Learning from Self-Report
Project Infobox 
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| Self researcher(s) | Brian Levine |
| Related tools | younlocked |
| Related topics | Mood and emotion, Sleep, Sports and fitness, Activity tracking, Food tracking, Fatigue |
Builds on project(s) |
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| Has inspired | Projects (0) |
| Show and Tell Talk Infobox | |
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| Featured image | |
| Date | 2015/05/16 |
| Event name | 2015 Quantified Self Public Health Symposium |
| Slides | |
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Learning from Self-Report is a Show & Tell talk by Brian Levine that has been imported from the Quantified Self Show & Tell library.The talk was given on 2015/05/16 and is about Mood and emotion, Sleep, Sports and fitness, Activity tracking, Food tracking, and Fatigue.
Description[edit | edit source]
A description of this project as introduced by Quantified Self follows:
Brian Levine is the co-founder of Tap2, a health analytics firm. Currently he's involved with the creation of a unique self-assessment tool called younlocked. In this talk, Brian discusses some of the interesting things he's found out by answering over 10,000 questions during the last six months while unlocking his phone.
Video and transcript[edit | edit source]
A transcript of this talk is below:
Brian Levine Learning from Self-Report
Hi everyone. I’m Brian Levine from Tap2. For the past 20 years I’ve been a builder, maker we’ve been saying here, making hardware, software and products that have a human analytics component to them. So I’ve studied baseball players from major league baseball. I’ve made your jeans fit better for the Gap, and slowly over time moved to telemedicine and applications for medical records. And for the past decade I’ve been working in biometric devices. And very recently over the past year I’ve switched from the biometric company that I started to healthcare analytics software. What I’ve had a great opportunity to do is to both create these amazing tools for these studies of other people, but also use them for myself and my own personal journey and that’s what I’m going to be sharing today. Specifically how I’ve approached some of my own research goals for myself--namely, why am I so tired and why can’t I be in a better mood? I think the mood piece came up earlier with someone else. For as long as I can remember I’ve just been tired and I think maybe a lot of people feel that way but I feel like I’m special somehow and I’m more tired that the rest of you. And I really want o understand why. I’ve had these amazing biometric devices I’ve been able to use. I’ve been able to build. I’ve been able to create anything to measure anything, and I’ve learned very little. I have really gone and amassed really great set of data. I’ve learned a lot about what I do, where I go, how things work. But not why, and I haven’t been able to find in any of these biometric devices real outcomes to help me. I moved from the biometric devices to just starting to download a bunch of apps to try out. You know mood trackers, food trackers and the like. And they’re really cool and I download them and I use them for three days, but I’m just so tired and I wind up not continuing. So I tried them all, but the one thing that’s really starting to become apparent was that I went to my phone a lot and I kept opening up my phone. So I decided to make a piece of software that would take a behavior that I’m already doing , unlocking and opening my phone and start tracking myself with it. so I created a piece of software that essentially whenever I opened the phone, there’s questions that show up at certain times and I started asking myself these various outcome variables; what’s my mood, how fatigued am I, what have I eaten lately, what have I drank lately. And so I had 10 core questions for the past six months that I’ve been primarily studying. I answer 50 to 60 a day. I’m approaching 10,000 self-reported data points on top of using the biometric devices and getting hundreds of thousands, or millions more. And it’s really the self-report that’s made a huge difference. And I want to share some of the things that I’ve been able to learn over time about myself. And some others are using now too, first our friends and family and network, and really just amassing great data. So I’ll start with one of the more fun ones, a little interesting piece which is at the same time I’m answering these questions, I’m tracking, the apps I used, the locations I go and other things like that on the phone. And I started to find that when I went to the phone to do social media or something distracting, it tended to be associated with a lift in mood. So basically, when I use my phone for some of these applications, I wasn’t as low in my mood as some others. I also started to learn when it was these distracting applications that I was using, I also wasn’t as high. Essentially the use of the phone was an emotional modulation, that it stopped me feeling too bad because I was always able to give myself some stimulation and keep myself up to a certain level. But it also took me out as fully interacting with others and exploring where I was and what I was doing. So sometimes you need those downs to experience those highs, so I wound up being somewhere in the middle, and it made me think a lot about how I’m using my phone and how my phone is changing me. But now to get back in which I was able to address my mood and fatigue, I studied some of the things you might expect. So number three of things that were most impactful was my exercise and activity. Fitbit measured really expected. The more active I was, the happier I was and the less fatigued I was. But I was able to learn with this intraday data I was tracking throughout the day so many times a day and fairly simply, that it was very important when I exercised. So it was hard to find data out there when you should exercise, and for me it was the morning. The morning was more specifically impactful. So still a kind of an obvious finding. Number two was sleep. The more I slept, potentially the less fatigued I was. But actually, for me it’s seven hours. So the biggest impact was if I got eight, nine or more was actually when I was in a worse mood, and when I was more fatigued. If I get two or three that’s bad too, but seven, not eight was really the peak for me. So at the same time I was able to track the pattern of sleep and learned that it’s better for me to go to sleep from 12 to seven, than say 10 to five. So again, learning more and again some of these things are obvious. The thing that I never intended to track, but a good friend of mine who build the software with me made me track was actually what I drank. And the single biggest impact in my life turned out to be water. There’s a 0.68 correlation in ten thousand of these data points basically not all of them, between the amount of water I drank and my mood. And what happened is as I started to drink more water, not only did I get in a better mood, I felt less fatigued. It actually helped, I felt more motivated. So as I’ve learned this information I’m taking more water. I’m exercising more. It turns out to be affecting my sleep and overall has made a huge change in my life from something that I never actually thought I was going to track. And really just a fantastic opportunity and what it’s really led me to as well is that we’re focused so much on some of these wearables and this passive data. And it’s so easy to do and collect that information, but the human brain; they rely on so many assumptions. And really right now the best way to understand these assumptions is really to use your mind. It’s the best interpretation engine. And if you can come to these things and start capturing the self-report and start capturing the context of your situation, your life, your own knowledge. It can pay huge dividends, and I think we have to think through not just tracking a wearable because we can, but let’s find ways to track things and we have to make it easier, and nobody wants to answer any questions. But let’s not forget the human element and what people bring to the table and know about themselves.
Thank you.
About the presenter[edit | edit source]
Brian Levine gave this talk.