Tag Archives: motivation
Some people base decisions on facts and data, while some people base them on other foundations or beliefs. Let’s immediately forget about this latter group.
For the former group, the quantity, frequency and overall reliability of the collected data is very important. However, capturing data seems harder in real life than it sounds. In addition to resolving technical challenges, such as designing reliable devices that provide usable data, the persistent action of collecting data for further analysis is sometimes a burden that people just don’t feel like carrying all the time.
In this breakout session, we will focus on people who get excited about the novelty of using a Fitbit device or a Runkeeper app to track their workout (the “Discovery Stage”). These are the people who diligently log the food they ingest into an iPhone app for a few weeks before they get a feeling that they are wasting their time. At some point, they feel that the benefit they get from logging data is much lower than the cost of the effort required to do it.
We hypothesize that the novelty effect behind these self-quantification efforts fades away in a few weeks or months and falls in a “Data Desert”. Whoever survives the crossing of the Data Desert is eventually rewarded with enough data and knowledge to pragmatically leverage them for effective decision making (“Data Legacy”). But abandonment is more likely than persistence during this difficult phase. Oftentimes, these people don’t have a vital problem to solve by collecting data. They are curious to uncover a vague concern, but no drastic and immediate consequence will ensue from stopping data collection.
Our hypothesis relies on personal observations of quantified-selfers (including ourselves) and also on the phenomena observed in subscription-based services (such as joining a gym) as well as on Geoffrey Moore’s “chasm” principle.
The proposed goal for this breakout session is to explore strategies, designs, technologies and incentives to help people persist in their data collection after the initial enthusiasm fades away.
This is a guest post by Patrick Burns, who is a PhD candidate in the School of Computing and Information Systems at the University of Tasmania in Hobart, Australia. He is researching the use of technology to promote physical activity. His interests include ubiquitous and wearable computing and ambient displays.
According to the World Health Organization more than one in ten adults worldwide in 2008 was obese. This figure has more than doubled since 1980. We know that obesity is a major risk factor for heart disease, diabetes, osteoarthritis and some forms of cancer. Unsurprisingly a combination of inadequate exercise and increased consumption of fatty, sugary foods is to blame. When it comes to a lack of physical activity, some people point the finger at technology. Cars let us drive to places we used to walk. Machines do jobs we used to do by hand. Video games, DVDs, television and the Internet provide a wealth of sedentary entertainment options. But could technology actually help us to do more physical activity, and ideally help prevent obesity?
One approach is to help people to better track their activity. The hope being that if we make a person more aware of their physical activity (or lack of it) that they will be motivated to do more. There are a number of existing devices designed to do just that. Examples are FitBit, Jawbone UP and Nike FuelBand. Each integrates a motion sensor (accelerometer) into a wearable device to track how much the wearer moves around during the day. There are also stand-alone smartphone apps which use the phone’s in-built accelerometer to track physical activity. In the case of the UP and some smartphone apps, the user can supplement their activity data with information on the type of food they’re eating. The UP and FitBit also monitor users’ sleep habits.
The data collected are processed and delivered to the user in the form numbers and graphs. A count of steps taken each day. Time active vs. sedentary. Steps climbed. Calories burned. There is an assumption that, when it comes to activity tracking, more is better. That we should collect more data from more sensors. That we should perform more analyses on that data and present it to the user in multiple forms. We should make our interfaces more engaging, to encourage users to continue to monitor their activity data. In the words of Jawbone’s VP of product development, “you have to create a Facebook-like engagement that keeps people coming back”.
The truth or otherwise of these assumptions is very much dependent on individual users, and the way in which they employ a particular technology in their lives. Users who are very motivated to do exercise, sometimes playfully called “fitness freaks” or “gym junkies”, employ activity monitoring technology in a supporting role. They already do a lot of physical activity and enjoy being able to record, quantify and analyse that activity. But what about less motivated users – people who don’t do enough physical activity and know that they should do more? For those users technology plays a motivating role, one in which the technology is (and should be) more peripheral to their day-to-day lives.
If we give these users an interface that is too complex, or that requires a continuing and significant time commitment, we run the risk that they will lose interest, “burn out” and return to old habits. Many of us have had the experience of starting a diet, joining a gym or buying exercise equipment only to give up soon after. These experiences underline the need to make small changes, slowly, that can be sustained in the long term. We need to design technology to support this type of change.
I argue that for these less motivated users, simpler interfaces could be just as effective as more complex, more engaging interfaces. Do we really need to know exactly how many steps we’ve taken or how many calories we’ve burned? Or is it good enough just to know that we’ve “done well” today or that we need to “do more”. Do we really need graphs and figures, or could we convey information in a simpler way. Say, through coloured lights.
I’m currently researching the use of simple interfaces to deliver physical activity information to users, with a specific focus on wearable technology. I recently designed and evaluated such a device – ActivMON. ActivMON is a wrist-watch like device containing a motion sensor and coloured light. The motion sensor detects the user’s physical activity and the light changes colour (on a spectrum from red to orange to green) to show the user’s daily activity level as compared to an activity goal. ActivMON then shares this data through the Internet with other devices. If you’re doing physical activity then the lights on your friends’ devices will pulse to let them know. If they’re doing physical activity, your device will pulse. Supporting social influence is important, and I wanted to see if this could be done using a wearable ambient display.
This work is still in its early stages, but I feel it raises some interesting questions. Should we deliver information differently (and more or less information) depending on a user’s level of motivation to change? How engaging should interfaces be? How little information can we deliver, and yet still realise a motivational effect? This is not to argue against the quantified self. Rather to pose the question of how best to present data to users once we’ve collected it.
I recently received an email from someone having trouble keeping up with her experiment. While there is lots of general advice about discipline and motivation, this got me thinking about how doing personal experiments might differ. Following are a few brief thoughts, but I’d love to hear ways that you keep motivated in your quantified self work.
The desire to get an answer. The main point of an experiment is to get an answer to the initial question. “Will a Paleo diet help me manage my weight?” “Does talking less bring me closer to my kids?” Maybe the principle at play is that experiments which motivate start with great questions.
Built-in progress indicators. If you’ve set up your experiment well, you should have measures that come in regularly enough to keep you interested. This is assuming, of course, that you care about the results, i.e., that you’ve linked data and personal meaning (see below). But unlike other types of projects, maybe we can use the periodic arrival of measurements to stimulate our motivation, such as celebrating when new results appear.
The joy of satisfying a mental itch. Curiosity is a deep human motivation, and experiments have the potential of giving your brain a tasty shift – such as when you are surprised by a result. I especially like when a mental model of mine is challenged by a result. Well, sometimes I like it.
Sharing with like-minded collaborators. At a higher level of motivation, experimenting on yourself is an ideal framework for collaboration with folks who are either 1) interested in your particular topic (e.g., sleeping better or improving your marriage), or 2) are living an experiment-driven life. It is encouraging to get together with people to share your work, and to receive support, feedback, and ideas. Of course it feels good to so the same for them.
Desire to make a change. Finally, if we come back to why we experiment, there should be a strong self-improvement component to what we are tracking. My argument is that, ultimately, it’s not about the data, but about making improvements in ourselves for the purpose of being happier. If the change you are trying is not clearly leading that direction, then it might make sense to drop it and try something more direct. Fortunately, with self-experimentation there is usually something new you can try.
Underlying all of these, however, is the fact that the work of experimentation takes energy. Every step of an experiment’s life-cycle involves effort, from thinking up what you’ll do (creating a useful design), through running the experiment (capturing and tracking data), to making sense of the results (e.g., the “brain sweat” of analysis). Given our crazy-busy lives, there are times when we simply can’t take on another responsibility. So if you find yourself flagging and losing interest in one of your self-experiments, then maybe that is itself some data. Thoughts?
[Image from Steve Harris]
Let’s admit it. People who do stuff are more interesting than those who don’t. Naturally we’re biased as Self-Quantifiers, but don’t you love running into folks at gatherings who have surprises and results to share about themselves, gained from experimentation and tasty data? It’s stimulating to hear about an insight (“I eat less when I’m happy”), a problem they’re getting a handle on (“I’m seeing if exercise helps my mood”), or a delightful surprise (“I’ll be darned – I’m smarter when I eat butter.”)
A meta question I’m curious about is whether we can quantify the self-quantifier. That is, can we find a personality type that’s common to all of us who experiment on ourselves? Let’s play with it by looking at a few possible attributes.
- The insatiably curious. If any of these dimensions are universally applicable, I’d guess it’s the trait that got the species to where it is now – the urge to answer innate questions like “Why did that happen?” or “What if I tried…?” Can there really be anyone who isn’t curious?
- Gadget lovers, early adopters. There’s no question that the explosion of self-tracking widgets is exciting. Electronics for measuring sleep, exercise, even power consumption provide motivation through novelty, and ease the tracking burden through automation. A little test: Anyone using low-tech tools? Graph paper and lab notebooks for example?
- Risk takers. Collecting data means trying new things, and as a species change is hard. In my case, some of the experiments I try out can feel pretty scary. In your life, how much of a stretch is it for you to do your experiments?
- Fans of Occam’s razor. Experimentation is a function of the scientific method, which requires a rational “prove it to me” mindset. Can we be motivated to collect data about ourselves yet not be skeptical?
- Problem solvers. Often our foray into experimentation is driven by a problem such as a major health concern. (There are over 600 of them at Alex’s CureTogether.) I wonder if motivation to solve a particular situation is at right angles to a general experimental sense. Or maybe it’s the other way around – those who work actively to address a problem are by definition self-experimenters.
- Tireless self-improvers. As Gary pointed out in his New York Times piece, we track data ultimately to peel back the layers of our behaviors: “The goal isn’t to figure out something about human beings generally but to discover something about yourself.” There’s probably a set of folks who are happy with themselves the way they are, but I don’t think they congregate here. Then again, I always appreciate when someone chimes in and questions our movement.
- Thrill-seekers. If it’s true that we have built-in novelty detectors, are we more likely to try things because results are more stimulating? I’d argue that, because of our curious nature, experimenting feels good. In your case, what kind of jolt do you get from discoveries?
- Willing to change. What’s the point of thinking up things to try, doing them, and then capturing and analyzing results if we don’t make a change, either in our thinking or behavior? I don’t mean that change is always the goal (I’m a firm believer that observation leads to awareness, which leads to change), but without change is this work simply waste? Maybe there are stages, starting with “data-curious?”
What do you think? Is it possible to define useful characteristics that capture the data-driven personality? Do any describe you? Which ones would you add or remove?
[image from x-ray delta one]
(Matt is a terminally-curious ex-NASA engineer and avid self-experimenter. His projects include developing the Think, Try, Learn philosophy, creating the Edison experimenter’s journal, and writing at his blog, The Experiment-Driven Life. Give him a holler at email@example.com)
They each pledged $2,000 one summer – they would lose this money if they didn’t daily follow their “yellow brick road” targets for everything from pushups to desserts to spending time with kids. Watch their fascinating story below.
Nathan Yau over at Flowing Data started a fascinating discussion last week. He asked his readers why they collect data about themselves, what they’ve learned from it, or why they don’t collect any data at all.
“I collect my blood glucose level every 5 minutes through a continuous glucose monitor stuck in my gut. I log carbs, protein and fat intake… I am a type 1 diabetic. All data I can collect, analyze and take action on improves my health and prolongs my life.
And it’s a huge pain in the ass.”