Link roundup

Gearing up into Christmas mode, so no proper write-up for these (interesting) links.

Personalized medicine is about data, not (just) drugs. Written by Thomas Goetz of The Decision Tree for Huffington Post. The Decision tree also has a nice post about why self-tracking isn’t just for geeks.

A Billion Little Experiments (PDF link). An eloquent essay/report about “good” and “bad” patients and doctors, compliance, and access to your own health data.

Latent Semantic Indexing worked well for NetFlix, but not for dating. MIT Technology Review writes about how the algorithms used to match people at Match.com (based on latent semantic indexing / SVD) are close to worthless. A bit lightweight, but a fun read.

A podcast about data mining in the mobile world. Featuring Deborah Estrin and Tom Mitchell.  Mitchell just recently wrote an article in Science about how data mining is changing: Mining Our Reality (subscription needed). The take-home message (or one of them) is that data mining is becoming much more real-time oriented. Data are increasingly being analyzed on the fly and used to make quick decisions.

How Zeo, the sleep optimizer, actually works. I mentioned Zeo in a blog post in August.

Measuring, understanding and fixing (?) your sleep habits

I’ve recently stumbled over articles describing two systems for quantifying and improving people’s sleep patterns. I’m sure there are more of them out there, but I’ll restrict myself to the two I’ve read about (out of sheer laziness).

The first system is called Proactive Sleep. It’s an iPhone-based application based on a couple of small tools. The “sleep diary” is used to track and graph the average sleep amount and the average number of times the user wakes up in the middle of the night. The “vigilance task” is a game that you play immediately on waking up. It “involves following a randomly moving ball with your pointer finger. While you are performing the task, your ability to follow the ball is measured. Performing the task quickly and accurately indicates healthier sleep and less sleep inertia.” The vigilance task is dynamic, so that the difficulty level is adjusted depending on the user’s performance.

The data that is generated through the playing of the vigilance game reflects the subject’s variations in alertness and can therefore be used to better understand his sleep patterns. When you have a good understanding of a person’s sleep, you could, for example, wake her up in a lighter sleep phase, so that she will feel more rested even though she has slept less than usual. As the web site says:

This would work by sampling wake-up times within a range of when you want to wake up and comparing how you perform on the stimulating game. Since performance on similar tasks as the game show that the deeper the stage of sleep just before awakening, the poorer the performance […], it is possible that the score on the game can be used to determine better or worse times for you to awaken.

The other system I’ve read about is called the Zeo Personal Sleep Coach. Like Proactive Sleep, it graphs your sleep habits, including average sleep duration, time taken to fall asleep, number of awakenings per night and so on. These things are measured using a nifty headband recording device.  Zeo also measures the time spent in REM, light and deep sleep. Instead of using a game, Zeo lets you quickly record how you feel about your sleep when you’ve just woken up. Then, you can “…compare how you feel you slept to the objective data Zeo provides.” The user also has access to a personal coach who suggests ways to improve poor sleep patterns.

The Decision Tree (nice blog by the way), Technology Review and USA Today have written about Zeo in some depth.

Presumably, both Zeo and Proactive Sleep will eventually have a pretty large collection of data on different individuals’ sleep patterns and how they have succeeded (or not) in improving them. This may then lead to different sorts of population-level analysis. For example, maybe people can eventually be classified into different “sleeper types” based on their sleep characteristics. Knowing a person’s sleeper type might then guide the choice of behavioural modification in order for the person to get more sleep.