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https://doi.org/10.1177/19322968221080199
Journal of Diabetes Science and Technology
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© 2022 Diabetes Technology Society
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DOI: 10.1177/19322968221080199
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Original Article
Introduction
Diabetes is one of the most common chronic conditions in
women, and the global incidence of type 1 diabetes (T1D)
and type 2 diabetes (T2D) has been on the rise for multiple
decades.1 Recently, therapeutic and technological advances
in diabetes care such as continuous glucose monitoring
(CGM) systems and continuous subcutaneous insulin infu-
sion (CSII) have facilitated the development of automated
insulin delivery (AID) systems—also called “(Hybrid-)
Closed-Loop Systems” or an “Artificial Pancreas.” The con-
trol algorithms used in AID systems automate and continu-
ously adjust insulin dosage based on changes in glycemic
levels and other factors such as carbohydrate intake.
Randomized controlled trials and observational studies have
1080199DSTXXX10.1177/19322968221080199Journal of Diabetes Science and TechnologyMewes et al
research-article2022
1Department of Pediatric Endocrinology and Diabetes, Charité—
Universitätsmedizin Berlin, Berlin, Germany
2School of Sociology, University College Dublin, Dublin, Ireland
3Berlin Institute of Health (BIH), Berlin, Germany
4Institute of Medical Informatics, Charité—Universitätsmedizin Berlin,
Berlin, Germany
*These authors contributed equally.
Corresponding Author:
Katarina Braune, MD, Fellow in Paediatric Endocrinology and Medical
Informatics, BIH Digital Clinician Scientist, BIH/Wellcome Trust SPOKES
Fellow, Department of Pediatric Endocrinology and Diabetes, Charité—
Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin,
Germany.
Email: katarina.braune@charite.de
Variability of Glycemic Outcomes and
Insulin Requirements Throughout the
Menstrual Cycle: A Qualitative Study on
Women With Type 1 Diabetes Using an
Open-Source Automated Insulin Delivery
System
Darius Mewes1,*, Mandy Wäldchen, MSc2,*, Christine Knoll, MD1,2,3,
Klemens Raile, MD1, and Katarina Braune, MD1,3,4
Abstract
Background: The impact of hormone dynamics throughout the menstrual cycle on insulin sensitivity represents a currently
under-researched area. Despite therapeutic and technological advances, self-managing insulin therapy remains challenging for
women with type 1 diabetes (T1D).
Methods: To investigate perceived changes in glycemic levels and insulin requirements throughout the menstrual cycle and
different phases of life, we performed semi-structured interviews with 12 women with T1D who are using personalized
open-source automated insulin delivery (AID) systems. Transcripts were analyzed using thematic analysis with an inductive,
hypothesis-generating approach.
Results: Participants reported significant differences between the follicular phase, ovulation, and luteal phase of the menstrual
cycle and also during puberty, pregnancy, and menopause. All participants reported increased comfort and safety since using
AID, but were still required to manually adjust their therapy according to their cycle. A lack of information and awareness
and limited guidance by health care providers were frequently mentioned. Although individual adjustment strategies exist,
achieving optimum outcomes was still perceived as challenging.
Conclusions: This study highlights that scientific evidence, therapeutic options, and professional guidance on female health-
related aspects in T1D are insufficient to date. Further efforts are required to better inform people with T1D, as well as for
health care professionals, researchers, medical device manufacturers, and regulatory bodies to better address female health
needs in therapeutic advances.
Keywords
glycemic variability, insulin sensitivity, sex hormones, menstrual cycle, automated insulin delivery, open-source
2 Journal of Diabetes Science and Technology 00(0)
supported the ability of these systems to improve glycemic
outcomes, decrease hypoglycemic events, and improve qual-
ity of life in people with diabetes (PwD) of various age
groups2-4 and in women with T1D during pregnancy.5-7
Prior to the availability of commercially developed AID
systems, a community of people affected by T1D behind the
hashtag #WeAreNotWaiting have collaboratively developed
open-source AID algorithms and openly shared their source
code and documentation online. In these systems, existing
medical devices are connected with an app running an open-
source control algorithm on their smartphones (AndroidAPS
for Android phones, Loop for Apple iPhones) or on a small
microcontroller (OpenAPS). Worldwide, an estimated num-
ber of several thousand PwD8,9 are currently using open-
source AID, of which approximately 44% are women.9-11
Observational studies9,11,12 have shown safety and efficacy
for open-source AID for PwD of various age groups and gen-
ders alike. User experiences reflect quality-of-life improve-
ments and describe the customizability and range of
personalized features of these systems as important
characteristics.13-17
For women living with T1D, managing diabetes can be
particularly challenging throughout different phases of life.
Several studies have shown that women with T1D and T2D
are less likely to reach targets in hemoglobin A1c, blood
pressure, and low-density lipoprotein cholesterol as recom-
mended by therapeutic guidelines,18 compared with men,
with possible explanations for these disparities remaining
unclear.19,20
The impact of sex hormone dynamics on insulin sensitiv-
ity and glucose metabolism is subject of constant scientific
debate.21-23 Variations in insulin sensitivity throughout the
menstrual cycle have been previously studied in women
without diabetes. However, the underlying molecular mech-
anisms are complex, and variable correlations of female sex
hormones and insulin sensitivity were observed. Several
studies that examined intravenous glucose tolerance in
smaller cohorts found either increased insulin resistance24-26
or no significant differences in insulin sensitivity27 during
the luteal phase of the menstrual cycle. A euglycemic, hyper-
insulinemic clamp study found no insulin sensitivity differ-
ences in relation to menstrual cycle phases.28 A longitudinal
study that investigated fasting glucose and insulin concentra-
tions in a larger cohort of 257 women without diabetes
showed significant changes in insulin resistance associated
with estradiol and progesterone concentrations and higher
insulin resistance during the luteal phase,22 in line with
observational studies that found significant correlations of
estradiol concentrations in saliva and insulin levels in 204
women regardless of their current menstrual cycle phase,29
and between estradiol and insulin concentration in 845 post-
menopausal women.30
Despite the available evidence on the influence of sex
hormones on glycemic levels in individuals without diabetes,
research on women with diabetes is sparse but equally
controversial. First observations suggesting an association of
diabetes and the menstrual cycle were made early in the his-
tory of insulin therapy in the 1940s, where cyclic changes in
blood glucose concentrations were observed in seven girls
with T1D prior to their menarche.31 Further studies from the
1990s and the early 2000s have found menstrual irregulari-
ties to occur more frequently in adolescents32 and adults with
T1D.33,34 Insulin sensitivity in relation to the menstrual cycle
was first investigated by hyperglycemic, hyperinsulinemic
clamp studies in the 1990s. A clamp study of 16 women
reported marked heterogeneity in glucose metabolism in all
and lower insulin sensitivity during the luteal phase in some
of the participants.35 These findings could not be confirmed
by others.36,37 A population-based study from 1996 on 124
women with T1D first highlighted self-reported changes in
glycemic levels around menstruation in 61% of the partici-
pants.38 Data throughout several complete menstrual cycles
were first assessed in 2004 by a pilot study of four women
with T1D using CGM,39 where different interindividual sen-
sor glucose patterns were found; however, these patterns
were consistent over several cycles of the same person.39 An
observational study of 12 women using CSII and CGM com-
bined found hyperglycemia to occur more frequently around
ovulation and the early luteal phase compared with the early
follicular phase.40 Controversially, a recent study of seven
participants found postexercise hyperglycemia to be more
prominent during the follicular phase.41
Despite these implications, sex hormone–related aspects
are—except for pregnant women with diabetes42—not suffi-
ciently considered in therapeutic guidelines, medical device
development, and clinical trials to date. The use of AID sys-
tems, and customizable open-source AID systems in particu-
lar, could facilitate the investigation of insulin needs and
glycemic patterns in relation to the menstrual cycle, and thus
contribute to the evidence base of this under-researched area.
Therefore, this explorative study aimed to investigate user
experiences of women living with T1D and using open-source
AID systems in relation to their menstrual cycles, thereby
leveraging experienced-based evidence and ideas for further
improvement of AID systems from the #WeAreNotWaiting
community and enabling further research in the field of T1D
and women’s health.
Methods
Study Design
As part of the patient-led OPEN project,43 a questionnaire for
the assessment of participant demographics and a schedule
for semi-structured interviews were created by the OPEN
team. Interview questions were designed based on previous
reports of open-source AID users in response to the DIWHY
survey,14,44 on discussions related to the study topic between
open-source AID users in online peer-support groups of the
#WeAreNotWaiting community and on the research team’s
Mewes et al 3
(KB, MW, KR) personal experience with T1D and using
open-source AID. The interview framework was pilot-tested
with two women using open-source AID before further par-
ticipants were enrolled.
Inclusion Criteria
Participants were eligible if they met the following inclusion
criteria: >18 years of age, biological sex was female, living
with T1D, using an open-source AID system for at least six
months, and were proficient in either English or German at
conversation level. No specific exclusion criteria applied.
Recruitment
To specifically target open-source AID users of different
ages and internationally, recruitment was carried out through
social media. Announcements were posted both publicly (eg,
on Twitter using the hashtag #WeAreNotWaiting) and in
online peer-support groups for open-source AID (eg, the
Facebook groups “Looped,” approximately 23 000 members,
and “Looped-DE,” approximately 2000 members in July
2020). Participation was entirely voluntary with no financial
compensation provided. The study was conducted according
to the guidelines of the Declaration of Helsinki, and approved
by the Institutional Review Board (or Ethics Committee) of
Charité—Universitätsmedizin Berlin (protocol code
EA2/122/20, July 7, 2020). Prior to the interviews, partici-
pants were informed about the professional background and
characteristics of the researchers performing the interviews
and their interests and aims in pursuing this research. In addi-
tion, a detailed information sheet was provided to all partici-
pants and their electronic consent was obtained. Participants
were recruited from July 2020 to January 2021. With a target
sample size of 10 to 15, participants were purposively sam-
pled until data saturation occurred.45-49
Procedures
Semi-structured interviews with 12 participants were con-
ducted via secure online video calls in either German or
English. Online interviews were conducted by DM and CK
with the use of encrypted online video chat services Zoom
(Zoom Video Communications, San Jose, California) and
Google Meet (Google Inc., Mountain View, California). The
calls lasted 45 to 60 minutes each. The questionnaire assess-
ing demographics and personal female health and diabetes-
related history (Supplemental Material) was sent to the
participants prior to the video call. During the interviews, the
researcher asked the participants to share their observations
and perceived challenges related to their diabetes during dif-
ferent phases of life (eg, puberty, menopause, pregnancy)
and throughout the menstrual cycle (eg, if they had noticed
variability in glycemic outcomes and an estimate of the rela-
tive changes in overall insulin requirements throughout the
menstrual cycle in percentage). Next, the interviewer
assessed participants’ individual solution strategies and man-
ual “workarounds” with their open-source AID system and
otherwise to address these challenges. Interviews finished
with discussions on users’ ideas of how to better automate
control algorithms and further improve future generations of
AID systems for women.
Data Collection and Analysis
Data collection was carried out in accordance with national
data protection regulations. The interviews were audio and
video recorded, transcribed, and de-identified. Transcribed
texts were imported into the MAXQDA Plus 2020 software
(VERBI GmbH Berlin, Germany).50 Given the scarcity of
existing qualitative research on the topic of women’s health
and T1D, an explorative and inductive approach was chosen
to generate new hypotheses and remain open to unexpected
findings. The analysis and generation of themes were car-
ried out by the research team collaboratively (DM, MW,
CK, KB). It should be noted that “themes” refer to interpre-
tive stories about particular patterns of shared meaning in
the data. These were developed in interaction with the
researchers’ theoretical assumptions, their analytic skill, and
the collected data. Thematic analysis was used to analyze
the data, including data familiarization, coding, generation
of themes, theme review, theme definition, and naming.50
The thematic analysis did not strictly follow procedures
such as coding or achieving inter-rater reliability between
researchers, and instead enabled reflection and engagement
by the researchers throughout the analytic process.50
Iterative discussion rounds were held until consensus
between researchers was achieved. The COREQ
(COnsolidated criteria for REporting Qualitative research)
checklist was used to guide reporting.51
Results
Of the 28 women who expressed their interest in participat-
ing, 12 participants based in four different countries were
recruited, meeting our target sample with no dropouts.
Participants had a median age of 39 years, ranging from 24 to
56 years, and a median experience of using an open-source
AID system (OpenAPS, Loop, or AndroidAPS) of 21
months, ranging from 12 to 48 months. Further demograph-
ics and health characteristics are presented in Table 1.
Content analysis of the data provided six themes with several
subthemes, as presented in Table 2.
Theme A: Improvements Through Open-Source
AID
All participants expressed high satisfaction with open-source
AID as their treatment option of choice, noting that it
increased their quality of life (subtheme A1). One participant
4 Journal of Diabetes Science and Technology 00(0)
called it a “huge relief for life in comparison to the past”
(33-year-old German woman, using AndroidAPS for one
year); another described it as “the easiest and safest my care
has ever been” (26-year-old American woman, using Loop
for 2.5 years).
Improved clinical outcomes were also reported (subtheme
A2). This was mostly associated with the availability of fast,
predictive, and automated dosing of.
correction insulin in response to changes in sensor glu-
cose, which to a large degree did not require frequent manual
intervention. Decreases in hemoglobin A1c, increases in
time-in-range, and fewer hypoglycemic events, especially at
nighttime, were described frequently:
I can tell: June 13th, 2018. First time I slept through the first
night, with Loop. [Previously], I was [. . . ] very often woken up
either by my own hypoglycemia, by noticing [the symptoms]
myself, or by an alarm. The loop has made it: It was really the
first night I didn’t wake up to some stupid alarms. [. . .] And I am
no longer afraid that it will happen. Because I know someone
will take care of it. My app. (52-year-old German woman, using
AndroidAPS for 2.5 years)
Theme B: Variations in Glycemia and Insulin
Requirements
All participants reported having experienced changes in gly-
cemic levels and insulin requirements associated with differ-
ent phases of their menstrual cycle (subtheme B1), which
required most of them (n = 10) to adjust their insulin ther-
apy. Most participants (n = 10) reported experiencing regu-
lar fluctuations in glucose levels and insulin needs throughout
the menstrual cycle, requiring them to change their settings.
An overview of the interindividual differences throughout
the follicular phase, around ovulation, and the luteal phase is
summarized in Figure 1 and Table 3 based on the data
reported by the participants (Supplementary Table 1).
This was often associated with frustration:
It’s like a major frustration for me because the first couple weeks
of my cycle are so nice and then the last half is kind of a disaster
zone. (31-year-old American woman, using Loop for 1.5 years)
Theme C: Additional Effort to Achieve Therapy
Outcomes
All participants stated that even with using AID, manual
therapy adjustments and “workarounds” related to the men-
strual cycle were still necessary on a regular level, which
caused them a constant time effort, cognitive load, and dis-
tress (subtheme C2), especially when compared with men
(subtheme C1):
I always find it so inequitable when men [. . .] brag about their
great blood sugar levels. I would wish them a month in the life
of a woman and then see how they deal with it. That is, I would
wish them humility. I think if you never experienced it yourself,
you can’t imagine what it’s like. (49-year-old German woman,
using AndroidAPS for 16 months)
Women of a younger age who managed multiple responsi-
bilities such as work and childcare especially mentioned a
lack of time to keep track of the changes and react to them.
Even those participants with professional backgrounds in
health care and personal interest in the topic of women’s
health, as well as active members of the open-source online
Table 1. Participant Demographics, Diabetes-Related and Gynecological History.
No. Age (years)
Country of
residence AID system(s)
AID experience
(mo)
No. of
pregnancies
Contraceptive
method(s)
Mean cycle
length (d)
Years since T1D
diagnosis (y)
1 56 Germany AndroidAPS,
OpenAPS
48 2 Nonhormonal In menopause 21
2 31 Germany AndroidAPS,
Loop
31 None Barrier and
sympto-thermal
methods
34 19
3 33 Germany AndroidAPS 13 2 Hormonal IUD 26 28
4 46 Germany AndroidAPS 25 1 None 28 29
5 26 United States Loop 31 None Hormonal IUD Not
menstruating
24
6 49 Germany AndroidAPS 16 3 Barrier methods 29 27
7 47 Australia AndroidAPS,
Loop
22 None Hormonal IUD In menopause 42
8 31 United States Loop 20 1 Copper IUD 36 20
9 26 Germany AndroidAPS 16 None Barrier methods 30 23
10 24 Germany AndroidAPS 12 None Nonhormonal 29 17
11 45 France Loop 17 2 None 26 23
12 52 Germany AndroidAPS 31 4 Barrier methods In menopause 43
Abbreviations: AID, automated insulin delivery; IUD: intrauterine device; T1D, type 1 diabetes.
5
Table 2. Content Analysis: Theme Structure, Definition, Example Quotes, and Respondent Profiles.
Theme Definition Example quote(s) Respondent profile
(A) Improvements through open-source AID
(A1) Increased quality of life Refers to perceived improvements in
everyday life and reduced diabetes-
related distress following the
implementation of open-source AID
“This is the easiest and safest my care has ever been.” 26-year-old American woman, using Loop
for 2.5 years
(A2) Improved clinical outcomes Refers to the perceived changes in clinical
outcomes (eg, fewer hypoglycemia and
hyperglycemia, more time-in-range) and
perceived increases in safety following
the implementation of open-source AID
“I can tell: June 13th, 2018. First time I slept through the first night, with Loop. [Previously,] I
was [. . .] very often woken up either by my own hypoglycemia, by noticing [the symptoms]
myself, or by an alarm. The loop has made it: It was really the first night I didn’t wake up
to some stupid alarms. [. . .] And I am no longer afraid that it will happen. Because I know
someone will take care of it. My app.”
52-year-old German woman, using
AndroidAPS for 2.5 years
(B) Variations in glycemia and insulin requirements
(B1) Intraindividual differences Refers to variability in glycemic outcomes
and insulin requirements throughout the
menstrual cycle and different life stages
observed by the women
“It’s like a major frustration for me because the first couple weeks of my cycle are so nice and
then the last half is kind of a disaster zone.”
31-year-old American woman, using Loop
for 1.5 years
(C) Additional effort to achieve therapy outcomes
(C1) Gender inequality Refers to the perceived differences
between men and women in therapy
effort needed to achieve the desired
outcomes
“I always find it so inequitable when men [. . .] brag about their great blood sugar levels. I
would wish them a month in the life of a woman and then see how they deal with it. That
is, I would wish them humility. I think if you never experienced it yourself, you can’t imagine
what it’s like.”
49-year-old German woman, using
AndroidAPS for 16 months
(C2) Causing distress Refers to the additional burden perceived
by the women related to female health–
related challenges in managing diabetes
“You know, you can do your best, but it won’t be good enough.” 31-year-old American woman, using Loop
for 1.5 years
(D) Limited awareness and support
(D1) Limited awareness pre-AID Refers to the novelty of the observations
since using open-source AID and the
participants’ unawareness of a possible
correlation between menstrual cycle and
diabetes prior to using an AID system
“I notice the [correlation] very prominently. I also noticed it with MDI, but I could not
attribute it that way.”
“I would say that you can see a tendency that in the second half of the cycle the levels and the
insulin requirements are higher. Before Loop, I didn’t even notice anything. With the closed-
loop, you notice it way more when something is off.”
56-year-old German woman, using
OpenAPS and AndroidAPS for four years
31-year-old German woman, using Loop
for 2.5 years
(D2) Limited HCP support Refers to therapy adjustments that were
often attempted “trial and error” with
limited professional medical guidance
and the perceived lack of awareness
toward sex- and gender-specific aspects
in diabetes care among physicians
and other HCPs, in the fields of both
endocrinology/diabetes care and
obstetrics/gynecology
“At a time when it would have been very important for me, for example, when I had children,
my endocrinologist never pointed out to me that we had to adjust anything. I’ve only just
noticed now that there is a women’s area in the Loop [groups]. Makes sense, that [the
therapy] is specifically adjusted during pregnancy. But to take a closer look at the cycle? [.
. . ] No one said ‘[. . .] please increase your basal rates every 26 days.’ That was just not a
discussion to have with the endocrinologist. [. . .] Now I am with a woman [endocrinologist]
and I asked her about menopause. She said yes, she didn’t know either, she’d have to read
up on it.
[. . .] I asked her: ‘How is it now with [diabetes], menopause, what to expect?’ And then she
started [telling me] about night sweats. And I said, ‘No, that’s not the question at all. I would
like to know how the blood glucose reacts to it?’ And then she did not know the answer.”
52-year-old German woman, using
AndroidAPS for 2.5 years
(E) Solution strategies
(E1) Peer-support Refers to support provided by other people
with diabetes, often online in social media
groups
“I am on a lot of Facebook groups including a ‘Looping in Pregnancy’ one and a ‘Breastfeeding
and Type 1 Diabetes’ one. Those are particular groups I’d be sooner to ask sort of women-
specific or hormonal questions on.”
31-year-old American woman, using Loop
for 1.5 years
(E2) Cycle documentation Refers to ways of documenting the
menstrual cycle
“I used to document the first day of my period in my phone. And in my paper calendar. Then
I have discovered the insulin age [field] in AndroidAPS which I am using now. Practically I do
not enter my insulin age in the app, but [use if for] the first day of my period. [. . .] That is
actually perfectly suited to get a bit of an overview, how far along [in the cycle] I am right
now.”
49-year-old German woman, using
AndroidAPS for one year
(continued)
6
Theme Definition Example quote(s) Respondent profile
(E3) Open-source AID features Refers to the use of already existing
features in open-source AID (eg
temporary overrides, profile switches)
“When I encounter higher levels, I switch to a temporary override relatively quickly. [. . .] You
can set [all parameters] to 110%, 120% and so on.”
31-year-old German woman, using Loop
for 2.5 years
(E4) Fake carbs Refers to carbohydrate entries in the AID
system without actually consuming them
“I tried changing my profile, it really means everything ISF and everything changed and that
did not work. [Now I am] only changing the basal rate and then possibly correct again if I
notice it is not working. Then I add a few more fake carbs. [. . .] The Loop thinks I still have
carbohydrates [on board], but there aren’t any. And [. . .] then, the Loop reacts a little more
aggressively.”
46-year-old German woman, using
AndroidAPS for two years
(E5) Manual changes of settings Refers to manual adjustments of AID
settings (eg, ISF, carb ratio, targets)
“The ISF. It’s not the case for older big ladies, like really big ladies. I think because they still
have a lot of estrogen anyway because they are big. But for average size ladies, and there are
some quite thin ladies I know, they are very very sensitive using ISFs of nine, ten, ten and a
half. Which is about what toddlers use. And I did not change my ISF from five until I talked
to some of these ladies and they’re like: ‘Yeah, I’ve had to put mine up to nine.’ And so, I
tried that, and I got a flat line.”
47-year-old Australian woman, using Loop
and AndroidAPS for 1.5 years
(E6) Exercise Refers to intentional exercise in phases
with high insulin resistance
“Before my period, for at least a week, I need a lot more insulin, so my insulin sensitivity is
a lot lower. [. . .] I guess I exercise on purpose just to try to not increase the insulin by so
much.”
31-year-old American woman, using Loop
for 1.5 years
(F) Ideas for further improvements
(F1) Further research and education Refers to the perceived scarcity of the
available literature and education on the
topic of female health and diabetes
“I think we absolutely all need to learn about this. It’s probably only been in the last five or six,
maybe ten, years that women had the chance to reflect on continuous glucose monitoring
during their cycles. Before that it was just, you know, whatever. And I think also we—
Because, I mean, everything is tested on men, generally white men, we do miss out on a lot
of research. And this stuff is so important. I think this is something little girls need to know
about. It’s not just the birds and the bees, it’s: ‘Hey, your insulin is going to need to do some
weird stuff. It’s all going to be different’. Because none of us had any idea, right? Just wasn’t
talked about. Wasn’t a thing.”
47-year-old Australian woman, using Loop
and AndroidAPS for 1.5 years
(F2) Menstrual cycle-related
automation
Refers to the ideas and suggestions for
further automation and additional
features to better cater to the users’
unmet needs
“It would help anticipate a little bit more in terms of, you know, I’m on day 20 and so this is
where things are starting to be a little more resistant, but I don’t realize that. [. . .] Loop
already talks to Apple Health, and I use the Apple Health app to track my cycle, so it doesn’t
seem very far to take that information from Apple Health. [. . .] If Loop could already take
into account when was the cycle ‘day one’ it would probably be helpful already.”
45-year-old French woman, using
AndroidAPS for 1.5 years
(F3) Machine learning Refers to the implementation of self-
learning algorithms based on user data
“If it was learning from the data—I love the idea of Autotune but I don’t think it’s necessarily
accurate for Loop specifically—if there was something like ‘I’ve noticed that it seems you
really need, [. . .] my need seems to ramp up over time and then ramp down as opposed
to being from day to day normal and then all of the sudden 20% more. [. . .] Your period
is predicted to start in 12 days so I’m going to go up by 5 percent. And now 10. And now
15’. [. . .] If [the algorithm] learned based on experience—you know: ‘Your last three cycles
your insulin needs were like this so I’m going to mimic that’.”
31-year-old American woman, using Loop
for 1.5 years
Abbreviations: AID, automated insulin delivery; HCP, heath care professional; ISF, insulin sensitivity factor; MDI, multiple daily injections.
Table 2. (continued)
Mewes et al 7
community who are in frequent exchange with other women
with T1D, expressed that they rarely felt they were “in con-
trol” of their diabetes at all times. Even though the partici-
pants reported that switching to open-source AID had
increased their knowledge about menstrual cycle effects on
glucose levels and made diabetes management easier and
safer, many expressed that certain challenges remain:
I’d say it’s still a major problem for me. I just remember being,
just a few weeks ago, so frustrated. I just kept spiking high after
meals, staying high overnight and stuff. So, I changed my
settings and then in that instance, for whatever reason, I needed
more than I thought, I guess. Or I would spike high after meals
but then I would be low otherwise, so my basal was too strong
but my carb ratio wasn’t good, or maybe I needed to pre-bolus
longer than usual. (31-year-old American woman, using Loop
for 1.5 years)
The frequent need for manual adjusting of settings and fine-
tuning was also seen as straining:
Because anything that keeps me from having to constantly
wonder about, you know: “Oh, okay. I’m getting a result I didn’t
expect so now I have to do this whole troubleshooting in my
head.” If the system just knew it’s the week before the period
then that would save me some manual troubleshooting, I guess.
(31-year-old American woman, using Loop for 1.5 years)
Theme D: Limited Awareness and Support
The effect of the menstrual cycle on glucose levels and insu-
lin requirements was first noticeably observed by the
participants following the initiation of open-source AID use
(subtheme D1):
I notice the [correlation] very prominently. I also noticed it with
MDI, but I could not attribute it that way. (56-year-old German
woman, using OpenAPS and AndroidAPS for four years)
Furthermore, support and awareness of women’s health and
diabetes from endocrinologists and obstetricians/gynecolo-
gists were perceived to be limited (subtheme D2) by all of
the participating women:
But I had a conversation with him about what sort of problems I
could expect for menopause. And he said “Oh, should be a
breeze.” [. . .] Yeah, so they’re clueless. Completely clueless.
(47-year-old Australian woman, using Loop and AndroidAPS
for 1.5 years)
If valuable suggestions regarding insulin therapy were
brought up by health care providers, they were highly
appreciated:
Usually she at least comes up with one helpful thing each time I
go. I do a lot of research myself, a lot of thinking and testing
myself. For her to come up with any additional thoughts, I think
is pretty good. (31-year-old American woman, using Loop for
1.5 years)
Theme E: Solution Strategies
Peer-support (subtheme E1) via online communities such as
the “Looped” Facebook groups was common among
Figure 1. Relative changes (%) in self-reported insulin requirements of female open-source AID users during different menstrual cycle
phases (blue: early follicular phase, orange: around ovulation, green: luteal phase). Abbreviation: AID, automated insulin delivery.
8 Journal of Diabetes Science and Technology 00(0)
interviewees. As an example, group video calls for setting
optimizations were mentioned, and one woman reported that
a friend with T1D regularly reminded her to consider the cur-
rent cycle phase in relation to glycemic outcomes outside
target range.
Except for three women who used a hormonal intrauterine
device (IUD), are in menopause, or both, all participants
stated that they regularly document their cycle and associ-
ated symptoms (subtheme E2). Methods of cycle tracking
included apps such as “Clue,” “myNFP,” “Mein Kalender
Flo,” “Period Tracker,” or default calendar apps on Android
and Apple smartphones. Some (n = 3) also used paper calen-
dars. Documented attributes were the beginning of menses,
duration, intensity of bleeding, and suspected or calculated
day of ovulation, and the fertile window. One woman
explained how she used the “insulin age” field of AndroidAPS
to document her cycle. She expressed not necessarily need-
ing this field for its intended purpose as she generally
Table 3. Self-Reported Perceived Changes in Glycemic Levels and Insulin Needs of Open-Source AID Users Throughout Different
Phases of the Menstrual Cycle and Special Situations (Pregnancy, Menopause).
Menstrual cycle phase or special
situation Perceived changes in insulin needs
Follicular phase With the onset of menses and the following two to three days, some (n = 5) women
reported a sudden increase in insulin requirements and therefore the necessity
to decrease their insulin delivery by 10% to 30%, while some (n = 4) needed to
increase their dose by 10% to 20%. One woman reported a small decrease in insulin
needs but does not regularly adapt settings accordingly. During menses, correlations
between changing insulin needs and the occurrence and intensity of menstrual
pain and other related symptoms, level of physical activity, and comorbidities were
suspected.
The late follicular phase up to the suspected day of ovulation was considered as the
most “stable” and “easy to manage” in relation to glycemic levels. Insulin needs
during that phase were considered as “normal” or “average.”
Around ovulation Participants identified their ovulation to take place between cycle day 13 and day 21,
depending on their cycle length. Some either reported to perceive specific physical
symptoms (n = 6) such as one-sided abdominal pain or a “pulling sensation”, and
increased libido, or used menstrual cycle tracking apps to identify their fertile
window. A sudden increase in insulin requirements on ovulation day and the
following one to two days of the cycle was reported by three participants, whereas
one woman explained to experience decreased insulin needs, which has been more
prominent after her first pregnancy but has become less noticeable since then.
Luteal phase Post-ovulation, insulin needs were reported to be increased by up to 35% until the
next cycle. Participants performed several different therapy adjustment strategies:
two of the three women who experienced higher insulin needs during ovulation kept
their more aggressive settings until the end of the cycle. One participant decreased
her insulin “back to normal” (100%) temporarily after ovulation and then increased
her insulin dose again for the last cycle week. One woman reported perceiving a
small decrease in insulin demand for the last days of the cycle, whereas another
woman explained being able to keep her settings on “default” (100%) throughout
ovulation and until the next cycle begins.
Of the five women who did not change settings during ovulation regularly, four
reported to have a steady increase in insulin needs leading up to the next cycle.
One woman reported being slightly more sensitive to insulin during that time and
therefore decreased her intake to 80%.
Pregnancy Different life phases and events, such as puberty, pregnancy, and menopause, were
perceived as particularly challenging with respect to managing diabetes. Participants
who had been pregnant in the past (n = 7) reported a constant effort of adapting
their insulin requirements to the dynamic hormonal situation. Following pregnancies,
participants reported that their insulin requirements needed to be reevaluated,
rather than returning to their prepregnancy profiles. Furthermore, cycle length
and strength of menstrual bleeding were perceived differently compared to before
pregnancy.
Menopause The transition into menopause was associated with decreased overall insulin
requirements, changes in the length of the menstrual cycle, a decrease in menstrual
bleeding, and ovulation frequency.
Abbreviation: AID, automated insulin delivery.
Mewes et al 9
replaces her insulin every few days. Instead, having her cycle
documented “at a glance” together with sensor glucose levels
and insulin delivery was described as helpful.
A significant concern among participants was increasing
their insulin delivery too early and provoking hypoglycemia
in return, which a participant described as at “minimum
annoying, maximum dangerous” (31-year-old American
woman, using Loop for 1.5 years). Therefore, many described
their management strategies as reactive rather than preventa-
tive, and changes were not being made until a significant
upward trend in glucose levels was witnessed after a few
days. The most commonly (n = 9) used features of open-
source AID (subtheme E3) were “Override Presets” (in
Loop) and “Profile Switches” (in AndroidAPS). Both fea-
tures enable the user to automatically apply relative changes
of all parameters affecting dosage calculation, including
basal rate (BR), insulin sensitivity factor (ISF), and carb ratio
(CR). Both AID systems allow for the saving and naming of
profiles:
In Loop there’s the “Override Presets” so I’ll do one at 80 or 90
percent of total insulin needs, and I’ll just put it on until I
eventually am running high because of the changes. But then I
put on the 120 percent preset to increase my insulin. (26-year-
old American woman, using Loop for 2.5 years)
Another mentioned strategy (n = 2) was the intentional over-
estimation of carbohydrate intake—referred to as “fake
carbs” (subtheme E4)—before or between meals. The par-
ticipants explained they used this method in addition to using
override presets or profile switches as described above, if
necessary. Some (n = 2) explained that a relative change of
BR, ISF, and CR combined does not work sufficiently for
them. Instead, through personal experience, they have found
that manually changing the settings one by one (subtheme
E5) gives them better results. Features such as “Autosens,”
an algorithm in AndroidAPS that estimates insulin sensitiv-
ity based on the user’s glucose deviations,52 were used for
fine-tuning their ISF. Other strategies unrelated to insulin
delivery were performing exercise (subtheme E6) in phases
with increased insulin resistance.
Theme F: Ideas for Further Improvements
Several ideas on how to further improve diabetes manage-
ment for women using an open-source AID system were
shared. First, the scarcity of information and research in the
field (subtheme F1) was acknowledged by many
interviewees:
I think we absolutely all need to learn about this. It’s probably
only been in the last five or six, maybe ten, years that women
had the chance to reflect on continuous glucose monitoring
during their cycles. Before that it was just, you know, whatever.
And I think also we—Because, I mean, everything is tested on
men, generally white men, we do miss out on a lot of research.
And this stuff is so important. I think this is something little girls
need to know about. It’s not just the birds and the bees, it’s:
“Hey, your insulin is going to need to do some weird stuff. It’s
all going to be different.” Because none of us had any idea,
right? Just wasn’t talked about. Wasn’t a thing. (47-year-old
Australian woman, using Loop and AndroidAPS for 1.5 years)
The required technical skills and levels of digital literacy
required to set up and use open-source AID systems were
also acknowledged (n = 3). Therefore, the desire to have a
better understanding of the automated decisions, for exam-
ple, rationales for temporary BR adjustments, was expressed.
Besides hardware improvements such as devices with
louder alarms and smaller dosage settings for the insulin
pump, all participants expressed that the linkage of AID to
the phases of their menstrual cycle (subtheme F2) would be
beneficial and already feasible. Suggestions included the
option to specify insulin dosage settings for individual cycle
phases (n = 7) and pattern recognition (n = 6) for personal-
ized profiles, for example, by the combination of different
information in Apple Health:
It would help anticipate a little bit more in terms of, you know,
I’m on day 20 and so this is where things are starting to be a little
more resistant, but I don’t realize that. [. . .] Loop already talks
to Apple Health, and I use the Apple Health app to track my
cycle, so it doesn’t seem very far to take that information from
Apple Health. [. . .] If Loop could already take into account
when was the cycle “day one” it would probably be helpful
already. (45-year-old French woman, using AndroidAPS for 1.5
years)
The implementation of self-learning machine learning algo-
rithms (subtheme F3) was also envisioned:
If it was learning from the data—I love the idea of Autotune but
I don’t think it’s necessarily accurate for Loop specifically—if
there was something like “I’ve noticed that it seems you really
need, [. . .] my need seems to ramp up over time and then ramp
down as opposed to being from day to day normal and then all
of the sudden 20% more. [. . .] Your period is predicted to start
in 12 days so I’m going to go up by 5 percent. And now 10. And
now 15.” . . . If [the algorithm] learned based on experience—
you know: “Your last three cycles your insulin needs were like
this so I’m going to mimic that.” (31-year-old American woman,
using Loop for 1.5 years)
In this context, concerns regarding the ability of algorithms
to cater to individual constellations and needs were raised:
I think you would have to have an absolutely regular cycle. And
certainly, I had that as a young woman. But teenagers aren’t
necessarily regular. Menopausal ladies aren’t necessarily
regular. In the middle women are often having babies and then
breastfeeding and having their cycles when breastfeeding. So,
there’s a lot, awful lot, of potential for irregularity which is
normal. It’s not abnormal to be irregular. And I think for the very
young women who are just starting their cycles, they’ve got all
10 Journal of Diabetes Science and Technology 00(0)
sorts of stuff going on and I think manual control of that would
be better, maybe. But I can see maybe for a few people yes. At
certain stages of their life. Nice regular cycles. A bit busy with
work and things to stop and think about it. Yeah, maybe it could
work. (47-year-old Australian woman, using Loop and
AndroidAPS for 1.5 years)
Discussion
This study reports that women with T1D using an open-
source AID system perceived a significant impact of changes
in insulin needs throughout their menstrual cycle and
throughout different events and phases of life, such as
puberty, pregnancy, and menopause. The influencing factors
were mostly unknown to them before they started using
open-source AID systems and caused them to perform sev-
eral workarounds to manually adjust their therapy. Although
using open-source AID had an overall positive effect on gly-
cemic outcomes and quality of life, the requirement to
respond to variability in insulin needs was perceived as an
individual burden. Health care provider awareness and
knowledge, as well as publicly available information on
menstrual cycles and diabetes, were perceived as limited.
Our findings provide valuable insights into the challenges
women face in managing T1D throughout life and yield sug-
gestions to further improve future generations of AID sys-
tems for women, contributing to gender equality and
improved quality of care.
Although qualitative studies on lived experiences with
AID systems among adults, teenagers,10 and younger chil-
dren53 exist, this is the first qualitative study focusing specifi-
cally on women as a user group outside the context of
pregnancy. The literature describes similar improvements of
clinical and patient-reported outcomes for PwD of various
ages and genders since commencing open-source AID.9-11,17
However, our findings suggest that women with T1D have to
undertake extra efforts to achieve these results. These find-
ings align with others that have highlighted where currently
available commercial AID systems do not meet their users’
expectations and either terminate use or come up with unex-
pected solutions.54 Findings like these should be a call to
action for academia, developers and manufacturers of diabe-
tes technology to closely work together with PwD in their
research and product development at an early level, to gener-
ate research questions that matter to them and improve the
products’ usability and efficacy.
Our findings on self-reported variable insulin require-
ments in relation to the menstrual cycle are in-line with the
few previous studies on women with T1D using therapies
other than AID40,55 and mirror the correlation of increased
insulin resistance in the luteal phase observed in women
without diabetes.22,24-26 However, this is the first qualitative
study to report how women’s health-related challenges were
experienced by and reacted to by women with T1D.
Furthermore, this is the first study to report on what strate-
gies and “workarounds” AID users perform to respond to
dynamic changes in insulin demands. Although it was
already self-reported by women in the 1990s that most per-
ceive differences in glycemia around their menstruation and
some adjust their therapy,38 there is still no therapeutic guid-
ance on the topic.
It is acknowledged that several strengths and limitations
apply to our study. Of particular strength is the multinational
character and wide age range despite the small sample size,
and the stakeholder engagement strategy directly including
the experience and ideas of people with T1D and the open-
source AID community during the study design.
For the purpose of this study, ethics approval was only
provided for adult participants. While we could identify sim-
ilarities between participants of different ages, women under
the age of 24, with a diabetes duration shorter than 17 years,
women with less than 12 months experience in using open-
source AID, and women using hormonal contraception
methods with higher systemic impact than in hormonal
IUDs, such as oral contraception or hormonal implants, are
not represented in our study. The likelihood of selection bias
when recruiting participants via social media further limits
broad generalizations to all women using open-source AID.
Further investigations on larger cohorts, including adoles-
cents and young adults <24 years of age and shorter diabetes
duration, and women using different methods of hormonal
contraception are necessary. With the increasing availability
of commercial AID options, it would also be of interest if
similar experiences were being made by women using com-
mercially developed AID systems.
In addition to our findings from this explorative study and
qualitative analysis, future studies should focus on the analy-
sis of diabetes device data from the AID systems in context
with documentation of menstrual cycle data to provide a bet-
ter understanding of the correlations that we found, identify
patterns, investigate the efficacy and actual benefit of using
workaround strategies compared to using an AID system
with fixed settings, and set the stage for further automation
tools and/or machine learning-supported AID for girls and
women with menstrual cycles.
Conclusions
Sex hormones are likely to directly or indirectly influence
insulin requirements in women with and without T1D,
although these correlations have so far not been sufficiently
researched. In this study, we generated experience-based evi-
dence of women of the #WeAreNotWaiting community
which provides an overview on current challenges to address
in future research and by developers of commercial and
open-source AID. Due to the automation of insulin dosing
and data tracking in AID, it should be possible to quantify
recurring patterns in glycemic outcomes and insulin needs
throughout the menstrual cycle.
Mewes et al 11
Furthermore, the “workaround strategies” the women cre-
ated provide useful information for potential further usability
improvements and automation of control algorithms. As an
example, the integration of menstrual cycle tracking data
into AID systems could further improve safety and efficacy
in users with menstrual cycles.
Last, awareness, existing scientific evidence, and profes-
sional guidance on the topic of female health in diabetes man-
agement are still insufficient. Therefore, we encourage an
open dialogue on women’s health between women with T1D
and health care professionals, and to consider cycle-related
changes in insulin sensitivity when reviewing data and adjust-
ing insulin dosage as part of their contacts. Moreover, further
education and advocacy efforts are required to better inform
PwD, health care professionals, and device manufacturers,
and more research is required to better address the needs of
women with T1D in therapeutic advances.
Abbreviations
AID, automated insulin delivery; CGM, continuous glucose moni-
toring; CSII, continuous subcutaneous insulin infusion; PwD, peo-
ple with diabetes; T1D, type 1 diabetes; T2D, type 2 diabetes.
Acknowledgements
The authors would like to thank the members of the
#WeAreNotWaiting community for their time and trust and for
sharing their valuable insights with the study team, and Jasmine
Schipp and Drew Cooper for critically proofreading the article.
Declaration of Conflicting Interests
The author(s) declared the following potential conflicts of interest
with respect to the research, authorship, and/or publication of this
article: Mandy Wäldchen was employed by Medtronic Diabetes
Germany until 2018. Katarina Braune received fees for medical
consulting and public speaking from Roche Diabetes Care, Dexcom,
Medtronic Diabetes, Diabeloop, BCG Digital Ventures, Novo
Nordisk, Abbott, Sanofi Diabetes, and Diabetes Center Berne, out-
side the submitted work. Klemens Raile is an advisory board mem-
ber at Lilly Diabetes Care and Abbott Diabetes Care; he received
fees for public speaking from Abbott Diabetes Care, Novo Nordisk,
Sanofi, and Springer Healthcare IME. The funders had no role in
the design of the study; in the collection, analyses, or interpretation
of data; in the writing of the manuscript; or in the decision to pub-
lish the results.
Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
work is part of the OPEN project (www.open-diabetes.eu). OPEN
has received funding from the European Commission’s Horizon
2020 Research and Innovation Program under the Marie
Skłodowska-Curie Action Research and Innovation Staff Exchange
(RISE) grant agreement number (823902). KB received funding
from the DFG-funded Berlin Institute of Health (BIH) Digital
Clinician Scientist Program and the BIH/Wellcome Trust SPOKES
Translational Partnership program.
ORCID iD
Katarina Braune https://orcid.org/0000-0001-6590-245X
Supplemental Material
Supplemental material for this article is available online.
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