This post is part of my liveblogged account of a conference. Two disclaimers: Liveblogging is hard, and I often get things wrong. If I did, please feel free to correct me via email or in the comments and I’ll make changes when appropriate. Second, the opinions expressed in these sorts of posts are those of the speakers, rather than mine.
There’s a quick lunch break at Quantified Self followed by a series of five-minute Ignite talks. I find these virtually impossible to blog because of the speed, but here’s an attempt.
Rick Smolan wants us to know that big data is not big brother. The producer of projects like A Day in the Life of America, Smolan is focusing his next project on showing us the human side of big data and what we can learn from it. He mentions Ushahidi as an example of data being used to save lives. As we collect data from thousands of individuals, we can map the need for water or healthcare in parts of Haiti.
For a project that looks at a day in the life of big data, he’s going to set up 10 million human sensors, many of whom will download smartphone aps that track GPS location, steps taken, their mental state and ask questions each hour that ask people to help map their experiences and environment. This will be complemented by inputs from 1000 journalists in 50 countries. The goal is to understand how reflecting on data we collect can change our behavior, much like we change our driving by monitoring through the dashboard of a Prius. “These are reflections in a digital mirror – we can use big data to take the pulse of the planet.”
Misha David Chellam from Scanadu tells us that he didn’t know what a tricorder was until his 50-something business partner mentioned the Star Trek device as a metaphor for what they could build together. Misha is a geek twentysomething who likes cool devices, and his partner doesn’t want to die, so together, they’re trying to build the medical tricorder.
There’s lots of health data available these days, from self-tracking devices like the Fitbit and the Zeo, from “macro-scanning” tools like full body scans and genomic analysis, like 23 and me, and we now have access to digital, “nomadic” health record systems like Practice Fusion, Google Health and Microsoft’s Health Vault. We could add to this “sequencing human lifestyles”, data that helps us understand behaviors on a population level.
The next step is interpreting this data. For starters, we can try to do interpretation using doctors in a Mechanical Turk fashion, perhaps using doctors who are solely in private practice and carrying a lower patient load. In the long run, we might do AI – and Watson’s victory on Jeopardy is an inspiration.
The tricorder is the metaphor because it does so many things… and the contemporary tricorder is the mobile phone. It’s got a vast number of sensors that are helpful to us, and we can add to it with interfaces like microfluidics readers. The vision behind Scanadu is developing a strategy that can win an XPrize, focused on building a tricoder that can evaluate a patient better than a board of physicians. Scanadu is taking first steps to this, collecting blood from alpha users and using Wolfram Alpha for contextualizing this data. It’s a first step, but they’ll have lots more to work with if they can partner with tracking device developers.
Alan Gale of Bio-Logic Health is interested in life extension through food and supplement tracking. In the past, Gale tells us, our methods for life extension were pretty weak: mummification, drinking blood, freezing Ted Williams’s head. Current approaches, advocated by people like Aubrey De Grey and Ray Kurzweil either focus on future technologies, or on new developments in regenerative medicine and hormone replacement.
At present, the best preventative techniques we know about are caloric restriction and supplements, a regimen that requires massive lifestyle changes. You have to take hundreds of supplements, some of which can be toxic in high doses. Managing this process requires lots of careful adherence and tracking.
Gale views the human body from an engineering point of view. It’s composed of subsystems, each with inputs and outptus. Systems are regulated both via feedback mechanisms, or through our conscious intervention. When it gets cold, we can shiver (activating endocrine and muscular systems) or we can put on a sweater. The same is true with food. With mobile tools, we can track inputs like what we eat, and outputs like our blood tests. Over time we can build a model of feedback mechanisms, guiding people towards their deficiencies and meeting their goals.
Sarah Gray tells a story about tracking her mood that starts, as most good stories do, with a boy. The boy lived in a different city, and she found herself unable to decide whether she should move to be with him, continue a long distance relationship or move on. So she built a website that allowed her to track her feelings. Over a few months, she rated her mood from 1-5 and looked for patterns. After a few months, her understanding of the situation was much clearer, and she decided to separate from the guy in question.
The app she built is the root of MercuryApp, a mood tracking website designed for easy use with smartphones. She suggests that it works because it encourages a ritual where we track every day, encourages reflection, where we stop and think about what’s going on, and helps us find a story, an arc, to our behavior.
She offers examples of individuals using the app:
– Sebastian, an pathological optimist, who thinks situations are always going to get better. After discovering that he was unhappy at work, week after week, he decided to quit his job and move back to Spain with his wife. “You can write off one sad panda, but not a string of them.”
– Dave, who manages an embedded software team. The team members use the tool to track their morale, and Dave has a real-time health check on the mood of the team.
The goal is to merge hard and soft data to help individuals become happier. Answering the question, “When are you happiest?” requires both quantifiable data and the data of your gut.
Marcy Swenson and Dale Larson offer a skit to explain what agile development might teach us about personal tracking. Dale’s worried about falling asleep during a session this afternoon. So he plans an experiment to use his Zeo, measure his sleep against caffeine consumption, mood, food and exercise data, then graph it all and engage in multivariate analysis to solve the problem!
Marcy observes that Dale seems to be more focused on data than on solving the problem. If we learned from software development, we might try weekly sprints, information radiators and a tight build/measure/learn cycle which might let us figure out what we really needed to know before investing months in a particular process. They’ve expanded on some of these thoughts at startuphappiness.com
Ron Gutman wants us to know about the untapped power of the smile. He’s a serious runner, and discovered that when he hits the wall in a long run (75 minutes in!), he often feels better when he smiles. He began tracking the data closely and discovered it was an unambiguous correlation for him. So he began a wide-ranging study of the power of the smile.
A 30 year longitudinal study tracked the relationship between people’s happiness (on a test of well-being) and success of their marriages and their high school yearbook photos. Based on people’s smiles, researchers could make very accurate predictions of the future of these students. Another study looked at pre-1950 baseball cards. The span of a player’s smile could predict span of the player’s life – bigger smiles predicts longevity.
Less than 15% of people smile less than 5 times a day. At the same time less than 1/3rd smile more than 20 times. It’s certainly possible to smile more: children can smile up to 400 times a day. It’s possible that smiling can, in and of iself, make us feel better. Charles Darwin speculated, “Even the simulation of an emotion tends to arouse it in our minds.”
Gutman tells us that one smile can create the same brain stimulation as 2000 bars of chocolate (with fewer calories.) If you smile, others see your smile and feel good. In turn, they smile and you feel good. He closes with a quote from Mother Theresa: “I will never understand all the good that a simple smile can accomplish.”
Sean Ahrens has Crohn’s disease, an inflammation in the digestive track caused from a disregulated immune system. He’s coped with the disease for 13 years, and recently decided to take some worms. He took pig whipworm, and a friend took human hookworm. It’s not a cheap thing to do – he spent $3000 to purchase worm eggs from Germany and Thailand, which he took every two weeks for five months. The eggs appeared, under a $12 microscope, to be worm eggs. And Ahrens monitored his symptons – gut pain and bowel movements – closely for the months he took the eggs and months afterwards.
It wasn’t a very successful experiment, both in that his symptoms didn’t get much better after taking the eggs, and that he can’t definitively say whether the eggs failed. First, he didn’t have much baseline data. Second, there were other changes in the time he tracked – a change of diet, other medications, and stress from participating in Y-Combinator. He ended up concluding that he didn’t have enough background in math to figure out causality in the data. The talk ends up being a cautionary tale about getting baselines and controlling experiments… which can be hard to do when you want pain to go away. In the meantime, Ahrens is working on a company, Crohnology, that’s a supportive social network for people with the disease.
Tina Park is a designer for Johnson and Johnson who worked on Project Health Design, a effort from the Robert Wood Johnson Foundation’s Pioneers Program to help teenagers with chronic conditions transition from pediatric to adult health care. This tends to be a difficult transition: teenagers go through lots of life changes, teens forget medications, and they can get sick and die.
It’s possible, she argues, to track teen’s moods through texts. And since teens identify health through mood, it’s possible to identify moments where teens may be unwell by reading their text messages. Many teens send hundreds of messages a day. Her project graphed the intensity of message sending on timelines – she shows us a visualization of data of a teen’s data for six months. You can tell when she’s asleep based on the flat periods in the timelines. And you can see what words are common at different times in the series, which can help a teen see what she was talking about and, perhaps, what stressors are happening in her life.
The benefit – this is data that already exists – perhaps we can get insights on mood without collecting any additional data.
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