It’s January, and like a lot of people I overindulged during the festive season (and, if I’m honest, the rest of the year too). As 2012 drew to a close, I resolved to shape up in the year ahead – just like I do every year.
But research regularly shows New Year’s resolutions are a waste of time; research for the Journal of Clinical Psychology found just 8% of people who make resolutions stick with them.
So how can we do this differently? Taking inspiration from my day job, I’ve turned to metrics – because you can only manage what you can measure, right? Like a growing number of people, I’ve been tracking my weight, food intake and exercise using a range of apps and sites in a bid to become fitter and healthier.
Organisations have long since recognised the power of data to effect change; as well as the obvious balance sheet and bottom line, data helps business to understand their resources and marketing, while the government is (selectively) pushing for greater scrutiny of spending and outcomes through the release of open data.
But while the impenetrability of (and lack of interest in) spending data means Eric Pickles’ army of armchair auditors is likely to remain small, understanding and analysing metrics about ourselves has fast become mainstream. Growing numbers of people are tracking their food, moods, exercise, their alcohol intake, how well they sleep, and much else besides, in order to make lifestyle changes – an approach known as self quantifying.
Recording personal data on nutrition and the like used to be difficult, which is why only those with a serious interest in doing so would bother. But smartphones have been a game-changer; they’ve bought the tools used to measure, monitor and analyse in business and science into the palms of our hands – and into the private sphere.
As people become more aware of the amount of data that organisations gather about them, they’re becoming more aware of the potential to use data themselves. By marrying technology and life improvement , and tapping into the stream of data we generate, we can find new ways to improve our quality of life.
Growing numbers of people are doing just that, using self-tracking tools and methods to gain knowledge about themselves, others and the world around them. Some of the things self-trackers at the London Self-Quantifiers Meetup Group measure include: self-experimentation, behaviour monitoring, lifelogging, location tracking, digitizing body info, biometric data, psychological self-assessments (mood tracking), medical self-diagnostics and even personal genome sequencing.
People with long-term illnesses are using self-tracking or quantifying to understand their own patterns of illness, and in particular lifestyle triggers, which helps them better manage their illnesses.
Over the past two years, my iPhone and apps have helped me understand my sleep patterns, keep track of my food intake, monitor my weight and train for two half-marathons.
Self-quantifying is being taken seriously by start-ups, with a wide range of companies launching new devices and software aimed at self-trackers – most notably the Nike FuelBand and the FitBit, which uses an accelerometer and altimeter to measure activity levels and sleep patterns.
The public sector has been quick to get in on the game; the NHS has developed a range of self-quantifying apps which help people measure (and so reduce) their alcohol intake and quit smoking. The cost of developing these will recouped if just a handful of people avoided a serious illness such as cancer by improving their lifestyle.
Insurers are looking at self-quantification, too, creating apps which help people to record their driving, and rewarding safer drivers with cheaper insurance. It’s been suggested these apps provide a glimpse of the future of health care, in which a greater emphasis is placed on monitoring, to prevent disease and reduce medical costs (or, more cynically, to more efficiently calculate actuarial risk).
But ultimately, this is about outcomes, not outputs; it doesn’t matter how many calories I consumed or burned off today, what I really want to know is whether I can fit into those skinny jeans. So as with all open data exercises, the data isn’t enough; it’s what you do with the data that’s important.
And this is where the secondary function of Quantified Self apps kicks into play; using the power of group dynamics and feedback. The weight loss industry has long since employed group feedback (positive and negative) within programmes such as Weightwatchers. Some self-trackers are using social networks to share their progress data, gain feedback and receive positive reinforcement that helps them reach their personal goals. This augments the already diverse range of health, fitness and weightloss forums out there with further opportunities for goal-setting and peer motivation.
A growing number of apps encourage self-tracking through gamification — using game mechanics to encourage participation and competition with friends. Android app Boozerlyzer, helps people track their drinking and uses simple games to help them measure the effect of alcohol on co-ordination, reaction times, memory and emotions.
Data from the Boozerlyzer app is anonymised and aggregated to investigate the variation in people’s response to alcohol – just one of many ways in which self-tracking is producing useful scientific data.
Users of the Zeo headband, which tracks sleep quantity and quality by measuring brainwave activity, have already generated the largest-ever database on sleep stages, which revealed differences between genders in REM-sleep quantity, and has vastly improved understanding of sleep disorders.
In fact, hundreds of thousands of patients are sharing data on symptoms, treatments and triggers for their illnesses on websites such as CureTogether.
With a growing number of self-tracking apps and gadgets on the market, the scope for data collection widens, enabling users to analyse their own behaviour (to make lifestyle changes) and aggregate their data with others (to further understanding). As the usability of tracking apps improves, we’re seeing a surprising growth in data nerds – auditing not the government, but themselves, often with very positive results.
For more on self-quantifying, see Gary Wolf’s Quantified Self blog.