Introduction

Gestational diabetes mellitus (GDM) refers to varying degrees of glucose intolerance that occur during pregnancy or are first diagnosed during pregnancy (Puhkala et al., 2017). GDM is a growing concern and is accompanied by disease burden and healthcare issues (Gilbert et al., 2019; Rasekaba et al., 2018). The high-risk factors associated with GDM include high maternal prepregnancy body weight, a family history of Type 2 diabetes, and previous pregnancy with glucose intolerance (Puhkala et al., 2013; Shen et al., 2019). Good control of blood glucose in GDM is important to minimize the risk of pregnancy-related complications such as premature birth, preeclampsia, and cesarean section births (Allehdan et al., 2019; Gilbert et al., 2019). Moreover, GDM has been associated with perinatal complications such as shoulder dystocia, fetal macrosomia, and neonatal death (Allehdan et al., 2019; Gilbert et al., 2019). Associations between GDM and increased risk of developing Type 2 diabetes (Rao et al., 2019; Werbrouck et al., 2019) and metabolic syndrome (MS) after delivery (Huvinen et al., 2018; Nouhjah et al., 2018) have been previously reported. Women with a history of GDM suffer from Type 2 diabetes in later life and face a seven-times-higher risk of MS and cardiovascular disease than women with no history of GDM (Hakkarainen et al., 2016; McKenzie-Sampson et al., 2018; Puhkala et al., 2017). MS is a clustering of cardiovascular disease risk factors, including dyslipidemia, hypertension, hyperglycemia, and abdominal obesity (Puhkala et al., 2017). A review study revealed that women with a history of GDM had a higher risk of developing MS than those without a history of GDM (relative risk [RR] = 2.36, 95% CI [1.77, 3.14]). Offsprings exposed to GDM in utero have a higher risk of developing MS than those not exposed to GDM in utero (RR = 2.07, 95% CI [1.26, 3.42]). Women diagnosed with GDM have an increased risk of developing MS during pregnancy (RR = 20.51, 95% CI [5.04, 83.55]; Pathirana et al., 2021). MS diagnoses are typically made at the first postpartum evaluation in accordance with the International Diabetes Federation classification (Alberti et al., 2005), with modifications made for Asian populations. A diagnosis is made if any three of the following five criteria are met: waist circumference (WC) of ≥ 80 cm, elevated blood pressure (BP; ≥ 130 or 85 mmHg), elevated TG (≥ 150 mg/dl), reduced high-density lipoprotein (HDL) cholesterol (<50 mg/dl), and elevated fasting blood glucose (FBG; ≥ 100 mg/dl).

Women at high risk of GDM are encouraged to alter certain lifestyle habits and attend regular follow-up examinations. As pregnancy progresses, the burden on pregnant women and health services increase (Hakkarainen et al., 2016; McKenzie-Sampson et al., 2018; Puhkala et al., 2017). The general prevalence of GDM is rising, with medical and insurance systems facing difficulties in coping effectively with this burden (Carolan-Olah & Sayakhot, 2019). Although interventions such as yoga, physical activity classes, lifestyle adjustments, and face-to-face nutrition counseling are currently provided to women with GDM, these interventions are often limited by time and place restrictions that make it difficult for some women to access these health resources (H. Chen et al., 2018). Thus, there is a need for a sustainable, innovative, and effective care system for GDM women that includes self-care behavior education and strengthening (Carolan-Olah & Sayakhot, 2019; Mackillop et al., 2018).

In previous studies, web-based interventions have been shown to be effective in improving lifestyle modifications; implementing blood glucose self-monitoring; achieving blood-sugar-control, maternal, and neonatal outcomes that are equivalent to the outcomes experienced by pregnant women receiving standard hospital care; and effectively reducing the rate of cesarean section (Homko et al., 2012; Rasekaba et al., 2018; von Storch et al., 2019). Web-based interventions employ a tracking system to improve self-monitoring that uses dietary logs, physical logs, reminders, and graphic progress indicators and, through peer support or real-time feedback interactivity, allow women to interact with one another and their health providers (Carolan-Olah & Sayakhot, 2019). Using this type of intervention in diabetes prevention may empower women to obtain appropriate resources and make positive decisions about lifestyle change and chronic disease control (Given et al., 2015; Homko et al., 2012; Rasekaba et al., 2018).

Over the past two decades, Taiwan’s total fertility rate has declined to one of the lowest in the world, with an average fertility rate of 1.3 children per woman (National Statistics, ROC, 2020). Consequently, developing health policies that prevent high-risk pregnancies, ensure a safe birth process, and protect the health of newborns and mothers should be prioritized. Systematic and meta-analysis appraisals of web-based interventions have been conducted for women at high risk of GDM (Rasekaba et al., 2018; Xie et al., 2020). However, only a few studies of MS and its postpartum components have been conducted in women who were found during early pregnancy to be at higher risk of developing GDM (Puhkala et al., 2013, 2017). Furthermore, long-term follow-up studies are lacking on the use of web-based interventions in preventing the development of MS in women at high risk of GDM.

The aim of this study was to examine the longitudinal effects on pregnant women at high risk of GDM of a nurse-led web-based health management program intervention that was initiated prior to 28 weeks of gestation and lasted through 6–12 weeks postpartum. Maternal anthropometric and metabolic profiles, including weight, body mass index (BMI), BP, FBG, TG levels, HDL levels, cholesterol levels, and WC, were used as the primary outcome measures. Pregnancy-related complications and neonatal outcomes were also accessed.

Methods Study Design and Setting

This randomized controlled study without blinding was conducted between February 2017 and February 2018 during regular maternity clinic visits at a medical center in northern Taiwan that delivers approximately 4,000 births per year and has nine obstetricians on staff. The trial was registered at ClinicalTrails.gov. The randomization and concealed allocation procedures were independently handled by a statistician who did not participate in this study using random allocation software (Random Allocation Software 1.0.0) block arrangement random allocation (permuted block randomization), with the number of groups set to 2, the number of samples set to 112, and the area block equal sample set to 4. The random serial numbers and groups generated by the computer were placed in consecutively coded, sealed opaque envelopes.

Participants

The inclusion criteria included (a) singleton pregnancy, (b) less than 28 weeks of gestation, and (c) having at least one of the GDM risk factors listed by the National Institute for Health and Care Excellence (2015) modified for Asian populations (i.e., > 34 years old, prepregnancy BMI ≥ 24 kg/m2, a macrosomia baby [weight ≥ 4.5 kg], history of GDM in a previous pregnancy, and family history of diabetes). Pregnant women with preexisting diabetes (Type 1 or 2), with limited mobility or inability to perform physical exercise, or < 18 years old were excluded.

G*Power Version 3.1.1 (Heinrich Heine University, Düsseldorf, Germany) was used to estimate the minimum sample size (Faul et al., 2007). An F test with three repeated measurements for two independent groups was used. According to Cohen’s (1988) rule for effect size, a sample of 70 is required to detect the differences in changes with an effect size of 0.25, a power of .80, and an alpha of .05 and, assuming a dropout rate of 20%–25%, a minimum sample size of 94 for the randomized controlled trial was needed in this study. A total of 112 participants were enrolled.

Measures

The participants in both groups filled out a questionnaire with demographic and health information at Time 0 (prior to 28 weeks of gestation). Anthropometric and metabolic measures were accessed at Time 0, Time 1 (36–40 weeks of gestation), and Time 2 (6–12 weeks postpartum). Maternal and neonatal outcome assessments were conducted at delivery.

Demographic characteristics

Demographic and personal health information with respect to height, prepregnancy bodyweight, parity, age, marital status, work status, educational level, family history of diabetes, previous history of premature birth or abortion, GDM, and preeclampsia was gathered using a self-report survey.

Maternal anthropometry and metabolic measures

To determine the effect of web-based health management on women’s outcomes, the maternal anthropometric and metabolic profiles (weight, BMI, BP, FBG, cholesterol, HDL, and TG) for each participant were evaluated. The metabolic measures for analysis after a 12-hour fast were determined. The results were obtained in a hospital setting using laboratory instruments tested by the hospital quality control team. The data were collected from the medical records at the antenatal clinic by a researcher.

Women are typically screened for GDM at 24–28 weeks of gestation by clinical order if risk factors such as advanced maternal age, previous history of GDM, and previous history of fetal macrosomia were present. The International Association of Diabetes and Pregnancy Study Groups' 75-g oral glucose tolerance test was used to diagnose GDM. The participants drank 75 g of glucose in 330 ml of water, and the samples were taken after 60 and 120 minutes and assessed in accordance with the criteria for FBG, 1-hour, and 2-hour oral glucose tolerance test plasma glucose concentrations mean values (92, 180, and 153 mg/dl, respectively) proposed by the Hyperglycemia and Adverse Pregnancy Outcome Study (International Association of Diabetes and Pregnancy Study Groups Consensus Panel et al., 2010).

Maternal and neonatal outcomes

Maternal outcomes compared the diabetic control between the groups. Weight and BMI were recorded at each visit, as was pregnancy-induced hypertension or preeclampsia, gestational age at birth, birth weight, and the proportion of babies who were large for their gestational age (> 90th percentile for gestation and gender), mode of birth, and severe perineal trauma. Neonatal outcomes of interest included birth-related injuries and neonatal intensive care unit (NICU) admission. The data were collected from the medical records at the antenatal clinic by a researcher.

Nurse-Led Web-Based Health Management Intervention and Control Development of a nurse-led web-based health management program

The development of the nurse-led web-based health management program was guided by discussion with an obstetrician, gynecologist, dietitian, sports coach (who provided pregnancy exercise guidance), nurse, and information technology engineer. The analysis, design, development, implementation, and evaluation model of system design (Reinbold, 2013) was applied to create the nurse-led web-based health management program (Figure 1). Twenty-two women with GDM were recruited using purposive sampling, and data were collected using in-depth, semistructured and open-ended interviews to explore the design needs of web-based health management. Themes were then mapped onto the web design (Table 1). The evaluation involved a two-stage process. In the first step, we invited nursing information experts and obstetrics and gynecology experts with clinical practice experience with GDM and MS (n = 5) to review the content relevance, wording clarity, and style design. The content validity index values were .97–.99. User evaluations were based on real case scenarios to simulate how women would use the system in a self-management process at home. We invited pregnant women with high risk of GDM of 30–40 years old (n = 10). They measured weight and BP, kept a diet and exercise log, recorded in paper logbooks, and input their personal health information into the website for 7 days. To confirm the consistency and stability of paper and electronic records, the intraclass correlation should be between .81 and .96. The researcher also interacted with the testers in 7 days to check network stability, operational convenience, and information content. The users evaluated the content relevance, wording clarity, and style design, finding the content validity index to be .91–.99. After evaluation, we made several modifications based on the experts’ and users’ suggestions. For example, the normal range of various metabolic indicators of the health plan were provided to help women set clear goals; embedded advertising was removed from videos and hyperlinks; and details for specific data upload, dietary, and exercise records were provided.

Figure 1:

Diagram Depicting the Five Steps of Analysis, Design, Development, Implementation, and Evaluation Model.

Table 1 - Mapping of Themes Onto Website Constructs Major Theme Website Module 1. Membership and user-friendly website interface 1. Unique account and password to log in
2. Website manual 2. Access to reliable information and resources Healthy lifestyle information
(1) What is gestational diabetes?
(2) How to avoid becoming diabetic?
(3) What to do if the oral glucose tolerance test check is abnormal?
(4) Healthy eating and exercise in GDM.
(5) Do I need to follow up after delivery?
(6) Life modification for GDM. 3. Provision of tailored and quick-link health information 1. Health plan in GDM: setting goals for blood sugar and metabolic indicator control levels
2. Weekly pregnancy and fetus changes: system automatically provides customized information on physical changes in the mother and fetus 4. Access to peer support Social networking group
(1) Online discussions, browse previous discussions
(2) Interact with other participants or the researchers on Facebook and LINE groups to provide and receive emotional support 5. Self-monitoring and learning tools 1. Maternal health log
(a) diet diary
(b) exercise log
(c) recommendations for recipes and excise
2. Maternal notepad
(a) my health data
(b) pregnant women’s body changes
(c) baby growth
(d) pregnancy highlight for this week
(e) fetal movements
3. Reminder service: motivated through e-mails, Facebook, and LINE messages

Note. GDM = gestational diabetes mellitus.

Intervention group

The participants in the intervention group received the standard clinic-based education class and were invited to use the web-based health management program. Each participant had a unique account and website log-in password, which was encrypted using Secure Socket Layer. The website was enabled to count the number of log-ins by each participant and record user usage patterns. The system determined course participation, self-monitoring (records related to the diet diary and exercise log), and satisfaction with online health information. Each participant was required to log into the system at least once per week to fill in their weight measurements and complete the diet diary and exercise log. Reward points were given to participants every time they completed this task, and participants could redeem these points for gifts (e.g., maternity and baby products). This reward mechanism encouraged participants to record information frequently and develop self-monitoring and management competencies. The intervention also included one-on-one, 20- to 30-minute LINE consultation sessions after each blood sample report that facilitated the provision of tailored health education, reinforced strategies, and elicited participant feedback.

Control group

Women in this group attended standard clinic-based care sessions. Women diagnosed with GDM were provided with a face-to-face health education program related to diabetes (same as the intervention group, conducted by the same educator). This program comprised diet control and guidance related to exercise during pregnancy and maintaining a healthy lifestyle, with each session lasting approximately 1 hour. Because all of the participants were covered by Taiwan’s national health insurance, participants followed the conventional schedule of examinations during pregnancy. Specifically, they received 10 examinations, with biweekly and weekly examinations conducted at 32–36 weeks and after 36 weeks, respectively.

Procedure

After institutional review board approval from the participating hospitals, three obstetrics and gynecological nurses with more than 5 years of respective experience assessed the eligibility of potential participants and obtained informed consent. These nurses were trained by the same researcher to ensure their understanding of the eligibility. To ensure the consistency and quality of the intervention, the intervention was carried out by one researcher. After the initial assessment, the qualified participants were transferred to the researcher to obtain consent. The researcher opened the envelopes in order and assigned the participants to the intervention group or control group according to the groups indicated on the envelope. The participants were randomly assigned to the intervention group or control group, and all of the participants received standard maternity care. With the intervention group, the researcher took approximately 15–30 minutes instructing each participant on using the website and setting up a personal account. The participants were then provided with the URL link or QR code and log-in password for the website and instructed that they could use the website at any time during the 6-month study period. To avoid interaction between the two groups, only the intervention group was permitted to log in with their account and password on the web-based health management program and to view/use the information.

The participants in the intervention group were required to log into the website at least once per week to complete dietary, exercise, and self-management information. Their health status levels, weight, and postpartum WC were also measured. The recruitment procedure is shown in Figure 2.

Figure 2:

Consolidated Standards of Reporting Trials Flow Diagram.

Data Analysis

Data analysis was performed using SPSS/PC for Windows 20.0 (IBM, Inc., Armonk, NY, USA). A t test and a chi-square test were conducted to analyze the demographic variables of the participants and determine whether differences in essential attributes were detected between the groups. The data related to MS indicators were processed using an independent-sample t test and a chi-square test to determine whether differences existed between the intervention and control groups. The significance level was set at a two-tailed p value of .05. Generalized estimating equations were applied to analyze intervention effectiveness. An intervention effectiveness evaluation was conducted after the covariates were controlled to assess the levels of MS indicators prior to and after the intervention. The aforementioned data were processed using intention-to-treat analysis.

Ethical Considerations

This study was approved by the regional ethics board in Taiwan (Chang Gung Memorial Hospital IRB No. 105-4129C). The researcher explained the purpose of the study, and all potential participants provided written consent prior to enrollment. The participants were informed they could withdraw during the study at any time for any reason without explanation.

Results Demographic Characteristics

The mean ages of participants in the intervention and control groups were 35.71 (SD = 4.31) and 35.82 (SD = 4.28) years, respectively. Forty-five and 38 participants in the two groups, respectively, completed the follow-up test. The descriptive analysis results are shown in Table 2. No significant difference between the groups in terms of sociodemographic and clinical characteristics was identified.

Table 2 - Baseline Characteristics and Components of Metabolic Syndrome, by Group (N = 112) Variable Intervention (n = 56) Control (n = 56) p n (%) n (%) Age (years), M ± SD 35.71 ± 4.31 35.82 ± 4.28 .355 Education .562  Less than high school 7 (12.5) 14 (25.0)  College 12 (21.4) 11 (19.6)  University 29 (51.8) 23 (41.1)  Master's degree or more 8 (14.3) 8 (14.3) Work status .510  Full time 32 (57.1) 37 (66.1)  Part time 6 (14.3) 5 (8.9)  None 16 (28.6) 14 (25.0) Marital status .495 a  Married 56 (100) 54 (96.4)  Divorced 0 (0.0) 2 (3.6) Gravidity .313  Primipara 35 (62.5) 29 (51.8)  Multipara 21 (37.5) 27 (48.2) Premature or abortion in any previous pregnancy 17 (30.4) 21(37.5) .162 GDM in any previous pregnancy 3 (5.4) 5 (8.9) .118 a Family history of DM 26 (46.4) 27 (48.2) 1.000 Preeclampsia in any previous pregnancy 3 (5.4) 2 (3.6) 1.000 a Oral glucose tolerance test  Fasting glucose (≥ 92 mg/dl) 3 (8.1) 2 (5.9) 1.000 a  1-hour glucose (≥ 180 mg/dl) 2 (5.4) 5 (14.7) .248 a  2-hour glucose (≥ 153 mg/dl) 3 (8.1) 6 (17.6) .672 a BMI (kg/m2) b, M ± SD 24.39 ± 5.45 25.15 ± 5.05 .446  18.2–23.9 31 (55.3) 27 (48.2) .385  24–26.9 9 (16.1) 15 (26.8)  ≥ 27 16 (28.6) 14 (25.0) Waist circumference (cm) b, M ± SD 76.20 ± 7.81 76.84 ± 7.83 .664 Waist circumference (≥ 80 cm) b 19 (33.9) 24 (42.9) .944 Fasting glucose (mg/dl), M ± SD 80.79 ± 15.5 79.80 ± 14.0 .823 Fasting glucose (≥ 100 mg/dl) 3(5.4) 5(8.9) .716 a Systolic blood pressure (mmHg), M ± SD 125.89 ± 20.7 126.50 ± 21.6 .880 Systolic blood pressure (≥ 130 mmHg) 21 (37.5) 21 (37.5) 1.000 Diastolic blood pressure (mmHg), M ± SD 75.61 ± 13.0 75.09 ± 12.3 .829 Diastolic blood pressure (≥ 85 mmHg) 9 (16.1) 12 (21.4) .333 HDL cholesterol (mg/dl), M ± SD 71.52 ± 17.1 70.55 ± 13.8 .743 HDL cholesterol (< 50 mg/dl) 4 (7.1) 4 (7.1) 1.000 Triglycerides (mg/dl), M ± SD 183.89 ± 70.84 200.63 ± 109.4 .132 Triglycerides (≥ 150 mg/dl) 36 (64.2) 40 (71.4) .544 Total cholesterol (mg/dl), M ± SD 216.32 ± 47.8 222.61 ± 46.9 .494 Total cholesterol (≥ 200 mg/dl) 37 (66.1) 37 (66.1) 1.000 Metabolic syndrome using IDF criteria 11 (19.6) 14 (25.0) .463

Note. GDM = gestational diabetes mellitus; BMI = body mass index; HDL = high-density lipoprotein; IDF = International Diabetes Federation.

a Fisher’s exact test; b Prepregnancy.

Effect of Intervention on Risk Factors of Metabolic Syndrome

After the intervention, at Time 1, the levels of diastolic BP (β = −4.98, p = .025) and TG (β = −33.69, p = .020) were significantly lower in the intervention group than the control group. Similarly, at Time 2, the intervention group had more favorable TG (β = −21.21, p = .036) and total cholesterol (β = −41.25, p = .006) levels than the control group (Table 3). As shown in Table 4, the weight gain differences between the groups were nonsignificant. However, BMI during pregnancy increased by 4.07 kg/m2 (95% CI [3.7, 4.4]) in the intervention group and 4.75 kg/m2 (95% CI [4.2, 5.3]) in the control group (p = .025). At Time 2, the BMI increase was 1.24 kg/m2 (95% CI [0.9, 1.6]) in the intervention group and 1.93 kg/m2 (95% CI [1.3, 2.5]) in the control group (p = .045). The intervention group had seven fewer participants with a WC of ≥ 80 cm than the control group (p = .042).

Table 3 - Generalized Estimating Equation (GEE) of Baseline and Follow-Up Assessment of Changes in Metabolic Syndrome Markers in Two Groups (N = 112) Characteristic Intervention (n = 56) Control (n = 56) Group × Time Interaction Effect in the GEE Model Mean SD Mean SD ß 95% CI p Systolic blood pressure  Time 0 125.89 20.74 126.50 21.57  Time 1 120.18 13.15 126.48 19.97 −5.69 [−12.47, 3.48] .242  Time 2 120.60 16.78 125.92 21.04 −4.71 [−12.61, 4.03] .101 Diastolic blood pressure  Time 0 75.61 12.97 75.09 12.32  Time 1 71.16 9.35 75.59 12.46 −4.98 [−9.33, 0.63] .025*  Time 2 73.20 10.38 75.39 13.58 −2.73 [−7.98, 2.68] .307 Fasting blood glucose  Time 0 80.79 15.50 79.80 14.01  Time 1 77.64 7.91 75.73 10.40 1.18 [−4.11, 6.48] .662  Time 2 81.36 7.49 78.42 10.69 2.24 [−4.19, 8.67] .495 Triglyceride  Time 0 183.89 70.84 200.63 109.41  Time 1 199.88 89.12 236.91 99.10 −33.69 [−77.38, 10.01] .020*  Time 2 178.62 94.23 221.79 140.46 −41.25 [−89.04, −23.46] .006** Cholesterol  Time 0 216.32 47.82 222.61 46.87  Time 1 231.74 52.86 248.95 43.43 −10.36 [−28.34, 7.62] .259  Time 2 213.62 52.06 241.45 46.29 −21.21 [−34.69, 2.26] .036* High-density lipoprotein  Time 0 71.52 17.11 70.55 13.78  Time 1 70.36 16.33 69.09 15.88 0.47 [−4.77, 5.70] .861  Time 2 68.31 16.07 70.18 16.74 −4.73 [−11.16, 1.70] .149

*p < .05. **p < .01. ***p < .001.

Table 4 - Baseline Data and Changes From Baseline Variable Prepregnancy Mean Weight Change From Baseline