Topic Archives: Lab Notes

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.

Posted in Lab Notes | Tagged , , , , | 1 Comment

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
 

Posted in Lab Notes | Tagged , , , , , | 1 Comment

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.

Posted in Discussions, Lab Notes | Tagged , , , , , | Leave a comment

QS Access App Visualization Showcase

Two weeks ago we announced the release of the QS Access App so you could access your HealthKit data in tabular format for personal exploration, visualization, and analysis. In that short period of time, we’ve seen a good number of downloads and positive feedback.



We know from our experiences hosting in-person and online communication about personal data that seeing real-world examples of what is possible is what inspires people to engage and ask questions of their own data. With that in mind we’re excited to announce our QS Access Visualization Showcase.

We are looking to you, our amazing community of trackers, designers, and visualizers, to show use what you can do with data gathered from using the QS Access App. Make heatmaps in D3, complete analyses and visualizations in Wizard, or just make meaningful charts in Excel. If you’re visualizing your QS Access data we want to see it.

We also know that data visualization design and creation is not trivial work. To support the community and help expose the visualization work we’ll be awarding free tickets to our QS15 Global Conference & Exposition to individuals who use QS Access to create unique and interesting visualizations. We’ve earmarked two tickets (a $700 value) for outstanding work.  If you’re selected, we’ll also work with you to showcase your work at the QS15 Conference and Exposition so other community members and attendees can explore and learn from their own data.

Submit your visualizations here.

 

If you’re in the Bay Area come to our QS Meetup on November 11th at the Berkeley Skydeck. You can showcase your visualization and tell our community what you’ve learned from accessing and visualizing your data.

Example Visualizations

HealthKit is still new and the number of apps that integrate with it is growing by the day. At QS Labs we’ve done a bit of work making simple visualizations that are meaningful to us.

Steps and Sedentary Activity

Gary3334

Gary19541

Gary has an iPhone 5s which has native step tracking. We used the QS Access app to export his hourly step totals and made these simple line graphs in Excel. You can read more about what he learned from these simple data visualizations here.

How Much Do I Run?

Ernesto is an avid runner and enjoys running along the quiet trails in Los Angeles. He was interested to see how often he actually runs and if there’s any pattern to his running. Using a well-designed D3 template he was able to make a calendar heatmatp of his running distance.

 

Screen Shot 2014-10-17 at 12.39.49 PM

Example Data

If you don’t have any HealthKit data to work with, or just want to play with some example data we’ve created a few files that you can use as examples. Download the files below from our GitHub account and make sure to read the documentation to understand where the data is coming from.  Descriptions of the data files and sources are available in our QS Access Data Examples repo on Github.

Example Data:
ER_HealthKit_DailyData
ER_HealthKit_HourlyData

Posted in Conference, Lab Notes | Tagged , , , , , | Leave a comment

QS Access App: See your HealthKit Data in a Table

On Wednesday this week we learned that the QS Access app we submitted to the Apple store was approved. This means you can download the QS Access app on iTunes. We hope you’ll find it useful. Our app is a very simple tool for accessing HealthKit data in a table so that you can explore it using Numbers, Excel, R, or any other CSV compatible tool.

It is still early days for HealthKit, but my conversations with toolmakers at Quantified Self events convinces me that there will be many device and software makers that integrate with Apple’s platform for collecting and analyzing personal data. I hope this will allow more people to learn from their own data by reflecting on changes over time and by combining multiple data streams – such as activity, sleep, and nutrition – into a single visualization for comparison.

To give you your HealthKit data in tabular format, we’ve had to simplify it. QS Access shows your data in either “hourly” or “daily” chunks.  These won’t be appropriate for all uses, but many interesting questions can be asked of data that is presented as a time series using hourly and daily values. This is just a starting point, and we’re looking forward to making it do more based on your feedback.

We very much hope that if you learn something from your data using QS Access, you’ll share your project by participating in a Quantified Self Show&Tell meetup and by joining us at QS15 Conference and Exposition next year in San Francisco. Suggestions about the app itself and interesting examples of usage can be shared with us directly by emailing us: labs@quantifiedself.com,

Read a short example of using QS Access to look at my activity data.
Find Support for QS Access in the QS Forum.

The QS Access App was authored by our long time QS Labs friend and collaborator Robin Barooah.

Posted in Lab Notes | Tagged , , , | 2 Comments

How to Download Minute-by-Minute Fitbit Data

IntradayDataChart

Earlier this week we posted an update to our How To instructions for downloading your Fitbit data to Google Spreadsheets. This has been one of our most popular posts over the past few years. One of the most common requests we’ve received is to publish a guide to help people download and store their minute-by-minute level step and activity data. Today we’re happy to finally get that up.

The ability to access and download the minute-by-minute level (what Fitbit calls “intraday”) data requires one more step than what we’ve covered previously for downloading your daily aggregate data. Access to the intraday data is restricted to individuals and developers with access to the “Partner API.” In order to use the Partner API you must email the API team at Fitbit to request access and let them know what you intend to do with that data. Please note that they appear to encourage and welcome these type of requests. From their developer documentation:

Fitbit is very supportive of non-profit research and personal projects. Commercial applications require additional review and are subject to additional requirements. To request access, email api at fitbit.com.

In the video and instructions below I’ll walk you through setting up and using the Intraday Script to access and download your minute-by-minute Fitbit Data.

  1. Set up your FitBit Developer account and register an app.
    • Go to dev.fitbit.com and sign in using your FitBit credentials.
    • Click on the “Register an App” at the top right corner of the page.
    • Fill in your application information. You can call it whatever you want.
    • Make sure to click “Browser” for the Application Type and “Read Only” for the Default Access type fields.
    • Read the terms of service and if you agree check the box and click “Register.”
  2. Request Access to the Partner API
    • Email the API team at Fitbit
    • They should email you back within a day or two with  response
  3. Copy the API keys for the app you registered in Step 1
    • Go to dev.fitbit.com and sign in using your FitBit credentials.
    • Click on “Manage My Apps” at the top right corner of the page
    • Click on the app you created in Step 1
    • Copy the Consumer Key.
    • Copy the Consumer Secret.
    • You can save these to a text file, but they are also available anytime you return to dev.fitbit.com by clicking on the “Manage my Apps” tab.
  4. Set up your Google spreadsheet and script
    • Open your Google Drive
    • Create a new google spreadsheet.
    • Go to Tools->Script editor
    • Download this script, copy it’s contents, and paste into the script editor window. Make sure to delete all text in the editor before pasting. You can then follow along with the instructions below.
    • Select “renderConfigurationDialog” in the Run drop down menu. Click run (the right facing triangle).
    • Authorize the script to interact with your spreadsheet.
    • Navigate to the spreadsheet. You will see an open a dialog box in your spreadsheet.
    • In that dialog paste the Consumer Key and Consumer Secret that you copied from your application on dev.fitbit.com. Click “Save”
    • Navigate back to the scrip editor window.
    • Select “authorize” in the Run drop down menu. Click run (the right facing triangle).
    • Select “authorize” in the Run drop down menu. This will open a dialog box in your spreadsheet. Click yes.
    • A new browser window will open and ask you to authorize the application to look at your Fitbit data. Click allow to authorize the spreadsheet script.
  5. Download your Fitbit Data
    • Go back to your script editor window.
    • Edit the DateBegin and DateEnd variables with the date period you’d like to download. Remember, this script will only allow 3 to 4 days to be downloaded at a time. 
    • Select “refreshTimeSeries” in the Run drop down menu. Click run (the right facing triangle).
    • Your data should be populating the spreadsheet!

If you’re a developer or have scripting skills we welcome your help improving this intraday data script. Feel free to check out the repo on Github!

Posted in Lab Notes, Personal Projects | Tagged , , , , , , , | 11 Comments

Downloading Fitbit Data: Update

We’re posting a quick note today to let you know that we’ve updated our “How To Download Your Fitbit Data” post. It now included separate instructions for both the old and new versions of Google Spreadsheets. This is just the first in a series of planned updates. We hope to post additional updates to allow you to have deeper access to your Fitbit data including, heart rate, blood pressure, and daily goal data.

If you’re using this how-to we’d love to hear from you! Are you learning something new? Making interesting data visualizations? Discussing the data with your health care team? Let us know. You can email us or post here in the comments.

ERFitbit_092214

Click to view the interactive version.

Posted in Lab Notes | Tagged , , , | 2 Comments

Welcome Steven Jonas

skjonas2

Today we are excited and honored to announce that Steven Jonas has joined QS Labs as our Senior Editor/Information Architect. As has been the case with previous additions to QS Labs, we welcome Steven as a friend and fellow community member. Steven serves as a co-organizer of the Portland QS meetup group, and has participated as our speaker coordinator for our past two conferences.

In addition to his work supporting our global QS community, Steven is an active self-tracker, having engaged in many different projects. We’ve been delighted to highlight a few of those here on the QS website. We invite you to welcome Steven and get to know him a bit by exploring the posts linked below.

Memorizing my Daybook
Tracking Stress
Stress Out Loud

Photo by Mark Krynsky

Posted in Lab Notes | Tagged , , , | 1 Comment

Access Matters

Someday, you will have a question about yourself that impels you to take a look at some of your own data. It may be data about your activity, your spending at the grocery store, what medicines you’ve taken, where you’ve driven your car. And when you go to access your data, to analyze it or share it with somebody who can help you think about it, you’ll discover…

You can’t.

Your data, which you may have been collecting for months or years using some app or service that you found affordable, appealing, and useful, will be locked up inside this service and inaccessible to any further questions you want to ask it. You have no legal right to this data. Nor is there even an informal ethical consensus in favor of offering ordinary users access to their data. In many cases, commercial tools for self-tracking and self-measurement manifest an almost complete disinterest in access, as demonstrated by a lack of data export capabilities, hidden or buried methods for obtaining access, or no mention of data access rights or opportunities in the terms of service and privacy policy.

Now is the time to work hard to insure that the data we collect about ourselves using any kind of commercial, noncommercial, medical, or social service ought to be accessible to ourselves, as well as to our families, caregivers, and collaborators, in common formats using convenient protocols. In service to this aim, we’ve decided to work on a campaign for access, dedicated to helping people who are seeking access to their data by telling their stories and organizing in their support. Although QS Labs is a very small organization, we hope that our contribution, combined with the work of many others, will eventually make data access an acknowledged right.

The inspiration for this work comes from the pioneering self-trackers and access advocates who joined us last April in San Diego for a “QS Public Health Symposium.” Thanks to funding support from the Robert Wood Johnson Foundation, and program support from the US Department of Health And Human Services, Office of the CTO, and The Qualcomm Institute at Calit2, we convened 100 researchers, QS toolmakers, policy makers, and science leaders to discuss how to improve access to self-collected data for personal and public benefit.  During our year-long investigation leading up to the meeting, we learned to see the connection between data access and public health research in a new light.

If yesterday’s research subjects were production factors in a scientist’s workshop; and if today’s participants are – ideally – fully informed volunteers with interests worthy of protection; then, the spread of self-tracking tools and practices opens the possibility of a new type of relationship in which research participants contribute valuable craft knowledge, vital personal questions, and intellectual leadership along with their data.

We have shared our lessons from this symposium in a full, in-depth report from the symposium, including links to videos of all the talks, and a list of attendees. We hope you find it useful. In particular, we hope you will share your own access story. Have you tried to use your personal data for personal reasons and faced access barriers? We want to hear about it.

You can tweet using the hashtag #qsaccess, send an email to labs@quantifiedself.com, or post to your own blog and send us a link. We want to hear from you.

The key finding in our report is that the solution to access to self-collected data for personal and public benefit hinges on individual access to our own data. The ability to download, copy, transfer, and store our own data allows us to initiate collaboration with peers, caregivers, and researchers on a voluntary and equitable basis. We recognize that access means more than merely “having a copy” of our data. Skills, resources, and access to knowledge are also important. But without individual access, we can’t even begin. Let’s get started now.

An extract from the QSPH symposium report

[A]ccess means more than simply being able to acquire a copy of relevant data sets. The purpose of access to data is to learn. When researchers and self-trackers think about self-collected data, they interpret access to mean “Can the data be used in my own context?” Self-collected data will change public health research because it ties science to the personal context in which the data originates. Public health research will change self-tracking practices by connecting personal questions to civic concerns and by offering novel techniques of analysis and understanding. Researchers using self-collected data, and self-trackers collaborating with researchers, are engaged in a new kind of skillful practice that blurs the line between scientists and participants… and improving access to self-collected data for personal and public benefit means broadly advancing this practice.

Download the QSPH Report here.

Posted in Discussions, Lab Notes | Tagged , , , , , | 1 Comment

A Code of Conduct

Quantified Self Labs is wholeheartedly dedicated to creating conferences and events that are safe and comfortable for everyone involved. This means providing a harassment-free experience for every participant, regardless of gender identity and expression, sexual orientation, ability, ethnicity, socioeconomic status, physical appearance, and beliefs.

Providing this experience for our members requires a concrete, visible commitment to the community. We introduced our first anti-harassment policy for the Quantified Self Global Conference in 2013. Quantified Self Boston has followed suit and introduced a Code of Conduct for all their events in April, 2014. Our Bay Area QS meetup has recently implemented this Code of Conduct as well. Meetup members from the Quantified Self Community have also started three women’s* meetups (QSXX San Francisco, QSXX Boston, QSXX New York City) to promote inclusion and safe spaces for sharing.

If you are involved in Quantified Self as an organizer or meetup member we encourage you to read our anti-harassment policy and the QS Boston Code of Conduct and use them as a basis for your own work in creating events that are welcoming to all.

Editor’s Note: Thanks to our friends Amelia Greenhall and Maggie Delano for their leadership. For more analysis and practical resources in fighting sexism in technology culture, see: The Ada Initiative.

Resources / further reading:
Code of Conduct 101 + FAQ
Why Women Need Women-Only Networks
First QSXX blog post
QSXX Boston Meetup Recap

Women’s* meetups:
QSXX San Francisco
QSXX Boston
QSXX New York City

* All women and people who identify or have identified as significantly non-male are welcome at QSXX, including those with non-binary and/or trans* gender identities.

Posted in Lab Notes | Tagged , , | 1 Comment