Betaworks recently announced that they had collected data from over 40,000 users who shared their iPhone homescreens through their apptly named #homescreen app. As they stated in their announcement, the apps people keep on their homescreen are often the apps they use the most. Being a data-minded individual I thought, “I wonder what kind of questions you could ask of this kind of data?” Of course, I immediately jumped to using the data to try and understand the landscape of self-tracking and quantified self app use. Let’s dive in.
I didn’t do anything fancy here. I used the search function to look up specific applications that I either use myself or have heard of. I also used the “in folders with” and “on homescreens with” lists to find additional applications that weren’t on my initial list. Each of the apps I found went into a Google Spreadsheet along with the “on % of homesceens” value reported by #homescreen. Additionally I categorized each of the apps into one of seven very broad categories: Activity, Fitness, Diet, Sleep, General Tracking, General Health, and Other.
If you search for an app this is the data that is returned.
I was able identify 65 unique applications reported as being present on #homescreen users iPhone homescreens. While in no way a complete list, I think it’s a good sample to see what people are using in their everyday life. So, how does the data actually break down?
The most popular category was Activity with 20 apps (30.8%) followed by Fitness (15 / 23.1%), General Tracking (11 / 16.9%), a tie between General Health and Diet (6 / 9.2% each), Sleep (5 / 7.7%), and Other (2 / 3.1%).
Frequency of Homescreen Appearance
Perhaps unsurprisingly, Apple’s Health app tops the list here with a whoping 23.45%. No other self-tracking application even comes close to that level of homescreen penetration. The next most frequent application is Day One (a journaling app) with 8.45%.
Clearly Apple Health skews the data so let’s look at homescreen frequency without it in the data set:
When you exclude Apple Health (a clear outlier) the average percentage for homescreen frequency is 0.83% with a standard deviation of 1.39%. This data is skewed by a high number of applications that appear very infrequently. How skewed?
If we disregard all apps that fall below this 1% cutoff what do we find? Fourteen apps meet this criteria: Coach.me, Day One, Fitbit, Health Mate (Withings), Moves, MyFitnessPal, Nike+, Pedometer++, Runkeeper, Runtastic Pro, Sleep Better, Sleep Cycle, Strava, and UP (Jawbone):
Interestingly, MyFitnessPal is the only “Diet” app that made this >1% list, but it has the second highest appearance percentage at 4.36. Sleep Cycle is not far behind at 3.98%.
Let’s start with the caveats. Obviously you can’t take these simple findings and generalize it to all individuals with smartphone, especially because this is only capturing iPhone users (many of the apps in this list have Android versions as well). Second, this data is based on a relatively small sample size of 40,000 individuals that are using the #homescreen app. Third, users of #homescreen are probably not representative of the general population of self-tracking application users. Lastly, it’s hard to draw conclusions about actual app use from this data. Like many people (myself included), I’m sure this data set has more than a few users who don’t regularly change their homescreen configuration when apps fall out of favor.
With that out of the way, what did I actually find interesting in this data set?
I found that sleep tracking, or having sleep tracking apps on a homescreen, was more popular than I thought it would be. Of the top 15 apps, 2 were sleep. When exploring by category, sleep had the highest mean appearance percentage (when also excluding Apple Health) at 1.32% (n = 5 apps).
For connected tracking devices, Fitbit (3.47%) is the clear winner, far outpacing it’s wearable rivals. The next closest application that is at least partially dedicated to syncing with a wearable device was Health Mate by Withings (2.14%).
I was reluctant to include Day One in this analysis. It isn’t commonly thought of as a “self-tracking” or “quantified self” application, but journaling and daily diaries are a valid form of tracking a life. Clearly it resonates with the people in this data set.
This was a fun exercise, but I’m sure there are many more questions that can be answered with this data set. I’ve compiled everything in an open google spreadsheet. I’ve enabled editing in the spreadsheet so feel free to add apps I might have missed or create additional charts and analysis.
We recently released our QS Access app, which allows you to see HealthKit data in tabular format. Not very many tools feed data into HealthKit yet, but Apple’s platform does pick up step data gathered by the iPhone itself. I have step data on HealthKit going back about two weeks. When Ernesto Ramirez and I were playing around with QS Access, loading the data into Excel and looking at some simple charts, I learned something: Even when I’m active, I’m sedentary.
My daily step totals ranged from a depressing 3334 steps on Thursday, September 18 to an inspiring 21,634 steps on Friday, September 25, but – as these charts clearly show – even on the extreme days my activity was concentrated into relatively short periods when I got up from my desk and went out to do something. Most hours, every day, were spent with hardly any movement at all. I’m sitting at my desk, and sitting at my desk some more, and sitting at my desk still more. That’s probably not good. No, not good at all.
Pulling my data out of HealthKit and seeing a few simple charts gave me a bit of insight that I hope will lead to a change in how much I sit. It was a great to be able to easily make some simple analysis of my data. I hope you’ll find QS Access useful also (you can learn more about it here). Please share what you learn in the QS Access thread in the QS Forum or by emailing us about your projects: firstname.lastname@example.org.
Today we are happy to bring you another interview in our Toolmaker Talk series. We had the great pleasure of speaking with Sampo Karjalainen, the designer and founder of Moves. Over fifty percent of U.S. adults have a smartphone. That’s a lot of people walking around with a multi-sensored computer in their pockets. Moves is another example of how developers and designers are focusing on the smartphone as a Quantified Self tracking and experience tool. This is an exciting space, and one we intend to keep a close eyes on moving forward.
Watch our conversation or listen to the audio (iTunes podcast link coming soon!) then read below to learn more about Sampo and the Moves app.
How do you describe Moves? What is it? Moves is an effortless activity tracker. It’s a bit like Fitbit or Jawbone UP, but in your smartphone. There’s no need to buy, charge and carry one more device. In addition to steps and active minutes, the app also automatically recognizes activity type: walking, running, cycling or driving. It also shows routes and places and builds ‘a storyline’ of your day. It helps you remember your days and see which parts of your day contribute to your physical activity. It’s a simple, beautiful app that hasn’t existed before.
What’s the backstory? What led to it?
We started Moves to motivate us to move more. Aapo Kyrola was doing his Ph.D at Carnegie Mellon University, working hard, gaining weight and lacking the motivation to exercise. We began discussing how to motivate people like Aapo to move more. The first prototype used game motivations: we had badges, leaderboards and virtual pet to motivate people. The problem was that they still had to remember to start and stop tracking. We quickly learnt that people didn’t remember to use it for everyday walks. That made us think that maybe we could make it work continuously in the background. It took plenty of R&D to find a way to minimize battery use while still collect enough data to recognize activity types and places correctly.
What impact has it had? What have you heard from users?
We’re seeing that when you make activity visible, people start to think about it. And when they think about it, they start to do small changes in their lives. They may park their car a bit further or consider biking instead of car. They may choose to walk just to get some steps and take a break from everyday hurries. It also helps people see how long it takes to travel between places and how much they actually use time in different places.
What makes it different, sets it apart?
Other phone-based trackers are good for tracking one run or one biking event. Moves is made to track all-day activity. Compared to activity gadgets, Moves recognizes activities by type, recognizes places and shows routes. It’s collecting a new type of a dataset that hasn’t been available before. And best of all, we now have a public API, so you can use your data as you like!
What are you doing next? How do you see Moves evolving? Currently we’re busy with the Android version of Moves and adding some features to the iPhone version. Over time we see that Moves will become a tool to understand not only your physical activity, but also your use of time, travels – your life in general.
Anything else you’d like to say? Moves is collecting your location in time and space. It’s a great ‘backbone’ for connecting all kinds of other data. We’re excited to see what type of visualizations and mashups people create!
This is the 20th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned? In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a toolmaker and want to participate in this series please contact Ernesto Ramirez.
Victor van Doorn describes himself as a nostalgic workaholic. He has tried and failed to keep diaries, so he decided to build an automatic one. His iPhone app Replay My Day (@replaymyday) collects his location and online activities each day, and builds it into a film – so when he’s lying in bed at night he can press play and literally replay his day. Victor also organized a fun launch party/contest to draw GPS snakes around Amsterdam! Check out his talk below. (Filmed at Amsterdam QS Show&Tell #3.)
What if you had a movie of your life that was made from a stream of pictures taken one every thirty seconds? Glenn Wolters and Jeroen Bos have built an iPhone app called Lifelapse to do this. They developed it as a school project. I noticed Joost Plattel using it at the recent QS conference – he showed me his entire bike ride across the Golden Gate Bridge as a powerful visual story in less than a minute. (Filmed at the Quantified Self Show&Tell meetup in Amsterdam at Mediamatic)
The Cophenhagen Wheel, a project of MIT’s Senseable City Lab, transforms an everyday bike into “a hybrid e-bike that provides feedback on pollution, traffic congestion and road conditions in real-time.” And yes, that’s an iPhone mounted on the handlebars.
Thanks to Nathan Yau of FlowingData
for the heads-up on this. Nathan writes:
The wheel stores energy when you pedal and brake, and turns on auto
pilot through your iPhone when you’re feeling lazy. Your iPhone is also used to switch gears and lock and
unlock your bike.
On top of that, or rather, inside the wheel, there are sensors for
torque, noise, carbon monoxide, nitrogen oxide, and location. Look back
on the environment around you, from the your data’s point of view, and
optionally, share your data with the community to contribute to a closer
view of your town.
I love this idea of passively capturing data while you cycle. There is so much environmental data available to us all the time – temperature, ambient noise, light levels, pollutants – why do we not have devices to easily capture all this information?
Can your cell phone replace your therapist, or make cognitive behavior therapy more accessible to a wider audience? Margaret Morris of the Digital Health Group at Intel sent in an article that addresses this question, published today in the Journal of Medical Internet Research.
One example is a participant who had been coping
with longstanding marital conflict. After reflecting on his mood data,
particularly a drop in energy each evening, the participant began
practicing relaxation therapies on the phone before entering his house,
applying cognitive reappraisal techniques to cope with stressful family
interactions, and talking more openly with his wife. His mean anger,
anxiety and sadness ratings all were lower in the second half of the
field study than in the first.
I recently came across Mikael Huss’ Follow the Data blog, which reports on data-driven trends in reality mining, self-tracking, and personalized medicine. In a recent post, Mikael talks about three bits of self-tracking news that are sure to create tingles up the spines of Quantified Self readers:
1. FitBit ships At long last! FitBit, the accelerometer with the beautiful clip-on form factor and wireless uploading of exercise and sleep data, has arrived. A one-time fee of $99 puts passive motion tracking in your pocket.
2. DailyBurn launches FoodScanner iPhone app Tracking your fitness and nutrition is going mobile. DailyBurn has a $0.99 iPhone app that lets you take pictures of the barcodes on foods you eat, helping you more smoothly track your caloric intake.
We recently started a program to invite QS Toolmakers to contribute directly to funding our events. We call this program Friends of QS. If you would like to participate we invite you email us to learn more.