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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Fabio Ricardo dos Santos on Using Relationship Data to Navigate a Chaotic Life

I’m fascinated by self-tracking projects that focus on things that are hard to quantify.

Such is the case here. Fabio Ricardo dos Santos is gregarious and likes to be around people. A lot of people. But he had a nagging sense that something was out of balance.

To better understand why, he began to track his relationships and interactions. He soon found that out of the people that he knows, only about 14% are what he considered to be important relationships and that they made up 34% of his interactions. He felt that this number was too low and it spurred him to spend more time with that important 14%.

But he didn’t just track his time with people and the number of interactions. He expanded his system to include the quality of his relationships and interactions. He found that this made him focus on face-to-face interactions and video chats over emails and texts.

The other side of this, though, is that when you have a system where you rate and rank your relationships, how does it not seem like you are rating people? What are the implications of doing so?

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Meetups This Week

There are an incredible 12 QS meetups getting together this week in seven different countries.

Both Toronto and St. Louis will discuss how to use self-tracking tools to keep New Year’s resolutions. In Indianapolis, people will talk about their recently acquired tracking devices from the holidays. Geneva will be doing a review of 2014, where attendees will mention their pick for the most interesting QS thing that occurred. Budapest will feature a couple toolmaker talks in addition to their show&tells, and Portland will be getting together for a workgroup session to make progress on their personal data projects.

QS meetups take many different forms. To see what the meetup in your area is like, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own!

If you organize a QS meetup, please post pictures of your event to the Meetup website. We love seeing them. Have a good week!

Monday, January 19
Oslo, Norway
Toronto, Canada

Tuesday, January 20
Miami, Florida
St. Louis, Missouri

Wednesday, January 21
Budapest, Hungary
Dallas/Fort Worth
Geneva, Switzerland
Madrid, Spain

Thursday, January 22
Grand Rapids, Michigan
Ljubljana, Slovenia
Portland, Oregon

Saturday, January 24
Indianapolis, Indiana

Here’s an image from last week’s meetups. The group in Dallas got together for an informal chat over dinner and one of the members tried out an HEG (hemoencephalography) headband, a device that measures blood flow in the prefrontal cortex.

DFWMeetup

 

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What We Are Reading

Enjoy these articles, examples, and visualizations!

Articles

OpenNotes: ’This is not a software package, this is a movement’ by Mike Milliard. I’ve been following the OpenNotes project for the last few years. There is probably no better source of meaningful personal data than a medical record and it’s been interesting to see how this innovative project has spread from a small trial in 2010 to millions of patients. This interview with Tom Delbanco, co-director of the OpenNotes project, is a great place to learn more about this innovative work.

Beyond Self-Tracking for Health – Quantified Self by Deb Wells. It was nice to see this flattering piece about the Quantified Self movement show up on the HIMSS website. For those of you looking to connect our work and the broader QS community with trends in healthcare and health IT you should start here.

So Much Data! How to Share the Wealth for Healthier Communities by Alonzo L. Plough. A great review of the new book, What Counts: Harnessing Data for America’s Communities, published by the Federal Reserve Bank of San Francisco and the Urban Institute. The book is available to read online and in pdf format.

The Ultimate Guide to Sleep Tracking by Jeff Mann. A great place to start if you’re interested in tracking sleep or just want to learn more about sleep tracking in general.

What RunKeeper data tells us about travel behavior by Eric Fischer. We linked to the recent collaboration between Runkeeper and Mapbox that resulted in an amazing render of 1.5 million activities a few weeks ago. The folks over at Mapbox aren’t just satisfied with making gorgeous maps though. In this post, Eric, a data artist and software developer at Mapbox dives into the data to see what questions he can answer.

General Wellness: Policy for Low Risk Devices – Draft Guidance for Industry and Food and Drug Administration Staff . On Friday, January 16, 2015, the Food and Drug Administration released a draft of their current approach to regulating “low risk products that promote a healthy lifestyle.” These guidelines point to a stance that will allow many of the typical self-tracking tools currently in use today to remain outside the regulations normally associated with medical devices. (A quick overview of this document is also available from our friends at MobiHealthNews)

Show&Tell

monkeyglandin_CaffeineSleep The Great Caffeine Conundrum. A wonderfully thorough post about using the scientific process, statistics, and self-tracking data (Jawbone UP) to answer a seemingly simple question, “Does eliminating caffeine consumption help me sleep better?”

 

ShrivatsIyer_BooksFour Years of Quantified Reading by Shrivats Iyer. Shrivels has been tracking his reading for the last four years. In this post he explains his process and some of the data he’s collected, with a special emphasis on what he’s learned from his 2014 reading behavior.

Visualizations

ChandlerAbraham_MessagesPretty Colors by Chanlder Abraham. Chandler spent his holiday break exploring his messaging history and creating some amazing visualizations. Above you see a representation of his messaging history with the 25 most contacted people since he’s began collecting data in 2007.

 

HR_proposal2Heart Rate During Marriage Proposal by Reddit user ao11112. Inspired by another similar project, this ingenious individual convinced his now fiancé to wear a hear rate monitor during a hike. Unbeknownst to her, he also proposed. This is her annotated heart rate profile.

Help CDC Visualize Vital Statistics by Paula A. Braun. The CDC has a new project based on the idea that better visualization can make the data they have more impactful. If you’re a data visualizer or design consider downloading the CDC Vital Statistics Data and joining #vitalstatsviz.

From the Forum

Fits & Starts
File format for centralized storage of Quantified Self data
Embedding/Sending data to Website
Data Collection & Analysis

 

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QS Access: New Data Privacy Legislation on the Horizon

“If we’re going to be connected, then we need to be protected. As Americans, we shouldn’t have to forfeit our basic privacy when we go online to do our business”.  - U.S. President Barak Obama

In a speech this week at the the Federal Trade Commission President Obama spoke about new measures he hopes to bring forward in 2015 focused on consumer and student data privacy. In his speech he outlined four key focus areas his administration will be working on in 2015:

  • Providing a federal standard for reporting data breaches. This will establish a 30-day notification requirement for companies if customer information has been exposed.
  • Signing up major financial institutions to agree to release credit score information to their customers free of charge.
  • Protection of data gathered by companies operating in the education sector. Companies will be prevented from selling student data to third parties.
  • The introduction of a Consumer Privacy Bill of Rights.

It remains to be seen how these initiatives will affect the companies currently collecting and storing personal self-tracking information. We’ll follow along closely in our Access channel so stay tuned.

Speech Transcript

Fact Sheet: Safeguarding American Consumers & Families

Quantified Self Labs is dedicated to the idea that data access matters. Moving forward, we’re going to be exploring different aspects of how data access affects our personal and public lives. Stay tuned to our QS Access channel for more news, thoughts, and insights.

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QS Access: Exporting Uber Trip Data?

Quantified Self Labs is dedicated to the idea that data access matters. Moving forward, we’re going to be exploring different aspects of how data access affects our personal and public lives. Stay tuned to our QS Access channel for more news, thoughts, and insights.

UberTrips

On January 13th Uber, a wildly popular and often scrutinized ride share company, announced they have entered into an agreement with the City of Boston to share anonymized data generated by users of the service. This is the first partnership between Uber and a local government body, but points to the ability to potentially partner with cities that want to take a peak at the vast amount of data about when and where people are traveling within their municipality. Our first reaction to this was to explore if Uber has provided any method for it’s own users to access and export their trip data. Surely if they can able to export and pass along data to a third party, they can pass that data to their own users?

In our exploration of the mobile and web user platforms we found that Uber currently does not offer users with an easy way to access their data. As an Uber customer, you are provided with email receipts of your trips that include travel information, a route of the ride, and cost. This information is also available through their online user account page. However, it is not exportable and accessible in a method that allows individuals to store information in a consistent and machine readable format (such as a csv file). In our search for methods to assist in exporting Uber ride data, I stumbled upon this data scraper on Github developed by Josh Hunt. It’s useful to know that Uber has a standard no scraping clause in in it’s Terms of Service, but individual users accessing their own data for their own reasons is probably not what these clauses are meant to protect.

Aside from data access issues there is of course open questions about how Uber will implement privacy protections governing sensitive user data. Of course, Uber is not without fault in this space. The now infamous blog post pointing to their ability to track one-night stands (archived here) was enough for some users to question ethical standards within Uber. In their announcement, Uber touched on this issue by stating that they will provide some privacy protections by only offering anonymized aggregated data to third party partners. Protecting user privacy through data aggregation and anonymization is a step in the right direction, but there remain these open issues around data access for users. Uber and the cities they partner with will learn a lot about how we travel, but the partnership between Uber and their users could be improved by helping users (myself included) understand their own data and behavior by allowing easier access to the data we contribute when we use the service.

We’re interested to hear from our readers about their experiences using the above mentioned tool, or similar tools to access and export their Uber trip data. Please let us know. We’ve also reached out to Uber for comment.

UPDATE

I reached out to Uber Support over Twitter and received the following response:

“Unfortunately this is not currently a feature, however we’re always looking to improve and I’ll pass your suggestion along! *NM” (link)

Source: Driving Solutions to Build Smarter Cities (Uber)

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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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Meetups This Week

The spirit of reflection that accompanies a new year is still present as eight QS meetups in four countries get together this week.  Munich, in particular, will be discussing how to use QS tools to help track and reach goals for 2015.

A common resolution is to lose weight, and weight management is the focus of the event in Stockholm today. Their show&tell talks include one person who, while on parental leave, lost 6 kilos by playing games and the story of a woman who used running and self-tracking to lose half her bodyweight.

One thing that I love about collecting the upcoming meetups is seeing the different formats that local organizers are adopting to serve their community. In England, the group in Cambridge are starting the year with an exciting launch event to engage their community. In addition to the classic Show&Tell talk, they are experimenting with their format by having a talk focused on QS technology, as well as, a “researchy” talk to tap into the academia presence in their membership. In the other direction, the Dallas group are keeping things informal and casual with a chat over dinner.

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own! If you organize a QS meetup, please post pictures of your event to the Meetup website. We love  seeing them!

Monday, January 12
London, England
Stockholm, Sweden

Tuesday, January 13
Boston, Massachusetts
Dallas/Fort Worth
Lansing, Michigan
Munich, Germany

Wednesday, January 14
Cambridge, England

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What We Are Reading

Enjoy the first What We’re Reading post of 2015!

Articles
Wearable Devices as Facilitators, Not Drivers of Health Behavior Change by Mitesh Patel, David Asch, and Kevin Volpp. This opinion piece seeks to describe the reasons why currently available health wearables are not “bridging the gap” between tracking and changing behavior.

Big Data Not A Cure-All in Medicine by Amy Standen. This story, which first appeared on All Things Considered, sheds some light on concrete examples of how data can be used to treat medical conditions, and the current roadblocks in place.

The Smart, Angry Home by Emily Anthes. Smarter homes, smarter grids, and more data about our energy use is undoubtably on the horizon. In this piece, Emily Anthes describes how providing data back to individuals about energy use, especially in multi-tenant dwellings, can be a source of tension.

Thoughts on the Quantified Self by Kevin Ripka. I really enjoyed this short post about the author’s reactions to Quantified Self. I was especially interested in his description of the “Four Types of Projects” that he believes one can undertake when self-tracking.

Show&Tell
SamBevReporterWhat 2439 Reports Taught Me by Sam Bev. Sam has been using the ReporterApp over the last year. Since he began he’s amassed over 2400 reports, and those have provided some interesting insights into his own life. Read this great post and make sure to visit his website where his reports are made visible.

Seen, Read 2014 by Steven Soderbergh. Steven Soderbergh is an acclaimed writer and director, who has been tracking his media consumption for a few years. This post chronicles the books, plays, TV, movies, and records he consumed during 2014.

Visualizations
MA_MapPics
Map Your Trips Using Pics From Your Phone by Marco Altini. In this how-to post Marco lays out a fun method for tracking travel and location using only the photos you take with your smart phone.

From the Forum
Data collection and analysis
Separation of cloud vs local storage?
Basis Peak
Timer/logger/tracker–what kind of gadget am I looking for?
What to do with GSR and skin temp data?

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