Topic Archives: Lab Notes

Comparing Apple Watch and Fitbit One for Step Tracking

ApplevFitbit

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.

Apple Watch
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
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.

Daily Steps

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)

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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.

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Hourly Steps
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.

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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.

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So What?

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.

What’s Next?

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.

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QS15: What Happened?

QS15.wide shot.DH.Medium

Two hundred sessions. Two thousand people. Thirty thousand square feet of exposition space on a San Francisco Pier. Did we really do that?

Over the next weeks we’ll be posting videos, photos, interviews and essays inspired by what happened last week at #QS15. But for the next day or two we’re just going to recover a bit, reflect on how things went, and enjoy the afterglow of spending 3 days with remarkable self-trackers, toolmakers, and scholars who share our interest in self-knowledge through numbers.

Our deepest thanks to to everybody who came to the event and to the hundreds of QS participants who worked with us for more than a year to create the program: to the speakers and session leaders who shared their self-tracking stories and ideas; to our courageous sponsors, whose support was indispensable; to the remarkable architects at The Living, who worked with us tirelessly to design an exposition space that supported conversation and discovery; and, to our friends at e2k events x entertainment, who managed the construction of the exposition from scratch.

QS15.vertical.DH.medium

 

 

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Communities, Climate, Environment, and Health

Today, we are participating in the “Data and Innovation at the Climate-Health Nexus” panel hosted by the White House Office of Science and Technology Policy. When we’ve spoken to people about this meeting the reaction we tend to receive is, “What does Quantified Self have to do with climate change?” It’s a valid question, and one we hope to answer during the panel. Today we wanted to take some time here to talk about why we’re a part of this important conversation.

It’s no surprise that data and data collection is becoming a part of the normal course of our everyday lives, from the data we choose to collect about our health and wellness to the so-called “data exhaust” we’re creating as we use different technological systems. The practice of self-tracking, collected data about yourself to answer interesting questions or help change behavior, has often been linked to narcissism or navel gazing. We know from our experience interacting with a worldwide community of self-trackers that this isn’t the case. Individuals who track, analyze, visualize, and learn from their own data also tend to do something else: share it. You just have to take a peek at our over 750 show&tell videos to see that sharing experiences, techniques, and outcomes is a core component of our work and our community. It’s the reason we hold conferences, support over 100 meetups around the world, and share on this website.

We also know that data is powerful. It can help us understand ourselves, but also the world around us. We’ve been watching closely as new citizen science, one-off projects, and commercial toolmakers have started to incorporate ways to sense and measure the personal and local environment. From air quality sensors integrated into in-home video monitors to crowdsourced DIY environmental sensing devices – we’re beginning to see the power of data for understanding the environment around us, and perhaps more importantly, how the environment plays a role in the health and wellness of our communities. A great example of this comes from our friends at Propeller Health. Recently they announced the launch of AIR Lousiville, a “first-of-its-kind data-driven collaboration among public, private and philanthropic organizations to use digital health technology to improve asthma.” By combining air quality data with geolocated asthma inhaler use data they hope to better understand and positively impact their local environment and reduce the burden of asthma in the Louisville community.

This is just one example of individuals coming together as a community to generate and contribute data about themselves, their environment, and their health to drive a much needed conversation. A conversation about the complex, and important, relationship between the environment and health. We’re hoping to see more and, to that extent, we’re excited to announce that starting at our 2015 Quantified Self Public Health Symposium we’ll be officially launching, in collaboration with with the U.S. Environmental Protection Agency, Personal & Community Environmental Data Challenges, calling on researchers and companies making wearables, sensing, data-visualization, and digital health-tools to join a national conversation about the importance of gaining a more detailed view of environmental impacts on health. This challenge is just one in a great list of commitments from leading companies and institutions designed to advance the Obama Administration’s Climate Data Initiative.

We invite you to learn more about our challenge announcement and our participation in the symposium on Data and Innovation at the Climate-Health Nexus by reading our brief press release here.

You can also learn more about national initiatives, programs, and newly released climate data from the following Fact Sheet: Administration Announces Actions To Protect Communities From The Impacts Of Climate Change

Update: The video from the panel is up and can be found here. The panel actually starts an hour and 19 minutes in to the video.

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Welcome Christopher Snider

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Today we are excited and honored to announce that Christopher Snider has joined QS Labs as our Associate Editor. In the run up to our yearly Quantified Self Public Health Symposium, Christopher will be assisting our efforts to grow our QS Access editorial channel in support of our mission to explore the role of data access for personal and public health benefit.

Christopher comes at self-tracking from a more “old school” perspective, living with type 1 diabetes since 2002. He believes in the power of storytelling, that the stories we share strengthen communities like Quantified Self, and that every story is worth telling no matter how ordinary it may appear to be on the surface. We invite you to welcome Christopher and get to know him a bit by exploring a few of his many online efforts.

A Consequence of Hypoglycemia
Just Talking Podcast
My Diabetes Secret

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Work With Us

Quantified Self Labs is growing and we’re looking for a few good people to join our team. If you share our interest in “self-knowledge through numbers” and enjoy working with a talented and experienced team both in person and remotely, please look at the open job and internships listed below. To apply, send a cover letter and resume/cv to labs@quantifiedself.com. Please include your salary requirements. If you want to work with us, but don’t see a specific job listed, feel free to get in touch. We welcome questions and referrals.

Community & Communications Intern

We’re looking for a great Community & Communications intern to help us engage with our worldwide Quantified Self community through in-person events, regular online communication, and ongoing research activities. This is a great job for somebody who believes in the QS mission, and is eager to gain experience building engagement in Bay Area cultural and technical communities.

Full job description for the Community and Communications Intern

 

About Quantified Self Labs
Quantified Self Labs is a California-based company founded by Gary Wolf and Kevin Kelly that serves the Quantified Self user community worldwide. Our mission is to inspire meaningful discoveries about ourselves and our communities that are grounded in accurate observation and enlivened by a spirit of friendship. We produce international meetings, conferences and expositions, community forums, web content and services, and guides to self-tracking ideas, methods, and tools.  We’re committed to creating a diverse and open work environment.

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Quantified Homescreens

Quantifying-Homescreens

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.

Methods

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.

Screen Shot 2015-02-14 at 9.52.05 AM

If you search for an app this is the data that is returned.

Results

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?

Category Representation

categoryrepresentationThe 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%.

homescreenfreq_WHealth

Clearly Apple Health skews the data so let’s look at homescreen frequency without it in the data set:

homescreenfreq_woHealth

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?

freqHistogram

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):

homescreenfreq_more1percent

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%.

Discussion

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.

And in the spirit of full disclosure, he’s my current homescreen.

This post first appeared on our Medium publication “Notes on Numbers, where we’re starting to share some of our thoughts and editorial experiments. Follow along with us there

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Fitbit vs. Moves: An Exploration of Phone and Wearable Data

Like many people paying attention to the press around Quantified Self, self-tracking, and wearable technology I was intrigued by the many articles that focused on a newly published research letter in the Journal of the American Medical Association. The letter, Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data, authored by Meredith A. Case et al., described a laboratory study that examined a few different smartphone applications and self-tracking devices. Specifically, they tested the accuracy of steps reported by the three different apps: Moves (Galaxy S4 and iPhone 5s),  Withings Health Mate (iPhone 5s), and the Fitbit app (iPhone 5s), three wrist-worn devices: Nike Fuelband, Fitbit Flex, and the Jawbone UP24, and three waist-worn devices: Fitbit One, Fitbit Zip, and the Digi-Walker SW-200. Participants walked on a treadmill at 3.0 MPH for trials of 500 steps and 1500 steps while a research assistant manually counted the actual steps taken. Here’s what they found:

JAMA_PhoneWearables

As the data from this research isn’t available we’re left to rely on the authors description of the data. They state that differences in observed vs device recorded steps counts “ranged from−0.3% to 1.0% for the pedometer and accelerometers [waist], −22.7%to −1.5% for the wearable devices [wrist], and −6.7% to 6.2% for smartphone applications [phone apps].” Overall the authors concluded that devices and smartphone apps were generally accurate for measuring steps. However, much of the press around this study dipped into the realm of sensationalism or attention grabbing headlines, for instance: Science Says FitBit Is a Joke.

Part of our work here at Quantified Self Labs is to encourage and help individuals make sense of their own data. After reading this research letter, or one of the many articles which covered it, you might be asking yourself, “I wonder if my device is accurate?” or “Should I be using a step tracking device or just my phone?” In the interest of helping people make sense of their data so that they can come to their own conclusions I decided to do a quick analysis of my own personal data.

For this analysis I examined the step data derived from my Fibit One and the Moves app I have installed on my iPhone 5. (Important note: the iPhone 5 does not have the M7 or M8 chip present on the 5s and 6/6+, respectively, which natively tracks steps.) I had a sneaking suspicion that my data experience differed from the findings of Case and her colleagues. Specifically, I had a hypothesis that the data from every day tracking via the Moves app would be significantly different than data from my Fitbit One.

Methods

First, I downloaded and exported my daily aggregate Fitbit data for 2014 using our Google Spreadsheets Fitbit script. I then exported my complete Moves app data via their online web portal. To create a daily aggregate step value from my Moves data I collapsed all activities in the summary_2014.csv file for each day. (Side note: We’ll be publishing a series of how-to’s for doing simple data transformations like this soon). This allowed me to create a file with daily aggregate step data from both Moves and my Fitbit for each day of 2014. Unfortunately I did not have my Fitbit for the first few weeks of 2014 so the data represents steps counts for 342 days (1/24/14 to 12/31/14).

Results

I found that my Fitbit One consistently reports a higher number of total steps per day than my Moves app. Overall, for the 342 days I had 689,192 more steps reported by Fitbit than by the Moves app. The descriptive information is included in the table below:

FitbitMoves2

Another way to look at this is by visualizing both data sets across the full time-frame:

Click for interactive version in Google docs.

Click for interactive version in Google docs.

There a few interesting things to point out in this dataset. On two days I have 0 steps reported from my Moves app. One day, Moves was unable to connect with their online service due to me being in an area with little to no cell signal. On the other day my phone was off, probably due to an iOS 8 release and having to reboot my phone a few times.

It is also clear to me that differences in data are related to how I wear my Fitbit and use my phone. For my Fitbit, it is basically on my hip from the time I wake up until the time I go to bed each night. However, my phone isn’t always “on my body” throughout the day. I think this is probably the case for more people.

Since I wear my Fitbit at all times some of the data it captures erroneously is included in the total step count. For instance, for the last few months in this data set I was commuting about 10 miles per day during the week by bike. This data is accurately captured as cycling by Moves, but captured as steps by my Fitbit. Therefore some over-reporting by Fitbit is present in the data.

Conclusion

For my own data I found that the Fitbit reports higher steps on most, if not all days, than the Moves app on my iPhone 5. There are a few caveats with this data and analysis that are worth mentioning. First, this exploration was intended to begin a conversation around the real-world use of activity monitoring apps and devices, and the data they collect. It was not intended as a statement on truth or validity (however I would welcome the help of a volunteer to follow me around with a manual clicker counting all my steps). Second, this analysis was undertaken in part to help you understand that scientists of all types, be it citizen or academic, have the ability to work with their own data in order to come to their own conclusions about what works or doesn’t work for them. Lastly, this analysis was completed very quickly and I am sure that other individuals may have different ideas about how to explore and analyze the data. For this reason I’m posting the daily aggregate values in a open Google Spreadsheet here.

If you’re inspired to analyze your own data in this way we’d love to hear from you. Reach out on twitter or send us an email. We’re listening.

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How to Represent a Year in Numbers

As the calendar turns over to a new year, it’s useful to look back and see what the last 365 days have been all about. Looking back is always easier when you have something to look back on, and, no surprise here, self-tracking is a great help for trying to figure out how things went. That’s what makes this time of year so interesting for someone like myself. I spend a good deal of my time trying to track down real-world examples of people using personal data to explore their lives. Sometimes it’s easy, and sometimes it’s hard finding people willing to expose themselves and their data. However, when late December rolls around, I perk up because this is the time for those yearly reviews.

I’ve spent the last few weeks gathering up some great examples from individuals from all over the world. I hope the following examples inspire you to track something new in 2015 and maybe share it with the QS community in person at a local meetup, at our QS15 Global Conference, or in our social channels. Okay, let’s dive in!

My Year 2014 in Numbers #QuantifiedSelf by Ragnar Heil. A brief, but fun post detailing a year of music, travel, and location checkins.

2014: A Year in Review with iPhone Pedometer Data by Geoffrey Litt. I really enjoyed this very thorough exploration of a year’s worth of pedometer data gathered from the Argus app (iOS). Not satisfied with just looking at his total step count for the year, Geoffrey ran a series of data explorations. Among my favorite, his visualization of his daily rhythms:

GeoffreyLit_StepHours

2014 in Numbers – My Life Behind the Command Line by Quincy Larson. Work, wellness (sleep and running) and reading – it’s all here. I like the idea of tracking what you’ve read by writing one tweet per book.

2014, Quantified by Sarah Gregory. Sarah does an amazing job of capturing and showcasing her 2014 activities in this beautifully simple post. With a balance of pure quantitative information and qualitative insights I found this review especially compelling. (It was also nice to see that she used our How to Download Your Fitbit Data tutorial.)

2014 in Numbers by Donald Noble. Speaking of our Fitbit data download tutorial, here’s a short post about a year’s worth of steps – 4.15 million steps to be precise.

Three Years of Running Data: 1,153km with Nike+ and Mind by Todd Green. As you can see from the title, this post details three years of running, but as a runner myself I always like peeking into other runner’s data. (Todd also has a fantastic post from early 2014 about tracking every penny he spent in 2013.)

Food, Glorious Food by Peter Chambers. A fun post detailing what Peter and his family ate for dinner nearly every day of 2014. One juicy bit – the most common meal? Chili – Peter’s favorite!

2014 in Numbers by Jill Homer. With the help of her Strava app, Jill details her cycling and running from 2014. Click for the numbers, stay for the gorgeous photos.

I wrote every day in 2014: Here’s an #infographic by Jamie Todd Rubin. It’s great fun following Jaime’s blog. He’s relentless on his journey of daily writing (and is quite the active Fitbit user as well). What was 2014 like for his writing? Over 500,000 words – almost enough to take on Tolstoy’s War and Peace. Plus, the visualization is great (click through for the full version):

JamieTR_Writing

2014 Stats by Dan Goldin. Amazing data gathered from a self-designed Google spreadsheet that includes mood, sleep, food, and drink.

Tracking My Life in 2014 by Mike Shea. Mike tracks his life using his own custom designed “Lifetracker app.” This includes his rating on six aspects of his life, daily activities, media, and location. In this post he turns his 8,400 rows of data into elegant visualizations and interesting analysis:

MikeShea_2014

A Year in Review of Personal Data, Should be, well, Personal. By Chris Dancy. As always, Chris has an interesting and entertaining post about his 2014 data and how it compares to 2013.

Tiny Preview By Lillian Karabaic. If her previous work is any indication this year’s review is going to be great. Keep in mind this is just a place holder until the full post is up.

Why #DIYPS N=1 data is significant (and #DIYPS is a year old!) by Dana Lewis. Along with her co-investigator, Scott Leibrand, Dana has been on a journey to better control, understand, and generate knowledge about her type 1 diabetes through augmenting CGM data, devices, and alerts. What started as project to make alarms more clear and useful has morphed into a full on DIY closed loop pancreas. In this post, Dana explores what they’ve learned over the last year of data collection. Truly inspiring work:

My Quantified Self Lessons Learned in 2014 by Paul LaFontaine. In this post Paul recounts what he’s learned from his various QS experiments during 2014, with a focus on stress and hear rate variability. Make sure to also take a peak at his 2014 Review and Gear Review.

2014 Year in Webcam and Screenshots by Stan James. We’ve featured Stan and his great LifeSlice project here on QuantifiedSelf.com before. It’s an ingenious little lifelogging application that tracks your computer use through webcam shots, self-assessments, and screenshots. Check out this post to see a fun representation of his data.

2014 by Kyle McDonald. A very interesting diary of a year.

What 2439 Reports Taught Me by Sam Bew. We highlighted this great post in our What We’re Reading a few weeks ago, but it deserves another mention here. Sam analyzes the data collected from using the Reporter iOS app and writes about what he learned.

2014 Personal Annual Report by Jehiah Czebotar. Coffee, travel, Citi bike trips, software development, laptop battery life, and webcam shots – all included in this amazing page. Presented without narrative or explanation, but meaningful nonetheless. The coffee consumption visualization is not to be missed (click through for the interactive version):JC_Coffee2

2014: My Year in Review by Sachin Monga. A mix of quantitative and qualitative data from Sachin.

My Q4 2014 Data Review by Brandon Corbin. While not a full “year in review” here, I still found this post compelling. Brandon created his own life tracking application, Nomie, and then crunched the numbers from the 60 different things he is tracking. Some great examples of learning from personal data in here.

20140101 – 20141231 (2014). Noah Kalina started taking a photo of himself on January 11, 2000. On the 15th anniversary of his “everyday” project he published his 2014 photos.

Reading
When I was spending late nights searching for variations on “2014”+”data”+”my year in review” I stumbled upon quite a few posts detailing reading stats. Here’s a good selection of what I can only assume is a big genre:

2014 Reading Stats and Data Sheets by Kelly Jensen. A great place to start if you want to track your own reading in 2015. Kelly provides links to three excellent spreadsheet examples.

My Year in Reading by Jon Page. Short and to the point, but a great exploration of format, genre, and authors.

My Year in Reading: 2014 by Annabel Smith.

My Year in Books, Unnecessarily Charted by Jane Bryony Rawson.

Well, that it for now. Special thanks to Beau Gunderson, Steven JonasNicholas Felton (and many others) for sending in links and tips on where to find many of the above mentioned work. If you have a data-driven year in review please reach our via email or twitter and we’ll add it to the list!

If you’re interested in learning about how people generate meaning from their own personal data we invite you to join us for our QS15 Global Conference. It’s a great place to share your experience, learn from others, and get inspired by leading experts in the growing Quantified Self Community. Early bird tickets are on sale. We hope to see you there.

If you’ve made it this far here’s a fun treat: Warby Parker made neat little tool you can use to generate a silly personal annual report.

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The State of Wearables

In our work supporting users and makers of Quantified Self tools we pay close attention to how others talk about trends and markets. In the past year, the most-used catch all term for devices that help us track ourselves has been “wearables.” Now, it’s clear that wearables covers only a fraction of QS practices. Many of the ways people are using numbers, computing, and technology to learn about themselves do not involve wearing anything special. However, the term is useful to us in following relevant research. Below you’ll find links to last year’s best reporting on the wearables market, gathered into a single post for easy reference.

Pew Research Center (January 2013)

The most important work in this space remains the Tracking for Health report from the Pew Research Center, which found that 69% of adults track their health or the health of others, and that 21% of those who track use technology.
Link: QS Analysis of the Pew Research Center Tracking for Health

Forrester, January 2013
A report about the market for fitness wearables “like the Nike+ Fuelband and Jawbone UP” predicts that 8 million US online will be purchasing such devices.
Link: Fitness Wearables — Many Products, Few Customers

Nike, August 2013
Announces in a press release for their “Just Do it” campaign that they have over “18 million global” members of their Nike+ ecosystem.
Link: Nike Redefines “Just Do It” With New Campaign

CCS Insight, October 2013
Surveyed over 700 adults in both the UK and US. They found smart watch adoption was low with only 1.3% of adults (both countries) currently owning and using one and 1.5% no longer using (had owned). For “Wearable Fitness Trackers” they found 2.3% currently owned and used one and 1.2% no longer use it.
Link: User Survey: Wearables UK and US

Endeavor Partners, January 2014 (Part 1)
A survey of “thousands of Americans” completed in late 2013 found that 10% own an activity tracker. Activity trackers were most popular with younger adults (25–34 years) when compared to other age groups. They found that 50% of individuals who have owned an activity tracker no longer use it and one third stopped using it within six months.
Link: Inside Wearables

IDC, March 2014
“This IDC study presents the five-year forecast for the worldwide wearable computing devices market by product category. The worldwide wearable computing devices market (commonly referred to as “wearables”) will reach a total of 19.2 million units in 2014”
Link: Worldwide Wearable Computing Device 2014–2018 Forecast and Analysis

Nielsen, March 2014
A survey conducted in late 2013 of 3,956 adults found that 15% currently “use wearable tech—such as smart watches and fitness bands—in their daily lives.” Device ownership leaned heavily toward “fitness bands” with 61% of wearable technology users reporting ownership. This was followed by smart watches (45%), and mobile health devices (17%).
Link: Are Consumers Really Interested in Wearing Tech on their Sleeves?

Rock Health, June 2014
“While the activity tracker segment has about 1-2% U.S. penetration, wearables overall are expected to grow significantly”
Link: The Future of Biosensing Wearables

Endeavor Partners, July 2014 (Part 2)
As of June 2014, they found that the percentage of adult consumers that still wear and use their activity tracker has improved with 88% still wearing it after three months, 77% after 3–6 months, 66% after 6–13 months, and 65% after a year. They also found that majority of respondents (1,024 of 1,700 surveyed) reported obtaining their divide within the last six months
Inside Wearables – Part 2

PWC, October 2014
“21% of American adults already own a wearable device” They also found in their survey of 1,000 adults that 2% no longer use it, 2% wear it a few times per month, 7% wear it a few times a week, and 10% use it everyday.
Links: The Wearable FutureHealth Wearables: Early Days

Acquity Group, November 2014
A survey of 2,000 US consumers found that 13% plan to purchase as wearable fitness device with in the next year, and 33% within the next five years. Additionally, smart clothing is on slower trajectory with 3% planning to purchase in the next year and 14% in the next five years.
Link: The Internet of Things: The Future of Consumer Adoption

Gartner, November 2014
Gartner forecasts that worldwide shipments for “wearable electronic devices for fitness” will reach 68 million units in 2015, a slight decrease from the forecasts from 2014 and 2013 (70.2 and 73 million units, respectively). Additionally, according to Angela McIntyre, Gartner has found that “20 million online adults in the U.S. own and use a fitness wristband or other activity monitor and that 5.7% of online adults in the U.S. own and use a fitness wristband.”
Link: Forecast: Wearable Electronic Devices for Fitness, Worldwide, 2014

Berg Insight, December 2014
This is a market research report that states “fitness and activity trackers is the largest product category” and shipments are forecasted to reach 42 million units in 2019. Smart watches are predicted to reach 90 million units.
Link: Connected Wearables

Accenture, January 2015
Using a survey of 24,000 individuals across 24 countries Accenture found that 8% currently own a “Fitness Wearable”. Furthermore, they found that 12% plan to purchase in the next year, 17% in the next 1–3 years, and 11% in the next 2–5 years.
Link: Engaging the Digital Consumer in the New Connected World

Global Web Index, January 2015
In their Q3 2014 Device Summary report, GWI labeled wearable devices as “highly niche” after finding that 7% of US online adults own a “smart wristband” (Nike Fuelband, Jawbone Up, Adidas miCoach) and 9% own a smart watch.
Link: GWI Device Summary – Q3 2014

Rocket Fuel, January 2015
A survey of 1,262 US adult consumers conducted in December of 2014 found that 31% currently use a QS tool to track their health and fitness. This includes apps, devices, and websites. More specifically, 16% use a wearable device and 29% use a website or app not associated with a wearable device to track health and fitness.
Link: “Quantified Self” Digital Tools
 

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How To Access & Export MyFitnessPal Data

MFP

MyFitnessPal is one of the leading dietary tracking tools, currently used by tens of millions of people all around the world to better track and understand the foods they consume every day. Their mobile apps and online tools allow individuals to enter foods and keep track of their micro- and macro-nutrient consumption, connect additional devices such as fitness trackers, and connect with their community – all in the name of weight management. However, there is no natively available method for easily accessing your dietary data for personal analysis, visualization, or storage.

With a bit of digging in the MyFitnessPal help section we can see that they have no official support for data export. However, they mention the ability to print reports and save PDF files that contain your historical data. While better than some services, a PDF document is far from easy to use when you’re trying to make your own charts or take a deeper look into your data.

We spent some time combing the web for examples of MyFitnessPal data export solutions over the last few days. We hope that some of these are useful to you in your ongoing self-tracking experiences.

Browser Extensions/Bookmarks

MyFitnessPal Data Downloader: This extension allows you to directly download a CSV report from your Food Report page. (Chrome only)

MyFitnessPal Data Export: This extension is tied to another website, FoodFastFit.com. If you install the extension, it will redirect you back to that site where your data is displayed and you can download the CSV file. (Chrome only)

ExportMFP: A simple bookmark that will open a text area with comma-separated values for weight and calories, which you can copy/paste into your data editor of choice.

MyFitnessPal Reports: A bookmarklet that allows you to generates more detailed graphs and reports.

 Web Apps/Tools

MyFitnessPal Analyser: Accesses your diet and weight data. It requires you to input your password so be careful.

Export MyFitnessPal Data to CSV: Simple web tool for exporting your data.

FreeMyDiary: A recently developed tool for exporting your food diary data.

Technical Solutions

MyFitnessPal Data Access via Python: If you’re comfortable working with the Python language, this might be for you. Developed by Adam Coddington, it allows access to your MyFitnessPal data programmatically

MFP Extractor and Trend Watcher: An Excel Macro, developed by a MyFitnessPal user, that exports your dietary and weight data into Excel. This will only work for Windows users.

Access MyFitnessPal Data in R: If you’re familiar with R, then this might work for you.

QS Access + Apple HealthKit

If you’re an iPhone user, you can connect MyFitnessPal to Apple’s HealthKit app to view your MyFitnessPal data alongside other data you’re collecting. You can also easily export the data from your Health app using our QS Access app. Data is available in hourly and daily breakdowns, and you should be able to export any data type MyFitnessPal is collecting to HealthKit.

As always, we’re interested to hear your stories and learn about your experiences with exploring your data. Feel free to leave comments here or get in touch via twitter or email.

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