Tag Archives: qs18

QS18 Preview: Maggie Delano and the Pomodoro Trail to a PhD

Maggie’s Tools: Strict Workflow and RescueTime

Maggie Delano is a professor of engineering and very experienced self-tracker whose pioneering work on DIY measurements systems includes a fluid status monitor for patients with congestive heart failure and a wearable device that continuously measures single lead electrocardiogram (ECG) and three axis acceleration data for up to one week. She wrote the first Quantified Self code of conduct, organized a sponsorship program for QS15, and started the QS Boston Women’s Meetup.

In addition to her DIY hardware projects, Maggie has also spent time tracking her work efficiency, a project she’ll present at QS18. While completing her PhD dissertation, she wondered if she could make her time more efficient rather than just adding in more work hours during the day. She began using the the Pomodoro Method (25 minutes of work followed by a 5 minute break) and collected a very large amount of data that helped her learn about her personal work habits.

Her instrumentation included RescueTime to track her computer usage and Strict Workflow to cut out distractions during each pomodoro segment. After collecting this data over five years and redefining a successful work day in terms of distraction-free time instead of total time, she established a “cadence” for productivity. This deceptively simple project underlines the fact that the value of self-tracking doesn’t lie in complex tools or advanced technology, even for people who are expert engineers. To learn and practice a good working cadence, complex tools would be disruptive. RescueTime and Strict Workflow are relatively simple ways to create a record and restrict distractions, and this alone formed the basis of important learning.

Maggie will present her project and answer questions at QS18 Conference coming up in just two weeks. If you come, you can also ask her insanely deep questions about recording electrical signals from the body.

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QS18 Preview: Esther Dyson and Three Sleep Trackers


From left to right: the Oura Ring, ResMed S+, and Whoop

Esther Dyson is a board member of 23andme, former chair of ICANN and the Electronic Frontier Foundation, and an investor in companies like Omada Health, PatientsLikeMe, and Medspace. But, like the rest of us, she spends a good portion of her life unconscious. While sleeping, she collects data with three different devices: Oura, Whoop, and ResMed. Each device tracks slightly differently: the Oura tracks sleep via a ring worn on the finger, while the Whoop is a wrist-based tracker and the ResMed S+ sits near the bed and uses radio waves to detect body movement. Unsurprisingly, the devices don’t always agree.

In her upcoming Show&Tell talk at QS18, Esther will talk about why she uses three sleep tracking devices and how she interprets their disparate outputs, providing a close look at the type of questions that interest many of us when we’re starting a self-tracking project. What constitutes “accurate” data? What are the differences between supposedly reliable tools? From the ill-fated Zeo to the S+, Whoop, and Oura, sleep has been a notoriously difficult metric to track. Whether it is because of an inaccurate sensor or the difficulty of distinguishing sleep patterns of the target individual from surrounding noise, sleep has been much tougher to track than many other metrics. Dyson’s talk provides insight into how to deal with those difficulties and still create useful insights from sleep data.

By looking at data from multiple sensors, Dyson gives a picture of how data can change according to the instrument used to record it, but also points to a central feature of a successful self-tracking project: that is, to create useful, actionable conclusions in the context of one person’s life, answering questions not about the universally verifiable accuracy of sleep measurement, but about her own sleep.

You can check out the full schedule of show&tell talks, breakout discussions and how-to workshops at the 2018 Quantified Self Conference in Portland, Oregon.

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QS18 Profile: Aaron Parecki and 10 Years of Location Data

If you were to look at Aaron Parecki’s map of his hometown, there is only a slight chance you’d recognize it as Portland, Oregon. Some roads are brightly colored thick lines that stand out against a black background and others are thin, barely visible filaments that are easy to miss. There are no marked roads and no scale. It’s the last tool you’d want to use to get from point A to point B. Even so, in a different sense might be the most detailed and most personal map of Portland ever created, made from a four years of location data recorded at increments of 1-6 seconds. Aaron’s map of self-tracking data is a strong reminder that we interact with the places we live on our own terms that aren’t necessarily dictated by the roads in front of us.


Aaron’s movement in Portland from 2008 to 2012. Aaron has continued to track his location and now has ten years of data.

The image shows Aaron’s location from 2008 to 2012, but he has continued to log this information and has recently reached the ten year mark.  The tools he’s used has changed over time, but they have increasingly mirrored the deeply personal element behind the maps. Aaron’s self-designed iOS app Overland passively tracks location while the tracking server Compass stores the data, making the actual recording process unobtrusive and creating an easy platform to retroactively analyze the data. Both are open source and allow other self trackers to modify the technology to their own projects. Likewise, Aaron has developed both the app and API with the intent of individuals owning their own data. Because location tracking includes not just the physical location of a person at any point in time, but also a detailed picture of their movement habits and areas of interest, both tools include privacy controls to make sure that the user retains ownership and access to the data.

A comprehensive record of location data can easily be mapped on to mood, biological markers, or any other data recorded at the same time. With these tools, it would be possible to see how passing through a specific area of town affects mood or if a particular commute correlates with a change in weight or heart rate, giving context to metrics on how our bodies are functioning that are normally dissociated from the surrounding environment.

Aaron’s project is a good map of his own experience of Portland and the tools he uses provide a blueprint for your own location-based tracking project. He’ll be leading a workshop at the QS Conference this month, so if you want to talk about how you can apply these approaches in your own projects, you’ll have a chance to meet him there.

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