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We are organizing a QS symposium on cardiovascular health for scholars and researchers and participants in the QS Community. The goal of our meeting is to support new discoveries about cardiovascular health grounded in accurate self-observation and community collaboration. This one-day symposium will be held on Thursday, April 19, 2018 at the University of California, San Diego.
Our “QS-CVD symposium” is free to attend, but space is limited, so if you’d like to be there we ask you to get in touch with us and tell us something about your research, tool development, and/or the personal self-tracking projects you’re doing that are relevant to the symposium there.
Learn more about the meeting here: QS-CVD Symposium.
Read about the community driven research that has influenced our planning for the symposium here: QS Bloodtesters.
From the Symposium program statement:
We know that data collected in the ordinary course of life holds clues about some of our most pressing questions related to human health and well being. Cardiovascular disease is the number one cause of death globally. CVD risk is strongly influenced by many of the factors commonly tracked in the QS community, including fitness, diet, stress, and sleep. But significant barriers stand in the way of using personal and public data for understanding and improving individual cardiovascular health. Perhaps the most important of these barriers is a lack of consensus about the legitimacy of self-initiated research and self-collected data. Our symposium is designed to advance progress in this field through exposing practical and innovative projects that would otherwise remain invisible, inviting critical comment, and documenting the state of the art for a wider public.
What is a QS guide?
The purpose of a QS guide is to make it easy for you to start tracking a new metric. Searching for the right device, head-scratching over how to use the thing, and figuring out what experiment to try first can be a huge time sink. Our goal is to offer a worked example of all of these steps with the device(s) we found to be the best on the consumer market. While the most sophisticated tools for physiological measurements are offered through professional laboratories, our guide is – of course – meant to help you with your own, DIY self-tracking projects. It’s not an extensive review of every option, but it will lead you from purchasing, to validating, to syncing the data, to doing a first experiment. As you go through the process yourself, much of your learning will come from building a mental model of how your own physiology works through additional reading and experimentation. Don’t shy away from that work – a QS project may not answer your question expediently, but it has the potential to teach you a lot.
The Guide to Tracking Cholesterol and Triglycerides will discuss two home lipid trackers: the CardioChek Plus and the Cholestech LDX. I will give an in-depth review of my experience with the former. The guide will touch on the science of the tests and devices, their accuracy and precision, and suggest a first experiment to try.
A little about blood lipids
While we normally think of cholesterol and triglycerides as risk factors for heart disease, there’s actually much more to them. In fact, it turns out that the basic functions of lipid components — including total cholesterol, triglycerides, HDL-c and LDL-c, not to mention their roles in heart health — are an active area of research and the center of an ongoing controversy. What lipid measurements can certainly do is reflect how your body is handling ingested animal products and fats. If you’d like to learn more, we put together an animation that goes a little deeper into the physiology.
Option 1: CardioChek Plus
What It Does: The CardioChek Plus measures blood lipids including total cholesterol, HDL-c and triglycerides using test strips. The device itself is battery powered and about the size of a Game Boy Color (and it makes similar sounds!). Each sample requires 40ul of blood and takes a few minutes. The major limitation of the device is its range of operation (it won’t report results if your lipids are very high or very low -and different lots of test strips have different operating ranges. Be sure to read the documentation before you purchase). It is an FDA approved, CLIA waved, testing system for clinical and paraclinical use.
Cost: New units retail for ~$800-$1000, but units appear on eBay for around $400. The tests cost ~$15 each and come in bundles of 15. Additionally, you will need rubbing alcohol, 2.8 mm lancets, 40 ul capillary tubes for blood collection and gauze wipes. Cost of extra supplies comes to about $200 for 15 tests.
Getting Your Data: The CardioChek stores data locally and has a limited memory. We recommend transferring the raw data by hand (3 numbers per test) to a personal spreadsheet.
Accuracy, Precision and Supporting Research: Finding information about the accuracy and precision of a new device can be non-trivial. Confirming what you learn can be even harder. We’ve had several months to figure out measurement validity for home lipid testing, and it’s a little complicated. At present, there is measurable variability (~13% is acceptable) in results obtained from clinical laboratory tests (Quest, LabCorp) as well as those from para-clinical tests like the CardioChek Plus. Chris Hanneman has written a great report that comments on the not-very-useful way validations are reported by glucose meter companies – and we acknowledge that the same is true here. The company that produces the device, PTS Diagnostics, reports numerous validations at the bottom of this page under resources, but we’ve averaged the basics across these many reports to produce a summary table.
Accuracy is a measure of how close a measured value is to the true value of the measurement (obtained via some gold-standard device). For accuracy, PTS diagnostics reports 18% error for total cholesterol (averaged across reported tests on the website in this document), 8% for HDL-c, and 13% for triglycerides.
At least one academic group has published a validation of this device: Gao et al., 2016 . They reported 3% error for total cholesterol, 7.1% error for HDL-c, and 7.6% error for triglycerides.
Precision is a measure of agreement between two measures which should be identical. In other words, it’s a measure of how much noise the device adds to the signal. During our own testing we measured the precision of the CardioChek Plus; you can view our results here. We actually found the CardioChek to be more precise than the company reports (so far).
Option 2: Cholestech
What it Does: The Cholestech LDX also measures total cholesterol, HDL-c and triglycerides. However, the device is larger (shoebox sized) and less mobile than the CardioChek Plus — it must be plugged into an outlet and calibrated in each new location.
Cost: New units retail for ~$2000, but used units can be easily found on eBay for around $50-$100 each.Note: make sure units have ROM pack version 3.40 or higher, and calibrate the used device.
Getting Your Data: Similarly, we recommend transferring the raw data by hand (3 numbers per test) to a personal spreadsheet.
Supporting Research: Whitehead, 2014 offers accuracy and precision measurements. Bias was 11.6% for total cholesterol, and 12.9% for HDL-c. The authors reported %CV of 2-3.5% for HDL-c and total cholesterol (pretty good!) – with the caveat that the venous blood samples they compared are less likely to introduce measurement error in comparison to the finger prick samples used at home.
My Experience, and What I Tried First
I only had the opportunity to use the CardioChek Plus, but my comments should apply to both devices. Setting up the device is trivial, but testing requires a few practice trials. The main challenge is the amount of blood required; it’s forty microliters (µl) which is equivalent to 2 large drops of blood. For some people I worked with, this was easy. But for others like myself, running the sampling hand under a hot tap is necessary to get the blood flowing. On top of this, the blood needs to be collected and deposited on the test strip within a couple of minutes to get an accurate reading. If this sounds a little off-putting, don’t worry too much- one becomes a blood collecting ninja fairly quickly. The pay of is in the ability to learn what my lipids are doing in near-real-time.
A First Experiment
While preparing for the project I wondered how fast my lipids really changed. I knew that seasonal, ovulatory cycle, and daily changes in lipids had been reported in the literature, but I wasn’t able to find any examples of how individual ambulatory humans varied hour by hour. The dynamic actions of these compounds on short timescales are less well characterized than changes on the timescale of years, but they are likely to contain useful health information. Because of all of this, I decided to measure my lipids every hour from the time I woke up, to the time I went to sleep. I won’t go into everything I saw here, but I will share one picture.
I’m 22 and in good health, yet across a single day I saw my total cholesterol nearly 50 mg/dL (almost plunking me into the at-risk for CVD category). Even more interesting, these changes seemed to occur at regular 3 h intervals, gradually climbing higher until they peaked around 8 pm. I learned that these changes might actually tell me more about my health than any one of those measurements alone could have. If you’re interested in getting a general sense of what your lipids are doing before you dive into more complex tests, I highly recommend setting a date with your CardioChek Plus or Cholestech LDX for some hourly measurements. Want a more in-depth argument for why you should try this first? Check out this animation.
This guide may have revealed that blood lipids are more complicated than you thought. But there’s no need to be overwhelmed — explore the metric, and you’ll build a deep understanding of your lipids in the process.
Smelling Food Makes You Fat by Robert Sanders. The late Seth Roberts, an influential contributor to the QS community and prodigious self-experimenter, wrote a book called the Shangri-La Diet based on radical weight regulation ideas stemming from his observation that his body seemed to treat calories from familiar sources differently. The germ of his theory came from a trip to Paris, where Seth found that he lost his appetite, and subsequently a good deal of weight, while trying a variety of sodas that, for him, had novel flavors. This seemed odd, given the calorie content. With further testing of his hypothesis, he theorized that the brain associates flavors with calories and will store calories from familiar flavors and burn calories from unfamiliar flavor sources (you can get his book or read this paper to get a full explanation of the theory and why the body would function this way). Related, he found that consuming flavorless calories in the form of extra light olive oil caused him to lose weight. This was a completely new model of weight regulation that, frankly, most people didn’t know what to do with. But this recent study from UC Berkeley seems to validate aspects of Seth’s theory. Scientists found that they were able to help obese mice lose weight by knocking out their sense of smell. Mice who still had their sense of smell ate the same food and increased in size to twice their starting weight. It’s an incredible example of the ability of self-experiments to create novel insights through accurate and tenacious observation. -Steven
“Mysteries in Reference Lists by Martin Fenner. Since we spend quite a bit of time trying to figure out how to get things right in recording measurements, communicating what we’ve done, and helping others do the same, we’ve come to enjoy a deep respect for how difficult it is create an accurate, explicit recitation of the steps involved in any action. There’s just so much ambiguity in what we say — and also so much tacit knowledge in what we do. But some things are much simpler than others: for instance, academic citations. There are only a few possible elements: Title, Author(s), Date, Journal Name, Volume, Issue, Page(s), DOI, URL, plus some specialty reference elements available to ultra-professionals when needed. You’d think that almost nothing could go wrong. That’s why I enjoyed this post by Martin Fenner so much: Even in the simple case of citations created by scholarly professionals, mysteries are common. -Gary
Evidence Based or Person Centered? An Ontological Debate by Rani Lill Anjum. This is a descriptive account of philosophical differences between two common ways of thinking about how we get sick and what we can do to improve our health. But for me Anjum described a deep underlying antagonism between two different philosophies of care, which helps me understand terrain I’m on when struggling with scientific and medical criticism of Quantified Self practices. I’m going to see how it works to address these criticisms not only as pragmatic doubts about QS methods but also as strong – if implicit – philosophical freak-outs. -Gary
My Scars by Ellis Bartholomeus. Ellis has taken a quantitative and thoughtful look at the form and meaning of the physical scars she has accumulated. She walks through a map of decades’ worth of scars and how she turned it into data, finding that when she added the lengths of her scars, the total is over a meter. -Azure
Max’s Vocabulary Acquisition by Nick Winter. Nick tracked the first 100 to 1000 English and Chinese words that his son learned through the first two years of his life. Comparing his son’s acquisition rate to other prominent examples, he found that his son’s progress appears to be rather linear. Nick also made Max’s Vocabulary data to look at yourself. -Steven
Fight For Your Right to Recess by Cantor Soule-Reeves. At Cantor’s school, recess is cancelled whenever it rains, an issue since he lives in Portland, Oregon. He wanted to make a case to the administrators that this policy is negatively affecting his activity levels by tracking his steps and comparing days with and without recess. -Azure
What I Learned from Weighing Myself 15 Times in a Day by Beth Skwarecki. If you are tracking your weight, a common and prudent piece of advice is to weigh yourself the same time every day. Since our weight fluctuates throughout the day, by taking a measurement at the same time, you reduce the amount of randomness in the result. However, what is not often explored is how much weight actually fluctuates during a day. In this example, the swing was over 8 pounds. -Steven (thanks to Richard Sprague)
Tracking Sleep and Resting Heart Rate by Jakob Eg Larsen. Jakob has tracked his sleep and resting heart rate (RHR) for the past four years. By tracking his RHR over a long period of time, has allowed Jakob to develop an intuition for connections between his RHR and physiological state. He has seen multiple times, for instance, that his resting heart rate will increase because of a coming flu before the onset of any other symptoms. -Azure
r/place Atlas by Rolan Rytz. On April Fool’s Day this year, Reddit tried an incredible community art experiment called r/place where every user is allowed to change only a single pixel every five minutes on a digital 1000×1000 canvas. The resulting 72 hour timelapse is an entrancing drama as various subreddits fought for space to have their imagery placed on the canvas. The reason I’m mentioning it now is that I recently came across an attempt to tell the story of this project by annotating all the images that showed up at r/place: what subreddit was behind the image, was there a conflict over that space, what new imagery arose from it, and what compromises were made between two warring factions (the r/France-r/Germany compromise was excellent). The atlas contains nearly 1500 entries. -Steven
#tabtQS 1: RescueTime in Tableau by Tim Ngwena. This novel visualization shows app usage over a three year period. For Tim, I’m sure that there are all sorts of stories embedded in the increased or decreased usage of certain applications during certain time periods. In the link, Tim walks through the workflow for creating this chart. -Steven
Activity Levels Around the World. This visualization is from a paper exploring “activity inequality” and comes 717,527 individuals’ smartphone data with over 68 million days of activity. -Steven (thanks to Richard Sprague)
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Jakob Eg Larsen has tracked his sleep and resting heart rate (RHR) for the past four years. His 7 minute talk is far better watched than read about: it’s a great illustration of data validation, longitudinal tracking, and data assisted self-awareness.
Briefly, by tracking his RHR over a long period of time, Jakob has developed an intuition for connections between his RHR and physiological state. He’s able to use the data to tune his self-awareness, but still keep a safety net when unexpected RHR elevations might portend a flu. To boot, the years of data across the Fitbit Blaze, Oura ring and Basis are one of the most extensive within-individual comparisons I can find anywhere of these devices.
You can watch the full video of Jakob’s talk at his QS Project page.
While a student at UC Berkeley, I was awed by the miniaturized devices created in the engineering department for neuroscientists. Eventually, these devices will enable entirely new kinds of Quantified Self projects. Here are three especially promising projects I came across while studying for my degree in neurobiology.
Temporary Tattoo EEG
Where: UC San Diego
Who: Professor Todd Coleman’s Neural Interaction Lab, and recent graduate Dr. Dae Kang
What it does: Continuous monitoring of vital signs can be uncomfortable, high noise, and restricted to hospital environments. However, recent developments in flexible, stretchable electronics are allowing metrics like brain activity (via EEG) to be measured wirelessly and with high precision. A typical EEG involves attaching electrodes to the scalp with glue and gel, connected to wires and heavy machinery. The temporary tattoos under development in the Coleman lab accomplish the same thing wirelessly. Due to improved conformability to the skin – they can even reduce motion artifacts in comparison to standard machinery. Further, the technique is modifiable: different miniature sensors can be added depending on the desired application.
QS Impact: Consumer versions of this bendable technology could help improve the notoriously low efficacy of wearable sleep staging and improve hospital visits. For example, Dr. Dae Kang is developing the use of a single wearable tattoo for determining sleep stage. Dae has also helped develop stretchable electronics for monitoring neonatal EEG and temperature. These permitted infants in the neonatal intensive care unit to be held by caregivers and freed of the isolating tangle of wires that normally monitor their vital signs.
Non Invasive Gut Activity Monitoring
Where: UC San Diego
Who: GI Innovation Group, and recent graduate Dr. Armen Gharibans.
What it does: Think of the electrogastrogram (EGG) as an EEG for your gut. Because your gut contains the largest number of nerve endings outside your central nervous system, it gives off quite a bit of electrical activity. The location and intensity of this signaling can be used to extract information about digestive activity comparable to that normally attained via invasive measures (is the activity stably periodic, or disrupted? Is the power of the activity lower or higher than is normal for you?). These invasive measures – picture a gastroduodenal manometry probe wired down your throat – are uncomfortable, can require sedation, and limit regular mobility. Further, current methods typically gather recordings for only a few hours, limiting the ability to observe digestion over the ‘cycle’ of a day or more. By contrast, the EGG is worn as an electrode array on the abdomen. It collects up to 24 hours of continuous gut activity and heart rate as the wearer walks, sleeps, eats and even exercises. Because it’s fairly comfortable (I was lucky enough to use it in a QS project and can attest to this!) it’s easy to collect multiple days of data – allowing comparison of that individual to themselves rather than to a population average. Dr. Ghariban’s technique is a breakthrough in filtering: locating a clean and biologically relevant signal through the skin and muscle wall as the electrodes are jostled by the person’s movements is no small feat. With a cleaner and easier-to-acquire signal, Armen can begin to gather enough recordings to start classifying which patterns are representative of ”healthy” and ”unhealthy” gut activity.
QS Impact: Researchers are currently using the EGG to study how our digestion works during wake, sleep, and recovery from illness. The goal is to map the periodic process of digestive motility and generate non-invasive biomarkers for health and impending illness. Rather than being constant through time, or changing linearly, gut activity oscillates across the day and night. These patterns need much more study, but hint that it might be possible to find a phase of oscillation during which it is better to eat a meal. The EGG was recently used as part of an incredible case study: observing the restart and re-stabilization of intestinal activity following bowel surgery. In concert with microbiome testing, target applications of the EGG include diagnosing functional gastric disorders like gastroparesis (a condition affecting more than half of diabetics and Parkinson’s patients, where food is not moved through the digestive tract in a timely manner), and helping you learn what times of day are physiologically best for you to eat.
Where: UC Berkeley
Who: Professor Kris Pister, PhD candidate David Burnett
What it does: Wearables are shrinking over time, but how small could they become? The Smart Dust project seeks to overcome size constraints in power source and radio communication in order to reduce the size of an autonomous sensor to 1 mm. In one fascinating part of this project, The Pister Lab and PhD candidate David Burnett are creating 4mm sq. chip. It can capture light, temperature and activity – but will be modifiable to carry more sensors. What is novel about the approach is the integration of a new kind of radio, and a solar rather than battery power source. Both provide engineering challenges, but the result will be a sensor that powers itself, and is able to send and receive information from a much much smaller chip.
QS Impact: Integrating these chips into clothing or jewelry, and scattering them about the environment have many potential applications. The application for which the chip is initially being designed is the continuous monitoring of circadian rhythms: our body’s way of anticipating periodic environmental change. Disruption of these rhythms is associated with myriad chronic diseases, but these rhythms are not usually monitored with an eye toward mitigating disruption.
For example, we all hear that we should limit blue light exposure in the evening – and that a weekend of camping can help re-align our bodies to the day night cycle. But we currently lack easy, consumer wearables that are tailored to measure just how ‘misaligned’ our bodies are. Smart dust that collects light, temperature and activity data from users and their ‘natural environments’ aims to create a poignant representation of health by helping people understand the stability of their behavior and physiology in relation to their environment. A more distant application is the development of autonomous sensor networks. Precise, wirelessly transmitting and energy harvesting, these networks could be used for health monitoring with zero input from the user, to allow them to truly forget they are ‘wired in’ to a device.
The push for smaller, more efficacious, and less invasive health monitoring devices continues to generate fascinating new technology. The projects deserve our attention and support. And while they aren’t on the consumer market yet, we can’t wait to try them.
How much do I trust this data?
This question has kept me awake many a night, both in the lab and during self-tracking experiments. Researchers do validation tests even when using expensive and widely trusted laboratory equipment, and these tests often expose unexpected problems. Commercial self-tracking devices present similar challenges, especially because company-sponsored validation tests may not be independently verified and may be difficult to understand and replicate individually. Though this problem won’t be solved overnight, there are several steps you can take to better understand the constraints of your new technology.
In this post, I’m going to take you through some lessons I’ve learned from a recent attempt to validate a widely used lipid test kit. Some of these lessons are generally applicable, and I hope they will be useful to you as you do your own tests.
I’ve been working on a Quantified Self project to support people doing unusually high frequency home lipid testing. For the project to succeed, we need to determine the accuracy and precision of the CardioChek® Plus from PTS diagnostics. (Accuracy refers to how close a device output is to its real value; precision measures how consistent a measure is across identical trials.) We chose this device from among almost a dozen different options and approaches, because it was in common use, was easily accessible, and was approved by the FDA for home use. But in order to ask sensible questions about our data, we needed to know how well it would do under real conditions. With some work, I was able to find the reported accuracy and precision of the device, verify both in my own hands, and alleviate considerable anxiety about generating believable data.
Here are some tips based on my experience. Of course success is not guaranteed; it will depend on the device you have, the time you’re willing to invest, and a bit of luck. But these general suggestions should get you on your way.
- Look carefully through the wearable’s website and read the fine print. Some manufacturers report their in-house testing online, but tucked in a corner where it’s hard to find. Although companies typically won’t report bad results, it’s at least a place to start.
- Pubmed. This is the watering hole for finding scientific literature. Try searching the name of your wearable here. Abstracts are generally available.
- Check the QS forum. Someone there may have the details of your device.
- Contact the company, and frame your questions about getting the most accurate and valid data as positively as possible. Many of these companies are small. Yes, they might brush you off, but they might be willing to give you insider tips on how to best use your device, or even raw data from their own trials to compare with your own. If you are able to explain a personal experiment requiring a particular degree of accuracy or precision, a one-on-one conversation is more likely to get you a relevant an honest answer than hours of googling. There are often hidden factors (lighting and humidity in my case) that make a huge difference in your data quality.
- Find a medical/industry standard to compare your device to. It’s very important to not only read reports of a device’s accuracy and precision, but to test it in your own hands. For me, this meant making a doctor’s appointment for a fasting lipid panel, taken at the same time as I did my own finger prick test with the CardioCheck. This is not always possible (most of us are unlikely to have access to polysomnography), but do your best.
- Replicate your results under similar conditions. This one is often easier.
If you measuring, say temperature, do so many times in a row to see the amount of variability. To see how accurate your step or distance tracker, walk from your house to the park several times and compare results. In my case, I pricked my fingers a few times in a row (ouch, but necessary).
- Take time of day into account when you are doing any measurement. Circadian rhythms are prominent in pretty much every system in your body. This means you should expect variability in any output by time of day. Let’s say that you’re tracking your basal body temperature (BBT) upon waking up as part of tracking ovulatory cycle. Sleeping until 11am on Saturday when you usually record at 6am on weekdays will confound the prediction of your cycle for sure! A perfectly accurate device can’t be a stand-in for good controls in your personal experiment.
- Once you know the constraints of your device, work within them. This may seem obvious, but it’s common to put too much faith in unverified data. Numbers aren’t magic. They are the outputs of sensors with strengths and weaknesses and calculations programmed by humans. Even a device with imperfect accuracy, but is consistent, can give useful information: you just have to figure out the right questions to ask.
- Don’t give up. This process takes time, but pays off in the long run. The trust gained from putting an honest effort into validation will save you hours, days or even weeks of confusion from trying to explain results that are just noise in the system, or from having to re-do an entire experiment. Save that time now.
- Embrace uncertainty. One of the toughest parts about navigating the validation of a new device is getting comfortable with uncertainty. A peek under the hood often reveals a lot we might wish we didn’t know. Sure, it would be nicer if the world delivered perfect data with every wearable purchase, but it isn’t so. Like all learning endeavors, it’s a continually evolving process that will not guarantee perfection. Questioning one’s potentially false sense of certainty, and leaning into the tricky process of confronting unknowns is a good practice to keep us honest anyways.
If you have done a validation test of a self-tracking tool, we’d like to hear about it.
A persistent theme at the 2017 Quantified Self Conference was how self-tracking can help those with chronic conditions spot associations between symptoms and lifestyle that a clinician might not have time to uncover. These personal discoveries can help improve one’s health.
In this show&tell, Justin Lawler talks about learning that he has early onset osteoporosis and the several metrics, including diet, microbiome, exercise, sleep and bone density, he tracks to help manage and understand the disease.
I love that the talk emphasizes that many QS projects are long term – even lifelong. Most conventional research projects have a start and end date, garnering a lot of information but only addressing a limited window in time. The self awareness that comes with self tracking can be useful across months and years, elucidating subtle patterns that might otherwise be undetectable.
We’re back from QS17 and eager to share the conference with you from beginning to end. This, our ninth conference, covered a lot of ground: we showcased self-tracking projects, investigated our relationship with technology, and discussed the past and future of QS. Over the coming weeks, we’ll share some conference highlights.
Today I want to share our opening Show & Tell from Robin Weis, which captures the personal discovery and data-driven spirit of QS. If you’re new to QS, you might not know that the community is about much more than tracking your steps or your hours of sleep: it’s about gaining personal insight by putting numbers to any important aspect of your life. Robin Weis tracked an unusual metric – crying – over a long period of time and did an inspiring job tying together her personal story with her data. Click the link above, check it out, and come back in a few days for another talk!
Sara Riggare: “I will share how I work to keep up with my progressive neurological illness by tweaking and re-tweaking my medications, including what I’ve learned from the most recent changes to my Parkinson’s medication.”
I love this clear illustration of the value of health-tracking between visits to the doctor – especially for disease management. At QS17, Sara will share the insights health tracking has allowed her to glean from decades of experience with Parkinson’s.
Managing Parkinson’s disease requires constant tuning. The symptoms result from decreased dopaminergic signaling from a brain region that helps set the tone for our movements. Without enough dopamine, movement is slow or impossible. Too much and movement is fidgety or ballistic. To add to the complication, the natural levels of dopamine in the brain fluctuate throughout the day – meaning that the same medication affects a patient differently depending on when it is taken. This makes Parkinson’s management a careful balancing act – not something that can be calibrated in just one doctor appointment per year.
Sara makes great use of the 8,765 hours she’s not in the doctor’s office to keep a record of how exercise, sleep, and shifts in the complicated dosing of her medications influence her symptoms. She has put her self-tracking to scientific use by conducting graduate research at the Karolinska Institute, and has been called “a thought leader in Parkinson’s in the new age of social media.” We’re excited to hear at QS17 how she re-calibrated her doses after adding a new medication to her drug regiment.
Just a few more weeks until the 2017 Quantified Self Global Conference! We can’t wait.