked him how much he was losing in his first few months, his friend wasn’t sure, so they set up a spreadsheet to start tracking his trading performance. In the video below, Ewart describes how he used data to modify his friend’s trading strategy to prevent big losses, as well as the interesting benefits of being tracked by someone other than yourself. (Filmed by the Washington DC QS Show&Tell meetup.)
This is the intimate story of the last 6 months of my life, and how it changed for the better by sharing my mood. For some reason, writing publicly about mood feels much more vulnerable than other kinds of data. Maybe it’s because emotions get more to the core of our beings than weight or steps counted.
But several people at the QS conference found it helpful and interesting to hear what I’ve been doing, so I feel encouraged to write this in case it inspires or motivates someone else.
It all started on November 28, 2010, when I watched and blogged Jon Cousins’ excellent video about Moodscope. I joined Moodscope immediately, and for the next twenty-seven days, calculated my mood according to their algorithm. It made the invisible visible for me:
1. My mood fluctuates much more than I realized! The highest point of 98%, on December 13, was after I had just walked part of the Honolulu marathon and celebrated seventeen years of togetherness with my sweetie. The lowest point of 13%, only three days later, I annotated as “jittery, lonely, sad, overwhelmed by work/family demands.”
2. Sharing with friends didn’t seem to have the stabilizing effect for me that it did for Jon. I started sharing this graph with two friends on December 4, but the ups and downs still happened.
3. Measuring mood needs more than one data point per day. I recorded these moods every day after my morning walk, which would be around 9-10 am, but I often found myself feeling very different as the day went on.
The dead end. The cul-de-sac. The walled garden. These are three different ways (using 2.5 different metaphors) to refer to services that allow you to communicate and display information but not to copy, transfer, or share your data with outsiders. It’s an internet dogma that dead ends, culs-de-sac, and walled gardens are bad.
I subscribe to this dogma. The belief that we should be able to use a
service, such as a mobile
phone, a social networking site, or a hospital, without being taken
hostage, has intuitive appeal. The favorite reference point for the
failure of walled gardens is the stagnation of the online pioneer AOL,
which, in the Great Internet Creation Myth, represents the
morally-pleasing downfall of an arrogant oligarch. Every new service
that holds on to its users by making it hard for them to export data
inevitably earns comparison to AOL.
At a recent QS Show&Tell we heard a good presentation from Brandon, who works at A&D Weighing about new devices that will automatically upload biometrics to a web site. (Video here.) Brandon was good humored about the hard time he got when he revealed that the data will not exportable, will not be shareable, and will be accessible only through Actihealth Monitoring System, a walled garden (dead end? cul-de-sac?) of data owned by a biometrics/fitness company called Fitlinxx. Biometrics belong to users – don’t they?
But here is a counter-example. Earlier in the week I visited the innovation research lab of Kaiser Permanente, where they are very keen on biometrics, and heard one of the docs talk about KP Connect, the online medical records system internal to Kaiser. This system is somewhat legendary, and not always for good reasons. Reported to cost upwards of 4 billion dollars, KP Connect is extraordinarily ambitious, combining what we ordinarily think of as medical records (lab results, prescriptions, physical complaints, observed symptoms, etc.) with appointment reminders, at-home biometrics, and expert-systems for advising doctors on treatment options.
It struck me, as I listened, that the Kaiser garden has very high walls. Most of the information in the system is government by privacy regulations, making it exportable and shareable only upon verified patient request. But that’s not really what makes these walls high. Kaiser, as an HMO, is both an insurer and a medical care provider – it knows more about your health than anybody else, and almost certainly more than you know yourself. The user experiences just the outward surface of KP Connect, the place where they see whether a child’s immunizations are up to date, or make an appointment to get a flu shot. But Kaiser can use the system to send reminders about medication, to suggest changes in treatment based on new research, and to aggregate data from which they can draw new inferences. They have begun to integrate the data on KP Connect with large genomic studies using volunteers who are Kaiser members – this will give ever more depth and complexity to the processes that control life in the walled garden. You can always choose to leave; but it may not be as verdant on the outside.
With these advantages, it is hardly necessary to keep individual data hostage. In fact, the biometrics gathered on KP Connect can be exported to Microsoft Health Vault, and from there they can be exported into files in standard formats. You can carry it around with you on your little flash drive, on your iPhone. You can publish your own little personal reports. But to make it meaningful, you may end up planting it somewhere.