Tag Archives: temperature

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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Stefano Schiavon: Using Data to Understand Personal Comfort

Stefano Schiavon is an assistant professor and researcher interested in sustainable building design. As he told us at last month’s Quantified Self meetup in Berkeley, California, “I am Italian. I love architecture. And I think buildings are beautiful.”

One goal of building design is to increase individual comfort. However, this poses a problem. Everyone is different. For instance, what should the temperature be set at? There is no one temperature that is comfortable for everyone. It doesn’t work to try find a temperature that is pleasant to the largest number of people. As Stefano puts it, it is like going around and measuring everyone’s foot to get an average, say 9, and then dictating that everyone wear size 9 shoes to the office.

So how does this connect with Quantified Self? Stefano and his colleagues have embarked on a series of studies to better understand people’s individual preferences for their environments and they are doing it with QS tools. The first study was fairly simple. They tracked the ambient temperature and air quality of the person’s surroundings and used an app for feedback on whether the environment was “acceptable” or not. Carbon dioxide and temperature measurements were taken throughout the day, while the person was in the car, at work, the restaurant, etc.

4_Stefano Schiavon Personal Comfort Quantified Self MeetUp.007
Stefano and his colleagues noticed a couple things. One is that there was higher exposure to CO2 in air conditioned rooms as opposed to naturally ventilated rooms. While Stefano says that this CO2 level is not a concern in of itself, it correlates with other pollutants, such as, airborne transmitted diseases (e.g., influenza).

4_Stefano Schiavon Personal Comfort Quantified Self MeetUp.008

Despite these data, Stefano and his colleagues found that just recording the environment gave him a limited ability to predict a person’s comfort. He is hoping, and the focus of his next study, is that by getting a person’s QS data (heart rate and body temperature), this predication ability will improve, making it easier to personalize a space’s comfort for each individual.

4_Stefano Schiavon Personal Comfort Quantified Self MeetUp.010For Stefano, all of this is in support of a larger cause, climate change. He was saddened to discover that nearly 40% of greenhouse emissions come from buildings. He hopes that by building better models for personal comfort by using QS tools, he can help people enjoy their environments more, while minimizing the environmental impact.

You can see the entire video of his talk at his QS project page.

Here are links to some of Stefano’s papers:

Get your tickets for QS17

Our next conference is June 17-18 in lovely Amsterdam. It’s a perfect event for seeing the latest self-experiments, debating the most interesting topics in personal data, and meeting the most fascinating people in the Quantified Self community. There are only a few early-bird discount tickets left. We can’t wait to see you there.

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Rain Ashford on Wearing Physiological Data

Rain Ashford is a PhD student in the Art and Computational Technology Program at Goldsmiths, University of London. Her work is based on the concept of “Emotive Wearables” that help communicate data about ourselves in social settings. This research and design exploration has led her to create unique pieces of wearable technology that both measure and reflect physiological signals. In this show&tell talk, filmed at the 2013 Quantified Self Europe Conference, Rain discusses what got her interested in this area and one of her current projects – the Baroesque Barometric Skirt.

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Does Shower Temperature Affect Brain Speed?

In November I learned about benefits of cold showers. So I tried them. I took cold showers that lasted about 5 minutes. I liked the most obvious effect (less sensitivity to cold).

Maybe a bigger “dose” would produce a bigger effect. Maybe the mood improvement cold showers were said to cause would be clearer. So I increased the “dose” in two ways: (a) more water flow (I stopped holes in the shower head) and (b) lower water temperature. After a week or so with the stronger dose, I saw I was gaining weight. It could be the cold showers, I thought. Fat acts as insulation and I couldn’t think of another plausible explanation. So I went from cold showers back to warm showers (48 degrees C.) — this time with greater water flow. My warm showers were 5-10 minutes long.

I began to lose weight, suggesting that the cold water did cause weight gain. More surprising was that my arithmetic speed (time to do simple arithmetic, such as 7-3, 8*4) began to decrease. Here is a graph of the results.

2011-01-04 shower temperature and arithmetic speed Continue reading

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