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On July 4th, 2009 Jan Szelagiewicz decided to make a change in his life. After taking stock of his personal health and his family history with heart disease he began a weight-loss journey that included a variety of self-tracking tools. Over the course of a few years Jan tracked his diet, activities such as cycling, swimming, and running, and his strength. In this talk, presented at the Quantified Self Warsaw meetup group, Jan describes how he used self-tracking to mark his progress and stay on course.
When we decide to track one thing, we sometimes find that we are indirectly tracking something else. That is the theme of today’s talk.
When Mark Leavitt was 57, he found out that he had heart disease, a condition that runs in his family. Mark set about making some life changes. He tracked his weight while adopting a low-fat diet. His tracking showed him that he was making progress and that progress encouraged him to keep tracking. But once Mark’s weight loss stalled and then started to backslide (though he had maintained his diet) his desire to track dwindled and was then snuffed out by a major life event.
Though he was ostensibly tracking weight, this experience gave him some insight into his motivation. He began to build a mental model of his willpower. When was it strong? When was it weak? Using his background as a doctor to make assumptions on the nature of his willpower, he used the tracking of other lifestyle changes, such as movement and strength-training, to test those assumptions and better understand how to follow through on his intentions.
Watch below to see what Mark found worked for him and if you would like to see how Mark’s keeping up with his habits, you can check out his live dashboard here.
One of the benefits of long-term self-tracking is that one builds up a toolbox of investigatory methods that can be drawn upon when medical adversity hits. One year ago, when Mark Drangsholt experienced brain fog during a research retreat while on Orcas Island in the Pacific Northwest, he had to draw upon the self-tracking tools at his disposal to figure out what was behind this troubling symptom.
Watch this invaluable talk on how Mark was able to combine his self-tracking investigation with his medical treatments to significantly improve his neurocognitive condition.
Here is Mark’s description of his talk:
What did you do?
I identified that I had neurocognitive (brain) abnormalities – which decreased my memory function (less recall) – and verified it with a neuropsychologist’s extensive tests. I tried several trials of supplements with only slight improvement. I searched for possible causes which included being an APOE-4 gene carrier and having past bouts of atrial fibrillation.
How did you do it?
Through daily, weekly and monthly tracking of many variables including body weight, percent body fat, physical activity, Total, HDL, LDL cholesterol, depression, etc. I created global indices of neurocognitive function and reconstructed global neurocog function using a daily schedule and electronic diary with notes, recall of days and events of decreased memory function, academic and clinical work output, etc. I asked for a referral to a neuropsychologist and had 4 hours of comprehensive neurocog testing.
What did you learn?
My hunch that I had developed some neurocognitive changes was verified by the neuropsychologist as “early white matter dysfunction”. A brain MRI showed no abnormalities. Trials of resveratrol supplements only helped slightly. There were some waxing and waning of symptoms, worsened by lack of sleep and high negative stress while working. A trial with a statin called, “Simvastatin” (10 mg) began to lessen the memory problems, and a dramatic improvement occurred after 2.5-3 weeks. Subsequent retesting 3 months later showed significant improvement in the category related to white matter dysfunction in the brain. Eight months later, I am still doing well – perhaps even more improvement – in neurocog function.
Memory, cognition, and learning are of high interest here at QS Labs. Ever since Gary Wolf published his seminal piece on SuperMemo, and it’s founder Piotr Wozniak, in 2008, we’ve been delighted to see how people are using space repetition software. Our friend and colleague, Steven Jonas, has been using SuperMemo since he read Gary’s article and slowly transition to daily use in 2010. Steven has been quite active in sharing how he’s used it to track his different memorization and learning projects with his local Portland QS meeup group. At the 2014 Quantified Self Europe Conference, Steven introduced a new project he’s working on, memorizing his daybook – a daily log he keeps of interesting things that happened during the day. Watch his fascinating talk below to hear him explain how he’s attempting to recall every day of this life. If you’re interested in learning more about spaced repetition we suggest this excellent primer by Gary.
You can also download the slides here.
What did you do?
I used a spaced repetition system to help me remember when an entry in my daybook occurred.
How did you do it?
Using Supermemo, I created a flashcard each morning. On the question side, I typed what I did the previous day. On the answer side, I typed down the date. SuperMemo would then schedule the review of these cards. I also played around with adding pictures and short videos from that day to the card, as well.
What did you learn?
First, that this seems to work. I’ve built up a mental map of my experiences, unlike anything I’ve ever experienced. I also learned that I hardly ever remember the actual date for a card. Instead, it’s a logic puzzle, where I can recall certain details such as, “It was on a Saturday, and it was in October, the week before Halloween. And Halloween was on a Thursday that year.” From there, I can deduce the most likely day that it occurred. I’m also learning which details are most helpful for placing a memory. Experiences involving other people and different places are very memorable. Noting that I started doing something, like “I started tracking my weight”, are not memorable.
Today’s post comes to use from our friend and co-organizer of the Bay Area QS meetup group, Rajiv Mehta. Rajiv and Dawn Nafus worked together to lead a breakout session that focused on self-tracking in the family setting at the 2014 Quantified Self Europe Conference. They focused on the role families have in the caregiving process and how self-tracking can be used in caregiving situations. This breakout was especially interesting to us because of the recent research that has shed a light on caregivers and caregiving in the United States. According to research by the Pew Internet and Life Project, “39% of U.S. adults are caregivers and many navigate health care with the help of technology.” Furthermore, caregivers are more likely to track their own health indicators, such as weight, diet and exercise. We invite you to read the description of the breakout session below and then join the conversation on the forum.
Families & Self-Tracking
by Rajiv Mehta
In this breakout session at the Amsterdam conference, we explored self-tracking in the context of family caregiving. In the spirit of QS, we decided to “flip the conversation” — instead of talking about “them”, about how to get elderly family members to use self-tracking technologies and to allow us to see their data, we talked about “us”, about our own self-tracking and the benefits and challenges we have experienced in sharing our data with family and friends. These are the key themes that emerged.
Share But Not Be Judged
Feeling like you’re being judged, and especially misjudged, by someone else seeing your data is a very negative experience. People want to feel supported, not criticized, when they open up. Ironically, people felt that reminders and “encouragement” by an app, knowing that it is based on some impersonal algorithm, was sometimes easier to accept than similar statements from family. The interactions we have with family members aren’t neutral “reminders” to do this or that; they’re loaded with years of history and subtext. One participant commented “What I really want is an app that trains a spouse how not to judge.”
Earn The Right
So much is about learning how to earn the right to say something—that’s an ongoing negotiation, and both people and machines have to earn this. Apps screw it up when they try to be overfamiliar, your “friend.” I recalled a talk from the 2013 QS Amsterdam conference of a person publicly sharing his continuous heart rate monitoring, whose boss had noticed that the person’s heart rate had not gone up and demanded to know why he was not taking a deadline seriously! Such misjudgments can kill one’s enthusiasm for sharing.
Myth Of Self-Empowerment
Just because you’re tracking something, and plan to stick to some regimen or make some behavioral change, doesn’t mean you’re actually empowered to make it so. Family members need to be sensitive to the fact that bad data (undesirable results, lack of entries, etc.) may be a “cry for help” rather than an occasion for nagging.
Facilitating Dialog and Understanding
On the positive side, sharing data can lead to more understanding and richer conversations amongst family members. One participant described his occasional dieting efforts, which he records using MyFitnessPal and shares the information with his mother. This allows her to see how he is able to construct meals that fit the diet parameters (and so learn from his efforts), and also to just know that he is eating okay. I described the situation of a friend with a serious chronic disease who was tracking her energy levels throughout the day. In considering whether or not to share this tracking with her family she realized that they had very little appreciation of how up-and-down each day is for her. So, before she’s going to get benefits from sharing continuous energy data, she’s going to have to help her family understand the realities of her condition.
Sense of Control
Everyone felt that one key issue was that the self-tracker feel that s/he is the one making the decision to share the data, and has control over what to share, when to share, and who to share with.
We hope that before people design and deploy “remote monitoring” or “home tele-health” systems to track “others”, they first take the time to share their own data and see what it feels like.
If you’re interested in reading further about technology and caregiving we suggest the recently published report from the National Alliance for Caregiving, “Catalyzing Technology to Support Family Caregiving” by Richard Adler and Rajiv Mehta.
At our 2014 Quantified Self Europe Conference, as with all our events, we sourced all of our content from the attendees. During the lead up were delighted to have some amazing interactions with attendees Alberto Frigo and Danielle Roberts, both of whom have been engaged with long-term tracking projects. This theme of “Tracking Over Time” was nicely rounded out by our longtime friend and New York QS meetup organizer, Steven Dean. Steven has been tracking himself off and on for almost two decades. In the talk below, Steven discusses what led him to self-tracking and how he’s come to internalize data and experiences in order to create his sense of self.
Quantified Sense of Self
by Steven Dean
Twenty years ago, I was in grad school getting an MFA. I was making a lot of objects that had very strong autobiographical component to it. Some I understood the source of. Many I did not. Continue reading
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
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:
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.