Original Research
ajog.org
OBSTETRICS
The impact of a daily smartphone-based feedback
system among women with gestational diabetes on
compliance, glycemic control, satisfaction, and
pregnancy outcome: a randomized controlled trial
Hadas Miremberg, MD; Tal Ben-Ari, MD; Tal Betzer, MD; Hagit Raphaeli, MD; Rose Gasnier, MD;
Giulia Barda, MD; Jacob Bar, MD, MSc; Eran Weiner, MD
BACKGROUND: Patient compliance and tight glycemic control have
been demonstrated to improve outcome in pregnancies complicated by
gestational diabetes mellitus. The use of advanced technological tools,
including smartphone-based platforms, to improve medical care and
outcomes has been demonstrated in various fields of medicine, but only a
few small studies were performed with gestational diabetes mellitus
patients.
OBJECTIVE: We aimed to study the impact of introducing a
smartphone-based daily feedback and communication platform between
gestational diabetes mellitus patients and their physicians, on patient
compliance, glycemic control, pregnancy outcome, and patient
satisfaction.
STUDY DESIGN: This is a prospective, single-center, randomized
controlled trial. Newly diagnosed gestational diabetes mellitus
patients presenting to our multidisciplinary diabetes-in-pregnancy
clinic were randomized to: (1) routine biweekly prenatal clinic care
(control group); or (2) additional daily detailed feedback on their
compliance and glycemic control from the clinic team via an
application installed on their smartphone (smartphone group). The
primary outcome was patient compliance defined as the actual blood
glucose measurements/instructed measurements 100. The secondary outcomes included diabetes-control parameters, pregnancy,
and neonatal outcomes. The study was adequately powered to
detect a 20% difference in patient compliance, based on a
Introduction
Gestational diabetes mellitus (GDM)
prevalence is estimated to complicate
8-9% of all pregnancies,1,2 and is rising
due to an increased rate of obesity and
sedentary lifestyle.3,4 Patients with
GDM have a higher risk for developing
preeclampsia,5 shoulder dystocia, birth
Cite this article as: Miremberg H, Ben-Ari T, Betzer T,
et al. The impact of a daily smartphone-based feedback
system among women with gestational diabetes on
compliance, glycemic control, satisfaction, and pregnancy outcome: a randomized controlled trial. Am J
Obstet Gynecol 2018;218:453.e1-7.
0002-9378/$36.00
ª 2018 Elsevier Inc. All rights reserved.
https://doi.org/10.1016/j.ajog.2018.01.044
preliminary phase that demonstrated 70% baseline compliance to
glucose measurements.
RESULTS: A total of 120 newly diagnosed gestational diabetes mellitus
patients were analyzed. The 2 groups did not differ in terms of age, parity,
education, body mass index, family history, maternal comorbidities, oral
glucose tolerance test values, and hemoglobin A1C at randomization.
The smartphone group demonstrated higher level of compliance (84 0.16% vs 66 0.28%, P < .001); lower mean blood glucose (105.1 8.6
mg/dL vs 112.6 7.4 mg/dL, P < .001); lower rates of off-target measurements both fasting (4.7 0.4% vs 8.4 0.6%, P < .001) and 1-hour
postprandial (7.7 0.8% vs 14.3 0.8%, P < .001); and a lower rate of
pregnancies requiring insulin treatment (13.3% vs 30.0%, P ¼ .044). The
rates of macrosomia, neonatal hypoglycemia, shoulder dystocia, and other
delivery and neonatal complications did not differ between the groups.
Patients in the smartphone group reported excellent satisfaction from the
use of the application and from their overall prenatal care.
CONCLUSION: Introduction of a smartphone-based daily feedback
and communication platform between gestational diabetes mellitus
patients and the multidisciplinary diabetes-in-pregnancy clinic team
improved patient compliance and glycemic control, and lowered the rate of
insulin treatment.
Key words: GDM, gestational diabetes melitus, glycemic control, pa-
tient compliance, patient satisfaction, smartphone
injury, and cesarean delivery.6
GDM is also associated with an
increased risk for early and late neonatal
complications.7,8
Tight glycemic control in GDM patients
has been shown in numerous trials to
reduce maternal, fetal, and neonatal
complications.9,10 The management of
patients with GDM poses a unique challenge, mainly due to the limited time
available for potential interventions
between the time of the diagnosis and
delivery. Furthermore, the management is
primarily based on self-performed blood
glucose (BG) monitoring and reporting,11,12 and clinical decisions, such as the
initiation of pharmaceutical therapy and
timing of delivery, are based on these
measurements.
Patient compliance with BG monitoring as well as tight glycemic control
have been demonstrated to play a
pivotal role in determining pregnancy
Telemedicine
and
outcome.10,13,14
advanced technology were shown to
improve glycemic control in the
nonpregnant diabetic population.15,16
However, only small scaled studies concerning the incorporation of advanced
technological platforms for GDM patients
were published.17,18 Moreover, the evidence regarding the use of smartphone
technology, compared to standard care, is
scarce.19 Therefore, in light of the smartphone revolution of recent years, we
aimed to fill this gap by studying the effect
of a daily smartphone-based feedback
system in the care of GDM patients on
APRIL 2018 American Journal of Obstetrics & Gynecology
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Original Research
OBSTETRICS
AJOG at a Glance
Why was this study conducted?
To resolve uncertainty as to whether smartphone-based daily feedback is effective
in improving gestational diabetes mellitus patient compliance and glycemic
control.
Key findings
Smartphone-based daily feedback between gestational diabetes mellitus patients
and physicians improved patient compliance and glycemic control, and lowered
the rate of insulin treatment.
What does this add to what is known?
This study adds information about the use of smartphone-based feedback
between gestational diabetes mellitus patients and their physicians on improving
patient compliance and glycemic control, and lowering the rate of insulin
treatment.
patient compliance, glycemic control, and
pregnancy outcome.
Materials and Methods
Population
A randomized controlled trial was conducted over a period of 12 months (May
2016 through May 2017) at a single,
tertiary, university-affiliated medical
center. The study was approved by the
local institutional review board (no.
0037-16-WOMC, 2/2016; clinical-trials.
gov identifier NCT02783612).
Eligibility was limited to women aged
18-45 years, singleton gestations, with
no pregestational diabetes (per history
and per first-trimester fasting glucose
assessment), and first diabetes-inpregnancy clinic visit <34 gestational
weeks. As per the design of the study, all
patients were also required to speak
English, at least to a level that enabled
them to use the application and
communicate with the clinic team.
According to our departmental protocol, GDM is diagnosed using the
2-step process.12 The first step is
screening at 24-28 weeks of gestation
using 50-g, 1-hour glucose challenge
test. Women whose glucose levels are
>140 mg/dL undergo a 100-g, 3-hour
oral glucose tolerance test (OGTT).
GDM is diagnosed in women who have
2 abnormal values on the 3-hour
OGTT (fasting 95 mg/dL, 1-hour
180 mg/dL, 2-hour 155 mg/dL,
3-hour 140 mg/dL).20 Additionally,
women with 1 abnormal value in the
OGTT and an additional risk factor
(obesity, GDM in a previous pregnancy,
or a first-degree family member with
diabetes mellitus type 2) are also diagnosed with GDM, as it was previously
shown that these patients are also at a
significantly higher risk of adverse perinatal outcome.21
Study design and group assignment
Patients diagnosed with GDM who
owned a smartphone were approached
for recruitment during their first visit in
our prenatal multidisciplinary diabetesin-pregnancy clinic.
Our clinic accepts patients screened for
GDM in low-risk community centers by
their primary obstetricians. According to
our local guidelines, GDM patients are
referred to a high-risk clinic upon diagnosis for the remaining prenatal care.
Therefore, all patients recruited to this
study were recruited upon GDM diagnosis and had no prior intervention.
After obtaining written informed consent, patients were randomly assigned
either to the intervention group (smartphone group) or to the control group in a
1:1 ratio. A blocked randomization
scheme was created using a computergenerated list of random numbers.
The routine prenatal care provided by
our diabetes-in-pregnancy clinic is
composed of a first visit, subsequent
biweekly visits up to 35 weeks of gestation,
and weekly visits from 35 weeks of
453.e2 American Journal of Obstetrics & Gynecology APRIL 2018
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gestation until delivery. The maternal-fetal
medicine specialist, according to each patient’s individual data, can modify the
appointment frequency, according to the
individual patients’ glycemic control.
During the first visit, the patient receives
consultation from a maternal-fetal medicine specialist, education regarding the
proper use of the glucometer by a trained
nurse, and dietary counseling by a certified
dietician, regarding the principles of
proper nutrition in GDM and the planning of a weekly menu. In addition,
patients receive counseling regarding recommended physical activity (moderateintensity exercise 3 times a week for 20-45
minutes each time).22e24 Subsequent
biweekly visits include blood pressure
monitoring, urine dipstick for proteinuria, reviewing of the BG measurements,
and decision-making regarding the need
for additional therapies, 20-minute nonstress test, and a sonogram for fetal weight,
amniotic fluid volume, and biophysical
profile. Patients noncompliant to physical
activity recommendation are counseled
again. Patients are instructed to monitor
BG 4 times a day (once at morning fasting
and after each primary meal), and
manually record the measurements on a
paper diary for review with their physician
at each visit.
Patients allocated to the control group
received the aforementioned care.
Patients assigned to the smartphone
group received our standard care, and
had an application installed on their
smartphones. The application (https://
www.glucosebuddy.com) is web-based,
freely available, simple to use, and available during the study period for all
smartphone users. All patients received a
10-minute demonstration regarding the
use of the application from one of our
research coordinators, in addition to a
detailed information brochure.
Each patient documented each of her
BG measurements using the application,
which generated a daily report transmitted by e-mail every evening to our
computerized research database. Every
evening (including weekends), the
patient received via e-mail individualized feedback from our clinic team
regarding her daily glycemic control.
This feedback could include reassurance
ajog.org
and positive messaging, dietary tips in
attempts to optimize specific off-target
measurements, modifications in insulin
treatment, or alerts to reschedule an
earlier appointment to the clinic. In
addition, patients were encouraged to
use the platform to ask questions and
receive immediate answers regarding any
aspect of GDM management. As per the
study protocol, medical treatment could
only be initiated in a formal clinic
appointment and not via the application.
As a rule of thumb, it is the policy of
our clinic to initiate medical therapy
when BG levels were >95 mg/dL (fasting)
or >140 mg/dL (1-hour postprandial) in
>30% of the measurements. The decision
is individualized by the clinic team
(maternal-fetal medicine specialist and
endocrinologist) for each patient. In
general, we prescribe long-acting insulin
(insulin detemir), and if needed shortacting insulin (insulin aspart). None of
the patients in the current study received
oral antiglycemic medications.
Physicians in our clinic, providing care
to the patients on their regular appointments, were not blinded to the group
allocation. However, all staff in our labor
and delivery ward and the neonatology
division were blinded to group allocation.
Data collection
Upon recruitment, the following
demographic data were collected:
maternal age, parity, obstetric history,
family history of diabetes mellitus, chronic
hypertension,25 thrombophilia, smoking
status, previous GDM, assisted reproductive technology, fasting glucose obtained at
first trimester, values of glucose challenge
test and OGTT, first-trimester fasting
glucose, hemoglobin A1C upon diagnosis,
educational status, and level of physical
activity. Each patient’s height, weight, and
body mass index were recorded.
Obstetrical data collected included
gestational age at delivery, mode of onset
of labor, the presence of preeclampsia
or gestational hypertension,25 polyhydramnios (amniotic fluid index >95th
percentile for gestational age),26 antenatal corticosteroids administration,27
mode of delivery, shoulder dystocia, the
use of episiotomy, or third-/fourthdegree perineal tears.
OBSTETRICS
Original Research
FIGURE
Study flow diagram
Study design
Miremberg. Smartphone-based daily feedback, GDM, and compliance. Am J Obstet Gynecol 2018.
Immediately after birth, a pediatrician
examined all neonates. Birthweight percentiles for gestational age were assigned
using the updated local growth charts.28
Large for gestational age (LGA) was
defined as an actual birthweight 90th
percentile for gestational age. We
collected data regarding birthweight,
birthweight percentile, rate of LGA,
neonatal length of stay, neonatal intensive care unit admission, hypoglycemia
of the newborn (BG <40 mg/dL), respiratory morbidity (respiratory distress
syndrome, transient tachypnea of the
newborn, mechanical ventilation, or
need for respiratory support), phototherapy, and neonatal death.
Women in the smartphone group
were approached during the last prenatal
visit, and were asked to complete a short
questionnaire (in Hebrew) regarding
satisfaction with their prenatal care, the
use of the application, and difficulties
with application use.
Secondary outcomes included: (1)
diabetes-control parameters: mean BG
(mean SD of all measured values),
need for insulin treatment, and percentage of off-target measurements
(thresholds: fasting >95 mg/dL and
1-hour postprandial >140 mg/dL); (2)
pregnancy outcomes: polyhydramnios,
preeclampsia, gestational hypertension,
need for induction of labor, instrumental or cesarean delivery, shoulder
dystocia, third- or fourth-degree perineal tears; and (3) neonatal outcomes:
neonatal weight, LGA, neonatal intensive care unit admission, hypoglycemia
of the newborn, respiratory morbidity,
phototherapy, neonatal death, and
composite adverse neonatal outcome.
Composite adverse neonatal outcome
was defined as the presence of 1 of the
following early neonatal complications:
hypoglycemia of the newborn, respiratory
morbidity, phototherapy, or neonatal
death.
Outcomes
Sample size calculation
The primary outcome was patient
compliance, expressed as percentage,
and defined as the actual BG measurements/instructed measurements 100.
The measurements were recorded from
each patient BG diary in the control
group, and from our research
application-based data system in the
smartphone group.
A total of 120 participants, 60 participants per group, were needed to be
recruited to detect a 20% difference in
compliance (from a baseline of 70-90%)
between the smartphone and control
group, with a ¼ 0.05 and b ¼ 0.20. This
calculation was based on preliminary
retrospective data from our clinic (from
the 6 months prior to the study) that the
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Original Research
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OBSTETRICS
TABLE 1
Maternal characteristics of study groups
Smartphone group
n ¼ 60
Control group
n ¼ 60
Maternal age, y
31.7 4.2
32 6.3
Nulliparity
29 (48.3)
19 (31.6)
BMI, kg/m2
27.1 5.1
27.1 5.2
Chronic hypertension
5 (8.3)
1 (1.7)
Thrombophilia
1 (1.7)
0
Smoking
3 (5)
7 (11.7)
P value
.973
.093
>.99
.207
>.99
.322
ART
3 (5)
7 (11.7)
.322
Previous CD
9 (15)
13 (21.7)
.479
Previous GDM
12 (20)
18 (30)
.291
Family history of DM
30 (50)
20 (33.3)
.095
First-trimester fasting glucose, mg/dL
88.8 6.8
87.6 8.5
.913
173.1 23
169.4 28.8
.919
OGTTefasting value, mg/dL
90.7 9.2
94.3 12.1
.813
OGTTe60-min value, mg/dL
202.3 23.3
197.5 22.9
.883
OGTTe120-min value, mg/dL
168.3 34.8
163.6 33.2
.922
OGTTe180-min value, mg/dL
110.4 38.4
107.5 33.5
.954
1 Abnormal OGTT value
6 (10.0)
8 (13.3)
Hemoglobin A1C (%) at randomization
5.2 0.33
5.2 0.4
GCT value, mg/dL
.777
>.99
Primary language Hebrew
59 (98.3)
59 (98.3)
>.99
College/university degree
21 (38.2)
18 (37.5)
>.99
Education, y
13.6 1.8
13.6 2
>.99
Physical exercise
5 (8.3)
7 (11.7)
.762
Data are n (%) or mean SD unless otherwise specified.
ART, assisted reproductive technology; BMI, body mass index; CD, cesarean delivery; DM, diabetes mellitus; GCT, glucose
challenge test; GDM, gestational diabetes mellitus; OGTT, oral glucose tolerance test.
Miremberg. Smartphone-based daily feedback, GDM, and compliance. Am J Obstet Gynecol 2018.
baseline expected compliance in the
control group is 70%.
Statistical analysis
Categorical variables were compared
between the groups using c2 test or Fisher
exact test and continuous variables were
compared between groups using Student
t test. P < .05 was considered statistically
significant. Data were analyzed by statistical analysis software (SPSS, v23.0; IBM
Corp, Armonk, NY).
Results
A total of 126 patients were randomized;
65 were assigned to the control group (of
which 5 were lost to follow-up) and 61
were assigned to the intervention group
(of which 1 patient was lost to followup). The final analysis included 120
participants, 60 in each group (Figure).
The 6 patients lost to follow-up were
allocated (due to patient convenience
reasons) to a different clinic after
randomization, and therefore were not
included in the analysis.
Maternal demographic characteristics
are shown in Table 1. There were no significant differences between the groups.
Six of the patients in the smartphone
group and 8 of the patients in the control
group were included based on 1 pathological OGTT value.
The glycemic control characteristics
are shown in Table 2. The primary
outcome, which was prespecified as
453.e4 American Journal of Obstetrics & Gynecology APRIL 2018
patient compliance, was higher in the
smartphone group as compared to the
control group (84 0.16% vs 66 0.28%, P < .001). Mean BG was significantly lower in the smartphone group as
compared to the control group (105.1 8.6 mg/dL vs 112.6 7.4 mg/dL,
P < .001). The overall rate of insulin
treatment was lower in the smartphone
group compared to the control group
(13.3% vs 30.0%, P ¼.044), as well as the
rates of off-target measurements both
fasting (4.7 0.4% vs 8.4 0.6%, P <
.001) and 1-hour postprandial (7.7 0.8% vs 14.3 0.8%, P < .001).
Pregnancy and delivery characteristics
are summarized in Table 3. There were
no differences between the groups in any
of the measured characteristics.
Table 4 presents the neonatal outcome
parameters of the 2 groups. We found no
differences between the groups in any of
the outcomes.
All 60 patients in the smartphone
group reported “high” or “very high”
satisfaction with their application-based
prenatal care, and 80% of the patients
reported no difficulty using the application (20% of the patients reported slight
difficulty mainly related to Englishlanguage barrier).
Comment
In the current study, GDM patients
randomized to use smartphone as part of
their GDM management demonstrated a
higher level of compliance to BG monitoring, lower mean BG values, and a
lower rate of off-target measurements,
both fasting and 1-hour postprandial. In
addition, patients in the smartphone
group also demonstrated a lower rate of
the need for insulin treatment. Patients
in the smartphone group reported a high
level of satisfaction from their care, and
thought that the use of this intervention
was convenient and acceptable.
It is well established in the literature
that the treatment of GDM by a multidisciplinary approach combining diet
modifications and medical treatment
improves pregnancy outcome.10 Pregnancy outcome is closely associated with
each patient’s glycemic control.29,30
Currently, the main principle of
decision-making in managing GDM
ajog.org
OBSTETRICS
TABLE 2
Glycemic control characteristics of study groups
Smartphone group
n ¼ 60
Control group
n ¼ 60
P value
84 0.16
66 0.28
<.001b
105.1 8.6
112.6 7.4
<.001b
Off-target 1-h postprandial glucose
measurement, %
7.7 0.8
14.3 0.8
<.001b
Off-target fasting glucose measurement, %
4.7 0.4
8.4 0.6
<.001b
Insulin treatment
8 (13.3)
a
Compliance, %
Mean blood glucose, mg/dL
18 (30)
.044b
Data are n (%) or mean SD unless otherwise specified.
a
Actual blood glucose measurements/instructed measurements 100; b Statistically significant.
Miremberg. Smartphone-based daily feedback, GDM, and compliance. Am J Obstet Gynecol 2018.
patient is each patient’s reported glycemic control, which depends on the
patient’s
compliance
with
selfperformed capillary BG monitoring.
Current estimations report that
>80% of the population in developed
countries (up to 95% in some countries)
own a cellular phone, most of which are
smartphones with Internet availability.
These numbers are probably higher in
the reproductive-aged population. In
recent years attempts are made to
translate this smartphone revolution to
improve medical care in all fields of
medicine,31,32 and specifically in the
diabetic population.16
GDM patients could potentially
benefit from advanced technology platforms to improve their compliance and
thereafter pregnancy outcome. Ming
et al,19 in a systemic review and
meta-analysis, concluded that there is
insufficient evidence that smartphone
technology is superior to standard care
TABLE 3
Pregnancy and delivery characteristics of study groups
Smartphone group
n ¼ 60
Control group
n ¼ 60
P value
Antenatal corticosteroids
7 (11.7)
3 (5)
.322
Gestational hypertension
0
1 (1.7)
>.99
Preeclampsia
3 (5)
2 (3.3)
>.99
Polyhydramnios
0
4 (6.7)
.118
Gestational age at delivery, wk
38.2 1.7
38.5 1.4
.892
Induction of labor
24 (40)
17 (28.8)
.248
Normal vaginal delivery
48 (80)
40 (67.7)
.147
Instrumental delivery
4 (6.7)
1 (1.7)
.364
Episiotomy
9 (15)
6 (10.2)
.582
Third-/fourth-degree perineal tear
0
0
>.99
Shoulder dystocia
0
0
>.99
Cesarean delivery
Emergent cesarean delivery
12 (20)
20 (33.3)
.147
4 (6.7)
7 (11.6)
.528
Data are n (%) or mean SD unless otherwise specified.
Antenatal corticosteroid treatment administrated between 24-34 wk of gestation.
Miremberg. Smartphone-based daily feedback, GDM, and compliance. Am J Obstet Gynecol 2018.
Original Research
for women with GDM. However, the 7
trials included in the meta-analysis were
all small and underpowered. Despite
proven feasibility,18,33,34 currently, the
literature lacks randomized controlled
trials comparing a smartphone-based
daily communication platform between
GDM patients and their caretakers to
standard care. In addition, none of the
previously published studies attempted
to investigate such an effect on detailed
pregnancy outcomes.
This current study demonstrated that
smartphone-based technology could
indeed improve not only the adherence
to self-performed BG monitoring but
also glycemic control parameters such as
mean BG, off-target measurements, and
the need for insulin treatment. Advanced
technology provided us the platform to
perform this study and maintain a daily
communication and feedback system
with our patients. We attribute our
encouraging results to the individualized
approach of our study. We were able
to quickly respond, address patient
concerns, reassure, warn, or modify individual patient’s treatment plan on a
daily basis, compared to our biweekly
standard protocol.
Our study has several strengths. First,
it was the first randomized controlled
trial to compare GDM patient compliance, glycemic control, and pregnancy
outcome between a smartphone-based
daily communication platform and
standard care. Second, the current study
was adequately powered to detect differences in the primary outcome. Third,
it was performed in a single-center
multidisciplinary diabetes-in-pregnancy
clinic, similar to those that exist in
most academic centerseemphasizing
the generalizability of the results.
The current study is not without limitations. First, despite being powered to
address the primary outcome, it was
underpowered to address the secondary
outcomes, which are of major clinical
significance. We did observe some promising trends such as lower birthweights, a
lower rate of cesarean delivery, and a lower
rate of composite adverse neonatal
outcome in the smartphone group; however, the study was underpowered for
these differences to reach statistical
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OBSTETRICS
TABLE 4
Neonatal outcomes in study groups
Birthweight, g
Birthweight percentile
Smartphone group
n ¼ 60
Control group
n ¼ 60
3097.8 548.2
3203.3 414.6
54.1 27.9
57.3 25.7
P value
.878
.933
>.99
LGA
7 (11.6)
7 (11.6)
Neonatal hospitalization, d
3.9 2
3.8 1.2
NICU admission
6 (10)
7 (11.6)
>.99
2 (3.3)
1 (1.7)
>.99
Hypoglycemia of newborn
Respiratory morbidity
a
Phototherapy
Neonatal death
Composite adverse outcome
b
.966
4 (6.6)
2 (3.3)
.680
2 (3.3)
4 (6.6)
.680
0
0
7 (11.7)
11 (18.3)
>.99
.444
Data are n (%) or mean SD unless otherwise specified.
LGA, large for gestational age; NICU, neonatal intensive care unit.
a
Includes respiratory distress syndrome, transient tachypnea of newborn, mechanical ventilation, or need for respiratory
support; b Defined as presence of 1 of following: hypoglycemia of newborn, respiratory morbidity, phototherapy,
or neonatal death.
Miremberg. Smartphone-based daily feedback, GDM, and compliance. Am J Obstet Gynecol 2018.
significance. Second, the nature of the
study did not allow blinding the physician
meeting the patient in the clinic routine
visits to the group allocation. Third, our
study included only patients who own a
smartphone, speak English, and are
compliant to seek medical care in a tertiary
center multidisciplinary diabetes-inpregnancy clinic. These inclusion criteria
probably underrepresent a high-risk low
socioeconomic population that does not
meet these criteria.
In conclusion, we believe that the current study sheds new light on an important concept: advanced smartphone-based
technology can be used for daily patientphysician communication and feedback
and subsequently potentially improve the
medical outcome. Indeed, GDM patients
in this study demonstrated better
compliance, improved glycemic control
parameters, required less insulin treatment, and reported a very high satisfaction
rate with the process. Our future research
will focus on performing larger randomized controlled trials powered to study the
effect of this platform on specific pregnancy outcomes. Indeed, we plan to begin
soon recruitment of patients to a second
study in which the primary outcome will
be delivery of neonates LGA. We will
recruit 378 women (189 in each group) to
identify a 50% decrease in LGA over a
period of 2 years. We will publish our
results as they are available.
n
Acknowledgment
The authors wish to thank the entire staff of the
multidisciplinary diabetes-in-pregnancy clinic at
the Edith Wolfson Medical Center, Holon, Israel,
headed by Essaev Stella, RN (nursing team
leader), for their dedicated contribution that
made this study possible.
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Author and article information
From the Departments of Obstetrics and Gynecology (all
authors) and Pediatric Endocrinology and Diabetes Unit
(Dr Ben-Ari), Edith Wolfson Medical Center, Holon, and
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv
(all authors), Israel.
Received Nov. 12, 2017; revised Jan. 30, 2018;
accepted Jan. 31, 2018.
The authors report no conflict of interest.
Presented in oral format at the 38th annual meeting of
the Society for Maternal-Fetal Medicine, Dallas, TX, Jan.
29-Feb. 3, 2018.
Corresponding author: Hadas Miremberg, MD.
dasile2@gmail.com
APRIL 2018 American Journal of Obstetrics & Gynecology
453.e7