Tag Archives: Health

What We're Reading

Enjoy this week’s list of articles, links, show&tells, and visualizations.

Articles
Personal Health Data: Five Key Lessons for Better Health by Patti Brennan and Stephen J. Downs. A fantastic post by two great thinkers in the world of personal health and data. They outline five key challenges that must be addressed in order to have meaningful use of personal health data.

It’s Time for Open Data on Open Data by Luke Fretwell. A short but meaningful post here. With all the clamor for more government open data portals it’s time to start exploring how they’re actually being used and what can be done to improve them.

The NFL Gets Quantified Intelligence, Courtesy Of Shoulder Pad-Mounted Motion Trackers by Darrell Etherington. As a sports fan and spouse of someone who works in sports media production I am fascinated by how the world of personal data is quickly colliding with professional athletics. We’ve long looked towards athletes for inspiration and examples of how data can be used to understand and improve and I’m very interested to see how the NFL will make use of this data. Maybe we’ll see more sabermetric-like player and team analysis?

Show&Tell
Heart Rate Variability While Giving a Public Speech by Pau LaFontaine. Paul gave a show&tell talk at a recent Bay Area QS meetup and tracked his heart rate variability. This post explains his data, and what he learned about the stress involved with public speaking. Be on the lookout soon for his show&tell talk video.

Chronic Diease and Self-Tracking – Part 1 by Sara Riggare. Sara is a longtime contributor in the Quantified Self community, having spoken at each of our three QS Europe Conferences. In this post she explains her new exploration of her resting heart rate and poses some interesting questions. We’d love to have you help her out!

Raspberry Pi Sleep Lab How-To by Nick Alexander. Nick was bothered by a common nightly occurrence, kicking off his covers in the middle of the night. Like any enterprising technologist, he enlisted his technical expertise to help examine this problem. This post is an amazingly detailed “How To” for building and setting up your own personal sleep monitoring tool complete with video, environmental information, sound, and sleep data.

Visualizations
This week I’ve been exploring how people are making using physical data visualizations. During some research I found a great resource, the List of Physical Visualizations. A few images below are from that great list, be sure to spend some time exploring the many different examples and then reading the excellent research paper linked below.

lego_timetrack_workweek

cyl3

GraphConfB

keyboard351-597x360

Evaluating the Efficiency of Physical Visualizations by Yvonne Jansen, Pierre Dragicevic, and Jean-Daneil Fekete. The first empirical study of the effectiveness of physical visualizations for conveying information. Using 3D bar charts as a primary example, the authors were abel to show that physical visualizations are more effective than their digital on-screen counterparts for some information retrieval tasks.

From the Forum

Data Aggregation
Idea for a Life Tracker Application
How can I log my teeth?
Home Potassium Testing

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Conference Preview: Discussing Families & Self-Tracking

Last June, the Pew Internet Research Project released a report entitled, Family Caregivers are Wired for Health. The authors - Susannah Fox, Maeve Duggan and Kristen Purcell - found that 40% of Americans are caring for an adult or child with significant health issues. Of special interest to us: “When controlling for age, income, education, ethnicity, and good overall health, caregivers are more likely than other adults to… track their own weight, diet, exercise routine, or other health indicator.” (Emphasis added.)

Our Bay Area co-organizer Rajiv Mehta was a community peer reviewer of the survey. At the upcoming 2014 Quantified Self Europe Conference, Rajiv will co-lead a breakout with Dawn Nafus of Intel Labs on the role of families in self-tracking practice. If you are involved in or curious about family caregiving, you’re invited to come and take part in what will be a great discussion.

The QS Europe Conference is just a few weeks away; come if you can!

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Fit Fifties, Sound Sixties: Maria Benet on Active Aging

Maria Benet began tracking her activity a few years ago as a way to lose weight and take control of her health. What started with a simple pedometer and a few custom Access databases has morphed into a multi-year tracking project that includes news apps and tools. Her progress and data has even spurred her on to new experiences and athletic endeavors. Watch her talk, filmed at the Bay Area QS meetup group, and read the transcript below.

(Editors Note: We’re excited to have Maria attending the 2014 Quantified Self Europe Conference where we hope to hear an updated version of this wonderful talk.)

What did I do?

Hi, my name is Maria Benet and I am happy to tell you that only about two-thirds of me is here to talk about my tracking project. I mean that literarily, because in the 10 years since I’ve been self-tracking I lost over 50 pounds while getting fitter.

In my early 50s, I was overweight, out of shape, with bad knees, and when not cranky, depressed. I was already on meds for high blood pressure and was looking at the prospect of more prescriptions down the road.

So, what did I do to change my situation? I set about tracking my activity levels, my weight and my food intake with the help of apps, wearable devices – plus — in databases and Excel spreadsheets that I designed. Until late 2011, I tracked inconsistently, but once I discovered mobile apps and wearable devices — I became more systematic and consistent about tracking weight, food intake, and fitness data.

How did I do it? 

When I first started — losing 50 pounds seemed daunting, but going for a walk at least 5 days a week seemed less formidable. To track walks I was going to take in the hilly neighborhood where I live, I created a simple Access database.

I bought a pedometer, hiking shoes, and off I went. After walking, I recorded the duration, the number of steps, and calculated the distances I covered. I also charted my routes by naming the streets, and made notes about the weather and my mood during the walk.

Recording the data turned out to be a form of reward in itself. At the start of this tracking project, I enjoyed seeing the database grow a little more than I enjoyed the actual walks themselves.

Over time, the walks got longer, steeper, and eventually included actual hikes. I also took up the practice of Yoga regularly, and then added Pilates to my exercise repertoire.

Along the way, I also started to lose weight. Though I didn’t weigh myself every day, I began to pay attention to the kinds of foods I ate and tried to wean myself off processed foods in general.

They say you get fit in the gym, but lose weight in the kitchen. In September 2011, when I discovered LoseIt, it became my virtual kitchen: LoseIt helped me see what foods I ate regularly, which of these spiked my weight, even if my calorie intake stayed the same. I noticed these relationships anecdotally, rather than by finding statistical correlations between them.

Tracking in LoseIt helped me realize that as much as I love bread and beer, they are not my friends. Two years ago, an allergist confirmed my wheat sensitivity through blood tests and an elimination diet.

I added Endomondo to my tool box a few months later, since I liked having the maps and stats it offered, in addition to the other data it showed. By December I also added a Fitbit, as with it I could track more accurately how many steps I took and approximate better the number of calories I burned. The Fitbit was like going back to the pedometer, but to one on steroids.

With the Fitibit, I focus mostly on the Very Active Minutes it claims to measure. Increasing that number over time became a game. In 2012, I was averaging about 57 minutes a day, which put me in the 98th percentile. Increasing to 69 minutes only brought me to the 99th percentile, as the Fitbit population also has increased over time.

The Fitbit turned out to be a catalytic tool, because it spurred me on to push the perceived limits of my fitness abilities and possibilities further. It ended up putting wheels under my dreams.

In the spring of 2012,I took up cycling to increase my active minutes and challenge a mental habit of opting out of things because of a fear of failure or thinking of them as not age appropriate. Biking, in turn, added to my collection of gadgets and apps for tracking the metrics involved.

By 2012 then, in addition to LoseIt and Fitbit, I was tracking workouts with a Garmin GPS watch with a HR monitor and my bike rides with a Garmin Edge computer, uploading the data to the Garmin site, to Endomondo and Strava, as each had strengths the other lacked, from my perspective.

To complicate data gathering, back in January 2012, I started a basic Excel spreadsheet that tracks highlights from each of these apps in an application-independent reference for me. In Excel I track the type of activity, duration, distance, if applicable, average and maximum heart rate, Strava suffer points, (a measure of exertion), the hours I slept and how that sleep seemed to me, and additional notes about the day I might think relevant.

The plethora of my gadgets and apps might earn me an entry into the next edition of The Diagnostic and Statistical Manual of Mental Disorders. But exploring these tools was, and still is, my way of looking for a comprehensive and personalized way to track the quantities in my habits and activities that make for a qualitative difference in my life … which brings me to what I learned so far:

What did I learn?

I learned that small quantitative changes in particular daily habits add up to a big difference in quality of life in general.

The incremental additions in my tracking methods and number of gadgets I added produced a lot of data, which I haven’t analyzed closely, because I was already getting a lot of return from them in the form of new experiences in my life.

The most memorable of these experiences is my having completed the metric century ride on the Tour de Fuzz in Sonoma last September. In the space of a little over a year I went from covering barely 8 miles in an hour on my first rides to completing 63 miles in 5 and ½ hours and feeling ready to ride a lot more.

It has been said that motivation is what gets us up and going, but it’s habit that keeps us going. So it is with my tracking: though the motivation was to lose weight, the habit of tracking and keeping an eye on the numbers are what allowed me to go from daily small changes to a much bigger transformation from the overweight, depressed, and achy person I was 10 years ago to who I am now: someone interested in health and fitness and setting goals I can meet.

I learned that for me the act of tracking is the project itself. Although the data I generate can be charted and described in numerical relationships the number that brings me the information that makes a difference in my life, is a simple 1 – or tracking one day at a time.

I love to see the numbers my Garmin and Fitbit generate, but in the end, the quantified self for me is not so much about the measured life as it is about keeping those numbers coming through a well-lived and, more importantly, well-enjoyed life as I go from my fitter fifties into what I hope will be my sounder sixties.

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Thomas Christiansen on Learning from 60,000 Observations

It’s an iterative process. I’m peeling an onion, and I can continue peeling that onion for the probably the rest of my life.

How many times have you sneezed today? This month? Over the last 3 years? Thomas Christiansen knows his sneeze count because he’s been tracking them since 2011. We’ve actually heard from Thomas before, but we were happy to have him give an update on his unique self-tracking project at the 2013 Quantified Self Global Conference.

To better understand his allergies and his overall health, Thomas began tracking a discrete phenomena, his sneezes. By plotting them over time and then exposing himself to other data like sleep, travel, and diet he’s been able to start to understand himself better. Watch his talk below to see what Thomas learned, and how he thinks about his process of continuous learning.

This video is from our 2013 Global Conference, a unique gathering of toolmakers, users, inventors, and entrepreneurs. If you’d like see talks like this in person we invite you to join us in Amsterdam for our 2014 Quantified Self Europe Conference on May 10 and 11th.

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Nancy Dougherty on Quantified/Unquantified

Nancy Dougherty has talked to us in the past about her experiences with exploring self-tracking and how mindfulness interacts with the technological processes of gathering and understanding personal data. In this short Ignite talk, given at the 2013 Quantified Self Global Conference, Nancy digs a bit deeper into her personal experiences when she gave up tracking while maintaining what she calls, “the QS mindset.”

This video is from our 2013 Global Conference, a unique gathering of toolmakers, users, inventors, and entrepreneurs. If you’d like see talks like this in person we invite you to join us in Amsterdam for our 2014 Quantified Self Europe Conference on May 10 and 11th.

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Pew Internet Research: 21% Self-Track with Technology

Today the Pew Research Center’s Internet & American Life Project released their latest findings in their ongoing research on the role of the Internet and technology in health and wellness. This latest report, Tracking for Health, is of particular interest to the Quantified Self community because it focuses on self-tracking. Thanks to Pew Associate Director, Susannah Fox, who gave us an advanced look at the results, we are able to bring you some reflections on this initial foray into measuring the impact of self-tracking.

Before we get to our discussion with Susannah it’s probably best to help set the stage with some of the most interesting findings.

Overview of Tracking

  • 69% of adults track a health indicator for themselves or others.
  • 34% of individuals who track use non-technological methods such as notebooks or journals.
  • 21% of individuals who track use at least one form of technology such as apps or devices.

Continue reading

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Stuart Calimport on The Memome Project

Stuart Calimport is on a quest to find the most useful memes for health and well-being. He started the Human Memome Project, and spent a year and a half collecting all his ideas about health. He classified 5137 of these ideas as healthy/ethical/optimal and 6581 of them as unhealth/unethical/sub-optimal. In the video below, Stuart shares some examples of his memes, as well as his process for optimizing meme rate generation, and what he has learned about himself on this adventure. (Filmed by the London QS Show&Tell meetup group.)

The Memome Project from Ken Snyder on Vimeo.

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Numbers From Around the Web: Round 9

Some people may be wondering how I find all the amazing people conducting neat self-tracking experiments and creating jaw-dropping personal data visualizations. Well, for the most part I just listen. I’m constantly paying attention to what’s being said on twitter about #QuantifiedSelf. When that doesn’t work I just use the power of Google to find people who are blogging about self-tracking, self-experimentation, or personal data. It’s great to look through the search results and see how many people are sharing their personal stories and insights. While doing some searching this morning I stumbled across a project that immediately brought a smile to my face. Hopefully you’re excited by this as much as I am.

Chris Volinsky is a statistician at AT&T Research and he’s no stranger to handling large data problems. Back in 2008 he was part of the team that won the $1 Million Netflix prize. He also has quite the impressive list of research papers that illustrate the many different uses of cellphone location data. But what is really interesting about Chris is his newest project: My Year of Data

Back in November of 2011 Chris started off a blog entry that with this:

My name is Chris. I am 40 years old. I am 5’9 1/2″ and weigh 174 pounds. I walked 9,048 steps and have consumed 1,406 calories today (so far).

Realizing that he’ld been gaining weight and wasn’t at his optimal health he decided to take a data-centric approach to improving his health. He is a statistician after all. So far, he’s found some interesting things. Take for instance his weight and dietary tracking.

As he explains in this post, Chris typically has a hard time tracking his diet consistently. This can be pretty frustrating when you hear about how important it is to eat this or not eat that to help with weight reduction. Rather than get frustrated Chris turned to the data to see what he could learn. When he stopped looking at the data he was entering and started looking at the missing data an interesting trend lept out. He found that fluctuations in his weight appeared to be correlated with whether or not he was logging food. Take for instance the plot below. It appears that there is a pretty clear association with periods of weight loss and periods of actively logging his food (pink zones). The opposite also appears to be true – no food logging = weight gain.

Weight chart with food tracking highlighted in pink

So this is where a typical NFATW post would stop. We have an interesting finding and a neat data visualization. But, Chris is doing something much more interesting than just talking about his weight data. He is on a long-term self-tracking and self-discovery journey and he is trying to enlist other interested parties to help him. Chris is going the extra step and posting all of his self-tracking data online for anyone to analyze, visualize, or just get inspired.

You can access all of his amazing data via a public dropbox folder that he’s set up. He even has a nice README file explaining the datasets and formats. So far he’s sharing the following:

  • Fitbit: sleep and activity data
  • FitLinxx: weight training data from gym activities
  • Livestrong: dietary tracking data
  • Runkeeper: running and other exercise activity data
  • RescueTime: productivity tracking (computer/internet use)

All the data is open and available for you to play with. This should be a really interesting project to keep “track” of in the future (pun definitely intended). To help inspire some action on your part I took some time today and looked at Chris’s most recent available data to see what I could find out. I downloaded his Fitbit data and decided to look for any interesting patterns. Turns out that when taking a look at his daily patterns of activity there seems to be something going on on Thursdays that reduces his step count and activity time . Also, Saturday is by far the best day with an average of 9,862.56 steps and a 5.3 hours spent being active (data available here).

Mean steps per day

Mean activity minutes per day

Make sure to reach out to Chris over at his blog and take a took at his data to see what interesting thing you can figure out!

Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.

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Sky Christopherson on The Quantified Athlete

Sky Christopherson is a velodrome cyclist who has been on the U.S. Olympic team. After retiring, he lived in the world of startups, and when his health started to decline as a result of that stress, he turned back to the kind of quantification he had been doing as an athlete to restore his health. In the video below, Sky talks about what he learned, like how temperature affects his deep sleep and how his blood glucose fluctuates. He also shares the exciting news of setting a world record, at age 35, after his self-tracking experiment. (Filmed by the Bay Area QS Show&Tell meetup group.)

Sky Christopherson – Self Quantification and Performance from Gary Wolf on Vimeo.

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Health Mashups: Helping People Find Long-Term Trends Between Wellbeing and Activities in Their Lives

Frank Bentley is a Principal Staff Research Scientist at the Motorola Mobility Applied Research Center outside of Chicago, IL. He creates new mobile applications and services that help people connect with each other and with data about their lives. He then studies how these systems are integrated into daily life over weeks and months.


Do you sleep better on days when it’s warmer? Walk less on days packed with meetings? Gain weight on the weekends? A growing number of consumers are turning towards specialized devices that track particular aspects of their lives and wellbeing. Whether it’s the Zeo to track sleep, the FitBit to track daily step counts, the MOTOACTV to track workouts, or the WiThings scale to track their weight, there is currently a wealth of personal data that is being stored about daily activities. However, most of these services continue to be silos. Even where the ability to import data from one device into another’s service exists, data is only combined superficially, providing at most a graph of steps and weight over time, obscuring long-term and periodic interactions. The questions presented above cannot be answered without great effort – effort that many in the Quantified Self community devote to understanding themselves. But can it be easier?

We see the key value of tracking multiple aspects of one’s life to be understanding the interaction of data from wellbeing sensors with other sensors as well as with contextual data about a person’s life (where they spent time, how busy their day was, the weather, etc.). We want to enable people to discover these hidden trends in their lives without resorting to complex Excel files and a PhD in statistics.

The Health Mashups system

The Health Mashups system was built through a collaboration between KTH University and the Motorola Mobility Applied Research Center. It consists of a server that aggregates data from a variety of sensors and a mobile application to automatically capture a user’s context and display the resulting correlations calculated by the server. Users can connect their FitBit accounts for step count and sleep data as well as their WiThings account for weight data. An Android application uploads contextual information automatically each day including the number of hours busy on the user’s calendar as well as the current location at a city level and weather for that location. After the initial setup, no further actions are required from the user to keep this data flowing to our server (although we also support manual food and exercise logging through the mobile phone application). Each night, our server computes correlations between sensors and deviations on data from a given sensor and generates a feed of items that are statistically significant. This feed is then accessible on the phone or web for users to view and reflect upon. Users can see feed items such as: “You lose weight on weeks when it is warmer” or “Yesterday you walked much less than you normally do on Saturdays.” This eliminates the need for manual log books and messy Excel files, and opens Quantified Self-style investigations to those with no technical background.

Field Trial

We wanted to understand how a broad range of users would integrate this system into their lives. We conducted a two-month field trial and recruited ten diverse participants in Chicago and Stockholm to take part. They came from a wide range of ages and educational backgrounds and had a variety of reasons for participating: from particular issues with sleep or excessive weight that they wanted to address to a general curiosity to understand themselves better. Participants were given a FitBit and a WiThings scale and asked to use these in their lives for the first month. Whenever they had an insight about their wellbeing, they were asked to call us and leave a voicemail describing their insight. For the second month of the trial, they were given the Health Mashups interface on their phone and again were asked to call us with new insights.

For the first month of the trial, none of our participants called with insights across sensors or time scales. While many reported general trends (e.g. “I’ve been losing weight this week” or “Yesterday I didn’t walk as many steps as I thought I did”), their insights did not connect their sleep, weight loss, or step counts to each other in any way. Nor did they include insights about patterns on specific days of the week or comparisons/deviations from week to week.

In the second month, participants were able to understand their wellbeing in much deeper and complex ways. The system showed them insights across sensors and varying timescales. Our participants reported understanding and relating to these feed elements. The mashups data helped our participants to better understand how aspects of their lives were related and to make positive changes in their lives (e.g. eating a little less fried chicken on Sundays or walking more on specific days of the week).

The Future of Health Mashups

We see a promising future for personal data analytics related to one’s wellbeing. With massive amounts of wellbeing and contextual data now being collected, systems are needed that make sense of this data for people and allow them to focus on what is significant to their lives without a large amount of effort. With Health Mashups our participants could gain these insights, combining data that is automatically collected as they live their lives. We believe these types of insights have the power to raise awareness about situations that lead to poor life choices, resulting in positive changes in behavior and ultimately happier, healthier lives. This summer we will be conducting a larger quantitative study to investigate the impacts of this system across a wider group of participants. If you are interested in participating, you can register your interest here.

 

This article is a summary of a position paper by Frank Bentley and his colleagues that will be discussed at the Personal Informatics in Practice workshop at CHI 2012 in Austin, TX on May 6, 2012. The workshop will be a gathering of researchers, designers, and practitioners exploring how to better support personal informatics in people’s everyday lives.
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