Rise: Support Your Health

I’ve been working hard for the past six months designing Rise, a new quantified self health tracker. It’s an accelerometer-powered device that sits on a person’s leg and helps them keep track of how much they’re sitting. Sitting, and being generally sedentary, have been linked to a whole host of health problems. Sitting too much can lead to blood clots in the legs in the short term, and diabetes and increased risk of heart disease in the long run.

The health problems that sitting causes are pretty simple to avoid: just sit less. Even getting up for just a few minutes every hour can have a marked impact on the health of the average office worker. So if it’s so easy to solve the sitting problem, then why did I spend so much time working on it?

It’s because sitting is insidious. People don’t notice how much they sit, and when they do notice it’s often because health problems are already starting to crop up. Rise, our sit tracker, will not only track how long a person has been sitting but send periodic alerts to let the wearer know its time to stand up and walk around for a bit. We’re building apps for both Android and iOS so that this is accessible to as many people as possible.

It’s been a fun project to work on, and a great extension of the research that I did when I got my Master’s. We’ve launched an IndieGoGo crowdfunding campaign to help turn the prototypes into final products. I’m excited to see how the campaign goes, and to see my work actually get out into the world and help some people. If you’re interested, you can check it out here.

Mood Monitoring and Fun Density

I’ve been getting more into the quantified self movement recently. The idea is that by tracking what you do and what the result is, you can understand yourself better. If you sift through the data, you can also find pretty easy ways to improve your life.

To get my feet wet measuring my life, I downloaded an app to my phone that will periodically ask me how I’m doing. I can answer anything from “normal” to “ok” to “excellent” on the positive end. On the negative end, I could answer “not good”, “terrible”, etc. When I make an entry about how I’m doing, I can also leave a note about what I was doing at that time. Once I have a large set of data from several weeks, I can go through and pull out patterns.

I first read about doing this in The Motivation Hacker, by Nick Winter. In it, he describes how he does a lot of new things and logs how he feels about them. Based on those logs, he figures out the fun density of different activities and tries to only do activities that have high fun density. He uses the example of white water rafting, where he has to travel to the river (not good), then rafts (ok with periods of awesome during rapids), then travel home (not good). His final conclusion after rafting was that it wasn’t worth it, because the fun density was low.

I like the idea of fun density as a way to measure activities, but I think that Winter’s application of it might be a bit flawed. Specifically, I think this because he talks about his own mood monitoring as a logarithmic measure. In his method, ok is twice as fun as normal, and good is twice as fun as ok. When he represents them with numbers, he only uses 1 to 10 (1=terrible, 10=excellent). He then takes the time average using that logarithmic scale of 1 to 10, not the experienced scale of 2^1 to 2^10. This means that his fun density measurements will undervalue short periods of high fun, which matches my surprise at him not wanting to go white water rafting again.

Now, I’m not saying that he should go white water rafting again. If he actually didn’t think it was worth it, that’s totally fine and he should do other things he finds more fun.

What I am saying is that logarthmic rating systems are a bit tricky. If rapids during rafting actually are an 8, and lasted for like twenty minutes of a two hour raft that was a 6 on average, and had a two hour ride there and back that was a 4, then his experienced average would be (20*2^8+100*2^6+240*2^4)/360 ~ 42. That 42 is about 5.4 on Winter’s scale, not the 4.7 that a simple average would give.

(To be fair, Winter also got a headache on his rafting trip that dropped the last hour of rafting down to a 4. Taking this into account, we get (20*2^8+40*2^6+60*2^4+240*2^4)/360 ~ 34.6667. This about 5.1 on his scale. That’s definitely less than his daily average (6.2!), but it’s more than the 4.4 that he was giving it.)

I’m pretty excited to start finding patterns in my own experienced fun levels. Having never tried to optimize my life for fun, I think my fun density might be pretty low. In a few weeks, I’ll have enough data to start finding things to do that improve my fun density. When I start doing that, I’m going to make sure that I don’t underestimate the impact of short periods of extreme emotion.