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Examining Menstrual Tracking
to Inform the Design of Personal Informatics Tools
Daniel A. Epstein, Nicole B. Lee*, Jennifer H. Kang, Elena Agapie, Jessica Schroeder,
Laura R. Pina, James Fogarty, Julie A. Kientz, Sean A. Munson
DUB Group, University of Washington | *Independent Researcher, San Francisco, CA
{depstein, jkang331, eagapie, jesscs, lpina, jfogarty, jkientz, smunson}@uw.edu, nikki@nicoleblee.com
ABSTRACT
We consider why and how women track their menstrual
cycles, examining their experiences to uncover design
opportunities and extend the field’s understanding of
personal informatics tools. To understand menstrual cycle
tracking practices, we collected and analyzed data from three
sources: 2,000 reviews of popular menstrual tracking apps, a
survey of 687 people, and follow-up interviews with 12
survey respondents. We find that women track their
menstrual cycle for varied reasons that include remembering
and predicting their period as well as informing conversations
with healthcare providers. Participants described six methods
of tracking their menstrual cycles, including use of technology,
awareness of their premenstrual physiological states, and
simply remembering. Although women find apps and
calendars helpful, these methods are ineffective when
predictions of future menstrual cycles are inaccurate.
Designs can create feelings of exclusion for gender and
sexual minorities. Existing apps also generally fail to
consider life stages that women experience, including young
adulthood, pregnancy, and menopause. Our findings encourage
expanding the field’s conceptions of personal informatics.
Author Keywords
Menstrual tracking; menstrual cycle; period; personal
informatics; lived informatics; women’s health; inclusivity.
ACM Classification Keywords
H.5.m. Information interfaces and presentation (e.g., HCI).
INTRODUCTION
Personal tracking for self-knowledge is commonplace, from
recording finances for accountability to tracking location for
pure curiosity. Health tracking has perhaps captured the most
attention, with nearly 70% of US adults tracking a health
indicator [14]. However, relatively little attention has been
paid to tracking factors specific to women’s health1, including
where a woman is in her menstrual cycle. When Apple
HealthKit launched in 2014 without support for menstrual
data, the public was outraged over the exclusion of such an
essential aspect of health tracking [11]. Apple later added this
feature, but its exclusion sparked a conversation about
inclusivity in design of personal tracking tools [33].
We consider menstrual tracking through the lens of personal
informatics, with two goals. We first contribute to an
ongoing conversation on women’s health in HCI (e.g., [1])
by examining the practice of menstrual cycle tracking. We
offer an understanding of why and how women track their
menstrual cycles, focusing on how they use technology to do
so. Second, we identify design challenges and concerns in
digital tools for menstrual cycle tracking, drawing upon such
insights to offer guidance and challenge current broader
assumptions in the design of personal informatics tools.
Although not about tracking a behavior, menstrual cycle
tracking fits Li et al.’s definition of personal informatics as
tracking to obtain self-knowledge [25]. The practice of
menstrual cycle tracking challenges many assumptions of
personal informatics. For example, women often track their
menstrual cycles without an explicit goal of action, but
instead for awareness of their place in their menstrual cycle.
Understanding the differences and commonalities between
menstrual cycle tracking and other domains of personal
informatics extends how we as a field consider personal
informatics and design our personal informatics tools.
Toward these goals, we collected and analyzed data from
three sources. We first collected and coded 2,000 reviews of
popular menstrual tracking apps on the iPhone App Store and
Android Market. We then surveyed 687 people to understand
their practices around tracking menstrual cycles. We finally
conducted follow-up interviews with 12 survey respondents
to gather in-depth perspectives of those practices.
In this paper we contribute:
• An empirical description of why women track their
menstrual cycles. Women track to better understand their
bodies and mental states, to have materials prepared for
1 We use the gendered term “women” in this paper to refer to
anyone who ha
s, or has previously had, a menstrual cycle. This
follows the general use of the term “women’s health” in the HCI
community to discuss health issues around pregnancy,
menstruation, menopause, and breast cancer. We acknowledge
not all people who have a mens
trual cycle identify as women,
and not all people who identify as women have a menstrual cycle.
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CHI 2017,
May 06 - 11, 2017, Denver, CO, USA
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DOI: http://dx.doi.org/10.1145/3025453.3025635
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their period, to predict ovulation, and/or to describe their
menstrual cycle to their doctors. These practices differ
from the traditional personal informatics focus on tracking
one’s behavior, rather than one’s experiences.
• An understanding of how women track their menstrual
cycles. We identify six common methods: using dedicated
apps, recording in digital calendars, using paper calendars
or diaries, following birth control intakes or schedules,
noticing early bodily symptoms, and simply remembering.
• A discussion of problems and issues associated with
menstrual cycle tracking. We specifically consider
concerns about the indiscrete nature of tracking, inclusivity,
and varied and evolving use cases for the same tool.
We then discuss the broader implications of our findings for
personal informatics, considering how the design of personal
informatics tools can be informed by problems and issues
women commonly encounter in menstrual cycle tracking.
BACKGROUND
The study of menstrual cycle tracking builds on prior research
in technology for women’s health and personal tracking.
Women’s Health and HCI
The HCI community has a rich history of studying
technology in support of women at different stages of
pregnancy and motherhood. Women often turn to web
searches, apps, and social media during pregnancy and
parenting for information on whether their experience is
normal [13,23,28]. Many turn to social networks for support,
information, and often commiseration [28,29,37]. HCI has
further considered designs for technology supporting
pregnancy and motherhood, including rethinking the
experience of breastfeeding [4,10] and aiding in tracking
child development [22,39]. Peyton et al. describe a pregnancy
ecology to aid in design, shifting from a focus on a woman’s
activity, diet, and weight tracking to supporting her
information seeking, self-knowledge, and social needs [34].
Prior research often focuses on maternal health. Designing
and understanding technology for broader women’s health
has received relatively limited attention [1]. One design in
the space is Labella, underwear with a visual marker and an
app designed to help women explore their vaginal and pelvic
region with a goal of breaking social taboos and promoting
exploration and self-understanding [2]. Another is Help Pinky,
a digital game aimed to bridge knowledge about menstruation
and puberty in a rural Indian community with limited support
and knowledge [18]. Work by Stawarz et al. is most closely
related to menstrual cycle tracking [38]. They suggest that
current pill reminder apps, including birth control apps, fail to
effectively address forgetfulness, and that apps do not always
integrate into people’s varied routines.
Many design explorations of women’s health technology have
taken a feminist HCI approach [2,10], which stresses engaging
with the perspectives of marginalized groups that are typically
left out of the design process. Our work makes what Bardzell
defines as a critique-based contribution [5] by “analyz[ing]
designs… to expose their unintended consequences”, such as
the downsides to normative design choices. In our work, we
highlight ways in which tracking apps fail to support the
marginalized populations of gender and sexual minorities.
We adopt many aspects of Bardzell and Bardzell’s Feminist
HCI methodology [6], such as indicating our goals as
technologists in our interactions with participants.
However, our research team’s expertise is in personal
informatics technology, rather than feminist theory. We
therefore primarily approach this study through the lens of
personal informatics, offering design insights for future
tools. Although we do not connect our findings to feminist
theory, we acknowledge the opportunity for considering
personal informatics design through a feminist lens.
Personal Informatics in Everyday Life
Li et al. define personal informatics as self-knowledge
gained by collecting and reflecting on personal data [25].
Early models of personal informatics describe how people
use technologies to collect and integrate data toward then
acting on the collected data, typically with a goal of behavior
change. However, personal tracking technology is now a part
of people’s everyday lives and is not necessarily associated
with a self-improvement goal [35]. People track for other
motivations, including pure curiosity and a desire to
instrument a particular activity [12]. In this paper, we consider
the everyday life experiences of menstrual cycle tracking to
understand people’s varied motivations, uses, and goals.
Personal informatics research has often investigated health
and wellness by developing or studying digital tools to help
people track and understand personal data. However, people
also use other means to track their health. A 2013 Pew survey
notes that 44% of US adults who track a health factor do so
only in their heads, with 34% tracking on paper [14]. Digital
systems for personal tracking offer benefits over paper
systems, including improving accuracy and ease of entry
while maintaining usability [43]. However, digital tools still
sometimes fail to support people’s needs or goals, and
tracking on paper is still prolific. As shown in research on
personal financial tracking, paper systems people use for
personal tracking may or may not resemble digital
methods [21]. People appreciate the flexibility paper affords,
more than a technical solution focused on data aggregation.
Women similarly use a variety of methods to track their
menstrual cycles, from digital to memory-based.
A few studies have explored personal tracking in the context
of sex and pregnancy. Lupton highlights self-quantifying
components of apps for tracking sex performance
(e.g., stamina, number of partners) as well as fertility
indicators (e.g., ovulation, body temperature) [27]. Apps for
tracking pregnancy often offer information on how the fetus
should be developing and provide reassurance and advice
about parenting [28]. Many apps for tracking pregnancy
focus on associated risks, while others highlight associated
joys [41]. However, little research focuses on examining the
needs and challenges of menstrual cycle tracking.
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DATA COLLECTION AND ANALYSIS
We gathered data from three sources: online app reviews, a
survey of women’s tracking practices, and follow-up interviews
with some survey participants. Table 1 summarizes participant
demographics. The supplemental materials contain more
detailed participant demographics, our analysis codebooks,
as well as survey and interview materials including consent
descriptions. We quote app reviews with identifiers AXXXX,
survey responses with SXXX, and interviews with IXX.
Method 1: App Reviews
In January 2016, we searched for “period tracker” on the
Android Market and Apple App Store and selected the 12
most reviewed apps (9 distinct apps, as 3 were
cross-platform). We downloaded the 2,000 most recent app
reviews (120-200 per app, with variability caused by rate
limits in our download script). We wanted to determine what
characteristics of specific apps people like or dislike, rather
than general opinions of the apps themselves. We therefore
focused on open-ended review text, ignoring review scores.
We analyzed review data through a grounded approach. Two
researchers open coded the reviews before paring down to 6
codes most relevant to our research questions. Another then
coded the entire corpus per these 6 codes, with two additional
researchers coding 25% each (κ=0.66-0.80). One of the
researchers who defined the codes broke any ties.
Method 2: Survey
After analyzing the app reviews, we designed a survey to
address open questions about why and how women track.
To understand whether menstrual tracking practices differ in
a generation who grew up with apps available or if practices
differ based on experience with menstruation, we developed
the survey to reach both teenagers (13-18) and adults (18+).
The survey first asked people whether or how they monitor
their menstrual cycle. For those who currently or previously
tracked, we asked primarily open-ended questions to
understand how people track their menstrual cycle and what
they like and dislike about their method.
We obtained IRB approval for this study from our university
as minimal risk research with a waiver of parental consent,
because requiring parental consent would impact the ability
to conduct the research and could increase risk of participation
(e.g., leading parents to make inferences about a minor’s
behavior). Adults granted consent after reading a description
of the study. Minors assented to participation after reading a
similar description adjusted for a grade school reading level.
We recruited 690 survey respondents via posts to Facebook,
Twitter, and a Reddit subreddit targeted at teenagers. These
participants were entered into a drawing for a $100 gift card
to Amazon or Starbucks. We excluded 3 spam responses
from our analysis (free text such as “yes” and “I like” for
questions asking for an explanation), leaving 687 responses.
Three researchers first read the open-ended survey responses
and discussed potential codes. The first author open coded
the responses before condensing to 14 codes most relevant to
our research questions. Two researchers then coded 10% of
the data. Code agreement varied on this initial pass (κ=0.31-1,
with 0.80 or higher for 10 codes). The two researchers
arbitrated the disagreements until reaching 100% agreement,
and one researcher then coded the remainder of the data.
Method 3: Interviews
Our analysis of survey responses revealed areas we wanted
to explore in more detail, including situations where tracking
was uncomfortable and how menstrual tracking data is
discussed with healthcare providers. In sampling for the
interviews, we contacted 19 survey respondents who had
agreed to be contacted for an interview. We aimed for
diversity in experiences and backgrounds (including race,
gender, sexual minorities, and health conditions) rather than
representativeness. 12 responded to our request. Two
researchers conducted each interview via phone or Skype,
with one leading the interview and the other taking notes and
asking follow-up questions. Interviews lasted about 30
minutes, and we compensated each participant with a $20
gift card to Amazon or Starbucks. An external service
transcribed the interviews.
The researchers discussed major themes and identified 10
codes. Each transcript was then coded once by a researcher
who did not participate in that interview, which helped each
researcher become familiar with more interview data. We did
not conduct inter-rater reliability on the interview data. It is
rarely calculated on semi-structured interview data because
people can apply the same code to different parts of a
conversation [3]. The interviews were conducted under the
same minimal risk IRB as the survey.
Limitations
As HCI researchers, we designed our study to inform the
design of menstrual cycle tracking technology through the
lens of personal informatics. Our research methods were
designed with a bias toward understanding how women
currently use technology to track. Women often track their
menstrual cycle through non-technological methods or in
their heads, and some do not formally track at all. The
prevalence of different tracking methods we report should
App Reviews (2000 reviews)
iPhone apps
Clue (200), Eve (150), Glow (200), Life (200),
P. Tracker (200) Period Diary (170)
Android
apps
Clue (160), Glow (160), My Days (120),
P. Tracker (160), Period Calendar (120), Pink Pad
(160)
Survey Demographics (687 people)
Gender
656 female, 11 nonbinary, 2 male (18 no answer)
Orientation
478 heterosexual, 91 bisexual, 21 homosexual,
8 asexual, 11 fluid, 38 other queer (40 no answer)
Age
min 13, max 60, mean 28.0, median 27 (14 no answer)
103 <18, 66 18-23, 265 24-29, 172 30-39, 67 ≥40
Apps used
(313 people)
Clue (104), P. Tracker (89), monthlyinfo.com (10),
Life (10), 33 apps with less than 10 people each
Interview Demographics (12 people)
Gender
10 female, 1 nonbinary (1 no answer)
Orientation
4 heterosexual, 3 bisexual, 2 homosexual, 1 fluid,
1 other queer (1 no answer)
Age
min 17, max 40, mean 28.6, median 28 (1 no answer)
1 <18, 1 18-23, 4 24-29, 4 30-39, 1 ≥40
Table 1. We collected data from three sources: app store reviews,
a survey of women’s practices, and follow
-up interviews.
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not be interpreted as representative of any particular
population, as our results likely exaggerate technology use.
The demographic makeup was heavily influenced by the
research team’s social networks and the Reddit population.
Both participant groups likely skew WEIRD: Western,
Educated, Industrialized, Rich, and Democratic [16]. Most
participants were from the United States. Compared to the
US population [17], a higher percentage of survey
respondents identified their race as “White” or “Asian”,
while fewer identified their race as “Black or African
American” or “Hispanic or Latino.” We did not specifically
target our recruitment at this population.
Although we focus on broad implications for menstrual
tracking, substantial care must be taken when applying our
findings to understanding tracking by women in cultures and
ethnic groups underrepresented in our study. Future work is
necessary to understand the challenges, motivations, and
methods of tracking of women from other cultures, education
levels, and economic statuses. As one example, S255
identifies as an Orthodox Jew, and tracks her cycle by
“remember[ing] what day I came back from the mikveh last,”
a purification ritual which immediately follows menstruation.
The keywords we selected when searching for apps led us to
examine apps focused on period tracking rather than fertility
and pregnancy. Many apps specifically aim to help women
track factors important to becoming pregnant. We did not
specifically investigate how women use these apps, as we
were interested in the lived experience of menstrual cycle
tracking. However, many apps that are described or marketed
as period trackers prominently include fertility and
pregnancy features. This point is discussed in our results.
WHY WOMEN TRACK MENSTRUAL CYCLES
We surfaced five reasons women track their menstrual
cycles. Women track to: (1) be aware of how their body is
doing, (2) understand their body’s reactions to different
phases of their cycle, (3) be prepared, (4) become pregnant,
and (5) inform conversations with healthcare providers.
Participants were typically motivated by multiple factors.
Many women in our study viewed menstrual tracking as a
“general health check” (S92). S250 describes tracking as an
indication that her body is doing well: “to be aware of my
body, making sure it’s happy and healthy.” A232 agrees,
noting “it’s a huge help in really getting to know your body.”
These people quickly glance at the data for awareness of their
health, similar to idea of a “financial touch” that many people
work to gain from tracking personal finances [21].
Survey respondents often tracked their menstrual cycles to
understand their body’s physical and emotional reactions at
different phases of their menstrual cycle, and to verify and
predict their body’s response. S466 tracks to “guess if
cramping is likely to get worse”, while S73 tracks “so I know
I’m not crazy when I start to PMS and so I can up my dosage
of my anxiety meds.” The tracked data can also be used to
make sense of current experiences. S465 notes “sometimes
I’m really emotional and irrational and I can look at my
tracker, see that my period is due in a week or less and chill
out and realize I’m PMSing instead of having real feelings.”
Participants reported tracking to be prepared, “so it doesn’t
surprise me” (S27). They often did so to ensure necessary
materials are accessible, such as “to predict when I will need
pads and tampons” (S135). The tracked menstrual cycle data
informs decisions in women’s everyday lives, echoing the
lived experience of tracking [35]. People plan life events
around their cycle, from leisure activities: “I can literally
plan my vacations and excursions around my time” (A1122),
to intimacy: “I also plan alone time with my husband based
on when my period comes… i.e. hotels without kids” (S537).
In extreme cases, people’s menstrual cycles interfere with
their professional life. S449 says “I suffer from debilitating
cramps that cause me to stay home from work, so I track my
period to plan in advance as best I can.”
Participants often started tracking when trying to get pregnant.
S228 notes “when I was younger I used to just remember.
When I started trying for a baby, it became important to
know my fertile times.” People also change their tracking
practices when they begin trying to become pregnant. For
example, S18 previously tracked on her paper calendar, but
later “switched to apps when trying to get pregnant to get
more symptom tracking.” However, these processes can be
“too complicated to keep up with [post-partum]” (S43).
Healthcare providers often ask a patient when her last period
was as part of exploring her needs for more information and
general attitude toward her body [7]. 25 women mentioned
tracking to inform conversations with their healthcare
providers. 6 other survey respondents began tracking at the
recommendation of their doctor, including A668: “it's great
because it lets me record unusual symptoms and then I can
remember them for my doctor visits.” For many, the
motivation is to know details related to their period when
their doctor asks: “my doctor asks, and I don’t want to seem
totally clueless about when it last was” (S420). Women’s
menstrual cycles tend to become irregular pre-menopause
[42] and immediately post-partum [32]. S181 is approaching
menopause, and tracks “so I can accurately tell my doctor
just how irregular my cycles are getting.”
For many women, tracking their menstrual cycle is an
obvious and essential activity. I03 said, “my mother just
taught me [tracking] was the thing you did. I don't think there
was ever any real explanation for why.” S580 noted “I didn’t
really think much about it. [tracking] was almost like a
routine.” Participants indicated the downside of not keeping
track was too high to not know where they were in their
cycle: “it really sucks to be caught unaware” (S48), “I want
to be prepared for it (emotionally and physically)” (S393).
This differs from other motivations typically considered in
personal informatics. Perhaps closest is the idea that people
tracking their finances seek out a financial touch, or a quick
glimpse at data for awareness of their financial situation [21].
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HOW WOMEN TRACK THEIR CYCLES
We surfaced six methods and tools women use to track their
cycle. Women (1) use phone apps, (2) use digital calendars,
(3) write in paper diaries, (4) follow cues in their birth
control, (5) notice symptoms, or (6) simply remember. We
also report on women who do not track. Similar to tracking in
other domains [12], women sometimes use multiple methods
simultaneously and switch between methods. Table 2 shows
the relative prevalence of each method in our survey
participants. We again note that our recruitment approach led
to a sample that over-represents women who track their
menstrual cycle, especially women using technology to do so.
Phone Apps
313 (47%) of our survey participants used an app on their
phone to track their menstrual cycle. For some, finding an
app to help track was the first method they thought of:
“common sense, there had to be an app for it. There’s an app
for everything.” (S622). Others arrived at it after struggling
to remember: “my memory is awful, so I like keeping track
using a dedicated app” (S517) or because they wanted
features other methods cannot provide: “I used to use paper
and pencil, but I like how the app can automatically predict
the start of my next period” (S41). For some, the switch to an
app was a logical step after they got a smartphone: “I used to
use a calendar when I was a teenager…, [I] started using the
app soon after I got a smartphone” (S88).
In addition to serving as a diary of past periods, apps often
use a woman’s average cycle length to predict when her next
period will occur and when she is ovulating. This information
is typically conveyed in the home screen of the app, or sent
in a push notification or email. Women often judge apps on
the accuracy of these predictions. 302 app reviews
mentioned how accurately the app predicted their cycle, with
65 reviewers describing apps as unhelpful due to inaccuracy.
Most apps support tracking factors beyond the timing of a
woman’s menstrual cycle, including information about the
period itself (e.g., color, volume), factors predictive of period
onset (e.g., cramping, mood), and other health-related factors
(e.g., exercise, sleep). Some people appreciate tracking these
factors. A94 was interested in understanding how her body
reacts at different cycle phases: “I have learned so much
about my body/cycle from using the app, like how certain
symptoms tell me I’m about to start my period… I always get
moody around this particular time every month, etc.”
However, too many features can make apps more difficult to
use: “they keep adding more functionality, which is good but
it makes things more cluttered” (S91).
Women who are trying to conceive often find phone apps
useful [28]. A395 says “I got pregnant right away, and I
believe it’s because of Glow’s algorithms and guided
suggestions for optimizing chance of pregnancy.” Apps often
surface details which are helpful for becoming pregnant.
S317 noted “it gave me clearer information about ovulation.”
A1764 used Glow, which “even tells you when to take a
pregnancy test… It also reminds you to have sex on those
important days.” Alternatively, 19 women in our study
reported using a phone app to avoid becoming pregnant.
S381 said, “I also have an active sex life and try to make a
note of potential fertile periods as well as make sure I don’t
miss a menstrual cycle to try to help avoid pregnancy.” In
her app review, A233 notes “you can breathe easy having
unprotected sex after your fertile window (99% of the time).”
Digital Calendars
83 (12%) survey respondents recorded their menstrual cycle
via a calendar on their phone and/or computer. Most selected
this method because it integrates with a tool they already use
to manage life events and check frequently. S324 says “I use
Google Calendar for other appointments, so this made sense
to keep everything tracked together.” Some people tried
using dedicated apps for menstrual tracking, but found “it is
too much trouble to use a separate app” (S255, 5 others
agreed). For many women, digital menstrual tracking followed
when they switched scheduling from paper to a digital system.
In order to increase awareness of their typical cycle, some
women record more information than when their period has
occurred or will occur even without the support of an app.
S274 records “two dots (..) for a heavy flow day, one dot (.)
for a light flow day, and a star (*) for my estimated ovulation
day.” 13 women mentioned marking a prediction of when
their next period would occur in their calendar, based on
when they got their period. S537 says, “I count 28 days and
put a dot so I know when to expect it next.” S45 encodes her
predictions: “I enter an M into my iCal calendar for day 1…
then an M? for when I think I’ll get my period.” 8 others,
including S468, set up “a repeating calendar event that tells
me about when to expect it every month.”
Paper Diaries and Calendars
52 (8%) survey respondents use a paper diary or calendar to
keep track, in much the same way that others use digital
calendars. Many women use a paper system “because I like
using pen and ink” (S142). Others, including S170, learned
to track on paper, and do not want to go through the effort of
switching to a digital system: “In the 90’s it was the only
option. [I] got used to it. I’m too lazy to search for an app.”
Although we expected younger women would be more likely
to use digital tools to track their menstrual cycles, Table 2
shows that younger respondents were no less likely to use a
paper system. Several young participants reported they had
adopted the paper-based tracking method their mothers used.
S596 tracks in a notebook, and noted “my mom uses this
method and she recommended it to me.” Conversely, some
Age
N
Phone app
Digital
calendar
Paper
calendar
Birth control
Early
symptoms
Remembering
Do not track
<18
103
50%
6%
9%
5%
9%
27%
6%
18-23
66
48%
9%
8%
17%
8%
15%
8%
24-29
265
43%
12%
6%
18%
6%
20%
9%
30-39
172
51%
17%
9%
6%
6%
14%
8%
≥40
67
34%
12%
9%
6%
9%
12%
20%
Overall
687
47%
12%
8%
12%
7%
19%
11%
Table 2. The majority of survey respondents used phone apps
to keep track of their menstrual cycles.
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participants also mentioned exploring tools that their
daughters had recommended. S537 currently tracks on paper,
noting “I just found out there is an app, my 16-year-old
daughter uses it.” S253 uses the P. Tracker app because “my
daughter suggested the app, and I find it easier than writing
it on the calendar.” Tracking methods are shared both from
mother to daughter and from daughter to mother.
Following Hormonal Birth Control
Pill-based hormonal birth control is typically in packs of 21
or 28 pills [40]. Both cases contain 21 days of pills with
active hormones, with a woman’s period typically arriving
after finishing these pills. The 28-day packs contain one
week of placebo pills, which are typically visually distinct,
to sustain the daily habit of taking a pill. The NuvaRing [30]
follows a similar cycle. A woman inserts it into her vagina
for three weeks, during which it releases hormones preventing
ovulation. She then removes it for one week, at which point
she experiences her menstrual period. Other hormonal birth
control pills have a longer cycle, with months between placebo
pills and thus the arrival of the period (e.g., Seasonale [31]).
These longer cycles are otherwise similar.
The women in our study who were using these types of
pill-based birth control often kept track by simply noticing
how far they were in their packs. S446 says, “when my pills
are gone for the month, I know my period is coming.” For
S21, “the approach of the [placebo] brown pills signal the
approach of my period.” S520 notes the ease of this method,
saying “it requires no additional effort since I would already
be taking my birth control.” Participants with a NuvaRing
typically used another method to track when they needed to
insert or remove the ring. For example, I8 said “when I was
on the NuvaRing… I used a website called Bedside that
would send me a text message about ring in and out.”
Noticing Early Symptoms and Physical Changes
Many women notice physical or emotional changes which
typically align with the arrival of their period, from bloating
or breast soreness to irritability or fatigue [36]. Some survey
respondents paid close attention to these changes, using them
to predict the arrival of their period. For S146, the physical
changes tell her to be prepared: “from the onset of soreness I
give myself a few days, and then I’m on high alert.” S639
mentioned she has an irregular period, and noticed “I get
depressed and moody a few days before I start… I am able
to recognize these emotions as a period coming.”
Remembering
Women also keep track simply by remembering when their
last period occurred: “I just remember the date when my last
period started and count ahead ~25-30 days” (S468). This
method is most effective for women who have especially
regular periods and can easily predict when their next one
will be. Others instead try to remember what they were doing
when their period occurred, such as S472: “I try to remember
what I was doing the day it started the prior month and
extrapolate from there.” 11 survey respondents relied only
on their memory because “it’s the least conspicuous method”
(S221). 19 others simply could not remember to use another
system: “I’m not good at remembering to note the dates of
my period in apps or paper calendars” (S132).
Not Keeping Track
89 survey respondents indicated they did not track at all. 17
of those 89 later described tracking in line with the styles
above, so we reclassified them. 25 respondents do not
currently have a period for various reasons, including birth
control: “Mirena IUD made it stop” (S118, 14 others),
menopause (6 respondents), surgery: “procedure to reduce
excessive bleeding during periods was very successful… I
stopped having them” (S293, 1 other), a current pregnancy
(1 respondent), or “intense sports” (S589).
The remaining 47 participants did not keep track, but many
still prepared for their period. 26 participants mentioned
having supplies always available: “[I] keep tampons at home
and in my car all the time” (S540) or beginning to carry or
use supplies preemptively: “if I think it may be coming soon,
I will buy a box of tampons and keep some in my backpack”
(S175). 11 participants reported they do nothing to monitor
or prepare for their period. This lack of monitoring is
sometimes problematic, such as for S107: “I do nothing at
all. It leads to quite a few ruined pairs of underwear.”
IMPLICATIONS FOR DESIGN IN MENSTRUAL TRACKING
Women described many challenges with how they track their
menstrual cycle. These challenges illustrate problems in the
design of technology to support menstrual tracking.
The Importance of Accuracy for Prediction
Predictions of where women are in their cycle must be
accurate to be useful. In the case of predicting the first day
of menstruation, women want to know for planning purposes.
For example, S399 relies on her app notification to not forget
and to be prepared: “it conveniently gives me notifications
two days early, otherwise I would always forget.” Menstrual
tracking apps vary in how they present these predictions.
Some apps present the prediction as a single-day estimate of
when a woman’s period or ovulation will begin (Figure 1a),
others present the prediction as a range of possible affected
days or include errors in their estimates (Figure 1b).
Similar to results in other domains [12], women abandon
inaccurate menstrual tracking apps and search for more
accurate alternatives. However, predicting the first day of a
period and time of ovulation is challenging. 90% of women
(a)
(b)
Figure 1. Phone apps predict a woman’s next period or
ovulation. Life (a) surfaces this prediction as a point estimate,
while Clue (b) provides a range of potential dates.
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have a period every 24 to 38 days, which clinicians define as
a “normal” cycle frequency [15,42]. This range is broad and
ignores the 10% of women whose cycles are more or less
frequent. Cycle regularity varies as well. Some women have
a fixed-length cycle, while others can vary by days or even
weeks [15]. To aid in this prediction, one of the first questions
apps often ask women is to enter their average cycle length
and flow duration (Figure 2a). This information is typically
used to predict the first day of a period, which is later
informed by the data recorded in the app (Figure 2b).
Many women find current modeling assumptions about
stability and regularity insufficient for predicting their
menstrual cycle. A1638 has an irregular cycle and found her
app assumed some regularity: “The whole reason I need a
period app is due to my extremely irregular periods… For
someone whose days vary it’s hard to use. Sometimes my
periods are longer than normal and the app assumes I must
have forgotten to hit the ‘period end’ button and does it for
me.” S369 notes that a particular cycle may be abnormal as
well, which impacts later prediction: “If something out of the
ordinary happens (Plan B, particularly stressful month), it
doesn’t take that into account when predicting your next
period.” For S188, this effect snowballs: “if I’m stressed I
usually run a week late. That messes up the accuracy of my
next cycle prediction… which then makes me more stressed.”
Models additionally fail to keep up with life changes. For
example, A81 was “very faithful at keeping track of her
periods up until I got pregnant”, but the app no longer works
for her: “a pregnancy, baby, and year and a half of
breastfeeding later, the app thinks my normal cycle length is
about every 700 days!” She does not want to switch apps and
lose her data: “Please add a pregnancy mode :) I don’t want
to have to start over!” However, apps also poorly account for
more routine changes, such as a woman switching her
method of birth control. S398 started using an IUD, noting
“the length of my cycles as reflected on the app is completely
out of sync. It would be nice to have options that account for
the fact that I still have a standard menstrual cycle, despite
not having a typical period every month.”
Similar to barriers in other domains [12], people often forget
to log their period. Most apps require logging both when a
period starts and when it ends. Logging the end can be
particularly difficult to remember: “it’s easy to forget about
towards the end of your period, so the actual cycle is often
not completely accurate… but that’s more of a user failing
;)” (S59). Beyond the annoyance of having an incomplete or
inaccurate record, S65 noticed it limited how well her app
predicted her period later: “I sometimes forget to enter my
data one month, which skews the data for the next month.”
Recommendation: Given how crucial prediction accuracy
is to menstrual cycle tracking, designers should evaluate
additional techniques for modeling and communicating
predictions about a woman’s cycle. Reasons for tracking likely
correspond to different prediction needs. A woman tracking
to avoid becoming pregnant would probably prefer her app
overestimate her ovulation window. A woman trying to
become pregnant may instead prefer a conservative estimate
of her ovulation. For both ovulation and period arrival,
designers should consider and evaluate interfaces that present
probabilities as an alternative to unreliable binary predictions.
Current app predictions do not sufficiently account for life
experiences that can impact a woman’s menstrual cycle
(e.g., stress, exercise or diet changes, some emergency
contraceptions). At minimum, apps should allow women to
record when the app’s prediction falls out of line with when
their period actually occurs, and should use this information
to improve future prediction accuracy. Designers should
consider how to account for the variability caused by
irregular cycles and life changes. For example, designs could
occasionally ask women to offer their own prediction, using
that opportunity to identify changes.
Gendered Design and Nonconformity
Similar to a trend noticed by Peyton et al. in the context of
pregnancy apps [34], the menstrual tracking apps we
reviewed tended to use stereotypically feminine attributes,
such as the interface being predominantly pink or using
flower and heart images (Figure 3). S98 found the design of
her app insulting: “they have tried to make it ‘feminine’ by
adding flowers… It makes me feel like you are trying to
‘dumb it down’ for me. Why can’t keeping track of my
menstruation be a professional and organized task?”
(14 other survey respondents agreed). I09 tried a couple of
different apps, finding: “a lot of them just felt kind of
condescending or like they were designed by dudes who were
designing what they thought a woman would like.”
(a)
(b)
Figure 2. Apps, such as My Cycles (a) and Period Tracker (b),
typically ask for average cycle duration and flow length to aid
in prediction. Although this prediction may be later aided by
journaled data, it is not resilient to variations due to irregular
c
ycles, stress, birth control, and even forgetting to journal.
(a)
(b)
Figure 3. Period tracking apps often employ feminine, flowery,
pink aesthetics. (a) is Period Diary, (b) is P. Tracker Lite.
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Our sampled app reviews often mentioned the femininity of
apps. 13 reviews appreciated the femininity, including A194
“I love the way it includes necessary info in a fashionable
girly way. :-).” However, 44 app reviews considered
femininity a more negative design trait, and valued when
they found more gender-neutral designs: “I spent quite a
while trying to find [a period tracking app] that wasn’t pink
and/or flowery, but I finally did and I’m impressed”
(A229 reviewing the app Clue, Figure 1b, 4b). 38 of these
reviews were for the Clue app, which was described as a
“gender neutral” alternative (A740).
Beyond pink and flowery interfaces, app designers often
assume that the person tracking their menstrual cycle
identifies as female (Figure 4a). A841 struggled to “find a
period tracker that didn’t misgender me”, before settling on
Clue. S365 identifies as male and also uses Clue, noting “it’s
hard to find tools that work for me! [Clue] uses gender
neutral language… it’s also not focused on pregnancy,
which I’m not interested in at all.” Tracking ovulation is
often a major feature of menstrual tracking apps, appearing
on the main app page alongside period information
(Figure 1). A1086 has no need or interest in seeing this
information: “I wish it were less catered to birth and family
planning… I suppose I’m in the minority since I’m asexual.”
Furthermore, menstrual tracking apps often enable logging
sexual activity. This feature is often designed to help women
track sex alongside ovulation, either to become or avoid
becoming pregnant. However, women track sex for reasons
unrelated to fertility. I06 appreciated logging to check how
her relationship is going. Her app puts a heart on the calendar
whenever she logs sex, which she appreciates: “I can tell if
we’re off or something, so I can look and can get back on
track… I just like looking back and seeing the little hearts
everywhere… it’s fun. I like to see how often we have sex.”
A1928 notes “to me, this is just as important!... it’s hard
sometimes to realize/remember how long it’s really been.”
Menstrual tracking apps often assume a sexual or
relationship partner is male. For example, Clue provides two
options for logging sex, with both icons suggesting a male
partner (Figure 4b). S240 was particularly bothered by this,
noting “sex options assume sex with a man, and reminder of
ovulation cycle both remind me I am not a ‘normal’ woman
whenever I use the app… but it’s not overly pink so I deal.”
A232 agreed, noting “as someone in a same sex relationship
protection isn’t really a concern, but I would like to keep
track of my ‘activity’ levels.” Other apps enable sharing of
period and fertility data with a partner. This partner is
sometimes implied to be male, such as in the iconography for
My Period Tracker (Figure 4c). The partner cannot log their
own menstrual cycle in some apps, despite the desire for the
couple to track together. A397 notes in her review of Glow,
“it’s an amazing app, but it’s meant for straight couples.”
Recommendation: More people can benefit from tools with
gender-neutral themes that do not assume gender in labels,
iconography, and functionality. Similarly, when logging sex,
apps should be careful not to assume the sexual orientation
or gender of either the person logging or their partner(s).
Logging sex and reporting on fertility can be desirable for
some women and uncomfortable for others. Designs should
consider including features to support tracking sex and
fertility, but should also support disabling those features.
The Discreet Nature of Menstrual Cycle Tracking
Women often treat menstruation as a personal matter they do
not want to disclose to others [24]. Women who track their
menstrual cycles with technology reported concern about
accidentally disclosing where they are in their cycle when
showing their calendar to friends or coworkers. In particular,
people often share their calendars permanently or
temporarily, which can cause discomfort for women who
have information about their menstrual cycle on their
calendar. S188 says, “It’s weird having it on my calendar so
publicly, I wish it was there but somehow more secretly”
(7 others expressed a similar sentiment). To combat this
disclosure, 21 survey respondents described using a method
to “encode the events” (S13) to keep the information private.
Women most commonly encode with a simple symbol, “X”
or “.”, instead of describing the event. Others aim to be more
discreet, such as S28: “I use ‘rrrrr’ for period… Secret, only
I know what ‘rrrrr’ means.” S409 describes her method:
“Luckily, I know an esoteric language, so I write [when my
period is coming] in that different language.”
Some women prefer using a dedicated app because of
privacy, including S192: “keeping info in an app instead of
written on my calendar gives me greater privacy.” However,
women were still concerned about revealing personal
information when these apps are used in front of others.
Privacy concerns are perhaps exacerbated by app designs.
A781 “used to be embarrassed when other people looked at
my phone and saw a bright pink tracking app”, which led her
to switch to Clue: “this design lets me feel more secure in
letting other people handle my phone” (17 others agreed).
S102 “renamed the app ‘tracker’ on my phone instead of
‘period tracker’” to make the app more discreet. A76 agreed,
suggesting “a less obvious title would be awesome” for the
app “Period Tracker.” 9 app reviewers of 5 different apps
(a)
(b)
(c)
Figure 4. In Glow (a), people who identify as male are directed
to an alternate view of the app. Clue’s iconography (b) suggests
a male sexual partner, while the iconography in My Period
Tracker (c) implies a female sharing data with a male partner.
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appreciated their app’s discreetness. A1503 said, “no one
will guess what it is because it looks like an average app.”
Although notifications can help women remember when
their period is coming, they also conflict with the desire for
discreetness. S440 has trouble remembering to monitor her
period: “you do still have to remember to use it”. She
suggests reminders could help, “but that makes it a little less
discreet. As is, the app could still be more discreet.” S283
disabled the notifications “since they’re kind of personal, but
as a result I sometimes forget to enter the period in and have
to try to remember when it was later.”
Recommendation: Apps for menstrual cycle tracking
should design for customization. Designs should either be
discreet by default or provide a neutral non-obvious interface
option. Such discreetness is similar to Consolvo et al.’s
design choice in UbiFit Garden, abstractly encoding physical
activity data as a garden [9]. Many women desire app color
schemes, icons, names, and notifications that do not draw
attention from others who casually glance at their phone.
Designs should support disabling and customizing
notifications. Any decision to show tracked data to someone
else should be explicitly triggered.
Supporting Varied and Changing Reasons for Tracking
Apps support many of the motivations women have for
tracking their menstrual cycle (Figure 4a). Supporting many
tracking motivations provides benefits, including supporting
people when their goals change. S148 “initially started
because I was fairly irregular and wanted to find any trend I
could”, but now has different goals: “now I track to make
sure I’m not missing my period – a pregnancy check,
basically.” S318 originally tracked for awareness: “before, it
was to know when it would next come”, and is now trying to
become pregnant: “more recently, it has been to know when
I’m ovulating for conception.” In practice, apps for menstrual
cycle tracking are not wholly successful at supporting varying
goals. S127 has tracked for both health and fertility, noting
“some [apps] have features for health and some have features
for fertility planning… to make the most, I have used various
apps at the same time and entered data into them twice.”
One option to better support these goals is to include more
information and options relating to fertility, pregnancy, and
post-partum. However, some women believe apps already
focus too much on these areas. S467 felt her app is “clearly
trying to support my getting pregnant (which is not my intent)
and not just agnostically for tracking” (8 others agreed).
A1936, who just started having her period, felt the ovulation
information in Clue was not relevant to her: “I would like it
if they made a kid’s version because idc [I don’t care] about
fertile!! I’m too young!!”
Moreover, ovulation information can be uncomfortable for
someone who has struggled with infertility. S104 writes: “my
app shows predicted ovulation. I wish it didn't. We dealt with
infertility and extensive treatments for 6 years. I am no longer
trying to get pregnant and I don't like the reminder of TTC
[trying to conceive] or the tiny glimmer of hope that maybe
by magic this will be the month when a miracle happens.”
She now primarily tracks “so I know when to expect [my
period]”, but the app she uses presents information about
ovulation despite her discomfort. Two others described the
same concern, saying tracking is a “constant reminder of
trying to conceive and not succeeding” (S131).
Recommendation: To resolve the problem of varied and
changing motivations, menstrual cycle tracking apps can
support reconfiguring the main interface. For example, apps
could provide a different view when someone begins trying to
conceive, utilizing their menstrual cycle history to accurately
predict when they will ovulate. Alternatively, apps could
enable exporting menstrual cycle history or could share data
among a collection of apps, so another app designed for a
woman’s new goal could utilize previously logged data.
Further, being able to retrieve tracked data outside the app to
share with a healthcare provider or when transitioning to a new
phone is often helpful. Unfortunately, personal informatics
data are largely siloed within a specific tool or company [26].
Some menstrual tracking apps enable data backups for
switching or resetting devices, but not consistently: “I went
from an iPhone system to an Android and nothing was saved
so I manually inputted what I had” (A782).
Menstrual Tracking Alongside Other Tracking
Some menstrual tracking apps support journaling other aspects
of wellbeing, such as mood, headaches, insomnia, and sexual
activity. Participants noted these aspects sometimes aligned
with their menstrual cycle. I04 noted, “I was consistently
being depressed right before it happened. I would be really
unhappy and grumpy, and then I’d get my period.” Tracking
her period alongside her mood helped better explain her
mood: “keeping track of it, then, when I remembered to look
at a calendar, I’d be, ‘oh, this is why I feel like this’.” In the
notes field of her menstrual tracking app, I05 tracks “whether
I started having some kind of symptom like headaches or
bloating… just to see if anything correlated.”
Some participants were particularly interested in aligning
their menstrual cycle data to other tracking. I09 said “I have
noticed a connection between my resting heart rate and my
menstrual cycle.” However, identifying the trend was hard:
“neither my menstrual cycle app nor the Fitbit allow me to
export the data, so I have to manually type the data into a
spreadsheet to see the trend. I’d love it if I could export the
data, or if I could track both via one app.” Some apps readily
support data integration and export. A596 says “I really like
how my FitBit info can be synced with Glow”, and A219
appreciates that her app “integrates with Apple Health.”
Recommendation: Menstrual cycles affect and are affected
by other aspects of women’s lives. Apps for tracking menstrual
cycles should support women in identifying these connections.
Apps could include the ability to journal concerns and
correlations alongside menstrual cycles, such as mood and
stress. Apps can further support developing open-ended
categories when a woman wants to journal something that is
unsupported. Apps should allow tracked data to be exported,
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ideally supporting interoperability with other popular
platforms for tracking health data (e.g., Fitbit, Apple Health,
MyFitnessPal). We note some apps include these capabilities
already, but their implementation should be widespread.
Discussing Tracked Data with Doctors
For many health concerns, patients report dissatisfaction
with how providers engage with the data that patients bring
to appointments [8]. Our participants similarly noticed that
doctors tended not ask about their data, particularly when
their health was normal. I07 said “none of [my doctors] asked,
‘oh, can I see your written calendar?’… just sort of my general
take of it… that was as much as they really seemed to care.”
On the other hand, when addressing a specific health
concern, healthcare providers appreciated having data about
the patient’s menstrual cycles. I11 used a spreadsheet to track
everything she and her husband tried while undergoing
fertility treatments. Whenever she visited a new doctor, she
would summarize the spreadsheet and give them a print-out
of what they had tried: “[the doctors] don’t have much time, I
felt like if I had just printed off the massive spreadsheet and
handed it to them, there’s pretty much zero chance they would
have looked at it… I really tried to just summarize it, and give
them a 1 or 2-page sheet… they loved that… otherwise they
have to go through my record, which is a massive packet of
stuff.” I06 has an especially irregular period and occasionally
bleeds outside of her primary cycle. Her doctor finds this
information useful: “when we’re trying to figure out whatever
was going wrong, it was very helpful for her to see the start
and end dates and how long it had been in between periods,
and how long the period had lasted… I would consolidate
what I have in a Google document… I always synthesize it.”
Recommendation: Designs should offer ways to summarize
and export data for a healthcare provider. Prior work notes
summaries of patient-generated data should be concise, follow
a standard format, and integrate into digital health records [8].
DISCUSSION
Our findings echo problems people encounter in many
personal informatics domains. People migrate between goals
and tools for tracking their menstrual cycles as their needs
change. Personal tracking tools fail to support such transitions
and do not enable data migration between tools [12]. Gendered
or heteronormative designs of such tools exclude people
from using them and alienate others who continue to use them.
Li et al. define personal informatics tools as “those that help
people collect personally relevant information for the
purpose of self-reflection and gaining self-knowledge” [25].
Although this definition is broad, most research has focused
on tracking behaviors and outcomes in support of behavior
change or tuning. Menstrual cycle tracking reminds the
community that tracking one’s experiences, rather one’s
behavior, can lead to important self-knowledge.
Personal informatics research has also focused on domains
where people have some amount of control over the behavior
being tracked, such as spending, physical activity, or food
choices. In menstrual cycle tracking, however, women
primarily observe what they are tracking, with little or no
control over how and when it will occur beyond hormonal
regulation through birth control. Rather than trying to change
the outcome being tracked, women track to learn how to
adjust their thoughts and behaviors around it. In other
domains where the person tracking lacks control, such as
tracking pain and allergic reactions, observing the event can
help explain symptoms and even identify causes.
People tracking physical activity or weight data often focus on
overall trends, rather than the accuracy of any individual data
point [19,44]. If overall trends are sufficiently accurate to
plan and adjust behavior, accuracy in individual data points
is less of a concern. For menstrual cycle tracking, however,
considering the overall trend is insufficient. Women plan
around the specific predictions provided by their app, which
rely on the accuracy of past data points. People’s needs
surrounding accuracy in personal informatics vary by
domain and how the data is being used. Designers of personal
informatics tools should work to build an understanding of
acceptable accuracy [20] for the domain in which they work
and the specific type of insights their tools will offer.
This paper aims to promote a conversation surrounding
menstrual cycle tracking within HCI. The tracking practice
is extremely widespread, spanning much of the lifetime of
much of the population. When we posted our survey to social
networks, we were overwhelmed by the amount of interest
the study received. On Facebook, participants expressed
appreciation that this topic was receiving attention, including
“hats off to [researchers] for tackling this” and “can’t wait
until they post their results.” Others lamented the current
state of technology: “I’ve tried 4 apps. They all suck… I
would think a creative woman would've created something
better by now…” Menstrual tracking tools are important, but
for many people current applications fall short. Designers,
engineers, and researchers can do more to make these tools
more inclusive and to reflect the changing needs of women.
CONCLUSION
We contribute an understanding of why and how women track
their menstrual cycles with an eye towards the problems they
encounter with current tracking methods. Designs should
avoid gendered coloring, iconography, and text to both be
discreet and inclusive of people’s gender identities and sexual
orientations. Designs should support the varied reasons people
track their menstrual cycles, and should support migration
between tracking goals or tools. Women rely on accurate
predictions from their menstrual trackers. Our research
emphasizes the importance of designing for inclusion,
acceptable accuracy, and discreetness in personal informatics.
ACKNOWLEDGMENTS
We thank Elizabeth Bales, Arpita Bhattacharya, and Sarah
Fox for their feedback. This work was sponsored in part by
the Intel Science and Technology Center for Pervasive
Computing, the University of Washington Innovation
Research Award, the AHRQ under award 1R21HS023654,
and the NSF under awards SCH-1344613 and IIS-1553167.
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REFERENCES
1. Teresa Almeida, Rob Comber, and Madeline Balaam.
(2016). HCI and Intimate Care as an Agenda for
Change in Women’s Health. Proceedings of the
SIGCHI Conference on Human Factors in Computing
Systems (CHI 2016), 2599–2611.
http://doi.org/bn88
2. Teresa Almeida, Rob Comber, Gavin Wood, Dean
Saraf, and Madeline Balaam. (2016). On Looking at
the Vagina through Labella. Proceedings of the
SIGCHI Conference on Human Factors in Computing
Systems (CHI 2016), 1810–1821.
http://doi.org/bn89
3. David Armstrong, Ann Gosling, John Weinman, and
Theresa Marteau. (1997). The Place of Inter-Rater
Reliability in Qualitative Research: An Empirical
Study. Sociology, 31(3), 597–606.
jstor.org/stable/42855840
4. Madeline Balaam, Rob Comber, Ed Jenkins, Selina
Sutton, and Andrew Garbett. (2015). FeedFinder: A
Location-Mapping Mobile Application for Breastfeeding
Women. Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems (CHI 2015),
1709–1718.
http://doi.org/bn9b
5. Shaowen Bardzell. (2010). Feminist HCI: Taking Stock
and Outlining an Agenda for Design. Proceedings of
the SIGCHI Conference on Human Factors in
Computing Systems (CHI 2010), 1301–1310.
http://doi.org/d7c545
6. Shaowen Bardzell and Jeffrey Bardzell. (2011).
Towards a Feminist HCI Methodology: Social Science,
Feminism, and HCI. Proceedings of the SIGCHI
Conference on Human Factors in Computing Systems
(CHI 2011), 675–684.
http://doi.org/dc47ft
7. Lynn S. Bickley and Peter G. Szilagyi. (2003). Female
Genitalia: The Health History: Menarche, Menstruation,
Menopause. In Bates’ Guide to Physical Examination
and History Taking (8th ed.). Lippincott Williams &
Wilkins, Philadelphia, PA.
8. Chia-fang Chung, Kristin Dew, Allison Cole, Jasmine
Zia, James Fogarty, Julie A. Kientz, and Sean A. Munson.
(2016). Boundary Negotiating Artifacts in Personal
Informatics: Patient-Provider Collaboration with
Patient-Generated Data. Proceedings of the ACM
Conference on Computer Supported Collaborative
Work (CSCW 2016), 770–786.
http://doi.org/bn9c
9. Sunny Consolvo, David W. McDonald, Tammy Toscos,
Mike Y. Chen, Jon Froehlich, Beverly Harrison, Predrag
Klasnja, Anthony LaMarca, Louis LeGrand, Ryan Libby,
Ian Smith, and James A. Landay. (2008). Activity Sensing
in the Wild: a Field Trial of UbiFit Garden. Proceedings
of the SIGCHI Conference on Human Factors in
Computing Systems (CHI 2008), 1797–1806.
http://doi.org/fj37wd
10. Catherine D’Ignazio, Alexis Hope, Becky Michelson,
Robyn Churchill, and Ethan Zuckerman. (2016).
A Feminist HCI Approach to Designing Postpartum
Technologies: “When I first saw a breast pump I was
wondering if it was a joke.” Proceedings of the
SIGCHI Conference on Human Factors in Computing
Systems (CHI 2016), 2612-2622.
http://doi.org/bn9d
11. Arielle Duhaime-Ross. (2014). Apple Promised an
Expansive Health App, so Why Can’t I Track
Menstruation? The Verge.
http://www.theverge.com/2014/9/25/6844021/appl
e-promised-an-expansive-health-app-so-why-
cant-i-track
12. Daniel A. Epstein, An Ping, James Fogarty, and Sean
A. Munson. (2015). A Lived Informatics Model of
Personal Informatics. Proceedings of the ACM
International Joint Conference on Pervasive and
Ubiquitous Computing (UbiComp 2015), 731–742.
http://doi.org/bdsr
13. Adam Fourney, Ryen W. White, and Eric Horvitz.
(2015). Exploring Time-Dependent Concerns about
Pregnancy and Childbirth from Search Logs.
Proceedings of the ACM Conference on Human
Factors in Computing Systems (CHI 2015), 737–746.
http://doi.org/bn9f
14. Susannah Fox and Maeve Duggan. (2013). Tracking
for Health. Pew Internet, 1–32.
http://www.pewinternet.org/Reports/2013/Tracki
ng-for-Health.aspx
15. Ian S. Fraser, Hilary O. D. Critchley, Malcolm G. Munro,
and Michael Broder. (2007). A Process Designed to Lead
to International Agreement on Terminologies and
Definitions used to Describe Abnormalities of Menstrual
Bleeding. Human Reproduction, 22(3), 635–643.
http://doi.org/ddcvtj
16. Joseph Henrich, Steven J. Heine, and Ara Norenzayan.
(2010). The Weirdest People in the World. Behavioral
and Brain Sciences, 33(2–3), 61-83-135.
http://doi.org/c9j35b
17. Karen R. Humes, Nicholas A. Jones, and Roberto R.
Ramirez. (2011). Overview of Race and Hispanic
Origin: 2010. United States Census Bureau.
http://www.census.gov/prod/cen2010/briefs/c201
0br-02.pdf
18. Minal Jain and Pradeep Yammiyavar. (2015). Game
Based Learning Tool Seeking Peer Support for
Empowering Adolescent Girls in Rural Assam.
Proceedings of the International Conference on
Interaction Design and Children (IDC 2015), 275–278.
http://doi.org/bvxh
19. Matthew Kay, Dan Morris, m.c. schraefel, and Julie A.
Kientz. (2013). There’s No Such Thing as Gaining a
Pound: Reconsidering the Bathroom Scale User
Interface. Proceedings of the ACM International Joint
Conference on Pervasive and Ubiquitous Computing
(UbiComp 2013), 401–410.
http://doi.org/bbdw
Personal Informatics & Self-Tracking
CHI 2017, May 6–11, 2017, Denver, CO, USA
6886
20. Matthew Kay, Shwetak N. Patel, and Julie A. Kientz.
(2015). How Good is 85%?: A Survey Tool to Connect
Classifier Evaluation to Acceptability of Accuracy.
Proceedings of the ACM Conference on Human
Factors in Computing Systems (CHI 2015), 347–356.
http://doi.org/bqd5
21. Joseph “Jofish” Kaye, Mary McCuistion, Rebecca
Gulotta, and David A. Shamma. (2014). Money Talks:
Tracking Personal Finances. Proceedings of the
SIGCHI Conference on Human Factors in Computing
Systems (CHI 2014), 521–530.
http://doi.org/bbdx
22. Julie A. Kientz, Rosa I. Arriaga, and Gregory D. Abowd.
(2009). Baby Steps: Evaluation of a System to Support
Record-Keeping for Parents of Young Children.
Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems (CHI 2009), 1713–1722.
http://doi.org/dz9vdr
23. Jennifer L. Kraschnewski, Cynthia H. Chuang, Erika S.
Poole, Tamara Peyton, Jaimey Pauli, Alyssa Feher,
Madhu Reddy, Ian Blubaugh, Jaimey Pauli, Alyssa
Feher, and Madhu Reddy. (2014). Paging Dr. Google:
Does Technology Fill the Gap Created by the Prenatal
Care Visit Structure? Qualitative Focus Group Study
With Pregnant Women. Journal of Medical Internet
Research, 16(6).
http://doi.org/bpnb
24. Janet Lee and Jennifer Sasser-Coen. (2015). Blood
Stories: Menarche and the Politics of the Female Body
in Contempary U.S. Society. Routedge.
25. Ian Li, Anind Dey, and Jodi Forlizzi. (2010).
A Stage-Based Model of Personal Informatics Systems.
Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems (CHI 2010), 557–566.
http://doi.org/bh8zsb
26. Ian Li, Anind K. Dey, and Jodi Forlizzi. (2011).
Understanding My Data, Myself: Supporting
Self-Reflection with Ubicomp Technologies.
Proceedings of the International Conference on
Ubiquitous Computing (UbiComp 2011), 405–414.
http://doi.org/cnsw9k
27. Deborah Lupton. (2015). Quantified Sex: a Critical
Analysis of Sexual and Reproductive Self-Tracking using
Apps. Culture, Health & Sexuality, 17(4), 440–453.
http://doi.org/bpnd
28. Deborah Lupton and Sarah Pedersen. (2015).
An Australian Survey of Women’s use of Pregnancy
and Parenting Apps. Women and Birth.
http://doi.org/bpnc
29. Meredith Ringel Morris. (2014). Social Networking Site
Use by Mothers of Young Children. Proceedings of the
ACM Conference on Computer Supported Cooperative
Work & Social Computing (CSCW 2014), 1272–1282.
http://doi.org/bpnf
30. Titia M. T. Mulders and Thom O. M. Dieben. (2001).
Use of the Novel Combined Contraceptive Vaginal
Ring NuvaRing for Ovulation Inhibition. Fertility and
Sterility, 75(5), 865–870.
http://doi.org/d37b65
31. Anita L. Nelson. (2005). Extended-Cycle Oral
Contraception: A New Option for Routine Use.
Treatments in Endocrinology, 4(3), 139–145.
http://doi.org/cpq5kn
32. Alfredo Perez, Patricio Vela, George S. Masnick, and
Robert G. Potter. (1972). First Ovulation after Childbirth:
the Effect of Breast-Feeding. American Journal of
Obstetrics and Gynecology, 114(8), 1041–1047.
http://doi.org/bpng
33. Sarah Perez. Apple Stops Ignoring Women’s Health
With iOS 9 HealthKit Update, Now Featuring Period
Tracking. TechCrunch.
https://techcrunch.com/2015/06/09/apple-stops-
ignoring-womens-health-with-ios-9-healthkit-
update-now-featuring-period-tracking/
34. Tamara Peyton, Erika Poole, Madhu Reddy, Jennifer
Kraschnewski, and Cynthia Chuang. (2014). “Every
Pregnancy is Different”: Designing mHealth
Interventions for the Pregnancy Ecology. Proceedings
of the ACM Conference on Designing Interactive
Systems (DIS 2014), 577–586.
http://doi.org/bpnh
35. John Rooksby, Mattias Rost, Alistair Morrison, and
Matthew Chalmers. (2014). Personal Tracking as Lived
Informatics. Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems (CHI 2014),
1163–1172.
http://doi.org/bbdz
36. Diana Sanders, Pamela Warner, Torbjöorn Bäckström,
John Bancroft, Torbjorn Backstrom, and John
Bancroft. (1983). Mood, Sexuality, Hormones and the
Menstrual Cycle. I. Changes in Mood and Physical
State: Description of Subjects and Method.
Psychosomatic Medicine, 45(6), 487–501.
http://doi.org/bpnj
37. Sarita Yardi Schoenebeck. (2013). The Secret Life of
Online Moms: Anonymity and Disinhibition on
YouBeMom.com. Proceedings of the International
AAAI Conference on Weblogs and Social Media
(ICWSM 2013), 555–562.
http://www.aaai.org/ocs/index.php/ICWSM/ICWSM1
3/paper/download/5973/6395
38. Katarzyna Stawarz, Anna L. Cox, and Ann Blandford.
(2014). Don’t Forget Your Pill! Designing Effective
Medication Reminder Apps That Support Users’ Daily
Routines. Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems (CHI 2014),
2269–2278.
http://doi.org/bpnk
Personal Informatics & Self-Tracking
CHI 2017, May 6–11, 2017, Denver, CO, USA
6887
39. Hyewon Suh, John R. Porter, Alexis Hiniker, and Julie
A. Kientz. (2014). @BabySteps: Design and
Evaluation of a System for Using Twitter for Tracking
Children’s Developmental Milestones. Proceedings of
the ACM Conference on Human Factors in Computing
Systems (CHI 2014), 2279–2288.
http://doi.org/bpnn
40. Patricia J. Sulak, Thomas J. Kuehl, Mirian Ortiz, and
Bobby L. Shull. (2002). Acceptance of Altering the
Standard 21-day/7-day Oral Contraceptive Regimen to
Delay Menses and Reduce Hormone Withdrawal
Symptoms. American Journal of Obstetrics and
Gynecology, 186(6), 1142–1149.
http://doi.org/dxgxmf
41. Gareth M. Thomas and Deborah Lupton. (2015). Threats
and Thrills: Pregnancy Apps, Risk and Consumption.
Health, Risk & Society, 17(7–8), 495–509.
http://doi.org/bpnp
42. Alan E. Treloar, Ruth E. Boynton, Borghild G. Behn,
and Byron W. Brown. (1967). Variation of the Human
Menstrual Cycle through Reproductive Life.
International Journal of Fertility, 12(1 Pt 2), 77–126.
http://doi.org/bzw45m
43. Christopher C. Tsai, Gunny Lee, Fred Raab, Gregory J.
Norman, Timothy Sohn, William G. Griswold, and
Kevin Patrick. (2007). Usability and Feasibility of
PmEB: A Mobile Phone Application for Monitoring
Real Time Caloric Balance. Mobile Networks and
Applications, 12(2–3), 173–184.
http://doi.org/bg67bf
44. Rayoung Yang, Eunice Shin, Mark W. Newman, and
Mark S. Ackerman. (2015). When Fitness Trackers
Don’t “Fit”: End-User Difficulties in the Assessment of
Personal Tracking Device Accuracy. Proceedings of
the ACM International Joint Conference on Perasive
and Ubiquitous Computing (UbiComp 2015), 623–634.
http://doi.org/bpnr
Personal Informatics & Self-Tracking
CHI 2017, May 6–11, 2017, Denver, CO, USA
6888
