Search Results for: sleep

New QS Devices

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

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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

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Where: UC San Diego

Who: Professor Todd Coleman’s Neural Interaction Lab, 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.

Smart ‘Dust’

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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.

 

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Is My Data Valid?

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.

  1. 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.
  2. Pubmed. This is the watering hole for finding scientific literature. Try searching the name of your wearable here. Abstracts are generally available.
  3. Check the QS forum. Someone there may have the details of your device.
  4. 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.
  5. 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.
  6. 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).
  7. 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.
  8. 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.
  9. 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.
  10. 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.

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QS17 Highlight: Taking on my Osteoporosis

Justin LawlerA 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.

Watch Justin’s show&tell talk at it’s project page.

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QS17 Amsterdam Highlight: Tracking Crying

Robin_Weis_CrymotionWe’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!

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QS17 Preview: Mental & Metabolic Dashboard

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Dr. Tara Thiagarajan: “I’ve created a dashboard to illustrate the inter-relatedness of my mental and metabolic machinery .”

While the terms ”dashboard” and ”machinery” may bring to mind the tidy and separable systems operating within a car, it’s the crossed wires of human neurophysiology that will be the subject of this conference preview. Though western medicine tends to treat physiological systems (cognition, digestion, hormonal axes) as disparate, centuries of wisdom and mounting scientific data suggest a great depth of inter-relatedness across physiological systems. Ancient thinkers like Plutarch, Ptolemy and Boethius have gone as far as to describe the interactions between human systems as musical chords – harmonious when healthy, dissonant when ill:

“Whoever penetrates into his own self perceives human music. For what unites the incorporeal nature of reason with the body if not a certain harmony and, as it were, a careful tuning of low and high pitches as though producing one consonance” – Boethius, Fundamentals of Music

At QS17, only a few weeks away, Tara will bring her background in math and neuroscience to show us how she has captured her ‘human music‘ by looking at interactions between her sleep, diet, water intake, and brain activity. Tara is the founder of Sapien Labs, which seeks to use EEG technology in a broad and more accessible context to generate massive data sets of human brain activity. She’s one of many excellent speakers presenting novel connections in personal data – check out our other participants and join us there!

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QS17 Preview: Managing Parkinsons 8,765 Hours a Year

Selfcare-infographic-English-278x300Sara 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.

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QS17 Preview: Dashboard of My Life

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David de Souza: I’ve been recording 35 of the most important areas of my life – and using Google Spreadsheets to create a personal dashboard that tracks my progress.

Tracking 35 metrics might seem like a daunting task. Everything from quantum theory to tracking-based anxiety shows that the mere act of observing affects the observed. Automating personal data collection might help us stress less, collect more, and (hopefully) be more accurate.

David has managed to create a streamlined workflow allowing him to record everything from sleep, weight and food intake to productivity, yoga and meditation. At QS17, David is going to share this dashboard and the correlations he’s drawn between diverse aspects of his behavior. He says his dashboard has done ”wonders to keep me accountable, and more importantly, to help me notice when I have fallen off the horse, allowing me to keep on track with my goals.” For those of us (all of us) looking to optimize our workflows and understand our habits, this is definitely a talk to see.

Join us at QS Amsterdam June 17-18, and if you haven’t already, check out our latest program. See you there!

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Hot Stuff: Body Temperature Tracking and Ovulatory Cycles

For the past eight months I’ve been tracking my temperature every minute using small, wireless sensors.

I work in a lab that recently showed minute-by-minute body temperature can tell you fascinating things about female physiology, at least in mice. Using temperature, we can tell what day a mouse will ovulate, whether or not it will become pregnant within hours of pairing with a male, and in the same time, whether or not its pregnancy will be successful. Just as interesting, the temperature reveals that some mice have stable ovulatory cycles and some don’t. We wanted to see if any of this holds up in humans (read: lab mates, a sporting family member and myself). I’ll show you what we did, what we found, and how to get started if you’d like to start tracking your temperature too.

Why Temperature?
Think of metabolism as a continuous symphony and body temperature as the din that carries through the concert hall walls.

Many of the metabolic reactions taking place throughout our bodies generate small amounts of heat and are actually coordinated in a similar way to musical chords. For example, during the luteal phase of the menstrual cycle progesterone levels will pulse in concert with estradiol, often following a luteinizing hormone pulse occurring 15 minutes prior. These fluctuations, as well as other things that affect metabolism (ovulating, eating a meal, etc.), translate into small temperature ripples which register on the surface of the body.

Temperature has long been used as a predictor of ovulation. But most temperature based techniques rely on a single measurement per day. Limiting data collection to one time point per day is the equivalent of listening to the symphony only at what we hope is the crescendo of each piece: with training, we might identify the chord, but we’ll still miss most of the show.

What are we doing and what have we seen already?
To see if we could use high temporal resolution temperature to recapitulate any of our previous findings, we began monitoring distal (wrist), axial (arm-pit) and core (ahem, core) temperatures every minute, using small devices called iButtons. We’ve seen some interesting things so far. I’ve shown the temperature data as a heat map, because it allows you to see many measurements while giving a clear picture of the overall pattern of rising and falling average temperatures over the course of 28 days.

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Temperature Can Predict the Start of Menstruation.
In the graph above, which uses my lab mate’s data, you’ll see that the range of temperatures she passes through in a day shifts a little higher every day leading up to the start of spotting/ menstruation. This timing is clear in her data, but it isn’t identical for everyone, though. My cycles are irregular and the chart below shows that menstruation starts when my average temperature reaches its highest level of the month. Note that this can be more than once per 28 days, as in the month graphed below.

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In my mom’s case, the heat map below clearly shows the shift between follicular (cooler) and luteal (warmer) phases. I’ve outlined the profiles of the Progesterone (P) and Estrogen (E) that my mom takes each day as part of hormone replacement therapy. In the valley where both hormones are low, she transitions from follicular to luteal phase. This corresponds to a temperature increase, and a few days later she gets her period.

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These findings keep us coming back for more: more subjects and more longitudinal data for each of us. Perhaps the differences we have observed between us support that there are different ‘types’ of cyclers in the population, just as there are different body types. And maybe the temperature features we have in common will apply to other women.

So how do we gather the data (and how might you)?

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iButtons are about the size of the button on your jeans, and one side has a sensor which is worn pressed to the skin. A sweat band is enough to secure one button to your wrist, and the axial button can be tucked into a bra strap or secured with a non-irritating skin tape (here is my favorite so far). Body temperature shouldn’t ever fluctuate more than a couple of degrees C, so devices with high precision are key. This model is accurate up to .0625 C. Both the resolution and the sampling rate can be user-specified, meaning you can take very precise measurements very frequently. I find that anywhere between one and three-minute resolution works well to capture changes throughout the day.

iButtons don’t ever need to charge, but the data needs to be read once the memory fills up. Depending on the sampling rate, that’s every 3-7 days. At the end of a recording period, the ibutton is touched to its reader, and a simple interface allows the user to view the data and export it as a csv. iButton will plot the data, but it won’t do any further analysis. We’ve taken these csv outputs into Matlab and Python for our analyses, and because they are widely used formats, anyone could make graphs and start to play with their data. I’m not associated with the company, but I’m excited to share what we’re finding and want others to know how to jump in. An ibutton and a reader together cost about $100.

Interested?
Temperature tracking is a scavenger hunt: we don’t know precisely what we’re looking for, but clues keep turning up that lead us in interesting and verifiable directions. Multiple hormonal systems in our bodies (the stress axis, the digestive system, the thyroid axis) affect body temperature, and the reproductive system is just one of those. This raises the question: could we see predictable changes in temperature associated with a long run, a large meal, or a bad night of sleep? Probably. Mapping the personal, research, and clinical applications of high temporal resolution body temperature tracking will take time and user participation. Luckily, it gives interesting and useful personal feedback along the way.

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Getting to Know the Gut: A QS Dublin Report

Earlier this month, the Quantified Self Dublin group got together for an engaging evening of talks on gut health by members of the local medical community.

CDSA Explained

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Francesco Polito, a nutritional therapist, talked about the markers that are found in a Comprehensive Digestive Stool Analysis (CDSA). This is a test that he has his clients get to understand the current state of their gut. Francisco walked through the test results, explaining what each marker represented and what it could mean if it is out of range. It’s an incredibly fascinating talk and I will be writing more about it in-depth next week. In the meantime, you can watch a video of the talk and review his slides, which contain an actual CDSA report from one of his clients.

Video of Francesco’s talk
Francesco’s slides

A Gut Hormone Primer

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Natasha Kapoor, a researcher at University College Dublin, gave a primer on hormones in the gut. She explained the relationship that ghrelin has with appetite.  Higher ghrelin levels correspond with increased hunger. This is concerning, since lack of sleep can cause ghrelin to rise, meaning that carrying a sleep debt could induce you to eat more than you otherwise would. It may follow, then, to try and manipulate ghrelin levels to help control appetite. However, clinical attempts to lower ghrelin levels are not advised since it is a complex hormone involved in more than just hunger, such as cardiovascular function, sleep and memory.

Still, there are other hormones that play a role in appetite. Natasha described three hormones that have the opposite effect as ghrelin, making you feel full while eating a meal: cholecystokinin, peptide YY and glucagon-like peptide-1. She is currently recruiting subjects for a study on whether these hormones could be manipulated to control appetite through a “gut hormone infusion” method. As Natasha explains in the video below, there are more mundane ways of taking advantage of these hormones to reach satiety quicker, such as eating your food in a certain order (hint: start with the protein portion).

Video of Natasha’s Talk
Natasha’s slides.

If you live in the Dublin area, you can join their meetup group and be notified about upcoming events (like the next Tuesday!). You can also keep up with QS Dublin on twitter.

If you are interested in exploring more about the microbiome, we’ve had a number of interesting Show&Tell talks on gut health:

QS17

You can meet Justin and other members of QS Dublin at our next conference on June 17-18 in lovely Amsterdam. It’s the perfect event to see the latest self-experiments, discuss the most interesting topics in personal data, and meet the most fascinating people in the Quantified Self community. There are a limited number of tickets left. We can’t wait to see you there.

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Meetups This Week in Washington D.C. and Copenhagen

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photo by Erica Tanamachi

Copenhagen has an amazing slate of presentations lined up for their Quantified Self meetup this week. Katarzyna Wac will speak about what she’s learned from using a continuous glucose monitor (there’s an interesting discussion on this topic going on in the QS forum).  Thomas Blomseth Christiansen will talk about what he tracked while training for a half-marathon. Jakob Eg Larsen will look at sleep and resting heart rate over a long time period. And finally, Frederik Ackermann will give pointers for designing N=1 experiments.

Wednesday
, March 22
Washington, D.C.

Thursday, March 23
Copenhagen, Denmark

Join us at QS17

Our next conference is June 17-18 in Amsterdam. It’s the perfect event to see the latest self-experiments, discuss the most interesting topics in personal data, and meet the most fascinating people in the Quantified Self community. There are a limited number of tickets. We can’t wait to see you there.

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