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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!
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.
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.
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?”
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.
This Week on QuantifiedSelf.com
QS Radio: Episode #4
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!
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!
How 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.
How 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!
Visualizing 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.
I 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?!
My 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.
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!
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.
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
Name: 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).
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!
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
Tuesday, July 21
Sunday, July 26
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.
Photo credit: Maxime Chabot
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.
Name: Beau Gunderson
Description: A homemade polysomnogram with a Zephyr Bioharness as the only data
Tools: IPython, matplotlib, pandas, seaborn, numpy.
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
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.
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!
It’s finally here.
Next Thursday we’re welcoming over 450 self-trackers, inventors, artists, toolmakers, researchers, and scientists to the 2015 Quantified Self Conference. Over two days were hosting over 130 different talks, sessions, and demos that showcase the ingenuity and expertise of our community. We create our program from the ground up, soliciting ideas from each individual that registers, and this year we’re excited to have over 100 different attendees contributing to the program. It’s going to be great.
Here’s just a few examples of the amazing Show&Tell talks, Breakout Discussions, Lunchtime Ignites, and Office Hours we have planned.
THREE YEARS OF LOGGING MY INBOX COUNT – Mark Wilson
The number of emails in my inbox correlates very well with my stress level. After passively tracking this number for three years, I explore what this and other data says about how I’ve controlled (and been controlled by) this stream of angst.
TRANSCRANIAL DIRECT CURRENT STIMULATION (TDCS) TO MANAGE MY STRESS. – JD Leadam
Transcranial Direct Current Stimulation (tDCS) is an emerging “at-home” method for influencing the brain using very low voltage electrical current applied to the scalp. I’ll show how I’ve used tDCS in conjunction with self tracking methods to assist in controlling my stress.
TIME AND INTENTION TRACKING – Allan Johnson
Does tracking my intentions affect how I spend my time? Using an app for self-reporting, I compared how I spent my time when tracking both my intentions and time.
CAN’T YOU SEE I WAS FALLING IN LOVE? – Shelly Jang
As I struggle with the iron discipline required for keeping consistent logs, I am often forced to look into what I call passively collected data sets. I explored whether I can excavate data artifacts from past and correlate them with known life events. Using Google hangout conversations, I ask “can’t you see I was falling in love?”
28 YEARS OF TRACKING: BUT WHAT HAVE I LEARNED? – Nan Shellabarger
I’ve got lots of data – weight, activity, sleep, and health. I find as I keep reviewing it, visualizing it in different ways, always looking for patterms, there are still things to be learned.
USING HEART RATE VARIABILITY TO ANALYZE STRESS IN CONVERSATION – Paul LaFontaine
I measured stress during conversations using off-the-shelf technology. The results were unexpected and at times funny; with some lessons for me about my “fight or flight” response.
IN PRAISE OF BAD DATA COLLECTION DURING EARLY FATHERHOOD – Thomas Richardson
Sleeplessness and the pressures of birth and postpartum life drove me to to collect information and quickly discard methods that appeared wasteful. Looking back, did the kinds of information I collected tell me more than the data itself?
RE-LIVING MY LIFE WITH MOOD TRACKING. – Kouris Kalliagas
I used an email-based mood tracking service for several months. I never used the data in any way till I noticed something which triggered me to look back at my mood tracking data and search for patterns.
TRACKING BABIES! – Morgan Friedman
Like many parents, I tracked my newborns. By comparing my records with those of other parents using the same app I learned some interesting things about my son. I’m curious to see how they play out as he grows up.
HACKING OUR MICROBIOME – Alexandra Carmichael, Richard Sprague
Today it’s possible to get data on the microbes that live in our gut using personal genomics. We’ll lead a breakout workshop on understanding and hacking our microbiome.
THE QUANTIFIED SELF AT WORK – Joost Plattel, Phoebe Moore
More than 13 million wearable fitness tracking devices will be incorporated into employee wellbeing and wellness programs 2014-19. We will discuss how self-tracking and monitoring are used in working spaces whether traditional or freelance. What are the advantages/disadvantages of quantifying the self at work?
AGGREGATING MULTIPLE DATA SOURCES FOR SELF-KNOWLEDGE – Anne Wright, Randy Sargent
We’ve been working on aggregating, visualizing, and analyzing data for personal benefit, using multiple self-tracking sources. We’ll share our methods, and invite you to comment, ask questions, or share your own.
SEX, SEXUAL HEALTH & QUANTIFIED SELF – Ilyse Magy
Cycles, lovers, positions, kinks, symptoms, stats, safety: how can tracking sexual activity benefit our experiences? We’ll talk about what tools you’re using but mostly dream up the tools we would want to use. This is a sex-positive, feminist, inclusive space open to all gender identities.
QSXX: WOMEN-SPECIFIC QS CONVERSATIONS – Amelia Greenhall, Maggie Delano
Women-centered QS meetups in SF, Boston, and NYC have created space for important conversations. Nicknamed “QSXX” (though not all women have two X chromosomes), this breakout session is specifically for people who identify as a woman to talk about QS experiences.
THINKING THROUGH DATA ACCESS AND PRIVACY – Kendra Albert
How do you view third-party access to your data: either by governments, advertisers, or corporations? Are certain types of data okay to share but others make us feel icky? We’ll focus not just on privacy “in general” but on specific types of circumstances in which data might be shared, trying to draw lines between types of data and uses.
WHAT IS THE SELF IN QUANTIFIED SELF? – Natasha Schull
How do digital tracking technologies engender new modes of introspection, understanding, and self-governance?
Lunchtime Ignite Talks
THE DIGITAL HEALTH COACH – Glennis Coursey
You might have everything you need to be healthy – wearables, health apps, a wireless scale. But without the motivation and support to actually get healthy, change can be hard. That’s where digital health coaches come in. Glennis shares what she’s learned building digital health coaching programs at Sessions and MyFitnessPal.
FIGHTING PARKINSON’S DISEASE WITH DATA: ROUND THREE – Kevin Krejci
Round three in the proverbial boxing ring between Kevin and Mr. Parkinson, as he updates us on his progress tracking multiples symptoms and therapies with multiple gadgets to slow the progression of this progressive neurodegenerative disorder. Sleep and biome discoveries highlighted in this round…
A QUEST FOR HIGH FIDELITY ACTIVITY TRACKING – Jamie Williams
Jamie will show us how he is building tools to capture a timeline of his daily activities and explore his habits through data visualization.
AM I BEING INTENTIONAL? – Beau Gunderson
The challenges of tracking (and defining) intentionality.
WHY I WEIGHED MY WHISKERS – Jon Cousins
When I was diagnosed with bipolar affective disorder, I noticed that my libido seemed to, er, rise and fall as my mood changed. Could this be due to a variation in testosterone? And might the rate of growth of my beard be one way of measuring this? I borrowed accurate laboratory scales and started daily mood tracking and whisker-weighing.
YOU HAVE NO IDEA WHAT YOU’RE DOING – Cara Mae Cirignano
Whatify allows you to collect data in a mindful way in pursuit of a specific question, instead of just gathering reams of data and then rooting around for insights. We use the most powerful tool of professional researchers, randomized experimentation, to help you easily isolate and understand one decision at a time. No experience whatsoever required.
FRICTIONLESS TRACKING WITH BEEMINDER AUTODATA – Danny Reeves
Beeminder is Quantified Self plus commitment contracts: data-oriented behavior change. But mustering the discipline to enter data can be a catch 22. We’ll discuss the myriad ways you can automatically collect data about yourself with Beeminder, highlighting our partnerships with other QS mainstays like RescueTime, Fitbit, Withings, Zapier, and IFTTT.
SHERBIT – Alexander Senemar
Your apps and devices are constantly generating data about you. Sherbit puts it all together so you can easily understand and analyze your information, keeping the integrated day firmly under your own control.
REVVO – Siva Raj
Revvo is a smart exercise bike. Unlike apps and wearables that track activity (steps, calories, distance etc.) Revvo actually tracks your fitness – and helps you train smart – so you see quick results.
EXPLORING TOMORROW – Ryan O’Donnell
Exploring Tomorrow focuses on teaching students how to quantify their daily interactions and goals through the use of self-management tools developed through the science of behavior to align each student’s values with their daily actions.
HEADS UP HEALTH – David Korsunsky
Heads Up Health helps consumers combine medical, wearable and self-collected data with personalized analytics and insights.
SIREN – Ran Ma
At Siren we believe that prevention is the best medicine – we combine smart textiles and user-centric software to give people actionable data in order to make informed decisions about their health. The first product that we are working on is a sensor embedded sock that tracks temperature, combined with a smart wearable anklet tracking motion that connects to a smartphone via BLE.
PERSONAL DATA BANK – Arkadiusz Stopczynski
Personal Data Bank with SafeAnswers allows users to collect, store, and give fine-grained access to their data all while protecting their privacy. With this infrastructure available as a service, developers can create applications powered by personal data in an easy and scalable way.
PROACTIVE LIFE – Daniel Gartenberg
I work on a variety of projects to track and improve sleep. This includes smart phone sleep trackers, providing different types of auditory stimulation during sleep, and figuring out alertness using simple reaction time tasks.
FITABASE – Aaron Coleman
My company helps researchers use Fitbit data to make discoveries in public health and behavioral science. Stop by and I’ll show you how.
That’s just a sample of the over 130 different sessions at the conference. We’re nearly sold out so register today!
Next week we’re hosting our QS15 Conference and Expo and we’re delighted that so many great toolmakers will be joining us to show off their devices, apps, and services. We’ve asked each of our toolmakers to give us a bit more background information about their company and what they’re excited about. If you’d like to meet these innovative companies and the amazing people behind them then make sure to register today!
1. How do you describe Addapp?
Addapp is a free iPhone app that provides personalized insights into your well-being from data you already produce with wearable devices & apps. For example, Addapp can detect a relationship between your protein intake and your weight, as well as between your deep sleep and your cycling.
Addapp has made a conscious decision to focus on providing awesome insights, but it also expands on them by giving users more background info and suggestions but also calls to actions.
2. What’s the backstory? How did you get started?
Around two years ago, CEO Kouris Kalligas started using all kinds of fitness apps/devices and put all of his data into one Excel document. He wanted to understand any important correlations or other relationship types within his data. He ended up with such a big spreadsheet filled with findings and he quickly realized keeping up with it was not sustainable. He got involved in the Quantified Self community and joined the QS conference in Amsterdam. It took him only one day to realize that he was not alone in this problem. This is why he started Addapp: to make sense of the data produced by wearable devices and apps people use to track their well-being (running, cycling, sleep, biometrics, etc.).
3. What impact has it had? What have you heard from users?
Addapp is at early growth stage with thousands of signups and active users. We are learning every day what users want and optimizing insights for it. They are asking for smarter and more engaging insights, and that’s exactly what our team is doing in the background. In the fall of 2015, we plan to make an even bigger splash in the market with new functionalities, which we are currently defining based on customer research.
4. How can people find out more about you?
If you want to know more about Addapp, browse to Addapp.io, find it for free in the App Store, tweet us @addappio or mail us at email@example.com. We guarantee everyone gets a reply!
Last week, together with our friends from InsideTracker, we ran a short contest to see what kind of experiments and tracking projects could be supported by having access to InsideTracker’s Ultimate Panel biomarker tests. And wow, the response was amazing.
We received so many great entries from the community. From individuals wanting to understand how their training and activity affect their hormones, to people just wanting help figuring out how chronic conditions are affecting them. It was hard to choose just two winners, so thanks to the generosity of the team over at InsideTracker we were able to choose three!
Congratulations to Dana Greenfield, Mary Eggers, and Felipe Gerhard! We’ll be doing more in-depth follow-ups with each of the winners here on the blog soon, but until then here are the great experiments and projects they proposed:
- Dana Greenfield - “I want to learn if certain foods I eat –such as spearmint tea, or omega-3 supplements–have actual effects on free testosterone or other altered biomarkers associated with my Polycystic Ovarian Syndrome diagnosis…”
- Mary Eggers - “I’ve been a triathlete for 20 years. Multiple Ironman races and and high volumes of training have left me with anemia, high levels of cortisol and some other issues. In August after age group nationals I’m going to switch my focus to swimming, with the goal of competing at USMS nationals. When I return, what will happen then? I am also switching from a paleo based diet to plant powered for this journey. What will that effect? This would be a fantastic chance to properly measure change…”
- Felipe Gerhard - “I want to study how a diet based only on Soylent will affect my overall health, hormones, and energy levels. How will I do it? I will switch to an almost exclusive Soylent diet for at least two months with one cheat day per week (~85% caloric intake from Soylent). One InsideTracker Ultimate Panel will be performed in the beginning to establish baseline value and a second one towards the end, 6-8 weeks into the diet. During the experiment, I will supplement with creatine, vitamin D, and any additional supplements recommended by InsideTracker after the first blood panel. I will continue ongoing tracking of basic QS stats such as body weight, body fat percentage, Fitbit data, daily and weekly habits, and sleep. I will start to track compliance with the diet and my energy levels through a subjective rating scale. What will I learn? Changing to Soylent as an almost exclusive source of food is a radical change in diet (#ForgetNormal). Though there are anecdotal long-term reports of people switching to Soylent, these reports are typically not accompanied with such an extensive blood panel that is offered by InsideTracker. In addition, there have been discussions around potential negative effects on hormonal levels. Therefore, tests for Testosterone, DHEAS, Cortisol, and other hormones included in the Ultimate Panel will be a crucial component of this experiment. I am personally interested in improving my sleep and energy levels and hope to see a correlation not just with the change of diet, but potentially also with the range of biomarkers that InsideTracker provides…”
Want to learn more about InsideTracker and what you can learn from blood and biomarker tests? Come see them in person at our QS15 Expo! Tickets are now on sale and readers of the blog get a special $10 discount! Register today!