Review of Life Mash

This is another post in the series on "Tracking: the other learning technology".

• Tracking: the other learning technology (overview article)
• Review of FitBit
• Review of myZeo
• Review of Lumosity brain games
• Review of 23andme.com personal genome service
• Today: Life Mash

What is it and what does it do?

Unlike other trackers we discussed, Life Mash is not an available service. It is a research experiment from Motorola lab's Frank Bentley. Back in April he asked for Quantified Self people to join his experiment and so I did. The article starts with 'do you sleep better when it is warmer?'. That's what Life Mash is all about indeed: via an Android app the app combines the data of different trackers and mines them for correlations. These relationships are then shown to you so you can learn about yourself. The service mashes up the various tracking data and uses data analytics to help you make sense of it. Below is a screenshot of the trackers available. You'll recognize the Fitbit we previously covered. I also own a withings scale that uploads my weight, BMI and fat percentage to my account automatically. You can also include automatic trackers from your smartphone: the tool can include data on your agenda, location, weather,...

The app comes with a few manual trackers for mood, food and pain as well. You can indicate when to prompt for these data, and the screen itself only takes a few seconds. I found it very user friendly, simple and quick to enter my data. (Because I'm a 'lazy' tracker: if it takes too much manual effort, I'm out.)

 The kind of relationships you end up with is similar to the screen below.

How good or bad is it?
As I've argued before: the tracking technology and Quantified Self tools and services are the most mature in the health and mood areas at this point in time. However, we need to get better at visualizing data, and at making sense of our data.  That's where this research tool comes in: it is an experiment in using data analytics to discover hidden correlations. But it still remains up to you to do anything with these insights.

So does it work? You can see from the observations above that not all entries tend to make sense. Especially combining two factors you don't have any influence on is not very actionable. (eg On Mondays it is warmer than on the rest of the week.). Sometimes the causal relationship is the other way around. (eg On days you walk more it is warmer - I wish I could influence the weather like that :-) ). But other observations were new and meaningful. (eg I sleep the least on Mondays - don't know why but I never realised.)

Where's the learning?
The learning is in the combination of the separate tracking data streams, and mining them for correlations and insights. The results of automated sense making are so-so. In our QS Meetup group in Brussels, we were wondering if there is no place for a 'Quant Coach' type of profession to help us making sense of our data. 
To come back to the question "Do you sleep better when it is warmer?", the answer is no. Below is a screenshot from the associated web site that makes reports over time of all the correlations.

I want to thank Frank Bentley for conducting this experiment and making the app. We still need to go further in sense-making of our data, and automating (part of) it is a useful step. 
(More screenshots here.)