For many women, controlling their fertility through applications can be a whirlwind of emotions

Have you ever used any mobile application to quantify some of your personal data such as diet, exercise or the menstrual cycle? Did the possibility of controlling all this data motivate you and make you feel good or did it stress you and become frustrating?

With the rise of personal quantifiers in the form of applications and wearables, there are many people who increasingly interact with their health-related data. According to a survey by the American Pew Research Center in 2012, 69% of American adults used some type of quantifier to manage their health or that of a loved one.

My colleagues and I are investigating a type of complex and emotional data quantification: fertility. We focus specifically on the way in which women use personal data quantification technologies to improve their efforts when trying to conceive. Fertility problems are not uncommon: in the United States 7.5 million women suffer from fertility problems and there are many who use these types of applications, although the impact they can have on their lives

Our research shows how women are exposed to multiple problems when quantifying their own fertility and how they react to the data in different ways: for some the experience is positive, while others feel overwhelmed or give up defeated

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Different ways of using the data

The main objective of the quantification of fertility is to determine the day of ovulation, since it is what defines the fertility period of each month. However, there is no single measure that can accurately identify the time of ovulation, hence women enter data from various indicators (such as body temperature, physical symptoms or the results of ovulation prediction kits) to arrive at an estimate of that period. Fertility applications aim to facilitate the collection of this type of data and analyze it.

We chose to first analyze data from an online fertility forum in order to focus on women's doubts, as well as their challenges and concerns. We analyzed 400 threads with more than 1900 entries between 2006 and 2016. In our results, published in November, we categorize women's experiences according to their data into five different types.

1. Positive

Women who had a positive attitude towards their data felt good to see the results. On many occasions they are in a learning process to quantify and understand how their bodies work, which makes them feel excited and confident. For example, one woman wrote: "Do you think I should do the test again tomorrow and the next two days? This is exciting!"

2. Overwhelmed

Women in this group tended to increase the amount of data collected over time, so quantification became somewhat tedious. These women showed a higher level of stress and anxiety compared to the first type. However, they still felt that the experience of controlling the data was positive.

For example, a woman felt overwhelmed because she was not able to follow the accuracy of her schedules: "I measure my temperature at 5:30 in the morning. For the last 2 days I have been exhausted and have fallen asleep. Yesterday I measured my temperature until 6:30 and today I did it at 6:50. Do you think I've spoiled my body temperature chart? "

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3. Obsessive

For women in this group, the control of the data begins to be obsessive and they tend to quantify even more data than the overwhelmed type, often including any type of symptom to their measurements. In this sense, they seem to be consumed by the data, often registering them in excess and even expressing higher levels of frustration and stress. However, they continue to believe in quantification and are unable to yield: "I am looking for any small inconvenience or irregularity that gives me hope ... You know how this is going."

4. Caught

This is the most intense type of behavior on an emotional level. Women with this type of relationship with the data tend to take time trying to get pregnant and often express signs of despair, guilt and dependence. They want to stop quantifying their data, but they feel incapable, as in the case of a woman who wrote: "I want to stop doing it at once, but I don't think I can forget all this. I seriously don't think I can make my brain Stop thinking 'today is the 10th day of my cycle, I should have sex, etc.' "

5. Abandonment

In some cases, quantification becomes an emotionally cumbersome and the frustration caused by negative results is so devastating that women decide to stop quantifying their data and even stop trying to get pregnant, either temporarily or permanently. As one woman wrote: "However, after all the stress, constant worries, measuring the temperature, having sex at the right time, visits to the doctor, blood tests and medication, I simply decided that I needed a break."

A possible feedback cycle

Needless to say, fertility problems are negative emotional and stressful experiences that do not arise simply from the use of personal data quantification applications.

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However, our research suggests that quantifying data may intensify these sensations because of some of the specific characteristics of fertility data tracking. For starters, fertility cycles are very different in each woman and the measurements are not accurate: they can be subjective or difficult to interpret and are not direct indicators of ovulation. For example, ovulation prediction kits indicate that ovulation will occur between the next 12 and 36 hours, although body temperature rises once ovulation has occurred. In addition, the objective may be unattainable, since a pregnancy may never occur despite the tracking of personal data.

In these circumstances, recording the data and emotional experiences resulting from the interaction with personal health data can create a feedback loop in which everything is gathered. Positive or overwhelmed women may experience some negative feelings, but their relationship with the data is mostly positive. In these cases, the control of the data is associated with positive emotions such as hope and control.

However, as our study shows, the other three types of interaction with the data show that there are more problematic relationships. For women of the obsessive type, measurements and quantification activities dominate their emotional responses, unlike trapped type women where the emotional component is more intense and dominates their quantification activities ...

Finally, women with a type of abandonment interaction have reached a point where their relationship with the data is so negative that it is unsustainable.

Better tools

Through our work we hope to contribute to the design of quantification technologies to help people manage their health avoiding a negative impact on their lives. Part of the problem lies in understanding individual emotions and behaviors when registering and following personal data.

This type of research shows that the same tools and activities can generate almost opposite consequences on different people, something that goes beyond fertility. For example, diet and exercise applications can help people improve their health habits, but they can also contribute to the creation of problematic experiences in people with eating disorders.

With this we can say that it is important to highlight the different individual experiences when developing tools that can help people better.

For example, a person may need a different kind of help, depending on their interaction with health data. In the case of fertility, if the interaction is rather problematic, the tools could suggest cycles with less quantification, offer suggestions for dealing with stress or even recommend taking a break. Applications could also highlight fertility variability; discuss the characteristics and problems of the different prediction systems and avoid presenting pregnancy as the only way to succeed.

In any case, our study shows that the data is not neutral: they can have strong moral and emotional implications, especially in delicate contexts. As more and more people quantify their daily activities, application creators should consider how it can affect the information they provide to their users regarding their emotions and well-being.

Authors: Mayara Costa Figueiredo. PhD student in computer science, University of California, Irvine. Yunan Chen Associate Professor of Informatics, University of California, Irvine.

This article has originally been published in The Conversation. You can read the original article here.

Translated by Silvestre Urbón