Tag Archives: fertility

QS17 Preview: My AMH Numbers Sucked, But I Made This Baby Anyway

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Whitney Erin Boesel: “In my case, AMH may not be as important as fertility clinics and egg-banking startups want people to believe.”

Women are understudied in most disciplines. Reproductive health is the general exception, but even then research on male reproductive problems often outnumbers that concerning women. One result among many is that our understanding female fertility isn’t as complete as it could be. For example,  anti-mullerian hormone (AMH) levels have been used to estimate a woman’s ‘reserve pool’ of eggs. Though AMH may become the “Gold Standard” of fertility, it still isn’t clear what levels are ideal for each woman. If you’re looking to get pregnant, there is a certain range (high but not too high) that is considered favorable for conception. At QS17 Amsterdam, Whitney is going to share her experience tracking her AMH, attempting to increase it, and finally having a successful pregnancy despite low-ish levels of the hormone. She’ll also hold a breakout session on tracking hormones, menstruation and fertility.

Aside being a new mum, Whitney is a writer, scholar, and active member of the QS community who, among her other work, has written about the incorporation of technology into medicine, Biomedicalization 2.0, and the nature of QS movement, “What Is The Quantified Self Now?

QS17 Amsterdam is coming up in just over a month on June 17-18.
We hope to 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.

HeatMap_LabMate

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.

heatmap_Azure

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.

HeatMap_Mom

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)?

ibutton_pile_2

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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Ahnjili Zhuparris: Menstrual Cycles, 50 Cent, and Right Swipes

Ahnjili50Cent

“I love reading random papers about the human body.”

Ahnjili Zhuparris came across a study on the menstrual cycle’s influence on cognition and emotion and was curious to see how hormonal changes may affect her day-to-day behavior. She figured her internet use may be a convenient and easy data set to assemble and examine for this effect. Using a few chrome plugins, Ahnjili was able to see not only where she spent her time online, but how she interacted with sites like Facebook and Youtube.

Her analysis yielded some interesting patterns. She found the most distinctive behaviors occurred during the fertile window, a span of about six days in the menstrual cycle when the body is most ready for conception. Looking at her shopping data from a clothing website:

 ”I found that there was no change in the amount of money I spent or the amount of time I shopped online… but while I was most fertile, I bought more red items. In fact, it was the only time I bought red items.”

In this talk, Ahnjili shows the differences in how she browsed Facebook, swiped in Tinder, and listened to music on YouTube.

Here are a few of the tools and papers that Ahnjili cites in her talk:

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