Tag Archives: Research

QS Access: Self-Report & Quantified Self in Health Research

As part of our new Access channel we’re going to highlight interesting stories, ideas, and research related to self-tracking data and data access issues and the role they take in personal and public health. We recently found this expert report, published in the International Journal of Obesity, that tackles issues with the data researchers rely on for understanding diet and physical activity behaviors, and ultimately concludes that the data is fundamentally flawed.

Researchers has known for a long time that relying on individuals to understand, recall, and accurately report what they eat and how much they exercise isn’t the best way to understand the realities of everyday life. Unfortunately for many years, this was the only way to track this information – interviews, surveys, and research measures. Only recently have tools, devices, and methods matured to a point where objective information can be captured and analyzed.

The authors of this article make the case that obesity and weight management fundamentally relies on getting these numbers right, and unfortunately most research hasn’t. Reading the background on self-report data and the call to action the authors make for developing and using more objective measures we can’t help but wonder about the role of commercial personal self-tracking tools. How can we, as a community of users, toolmakers, and researchers work together to open up access pathways so that the millions of people tacking pictures of their meals and uploading their step data can have a positive impact on personal and public health? This is an open question, one that we’re excited to be working on.

If you’re interested in these type of questions, or working on projects related to data access we invite you to get in touch and keep following along here with us.

 

 

 

 

 

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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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What We Are Reading

We hope you enjoy this week’s list!

Articles
Big Data in the 1800s in surgical science: A social history of early large data set development in urologic surgery in Paris and Glasgow by Dennis J Mazur. An amazing and profoundly interesting research paper tracing the use of “large numbers” in medical science. Who knew that is all began with bladder stones!

Civil Rights, Big Data, and our Algorithmic Future by Aaron Rieke, David Robinson and Harlan Yu. A very thorough and thoughtful report on the role of data in civil and social rights issues. The report focuses on four areas: Financial Inclusion, Jobs, Criminal Justice, and Government Data Collection and Use.

Caution in the Age of the Quantified Self by J. Travis Smith. If you’ve been following the story of self-tracking, data privacy, and data sharing this article won’t be all that surprising. Still, I can’t help but read with fascination the reiteration of tracking fears, primarily a fear of higher insurance premiums.

Patient Access And Control: The Future Of Chronic Disease Management? by Dr. Kaveh Safavi. This article is focused on providing and improving access and control of medical records for patients, but it’s only a small mental leap to take the arguments here and apply them all our personal data. (Editors note: If you haven’t already, we invite you to take some time and read our report: Access Matters.)

Perspectives of Patients with Type 1 or Insulin-Treated Type 2 Diabetes on Self-Monitoring of Blood Glucose: A Qualitative Study by Johanna Hortensius, Marijke Kars, and Willem Wierenga, et al. Whether or not you have experience with diabetes you should spend some time reading about first hand experiences with self-monitoring. Enlightening and powerful insights within.

Show&Tell
Building a Sleep Tracker for Your Dog Using Tessel and Twilio by Ricky Robinett. Okay, maybe not strictly a show&tell here, but this was too fun not to share. Please, if you try this report back to us!

Digging Into my Diet and Fitness Data with JMP by Shannon Conners, PhD. Shannon is a software development manager at JMP, a statical software company. In this post she describes her struggle with her weight and her experience with using a BodyMedia Fit to track her activity and diet for four years. Make sure to take some time to check out her amazing poster linked below!

Visualizations
The following two visualizations are part of Shannon Conners’ excellent poster detailing her analysis of data derived from almost four years of tracking (December 2010 through July 2014). The poster is just excellent and these two visualizations do not do it justice. Take some time to explore it in detail!

SC_calorieweight

SC_sleep

Tracking Energy use at home by reddit user mackstann.

EnergyApp

“The colors on the calendar represent the weather, and the circles represent how much power was used that day. The three upper charts are real-time power usage charts, over three different time spans. I use a Raspberry Pi and an infrared sensor that is taped onto my electric meter. The code is on github but it’s not quite up to date (I work on it in bits and pieces as time permits I have kids).”

From the Forum
Help With Livestrong Data Export
Need Help Deciding Which Device
New to Fitness Tracking

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QS | Public Health Symposium: Margaret McKenna

This week we’re taking a look back at our 2014 Quantified Self Public Health Symposium and highlighting some of the wonderful talks and presentations.  We convened this meeting in order to bring together the research and toolmaker communities. Both of these groups have questions about data, research, and how to translate the vast amount of self-tracking data into something useful and understandable for a wider audience.

As part of our pre-conference work we took some time speak with a few attendees who we thought could offer a unique perspective. One of those attendees was Margaret McKenna. Margaret leads the Data & Analytics team at RunKeeper, one of the largest health and fitness data platforms. In our conversation and in her wonderful talk below Margaret spoke about two important issues we, as a community of users, makers, and researchers, need to think about as we explore personal data for the public good.

The first of these is matching research questions with toolmaker needs and questions. We heard from Margaret and others in the toolmaker community that there is a near constant stream of requests for data from researchers exploring a variety of questions related to health and fitness. However, many of these requests do not match the questions and ideas circulating internally. For instance, she mentioned a request to examine if RunKeeper user data matched with the current physical activity guidelines. However, the breadth and depth of data available to Margaret and her team open up the possibility to re-evaulate the guidelines, perhaps making them more appropriate and personalized based on actual activity patterns.

Additionally, Margaret brought up something that we’ve heard many times in the QS community – the need to understand the context of the data and it’s true representativeness. Yes, there is a great deal of personal data being collected and it may hold some hidden truths and new understanding of the realities of human behavior, but it can only reveal what is available to it. That is, there is a risk of depending too much on data derived from QS tools for “answers” and thus leaving out those who either don’t use self-tracking or don’t have access or means to use them.

Enjoy Margaret’s talk below and keep an eye out for more posts this week from our Quantified Self Public Health Symposium.

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QSEU14 Breakout: Measuring with Muppets

Today’s post comes to us from June Lee and Jennifer Kotler. June and Jennifer are researchers at Sesame Workshop, where they are conducting work exploring children’s media use. Below you can read their description of a breakout session they led on the topic at the 2014 Quantified Self Europe Conference. If you have ideas about measuring media use or want to continue this conversation we invite you to join the discussion in the forum.

JenKJuneL_QSEU14

Measuring with Muppets
by June Lee & Jennifer Kotler

The goal of the session was to exchange ideas for ways to measure and track children’s media use across contexts (which include physical spaces such as work vs. school, and social contexts such as with whom they are using media). An ideal device would be a wearable device that’s a “Shazam meets LENA,” which would identify the media content being used, as well as capture the conversation taking place around media use.

Currently, different technologies approximate what we would like to do. For instance, iBeacon is used in shopping malls to track and deliver messages to shoppers; smartwatches could be good for capturing audio; Bluetooth recognition could identify devices that are nearby and partly capture the social context. Different apps, however, don’t use the same system and are difficult to integrate. The main takeaway from the session is that nothing exists yet that does what we would like to do. We would need different apps and systems.

The session generated other useful ideas, such as the asking what parents would like to track in terms of media and their child, and what parents currently track (if they do). Another suggestion was to look at the rare disease or health care community, which is ahead of the curve in terms of tracking and managing child health; Human-Computer Interaction departments or Interaction Design departments at universities could be another good resource. Many agreed that we could start with simple, low-tech approaches: observations and/or manual paper recording. Or do the research in stages, using technology that does exist. In short, we needed to narrow our research questions because the tool we’re looking for does not (yet) exist.

Editor’s note: While doing some research around measurement and children we stumbled upon this great Sesame Street video. Enjoy Elmo singing about the power of measuring!

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QSEU14 Breakout: Cartographies of Rest

Today’s post comes to us from Josh Berson. Josh is a anthropologist and researcher at the Max Plank Institute. In the fall of 2014 Josh and his colleagues will be embarking on an ambitious research program to explore how we understand and engage in activity and rest. This research project, housed at the Hub at Wellcome Collection, is still in it’s early stages and Josh sought to discuss and gain insight into how people think about measuring activity and rest, as well as how they perceive participatory research projects. You’re invited to read Josh’s description of the session below and then join the discussion on the QS Forum.

jb_qseu14

Cartographies of Rest
by Josh Berson

In the Cartographies of Rest breakout, we were looking for practical guidance on setting up a large study using self-tracking technologies to elicit a synoptic picture of contemporary rhythms of rest and activity in densely inhabited urban spaces. The response was great. We had participants with backgrounds in voice analysis, geovisualization, and the design of participatory public health interventions. Some of these conversations will probably lead to formal working relationships with the Hub at Wellcome Collection, the umbrella project under which Cartographies of Rest is being carried out.

But the key insight we came away with had little to do with the technical apparatus or tracking channels for the study. Rather, it was that we would do well to present the demands we’re making of our research participants not as an inconvenience but as an opportunity to be part of an innovative collective approach to generating knowledge about how we move (and stop moving) through shared space. We need to make sure the design of the study, and the way we communicate the study’s aims to participants, reflects our core conceit, to wit, that activity and rest are social phenomena, and their physiological and behavioral correlates are conditioned by social context, not the other way around. In contrast to previous “reality mining” studies, we’re not looking to study the evolution of a preexisting social network, and in fact we’d been thinking recruitment would need to stratify against too many preexisting relationships among participants, since that might confound the rest:activity timelines they generate as individuals. We were also concerned with how to control for the fact that participating in the study would lead participants to change their behavior.

But if, instead, we focus on creating a community among the participants, and on letting the fact that participating in the study will inevitably change their behavior (through new relationships, through new concern, individually, for how active and restful they’re being) be part of what we’re looking at, we will end up with results that have greater translational value and are truer to the aspirations to self- (as well as social) improvement out of which self-tracking methods originate

If you’re interested in exploring tracking activity and rest we invite you to head to our forum to join the discussion on the QS Forum.

Image by Ian Forrester

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The State of Self Tracking (QS London Survey)

The excellent organizers of the London Quantified Self Show&Tell recently fielded a detailed survey about the self-tracking practices in their group. In the video below Ulrich Atz presents their findings.

Some of the interesting results from the survey:

  • 105 respondents (22 identified as female, 76 as male).
  • Over 500 unique tools were being used.
  • 47% of the respondents are currently measuring weight (17% have in the past).
  • Pen & paper is being used by 28% of respondents.
  • 90% of respondents who answered a question about data sharing would share their data (or share it for medical research).

QSLondon_tracking2

The presentation is available online here (PDF) and an aggregate view of the survey results is also available for you to explore here. We’re excited to see and learn more from this interesting data set in the future.

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The Quantified Self Institute

We are excited to be bringing a scientific and research track to the upcoming 2014 QS Europe Conference. We’ve been pushed and prodded by many of our friends in the QS community to make this happen. Today we’re highlighting one of those friends and collaborators, the Quantified Self Institute. Read below to learn more about their work and then register for the conference to join the conversation in person!

QSI logo

In 2012 the Hanze University of Applied Science founded the Quantified Self Institute (QSI) in collaboration with Quantified Self Labs. The mission of QSI is to encourage a healthy lifestyle through technology, science, and fun. We aim to bring the knowledge and experience of the QS community and the science community together in order to learn from each other.

We are a multidisciplinary group of researchers and teachers who work together with a network of universities, health care institutions and industry partners on personalized science, health and self-tracking.

We focus on the Big Five for Healthy Life (physical activity, food, sleep, stress & relaxation and social interaction) and conduct research on the availability, validity, and efficacy of self-tracking technologies.

Our ultimate goal is to find out by what means and to what extend self-tracking is useful for personal health. We look forward to exploring and along with worldwide QS Community. We hope you join us at the upcoming 2014 QS Europe Conference!

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Submit Your Quantified Self Research

QSEU14_small
We’ve been holding Quantified Self Conferences since 2011. Every year since then we’ve been approached by scientists and researchers in the academic community to help them find a way to incorporate their work and their ideas into our structure. After a few years of holding back, listening, and watching the research community become engaged with other scientists and the real-world QS practitioners we’re ready to take that next step.

We are excited to announce today that we are inviting scientists and non-scientists to join a research oriented poster session at our upcoming Quantified Self European Conference on May 10th and 11th.

These sessions are a way for us to support interesting work that doesn’t fit into our established show&tell format, including research results from academic and scientific studies relevant to QS practitioners. Possible topics include (but are not limited to):

  • Validity, reliability, usability, and effectiveness of self-tracking devices
  • Experiment design
  • Statistical and/or visualization methods
  • Social and psychological investigation into self-tracking practices
  • Social science research on the QS community

Our hope is that these posters and the conversations around them will help us (scientists and non-scientists) learn from each other, stimulate new ideas/projects, and to uncover new applications for the research findings.

How to submit a poster

The process is very simple. Simply send us a draft of your poster submission via email. We will be accepting submissions until April 14, 2014.  For format and other info, please read the instructions below. The posters will be reviewed for content and relevance; if you would like to be involved with the review process, or have any questions, please contact us.

Details

Posters should contain the following elements:

  • Title
  • Authors and affiliations
  • Sections:
    • Background
    • Method
    • Results
    • Discussion/Conclusions
    • QS Relevance
  • Contact information. We recommend including a picture of yourself so others at the conference can find you, and, if applicable, your twitter account.

Format:

  • You must use the A0 size (841 × 1189mm or 33.11 × 46.81 inches)
  • A PowerPoint template is provided for you to use.

Remember to Keep It Short and Simple (KISS). We want to stimulate creativity and strongly recommend the use of tables, figures, and visualizations. For examples and design tips we recommend the following articles:

Dates & Deadlines

Deadline for submission is April 14, 2014. We will conducting reviews and informing submitters of acceptance on a continual basis. All submitters will be notified by April 21, 2014. We look forward to seeing your inspiriting projects and findings.

Submit your poster now!





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The State of Self-Tracking

(Co-written with Gary Wolf)

In January we started asking ourselves, “How many people self-track?”  It was an interesting question that stemmed from our discussion with Susannah Fox about the recent Pew report on Tracking for Health. Here’s a quick recap of the discussion so far.

The astute Brian Dolan of MobiHealthNews suggested that the Pew data on self-tracking for health seems to show constant – not growing – participation. According to Pew, in 2012 only 11% of adults track their health using mobile apps, up from 9% in 2011.

All this in the context of a massive increase in smartphone use. Pew data shows smartphone ownership rising 20% just in the last year, and this shows no signs of slowing down. Those smartphones are not just super-connected tweeting machines. They pack a variety of powerful sensors and technologies that can be used for self-tracking apps. We notice a lot of people using these, but our sample is skewed toward techies and scientists.

What is really going on in the bigger world? How many people are actually tracking?

A few weeks ago ABI, a market research firm, released a report on Wearable Computing Devices. According to the report there will be an estimated 485 million wearable computing devices shipped by 2018. Josh Flood, the analyst behind this report indicated that they estimated that 61% of all devices in wearable market are fitness or activity trackers. “Sports and fitness will continue to be the largest in shipments,” he mentioned “but we’ll start to see growth in other areas such as watches, cameras, and glasses.”

One just needs to venture into their local electronics retailer to see that self-tracking devices are becoming more widespread. So why are our observations out of synch with the Pew numbers?

The answer may lie in the framing of the Pew questions as “self-tracking for health?” For instance:

On your cell phone, do you happen to have any software applications or “apps” that help you track or manage your health, or not?

Thinking about the health indicator you pay the most attention to, either for yourself or someone else (an adult you provide unpaid care for), how do you keep track of changes? Do you use paper, like a notebook or journal, a computer program, like a spreadsheet, a website or other online tool, an app or other tool on your phone or mobile device, a medical device, like a glucose meter, or do you keep track just in your head?

We think it is likely that many practices we include in our definition of Quantified Self are not being captured by the Pew Research. A person who tracks a daily run with a Garmin GPS watch might show up in the wearables data that ABI looks at, and might look to us as a self-tracker for health, but might be invisible to Pew. There may be even self-tracking practices that fall outside health or wearables. We’ve seen a large number of people who track time and productivity using computer applications such as RescueTime, apps that support well-being such as meditation trackers, mood trackers, and diet trackers; and apps that support general self-reflection and journaling, such as a life-logging app. Many self-tracking practices do not fit neatly into “health.” (Though they may influence health!)

In a way, there is a parallel here to what we found when we compared Fitbit with Fuelband data. Both of them produced different numbers for “steps.” When we got into the details, we ended up thinking that this was not a matter of one being closer to the “ground truth,” but of intentionally different interpretations of messy accelerometer data. Fitbit gives more step credit for general movement, because it is a lifestyle/activity tracker; Nike might prefer to credit intentional exercise, since the Nike brand sits closer to sports. Context matters.

This confusion about what is health tracking, what fits in the frame, is closely analogous to many other confusions in the conversation about health generally. It is common now in the healthcare world to talk about how the larger determinants of public health are outside the control of the healthcare industry; for instance, diet, exercise, stress, and exposure to environmental toxins. Sometimes people who make these observations follow them with a call for the healthcare industry to begin addressing these larger concerns; for instance, to “medicalize” tracking apps by making them prescribable and reimbursable by health insurers.

But maybe this isn’t the only approach. If the “healthcare” frame isn’t adequate to capture the most important determinants of health, we could try switching frames. What our journey through the self-tracking data suggests is that the opposite approach might be useful to consider: start with the bigger world of self-care practices, and enhance these. Why? Because that’s where we trackers already are. That is, how are we deriving meaning from self-tracking? That’s the mental framework that we typically use, and that we like to use. That’s where the growth – in terms both of us, and of cultural understanding, engagement, and knowledge-making – might really be happening.

We don’t know this for sure. We take the Pew data as evidence that this approach is worth trying.

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