Our QS Conferences are organized to maximize discovery and serendipity. The entire program results from us inviting attendees to present and participate. You’re never quite sure what you’ll get, but it’s hardly ever boring! I didn’t know what to expect when Caspar Addyman took the stage in Amsterdam to talk about “Tracking your brain on booze”, but he very quickly grabbed my attention. His talk reminded me that, as Malcolm Gladwell once reported, “How much people drink may matter less than how they drink it.”
Q: How do you describe Boozerlyzer? What is it?
Addyman: The Boozerlyzer is a drinks-tracking app for Android phones. It lets you count your drinks and their calories and tells you your current blood alcohol. Crucially, it also lets you record your mood and play a range of simple games that measure your coordination, reaction time, memory and judgment.
What Boozerlyzer explicitly does not do is tell people how much to drink. We think people would find it patronizing and off-putting. Rather we hope that it will help people get better insight into how drinking affects them.
In addition, if users agree, their data is sent to our servers to contribute to our research on how drink affects people. I’m a researcher with the Center for Brain and Cognitive Development, Birkbeck College, University of London, and this project was started as a way to collect data beyond the artificial setting of a laboratory.
Addyman: I originally had the idea back in 2003 while doing my undergraduate psychology degree. I was interested in how to study the affects of recreational drugs. The web technology of the time couldn’t be used when people were out at the pub or club so I didn’t pursue it.
In summer of 2010 I took part in a science & technology hack day in London and the idea occurred to me again, this time using smartphones. So I told a few friends about it. Mark Carrigan, a sociologist at Warwick University, opened my eyes to the more sociological types of data that we could gather. This broadened the aims from my initial very cognitive focus to think about the emotional and social experiences involved with drugs and alcohol. That was at the end of 2010. All that remained then was to invent the app. I’m not really a developer and have been working on this in my spare time so it has taken longer than I’d expected.
Q: What impact has it had? What have you heard from users?
Addyman: I have been using the app myself for 6 months now and the thing that has surprised me the most is how rapidly the drinks accumulate if I’m out with friends. A few drinks early in an evening, then a couple of glasses of wine with a meal and then more drinks all through the night. Over a particularly sociable weekend I find myself drinking a disturbing amount even though it doesn’t seem that way at the time.
We started our first public beta in December 2011 and have a hundred or so users. I still have to analyse the first batch of data and usage statistics. But, a first look at the data from December and January showed something surprising: the Christmas season seems to ratchet up drinking levels, normalising heavy drinking on into January. Unfortunately, I don’t think I’ve got enough data to tell if this is real trend.
In terms of direct feedback from users, generally, we’ve had positive reaction to the idea but there are plenty of things we can improve. One of the biggest problems with the enterprise is that our users forget to actually use the app when in the bar, or when they’ve stopped drinking. Also, people are willing to track their drinks and their mood as they go along, as that takes very little time. But at the moment the games take a little too long to play, and the game feedback is a bit too abstract. We aren’t yet giving estimates of drunkeness based on game performance. Here we are in a bit of Catch 22: more compelling feedback ought to be possible once we’ve got a reasonable base set of group data to run some regression analysis but without interesting feedback we have trouble getting people to play the game in the first place.
Q: What makes it different, sets it apart?
Addyman: One big difference between our app and many tools in the personal health world is that our focus is not on behavior change, but instead on data for scientific research and self-learning.
Also, this is an academic, non-commercial project. Our app will always be free. We will never collected any data that could directly identify you nor will we sell any of the data we collect. We believe in open systems, open data and open minds. The code we write is open sourced. The data we collect will be available to anyone that wants to study it.
Q: What are you doing next? How do you see Boozerlyzer evolving?
Addyman: The Boozerlyzer is our first app and there are still plenty of improvements to make to it. But, in addition, we want to broaden our scope and apply the same principle to recreational drugs and the effects of various medications.
As an example, I met Sara Riggare Sara Riggare from the Parkinson’s Movement at the Amsterdam QS conference. She pointed out that a version of Boozerlyzer could help Parkinson’s patients track their medication intake and quantify the effects of the medications on mood, coordination, memory, etc. We are starting a collaboration to redesign the app for this purpose.
Meanwhile, my own motivation for starting this project was always to be able to do better research into recreational drugs. This has never been a more pressing concern, and I am hoping that a drugs tracker app can help. Obviously, this is fraught with legal and ethical difficulties so we are having to tread carefully. See here and here for more background on this.
Q: Anything else you’d like to say?
Addyman: We have already benefited greatly from our contact with QS community. The conference was a great inspiration and I wish could get to more of the lively London meet ups. If anyone out there would like to get involved with our project, we’d love to hear from you. Any advice or experience you could lend us would be greatly appreciated. Our project is both open source and open science. We believe in the power of collaboration and so would love to hear from anyone with similar projects in mind.
This is the 12th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned? In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at firstname.lastname@example.org.