Tag Archives: Research
“Open Humans aims to break down data silos in human health and research. We believe data has a huge potential to live and grow beyond the boundaries a single study or program. Our online portal allows members to aggregate data from the research they participate in. By connecting individuals willing to share existing research data about themselves with researchers who are interested in using that data, data can be re-used and built upon.” — OpenHumans.org
On March 24, 2015 the Open Humans Network officially opened their virtual doors and began allowing individuals to sign up and engage in a new model of participatory research. We spoke with Co-founder & Principal Investigator of the Public Data Sharing study, Madeleine Ball, Ph.D. about Open Humans, what it means for research, and what we can look foward to from this exciting initiative. The following is an edited transcript of that conversation.
It’s been a lot of work up to this point.
We’re grateful to have the funding support of two organizations to help get this off the ground, the Knight Foundation and the Robert Wood Johnson Foundation. It’s been a lot of work to get to this point, from hiring Beau Gunderson as our Senior Software Gardener to launching with our first three studies. We’re excited to be partnering with the Harvard Personal Genomes Project, the American Gut study, and the GoViral study. These are the seed studies, what we’ll build off of in the coming months and years. Today, we’re excited to start letting participants in these projects, and all individuals interested in participating in research, know about Open Humans.
This is an open invitation to join us.
We’re also working to make it easier for research partners to join the Open Humans Network. We’ve already started receiving interest from researchers that want to integrate with Open Humans or start working with our already growing public data sets. We’ve set parameters regarding how you have to behave as a study as well as how researchers looking to work with our members should engage with us. (You can find out more about that here.)
For members who sign up with us we’ve developed methods for them to control access to their data. Whether that is data from personal health devices and apps like Runkeeper (adding this to our next project), genetic data, or other data sources derived from participating studies, each individual member will have the ability to establish a peer-to-peer interaction. Members can allow access to some data, but not others. They may choose to release some or all of their data publicly, or the may choose to only share with one study. In the end it’s up to them and their individuals goals.
What excites me about Open Humans is the potential we have to transform future research studies — from how they treat data to how they think about data sharing. We’re building our system so that participants are central to the data process. A good example of this when researchers use our member’s data they must also agree to return any new data that results from their research back to the original participant. This decentralization of data is a key component of our design. No single person, researchers, or study has all the data.
We’ve also built in the ability for researchers to contact our members who contribute data. The idea that researchers must come up with all the right questions before starting a study is a recipe for failure. Researchers are not psychic, that can’t forsee what interesting questions might come up in the future. By opening up the ability for these connections to take place in the design of Open Humans, we’re creating the ability to continue asking questions of specific individuals, or groups of people, far in to the future.
I think this work is creating a new form of data sharing that will unlock a world of new exciting possibilities. Our hope is that when participants start getting data back from studies, and have the ability to use it and share it how they wish, that participation in research will be more rewarding. This model helps participants become a respected member of the evolving research conversations happening all over world. We know a lot of people don’t participate in research, even researchers who rely on participants don’t participate in studies. Hopefully this work will help move the needle.
It’s wonderful to see the long scroll of members.
As of this writing the Open Humans Network has over 200 individuals who have created member profiles. If you’re interested in participating in open research you can learn more and sign up here. If you are a researcher or personal data company interested in integrating with Open Humans you can get in touch with the team here.
Ernesto is in sunny Austin for SXSW, so I’m filling in to gather this week’s articles and links for your reading pleasure.
Apple ResearchKit concerns, potential, analysis by MobiHealthNews. ResearchKit was a big surprise coming out of Apple’s Special Event this week. It was quite difficult to select just one representative article about the ensuing conversation, so this round-up serves nicely.
#WhatIfResearchKit: What if Research Kit actually, truly, worked… by Christopher Snider. Okay, I failed to keep to one article on ResearchKit. This post chronicles a series of Twitter conversations on the question: if ResearchKit does work, what are the possibilities?
The Electric Mood-Control Acid Test by Kevin Bullis. Thync is a sort of evolved version of a transcranial direct current stimulation (TDCS) device. A technology with a lot of potential and controversy, this article explores why the brain-enhancing effects of the TDCS only work for some people. By the way, if you are a fan of Philip K. Dick, Thync may remind you of the mood organ that was in Do Androids Dream of Electric Sheep?
Automated Learning by Nichole Dobo. Some school classrooms are experimenting with ”Blended learning”, a method of combining classroom teachers and computer-assisted lessons. A detail that stuck with me is the description of three large displays that show where each student is supposed to go that day, based on the results of the previous day’s lesson.
The Mouse Trap: Can One Lab Animal Cure Every Disease? by Daniel Engber. An in-depth how science’s predominant use of lab mice could be limiting our knowledge of disease. Of relevance to self-trackers because many models of optimal health are in part based on mouse studies.
Analyzing a Year of My Sleep Tracking Data by Bob Troia. This is a superb exploration of Bob’s sleep data from 2014 as collected by his Basis watch.
Notes on 416 Days of Treadmill Desk Usage by Neal Stephenson. The author of Snow Crash and The Cryptonomicon is a long time user of a treadmill desk, but when he began having pain in his left leg, he had to reevaluate how he used his favored tool.
Qualities of #QuantifiedSelf by Christina Lidwin. A fascinating analysis of the #quantifiedself hashtag.
First medical apps built with Apple’s ResearchKit won’t share data for commercial gain by Fred O’Connor
Talking Next-Gen Diabetes Tools with Dexcom Leaders by Mike Hoskins
From the Forum
Mood Tracking Methods?
Howto track laptop uptime
CCD or CCR conversion tools?
What gets measured, gets managed – Quantified Self in the workplace
Best ECG/EKG Tool for Exercise
Best iOS app to track water/coffee/alcohol intake?
This Week on QuantifiedSelf.com
QS15 Sponsor Highlight: RescueTime
Quantified Self and Apple’s ResearchKit
Better by Default: An Access Conversation with John Wilbanks
QS15 Conference Preview: Jamie Williams on Tracking My Days
Quantified Self Styles
Lastly, I’ll leave you with a lovely little comic with a message that many self-trackers can relate to.
The Secret by Grant Snider
Apple’s announcement of ResearchKit is strong evidence that Quantified Self practices are emerging as a major influence on medical research and other forms of knowledge making.
Apple talked about how their new effort focused on opening up health research is designed to combat five main current issues:
- Limited Participation
- Small sample sizes limit our understanding of diseases
- Reliance on subjective data
- Infrequent data provide only snapshots through time
- One-way communication from researcher to participant (and only at the end of the study, if at all)
Furthermore, the design of ResearchKit allows the participant to decide how data is shared. Apple will not see the data. Participants are allowed to be involved in the data collection in real-time, using the data they’re collecting to understand and inform their own health improvement plans.
In light of today’s announcement we wanted to highlight some of our favorite and most powerful examples of taking the research process into one’s own hands, making their own knowledge through thoughtful data collection and reflection. We invite you watch what’s possible now, and imagine with us what could be accomplished tomorrow.
Last year we gather a fantastic group of researchers, toolmakers, and science leadership at the 2014 Quantified Self Public Health Symposium to discuss how personal data can impact personal and public health. That meeting culminated in a great report that touches on many of the aspects discussed today regarding ResearchKit. We invite you to download, read, and share that report. For a more nuanced look into how ResearchKit may impact the research community, we’re highlighting four great talks from the the meeting.
Susannah Fox shares research from the Pew Internet and Life Project and describes the challenges ahead for promoting self-tracking.
Margaret McKenna explores the issues, challenges, and ideas large scale self-tracking applications have in mind when they consider working with the research community.
Jason Bobe talks about the lessons learned from involving research participants in the data ownership and discovery process.
Doug Kanter describes what he’s learned from tracking and visualizing his diabetes data.
If you’re interested in how ResearchKit will be affecting self-tracking, personal data, and access to information, research and knowledge making, then stay tuned to our Access Channel here on QuantifiedSelf.com and on Medium.
We are sure to have many great talks and sessions that focus on ResearchKit at our QS15 Conference and Actrivate Exposition. We invite you to join us.
This past fall we learned about a unique study, conducted at Stanford University, designed to contribute to the understanding of the human microbiome. This study also has a component not common to academic research — data is being returned to the participants. Intrigued, I contacted the principle investigator, Les Dethlefson, to learn more.
Ernesto: Tell me about the Dynamics of Human Microbiota study.
Les Dethlefsen: Since I joined the Relman Lab at Stanford, I’ve been looking at the human gut microbiota, focusing on what affects it and how it changes over time. In our study, we are looking at three different perturbations, deliberate changes to the gut ecology, to see how the microbiota population is affected.
We are very interested in the patterns that emerge. In people who have very stable gut microbiota, does their microbiota remain that way when they undergo diet shifts, a colon cleanout, or an antibiotic? Or maybe people who have a stable gut microbiota most of the time are the ones who are most affected by something unusual such as taking antibiotics. We just don’t know enough to understand these patterns right now. So, we’re really looking for basic ecological information.
Ernesto: If you look at the popular press, it seems the microbiome is the new golden child of biological life sciences. We’re even seeing companies in Silicon Valley get involved with this kind of work.
Les: It is broader than that. It really is a worldwide interest on the parts of both the scientific community and the public. And unfortunately, we are probably going to see some overhype, just as we did with the Human Genome Project. But I do believe this is a very important area. I think there will be a lot of payoffs and health impacts from this research, although it’s not going to be everything.
The shift that, I think, would be good for us to make intellectually is to get rid of the “us vs. them” thinking, because we are symbiotic organisms.
We have evolved with a native gut microbiota, and native microbiota is pretty much everywhere. We have evolved together, so it’s fallacious — an artifact of our past ignorance — that we don’t think of our microbes as part of our physiology.
Ernesto: It seems like exploring the deep sea, an unknown world that we’re just starting to peek into.
Les: It’s along those lines. You’re not wrong about that. But unlike, let’s say, the deep waters surrounding an undersea hydrothermal vent, we already know a lot about human physiology. There are a lot of molecular details and genetic pathways that we already have worked out. The context is somewhat understood.
And now, we have a reasonable start on the initial research: What microbes are present, and where? What’s the range of what we think is the normal distribution? We certainly don’t know enough, because we only know about people in the developed world. However, this may not represent all of human diversity or a very natural state of the gut microbiota.
Ernesto: Let’s get back to your study. You are asking participants to send microbiome data in the form of fecal matter and urine to your lab. What are you doing with those samples?
Les: We ask participants to provide both stool and urine samples. With the stool sample, we apply four different methodologies to turn it into data. One is the very common 16S ribosomal RNA (16S rRNA) gene sequencing approach. It’s relatively standard and inexpensive. It acts like an ID card for microbial taxa — telling us approximately what strains are present and in what relative abundance. We have a lot of data like that already for comparison.
The second approach we will be applying is metagenomic sequencing, wherein we will be sequencing a random selection of all the genomes of the microbial types that are present. We can’t take this to completion, even with the dropping cost of sequencing, especially because there are some very, very rare microbes that we barely even have the chance to see at all. But we can get a pretty good swathe of genetic sequence data from all the microbes.
The third approach is even more ambitious. It’s called metatranscriptomics. Genes can be carried by any critter, you and I included, but not expressed. Knowing which genes are turned on, and to what extent they’re turned on is a better measure of the biological activity that is actually happening. The metagenome is a measure of potential activities, what the bugs can do. The metatranscriptome shows what the microbes are actually doing. Metatranscriptomics is even more challenging than metagenomics partly because of the nature of messenger RNA (mRNA). It’s a highly unstable molecule. There are technical challenges, but we’re ambitious enough to try to collect information on gene expression.
The fourth approach is not based on gene sequences, but on chemical composition. Metabolomics is the name given to a number of these approaches that are not directed to a specific chemical. These are techniques that try to measure a broad swathe of chemicals present in the environment and their relative abundance. This is a technology that we, in the Relman Lab, know very little about. We’re collaborating with the Nicholson Lab in Imperial College in London, and they will be doing the metabolomic analyses on the stool samples. That may be even closer to where the rubber meets the road — knowing not just the gene expression but also the resulting chemical changes that are happening in the environment.
Metabolomics takes us to the other type of sample we’re collecting: the urine samples. We aren’t doing this because we have an interest in the urinary microbiome itself, but because, as the Nicholson Lab suggested, the urine provides a more complete, integrated picture of the co-metabolism between the human host and most of the gut microbiota. So while metabolomics for the stool samples would primarily measure the gut microbial activity and what they contribute to the host’s physiology, the urine provides a more integrated picture about how the host metabolism works in concert with the gut microbiota.
Ernesto: If a participant is going to be contributing all of that data, will they have access to it?
Les: As someone with similar interests, I certainly knew that a huge motivation for people to join the study would be the access to their own data. We offer monetary compensation, but for the amount of time that will be spent in contributing samples, it is probably trivial. We knew we would attract the curious, scientifically inclined, and practising scientists. Of course, they would want to see their data.
The Institutional Review Board (IRB) was quite open to us sharing information with the participants about their own microbiota. It probably helped that there’s publicity about ways people can get this information. There is the American Gut project, offering an assessment of your microbiota for a donation, and uBiome, a private company offering the same kind of service.
I, or another staff member of the study, are going to share this microbiota data with each participant in a conference call. So in effect, I’m going to be a microbiota counselor. It’s nowhere near as high-stakes as sharing genome information. We don’t know enough to say, for example, that this microbiome is definitively healthy, or that it’s unhealthy, or what the exact risks of diseases are due to this particular composition. So we will be putting this information in context, and we will be available as interpreters of the scientific literature. We may be able to say that there is a statistical association between a particular microbial group that someone may have in their gut and some health-related outcome.
Ernesto: Will participants be getting a copy of their data as well?
Les: Yes, we will provide that. I have an open source mentality. Added to that is the fact that there are many practicing scientists signing up for the study and saying they want data, not just a PDF summary. I am happy to provide the data in as raw a format as people want. They can get the raw sequence information, a low-level summary (which is the result of the first pass of data processing), or the final summary. I have permission and full intention to share all the data derived from a person’s samples with that person.
Ernesto: Do you think we will see this happening more in the future?
Les: I think we will probably see more of it in the future. We’re moving in the direction of access to information. The open source movement has reached the health and medical realm from its origins in tech and computing. I think the participatory nature of access to data and scientific information is a good thing. It has started, and I don’t see any way of reversing the trend. I would hope that it becomes the norm that there is some appropriate level of sharing, that research participants have access to their data if they wish, and in a way that lets them interpret that data appropriately.
I believe that people have a right to that level of knowledge about their bodies, and if we, scientists, are generating that knowledge, there’s no reason not to share it with the individuals.
The Dynamics of Human Microbiota study is currenlty recruiting participants. If you’re interested in learning more about the ecosystem within read more about the study and check to see if you’re eligible to participate here.
This morning President Barack Obama announced a new Precision Medicine Initiative, a key $215 million piece of the proposed 2016 budget. Much has been written since last week’s State of the Union, when this initiative was first mentioned by President Obama. In brief, the initiative is an investment in new programs and funding initiatives at major government bodies that influence the current and future health of all Americans, including the National Institutes of Health (NIH), the Food and Drug Administration (FDA), and the Office of the National Coordinator for Health Information Technology (ONC). These programs will focus on developing “a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.”
There is a lot of information being circulated about this new initiative, and we’ve collected some links below, but we’d like to highlight something directly related to our interests in self-tracking data, personal data access, and new models of participatory research. In this morning’s announcement President Obama mentioned a long-term goal of creating a participatory research cohort comprised of 1 million volunteers who will be called upon to share personal medical record data, genetic samples, biological samples, and diet and lifestyle information. This is truly an ambitious goal and we are happy to see the President take care to mention the importance of including patients and the individuals who collect this data in the decision making and research process. For example, here is the description of this specific program from the NIH Precision Medicine Infographic
Here at QS Labs, we’re dedicated to helping create and grow a culture that enables everyone to generate personal meaning from their personal data. Sharing, participation, and exploring new models of discovery are a core themes we’re exploring as part of our QS Access work. We’ll be following this initiative as it moves from today’s announcement to tomorrow’s reality. Be sure to stay tuned to our QS Access Channel for more updates as we learn more.
Learn more about the Precision Medicine Initiative
NIH mini site describing the initiative
White House Blog: The Precision Medicine Initiative: Data-Driven Treatments as Unique as Your Own Body
FACT SHEET: President Obama’s Precision Medicine Initiative
A New Initiative on Precision Medicine by Francis Collins and Harold Varmus (New England Journal of Medicine).
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.
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 Future, Health 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
We hope you enjoy this week’s list!
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.
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!
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!
Tracking Energy use at home by reddit user mackstann.
“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).”
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.