From Self-Observation to Medicine

Kevin Kelly

The art of constant self-awareness and self-experimentation is essential to the habit of self-metrics. Occasionally a trained scientist can take a small signal from their own life and turn it into a falsifiable result. I found the following note of such self-observation on the website for The People's Pharmacy. This center for patient submitted alternative remedies has been run by Joe and Terry Graedon since the 1970s.  Among the many recent examples of self-awareness leading to quantifiable medical results they list this story:

This is one of the most bizarre discoveries we have ever heard about.

A scientist noticed that when he got up out of his chair after watching a football game, his arthritis pain was greatly diminished.

During the course of the game he had consumed a six-pack of diet soda containing aspartame. Putting two and two together, he thought this artificial sweetener might have contributed to his relief.

He organized a placebo-controlled trial involving aspartame (aka Equal, NutraSweet) and confirmed that doses of 76 to 152 mg did indeed provide pain relief, roughly comparable to anti-inflammatory agents.

This research was published in the very respectable scientific journal, Clinical Pharmacology and Therapeutics.*

*Edmundson, A. B., and C. V. Manion. "Treatment of Osteoarthritis with Aspartame." Clin. Pharmacol. Ther. 1998; 63:580-593.

Detailed quantifiable self-observation has a new handle. It is called ODL or Observations of Daily Living. The idea is that if you monitor your body in your daily life over time you'll have more than just a snapshot of your health, you'll have baselines and long-term trends.  As Project Health Design suggests:

..The importance of observations of daily living (ODL) [is] moving toward next-generation personal health records and health management. 

Things like:  Does my chronic pain spike when the temperature dips below a certain threshold?  What effects might a particularly stressful month, with long hours at work and marginal sleep, have on my eating and activity behaviors, and hence my diabetes?  Can the fact that a 17-year-old with a chronic illness is regularly self-reporting his or her mood to be bad or sad play a role in the self-management of his or her disease?  And, if today’s pollen counts are really high, can my PHR device send me an alert in the morning to remember my inhaler, and then delete that point-in-time data capture because it may not be useful if conditions change tomorrow?

Trixie Tracker: Data-driven Parenting

Kevin Kelly

The Quantified Self is primarily about self-monitoring, and not about monitoring others. But your baby is close to the self, so there may be some technology in baby monitoring to be of use to adults.

Trixie Tracker tracks and displays the activity patterns of babies. As they claim on their website:

"Uncover patterns in your baby's sleep rhythms and daily activity. Develop a good sleep schedule with helpful charts. Share online with family and friends."

Using this software parents can track what goes into baby, and when; what comes out, when; when baby sleeps, when baby wakes, and any other activity you want to collect data for.

This is a primitive version 1.0 of tracking tools because you need to manually enter all data. The tool provides a web-based fancy spreadsheet with cute charts. You provide the data entry.  It has an iPhone input option, too, which could make a difference. What you can take away -- particularly if you are willing to share your baby' data -- is some sophisticated analysis of say baby's sleep probability.

Trixie Tracker is part of a larger idea called data-driven parenting, which I suspect has a small following right now, because who wants to spend their lives inputing data? But once all these ubiquitous devices collect data for us, crunching your kids' day later in the evening after they go to bed may be the new parental chore.

Annals of Self-Experiment - Seth Roberts is His Own Mouse

Gary Wolf

I'm becoming a devoted fan of Seth Roberts, one of the great champion of self-experimentation. Roberts, an emeritus professor of psychology at UC Berkeley, has spent many year studying himself, and, even better, offering many practical clues about how to construct your own "experiments of one." I first found out about his work in the most obvious way: searching on "self-experimentation" in Google.

This lead me to Roberts paper: "Self-experimentation as a source of new ideas: Ten examples about sleep, mood, health, and weight." The problems he describes are so common, and his solutions so counter-intuitive, that you can't help being intrigued. One of the great things about reading Roberts is getting a feeling for how different self-experimentation is from other forms of self-knowledge. While Roberts often begins his experiments with a hypothesis, using his stock of common knowledge, suggestions from friends, and categories of analysis typical of a well-trained college professor, this first idea is usually proven, through experiment, to be wrong. Not superficial, or too narrow, or distorted by delusion or prejudice; simply incorrect, provably irrelevant. So then Roberts has to come up with new ideas. The data, expressed as charts, no longer merely test his hypotheses; the data becomes the source of his theories. And the theories bear the mark have having emerged from data. Often, they seem very, very odd. They seem to have no link to received wisdom, to folk knowledge, to intuitive "rightness." To me, they seem like the kinds of theories a computer might have about a person. (A confession: this biases me in their favor.)

Does standing up a lot during the day reduce susceptibility to colds? Go ahead and doubt it; I did. But Roberts has data to back it up, and while it would be foolish to believe that standing up a lot  during the day would eliminate colds across an entire population - foolish, that is, without experiments to prove it - Roberts' own practice of standing up a lot has a lot more empirical back-up than many of the more "sensible" things we naively believe.

Here's anther one: for a long time Roberts had a problem with his sleep. He woke too early, could not go back to sleep, and then was tired in the morning. He tried different ways to cure this problem until, through a combination of coincidence, experiment and analysis of the data, he discovered an expected correlation: his problem disappeared when he skipped breakfast. He cured his early awakening by not eating until 11 a.m.

The idea that skipping breakfast may reduce early awakening was, wrote Roberts, "a new idea in sleep research." Strangely, Roberts was not hungry in the wee hours when he was troubled by early awakening, which lead him to suspect that it was not discomfort that roused him, but rather some glitch in his sleep cycle caused by anticipation of food.
In his paper, Roberts cites a number of studies showing that:

Food-anticipatory activity is a well-established effect in animals (Bolles & Stokes 1965; Boulos & Terman 1980). Mammals, birds, and fish become more active a few hours before feeding time (Boulos & Terman); as far as I know, no effect present in mammals, birds, and fish has ever been absent in humans. Because activity requires wakefulness, food should produce anticipatory wakefulness as well.

Roberts' theory came to mind recently because just last week, in the May 23, 2008 issue of Science, Patrick M. Fuller, Jun Lu, and Clifford B. Saper report on some experiments that precisely locate an important mechanism that links food with circadian rhythms in mice. The idea that circadian rhythms in mice are influenced by food availability is not new, but, through an elegant experiment, the authors show that there is a food-entrainable clock in the dorsomedial nucleus of the hypothalamus (DMH), and that this clock can override the light-sensitive circadian clock in the suprachiasmatic nuclei (SCN).

Our data indicate that there is an inducible clock in the DMH that can override the SCN and drive circadian rhythms when the animal is faced with limited food availability. Thus, under restricted feeding conditions, the DMH clock can assume an executive role in the temporal regulation of behavioral state. For a small mammal, finding food on a daily basis is a critical mission. Even a few days of starvation, a common threat in natural environments, may result in death. Hence, it is adaptive for animals to have a secondary "master clock" that can allow the animal to switch its behavioral pattern rapidly after a period of starvation to maximize the opportunity of finding food sources at the same time on following days.

How strongly this mechanism operates in humans - if at all - is unknown. But, thanks to Seth Roberts' experiments, we have data on a human whose sleep problem was cured by an alteration in the schedule of food. One of the regular contributors to the forum Roberts runs on his Web site uses the tag line: "Proud member of Lab Rats United."  This is a joke, but more than a joke. When we experiment on ourselves, we can fruitfully adapt the methods used by psychologists on mice; but that's not so surprising, because we share a lot of their biology, too.
 
 

Home Monitoring as Long Term Care

Gary Wolf

This story from the New York Times today gave me a sense of how far along the personal monitoring movement has come in the last few years.

...Sensors attached to the wall are able to register when Mrs. Trost gets out of bed and whether she stops at her medication dispenser, and to alert her daughters to any deviations from her routine that might indicate an accident or illness. The family is updated by electronic report every morning.

Monitoring systems like these, which go far beyond the emergency response buttons that have been around for years, are not found in many homes yet. Privacy is an issue for some older people, and the basic package can range from $50 up to $85 a month for the motion sensors and remote monitoring system like Mrs. Trost uses. More comprehensive packages can include devices to track blood pressure, weight or respiration.

Experts on aging say the systems will become commonplace as the 76 million baby boomers approach ages when disabilities or conditions like diabetes and failing eyesight jeopardize the ability to live independently. The population of those 65 years and older is almost 40 million today, and the federal Census Bureau says that will more than double, to nearly 87 million, by midcentury....

The growing number of Alzheimer's sufferers, which is expected to more than triple from the current four million by 2050, may also spur wider adoption of technologies like motion sensors to alert others to deviations in routine, trackers to assure medications are taken and emergency response buttons....

The story by Elizabeth Olson gives a good general description of how personal monitoring is being used to supplement care for the elderly. It only mentions two specific technologies currently in use. They are:

Motion sensors that can inform relatives or care-givers when a person has gotten out of bed, and whether there's been a stop at the medicine cabinet.

Blood pressure monitors and scales that automatically note anomalies and send alerts to doctors.

Examples of the first of these are not hard to find. One of the companies offering them is Simply Home, which sells a programmable motion detecting system designed to identify ominous changes in behavior.

This system is not cheap: $400, plus $54 per month. At first it seems rather Byzantine - alerts go via two way radio to a central processing station, and are automatically send back out to relatives or care givers via the Web and PDAs. But when you realize that there is no call center, no live monitoring, but rather just a data center than processes the alerts and resends them; suddenly, it makes good sense. The data can be interpreted by somebody who knows the person being monitored. Simple rules become useful; if such-and-such a door isn't opened by 9 a.m., send an alert..."

The integration of human watchers into automated systems will be one of the things that makes them smarter more quickly than most people expect. These watchers are not anonymous employees, but relatives and friends, picking up cues from the data stream, and making inferences immediately based on their knowledge of your patterns of life.

Testing Genetic Test Chips

Kevin Kelly

Ann Turner, co-author of the best book on DNA-based genealogy: Trace Your Roots With DNA, wrote me to say that she too has been comparing results from the two big genetic test companies, 23andMe and deCode.  She wrote in response to my earlier posting comparing results between the two vendors.

The big news is that places where errors are showing up are probably not random. Here's the argument, starting with her post on ancestry.com: 

The two companies overlap on 562,532 SNPs. They agreed on 560,128 calls, or 99.6%. 23andMe didn't make a call on 1,970 SNPs where deCODEme did, and deCODEme didn't make a call on 399 records where 23andMe did. That leaves a mere 35 records where they actually made different calls [see the list below]. In all of those cases, one company would make a homozygous call while the other company made a heterozygous call -- there were no cases where they made a completely discordant call.

 
Here's the kicker from Ann's letter to me:

Four of those (rs11149566, rs4458717, rs4660646, and rs 754499) were also found in Antonio's list. That's more than you would expect by chance.

Four out of 23 from Antonio's list and four out of 35 on Turner's list of discordant results indicates that these regions (at least) are unreliable.

This is why sharing results is so valuable and a key to great quantified self understanding.

This is a micrograph of the bead array on which these tests are conducted.

Turner's 35 SNPs with different results, if case you also have done a comparison.

rs10435795 rs1045363 rs10743414 rs10945383 rs11149566 rs11179382 rs11707159 rs11915402 rs1209171 rs1221986 rs12907462 rs1303912 rs13422439 rs161381 rs17328647 rs1961196 rs1966357 rs2016461 rs2064034 rs2290516 rs2853981 rs3952469 rs4336661 rs4423481 rs4458717 rs4572718 rs4660646 rs6531490 rs6942478 rs7102702 rs754499 rs7812884 rs845217 rs9332128 rs9476380

How Accurate Are Personal Genome Tests?

Kevin Kelly

I've had my DNA sequenced by 2 of the 3 companies now offering this service to the paying public. I purchased the tests for 23andMe and Iceland-based deCode. I am still plodding my way through the results -- it's sort of an education. One question I had was how well do the two results matched?  I give the same DNA to both companies; the results ideally should be identical. DeCode claimed to test for 1,000,000 SNPs and 23andMe for 500,000, so the problem of lining all these results up to see what differs is not trivial.  Luckily another user has just done this. 

Antonio Oliveira also used both 23andme and deCode. He writes in his new blog: 

In order to determine the accuracy of the genome profile provided by 23andMe and deCODEme I arranged to be genotyped by both companies and wrote a computer program to compare the results. The downloaded files contains 576,105 snips in the case of 23andMe and 1,013,349 snips for deCODE. After removing the no-calls and matching the two files by SNP identification, 560,299 snips were present in both files. The comparisson revealed 23 cases in which the results do not agree. 

Oliveira made a chart of his results, categorized by chromosome.

The 23 errors makes the agreement between the two sets of data about 99.995% accurate, or an error rate of .005%, which is pretty good for medicine. A better test might be to repeat the test on the same DNA, but I assume the manufactures of the chip have done that. The 23 "unequal" SNPs caught here in disagreement are not SNPs currently associated with any diseases, so these particular errors are inconsequential. I don't know if there are location biases in the errors, but presumably errors can appear in significant locations -- at that very low rate. However if your computer had the same error rate, you'd notice.

"Productivity" Dashboard Monitor

Kevin Kelly

In the annals of self-monitoring tools, here is one that monitors your computer time. It's a fancy version of time management software. You assign certain tags for various functions and websites -- say "surfing" for Digg, Reddit, or Popurls, or "research" for Wikipedia. After you label your activities once, then RescueTime will gather the stats and present you with your accumulative totals in a kind of productivity dashboard. You can get a time budget showing how you actually use time on your computer.

Assuming you can accurately classify which activities are productive, then you can measure your productivity -- at least in terms of how much time you spend on "productive" tasks. (This particular software will also require a certain level of trust since your self-monitoring activities are transmitted to the software's website.) I haven't used it, though it's if free and available on Windows or Mac.

The BodyBugg

Gary Wolf

I'm fascinated by the BodyBugg. Not convinced, but fascinated. This is the most complete self-monitoring system I've yet seen. With an accelerometer, a skin-temperature sensor, a sensor to measure the electrical conductivity of the skin (known as GSR, for Galvanic Skin Response), and a sensor to measure "heat flux" (the rate of heat transfer from the skin), the BodyBugg truly aspires to track a complex behavior – physical exercise – not in terms of outward factors, such as miles run or laps swum, but in terms of inward factors: how much energy has your body used?

This is a hard task, and it's inspiring that somebody has come so far in figuring it out. The goal is round-the-clock self-surveillance:

We recommend wearing the armband as much as possible during waking hours. the more you wear your bodybugg™, the more accurate and effective you will be at maintaining your calorie deficit goal. During low activity level periods of time (such as sleeping), the program will estimate your calorie burn at rest, based on your body parameters, so it is not 100% necessary to wear to sleep.

There is some science available for those who want to calculate energy expenditure through measurements like heat flux. Still, the assumption behind the current version of the BodyBugg is not that users want to experiment on themselves, or participate in scientific research. Instead, they want the Body Bugg to help them lose weight. The problem is that a device that involves such total commitment to rational self-analysis seems ill-suited to such a straightforward goal. If the goal is simply to lose weight, you don't need to measure yourself 24 hours a day, seven days a week. You simply need to eat a little less and exercise a little more than usual. You can track these variables with any calendar program and a scale.

Something like the Body Bugg could clearly do more interesting work. It could show energy expenditure through time, and allow analysis of the relationship between work, sleep, or mood, on the one hand, and patterns of energy use, on the other. It could be used by two or more people, and allow us to test theories of how we influence each other. It could do a lot of fun things. Right now, the Body Bugg is just the technical part of a program of weight loss coaching. But it, or something like it, has a higher destiny.

For people interested in BodyBugg as it is currently intended to be used, there's a good conversation about various issues here. It has apparently been promoted on the TV show, The Biggest Loser, which I've never seen.

Emotion Map of San Francisco

Gary Wolf

How do you feel in different places? The precise correlation of location and emotional arousal is the topic of Christan Nold's long running biomapping project. The project used a simple galvanic skin response meter, which gives a reading of how excited you are.

A GSR device is simple. Here's the Lego version.

These GSR readings are not very specific. They do not tell you whether you are disgusted, shocked, thrilled, or fascinated. But once Nold added GPS tracking, and invited people to annotate their readings, he could produce a map that correlates emotion with locations. This can be mashed up in Google Earth with contributions from others.

Nold's device looks like this.

You can download a printable version of the San Francisco map (PDF). But, better yet, you can get the raw data (kmz) and load it onto Google Earth to browse. Right now this is an art project, a vision of the future, a hint of the utopian upside in surveillance and tracking.

Next step – getting my own version!

Reality Mining at MIT

Gary Wolf

Earlier this week I had a chance to drop in on Nathan Eagle's presentation at ETech about using the Bluetooth feature on mobile phones to keep track, not only of where people are, but who happens to be nearby. This research is part of the larger Human Dynamics Group at MIT run by Sandy Pentland.

Eagle gave a great talk, which led me to read the description of his research at the Reality Mining site. Here one statement that jumped out:

[O]ur ultimate goal is to create a predictive classifier that can learn aspects of a user's life better than a human observer (including the actual user)...

Can our devices know us better than we know ourselves? It seems obvious that this must be true. Human self knowledge is plagued by all kinds of limits: bias, sampling error, memory failure, and lack of sufficient processing power to recognize complex patterns. Machines do not suffer from the first three of limits, and the last is under steady assault from Moore's law. But for computers to help us know ourselves better, they need two things: better data, and new analytical tools for transforming this data into predictions. These are problems that the Reality Mining researchers (among others) are trying to tackle.

In the experiment he described at ETech, Eagle's group gave 100 MIT students free use of a Nokia smart phone in exchange for being tracked whenever the phone was turned on. Some filled out questionnaires, others kept diaries.

In return for the use of the Nokia 6600 phones, students have been asked to fill out web-based surveys regarding their social activities and the people they interact with throughout the day. Comparison of the logs with survey data has given us insight into our dataset's ability to accurately map social network dynamics....Additionally, a subset of subjects kept detailed activity diaries over several months. Comparisons revealed no systematic errors with respect to proximity and location, except for omissions due to the phone being turned off.

Proving that people can be effectively tracked using low-power Bluetooth transmissions has a certain technical interest, but of course the true power of this work lies in beginning to understand what kinds of things can be learned from such tracking. Eagle and his colleagues, for instance, found it easy to predict when two people were likely to encounter each other, as long as the users had fairly regular habits:

In contrast to previous work that requires access to calendar applications for automatic scheduling [Roth and Unger (2000)], we can generate inferences about whether a person will be seen within the hour, given the user's current context, with accuracies of up to 90% for 'low entropy' subjects.

By 'low entropy,' the researchers mean 'easily predictable.' Their claim is that their system can predict social behavior among people who are easily predictable. Such a result might seem the very definition of trivial, but it's not as pointless as it sounds. Such a result functions as a kind of system tuning, a check on whether the basic parameters of Bluetooth tracking and social predictions are plausible. Once you know that it works on the easy cases, you can start trying to generate the more interesting analytical tools necessary to get more surprising results.

Research is being pursued to develop a new infrastructure of devices that while not only aware of each other, are also infused with a sense of social curiosity. Work is ongoing to create devices that attempt to figure out what is being said, infer the type relationship between the two people, and even suggest additional subjects to discuss. These devices see what the user sees, hear what the user hears, and are beginning to learn patterns in people's behavior. This enables them to make inferences regarding whom the users knows, whom the user likes, and even what the user may do next. Although a significant amount of sensors and machine perception are required, it will only be a matter of a few years before this functionality will be realized on standard mobile phones.

To perform these experiments, more than 100 subjects on the MIT campus will be needed. That's where you come in:

While Symbian Series 60 phones have become a standard for Nokia's high-end handsets, they represent a small fraction of today's Bluetooth devices. We are in the final stages of developing a MIDP (Java) version of the BlueAware application that will run on a wider range of mobile phones. The final test of Serendipity will be its public launch on www.mobule.net. We hope that not only will the application prove to be robust, but also quite popular within the realms described above, as well as those unanticipated.

The Mobule site does not seem to be functional yet, though there is a light description of the next phase of the project here, where it is described as a social introduction service. If your phone knows who is in your proximity, it can match profiles and make introductions. To me, this application seems boring and redundant. The world has gone crazy for social networking, but I don't want new ways to make social and business contacts. There is a lot of fear that social tracking will simply be a new channel for exploitative marketing, oppressive government tracking, and annoying, spam-like requests for "friendship." In some ways, the Reality Mining group is underselling their own interesting discoveries, because the promise of new understanding our social behavior goes beyond this impoverished definition of "networking."

Another section of the site offers a clue as to the more interesting applications:

In collaboration with Push Singh and Bo Morgan, we have created an interactive, automatically generated diary application which will allow users not only to query their own life (ie: "When was the last time I had lunch with Mike? Where were we? Who else was there? What did I do next?") but also (after a few months of training data) visualize the model's predictions about upcoming behavior in the immediate future.

The reference to Push Singh and Bo Morgan offers a clue that this work goes deeper than finding friends or hustling sales. The question "what did I do next?" is easily transformed into a prediction about "what will I do next?" Or how about "what should I do next?" The day when we consult devices for advice is closer than we think. It already works in the stock market, and in many expert systems. Many of our decisions are less complex; but until now, both data and models have been missing. Eagle's work is part of a bunch of efforts that will help fill the gap.

In his talk, he spoke of getting the next phase of his experiment going with 100,000 users.