You are here

  • Home
  • Archive
  • Volume 12, Issue 11
  • Comprehensive self-tracking of blood glucose and lifestyle with a mobile application in the management of gestational diabetes: a study protocol for a randomised controlled trial (eMOM GDM study)

Article Text

Article menu

Download PDFPDF

Obstetrics and gynaecology
Protocol
Comprehensive self-tracking of blood glucose and lifestyle with a mobile application in the management of gestational diabetes: a study protocol for a randomised controlled trial (eMOM GDM study)
  1. Mikko Kytö1,2,
  2. Lisa Torsdatter Markussen1,3,
  3. Pekka Marttinen4,
  4. Giulio Jacucci2,
  5. Sari Niinistö5,
  6. Suvi M Virtanen6,7,
  7. Tuuli E Korhonen5,
  8. Harri Sievänen8,
  9. Henri Vähä-Ypyä8,
  10. Ilkka Korhonen9,
  11. Seppo Heinonen10,
  12. Saila B Koivusalo10,11
  1. 1Department of IT Management, Helsinki University Hospital, Helsinki, Finland
  2. 2Department of Computer Science, University of Helsinki, Helsinki, Finland
  3. 3Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
  4. 4Department of Computer Science, Aalto University, Aalto, Finland
  5. 5Department of Public Health, Welfare Finnish Institute for Health and Welfare, Helsinki, Finland
  6. 6Department of Public Health and Welfare, The National Institute for Health and Welfare, Helsinki, Finland
  7. 7Faculty of Social Sciences, Unit of Health Sciences, University of Tampere, Tampere, Finland
  8. 8UKK Institute for Health Promotion Research, Tampere, Finland
  9. 9Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
  10. 10Department of Obstetrics and Gynaecology, Helsinki University Central Hospital, Helsinki, Finland
  11. 11Department of Obstetrics and Gynecology, Turku University Hospital, Turku, Finland
  1. Correspondence to Dr Mikko Kytö; mikko.kyto{at}hus.fi

Abstract

Introduction Gestational diabetes (GDM) causes various adverse short-term and long-term consequences for the mother and child, and its incidence is increasing globally. So far, the most promising digital health interventions for GDM management have involved healthcare professionals to provide guidance and feedback. The principal aim of this study is to evaluate the effects of comprehensive and real-time self-tracking with eMOM GDM mobile application (app) on glucose levels in women with GDM, and more broadly, on different other maternal and neonatal outcomes.

Methods and analysis This randomised controlled trial is carried out in Helsinki metropolitan area. We randomise 200 pregnant women with GDM into the intervention and the control group at gestational week (GW) 24–28 (baseline, BL). The intervention group receives standard antenatal care and the eMOM GDM app, while the control group will receive only standard care. Participants in the intervention group use the eMOM GDM app with continuous glucose metre (CGM) and activity bracelet for 1 week every month until delivery and an electronic 3-day food record every month until delivery. The follow-up visit after intervention takes place 3 months post partum for both groups. Data are collected by laboratory blood tests, clinical measurements, capillary glucose measures, wearable sensors, air displacement plethysmography and digital questionnaires. The primary outcome is fasting plasma glucose change from BL to GW 35–37. Secondary outcomes include, for example, self-tracked capillary fasting and postprandial glucose measures, change in gestational weight gain, change in nutrition quality, change in physical activity, medication use due to GDM, birth weight and fat percentage of the child.

Ethics and dissemination The study has been approved by Ethics Committee of the Helsinki and Uusimaa Hospital District. The results will be presented in peer-reviewed journals and at conferences.

Trial registration number NCT04714762.

  • OBSTETRICS
  • Diabetes in pregnancy
  • PERINATOLOGY
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2022-066292

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    STRENGTHS AND LIMITATIONS OF THIS STUDY

    • A mobile app for self-management of gestational diabetes (GDM) that combines lifestyle (eg, nutrition, physical activity and sleep) with continuous glucose levels in a real time and without support from healthcare personnel is evaluated in randomised controlled trial.

    • Maternal outcomes and neonatal outcomes are compared between the intervention and the control group with different measurements, including physical activity sensors and neonatal air displacement plethysmography.

    • Usage logs enable investigations of the effect of compliance with the eMOM GDM app on the outcomes.

    • As a limitation, we cannot fully isolate or mitigate the effect of sensors’ own apps on the outcomes.

    Introduction

    Digital health holds a promise for more efficient and improved healthcare with minor additional human resources. Gestational diabetes (GDM) is a type of diabetes that develops during pregnancy.1 Today, the burden of GDM on healthcare system and economy is remarkable since the incidence of GDM is increasing together with obesity globally,2 and it is associated with a range of adverse short-term and long-term consequences for both the mother and child.3–7 The primary treatment for GDM and glycaemic control is through adjustments toward heathier lifestyle, especially changing the diet and increasing exercising.8 9 It is critical that women with GDM are supported in this behaviour change.10 The lifestyle interventions for the treatment of women with GDM have been shown to reduce the incidence of large-for-gestational-age (LGA)8 11 and post partum weight retention.12 For this support different digital health interventions have been tested in women with GDM and a recent meta-analysis shows that interventions where participants receive weekly or more frequent guidance and feedback from healthcare personnel have shown the potential to improve glycaemic control.13 For example, in a study by Miremberg et al14 women with GDM received dietary tips for optimising off-target measurements and reassuring and positive messages every evening via email. However, mobile app interventions without such substantial input from healthcare professionals are limited and lack effectiveness.15–17 Recently, Yew et al18 found improvements in mean glucose and in proportion of off-target preprandial and 2-hour postprandial measurements. However, their app included a chat feature with healthcare team, which use and effect on the results remained unclear.19 In this study, we evaluate if the effectiveness can be increased with the eMOM GDM mobile app, which visualises continuous glucose data together with lifestyle data in a single view in a real time without involvement of healthcare personnel. Comprehensive self-tracking with wearable sensors has been identified as a desirable feature for increasing competence to self-manage GDM,20 21 but has not been clinically evaluated before.

    Methods

    Study design

    Participants are randomised 1:1 into the intervention and the control group. The intervention group receives standard antenatal care and the eMOM GDM app, while the control group will receive only standard care. Participants in the intervention group are instructed to use the eMOM GDM app with continuous glucose metre (CGM) and activity bracelet 1 week/month until delivery. During the same week when participants have the CGM installed, participants are instructed to keep an electronic food diary for 3 days. After keeping the food diary, the nutrition entries are checked and corrected in a call by a nutritionist. In addition to this instructed use, participants can use the eMOM GDM app with activity bracelet and food-tracker freely. Participants in both groups are invited to the follow-up visit at 3 months post partum.

    We follow the Standard Protocol Items: Recommendations for Interventional Trials checklist22 in the study design and reporting. Study design and data collection is depicted in figure 1, please see details for laboratory tests, measurements and questionnaires in the Measurements section.

    Figure 1
    Figure 1

    Design of the randomised controlled trial.

    Participants

    Inclusion and exclusion criteria are given in table 1. Women with GDM are randomised into the intervention and the control group using stratification at gestational week (GW) 24–28. Strata are GW of GDM diagnose (early: < 24 GW or late: 24–28 GW), parity (primiparous or multiparous) and body mass index (BMI) (< or ≥30 kg/m2). Three strata resulted in eight blocks. The Finnish Current Care Guidelines provide the thresholds for diagnosing GDM based on 2-hour 75 g oral glucose tolerance test (OGTT): 0 hour >5.3 mmol/L, 1 hour >10.0 mmol/L and 2 hours >8.6 mmol/L.23 Participants are voluntary and they are allowed to withdraw any point without any reason, and they have also right to cancel the consent. If a participant withdraws, the research data collected in the study will be stored and included in the analysis. If a participant cancels the consent, all the data which are not processed as research results, will be deleted according to General Data Protection Regulation (GDPR) (https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679&from=EN%23d1e2001-1-1) of European Union. If medication is started, the participant will drop out from the study and the data collected until the decision to start medication will be collected.

    View this table:
    Table 1

    Inclusion and exclusion criteria

    Recruitment and consent

    Participants are recruited from antenatal clinics from the Helsinki metropolitan area at GW 24–28. Local nurses at the municipal maternal clinics in Helsinki, Espoo and Vantaa inform about the possibility to participate in the eMOM GDM study in peer-support groups and share flyers in one-to-one meetings. We started the recruitment in March 2021 and we plan to continue until January 2023. Women who agree to participate sign two consent forms, one copy for themselves and one for the study nurse.

    Blinding

    For each block (see the Participants section), envelopes consisting of equal number of intervention and control cases were created for blinding. Based on the participant’s status, a study nurse opens a sealed envelope from the respective block. The content of the envelope determines whether the participant is assigned to the intervention or to the control group. This approach balances the allocation of subgroups of participants to intervention and control groups in a blinded manner.

    Description of the eMOM GDM app and its wearable sensors

    The eMOM GDM app and its integration to the medical personnel user interface (UI) was developed in 2020. The eMOM GDM app visualises lifestyle data (physical activity, sleep and nutrition) together with tissue glucose data in a single view (see figure 2A,B). For displaying the data, the eMOM GDM app has two views, a week view (see figure 2) and a day view (see figure 2B). The information from the sensors and food tracker are updated each 10 min to the eMOM GDM app. We included an information section regarding pregnancy and GDM (see figure 2C). Based on recent studies20 21 providing reliable information about how to manage GDM (particularly what to eat and how the fetus is developing) should be integrated in the GDM apps, so that no separate app for this is needed.

    Figure 2
    Figure 2

    Screenshots of main views in the eMOM GDM APP. (A) A week view for self-tracking data (Copyright: Fujitsu Finland), (B) a day view for self-tracking data (Copyright: Fujitsu Finland), (C) pregnancy and GDM related information (Copyright: Helsinki university hospital), (D) detailed glucose view (Copyright: Fujitsu Finland) and (E) detailed nutrition view (Copyright: Fujitsu Finland). GDM, gestational diabetes.

    Medtronic Guardian Connect CGM with Enlite sensor (Medtronic, Dublin, Ireland; see figure 3) continuously measures tissue glucose. A flexible filament is inserted just under the skin to measure glucose levels in interstitial fluid approximately every 5 min. The eMOM GDM app shows these detailed glucose values when tapping the glucose curve on the screen (see figure 2D). Glucose values are sent to the mobile phone via Bluetooth. Medtronic requires calibration of the sensor by capillary blood glucose measurements two times a day.

    Figure 3
    Figure 3

    The sensors used in the study. The sensors on the left (1. HRV sensor and 4. movement sensor) are worn by both control and intervention group. The movement sensor is a small box, which is attached either to a belt or to a bracelet. sensors on the right (2. CGM and 3. activity bracelet) are part of the intervention.

    Garmin Vivosmart 3 (Garmin International, Kansas, USA; see figure 3) is a wrist-worn optical heart rate and activity tracker which measures activity, sleep and heart rate continuously based on its optical heart rate sensor, and 3D acceleration. Garmin Vivosmart 3 has been found to be feasible among pregnant women with good ratings on user experience24 and measures steps well at slow walking speeds.25 The tracked parameters include steps, estimated energy expenditure in calories (based on motion and heart rate), stairs, walking and running distance, all-day heart rate and stress. It connects automatically to a mobile app (Garmin Connect) and data are stored automatically to the cloud, where it can be accessed via application programming interfaces (APIs).

    A dedicated food-tracker was developed by Helsinki University Hospital to make the food intake data collection easier for pregnant women. A user enters the time of each meal, food items consumed and portion sizes to the food-tracker. The food-tracker fetches the food items and their nutrient contents from Fineli food composition database (Finnish National Institute for Health and Welfare, http://www.fineli.fi), calculates nutrient intake and shows the amounts of the energy yielding nutrients and fibre to the user. This data is transferred to the eMOM GDM app where the energy intake from each energy yielding nutrient is visualised as stacked bars (see figure 2B). More detailed information (recorded food items, nutrient intake in grams) can be accessed (see figure 2E) by tapping the stacked bar (see figure 2B). In addition, participants were asked to add their weight to the eMOM GDM app once a week.

    Standard care for both groups

    All participants in the study receive standard care, in addition to the study protocol. The public healthcare system in Finland offers all pregnant women antenatal healthcare on a regular basis in municipal maternity clinics at primary healthcare centres. A doctor and a nurse are following the pregnancy in collaboration. Sessions are divided into periodic audits (basic visits) and discretionary additional visits. The periodic health check includes a minimum number (9–10) of visits with a nurse, and two doctor examinations designed for normal, low risk pregnancies. The first medical check-up is during GW 8–10. Maternity clinics and hospitals have provided detailed instructions on when the mother should be sent to the hospital for further examinations and follow-up.26

    A 2h-OGTT is performed normally at GW 24–28, but if the risk of GDM is considered high (BMI >35 kg/m2, previous GDM, glycosuria in early pregnancy, incidence of type 2 diabetes in grandparents, parents or siblings, oral corticosteroid therapy, polycystic ovary syndrome), OGTT is performed already at GW 12–16. After a GDM diagnosis, the women receive guidance on diet, physical activity and self-monitoring of blood glucose with electronic capillary glucose meters.23 In case of repeated fasting capillary glucose of ≥5.5 mmol/L or a 1-hour postprandial value of ≥7.8 mmol/L, the maternity clinic refer the woman to a maternity hospital for further assessment regarding need of medication.23

    Objectives and hypotheses

    The principal aim of the study is to evaluate the effect of the eMOM GDM application on maternal glucose levels by comparing intervention and control groups. We also study the effects of the application on different other maternal and neonatal outcomes (please see the specific objectives below). The follow-up visit after the delivery and intervention takes place 3 months post partum.

    The specific objectives of the intervention study are to compare between the intervention and the control groups:

    1. Differences in maternal glucose levels during pregnancy.

    2. Differences in physical activity and stress levels during pregnancy.

    3. Differences in total diet during pregnancy.

    4. Difference in the need for medication due to GDM.

    5. Difference in gestational weight gain (GWG).

    6. Birth weight, incidence of large-for-gestational-age (LGA) and macrosomic (birth weight >4000 g) newborns, and infant’s body composition.

    7. Differences in motivation during the pregnancy.

    8. Differences in physical activity, total diet, weight retention and glucose values 3 months post partum.

      Main hypothesis: Fasting plasma glucose, between baseline (BL) and GW 35–37, will decrease more in the intervention than in the control group.

    Outcomes

    Primary outcome is the change in fasting plasma (fP)-glucose from BL (GW 24–28) to GW 35–37. The main secondary outcomes are given in box 1.

    Box 1

    The main secondary outcomes

    1. Difference in capillary fasting glucose and 1-hour postprandial glucose values during pregnancy.

    2. Difference in capillary glucose area under the curve during pregnancy.

    3. Change in Homeostatic Model Assessment for Insulin Resistance during pregnancy and 3 months post partum.

    4. Difference in physical activity level during pregnancy and 3 months post partum.

    5. Difference in total diet during pregnancy and 3 months post partum.

    6. Difference in medication use due to gestational diabetes mellitus (GDM) (insulin, metformin).

    7. Difference in gestational weight gain and postpartum weight retention.

    8. Difference in birth weight (SD) of child.

    9. Difference in large-for-gestational-age of child and macrosomia.

    10. Difference in infant’s body composition.

    11. Difference in neonatal hypoglycaemia (<2.6 mmol/L) incidence within a week from birth.

    12. Difference in motivation to manage GDM during pregnancy.

    13. Difference in fP-glucose and 2-hour blood glucose levels measured with oral glucose tolerance test (75 g) at 3 months post partum.

    Measurements

    We take measurements with multiple techniques and sensors. All the measurements, except measurements specific to the eMOM GDM app intervention are taken from both groups (see a summary in table 2).

    View this table:
    Table 2

    Measurements for intervention and control groups

    Background information

    Age, BMI, parity and previous GDM status are collected from hospital registries, and socioeconomic status (eg, education, occupation), alcohol use, possible special diet, and experience with self-tracking from a background questionnaire.

    Maternal glucose levels

    Maternal glucose levels are collected in the intervention and the control groups with three measurement types: (1) 2-hour OGTT at GW 12–16 (early) or GW 24–28 (late) as well as 3 months post partum, (2) capillary glucose measures (eg, fasting glucose, postprandial glucose, area under the curve (AUC)) (Contour Next One, Ascensia Diabetes Care, Basel, Switzerland) from BL until delivery and (3) laboratory fP-glucose, glycated hemoglobin (HbA1c), fP-insulin at study visits at BL, GW 35–37 and 3 months post partum. In addition in the intervention group, glucose levels are collected with a CGM (Medtronic Guardian Connect) 1 week/month from BL to delivery.

    Maternal physical activity

    Maternal physical activity is extracted from both groups at BL, GW 35–37 and 3 months post partum with two ‘blind’ sensors: UKK RM 42 and Firstbeat Bodyguard 2 (see figure 3).

    Movement is measured with UKK RM42 (UKK Institute, Tampere, Finland). It is a triaxial accelerometer that measures the device movement located either at the hip during waking hours and at the wrist during the time in bed for sleeping (see sensor 4 in figure 3). The data analysis is based on validated MAD-APE algorithms.27 28 The amount of daily physical activity is described in durations and intensity in METs, the amount of sedentary behaviour (lying, sitting and standing) in durations, and sleep as movement categories. These analyses have been employed in population-based studies of Finnish adults.29 30

    Heart rate variability (HRV) is measured with Firstbeat Bodyguard 2 sensor (Firstbeat Technologies, Jyväskylä, Finland), which is a chest worn wearable device which measures beat-to-beat HRV and activity (3D acceleration) continuously for 3 days. The device is attached to the chest with two disposable clinical grade ECG electrodes (see sensor 1 in figure 3). The device is able to continuously measure beat-to-beat HRV with <3 ms error and >99.9% detection rate as compared with clinical grade ECG.31 Proprietary analytics software (Firstbeat Lifestyle Assessment, Firstbeat Technologies, Jyväskylä, Finland) is used to transform the recorded beat-to-beat and motion data into continuous assessment of energy expenditure, VO2, physical activity, stress and recovery. These measurement methods have been validated widely in several studies.32 33

    Physical activity measures are also collected in the intervention group with activity bracelet Garmin Vivosmart 3 1 week/month from BL until delivery (see sensor 3 in figure 3 and Description of the eMOM GDM app section and its wearable sensors).

    Maternal nutrition

    We measure maternal total diet including food consumption and nutrient intake among intervention and control women during the preceding month with a digital semiquantitative 142-item Food Frequency Questionnaire (FFQ) (updated from34) at BL, GW 35–37 and 3 months post partum. FFQ is used for intervention effect estimation. Nutritional outcomes include, for example, intake of fruits and vegetables, wholegrain cereals, sugar, protein, carbohydrates, fibre, unsaturated and saturated fat, vitamins and minerals. In addition, participants in the intervention group are instructed to keep an electronic food diary for 3 days/month while they are wearing the CGM.

    Maternal gestational weight gain and weight retention 3 months post partum

    Weights (in kg) are extracted from the maternity card and at study visits at BL and GW 35–37, within 2 days after birth, and 3 months post partum.

    Neonatal outcomes

    Neonatal anthropometric measures at birth are extracted from hospital registry; birth weight (SD), birth length, incidence of LGA/macrosomic newborn (>+2 SD/>4000 g), and newborn body composition (body fat%, lean body mass%) by Pea Pod (COSMED, Italy, Rome) air-displacement plethysmography,35–37 within 2 days after birth. Also, Apgar scores, neonatal hypoglycaemia (<2.6 mmol/L), transfer to intensive care and intravenous glucose infusion of newborn are collected.

    Psychological factors, motivation and health-related quality of life

    Maternal depression and health-related quality of life are collected with the digital questionnaires at BL, GW 35–37 and 3 months post partum. Motivation is collected with the digital questionnaires at BL and GW 35–37. Depression is measured with Edinburgh Depression Postnatal Depression Scale,38 which is also used during pregnancy (eg,39), health-related quality of life with 15D questionnaire40 and motivation with Treatment Self-Regulation Questionnaire41 and Perceived Competence for Diabetes Scale (PCDS).41

    Measures about the intervention

    We measure the app usage by logging the time and type of every interaction with the eMOM GDM app. Human factors relating to app use are measured with Unified Theory of Acceptance and Use of Technology questionnaire,42 Software Usability Measurement Inventory43 and with a semistructured interview on user experience with the eMOM GDM app with randomly selected 20 participants.

    Data analysis

    We will analyse the data using RStudio (V.1.1.456, RStudio, Boston, USA) and perform the analyses according to intention-to-treat principle. Missing data will not be replaced. For comparing maternal outcomes and neonatal outcomes between intervention and control group we will use classical statistical tests (such as t-test, χ² test and analysis of covariance (ANCOVA)) depending on type of the variable and its distribution (ie, normality and homogeneity). The comparisons of changes between the control and the intervention group will be performed using either analysis of variance of change from BL or ANCOVA, having the BL measurements as covariate.44 The selection depends on the possible differences at BL.44 Other maternal covariates will be maternal socioeconomic situation (education, occupation), age at childbirth, BMI, parity and smoking during pregnancy. The birthweights will be adjusted according to Sankilampi et al.45 Regarding the eMOM GDM app use, we will investigate correlations between usage patterns of the eMOM GDM app and outcomes (maternal and neonatal). We will conduct a cluster analysis for the usage patterns to identify effective usage strategies of the eMOM GDM app similar to apps designed for type 2 diabetes management.46

    For the CGM data, we will conduct time series analysis using standard techniques,47 such as AUC and incremental AUC48 49 and mean amplitude of glycaemic excursions.50 Interim data analysis will be performed by data analysis team (MK, LTM and PM) when the research data from a half of the participants (N=100) is collected. Study nurses will remain blinded to the interim results, and we will blind the statistical analyses by asking an external person to recode the participants before conducting final analyses. The final dataset is accessed by data analysis team (MK, LTM and PM). The final decision to terminate the trial will be made by the principal investigator (SBK).

    Sample size

    To detect at least a 0.32 mmol/L between-group mean difference in the fP-glucose (primary outcome) response to the intervention (α<0.05, power=95%, an assumed drop-out rate of 40%), a sample of 200 women (100 in each intervention arm) is needed for the intervention study. This anticipated difference of 0.32 mmol/L between the intervention group and control group corresponds to 0.7 SD of variation in fP-glucose change observed previously in similar women and stage of pregnancy (SD needed for power calculation has been calculated from the Finnish Gestational Diabetes Prevention Study-population; SD for change in fP-glucose value between II and III trimesters in women who got GDM diagnoses at II trimester).51

    eMOM GDM system implementation and data maintenance

    The system implementation was designed to support wider employment of eMOM GDM service concept as a part of future digital healthcare path for women with GDM in Helsinki metropolitan area. Technical implementation of the eMOM GDM app as well as browser-based UI for healthcare professionals were conducted by Fujitsu Finland Oy. The data transfer to the eMOM GDM app and professional UI is implemented in the cloud so that eMOM app’s back-end server fetches data from a data integration server. Data integration server (implemented by Elisa Corp.) gathers data from sensors’ and food tracker’s cloud services. The data from eMOM GDM service’s back-end server are further transferred to secure HUS Data Lake environment (Microsoft HDInsight Hadoop cluster).

    Feasibility study

    Before this randomised controlled trial, we conducted feasibility studies of the eMOM GDM app, wearable sensors and food-tracker involving women with GDM (data not published yet). The digital food-tracker used in this study was originally speech-enabled, but in the feasibility study we noticed that a large majority of participants preferred typing to speech. Thus, we discontinued the support for speech recognition.

    Ethics and dissemination

    The eMOM GDM study is in accordance with the Declaration of Helsinki (www.wma.net). All the participants sign informed consent forms, and they are instructed that they can withdraw at any point during the study. Data during any processes or analyses is pseudonymised, and it does not contain personal information that could be directly linked to any individual. For example, a participant of the intervention group gets a pseudonym (ID based) email address, which the participant uses when she logs in to the eMOM GDM app and the sensor-associated apps. We have ethical approval from ethical committee of HUS, and study permissions from three cities (Helsinki, Espoo and Vantaa) in the Helsinki Metropolitan area to recruit participants.

    As a part of quality assurance, the study is being monitored by an external institution, Helsinki University Central Hospital’s Study Monitor of Clinical Research Institute (https://hyksinstituutti.fi/clinical-research-institute-huch/?lang=en), according to legislation, official guidance and Good Clinical Practice (https://hyksinstituutti.fi/clinical-research-institute-huch/?lang=en). The auditing visits are conducted every 6 months during the trial, and the auditing process is independent of investigators and the sponsor.

    Important protocol modifications will be communicated with research group, Ethical committee of Helsinki University Hospital and ClinicalTrials.gov.

    Sponsor provides facilities for the trial and funder partly funds the trial, but they do not have a role in study design. There are monthly technical steering group meetings by project partners, in which the progress of the trial is followed. However, the ultimate authority to make decisions regarding the clinical trial is the principal investigator (SBK).

    The results of the eMOM GDM study will be published in peer-reviewed journals and at national and international scientific conferences. We promote open science by blogs and interact actively with the media, including social media (eg, Twitter and LinkedIn), to gain visibility to our findings among journalists and among non-research community as well.

    Patient and public involvement statement

    Both women with GDM and registered nurses from the Helsinki Metropolitan Area were involved in the design process of the eMOM GDM service concept. Based on the feedback from the use of the first version of the eMOM GDM app, it was modified into the version, which is used in the eMOM GDM clinical trial. The user-centred design process involved women with GDM (N=21) who did not participate in the clinical trial and the results will be published in a separate article. The local nurses gave insights into the design of the professional UI.

    Adverse events

    All participants will be under standard care, and thus will be in close medical observation. In the case of adverse or harmful events, study nurses will report to principal investigator (SBK) and the appropriate response to these will be discussed within the research group. All participants are insured with the patient injury insurance by Helsinki University Hospital.

    Discussion

    Our approach differentiates from existing GDM app interventions in three main aspects. First, we have wearable sensors integrated with the eMOM GDM app. This enables viewing self-tracking data in one place. Objectively and automatically measured, and constantly available data through wearable sensors data can be expected to support learning. However, learning through self-tracking requires active agency from women with GDM, as associations between lifestyle and nutrition are complex and difficult to identify, especially within first weeks after the GDM diagnosis.52 Thus, it remains to be seen if this type of feedback, being quite different from what women with GDM receive in standard care, is effective enough for improving maternal glycaemic levels and neonatal outcomes. Second, our intervention differentiates from other app-based interventions in timewise. Our intervention has clearly defined instructions when participants should use the app (1 week per month) and keep food diary (3 days per month). These time periods were chosen based on the validity (3-day food diary have shown to provide equally valid results with 9-day food diary53), decrease of engagement with mHealth over time,54 practicality (battery of CGM lasts approx. a week) and costs (the price of the sensors; we were able to rotate them among participants). The effects of this type of periodical intervention remain unclear, and we will collect experiences on this with interviews with the participants. Third, the system does not consist of an app only, as there is UI for professionals to view the data from the CGM, activity bracelet and food diary from each participant in the intervention group. In this study, the professional UI is used only for remote monitoring of the technical data flow from the participants in a real time. In the future, the professional UI is planned to be used as a resource for monitoring and giving guidance for women with GDM. Together with the eMOM GDM app, the professional UI forms a novel service concept, which can be employed as future digital healthcare path for women with GDM.

    The comprehensive self-tracking approach also poses challenges. As we are using multiple sensors and they have their own apps, it is difficult to identify how much the eMOM GDM app and other apps (CGM’s app and activity bracelet’s app) influence the learning. Especially, the CGM solely has been identified to facilitate self-discovery, that is, finding associations between lifestyle (eg, nutrition) and glucose levels.55 56 In order to evaluate the eMOM GDM app’s role, we will log the interactions with the app, and we will interview a subgroup of participants about the user experience with the app. By doing so, we are able to evaluate the compliance and engagement with the app, which has been identified as a shortcoming in studies evaluating the effectiveness of GDM apps.19 The other challenges emerge from technical implementation and maintenance. The sensors’ apps need to be installed in compatible mobile phones and the data is fetched to the eMOM GDM app through APIs in the cloud services. This requires active monitoring for the updates regarding compatibility issues between phones and sensors and their APIs.

    Ethics statements

    Patient consent for publication

    Ethics approval

    The Ethics Committee of the Helsinki and Uusimaa Hospital District has accepted the study protocol (HUS-2165-2018-3).

    Acknowledgments

    We gratefully acknowledge the contribution of Fujitsu Finland Oy for the technical implementation of eMOM app and Elisa for sensor integration. We wish to thank all the women with GDM in Finland taking part in the research so far, and the study nurses Jaana Palukka, Sanna Lampi and Milla Tuhkanen.

    References

    1. ↵
      1. World Health Organization
      . Definition, diagnosis and classification of diabetes mellitus and its complications : report of a WHO consultation. Part 1, Diagnosis and classification of diabetes mellitus, 1999.
    2. ↵
      1. International Diabetes Federation
      . IDF diabetes atlas. 10th. Brussels, Belgium, 2021.
    3. ↵
      1. Farrar D,
      2. Simmonds M,
      3. Bryant M, et al
      . Hyperglycaemia and risk of adverse perinatal outcomes: systematic review and meta-analysis. BMJ 2016;354:i4694.doi:10.1136/bmj.i4694pmid:http://www.ncbi.nlm.nih.gov/pubmed/27624087
      OpenUrlAbstract/FREE Full Text
    4. ↵
      1. Burlina S,
      2. Dalfrà MG,
      3. Lapolla A
      . Long-Term cardio-metabolic effects after gestational diabetes: a review. J Matern Fetal Neonatal Med 2021;0:1–8.doi:10.1080/14767058.2021.1903863pmid:http://www.ncbi.nlm.nih.gov/pubmed/33779467
    5. ↵
      1. Hoodbhoy Z,
      2. Mohammed N,
      3. Nathani KR, et al
      . The impact of maternal preeclampsia and hyperglycemia on the cardiovascular health of the offspring: a systematic review and meta-analysis. Am J Perinatol 2021. doi:doi:10.1055/s-0041-1728823. [Epub ahead of print: 03 May 2021].pmid:http://www.ncbi.nlm.nih.gov/pubmed/33940650
    6. ↵
      1. Ornoy A,
      2. Becker M,
      3. Weinstein-Fudim L, et al
      . Diabetes during pregnancy: a maternal disease complicating the course of pregnancy with long-term deleterious effects on the offspring. A clinical review. Int J Mol Sci 2021;22:2965–38.doi:10.3390/ijms22062965pmid:http://www.ncbi.nlm.nih.gov/pubmed/33803995
      OpenUrlPubMed
    7. ↵
      1. Bianco ME,
      2. Josefson JL
      . Hyperglycemia during pregnancy and long-term offspring outcomes. Curr Diab Rep 2019;19:143.doi:10.1007/s11892-019-1267-6pmid:http://www.ncbi.nlm.nih.gov/pubmed/31754898
      OpenUrlCrossRefPubMed
    8. ↵
      1. Brown J,
      2. Alwan NA,
      3. West J, et al
      . Lifestyle interventions for the treatment of women with gestational diabetes. Cochrane Database Syst Rev 2017;5:CD011970.doi:10.1002/14651858.CD011970.pub2pmid:http://www.ncbi.nlm.nih.gov/pubmed/28472859
      OpenUrlPubMed
    9. ↵
      1. McIntyre HD,
      2. Catalano P,
      3. Zhang C, et al
      . Gestational diabetes mellitus. Nat Rev Dis Primers 2019;5:47.doi:10.1038/s41572-019-0098-8pmid:http://www.ncbi.nlm.nih.gov/pubmed/31296866
      OpenUrlPubMed
    10. ↵
      1. Craig L,
      2. Sims R,
      3. Glasziou P, et al
      . Women’s experiences of a diagnosis of gestational diabetes mellitus: a systematic review. BMC Pregnancy Childbirth 2020;20:1–15.doi:10.1186/s12884-020-2745-1
      OpenUrlCrossRefPubMed
    11. ↵
      1. Martis R,
      2. Crowther CA,
      3. Shepherd E, et al
      . Treatments for women with gestational diabetes mellitus: an overview of Cochrane systematic reviews. Cochrane Database Syst Rev 2018;8:CD012327.doi:10.1002/14651858.CD012327.pub2pmid:http://www.ncbi.nlm.nih.gov/pubmed/30103263
      OpenUrlPubMed
    12. ↵
      1. Ruchat S-M,
      2. Mottola MF,
      3. Skow RJ, et al
      . Effectiveness of exercise interventions in the prevention of excessive gestational weight gain and postpartum weight retention: a systematic review and meta-analysis. Br J Sports Med 2018;52:1347–56.doi:10.1136/bjsports-2018-099399pmid:http://www.ncbi.nlm.nih.gov/pubmed/30337461
      OpenUrlAbstract/FREE Full Text
    13. ↵
      1. Xie W,
      2. Dai P,
      3. Qin Y, et al
      . Effectiveness of telemedicine for pregnant women with gestational diabetes mellitus: an updated meta-analysis of 32 randomized controlled trials with trial sequential analysis. BMC Pregnancy Childbirth 2020;20:198.doi:10.1186/s12884-020-02892-1pmid:http://www.ncbi.nlm.nih.gov/pubmed/32252676
      OpenUrlCrossRefPubMed
    14. ↵
      1. Miremberg H,
      2. Ben-Ari T,
      3. 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.e1–453.e7.doi:10.1016/j.ajog.2018.01.044pmid:http://www.ncbi.nlm.nih.gov/pubmed/29425836
      OpenUrlPubMed
    15. ↵
      1. Borgen I,
      2. Småstuen MC,
      3. Jacobsen AF, et al
      . Effect of the Pregnant+ smartphone application in women with gestational diabetes mellitus: a randomised controlled trial in Norway. BMJ Open 2019;9:e030884–12.doi:10.1136/bmjopen-2019-030884pmid:http://www.ncbi.nlm.nih.gov/pubmed/31719080
      OpenUrlPubMed
    16. ↵
      1. Mackillop L,
      2. Hirst JE,
      3. Bartlett KJ, et al
      . Comparing the efficacy of a mobile Phone-Based blood glucose management system with standard clinic care in women with gestational diabetes: randomized controlled trial. JMIR Mhealth Uhealth 2018;6:e71.doi:10.2196/mhealth.9512pmid:http://www.ncbi.nlm.nih.gov/pubmed/29559428
      OpenUrlPubMed
    17. ↵
      1. Rigla M,
      2. Martínez-Sarriegui I,
      3. García-Sáez G, et al
      . Gestational diabetes management using smart mobile telemedicine. J Diabetes Sci Technol 2018;12:260–4.doi:10.1177/1932296817704442pmid:http://www.ncbi.nlm.nih.gov/pubmed/28420257
      OpenUrlPubMed
    18. ↵
      1. Yew TW,
      2. Chi C,
      3. Chan S-Y, et al
      . A randomized controlled trial to evaluate the effects of a smartphone Application-Based lifestyle coaching program on gestational weight gain, glycemic control, and maternal and neonatal outcomes in women with gestational diabetes mellitus: the SMART-GDM study. Diabetes Care 2021;44:456–63.doi:10.2337/dc20-1216pmid:http://www.ncbi.nlm.nih.gov/pubmed/33184151
      OpenUrlAbstract/FREE Full Text
    19. ↵
      1. Immanuel J,
      2. Simmons D
      . Apps and the woman with gestational diabetes mellitus. Diabetes Care 2021;44:313–5.doi:10.2337/dci20-0068pmid:http://www.ncbi.nlm.nih.gov/pubmed/33472965
      OpenUrlFREE Full Text
    20. ↵
      1. Kytö M,
      2. Strömberg L,
      3. Tuomonen H, et al
      . Behavior change Apps for gestational diabetes management: exploring desirable features. Int J Hum Comput Interact 2022;38:1095–112.doi:10.1080/10447318.2021.1987678
      OpenUrl
    21. ↵
      1. Kytö M,
      2. Koivusalo S,
      3. Ruonala A, et al
      . Behavior change app for self-management of gestational diabetes: design and evaluation of desirable features. JMIR Hum Factors 2022;9:e36987.doi:10.2196/36987pmid:http://www.ncbi.nlm.nih.gov/pubmed/36222806
      OpenUrlPubMed
    22. ↵
      1. Chan A-W,
      2. Tetzlaff JM,
      3. Gøtzsche PC, et al
      . Spirit 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ 2013;346:e7586–42.doi:10.1136/bmj.e7586pmid:http://www.ncbi.nlm.nih.gov/pubmed/23303884
      OpenUrlAbstract/FREE Full Text
    23. ↵
      1. Finnish Medical Society
      . Gestational diabetes. current care guidelines. Working group set up by the Finnish medical Society Duodecim and the Finnish cardiac Society. Helsinki: The Finnish Medical Society Duodecim, 2013. https://www.kaypahoito.fi/hoi50068
    24. ↵
      1. Grym K,
      2. Niela-Vilén H,
      3. Ekholm E, et al
      . Feasibility of smart wristbands for continuous monitoring during pregnancy and one month after birth. BMC Pregnancy Childbirth 2019;19:1–9.doi:10.1186/s12884-019-2187-9
      OpenUrlCrossRefPubMed
    25. ↵
      1. Fokkema T,
      2. Kooiman TJM,
      3. Krijnen WP, et al
      . Reliability and validity of ten consumer activity trackers depend on walking speed. Med Sci Sports Exerc 2017;49:793–800.doi:10.1249/MSS.0000000000001146pmid:http://www.ncbi.nlm.nih.gov/pubmed/28319983
      OpenUrlPubMed
    26. ↵
      1. Tiitinen A
      . Äitiysneuvolaseuranta, 2022. Available: https://www.terveyskirjasto.fi/dlk00186. Duodecim
    27. ↵
      1. Vähä-Ypyä H,
      2. Vasankari T,
      3. Husu P, et al
      . Validation of cut-points for evaluating the intensity of physical activity with accelerometry-based mean amplitude deviation (Mad). PLoS One 2015;10:e0134813–13.doi:10.1371/journal.pone.0134813pmid:http://www.ncbi.nlm.nih.gov/pubmed/26292225
      OpenUrlPubMed
    28. ↵
      1. Vähä-Ypyä H,
      2. Husu P,
      3. Suni J, et al
      . Reliable recognition of lying, sitting, and standing with a hip-worn accelerometer. Scand J Med Sci Sports 2018;28:1092–102.doi:10.1111/sms.13017pmid:http://www.ncbi.nlm.nih.gov/pubmed/29144567
      OpenUrlPubMed
    29. ↵
      1. Husu P,
      2. Suni J,
      3. Vähä-Ypyä H, et al
      . Objectively measured sedentary behavior and physical activity in a sample of Finnish adults: a cross-sectional study. BMC Public Health 2016;16:1–11.doi:10.1186/s12889-016-3591-y
      OpenUrlCrossRefPubMed
    30. ↵
      1. Husu P,
      2. Tokola K,
      3. Vähä-Ypyä H, et al
      . Physical activity, sedentary behavior, and time in bed among Finnish adults measured 24/7 by Triaxial Accelerometry. J Meas Phys Behav 2021;4:163–73.doi:10.1123/jmpb.2020-0056
      OpenUrl
    31. ↵
      1. Parak J,
      2. Korhonen I
      . Accuracy of Firstbeat bodyguard HR monitor, 2015.
    32. ↵
      1. Kuula L,
      2. Pesonen A-K
      . Heart rate variability and firstbeat method for detecting sleep stages in healthy young adults: feasibility study. JMIR Mhealth Uhealth 2021;9:e24704–8.doi:10.2196/24704pmid:http://www.ncbi.nlm.nih.gov/pubmed/33533726
      OpenUrlPubMed
    33. ↵
      1. Föhr T,
      2. Tolvanen A,
      3. Myllymäki T, et al
      . Physical activity, heart rate variability-based stress and recovery, and subjective stress during a 9-month study period. Scand J Med Sci Sports 2017;27:612–21.doi:10.1111/sms.12683pmid:http://www.ncbi.nlm.nih.gov/pubmed/27037904
      OpenUrlPubMed
    34. ↵
      1. Erkkola M,
      2. Karppinen M,
      3. Javanainen J, et al
      . Validity and reproducibility of a food frequency questionnaire for pregnant Finnish women. Am J Epidemiol 2001;154:466–76.doi:10.1093/aje/154.5.466pmid:http://www.ncbi.nlm.nih.gov/pubmed/11532789
      OpenUrlCrossRefPubMedWeb of Science
    35. ↵
      1. Ellis KJ,
      2. Yao M,
      3. Shypailo RJ, et al
      . Body-composition assessment in infancy: air-displacement plethysmography compared with a reference 4-compartment model. Am J Clin Nutr 2007;85:90–5.doi:10.1093/ajcn/85.1.90pmid:http://www.ncbi.nlm.nih.gov/pubmed/17209182
      OpenUrlAbstract/FREE Full Text
    36. ↵
      1. Ma G,
      2. Yao M,
      3. Liu Y, et al
      . Validation of a new pediatric air-displacement plethysmograph for assessing body composition in infants. Am J Clin Nutr 2004;79:653–60.doi:10.1093/ajcn/79.4.653pmid:http://www.ncbi.nlm.nih.gov/pubmed/15051611
      OpenUrlAbstract/FREE Full Text
    37. ↵
      1. Hawkes CP,
      2. Hourihane Jonathan O'B,
      3. Kenny LC, et al
      . Gender- and gestational age-specific body fat percentage at birth. Pediatrics 2011;128:e645–51.doi:10.1542/peds.2010-3856pmid:http://www.ncbi.nlm.nih.gov/pubmed/21824882
      OpenUrlCrossRefPubMedWeb of Science
    38. ↵
      1. Cox JL,
      2. Holden JM,
      3. Sagovsky R
      . Detection of postnatal depression. development of the 10-item Edinburgh postnatal depression scale. Br J Psychiatry 1987;150:782–6.doi:10.1192/bjp.150.6.782pmid:http://www.ncbi.nlm.nih.gov/pubmed/3651732
      OpenUrlAbstract/FREE Full Text
    39. ↵
      1. Rubertsson C,
      2. Börjesson K,
      3. Berglund A, et al
      . The Swedish validation of Edinburgh postnatal depression scale (EPDS) during pregnancy. Nord J Psychiatry 2011;65:414–8.doi:10.3109/08039488.2011.590606pmid:http://www.ncbi.nlm.nih.gov/pubmed/21728782
      OpenUrlCrossRefPubMedWeb of Science
    40. ↵
      1. Sintonen H
      . The 15D instrument of health-related quality of life: properties and applications. Ann Med 2001;33:328–36.doi:10.3109/07853890109002086pmid:http://www.ncbi.nlm.nih.gov/pubmed/11491191
      OpenUrlCrossRefPubMedWeb of Science
    41. ↵
      1. Williams GC,
      2. Freedman ZR,
      3. Deci EL
      . Supporting autonomy to motivate patients with diabetes for glucose control. Diabetes Care 1998;21:1644–51.doi:10.2337/diacare.21.10.1644pmid:http://www.ncbi.nlm.nih.gov/pubmed/9773724
      OpenUrlAbstract/FREE Full Text
    42. ↵
      1. Viswanath V,
      2. Morris G
      . User acceptance of information technology: toward a unified view. MIS Quarterly 2003;27:425–78.doi:10.2307/30036540
      OpenUrl
    43. ↵
      1. Kirakowski J,
      2. Corbett M
      . SUMI: the software usability measurement inventory. Br J Educ Technol 1993;24:210–2.doi:10.1111/j.1467-8535.1993.tb00076.x
      OpenUrlCrossRef
    44. ↵
      1. Van Breukelen GJP
      . ANCOVA versus change from baseline: more power in randomized studies, more bias in nonrandomized studies [corrected]. J Clin Epidemiol 2006;59:920–5.doi:10.1016/j.jclinepi.2006.02.007pmid:http://www.ncbi.nlm.nih.gov/pubmed/16895814
      OpenUrlCrossRefPubMedWeb of Science
    45. ↵
      1. Sankilampi U,
      2. Hannila M-L,
      3. Saari A, et al
      . New population-based references for birth weight, length, and head circumference in singletons and twins from 23 to 43 gestation weeks. Ann Med 2013;45:446–54.doi:10.3109/07853890.2013.803739pmid:http://www.ncbi.nlm.nih.gov/pubmed/23768051
      OpenUrlCrossRefPubMed
    46. ↵
      1. Bradway M,
      2. Pfuhl G,
      3. Joakimsen R, et al
      . Analysing mHealth usage logs in RCTs: explaining participants' interactions with type 2 diabetes self-management tools. PLoS One 2018;13:e0203202–18.doi:10.1371/journal.pone.0203202pmid:http://www.ncbi.nlm.nih.gov/pubmed/30161248
      OpenUrlPubMed
    47. ↵
      1. Broll S,
      2. Urbanek J,
      3. Buchanan D, et al
      . Interpreting blood glucose data with R package iglu. PLoS One 2021;16:e0248560–17.doi:10.1371/journal.pone.0248560pmid:http://www.ncbi.nlm.nih.gov/pubmed/33793578
      OpenUrlPubMed
    48. ↵
      1. Danne T,
      2. Nimri R,
      3. Battelino T, et al
      . International consensus on use of continuous glucose monitoring. Diabetes Care 2017;40:1631–40.doi:10.2337/dc17-1600pmid:http://www.ncbi.nlm.nih.gov/pubmed/29162583
      OpenUrlAbstract/FREE Full Text
    49. ↵
      1. Pruessner JC,
      2. Kirschbaum C,
      3. Meinlschmid G, et al
      . Two formulas for computation of the area under the curve represent measures of total hormone concentration versus time-dependent change. Psychoneuroendocrinology 2003;28:916–31.doi:10.1016/S0306-4530(02)00108-7pmid:http://www.ncbi.nlm.nih.gov/pubmed/12892658
      OpenUrlCrossRefPubMedWeb of Science
    50. ↵
      1. Service FJ,
      2. Molnar GD,
      3. Rosevear JW, et al
      . Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes 1970;19:644–55.doi:10.2337/diab.19.9.644pmid:http://www.ncbi.nlm.nih.gov/pubmed/5469118
      OpenUrlAbstract/FREE Full Text
    51. ↵
      1. Rönö K,
      2. Stach-Lempinen B,
      3. Klemetti MM, et al
      . Prevention of gestational diabetes through lifestyle intervention: study design and methods of a Finnish randomized controlled multicenter trial (RADIEL). BMC Pregnancy Childbirth 2014;14:1–11.doi:10.1186/1471-2393-14-70
      OpenUrlCrossRefPubMed
    52. ↵
      1. Parsons J,
      2. Ismail K,
      3. Amiel S, et al
      . Perceptions among women with gestational diabetes. Qual Health Res 2014;24:575–85.doi:10.1177/1049732314524636pmid:http://www.ncbi.nlm.nih.gov/pubmed/24682021
      OpenUrlCrossRefPubMed
    53. ↵
      1. Yang YJ,
      2. Kim MK,
      3. Hwang SH, et al
      . Relative validities of 3-day food records and the food frequency questionnaire. Nutr Res Pract 2010;4:142.doi:10.4162/nrp.2010.4.2.142pmid:http://www.ncbi.nlm.nih.gov/pubmed/20461203
      OpenUrlCrossRefPubMed
    54. ↵
      1. Böhm A-K,
      2. Jensen ML,
      3. Sørensen MR, et al
      . Real-World evidence of user engagement with mobile health for diabetes management: longitudinal observational study. JMIR Mhealth Uhealth 2020;8:e22212.doi:10.2196/22212pmid:http://www.ncbi.nlm.nih.gov/pubmed/32975198
      OpenUrlPubMed
    55. ↵
      1. Lane A,
      2. Mlynarczyk M,
      3. de Veciana M, et al
      . 85: real-time continuous glucose monitoring in gestational diabetic pregnancies: a randomized controlled trial. Am J Obstet Gynecol 2019;220:S68–9.doi:10.1016/j.ajog.2018.11.094
      OpenUrl
    56. ↵
      1. Wei Q,
      2. Sun Z,
      3. Yang Y, et al
      . Effect of a CGMS and SMBG on maternal and neonatal outcomes in gestational diabetes mellitus: a randomized controlled trial. Sci Rep 2016;6:1–9.doi:10.1038/srep19920
      OpenUrlCrossRefPubMed

    Footnotes

    • Contributors SBK, SH, MK, PM and GJ planned the design of the study. MK, LTM, PM and SBK planned the analysis of the data and performed the power calculation. SN, SMV and TEK were responsible of methods of dietary data collection and calculations. HS, HV-Y and IK designed the collection of physical activity data. MK and SBK wrote the draft and all authors MK, SBK, LTM, PM, GJ, SN, SMV, HS, HV-Y, IK, TEK and SH have reviewed and approved the final manuscript.

    • Funding This work was supported by Business Finland grant number 860/31/2018 (eMOM GDM).

    • Competing interests IK is a shareholder of Firstbeat Technologies and products of Firstbeat Technologies are used in the present study. These are sold on commercial basis to researchers. Firstbeat does not fund or supervise the study as an organization.

    • Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

    • Provenance and peer review Not commissioned; externally peer reviewed.