Tag Archives: behavior change
Today’s post come to us from Lukasz Piwek. Lukasz is a behavioral science researcher at the Bristol Business School, University of West England. We were happy to welcome Lukasz, who led an well attended breakout session at the 2014 Quantified Self Europe Conference where conference attendees discussed current issues and new dimensions of behavior change. We encourage you to read his description below (which first appeared on his cyberjournal, Geek on Acid) and join the conversation in our forum.
The Future of Behavior Change
by Lukasz Piwek
I gave a short talk, and moderated a breakout discussion, on the future of behaviour change in the context of quantified self approach. It was an inspiring session for me so I summarised my slides here with the discussion that followed.
First, I highlighted that behaviour change interventions require multidisciplinary approach in order to target a broad range of behaviours related to health (e.g. healthy eating, alcohol & drug use, stress management), sustainability (e.g. travel habits change, energy saving, recycling) or education.
Health interventions are good example where behaviour change can enormously benefit from smart technology. Currently we have what we call a “sick care” model: when we notice a specific symptoms of illness we share it with our GP, and we get prescription, or we’re referred for more detailed diagnosis. This classic and dominant “sick care” model focuses on relatively passive way to manage illness “after” it occurs.
However, in the future we can envision ourselves being empowered by smart devices that track various variables in our daily life (such as heart rate, body temperature, activity levels, mood, diet). This variables will get combined in sophisticated analysis merged with our illness history and DNA screening. This continuously provides us with information about “risk factors” for illnesses, which enables us in turn to act and change our behaviour before the onset of a disease. This is what we call a real “preventive care” model of healthcare. Clearly we’re not there yet.
The key question we discussed was: “what critical features or solutions we are missing to make a breakthrough in behaviour change interventions with quantified self approach?” I started the discussion with giving two possible answers.
First, we lack long-term user engagement for smart wearables and self-tracking solutions. A recent study showed that 32% of users stop using wearables after 6 months, and 50% – after just over a year. Similarly, there is a high drop rate amongst smartphone apps users: 26% of apps being used only once and 74% of apps are not used more than 10 times (although discussion pointed out that we might not need long-term engagement for many interventions).
Second, existing devices for self-tracking lack data validity and reliability. Proprietary closed platforms and limited access APIs make it difficult for us scientists to validate how well self-tracking devices measure what they intend to measure. This is a major problem from the perspective of methodology for behaviour change interventions in clinical context.
In the discussion that followed my presentation, the major reoccurring theme was a lack of robust and reliable feedback provided to users/clients. We agreed that new model of feedback would incorporate such concepts as: narratives, actionable advices on specific consequences of behaviour, and personalised, rapid, relevant data visualisation.
Another problem highlighted was related to psychological resistance towards smart technologies in our lives, especially in the groups that are not motivated to use wearables/self-monitoring.
Finally, it seems clear that we’re currently focusing on “exploratory” side of quantified self, and its important we start moving towards more “explanatory” and predictive approach, like in the healthcare example described above. This requires a development of new methodology for n=1 research and creation of data bank of personal analytics. Such bank would enable better generalisation and evaluation of results for larger-scale interventions.
I’m totally on it.
If you’re interested in the intersection of Quantified Self and behavior change we invite you to join the conversation in our forum.
Nir Eyal writes about “behavior engineering” encompassing user experience design, behavioral economics, and a dash of neuroscience. In this Show & Tell talk at the May 3, 2012 Silicon Valley QS meetup, Nir offers suggestions for behavior change design based on understanding the distinction between four different behavior types: amateur, skillful, habitual, and addictive. He advocates responding to each in an appropriate and unique way.
Robby MacDonell from RescueTime tried many different tools to form habits, and didn’t find that any of them worked. After a good deal of frustration, he started to investigate the idea of having goals at all. In this great talk, Robby honestly shows data that isn’t pretty – hooray! He also shares some really interesting insights on how to make the process of behavior change gentler, from personal and system design perspectives. (Filmed by the Seattle QS Show&Tell meetup group.)
Nick Crocker‘s QS journey started with his dentist telling him, “Floss the teeth you want to keep!” Nick tells the story below of how he spent five years figuring out how to implement changes in his life, and how hard it was to add this habit to his routine. He also shares, Ignite-style, ten lessons he learned to make change easier. (Filmed by the New York QS Show&Tell meetup group.)
By popular request, we have just launched a global QS forum at: http://forum.quantifiedself.com/
Gary, Dan Dascalescu, and I took some exciting topics from the conference and turned them into forum discussions, with expert moderators to help explore ideas and answer questions. You’ll find discussions on:
Please join in the conversations, ask questions, share what you’ve learned, and come play with us!
From the Boston QS Show&Tell meetup group: Ian Ayres, founder of Stickk.com, talks about his book, Carrots and Sticks, and how to “unlock the power of incentives to get things done.” His video is in three parts below, with over an hour of behavior change insights and goal-sticking goodness. Enjoy! (and big thanks to PK Shiu for getting the videos online.)
First video ever from the Boston QS Show&Tell group: Trapper Markelz of MeYouHealth is introduced by QS organizer Michael Nagle. MeYouHealth is trying to solve the behavior change problem and especially influence women, who tend to make health decisions for their families. Watch the video below to hear Trapper’s tips on how to bring quantifying to non-quantifiers.
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 firstname.lastname@example.org)
How do I quit smoking, or start a running program? Drink tea instead of coffee, or eat more vegetables and less sugar? What about focusing on one task at a time instead of checking my RSS reader every 20 minutes?
If you are facing questions like this in your life or as part of your company, self-tracking can help bring awareness to patterns that you want to change. But what happens if awareness on its own is not enough to alter your behavior?
That’s where this guy comes in.
Crowdsourcing Your Future is a postcard that you send to your friends to have them predict your preferable and probable future timelines, so you can take action to follow or avoid certain futures that your friends see for you.
Personal Microtrends is a daily diary that asks provocative questions and suggests behavior changes for the next day to continue or alter trends depending on your goals. Jessica says,
What if you could create a self-reflective diary that made use of
our everyday thoughts to provoke us in such a way that you were able to
change your future actions?
The Microtrend Diary
is a mirror of your daily actions and emotions that reveals provocative
ways to alter your future actions.
This personalised diary is printed to order based on a set
of preliminary personality questions. As the owner makes a daily record
of their actions, a unique set of provocative aide memoirs are revealed
under a perforated flap that suggest changing your behaviour in certain
ways for the following day.
Right now Jessica’s diary is just at the concept stage, but the idea of looking at microtrends in your daily life, based on whatever data you collect, could allow self-quantifiers to spot patterns and make any needed changes on a more granular basis. It’s like rapid prototyping for self-experimentation.