Tag Archives: apple watch
We’re back again with another round of What We’re Reading. Before diving into the great articles and links below why not take some time to subscribe to our QS Radio podcast! We just released our fourth episode and would love to know what you think!
Want to tell us in person? Why not join us for our fourth QS Europe Conference this September in Amsterdam! Register now to take advantage of our early bird pricing.
Got Sleep Problems? Try Tracking Your Rest with Radar. by Rachel Metz. Researchers at Cornell University, the University of Washington and Michigan state are conducting research using off the shelf components to see if non-contact sleep tracking is possible. Turns out it is!
Apple’s Fitness Guru Opens Up About the Watch by Scott Rosenfield. A nice interview with Jay Blahnik here, where he speaks to Apple’s focus on self-tracking and fitness with the Apple Watch.
To share is human by Laura DeFrancesco. In this great news feature, Laura DeFrancesco exposes some of the issues with sharing personal data, as well as the initiatives hoping to break through those issues to help bring more data into the public sphere.
Using Twitter data to study the world’s health by Elaine Reddy. A great post here profiling John Brownstein and his work in Computational Epidemiology, specifically how he and his research team use public data sources like Twitter to tease out signals for health research.
Comparing Step Counts: Apple Watch, Fitbit Charge HR, And IOS Withings App by Victor Lee. An awesome and in-depth post comparing almost two months of steps counts from three different tracking methods by our friend Victor Lee. Glad to see he put our QS Access app to good use!
Follow-up to how I lost over 40 pounds using HealthKit and Apple Watch by Jim Dalrymple. Jim tells his story of how using a variety of apps and tools, all linked to his Apple Healthkit app, helped him learn about himself and eventually put him on the path to sustained weight loss.
Tracking Confidence by Buster Benson. Buster always has something interesting to say about self-tracking. This time is no different. Here he briefly talks about asking himself, “how confident do I feel right now?”
The Heart Chamber Orchestra – HCO – is an audiovisual performance. The orchestra consists of 12 classical musicians and the artist duo TERMINALBEACH. Using their heartbeats, the musicians control a computer composition and visualization environment. The musical score is generated in real time by the heartbeats of the musicians. They read and play this score from a computer screen placed in front of them.
This Week on QuantifiedSelf.com
QS Radio: Episode #4
When the Apple Watch was announced I started waiting with bated breath to see how it could be useful for Quantified Self and self-tracking purposes. Of course this means staying up late and making sure I had one on order as soon as possible. I put in my order shortly after midnight on launch day for a 42mm Space Gray with the black sport band.
On May 19th my Apple Watch arrived, coincidentally just after we wrapped on our first Bay Area Apple Watch Users Group meeting (which was fantastic and I highly recommend joining). I set it up and started figuring out how it worked as an activity tracker. I have a keen interest in activity tracking, not just as a self-tracker, but also as a graduate student studying how people use activity tracker data to understand and impact their lives. In that vein, I’ve been a consistent Fitbit user for over four years, transitioning from the original Fitbit to the Ultra, and then to my current Fitbit One. I’m a big fan of the Fitbit and use it as my personal “gold standard” for activity tracking. It’s accurate, consistent, and easy to use. Does that hold true for the Apple Watch? Let’s find out.
What did I do?
I wore my Apple Watch every day, from the moment I woke up to when I went to sleep at night. I set up my charging station on my nightstand, which is also where my Fitbit One spends its nights. I wasn’t thinking about this data analysis when I first started wearing the watch, but looking back over the past month I am confident saying that if I was wearing my Fitbit I was also wearing the watch.
This data analysis includes data from May 20th to June 23rd, or 35 days of data collection. My activities varied as a normal function of my work and life, meaning I didn’t purposefully mix things up or engage in activities just for testing purposes. Many days were sedentary, some days had longer walking periods, and in the 35 days I ran seven times at distances between four and nine miles.
How did I do it?
Exporting the data from both the Fitbit and the Apple Watch is not a trivial task, but thanks to a few pieces of software I was able to access and analyze both data sets.
The Apple Watch stores the data it collects in Apple’s Health app using Healthkit. A quick glance into the Health app indicates that it is storing minute-level step data from the Apple Watch. Apple built in a data export function for the Health app, but it’s in a proprietary XML format that I’m not super familiar with. Thankfully there is QS Access. Our team at QS Labs created simple app that connects to Apple Health and allows you to export your data in a easy to use .csv file.
To export my data I first made sure that the Apple Watch had the highest priority for the data sources that feed the “steps” data for Apple Health. This is important because all newer iPhones (5s, 6, 6+) also natively create step data and store it in the Health app. I then used QS Access to create a data export for steps. I chose the hourly function as it’s the highest level of granularity the QS Access app currently offers for data export.
Fitbit recently introduced a data export feature. While this is a great step forward for them, and for their millions of users, the export feature is a bit limited. You can only export daily aggregate data and only one month of data is exportable at a time. Since I had access to hourly data from the Apple Watch I wanted to match that granularity.
I turned to my good friend, and past colleague, Aaron Coleman. Aaron runs a unique startup called Fitabase, which was built to help researchers, organizations, and individuals get easy access to activity tracker data. I spun up my account at Fitabase, which has been collecting and storing my Fitbit data for the last few years, chose the date range and downloaded my hourly step data.
I wanted to get right to my core question, “How accurate is the Apple Watch compared to the Fitbit One?” so I imported both data files into Google Spreadsheets, did a bit of data formatting, created a pivot table, then made some simple graphs. The full data set is available here if you want explore more complex statistics or visualizations.
What Did I Learn?
When compared to the Fitbit One, the Apple Watch is fairly accurate for step tracking. What do I mean by fairly accurate? Let’s dive into the data.
When I explored my daily step totals it appeared that the Apple Watch counts more steps than my Fitbit One, but not that many more. Here’s the data you need to know:
- Fitbit Total Steps: 308,955
- Apple Watch Total Steps: 317,971
- >Difference: 9,016 or 2.91% of the total steps (counted by Fitbit)
I created a difference category by subtracting Fitbit steps from Apple Watch steps for each day. This allowed me to see how different the data was day over day. The mean difference indicated that Apple Watch counted 258 steps more per day on average. Important to note that the daily difference was highly variable with a standard deviation of 516 steps. Looking at the scatterplot and histogram below you can see a few clear outliers, but what appears to be an otherwise normal(ish) distribution for the difference in step counts.
What about when we look at a higher level of granularity? I also explored the hourly steps data and compared the Fitbit and Apple Watch. On average the Apple Watch counted 11 more steps per hour than the Fitbit One during this period. Again, this was highly variable with a standard deviation of 85 steps, and a range from overcounting by 462 steps to undercounting by 696 steps. I haven’t yet filtered out sleep time (0 steps) so the mean difference per hour in this data set is likely skewed low.
I also looked into one more question that I though was interesting. Is there a significant difference in daily or hourly step data as a function of the total steps? Or, more simply, when I’m more active does the Apple Watch still stay consistent?
It appears that being more active doesn’t have a significant impact on how accurate the Apple Watch is tracking and counting steps. I created scatterplots for this relationship and added a simple linear trendline. In both cases, the trendline indicated that only a small amount of variability in the difference between the devices was accounted for by the total steps taken.
I’m not ready to give up my Fitbit just yet, but I was happy to see that the Apple Watch is an accurate step tracking device. Of course there are caveats to this data set. It’s somewhat small, a little over a month of data, and I didn’t do any “ground truth” testing where I counted my actual steps. However, I feel more confident now that whether I’m walking around my apartment, my nieghborhood, or going on runs, the Apple Watch will accurately reflect those activities.
Like most other runners who are using the Apple Watch I’m interested to dive into the heart rate data to test it’s accuracy. I’ve already collected a few runs, but will doing a bit more testing to compare to other common heart rate trackers.
We’re interested in the Apple Watch. So you might expect there to be a bunch of interesting self-tracking projects using the Apple Watch presented at QS15: The Quantified Self Global Conference and Exposition.
We expected that too. But, as you might have noticed, even devoted Apple fans are still (mostly) waiting for their watches to arrive. There hasn’t been enough time to learn very much.
So, to prime the pump, we got our hands on a new Apple Watch, and we’re going to give it away to somebody who has an idea for a QS project to try. The model is exactly as shown above: 42mm Space Grey Aluminum Case with Black Sport Band.
Here’s a picture of the actual watch, still in the brown delivery box.
Here’s a picture of the brown boxed, opened up.
And that’s where we’re stopping. The person who wins the watch should get to open it, right?
Let us know if you have a project to propose using our very short application form. We don’t expect there to be more than a few dozen entries, so your odds of winning are quite high compared to most promotions. But you do have to be at the conference on June 18 and 19, 2015, to receive the watch.
(You can register here: http://qs15.quantifiedself.com/register/)
How to Enter
Use this form to tell us what you want to learn about yourself and how an Apple Watch can help you make these discoveries. We will select an idea that we think will be especially meaningful for everybody to learn from, and we’ll hand over the watch to you on June 18. The conference starts in a couple of weeks, so please act fast!
You can share your ideas with us and the Quantified Self community on Twitter using #QSAppleWatch. We’d love to see what you want to learn!
So much going on this week! We just announced and opened registration for our QS15 Exposition. If you’re in the Bay Area join us on June 20th for an amazing day of demos, talks, and sessions highlighting the very best of the Quantified Self. Readers of What We’re Reading get a special discount. Just click here to get $10 off your ticket price.
We also just announced the Future Normal QS15 Challenge. Thanks to our long time friends and QS sponsors InsideTracker, we’re inviting you to take part in an exciting challenge to develop new ideas and questions about what we can learn from unlocking the information stored in our blood. Click here to learn more and enter to win two free Ultimate Panels!
Now, on with this week’s list!
How accurate is the Apple Watch’s step counter and distance tracking? by Dan Graziano. Great article testing how accurate the new Apple Watch is versus other activity trackers and smart watches. It’s a short(ish) controlled experiment, but the initial results seem positive. Key takeaway: if you have an Apple Watch take the time to calibrate it!
Forget the Fitbit: Can Wearables be Designed for the Developing World? by Jessica Leber. Together with Frog and ARM, UNICEF is launching a “Wearables For Good” challenge to generate ideas and designs for new sensors and technologies that can be used in “resource constrained environments.” Check out the challenge website here: Wearables For Good.
Is the Wearable Health Movement Sustainable by Tim Bajarin. TL;DR: Yes.
With Great Data Comes Great Responsibility by Paz. Ownership, privacy, and longevity. These are the three topics taken on in this long, but well written article on data. If you’re working with or collecting a user’s data this is a must read. And if you’re sending your data to a service, you might as well read it too.
Data is Personal by Frances Angulo. A beautiful post by Frances about using a few simple tools to track anxiety, including a few wonderful visualizations. Once again, I’m astounding by the simple power of using a tool like Google Forms to ask oneself the question that matter.
This is what it is like to be charged by a hippopotamus. by Jer Thorp. Turns out it’s super scary and makes your heart rate jump through the roof. Make sure to click through to see and hear the visualization. It’s well worth it.
From the Forum
This Week on QuantifiedSelf.com
A few notes up top here. First, if you haven’t yet checked it out please give our new QS Radio podcast a listen. We’d love to hear what you think!
Second, our QS15 Conference & Exposition is fast approaching. It’s going to be a wonderful and jam-packed three days of talks, sessions, and amazing demos. Our Early Bird tickets are almost gone. Register before Monday (May 11th) to get $200 off the regular price!
Now, on to the links!
Data (v.) by Jer Thorp. So many people in my network were sharing this over the last few days I had to give it a read, and I’m happy I did. Jer Thorp makes a succinct argument for turning the word “data” from a amorphous blob of a noun into a verb.
By embracing the new verbal form of data, we might better understand its potential for action, and in turn move beyond our own prescribed role as the objects in data sentences.
How Not to Drown in Numbers by Alex Peysakhovich and Seth Stephens-Davidowitz. In this great article, two data scientists make the case for “small data” – the surveys and rich contextual information from open-ended questions.
We are optimists about the potential of data to improve human lives. But the world is incredibly complicated. No one data set, no matter how big, is going to tell us exactly what we need. The new mountains of blunt data sets make human creativity, judgment, intuition and expertise more valuable, not less.
Data, Data, Everywhere, but Who Gets to Interpret It? by Dawn Nafus. We’ve been collaborating with Dawn and her team at Intel for quite a while, and we’ve learned a lot. Reading this wonderful piece lead to even more learning. Dawn uses this article to describe not only the community of individuals who track, but also why, and what happens when it comes time to interpret the data. (You can explore DataSense, the tool Dawn and her team have been working on, here: makesenseofdata.com)
Applying Design Thinking to Protect Research Subjects by Lori Melichar. Lori is a director at the Robert Wood Johnson Foundation and recently did some work related to how institutional review boards (IRBs) function. For those who don’t know, IRBs are the groups/committee that evaluate the benefits and harms of human subjects research. Their process hasn’t changed much in the few decades, but the face of research has. In this short post Lori describes the ideas that came from thinking about how we might re-design the current system.
ResearchKit and the Changing Face of Human Subjects Protections by Avery Avrakotos. As mentioned above, research is changing, and one of the big changes we’re currently seeing is the use of mobile systems like Apple’s ResearchKit. It’s not all sunshine and roses though, the popularity and excitement that goes along with these new methods also means we have to think hard about we protect those who choose to participate.
I measured my brain waves and task performance on caffeine- here’s what I found by John Fawkes. John was interested in how much caffeine he should be ingesting to help with his mental and physical performance. In this post he details some of what did, how he tested himself, and what he learned about how caffeine, and how much of it, affects different aspects of his life.
The Quantified Self & Diabetes by Tom Higham. Tom was diagnosed with diabetes in the late 80s. In this short post he details some of the different apps and tools he uses to “get my HbA1c down to the best levels it’s ever been.”
2014: A Year in New Music by Eric Boam. I had the pleasure of meeting Eric recently in Austin and was blown away by his ongoing music tracking project. I’m excited to see this new report and learn a bit more about what he’s discovered.
Apple Watch Heart Rate Comparison by Brad Larson. Brad used a simple script to export the heart rate values from his Apple Watch and compare it to two different heart rate measurement devices. Above is a comparison with the Mio Alpha, and he also compared is to a more traditional chest strap and found the readings to be “nearly identical.”
From the Forum
This week on QuantifiedSelf.com