Mappiness: transforming wellbeing research one chirp at a time

One of the many reasons I am such an iPhone enthusiast is the amount of innovation it has unleashed on the part of app designers, and the potential for entirely new programs that use the iPhone’s built-in functions to create something exciting and special. The percentage of apps which actually do this, of course, is ridiculously small, but for every thousand Pretend Pints there’s an Angry Birds, and for every half-million of all of them there’s a Mappiness.

I started using Mappiness in the week it was released last August and my love for it borders on obsession. Created by George MacKerron and Susana Mourato, environmental researchers at the LSE, Mappiness is a data collection tool which focuses on a simple but extraordinarily important question: what makes us happy?

With the Government’s focus on national wellbeing (and the launch of the ever-so-slightly-patronising Action for Happiness campaign), the need for a way to accurately capture this most simple of questions is crucial. We all agree that the point of economics is to serve the interests of people, not the other way around; and the main thing people want is to be happy (however they achieve this). But if there’s no way to measure what affects happiness, then there’s no way to know what direction policy should take. It’s a field of economics which is notoriously difficult and contentious.

According to the World Values Survey, the largest evaluation of happiness levels globally, the Danes are happier than anyone else in the world. But what this actually means is simply that, when asked how happy they are, they respond with higher levels of enthusiasm. Are they actually happier, or do they just report that they are? Do their happiness levels mean the same as ours, or are they on a different scale entirely? And if their reported happiness changes, how can we accurately deduce the causes? What makes people happy is extraordinarily complicated, it’s almost impossible to fully control for all influencing factors, and there’s no reason at all to think that people’s subjective happiness levels can be compared. Mappiness is getting incredibly close to circumventing these problems, in a pleasingly simple way.

Five times a day (and night), the app chirps in a cheerful way and invites me to record how happy, relaxed and tired I am, what I’m doing, where I’m doing it and who with. If I want to, I can take a photo of my surroundings. Because of the phone’s built-in GPS it knows where I am and, therefore, information about my physical environment along with things like the weather, air pollution and how noisy it is.

The payoff for me is that I get a record of my general wellbeing across time, broken down into my average mood at each hour of the day and day of the week, and which activities, people and places correlate with a high (or low) happiness score. (Plus, as the researchers state, “The warm glow of helping increase the sum of human knowledge”.) It’s a data-geek’s dream, and ridiculously addictive. Last week a data-download function was added, and I spent a happy hour looking at my happiness markers on a map of the world (Old Street roundabout scores pretty highly).

The self-reporting aspect is crucial because it avoids some of the inherent (unconscious) biases which traditional wellbeing survey questions can tap into. The app doesn’t directly ask the causes for my current mood, which is important because humans aren’t always particularly good at identifying what’s affecting their emotions, especially when it comes to external factors.

The researchers happily acknowledge (see the TED talk here) that some of the raw statistics are meaningless without controls. (Is “queuing” in my top 5 activities just because I’m English, or because the activity coincides with my being at an airport? Do I intrinsically hate “washing, dressing”, or is it because I’ve just woken up?)

But the project really comes into its own in the bulk analysis. Last month Mappiness hit 40,000 participants, each contributing an extraordinary amount of data on a daily basis – over 2 million responses so far. It doesn’t solve the issue of interpersonal comparisons but it does, to an important extent, make it irrelevant – by replacing the traditional research tactic of asking a lot of people the same question with one that asks the same people the same question over and over again. It doesn’t really matter if we haven’t got a calibrated, baseline happiness level or a way to compare people’s responses; what matters is the variation within each individual’s own responses according to different inputs and  internal and external factors. In short, whether your baseline happiness is a 4 or a 7, and what this ‘means’, is irrelevant – what matters is whether a sunny day pushes you a point higher, and if being in an area of urban deprivation drags it down.

On the effect of environment in particular, the ability to know exactly where respondents are is revolutionary. Traditional surveys ask people about the environment in which they live or work and ignore everything else, letting in a huge number of uncontrolled factors (as MacKerron points out in the TED talk, there might be reasons that people who live in run-down housing estates are unhappy other than the buildings being made of concrete).

The results so far are exactly what the researchers expected: regardless of all other factors, people are happier in any natural environment than they are in cities (by about 5 percentage points) and happier in green areas of cities than grey ones. This feels obvious, but it’s something which hasn’t been provable at such scale before.

(On a more frivolous note, the research also conclusively debunked all those stories about ‘Blue Monday’ being the grumpiest day of the year.)

Mappiness has the potential to lend an enormous amount of weight to the arguments of those who want to design buildings and cities in ways which improve, rather than detract from, our wellbeing. For a small, friendly program that runs on a mobile phone, that’s pretty impressive.