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Original Paper

Continuous 7-Month Internet of Things–Based Monitoring of HealthParameters of Pregnant and Postpartum Women: Prospective
Observational Feasibility Study
Johanna Saarikko1, RM, MHSc; Hannakaisa Niela-Vilen1, RM, PhD; Eeva Ekholm2,3, MD, PhD; Lotta Hamari1,4,
PT, PhD; Iman Azimi5, PhD; Pasi Liljeberg5, PhD; Amir M Rahmani6, MBA, PhD; Eliisa Löyttyniemi7, MSc; Anna
Axelin1, RN, PhD
1

Department of Nursing Science, University of Turku, Turku, Finland2

Department of Obstetrics and Gynecology, Turku University Hospital, Turku, Finland

3

Faculty of Medicine, University of Turku, Turku, Finland

4

Faculty of Communication Sciences, University of Tampere, Tampere, Finland5

Department of Future Technologies, University of Turku, Turku, Finland

6

School of Nursing and Department of Computer Science, University of California, Irvine, CA, United States

7

Department of Biostatistics, University of Turku, Turku, Finland

Corresponding Author:
Amir M Rahmani, MBA, PhD
School of Nursing and Department of Computer Science
University of California
106D Berk Hall
Irvine, CA, 92697-3959
United States
Phone: 1 949 824 3590
Email: a.rahmani@uci.edu

Abstract
Background: Monitoring during pregnancy is vital to ensure the mother’s and infant’s health. Remote continuous monitoring
provides health care professionals with significant opportunities to observe health-related parameters in their patients and to detect
any pathological signs at an early stage of pregnancy, and may thus partially replace traditional appointments.
Objective: This study aimed to evaluate the feasibility of continuously monitoring the health parameters (physical activity,
sleep, and heart rate) of nulliparous women throughout pregnancy and until 1 month postpartum, with a smart wristband and an
Internet of Things (IoT)–based monitoring system.
Methods: This prospective observational feasibility study used a convenience sample of 20 nulliparous women from the Hospital
District of Southwest Finland. Continuous monitoring of physical activity/step counts, sleep, and heart rate was performed witha smart wristband for 24 hours a day, 7 days a week over 7 months (6 months during pregnancy and 1 month postpartum). The
smart wristband was connected to a cloud server. The total number of possible monitoring days during pregnancy weeks 13 to
42 was 203 days and 28 days in the postpartum period.
Results: Valid physical activity data were available for a median of 144 (range 13-188) days (75% of possible monitoring days),and valid sleep data were available for a median of 137 (range 0-184) days (72% of possible monitoring days) per participant
during pregnancy. During the postpartum period, a median of 15 (range 0-25) days (54% of possible monitoring days) of valid
physical activity data and 16 (range 0-27) days (57% of possible monitoring days) of valid sleep data were available. Physicalactivity decreased from the second trimester to the third trimester by a mean of 1793 (95% CI 1039-2548) steps per day (P<.001).
The decrease continued by a mean of 1339 (95% CI 474-2205) steps to the postpartum period (P=.004). Sleep during pregnancy
also decreased from the second trimester to the third trimester by a mean of 20 minutes (95% CI –0.7 to 42 minutes; P=.06) andsleep time shortened an additional 1 hour (95% CI 39 minutes to 1.5 hours) after delivery (P<.001). The mean resting heart rateincreased toward the third trimester and returned to the early pregnancy level during the postpartum period.

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Conclusions: The smart wristband with IoT technology was a feasible system for collecting representative data on continuous
variables of health parameters during pregnancy. Continuous monitoring provides real-time information between scheduled
appointments and thus may help target and tailor pregnancy follow-up.
(JMIR Form Res 2020;4(7):e12417) doi: 10.2196/12417
KEYWORDS
prenatal care; postnatal care; wearable electronics; biosensing; cloud computing; mHealth; physical activity; sleep; heart rate

Introduction
Monitoring and follow-ups during pregnancy are vital to ensure
the health and well-being of pregnant women and their unborn
infants. Thus far, monitoring of pregnancy is performed atscheduled appointments by health care professionals in maternity
care units [1]. Instead of intermittent measurements tied to timeand place, remote and continuous monitoring could provide
significant opportunities for health care professionals to observe
the health-related parameters of their patients [2] and detect
abnormal changes in maternal adaptation to pregnancy andliability to pregnancy complications early. Remote monitoringmight support personalized care as maternity care could betailored according to the received information. A personalized
monitoring approach could also enhance a woman’s
self-management because it makes the woman more aware ofher health and might commit her to pregnancy care [3].
The Internet of Things (IoT) provides methods for ubiquitousand continuous maternity monitoring. The IoT is a high-levelnetwork of objects (ie, things) that are wirelessly connected toservers to provide efficient and comprehensive services [4,5].
In practice, the pregnant woman wears sensors that monitor
health-related parameters, enabling her and health care
professionals to track the data through web-based user interfaces
anywhere and at any time [5]. Recently, the use of various
eHealth applications in pregnancy care has sharply
increased—for example, for remote monitoring of blood
glucose—and the number of maternity care visits for womenwith gestational diabetes mellitus has decreased with nodifferences in maternal or neonatal outcomes noted [6,7].
Further, pulse and blood pressure sensors in a smartphone
application have been developed, and the feedback frompregnant women has been positive [8,9].
Physical activity and sleep are significant for a pregnant
woman’s general well-being and quality of life [10]. Physicalactivity decreases as pregnancy progresses [11], and sleep
disorders are common among pregnant women, especiallyduring the third trimester and during the postpartum period [12].
Both physical activity and sleep are usually measured bysubjective self-reports [11-13], which is complicated because

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self-reports may over or underestimate the duration of sleep or
activity [13,14]. On the other hand, device-based monitoring
has the potential to increase physical activity because having
visibility to one’s daily activity is known to be an incentive toincrease physical activity [15]. Although smart wristbands arewidely utilized to measure activity, sleep, and heart rate, theiruse in pregnant women is still scarce [2]. Continuous
device-based monitoring would provide unique andrepresentative data on the levels and changes of activity andsleep during pregnancy and the postpartum period. Furthermore,heart rate measures could be utilized to evaluate the intensityof the physical activity of pregnant and postpartum women.
Continuous monitoring could also be useful to target
interventions to those needing them the most and to measure
health outcomes systematically.
This study aimed to evaluate the feasibility of continuous
monitoring of health parameters (physical activity, sleep, andheart rate) of nulliparous women throughout pregnancy anduntil one month postpartum with a smart wristband and an
IoT-based monitoring system.

Methods
Study Design and Settings
This study was conducted as a prospective observational
feasibility study on nulliparous women attending two maternity
outpatient clinics in Southern Finland between May 2016 andJune 2017. Physical activity, sleep, and heart rate data werecollected with a smart wristband integrating aphotoplethysmogram bio-sensor to measure heart rate [16] andan inertial measurement unit to track activity and sleep [17].
The study design is presented in Figure 1.
The study was carried out per the Code of Ethics of the World
Medical Association (Declaration of Helsinki) and approvedby the Joint Ethics Committee of the Hospital District ofSouthwest Finland (35/1801/2016) and the university hospital.
Permission to use Garmin Vivosmart (HR, Garmin) smart
wristbands in this study was obtained from the manufacturer.
The study was not registered due to the feasibility design.

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Figure 1. The study design.

Participants and Recruitment
In Finland, all pregnant women are offered a screening
ultrasound free of charge at the end of their first trimester. A
convenience sample of 20 pregnant women was recruited fromthis visit. Criteria for eligibility were women (1) expecting theirfirst child, (2) ≥18 years of age, and (3) ≤15 of weeks ofsingleton gestation. Women were excluded if they did notunderstand Finnish or English or did not have a PC, tablet, or
mobile device with which to synchronize data.
The midwives at the maternity outpatient clinics informed theeligible women about the study. After providing oral and written
information, the midwives asked permission for the researchers
to be in contact with potential participants (N=22). The
researchers explained the study purpose and procedures to thewomen by telephone and scheduled a meeting if a woman was
willing to participate. Two women declined participation
because they felt that they would not wear the smart wristband.
Written informed consent was obtained from all participants at
the meeting with the researchers. In addition, a smart wristband
and instructions on how to use it were given to each participant,and background information was obtained through aquestionnaire.

Outcome Measures and Data Collection
Maternal background characteristics of age, BMI, marital status,
education, employment status, smoking, and pre-pregnancy
physical activity habits were collected with a questionnaire at
the meeting with the researcher. Data on pregnancy and delivery
were collected from the maternity card and hospital electronic
patient records.
Physical activity, sleep, and heart rate data were collected
objectively using a Garmin Vivosmart, small (21 mm × 12.3mm) and light (29.6 g) smart wristband. The device has shown
an acceptable level of validity for step counts tested underlaboratory conditions against the Optogait system (OPTOGait,
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Microgate Srl) and a manual hand counter on the treadmill[18,19]. The device detected sleep automatically based on heart
rate and hand movements during regular sleep hours, not during
the day. Physical activity intensities were estimated bycomparing heart rate data with the data collected by anaccelerometer sensor when acceleration was detected, incomparison to the patient’s average resting heart rate. If theheart rate sensor was not on, the device calculated the numberof steps per minute to evaluate moderate-to-vigorous physicalactivity (MVPA) minutes. At least 10 consecutive minutes must
be recorded to earn intensity minutes so that either step countrate or heart rate was elevated above the predefinedmoderate-intensity threshold [20]. The women were instructed
to wear the activity tracker for 24 hours a day during theirpregnancy and for 1 month after delivery, totaling 7 months.
They were advised to synchronize the devices once a day or atleast when charging the devices every 5 days. Data wereaccessible for both the researchers and the women.
An IoT-based system was implemented to provide remote healthmonitoring throughout pregnancy and postpartum. The
monitoring system leverages an amalgamation of different
sensing, communication, and computing resources to collect,transmit, and analyze data [5,21]. The architecture of this system
is illustrated in Figure 2. First, a smart wristband was used tocollect health data from the mother remotely. Second, thecollected data were transmitted to cloud servers via a gateway
device, which was a smartphone or a personal computer. Third,
the cloud was responsible for storing the data and for performing
the following data preprocessing and analysis methods.
Data collection rates were not fixed throughout monitoring, as
the Garmin provided the parameters at different rates. Therefore,
we first homogenized the heart rate values by interpolating or
averaging them. We then selected a 15-minute interval between
successive values. Moreover, step counts were obtained fromthe Garmin. We leveraged step count values to specify physicalactivity levels. Sleep duration per night was extracted from the
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sleep data provided by Garmin. To validate this data, we carriedout a manual cross-check between the sleep data and other data,

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including hand movement and heart rate values. The sleep data
were corrected or removed in cases of mismatch.

Figure 2. An Internet of Things–based system for remote maternity monitoring.

Statistical Data Analysis
Physical activity (step count) and heart rate data were accepted
as valid for analyses if the participant wore the device for atleast 10 hours per day while awake [22,23] at least 4 days per
week [24]. The only exception was the week of delivery, fromwhich all awake data were included in the analyses because thedelivery week is shorter in most cases (if woman deliverers at
40+1 gestational week, this week includes only 2 days). Sleep
measurements were considered successful if there was nooff-wrist time during the sleep period [25] and at least 4 sleep
periods measured per week. Sleep duration was defined as thenumber of minutes scored as sleep by the Garmin algorithm.
To describe the women’s background information and monitored
health parameters (step counts, sleep and awake minutes, heartrate, and MVPA minutes), means, SD, or CI and medians withranges were used as continuous variables and counts withproportions for categorical variables. Daily step counts, sleep,and heart rate data from 13 weeks of gestation to 1 month
postpartum were presented for the second and third trimesters
and the postpartum period (averaging all accepted data during
each period). The changes in weekly step counts and sleep
minutes were assessed throughout pregnancy. These analyses
were performed with a linear mixed model with repeatedmeasures, including one within factor (time = gestational
weeks). A compound symmetry covariance structure was used

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for time. This method allows women with missing values to be
included in the analyses. The same method was used to study
differences in average step count, sleep, and awake minutes
between pregnancy trimesters. Changes over trimesters in
MVPA minutes were analyzed using the Friedman test because
the normality assumption was not met.
Spearman correlation testing was used to explore associations
between mean daily step count by the second and third trimesters
and the postpartum period as well as mean daily sleep minutesby the trimesters and postpartum period. MVPA minutes werecompared to the recommendation for physical activity in healthy
pregnant women: 150 minutes of moderate-intensity or 75
minutes of vigorous-intensity activity per week [26]. All tests
were performed as two-sided tests with a significance level set
at .05. The analyses were performed using SAS System, version
9.4 for Windows (SAS Institute).

Results
Participants
A total of 20 of 22 eligible pregnant women (refusal rate of 9%)
were enrolled in the study. The mean gestational age at
recruitment was 12 (SD 2) weeks of gestation. The backgroundcharacteristics of the women are described in Table 1 and theperinatal outcomes are described in Table 2.

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Table 1. Descriptive statistics for maternal background characteristics (N=20).
Characteristic

ParticipantsAge (years), mean (SD)26 (5.0)

Gestational age at recruitment (week), mean (SD)

12 (2.1)

Highest educational qualification, n (%)
Primary education

4 (20)

Secondary education

9 (45)

College or university of applied sciences

4 (20)

University

3 (15)

Marital status, n (%)
Married or living with a partner

17 (85)Single3 (15)

Employment status, n (%)
Working

13 (65)

Unemployed2 (10)

Student

5 (25)

Pre-pregnancy BMI (kg/m2), median (range)

24.4 (17.7–43.5)

Pre-pregnancy smoking, n (%)

7 (35)

Smoking during pregnancy, n (%)

5 (25)

Pre-pregnancy physical activity (almost daily), n (%)

12 (60)

Table 2. Descriptive statistics for perinatal outcomes (N=20).
Perinatal outcomes

ParticipantsGestational age at delivery (week), mean (SD)39.4 (2.6)

Gestational diabetes, n (%)

5 (25)

Means of delivery, n (%)
Vaginal

14 (70)Vacuum-assisted

4 (20)

Emergency cesarean section

2 (10)

Birth weight (g), median (range)

Physical Activity and Sleep
The recordings yielded valid physical activity data for a medianof 144 (range 13-188) days, which represented 75% of the
6-month data collection period during pregnancy. During the
postpartum period, valid data were available for a median of 15(range 0-25) days, representing 54% of the 1-month datacollection period.
Physical activity decreased from the second trimester to thepostpartum period (Figure 3). The mean daily steps during thesecond trimester were 6838 (95% CI 5866-7810) and decreased
by a mean of 1793 (95% CI 1039-2548) steps per day in thethird trimester (P<.001). The daily steps further decreased bya mean of 1339 (95% CI 474-2205) steps in the postpartum
period (P=.004). In weekly comparisons, the average daily step
count was between 6000 and 7000 from 13-31 gestational
weeks. After gestational week 32, the mean daily step countdecreased to 5000 by 36 gestational weeks and further tohttp://formative.jmir.org/2020/7/e12417/

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3415 (1100-4445)

approximately 4000 steps/day after that. The most significant
decrease in physical activity occurred at 32 gestational weeks(P<.05 in most weekly comparisons) (Table 3).
Valid sleep data were available from a median of 137 days
(range 0-184), representing 72% of data during pregnancy.
During the postpartum period, a median of 16 days (range 0-27)
of valid data (57%) was used in the analyses (Table 3). Sleep
minutes decreased, and nightly awake minutes increased fromthe second trimester to the postpartum period (Figure 3). The
participants slept a mean of 8 hours (95% CI 7.6-8.3 hours)
during the second trimester and a mean of 20 minutes (95% CI–0.7 to 42 minutes) less per night in the third trimester (P=.06).
The total night sleep time shortened an additional 1 hour (95%
CI 39 minutes to 1.5 hours) in the postpartum period (P<.001).
In weekly comparisons, the average daily sleep minutes were
between 450 and 500 from 13 to 38 gestational weeks. After

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gestational week 38, the mean daily sleep minutes decreased to

435 (P<.05 in most weekly comparisons; Table 3).

Figure 3. Mean and standard deviation of the sleep data and step counts per day by week during pregnancy and postpartum (n=2-19).

Table 3. Mean step counts and sleep minutes by trimesters and the postpartum period.
Variables

Second trimester(n=19)

Third trimester(n=17)

Postpartum period(n=12)

Valid physical activity days, n (%a)

76 (70)51 (67)

12 (42)

Mean (CI)6838 (5866-7810)

5045 (4049-6041)

3705 (2635-4776)

Pairwise differences between periods (P value)<.001c.004d<.001e69 (63)48 (63)

12 (41)

Mean (CI)

477 (433-501)

457 (433-481)

393 (367-420)

Pairwise differences between periods (P value).06c<.001d<.001eMean (CI)

21 (15-27)

32 (25-40)

67 (52-81)

Pairwise differences between periods (P value).02c<.001d<.001eOverall differences between periods (P value)

Steps per dayb

Valid sleep days, n (%a)<.001

Sleep (min/night)b<.001

Awake (min/night)b

a

<.001

Calculated days during the follow-up period.

b

Measured by Garmin Vivosmart.

cSecond trimester versus third trimester.

dThird trimester versus postpartum period.

eSecond trimester versus postpartum period.

Heart Rate and the Intensity of Physical Activity
The resting heart rate increased progressively by 17% from 60
bpm (SD 5) at 13 gestational weeks to 70 bpm (SD 8) at 32
gestational weeks and remained at that level until delivery. The

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resting heart rate decreased to the early pregnancy level by 4
weeks postpartum (Figure 4).
The intensity of physical activity decreased from the second
trimester to the postpartum period. A median of weekly MVPA
minutes during the second trimester was 46 (range 0-288) and
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decreased by a mean of 14 minutes (95% CI –3 to 32 minutes)
per week in the third trimester. After delivery, the MVPA
minutes further decreased to a median of 15 (range 0-188)
minutes per week, but the change was not significant (P=.08;
Figure 4). Comparing the MVPA minutes with the generalrecommendation of physical activity for pregnant women, only
47%, 24%, and 25% of the participants achieved therecommended activity level in at least 1 week in the second andthird trimesters and the postpartum period, respectively (Table
4).

Figure 4. Resting heart rate values and moderate-to-vigorous physical activity minutes per week during pregnancy and postpartum (n=3-19). HR: heart
rate; MVPA: moderate-to-vigorous physical activity.

Table 4. Median MVPA minutes by trimester and the postpartum period.
VariablesaSecond trimester

Third trimester

Postpartum period(n=19)(n=17)(n=12)Overall difference between periods (P value)

MVPAa (min/week)b, median (range)

46 (0-288)

27 (0-279)

15 (0-188).08

Participants meeting the recommended MVPAc, n/N (%)

9/19 (47)

4/17 (24)

3/12 (25)

N/Ad

Weeks of 150 MVPA minutes, n/Ne (%c)

43/239 (18 )19/154 (12)7/40 (18)N/A

MVPA: Moderate-to-vigorous physical activity.

b

Measured by Garmin Vivosmart.

cModerate-to-vigorous physical activity at least in 1 week during follow-up.

dN/A: not applicablee

Calculated weeks during the follow-up period.

Discussion

Evaluation of the Health Parameters: Physical Activity,
Sleep, and Heart Rate

Principal Findings

Physical activity (daily steps) decreased during pregnancy andwere quite low in the third trimester and especially during thefirst month after the delivery. Previous studies using intermittent
and subjective measurements also showed that physical activity
decreases as the pregnancy progresses [27,28], and one previous
study using continuous monitoring also found that step counts
decrease in inactive pregnant women as pregnancy proceeded[29]. According to a previous review [15], the smart wristband
itself might increase participant physical activity. However, theeffects of physical activity interventions are often short term.
Thus we estimated that this was not relevant in our study due
to the long follow-up period. After the delivery, it was notexpected that the women’s physical activity would have returnedto the early pregnancy level during the first month. In addition
to the recovery from delivery, the transition to first-timeparenthood takes time. A smart wristband could be used toillustrate the level of physical activity to a pregnant woman

To our knowledge, this is the first continuous, long-term
follow-up study measuring physical activity, sleep, and HR
during pregnancy and the postpartum period. The results of thisstudy confirmed that this IoT-based system was feasible tomonitor health parameters during pregnancy when we collecteda large amount of valid physical activity (75% of the datacollection period) and sleep (72%) data. After delivery, theamount of data obtained was not representative. Overall physicalactivity, measured objectively with step counts, was low during
pregnancy and postpartum. Sleep time decreased during
pregnancy and after delivery, and heart rate increased due tohemodynamic changes. These expected results support the
preliminary reliability of a smart wristband with IoT-technology.
However, more research is needed to validate the continuous
monitoring of pregnant and postpartum women.
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herself and, by implication, support her in changing her lifestyle
to a more active one. Furthermore, physical activity improves
sleep quality during the postpartum period [12,30], andwell-rested women are most likely to be physically active; thus,
both elements of health should be equally supported.
Our study confirmed previous findings on declining sleep quality
and more frequent nocturnal awakenings. In line with theprevious study [31], we found sleep quality decline startingfrom the ﬁrst trimester. In addition, both the number andduration of nocturnal awakenings seemed to increase as
pregnancy proceeded [30] and in the postpartum period [12].
However, the sleep time remained rather high throughout
pregnancy and after delivery. Pregnant women’s abnormal sleep
duration is associated with several maternal problems, such as
hypertensive disorders [32], increased body mass index after
delivery [33], and depression and anxiety [34]. Moreover,extreme sleep duration during pregnancy has been associatedwith gestational diabetes [35]. Although the sleep duration of
the study participants was mostly sufficient according to thepresent recommendations, the awake minutes during sleep
periods increased during pregnancy. The nocturnal awake
minutes reduced sleep quality and probably compromised sleep
efficiency. The data on sleep quality in pregnancy are limited;
thus, the present study adds significantly to the present
knowledge of this subject. Smart wristbands provide many
opportunities to measure and support the sleep hygiene ofpregnant women in maternity care.
The resting heart rate increased until 32 gestational weeks, anormal hemodynamic change during singleton pregnancy [36];thus, it appears that a smart wristband is an appropriate tool formeasuring heart rate during pregnancy. In the future, heart rateand heart-rate variability could be utilized to study the level ofstress or the recovery from physical or mental exertions.
Maternal stress is associated with some pregnancy complications[37], and identifying the increase in stress could help withfinding suitable interventions to support the pregnant woman.
The intensity of the physical activity levels in this study waslow, as expected, and decreased considerably during the lastmonth of pregnancy. A comparison with the global physicalactivity recommendation for pregnant women showed that overhalf of the participants did not reach the recommended 150
minutes of moderate or vigorous physical activity per weekduring the second or third trimester.

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Limitations
The study includes distinct limitations due to the feasibility
design. The participants were recruited as a convenience sample;
however, the sample consisted of a diverse group of primiparous
women. Due to the small sample size, the results from this study
may not be generalizable. Furthermore, the small sample size
did not allow us to perform statistical analyses between outcome
predictors and maternal background characteristics or perinatal
outcome variables. Although Garmin Vivosmart has been shown
to provide a valid measure of step count [18,19] and total sleep
time compared with a sleep diary in a healthy adult population[38], it is notable that the device has not been validated in
pregnant women. A sleep diary would have strengthened our
results. However, due to the extended data collection period,
we made the decision not to use such a method. The intensityof physical activity was estimated based on heart rate elevation.
This approach is not unambiguous in pregnant women, becauseeven if the resting heart rate increases, the maximal heart rate
decreases during pregnancy, resulting in a smaller heart rate
reserve.
Furthermore, the specificity of intensity measures is only
moderate as the device may underestimate the intensity at higher
rates [19]. Therefore, moderate and vigorous activity minutes
may be biased. Missing data due to the participants not wearing
the smart wristband or not remembering to synchronize thedevice might have affected our results. The data collection
period was, however, very long, and only valid weeks were
included in the analysis. The data during pregnancy wererepresentative and covered the long follow-up period well. Afterdelivery, however, other methods for monitoring should beconsidered. In addition, the smart wristband used in this study
was found to be a feasible tool for continuous monitoring during
pregnancy [39].

Conclusions
The smart wristband with the IoT solution seems to be a feasible
system to collect representative data on continuous variables
such as physical activity, sleep, and heart rate throughout
pregnancy. However, more research is needed, and othermethods for monitoring after delivery should be considered.
Continuous monitoring provides personalized information andthus may help target and tailor pregnancy follow-up. Remotemonitoring provides vast opportunities to observe health-relatedparameters in pregnancy and thus detect pathological signs at
an early stage and partly replace traditional appointments. Futurestudies on predicting complications or support of women withhigh-risk pregnancies are needed.

Acknowledgments
This research was supported by the University Foundation (Award 5-863) and Academy of Finland (Awards 313448, 313449,
316810, and 316811). The authors would like to thank the personnel of the maternity clinics for their assistance in data collection.
Special thanks to the women who participated in the study.

Authors' Contributions
JS drafted the manuscript and contributed to the design of the study and the acquisition of data from the participants. JS also
analyzed and interpreted the data from the questionnaires and the participant data related to background information and healthrecords. HNV contributed to the design of the study and the acquisition of data from the participants. HNV was also a major
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Saarikko et alcontributor to drafting the manuscript and revising it critically. EE contributed to the design of the study and revised the manuscript
critically throughout the process. LH contributed to drafting the manuscript and analyzed and interpreted the data associated withphysical activity, sleep, and heart rate. IA, AR, and PL contributed to the design of the study, facilitated the cloud service for thedata collection, and analyzed and interpreted the data from the smart wristbands. EL was responsible for the overall biostatistical
data analysis and interpretation. AA was a major contributor to the design of the study, data analysis, and interpretation. AA also
contributed to drafting the manuscript and to revising it critically throughout the process. All authors read and approved the final
manuscript.

Conflicts of Interest
None declared.

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Saarikko et al

Abbreviations
GDM: gestational diabetes mellitus
IoT: Internet of Things
MVPA: moderate-to-vigorous physical activity

Edited by G Eysenbach; submitted 24.05.19; peer-reviewed by M Aittasalo, L Mackillop, J van den Heuvel; comments to author
09.12.19; revised version received 03.02.20; accepted 14.05.20; published 24.07.20
Please cite as:
Saarikko J, Niela-Vilen H, Ekholm E, Hamari L, Azimi I, Liljeberg P, Rahmani AM, Löyttyniemi E, Axelin A
Continuous 7-Month Internet of Things–Based Monitoring of Health Parameters of Pregnant and Postpartum Women: Prospective
Observational Feasibility StudyJMIR Form Res 2020;4(7):e12417
URL: http://formative.jmir.org/2020/7/e12417/
doi: 10.2196/12417
PMID: 32706696©Johanna Saarikko, Hannakaisa Niela-Vilen, Eeva Ekholm, Lotta Hamari, Iman Azimi, Pasi Liljeberg, Amir M Rahmani, Eliisa
Löyttyniemi, Anna Axelin. Originally published in JMIR Formative Research (http://formative.jmir.org), 24.07.2020. This is an
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License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information,a link to the original publication on http://formative.jmir.org, as well as this copyright and license information must be included.

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