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\begin{document}

\title{Attitudes of pregnant women toward wearable technology: an observational
cross-sectional survey study}



\author[1]{Colin Wakefield}%
\author[2]{Lena Yao}%
\author[2]{Steve Self}%
\author[1]{Martin Frasch}%
\affil[1]{University of Washington}%
\affil[2]{Fetal Precision, LLC}%


\vspace{-1em}



  
  \date{May 23, 2024}


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\selectlanguage{english}
\begin{abstract}
Objective: Telemedicine has advanced to the forefront of healthcare
delivery, including maternal-fetal medicine. Smart wearable
electrocardiogram (ECG) devices can enable pregnant women to monitor
their health and that of their fetuses. Such technology would be a
logical extension of the telemedicine ecosystem. However, it is not
known how pregnant women perceive the ability to use such technologies.
Design: Observational cross-sectional study. Setting: Online survey in
the United States in 2019. Population: A representative sample of 507
women aged 18-45 were polled from 45 states. Methods: Study participants
were recruited using the SurveyMonkey Audience Polling system and
responded virtually. Main Outcome Measures: Women were asked to identify
willingness to use a wearable ECG device the size of a patch-sized large
band-aid on their abdomen. Ten binary or multiple-choice questions were
used to gauge population interest and related demographics towards the
usage of a wearable ECG device. Results: 91\% of women expecting to
become pregnant in the next five years accept wearable ECG technology as
a mechanism for increased frequency of monitoring of maternal and fetal
health throughout the pregnancy outside the hospital. 78\% of women
demonstrated a willingness to wear devices day and night or at least
during sleep and 42\% of women would spend up to \$200 on such a device.
Conclusions: Even though conducted prior to the COVID-19 pandemic, this
study clearly indicates a high degree of readiness of prospective
pregnant women for telemedicine with continuous health monitoring of the
mother-fetus dyad during the entire antepartum period.%
\end{abstract}%



\sloppy


\textbf{Introduction}

The COVID-19 pandemic has drastically altered the landscape in which
healthcare is delivered. Prior to 2019, telemedicine was pitched as the
next frontier. However, little was understood on how it would become
integrated into modern healthcare delivery. Over the past two years,
patients have become increasingly accepting of receiving medical care
remotely.\textsuperscript{1}This represents an exciting social shift
that offers enormous opportunities for the improvement of Maternal and
Fetal Medicine.

The use of fetal electrocardiogram (ECG) derived from maternal abdominal
ECG as a biomarker of fetal well-being offers a prime entry point for
the implementation of remote monitoring and telemedicine for this
underserved patient population. ECG patterns are currently being
investigated as early biomarkers of poor fetal and postnatal
development.\textsuperscript{2,3}Studies indicate that in-home stimuli
like maternal stress can negatively impact lifelong neurodevelopmental
trajectories.\textsuperscript{4}Current practice is only beginning to
use ECG as a source of biomarkers intrapartum and not at all antepartum.
This leaves minimal time for intervention and correction of fetal
development. Applying this technology to the in-home setting will expand
the intervention window for providers, improving pregnancy outcomes.
Mothers would continuously wear ECG devices monitoring both maternal and
fetal ECG and its derivatives, such as heart rate (HR) and HR
variability metrics, providing instant on-site and remote access to the
health of the mother-fetus dyad. The first step in the implementation of
such practices is gauging how perceptive prospective and existing
mothers would be to the integration of wearable ECG devices in their
daily lives.

\textbf{Word count: 255/400.}

\textbf{Methods}

To gauge their interest in the use of a wearable maternal-fetal ECG
device, we used the SurveyMonkey Audience system, from July 31st to
August 1st of 2019, to survey 507 female participants across the United
States of the ages 18-45, with annual income brackets of \$0-\$200,000.

SurveyMonkey is an online survey tool used to collect data from
individuals across the US population. A pool of over two million people
is maintained through an agreement wherein participants agree to take
part in a survey, in exchange for SurveyMonkey making a donation of
\$0.50 to the charity of that individual's choice. The SurveyMonkey
algorithm then randomly assigns participants to surveys in a manner that
creates a sample representative of the demographics specified by the
investigator. Participants then receive a URL via email from which the
survey can be completed on a laptop or mobile device.

This study was conducted under the parameters of a nationally
representative sample containing 500 females ranging 18-45 years of age
with an annual income range of \$0-200,000. Participants were asked a
total of 10 questions. We screened the participants by asking upfront:
``Are you planning to be pregnant in the next 5 years?'' If no, the
questionnaire was stopped, if yes, participants answered four questions
to gauge interest in a wearable ECG device as seen in \textbf{Figure 1}
. When asked question four, participants were given a description of a
wearable device the size of a ``patch-sized large band-aid'' worn on the
abdomen for at least 8 continuous hours throughout the day to help
doctors ensure they are doing ok during pregnancy when on the go.
Demographics were then gathered via five additional questions to
identify income, age, location, gender, polling device type distribution
among the cohort.

To achieve adequate power to accurately represent the roughly 60 million
US females between the ages of 18-45, we determined 500 participants
were needed. In order to gather enough responses for each of the ten
questions, SurveyMonkey recruited a total of 527 participants. The
response rate among recruited individuals was 96\%, with twenty
recruited individuals failing to complete all ten questions. The initial
screening question had a margin of error of 4.44\% due to 507
individuals responding ``yes'' on ``planning to become pregnant in the
next 5 years''. Questions 2-5 had a margin of error of 6\% due to
participants choosing not to answer. Questions 6-10 which were used to
gather cohort demographics had a margin of 4.4\%. In total, the data
collected using the SurveyMonkey Audience System had a confidence level
of 95\%.

\textbf{Funding. N/A.}



\textbf{Results}

The cohort represented individuals from forty-five states across the
U.S.A., with response density reflecting population densities (Fig. 2).

A 97\% response rate was achieved because participants were recruited
and agreed to complete a survey in principle prior to participation.
Individuals had no knowledge of the survey content prior to agreeing to
participate. SurveyMonkey randomly assigned participants to the survey
until all questions had a response rate of 250. This led to a total
recruitment of 527 individuals, twenty of which did not complete the
survey and whose response could not be included. Participants make up
females 18-45 with incomes ranging \$0-200,000 from 45 of the 50 states
in the U.S.

As Table 1 summarizes, a large proportion (43.8\%) of participants had
already gone through a prior pregnancy. The screening question
identified 258 participants, or roughly half of the participants
(50.89\%) as planning to become pregnant within five years of
participating in the study. These participants expressed a willingness
to wear an ECG device continuously (46.9\%), only while sleeping
(31.4\%), or only while awake (12.8\%) (Fig. 3). Individuals were also
polled on their inclinations to spend money on said device, 44.7\% were
willing to spend up to \$100 on such a product and another 41.7\%, \$100
to \$200 (Fig. 3). A subset (9.79\%) expressed a willingness to spend up
to \$300, and an additional 3.83\% demonstrated a readiness to spend
more than \$300 on a wearable ECG device.



\textbf{Discussion}

Main Findings

A cohort of 508 women of childbearing age, the largest population sample
surveyed to date for this purpose, demonstrated an overwhelming
enthusiasm towards wearable ECG devices for monitoring maternal-fetal
health during pregnancy in-home, while asleep and on the go. These
findings indicate that the public is ready for the integration of
wearable continuous ECG monitoring into the modern healthcare model of
telemedicine.

Strengths and Limitations

The strengths of the present study include a large cross-sectional
US-wide representative population sampling approach with a response rate
over 90\% and a margin of error around 4\%.

The key limitation is that we conducted no piloting and validation of
the questions 4 and 5 deployed in our survey. A mitigating circumstance
is the fact that the questions were either binary or simple
multiple-choice queries leaving little room for interpretation.

Interpretation

The time for disruption of the present antiquated maternal-fetal
monitoring antepartum has arrived. The required hardware and software
technologies have matured and converged with the people's readiness for
telemedicine throughout the antepartum period.

Wearable products that monitor adult health and fitness do exist but are
not targeted specifically towards pregnant women. Therefore, currently,
no technology exists that provides information on the pregnancy
physiology or the health status of the fetus or the pregnant mother.
Non-wearable products that monitor aspects of fetal health exist in the
consumer space. However, such devices all share common pitfalls like low
fidelity ultrasound technology, very short-term (seconds to minutes)
observations without data recordings and provide minimal data analytics
(if any) value beyond brief entertainment and possibly perilous false
reassurance.

At the same time, the cost of hardware components required to build
high-quality ECG devices for this purpose has come down to such a level
that continuous antepartum monitoring technology is now achievable
within the price range identified in the present study. All required
software components are also available.\textsuperscript{3,5--7}

From physiological viewpoint, a pregnant woman and her unborn baby are
intimately connected forming one single physiological system.
Consequently, important information about their joint wellbeing is lost
when they are not monitored together.\textsuperscript{8}With one recent
exception, all current devices use periodic ultrasound-based monitoring
which is less precise than ECG-based monitoring and cannot provide the
continuous monitoring (during maternal sleep for example) that is
possible with ECG-based wearable devices.\textsuperscript{9}

\textbf{Conclusion}

ECG-based monitoring devices for pregnant women will enable deeper
analyses and more precise reporting of health status than current
ultrasound-based devices. This report shows that this is a technology
that mothers want and suggests that the time is right for maternal and
fetal medicine to integrate it into healthcare delivery.

\textbf{Word count: 416/1200}

\textbf{Ethics statement}

This study was conducted using the SurveyMonkey Audience system. This
platform is an online survey tool used to collect data from individuals
across the US population. A pool of over two million people is
maintained through an agreement wherein participants agree to take part
in a survey, in exchange for SurveyMonkey making a donation of \$0.50 to
the charity of that individual's choice. The SurveyMonkey algorithm then
randomly assigns participants to surveys in a manner that creates a
sample representative of the demographics specified by the investigator.
As such, no additional specific IRB approval was required or obtained.

\textbf{Acknowledgments}

The authors gratefully acknowledge the assistance of D. Levitan and Y.
Kimelfeld during the survey design.

\textbf{Conflicts of interest} \textbf{statement}

The study was conducted as part of a research project by Fetal Precision
LLC. The company has been since dissolved and the findings have been
open sourced. M. G. Frasch has patents on aECG (WO2018160890) and EEG
technologies for fetal monitoring (US9215999). The authors declare that
the research was conducted in the absence of any other commercial or
financial relationships that could be construed as a potential conflict
of interest.

\textbf{Word count: 1592/3500.}

\textbf{References}

1. Mann DM, Chen J, Chunara R, Testa PA, Nov O. COVID-19 transforms
health care through telemedicine: Evidence from the field. J Am Med
Inform Assoc {[}Internet{]}. 2020 Jul 1;27(7):1132--5. Available from:
http://dx.doi.org/10.1093/jamia/ocaa072

2. Frasch MG. Letter to the Editor: Mind the gap: epistemology of heart
rate variability. Am J Physiol Regul Integr Comp Physiol {[}Internet{]}.
2020 Sep 1;319(3):R343--4. Available from:
http://dx.doi.org/10.1152/ajpregu.00183.2020

3. Sarkar P, Lobmaier S, Fabre B, Gonz\selectlanguage{ngerman}ález D, Mueller A, Frasch MG, et
al. Detection of maternal and fetal stress from the electrocardiogram
with self-supervised representation learning. Sci Rep {[}Internet{]}.
2021 Dec 17;11(1):24146. Available from:
http://dx.doi.org/10.1038/s41598-021-03376-8

4. Lobmaier SM, Müller A, Zelgert C, Shen C, Su PC, Schmidt G, et al.
Fetal heart rate variability responsiveness to maternal stress,
non-invasively detected from maternal transabdominal ECG. Arch Gynecol
Obstet {[}Internet{]}. 2020 Feb;301(2):405--14. Available from:
http://dx.doi.org/10.1007/s00404-019-05390-8

5. Su L, Wu H-T. Extract Fetal ECG from Single-Lead Abdominal ECG by
De-Shape Short Time Fourier Transform and Nonlocal Median. Frontiers in
Applied Mathematics and Statistics {[}Internet{]}. 2017;3:2. Available
from: https://www.frontiersin.org/article/10.3389/fams.2017.00002

6. Frasch MG. Heart rate variability code: Does it exist and can we hack
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http://arxiv.org/abs/2001.08264

7. Frasch MG. Comprehensive HRV estimation pipeline using Neurokit2
{[}Internet{]}. Available from:
https://zenodo.org/badge/latestdoi/427111001

8. Frasch MG, Lobmaier SM, Stampalija T, Desplats P, Pallarés ME, Pastor
V, et al. Non-invasive biomarkers of fetal brain development reflecting
prenatal stress: An integrative multi-scale multi-species perspective on
data collection and analysis. Neurosci Biobehav Rev {[}Internet{]}. 2018
May 30; Available from:
http://dx.doi.org/10.1016/j.neubiorev.2018.05.026

9. Mhajna M, Schwartz N, Levit-Rosen L, Warsof S, Lipschuetz M, Jakobs
M, et al. Wireless, remote solution for home fetal and maternal heart
rate monitoring. American Journal of Obstetrics \& Gynecology MFM
{[}Internet{]}. 2020 Mar 17;100101. Available from:
http://www.sciencedirect.com/science/article/pii/S2589933320300318

\textbf{Tables}

\textbf{Table 1. Survey results.}\selectlanguage{english}
\begin{longtable}[]{@{}l@{}}
\toprule
\vtop{\hbox{\strut \textbf{Question} \textbf{Response Options}
\textbf{Participant Responses} \textbf{Question 1} \textbf{Question 1}
Are you planning to be pregnant in the next 5 years? Yes}\hbox{\strut No
50.89\% (258)}\hbox{\strut 49.11\% (249) \textbf{Question 2} What is
your age? What is your age?
18-24}\hbox{\strut 25-34}\hbox{\strut 35-44}\hbox{\strut 45 or over
28.68\% (74)}\hbox{\strut 52.71\% (136)}\hbox{\strut 18.60\%
(48)}\hbox{\strut 0\% (0) \textbf{Question 3} \textbf{Question 3} Do you
have any children? 1}\hbox{\strut 2}\hbox{\strut 3}\hbox{\strut More
than 3}\hbox{\strut None 20.16\% (52)}\hbox{\strut 17.44\%
(45)}\hbox{\strut 4.65\% (12)}\hbox{\strut 1.55\%
(4)}\hbox{\strut 56.20\% (145) \textbf{Question 4} \textbf{Question 4}
Would you wear a pregnancy monitor to know how you and your baby are
doing at home or on the go in real-time between doctor appointments? At
night}\hbox{\strut During the day}\hbox{\strut Day and
night}\hbox{\strut No 31.4\% (81)}\hbox{\strut 12.79\%
(33)}\hbox{\strut 46.90\% (121)}\hbox{\strut 8.91\% (23)
\textbf{Question 5} \textbf{Question 5} How much would you be willing to
pay for such a pregnancy monitor? \$100 or
less}\hbox{\strut \$100-\$200}\hbox{\strut \$200-\$300}\hbox{\strut More
than \$300 44.68\% (105)}\hbox{\strut 41.70\% (98)}\hbox{\strut 9.79\%
(23)}\hbox{\strut 3.83\% (9) \textbf{Question 6-10} \textbf{Question
6-10} Verified age, and gathered gender, income range, geographical
region, response device type Verified age, and gathered gender, income
range, geographical region, response device type Verified age, and
gathered gender, income range, geographical region, response device
type}}\tabularnewline
\bottomrule
\end{longtable}



\textbf{Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1. Survey question algorithm.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2. Geographic representation of surveyed participants.}
The cohort represented individuals from forty-five states across the
U.S.A., with response density reflecting population densities\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 3.} \textbf{Survey findings.}

\selectlanguage{english}
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