With the popularity of self-tracking technologies like apps and wearables, many people are becoming more engaged with their health data. According to a 2012 Pew Research Center survey, 69 percent of U.S. adults use tracking to manage their health or that of a loved one.
My colleagues and I are currently investigating a complex and emotional type of self-tracking: fertility. We specifically focus on how women use self-tracking technologies to assist their efforts to conceive. Fertility challenges are not uncommon: 7.5 million U.S. women experience fertility issues. Many of these women use fertility apps, but it’s not yet clear how these tools can potentially impact their lives.
Our research shows that women face multiple challenges when self-tracking for fertility, and they respond to the data in different ways. Some may find the experience positive, while others feel overwhelmed or give up altogether.
Different types of engagement with data
The main goal of fertility tracking is to detect the ovulation day, because that defines each month’s fertile period. However, there is no unique measure that can precisely identify ovulation, so women track multiple indicators – like temperature, physical symptoms or results from ovulation predictor kits – to estimate this period. Fertility apps aim to facilitate this data collection and reflection.
We chose to first analyze data from an online fertility forum, so we could focus on women’s questions, challenges and concerns. We analyzed 400 threads with more than 1900 posts between 2006 and 2016. In our results, published in November, we categorize women’s experiences with their data into five different types.
Women who are positively engaged with their data see their data and feel excited with their results. They are often learning to track and understand their bodies, which leaves them feeling hopeful and confident. For example, one woman posted: “Do you think I should test again tomorrow and the following 2 days? This is exciting!”
Women in this group tend to increase the amount of data they track over time, so tracking becomes more burdensome. They express a higher level of stress and anxiety when compared to the first type. However, they still have a generally positive experience tracking and viewing data.
For example, one woman felt anxious because she could not follow her precise tracking schedule: “I measure my temperature at 5:30 in the morning. The past 2 days I have been totally exhausted, and I over-slept. Yesterday, I did not measure my temperature until 6:30 and today I did it only at 6:50. Do you think I screwed up my temperature chart?”
For women in this group, tracking begins to occupy much of their attention. They tend to track even more data than the burdened type, often seeing any symptom as a possible measure to track. In this sense, they seem to be consumed with data, often over-tracking, and they express even higher levels of frustration and stress. However, they still believe in tracking and are unwilling to give up on it: “I am searching for any little pain or irregularity to give me hope. You understand how it works!”
This is the most emotionally intense type of engagement. Women with this relationship with data often have been trying to conceive for longer. They tend to express signs of despair, guilt and dependence. They want to stop tracking, but they feel unable to, like the woman who wrote: “I want to stop trying so badly, but I just do not think I can forget about all this. I seriously do not believe I can refrain my brain from thinking ‘today is the 10th day of my cycle, I should have sex, and so on.‘”
In some cases, tracking becomes so emotionally burdensome and the frustration with the negative results becomes so devastating that women decide to stop tracking and trying to conceive, either temporarily or permanently. As one woman wrote: “But after all the stress, constant worrying, tracking temperature, having intercourse on time, visits to doctors, blood tests and medications, I just decided I needed a break.”
A potential feedback loop
Fertility challenges are naturally emotional and stressful. These negative experiences are likely not generated by self-tracking alone.
However, our research suggests that self-tracking may intensify these feelings. Some specific characteristics of fertility self-tracking may contribute to this. First, fertility is very personalized, as cycles vary from person to person. Also, the measures aren’t straightforward; they can be subjective or hard to interpret, and they don’t directly pinpoint ovulation. For example, ovulation predictor kits indicate ovulation will occur in the next 12 to 36 hours, while temperature rises the day after ovulation. Besides, the goal itself may even be unachievable – pregnancy may not occur through self-tracking or at all.
In this scenario, the act of tracking and the emotional experiences resulting from engaging with health data can create a feedback loop, progressing together and influencing each other. Women in the positive or burdened groups may experience some negative feelings, but their relationship with data is mostly positive. In these cases, self-tracking is reinforced by positive emotions, such as hope and feelings of agency.
However, as our study showed, the other three types of engagement demonstrate a more problematic relationship with data. For women in the obsessive group, the measures and tracking activities dominate their emotional response. This is flipped for women in the trapped group: The emotional component is more intense and dominates their tracking activities.
Finally, for women with an abandoning engagement, they reach a point where the relationship with data is so negative that it can become unsustainable.
Through this work, we hope to contribute to the design of self-tracking technologies that support people in managing their health without negatively impacting their lives. Part of this lies in understanding individuals’ emotions and behaviors when self-tracking.
Research like ours shows that the same tools and activities can generate almost opposite consequences for different people. This can be true beyond fertility. For example, diet and exercise apps can help people improve health behaviors, but may also contribute to problematic experiences for people with eating disorders.
That suggests it’s important to make individuals’ different emotional experiences visible. This knowledge can help us develop tools that can better support these populations.
For example, people may need different types of support, depending on their engagement with health data. In the fertility case, if the engagement is more problematic, tools could suggest cycles with reduced tracking, offer stress management suggestions or maybe even suggest taking breaks. Apps could also highlight the variability of fertility; note the characteristics and problems of different measures; and avoid presenting pregnancy as the only possible success.
If anything, our study reinforces that data are not neutral: They can have strong moral and emotional implications, especially in such sensitive contexts. As more people track their daily activities, designers need to consider how information they provide can affect users’ emotion and well-being.