Topic Archives: Personal Projects
Typically when the Quantified Self-er talks about using photography and image capture for self-tracking they’re talking about taking pictures of their food. Pictures are a very powerful way to capture information for better understanding, you know, they are worth a thousand words. On the blog here we’ve also highlighted a few really interesting projects that take the idea of using visual images for tracking and decided to turn the lens around such as Jeff Harris and his 13 years of self portraits.
One of the projects that I found super interesting was LifeSlice by Stan James.
For those of you who want to try LifeSlice Stan has put the code online for you to use and possibly tinker with. As a new user I can say that it is pretty interesting to see how my facial characteristics map to what I’m doing on the computer. For examples here’s me looking at a new statistical software package for mac (Wizard).
And here’s me writing this post while listening at a conference on health data.
The last project I want to highlight here is the self-portrait project of Noah Kalina. Noah is a photographer who has been taking self portraits every day for 12.5 years (January 11, 2000 – June 20, 2012). A few months ago he put all 4514 images together into one amazingly insightful video.
Than Tibbetts was so intrigued by this project he decided to work some fancy image processing magic to find out what “Average Noah” looked like and found this:
I’m sure there are more projects out there that involve individuals turning the camera on themselves. We all have cameras with us in our pockets and on our computers. How are you using those image capture technologies to better understand yourself? If you’re working on something interesting let us know!
A great talk on a Polyphasic Sleep experiment by Emi Gal, the CEO of the interactive advertising platform Brainient.
“The main takeaway was that it is fun, but not sustainable. None of the polyphasic sleepers have succeeded in doing it more than six months. You always act tired…”
If you’ve experimented with polyphasic sleep, we’re interested in your stories.
My personal science introduced me to a research method I have never seen used in research articles or described in discussions of scientific method. It might be called wait and see. You measure something repeatedly, day after day, with the hope that at some point it will change dramatically and you will be able to determine why. In other words: 1. Measure something repeatedly, day after day. 2. When you notice an outlier, test possible explanations. In most science, random (= unplanned) variation is bad. In an experiment, for example, it makes the effects of the treatment harder to see. Here it is good.
Here are examples where wait and see paid off for me:
1. Acne and benzoyl peroxide. When I was a graduate student, I started counting the number of pimples on my face every morning. One day the count improved. It was two days after I started using benzoyl peroxide more regularly. Until then, I did not think benzoyl peroxide worked well — I started using it more regularly because I had run out of tetracycline (which turned out not to work).
2. Sleep and breakfast. I changed my breakfast from oatmeal to fruit because a student told me he had lost weight eating foods with high water content (such as fruit). I did not lose weight but my sleep suddenly got worse. I started waking up early every morning instead of half the time. From this I figured out that any breakfast, if eaten early, disturbed my sleep.
3. Sleep and standing (twice). I started to stand a lot to see if it would cause weight loss. It didn’t, but I started to sleep better. Later, I discovered by accident that standing on one leg to exhaustion made me sleep better.
4. Brain function and butter. For years I measured how fast I did arithmetic. One day I was a lot faster than usual. It turned out to be due to butter.
5. Brain function and dental amalgam. My brain function, measured by an arithmetic test, improved over several months. I eventually decided that removal of two mercury-containing fillings was the likely cause.
6. Blood sugar and walking. My fasting blood sugar used to be higher than I would like — in the 90s. (Optimal is low 80s.) Even worse, it seemed to be increasing. (Above 100 is “pre-diabetic.”) One day I discovered it was much lower than expected (in the 80s). The previous day I had walked for an hour, which was unusual. I determined it was indeed cause and effect. If I walked an hour per day, my fasting blood sugar was much better.
This method and examples emphasize the point that different scientific methods are good at different things and we need all of them (in contrast to evidence-based medicine advocates who say some types of evidence are “better” than other types — implying one-dimensional evaluation). One thing we want to do is test cause-effect ideas (X causes Y). This method doesn’t do that at all. Experiments do that well, surveys are better than nothing. Another thing we want to do is assess the generality of our cause-effect ideas. This method doesn’t do that at all. Surveys do that well (it is much easier to survey a wide range of people than do an experiment with a wide range of people), multi-person experiments are better than nothing. A third thing we want to do is come up with cause-effect ideas worth testing. Most experiments are a poor way to do this, surveys are better than nothing. This method is especially good for that.
The possibility of such discoveries is a good reason to self-track. Professional scientists almost never use this method. But you can.
We’ve already published this QS Show&Tell talk by Mark Drangsholt about using self-tracking to identify the triggers of his heart problems, lessen their frequency, and make good decisions about treatment. I’m re-posting it here to focus on attention on the interesting and powerful method Mark used, the case-crossover design, and invite you to think about whether this has promise for your own self-tracking projects.
Mark is a professor and chair of oral medicine at the University of Washington School of Dentistry. He’s a triathlete and long time self-tracker. He is in good physical condition, but suffers from heart ailments that are frightening and dangerous. For instance, he has tachycardia (sudden acceleration of heart rate). At times his heart goes from 60 to 220 beats per minute. It feels like his heart is going to jump out of his chest. He also has atrial fibrillation, with palpitations, a feeling of immanent doom, and a sense that he is choking.
“The first time it happened in 2003 I really thought I was dying,” Mark says in his talk. He had always assumed that if he ever had a heart attack he, of all people, would know to pick up the phone and call 911, but the opposite happened. He just thought to himself “this is it,” and slumped down in his chair. Fortunately, he survived, and when he recovered he asked himself whether he could identify the triggers of these unpleasant events and avoid them. He created a simple Excel table of all episodes for one year, on which he recorded information about his attacks.
Mark is an expert on evidence based medicine, so he was naturally curious about what kind of evidence his self-tracking data contained. In standard reference material on medical evidence, students learn about a hierarchy that goes something like this:
- 1 or more randomized controlled trials
- 1 or more cohort studies
- 1 or more case-control studies
- 1 or more case-series
- expert opinion without above evidence
Mark’s self-tracking data didn’t naturally fit with any of these approaches. To understand whether these triggers actually had an effect on his arrhythmias, he used a special technique originally proposed by the epidemiologists Murray Mittleman and K. Malcolm Maclure. A case-crossover design is a scientific way to answer the question: “Was the patient doing anything unusual just before the onset of the disease?” It is a design that compares the exposure to a certain agent during the interval when the event does not occur to the exposure during the interval when the event occurs.
Using this method, Mark discovered that events linked to his attacks included high intensity exercise, afternoon caffeine, public speaking to large groups, and inadequate sleep on the previous night. While these were not surprising discoveries, it was interesting to him to be able to rigorously analyze them, and see his intuition supported by evidence.
“A citizen scientist isn’t even on the conventional evidence pyramid,” Mark notes. “But you can structure a single subject design to raise the level of evidence and it will be more convincing.”
Please let us know if you use this method in your own projects. We’ll post more reports when we have them.
REFERENCES AND GUIDES
There are some tricks to doing a good case-crossover study on yourself. Mark’s video provides a basic introduction. For technical details, this detailed introduction to case-crossover design by Yue-Fang Chang especially useful.
The seminal paper on case-crossover design is “The Case-Crossover Design: A Method for Studying Transient Effects on the Risk of Acute Events” by Malcom Maclure. (1991) [PDF] A search on Google Scholar for case-crossover design will get you deep into this literature. Unfortunately very little of it involves the kind of n-of-1 studies we’re usually interested in, but there are many technical details that may contain clues for dedicated experimenters.
One paper that will be of special interest is this one: “Should We Use a Case-Crossover Design?” by K. Malcolm Maclure and his collaborator Murray Mittleman. (2000) [PDF] In the midst of discussing technical details important for scientists proposing to use this method in studies funding by research grants whose reviewers may not be familiar with it, Maclure and Mittlemen describe using case-crossover analysis to retrospectively understand more about the death of Maclure’s father. I quote the relevant section below:
We did an n-of-1 case-crossover study of hypothesized triggers of repeated syncope experienced by Kenneth Maclure (MM’s father), who was diagnosed with sick sinus syndrome and died of fatal MI at age 73 during a morning swim, after several other potential triggers. The target person times wereKenneth’s 62nd–74th years (and subsequent years if he had lived longer). The study base comprised the years 1980–1981 and 1986, during which there were 33 instances of syncope. We restricted the study base to those years because his wife, Margaret, was willing to review only 3 years of her diaries because the memories rekindled her grief. We had no intention to generalize the findings to other individuals, only to other years. Our goal was to identify triggers to which Kenneth may have been susceptible and to test Margaret’s general hypothesis, “Perhaps I should have done more to help him avoid stress.” Hypothesized triggers included visitors to the home, trips out of town, eating out, unusual exertion, and so on. The 24-h period before an episode of syncope was classified as a case day. Each case day was matched with a control day, the same 24-h period 2 weeks before. Margaret was surprised by our null findings and relieved some lingering feelings of guilt.
Aarti Vashisht has done some interesting QS-related work for her MFA at Art Center College Design.
She designed some prototype sensors that could be worn on our bodies in the future, and interviewed people to learn their thoughts on how these integrated sensors might impact their lives.
This is an image of the sensors she designed, to be worn across the shoulders and on the wrist, among other places. Take a look at her report here, called Temple of Self.
This interesting post by Dan Catt (@revdancatt) describes how he used Quantified Self ideas to get a handle on his depression:
I’d never been depressed before, or at least not that I could remember. …
Spotting the depression was interesting. Obviously I knew something was up, but when it started it kind of blinded me to itself. I didn’t really have the energy to spot what was going on.
But, because I back-up my data regularly, grabbing content of various social networks either with scripts or services that do it for you, I noticed something. The amount I was tweeting was way down, it had suddenly dropped. Not so much general tweets but conversations with people, @ messages and direct messaging was down, I could see the numbers right in front of me.
The amount of photos I was posting to Flickr had also dropped (cross posted from Instagram I’ll get to in a second).
I could see the interactions with people I was having around the internet had reduced, weeknotes had stopped, emails slowed down, I was leaving my IM client off more, blogged (or at least writing drafts) took even longer than normal.
Dan Hon wrote about the Quantified Self as a way to measure his blood sugar (and more). All these services, hardware and tools we can monitor our body with, glucose levels, weight and so on. What I was seeing was a change in my behaviour, a measurable mental state. And once I’d seen the numbers it made it easier to figure out what was going on.
[Read the whole post: Leaving the Guardian, creativity vs mild depression, the quantified self and running]
After a year of research and writing, I’m finally finished with what could be called the “first master’s thesis on Quantified Self.” If some of you didn’t catch my first post, you can find it here. I spent about a year conducting research on Quantified Self for an MA in Applied Anthropology at San Jose State University. Technically, I didn’t write a “thesis” but a “project report,” because I conducted an applied research project on QS Meetup groups rather than “thesis” type research. To fulfill the requirements for the MA I had to produce two reports. The first was a report containing the findings from the research on the meetups, which I presented to QS Labs (see the earlier post). The second was a project report, which is the document I submitted to my department (which I would like to present here). For the most part, the purpose of the project report is to document the research process, including methods and theory. In addition to that, I was able to fit in some general info about QS and self-tracking into my report. Section one and section four will probably be the most interesting to members of the greater QS community. The following is an excerpt from section four.
You can see the full report here.
The range of self-tracking projects that people take on is diverse and tracking can focus on almost any aspect of life. Grouping by domain, such as sleep or weight, is one way to describe self-tracking practices. However through my research, I identified three axes that can be used to describe or locate self-tracking projects within the spectrum of these phenomena. Figure 1 (below) represents the three axes as a three-dimensional field, with sample self-tracking projects plotted as examples of how projects configure within this space.
The first axis is the degree of technological involvement. Self-tracking projects can heavily rely on complex devices with advanced sensors or on sophisticated laboratory tests. In other cases, self-trackers may use only a pencil and paper. The technology axis is essentially the initial line that delimits what can or cannot be tracked. In order to monitor and record data, you need certain sensors and recording devices. In some cases, self-tracking projects are driven by the technology; because there are sensors and devices to take measurements, people are using these technologies to monitor and collect data. In other cases, the self serves as both the sensor and the recording device.
The second axis is the level of complexity in the design of the self-tracking project. On the more complex side are projects often referred to as “self-experiments.” Self-experiments usually employ the scientific method to some degree, collecting baseline data, testing hypotheses, and in some cases controlling for variables. Some self-trackers try to find correlations across data sets, for example, trying to figure out what factors affect their sleep quality, by monitoring and recording data on sleep quality, and correlating that data on their activity before bed or even ambient data such as room temperature at night. On the less complex side of project design are practices like “life-logging.” There is a range of different practices people will call life-logging, but one example of a simpler form in terms of project design, would be basic journaling. A practice such as keeping a dream journal is considered self-tracking, in that it produces knowledge about the self through recording information, even though the data are not numerical.
The third axis self-tracking projects can be plotted on is the extent to which projects are explicitly goal driven or exploratory. Some self-trackers have particular goals when starting a project, like wanting to lose weight or improve sleep. Other self-trackers collect data with the intention that they will be able to do something with the data in the future (what that something is, may or may not be known), or in some cases track things just to keep track of them. Examples of this last case are things such as tracking daily step count with a pedometer or using mobile phone applications to “check in” to places you visit each day simply to have a record of that.
Self-tracking projects can configure onto these axes in almost any way imaginable. The categories on each of these axes are also negotiable. Different people may not agree what constitutes a high or low level of technology involvement. In some cases, someone might not even consider things like pencils and paper technologies. Similarly with practices such as journaling, some might argue the extent to which journaling would be considered life-logging or even self-tracking. There are also secondary factors that can help classify a project, such the length of a project, whether the project is an intervention or data collected to inform an intervention, and whether the data collected is primarily objective/collected by passive sensors, or subjective/based on self-assessment.
I have blogged many times about biohacker Tara Grant’s discovery that she slept much better if she took Vitamin D3 in the morning rather than later. Many people reported similar experiences, with a few exceptions. Lots of professional research has studied Vitamin D3 but the researchers appear to have no idea of this effect. They don’t control the time of day that subjects take D3 and don’t measure sleep. If the time of day of Vitamin D3 makes a big difference, measuring Vitamin D3 status via blood levels makes no sense. Quite likely other benefits of Vitamin D3 require taking it at the right time of day. Taking Vitamin D3 at a bad time of day could easily produce the same blood level as taking it at a good time of day.
I too had no idea of the effect that Grant discovered. I had taken Vitamin D3 several times — never in the morning — but after noticing no change stopped. I tested Grant’s discovery by taking Vitamin D3 at 8 or 9 am. First, taking it at 8 am, I gradually increased the dose from 2000 IU to 8000 IU. Then I shifted the time to 9 am. The experiment ended earlier than I would have liked because I had to fly to San Francisco.
When I woke up in the morning I rated how rested I felt on a 0-100 scale, where 0 = not rested at all and 100 = completely rested. I’d been using this scale for years. Here are the results (means and standard errors):
Vitamin D3 had a clear effect, but the necessary dose was more than 2000 IU. If Vitamin D3 acts like sunlight, you might think that taking it in the morning would make me wake up earlier. Here are the results for the time I woke up:
There was no clear effect of dosage on when I got up. Shifting the time from 8 am to 9 am may have had an effect (I wish I had 3 more days at 9 am).
Many people have reported that taking Vitamin D3 in the morning gave them more energy during the day. I usually take a nap in the early afternoon so I measured its effect on the length of those naps:
Maybe my naps were shorter with 6000 and 8000 IU at 8 am. It’s interesting that 4000 IU seemed to be enough to improve how rested how I felt but not enough to shorten my naps.
What do these results add to what we already know? First, the large-enough dose was more than 2000 IU. (A $22 million study of Vitamin D3 is using a dose of 2000 IU.) The dose needed to get more afternoon energy may be more than 4000 IU. Second, careful experimentation and records helped, even though many people found the effect so large it was easy to notice without doing anything special. For example, these results suggest the minimum dose you need to get the effect. Three, these support the value of supplements. Many people say it is better to get necessary nutrients from food rather than supplements. However, supplements allow much better control of dosage and timing and these results suggest that small changes in both can matter. I cannot imagine this effect being discovered with Vitamin D3 in food.
Note from Alex: Gwern Branwen also sent in this detailed post on Vitamin D and sleep.
A New York lawyer named Greg reports remarkably clear evidence about the effect of butter on blood lipid levels: It improved them. For a few years he measured his HDL and LDL regularly with a home cholesterol device. For unrelated reasons, he started eating more butter. He ate a half stick (about 60 g)/day, like me. Here’s what happened.
The first five measurements are from lab tests. The rest are from his home machine.
I asked Greg for details.
I’m 36. I bought the cholesterol meter last July after my doctor said he couldn’t figure out why my numbers were a bit high. We both agreed it was not something to worry too much about and that there was no point charging my insurance company for a VAP test every 6 months. We both also agreed that going on a statin was a bad idea. I picked up the meter out of curiosity. I had previously been monitoring my blood sugar (since 2009) and found it to be very interesting, so I thought I could have some fun with the numbers. The result is all the more surprising because I did not expect it. I was tracking my numbers around the time of the experiment [with butter] to make sure they did not go the wrong way like everyone says they should.
The machine is a CardioChek PA [about $600], which is designed for use in doctors offices, not for the consumer market. The device is “CLIA-waived”, which means that the FDA considers it so simple that the user does not need any special training in clinical chemistry (home glucometers fall into the same category). The machine gives significantly different numbers for different people, suggesting it is measuring something real and not spitting out random numbers.
I asked what the reaction to this data has been.
Most people I’ve spoken to have been receptive to the idea [that butter improves blood lipids], but I got no sense that they would be willing to try it for themselves. Most people I know seem to be quite willing to accept the fact that the old stories about cholesterol are not true. In contrast, one conservative cardiologist said I must have “unique genetics”.
I have finally figured out my mood! After 16 months and 300,000 words of mood tracking data, which I shared with a friend, I have a painstakingly compiled list of hacks that balance my extreme mood swings and make life much smoother for me.
So, like a good QS’er, I’m sharing what I learned. Maybe it will help someone else out there. I’ve broken down the insights into four categories.
1. Accept, accept, accept
The practice of acceptance has been incredibly transformative. If you can accept yourself as you are, accept other people as they are, and accept situations around you, you will be free from secondary layers of emotion that prevent you from just dealing with whatever you need to deal with.
For example, say that someone you love promises to do something for you, and then doesn’t do it. You have a choice here – layer frustration and anger on top of the situation, or accept it and check in with the person to see what happened. Maybe they forgot because they were feeling sick, or are stressed out at work. It probably doesn’t mean they don’t care.
As Zen master Suzuki Roshi said, “It’s like putting a horse on top of a horse and then climbing on and trying to ride. Riding a horse is hard enough. Why add another horse?” Acceptance helps you just ride one horse at a time.
Expectations come into play here as well. What I’ve learned is, the fewer expectations you have, especially about other people doing anything in particular, the easier life becomes. Keep moving strongly towards your inspiring intentions, just don’t expect anything to work out in the exact way you imagine. And if you do have a particular expectation, make sure to communicate it to the people who you’re expecting to meet it!
2. Create social algorithms
I used to have massive social anxiety coupled with an intense fear of abandonment, which often led me to isolation and depression. I was even afraid to go to QS meetups for a long while! Here are three tips I learned to make socializing smoother for me:
- Always have a buddy. Before I go to a meetup or a conference, I always ask someone to be my buddy. It has to be someone I feel safe with and who is also going to the same event (or willing to be invited.) Being a buddy means I can sit next to them, and check in with them for a hug or a quick update on how I’m feeling. At first it was hard to ask, but I soon realized that other people were also often relieved to have a buddy!
- Figure out how you engage best. For me, conference calls and group meetings are death. Coffee shops and most restaurants are too loud. So I suggest one-on-one walks with people, usually in a beautiful park or outside nature space. Again, most folks are happy to have this option of getting fresh air and exercise as well as connection. And I’m more comfortable, so I’m able to listen, give, and connect better.
- Spread out social events. Everyone will have their own balance for this one. I noticed that I get depressed if I’ve been at home for more than two days in a row, and I get a tad too manic if I’m out having meetings for more than two days in a row. This is a simple heuristic that makes it easy to decide when to put things in my calendar.
3. Pay attention to sensory experiences
I became aware of the importance of sensory experience after reading up on sensory processing differences and a sensory theory of autism. Once I tuned in and started noticing my environment, I discovered I could:
- Comfort my skin. Wearing uncomfortable clothes makes me irritable. So I gave away all my jeans, high heels, bras, anything that felt constricting or tight. I’m so much happier wearing comfy clothes all the time.
- Protect my ears. Loud machines are very draining. I noticed that riding on a train or airplane, walking down a busy street, or working at a coffee shop with that grinder going off periodically was tiring me out quite a lot. So I bought myself a pair of Bose QC15 noise canceling headphones, and I wear them every day. It also helps to put me in the zone for productive work, with playlists full of beats (for coding), love songs (for community building), or mellow ambient music (for chats.)
- Improve my sleep. I started tracking sensory experiences that interfered with my sleep, and discovered sound and light to be the main challenges. I now wear blue blockers for the last two hours before bed, have a white noise machine in my room, wear these earplugs, and try to make sure everyone in my house has used the bathroom before I go to bed so they won’t have to come in and turn on the light too often. My sleep is so much better, and my mood is too!
- Have awesome hugs! After hearing about Temple Grandin’s experience of being calmed by a squeeze machine, I started noticing that hugs (especially good, solid, squeezy hugs) really calmed me down too. People who I feel safe enough to hug deeply will notice it after ten or twenty seconds – my body just starts to melt and relax. So I started offering to hug people more, and even proudly broadcast on my social media descriptions and chat status that I love hugs. Now people send me random hugs by chat, which always make me smile. I also get more hugs in person, which further helps me to be comfortable in social situations!
4. Do the opposite from what you feel
This is the final piece that made me feel like I’d finally solved mood. Credit goes to Marcin Kowrygo from QS Poland and Simon Frid from the first QS discussion group for giving me the pieces of this last puzzle.
Basically, if you’re depressed, act as you would if you were excited, and if you’re manic, act as you would if you were depressed.
So now when I’m feeling down, I go for a fast walk in the bright sunshine with loud music, I eat less, try to chat with people more or invite someone out, and try to wake up extra early. When I’m feeling too hyper, I slow down, lie in a dark room with quiet or sad music, eat a bit extra, try to be alone, and try to get lots of sleep.
This is going against what my body feels like doing in the moment, but it definitely works to curb the extreme ups and downs that I otherwise feed and amplify. I like to think of it as CBT (cognitive behavioral therapy) without the C.
So these are my distilled insights from 16 months of intense mood tracking, at least an hour of writing a day. If you have any insights or hacks of your own on how you balance mood, I’d love to hear them in the comments below!!
As for my next experiment, I think it might have to do with measuring and modifying time perception. Stay tuned for that…