Search Results for: weight

What We Are Reading

WWAR

Articles

The DIY Scientist, the Olympian, and the Mutated Gene by David Epstein. There are several surprising twists in this story of a non-professional scientist named Jill Viles, who made an important discovery about her own rare genetic disorder. What inspired me mostwas Viles’ tenacious reliance on her own capacity to reason, even in the face of skepticism from professionals who had less knowledge (though more confidence) than she did. Eventually, she connects with highly technical scientists whose research direction she influences with her ideas. Epstein got a fantasic quote from one of them when he asked the scientist if this has ever happened before. “In my life, no,” he says. “People from outside coming and giving me hope? New ideas? I have no other example of this kind of thing. You know, maybe it happens once in a scientific life.” I found myself wondering if this kind of thing will be less rare in the future. -Gary

A Drug to Cure Fear by Richard A. Friedman. This article intersects two of my interests that stem from my own self-experimentation. From my stress tracking I realized that many of my reactions in my day-to-day life are influenced by traumatic memories. From my spaced repetition practice I learned how memories can change over time through retrieval and consolidation. A study done in the Netherlands suggests that a memory can be decoupled from an associated fear response by using propranolol which blocks the effects of norepinephrine, a  chemical  that strengthens connections in the brain. The study has yet to be replicated, but hopefully it will increase our understanding of trauma.  -Steven

Internet of Things security is so bad, there’s a search engine for sleeping kids by J.M. Porup. Ever since doing a research project on data flows for our first Quantified Self symposium we’ve had what you might describe as a below average level of confidence in the security and reliability of information traveling outside the immediate context of its collection, now that APIs connect to APIs connecting to yet other APIs. Still, even I was surprised by the recklessness and potential harm described in J.M. Porup’s brief account of a search engine that displays random pictures from internet connected consumer cameras around the world. -Gary

Algae bloom toxin linked to Alzheimer’s, other diseases by Amy Kraft. One consequence of the climate change and the depletion of fish stocks in ocean’s is the increase occurrence of algae blooms. Ethnobotanists found a correlation between algal blooms and neurodegenerative diseases among remote populations in the Pacific. New research suggests that cyanobacteria, the microorganism in these blooms, has a neurotoxin that can cause neurodegenerative precursors that develop. This neurotoxin enters the human food chain as it bioaccumulates in fish and shellfish. -Steven

Show&Tell

Glass Half Full Succeeds in Unwinding Upsets by Paul LaFontaine. Most people have moments of irritation or worry throughout the day. Paul wanted to find out what worked better as a response to these moments. Option A was to step back and observe his emotions in a manner similar to that taught by some schools of meditation. Option B was to figure out the source of irritation or concern and think of a positive angle to the situation. What is great about this post is the very simple but illuminating experiment that he devised to explore this question. -Steven

Finding My Optimum Reading Speed by Kyrill Potapov
As an English teacher Kyrill Potapov spends a lot of time working with 12 year old kids who are trying to improve their reading, writing, comprehension, and analytical skills. In this talk, he explores a remarkable method of speed reading, called Spritz, that promises to let you “read Harry Potter in three hours” with full understanding and recall. Could such a promise possibly be true? -Gary

Heart Rate Variability, Body Metrics, and Cognitive Function by Justin Lawler. This is a great examination of how Justin’s HRV measurements correlate to all other personal data he has collected. -Steven

Using Spectrograms to Visualize Heart Rate Variability by Randy Sargent
Randy’s idea about using spectrograms, normally used for audio signals, to create a portrait of your own time series data, is completely novel as far as I know. -Gary

Visualizations

spuriouscorrefations

Spurious Correlations by Tyler Vigen. An entertaining collection of unrelated facts that can be correlated with a high degree of confidence. -Steven

FireCalcWeight

Hackers Diet, FIRECalc and weight loss by u/Thebut_. This chart is a mess, but the idea behind it is fascinating. This reddit user was inspired by FIRECalc, a financial tool that “projects your future assets based on historical market data” and tried to apply it to his weight data. Instead of giving a single projection, the tool shows a range of possibilities. This is similar to how Baseball Prospectus’s PECOTA system uses a weighted range of possibilities (probability distribution) rather than a single guess (point estimate) for forecasting a prospect’s future performance. I would like to see more of this kind of thinking applied to personal data. -Steven

Projects

Darwin Tunes by Bob MacCallum, Armand Leroi, Matthias Mauch, Steve Welburn, and Carl Bussey. A fascinating project that treats pieces of music like organisms that can mate and reproduce based on listeners’ votes. These audio loops started off as random noise, but as the generations moved into the thousands, the presence of chords and higher order melodies emerged. At this point, there have been over 8700 generations. You can take part yourself! -Steven

On the QS Blog
Quantified Self Public Health Symposium
Explaining Nightscout by Lane Desborough

From the Forum
Central repository for QS data
Best Pulse Oximeter of all-night logging
Open Source wearable bio-sensor: TrueSense Kit
How about quantifying and tracking your blood alcohol?

Posted in What We're Reading | Tagged , , , , , | Leave a comment

Re-Living My Life with Mood Tracking by Kouris Kalligas

Kouris Kalligas, a long time participant and contributor at Quantified Self meetings, is the creator of the very easy to use data aggregation service AddApp. AddApp is an iPhone app that makes it simple to gain insights from data gathered on dozens of different devices. While running his startup, Kouris has also been doing  ongoing self-tracking experiments. At QS Europe 2014, he gave a excellent show&tell talk about his sleep, diet, and exercise data. In the talk below, he discusses using mood data in combination with calendar data to reflect on the relationship between emotion, experience, and self-image.

Posted in Conference, Personal Projects, QS15 | Tagged , , , , | Leave a comment

What We Are Reading

WWAR

Articles

A man who tracked five years of sneezes might have a fix for your pollen allergy by Akshat Rathi. Thomas Blomseth Christiansen has spoken about tracking his sneezes at QS conferences. This article is a good telling of Thomas’s story.

Good tool with too small market can get a second chance – a hardware hack saves Zeo by Portabla Media. A short article on how Philipp Kalwies responded to the demise of Zeo. Since the sensors in the headband need to be replaced every three months and official supplies were dwindling on the secondary market, Philipp began to make his own and hopes to have this resource available to the small group of users who continue to get value from their Zeo devices.

The Right to Repair Ourselves by Kim Bellard. A common question in the QS community is “who owns your data?” Another question that should be given more time and is explored here, is “who owns the knowledge of how to ‘fix’ yourself?”

Show&Tell
ShannonConnorsSkulptAim

The Habits of Tracking My Diet and Exercise Data by Shannon Connors. Shannon has some of the most impressive personal data sets that I have ever seen. In this post, she gives an overview of the tools that she uses, what about the data she finds useful, and how she integrates the data collection into her day.

What you can learn from 2 years of Coach.me habit tracking + Machine Learning by Bryan Dickens. Applying association analysis to his coach.me data, Bryan was able to see which of his habits tended to occur together. There are some intriguing insights in here.

Visualizations
RobertRouseSleep
Visualizing Data in My Sleep with Tableau by Robert Rouse. Robert shows how his sleep patterns changed after the birth of his child.

From the Forum
What Keeps You Tracking?
My Phone and Me
Zeo iOs / Android application

Posted in What We're Reading | Tagged , , , , | Leave a comment

What We Are Reading

On with the links!

Articles

Get your electronic health record: It’s your right by Lisa Zamosky. Make sure to know your rights when you ask for your medical records. A good overview of the regulations and some good links to have in your arsenal.

Open Data Rockstar: Jennifer Pahlka by Maria Renninger. We’re big fans of what Code for America is doing to make open data useful for people in communities around the United States. Great to hear a bit from Executive Director.

Awash in Data, Thirsting for Truth by Margaret Sullivan. The public editor for the New York Times goes deep on how data can be used, and sometimes abused, in the new era of data-driven journalism.

Meet the Hackers Who Are Decrypting Your Brainwaves by Sean Captain. I’m fascinated by the growing presence of brain tracking devices out there. Great to see some grassroots groups looking to make sense of all that data.

When Discrimination Is Baked Into Algorithms by Lauren Kirchner. The code that governs our machines are written by people. Fallible people who have their own opinions and biases. Who make mistakes. But what kind of legal protections are needed when discrimination is the result of the computers that run that code?

Show&Tell
589aef6f-86b9-4a43-8919-551427cbaa7a-620x313 Why your bathroom scales are lying to you and how to find your true weight by Martin Robbins. Brilliant and fascinating post by Martin Robbins, who weighed himself every hour over a three-day period.

Soylent: What Happened When I Went 30 Days Without Food by Josh Helton. Say what you will about the techno-utopian food replacement product, but this is a great write-up on a month-long experiment to live off the stuff even while running nearly 70 miles per week. (Don’t forget to check out the data!)

Visualizations

open_jamierubin_net_v8_heatmap_html A Heatmap of Over 900 Days of Writing Data from My Google Docs Writing Tracker by Jamie Todd Rubin. Jamie shows us the data from the last two and a half years of tracking his writing.

From the Forum
Can You Quantify Inner Peace?
Psych Graduate Student Interested in QS Research

This Week on QuantifiedSelf.com
Announcing the 2015 Quantified Self Europe Conference Program
Track HRV, Make a Dashboard, and Have Fun with Fitbit at QSEU15

Posted in What We're Reading | Tagged , , , , | Leave a comment

QSEU15 Preview: Morris Villarroel on Slowing Time with a Lifelog

Morris Villarroel at QS14

The 2015 Quantified Self Europe Conference will commence in less than four weeks, bringing together the QS community to share what they’ve been learning with personal data.

Anyone who engages in any sort of self-tracking discovers that the data collected is not a mere recording of some aspect of your life. Rather, engaging with and reflecting on that data can change the way that you relate to an aspect of yourself. Something as simple as getting on a scale each morning can change the way you think about weight. Morris Villarroel has discovered a novel way that this relationship can develop. At this year’s conference, Morris will talk about how using a Narrative camera to keep a visual record of his days, along with detailed notes, has changed his subjective experience of time, “bringing it closer to the present.”

I experienced something similar when I used a spaced repetition system to memorize entries from my daybook. Frequently recalling recent events kept the past distinct and novel. When a month passed, it no longer seemed like a blur, but a container filled with distinct experiences that differentiated itself from any other month.

You can find out more about how Morris gleans value from his lifelog at the 2015 QS Europe Conference. In addition to his show&tell talk, Morris will be leading a breakout discussion on how we can learn more from our lifelogs. We invite you to join us in Amsterdam on September 18th & 19th for two full days of talks, breakout discussions, and working sessions! Early bird tickets are still on sale. Register today for only €149!

Posted in Conference, QSEU15 | Tagged , , , , | Leave a comment

What We Are Reading

We’re back again with another round of What We’re Reading. Before diving into the great articles and links below why not take some time to subscribe to our QS Radio podcast! We just released our fourth episode and would love to know what you think!

Want to tell us in person? Why not join us for our fourth QS Europe Conference this September in Amsterdam! Register now to take advantage of our early bird pricing.

Articles
Got Sleep Problems? Try Tracking Your Rest with Radar. by Rachel Metz. Researchers at Cornell University, the University of Washington and Michigan state are conducting research using off the shelf components to see if non-contact sleep tracking is possible. Turns out it is!

Apple’s Fitness Guru Opens Up About the Watch by Scott Rosenfield. A nice interview with Jay Blahnik here, where he speaks to Apple’s focus on self-tracking and fitness with the Apple Watch.

To share is human by Laura DeFrancesco. In this great news feature, Laura DeFrancesco exposes some of the issues with sharing personal data, as well as the initiatives hoping to break through those issues to help bring more data into the public sphere.

Using Twitter data to study the world’s health by Elaine Reddy. A great post here profiling John Brownstein and his work in Computational Epidemiology, specifically how he and his research team use public data sources like Twitter to tease out signals for health research.

Show&Tell

Charts.001 Comparing Step Counts: Apple Watch, Fitbit Charge HR, And IOS Withings App by Victor Lee. An awesome and in-depth post comparing almost two months of steps counts from three different tracking methods by our friend Victor Lee. Glad to see he put our QS Access app to good use!

Follow-up to how I lost over 40 pounds using HealthKit and Apple Watch by Jim Dalrymple. Jim tells his story of how using a variety of apps and tools, all linked to his Apple Healthkit app, helped him learn about himself and eventually put him on the path to sustained weight loss.

Tracking Confidence by Buster Benson. Buster always has something interesting to say about self-tracking. This time is no different. Here he briefly talks about asking himself, “how confident do I feel right now?”

Visualizations


The Heart Chamber Orchestra.

The Heart Chamber Orchestra – HCO – is an audiovisual performance. The orchestra consists of 12 classical musicians and the artist duo TERMINALBEACH. Using their heartbeats, the musicians control a computer composition and visualization environment. The musical score is generated in real time by the heartbeats of the musicians. They read and play this score from a computer screen placed in front of them.

From the Forum
How to acquire info about sent e-mails using gmail?
Looking for Android exercise tracking app
Resting Heart Rate tracking

This Week on QuantifiedSelf.com
QS Radio: Episode #4

Posted in Uncategorized, What We're Reading | Tagged , , , , | Leave a comment

What We Are Reading

A long list for this week’s What We’re Reading. I actually had to stop myself from adding in even more visualizations and show&tell examples! We’re always on the lookout for more though, so make sure tweet us your favorite links!

Articles
Fitted by Moira Weigel. A very thoughtful essay on gender, identity, and confession – all while using the Fitbit as the narrative backdrop.

What kind of love does the FitBit prepare us to feel? Is it self-love? Or is even the self of the exorexic a kind of body armor?

How to Build a Smart Home Sensor by Dave Prochnow. If you have 2 hours, $95, and know how to solder, then you too can build this DIY sensor to measure the temperature, humidity, light, and noise for any room in your home. If someone builds and tests this please let me know (Would love to see air quality sensors included too!)

It’s Hard to Count Calories, Even for Researchers by Margot Sanger-Katz. New research shows Americans are eating less, but can we really trust the data? Margot does an excellent job here of rounding up the various ways we measure food consumption in the United States while coming to a commonly heard conclusion – food tracking is just plain hard.

Hadley Wickham, the Man Who Revolutionized R by Dan Kopf. If you’re knee deep in data analysis, or just like poking around in stats software, you’ve probably heard of and used R. And if you’ve used R, then there is a good chance you’ve used many of the packages written by Hadley Wickham. Great read, if for nothing else you learn what the “gg” in ggplot2 stands for.

Heart patient: Apple Watch got me in and out of hospital fast by Neil Versel. When Ken Robson wasn’t feeling well he turned to his Apple Watch. After noticing lower than normal heart rate readings his checked himself into the emergency room and soon found out his hunch was right, he had sick sinus syndrome.

New Australian experiment rewards joggers with 3D printed chocolate treats based on exercise data by Simon Cosimo. Sign me up!

Show&Tell

Screen_Shot_2015-07-24_at_11.41.15_AMHow Does Giving Blood Affect Your Iron Levels? by Ryan W. Cohen. Simple and to the point blog post by Ryan explaining how he discovered elevated iron levels in his blood, and the simple test he tried to find out why.

The Quantified Athlete by Matt Paré. Matt is a minor league catcher in the San Francisco Giants organization. In this post, the second in a series (read Part 1 here), Matt discusses how he became interested in tracking his biomarkers, and what he’s experimenting with.

What I Learned When I Stopped Wearing a Fitbit After Seven Years by Michael Wood. Michael writes up a brief post on how he felt when he was separated from his Fitbit activity tracker.

1*n0JEGs6Mzgiri0kAUYPDkgHow I tracked my house movements using iBeacons by Joe Johnston. Joe uses a few iBeacons to find out where he spend time in his house. Fascinating idea, makes me want to play with this technology as well!

Visualizations

Screen Shot 2015-06-30 at 8.55.26 amVisualizing a Simpler RunKeeper Training Plan by Andy Kriebel. Andy presented his running data, and how he uses a few tools to keep track and visualize his data as he trains for a marathon. Follow the link and you can see his Tableu workbook, which includes a screencast of his presentation, and links to his workflow.

gIV8BlGI decided to take a peek at my Netflix viewing data by Reddit user AmericanPicker69. This enterprising individual decided to take a peak into his user account to understand his Netflix viewing habits. Turns our a simple copy/past is all you need to do to get the raw data. Who knew?!

eUKFHBOMy weight loss journey by Reddit user IMovedYourCheese. Loved this graph and the implementation of BMI categories, a moving average, and lower/upper bounds for weight loss. He even provided the excel template if you’d like to use it with your own weight tracking.

From the Forum
Teach Arduino from Beginner to Making a Quantified Monitor
Google Fit

Posted in What We're Reading | Tagged , , , , | Leave a comment

2015 QS Visualization Gallery: Part 3

We’re excited to share another round of personal data visualizations from our QS community. Below you’ll find another five visualizations of different types of personal data. Make sure to check out Part 1 and Part 2 as well!

eddie-flights Name: Edward Dench
Description: All recorded flights I’ve taken.
Tools: Manual entry into openflights.org (there is an interface using TripIt though).

 

QS Visualization Name: Siva Raj
Description: After 6 months of regular exercise failed to improve my fitness and blood pressure levels, I switched to training above my endurance limit (anaerobic threshold). This was higher intensity but half the cycling time, yet my fitness and blood pressure improved within weeks.
Tools:Revvo – tracking fitness and intensity of workout; Withings – weight; iHealth BP Monitor – BP. Visualization created by overlaying Revvo screenshot with other information in photoshop.

 

Screenshot 2015-06-05 08.07.14 Name: Kurt Spindler
Description: Grafana is a common tool in the Software community to create beautiful dashboards to visualize server health (network, requests, workers, cpu, etc.) and therefore more easily diagnose problems. I created a custom iOS app that allows me to publish metrics to the same backend as Grafana, giving me Grafana dashboards for my personal health.
Tools:Custom iOS app, Grafana, Graphite
RyanODonnell_PagesReadPerMonthName: Ryan O’Donnell
Description: This semi-logarithmic graph is called the Standard Celeration Chart (SCC). It’s beauty is that anything a human does can be placed on this chart (i.e., standardized display). This also allows for cool metrics to be developed that lend well to predictability. I charted the number of pages that I read for my field of study, Behavior Analysis. I wrote a blog post on the display to speak some to the reading requirements suggested by professionals in the field. There were many variables that led to variations in reading rate, but the point of this work was to try and establish a steady reading repertoire. A recent probe in May of 2015 was at 2800 pages read. Essentially, I learned how to incorporate reading behavior analytic material almost daily in my life, which indirectly aids in the effectiveness I have as a practitioner and supervisor.
Tools: Standard Celeration Chart and paper-based data collection system (pages read each day on a sheet of paper).

 

Graph4_red_black Name: Francois-Joseph Lapointe
Description: This *Microbial Selfie* depicts the gene similarity network among various families of bacteria sampled from my gut microbiome (red) and oral microbiome (black). Two bacteria are connected in the network when their gene sequences are more similar than a fixed threshold (80%). The different clusters thus identify bacterial families restricted to a single body site (red or black) versus those inhabiting multiple body sites (red and black).
Tools: In order to generate this data visualization, samples of my oral and gut microbiome have been sequenced on a MiSeq platform by means of 16S rRNA targeted amplicon sequencing, and the resulting data have been analyzed using QIIME, an open-source bioinformatics pipeline for performing microbiome analysis. The gene similarity network was produced with the open graph viz platform Gephi, using the Fruchterman–Reingold algorithm.

Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!

Posted in QS Gallery, QS15 | Tagged , , , , , | Leave a comment

Meetups This Week

There are three very interesting QS meetups occurring this week. Chicago’s event will be fitness focused, with talks on what it’s like to work out with a weight system that changes it’s resistance in real-time based on your performance and effort and learning from DXA body composition data. Shanghai will have a researcher talk from Preston Estep on using genetic data to improve health.

Ashland will have an amazing sharing of progress on current n=1 projects. Projects include exploring deep sleep with Beddit, looking at the difference between breath-based and blood-based ketone readings, and testing the effects of berberine on postprandial glucose rise. The last one is interesting is because it is placebo-controlled and double-blind, which can be difficult to pull off. I would love to hear more about his experiment design.

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own!

Monday, July 20
Chicago, Illinois

Tuesday, July 21
Shanghai, China

Sunday, July 26
Ashland, Oregon

Photo from QS Montreal’s meetup last week

What a beautiful venue. If you organize a QS meetup, please post pictures of your event to the Meetup website. We love seeing them.

QSMontrealJuly
Photo credit: Maxime Chabot

Posted in Meetups | Tagged , | Leave a comment

2015 QS Visualization Gallery: Part 1

In 2013, just prior to our our Quantified Self Global Conference, we asked conference attendees to send us examples of their own personal data visualizations that they found especially meaningful. We were blown away by what everyone shared with us. From visualizations of blood glucose readings to GPS traces and plots of time tracking and productivity, the range of visualizations was astounding (you can view some of those visualization by searching the blog for the QS Gallery tag).

This year, we sent out the request once again to attendees of our QS15 Conference and Expo. Once again, our inbox immediately started to fill up with images, graphs, and visualizations describing the tracking experiences of our amazing community. Today, we’re excited to start sharing those visualizations with you here.

Beau Name: Beau Gunderson
Description: A homemade polysomnogram with a Zephyr Bioharness as the only data
source.
Tools: IPython, matplotlib, pandas, seaborn, numpy.

 

Seasonal compliance Name: Shannon Conners
Description: This graph shows what initially looks like an interesting trend in my activity data. I seem to be less active during the summer months, but when I pair my activity and wear time for the BodyMedia FIT armband I used to generate the data, the real reason for the drop becomes clear. I’m wearing the armband less in the summer months to avoid upper arm strap tan! I know my own device usage patterns, so when I graphed the two measures together, it was immediately clear to me what was going on. To me, this is a simple example that illustrates one of the big challenges of looking at activity monitor data in the absence of data about device usage. Usage patterns can and do change over time and the reasons for these changes may not be as obvious as the change of the seasons. For example, something as simple as breaking the clip-on case you use to carry the phone that counts your steps could greatly impact how often you carry it, and therefore the quality of the data you collect. Some monitors don’t even record a usage metric with which to compare activity data. I like this graph as a reminder that interesting patterns may in fact be data collection or data quality issues in disguise.
Tools: BodyMedia FIT Core BW, JMP

 

HeadsUp Name:: David Korsunsky
Description: Mashing data from my favorite wearables, my medical records as well as data I track manually into a custom dashboard.
Tools: Heads Up Health is software that can enable anyone to create their own custom configurations.

 

4fcfb36b86f2241013000002_graph Name:: Daniel Reeves
Description: Number of (read) messages in my inbox over time.
Tools: Beeminder’s GmailZero.com

 

QSHRVSeasonalTrend Name:: Jo Beth Dow
Description: Trend analysis of my HRV over a 2.5 year period. Displays a stunning seasonal trend.
Tools: iPhone running SweetBeatLife app to measure clinical grade HRV on a daily basis.

Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!

Posted in QS Gallery | Tagged , , , , , | Leave a comment