Introduction

Gestational diabetes mellitus (GDM) is defined as abnormal glucose metabolism that is first recognised during pregnancy, accounting for 5%–25% of all pregnancies.1 In 2021, the International Diabetes Federation reported that 16.7% of women had hyperglycaemia during pregnancy, 80.3% of whom had GDM, and the prevalence of GDM increased from 4% in 2010 to 21% in 2020.2 GDM not only increases the risk of preeclampsia, preterm birth, miscarriage, type 2 DM and cardiovascular disease in mothers but also may lead to fetal macrosomia, intrauterine growth retardation and future obesity, cardiovascular disease in the adulthood of infants.3–5 The increase in GDM brings a huge burden on the global economy.6 7 For example, in China, the prevalence of GDM in 2015 was 17.5%, with an economic burden of 19.36 billion yuan, accounting for 0.5% of public health spending.8 GDM is not covered by maternity insurance, and 60% of births are paid for out-of-pocket by the family, putting great financial pressure on the family.9

Risk factors for GDM include genetic predisposition and environmental factors.10 Genetic factors are strongly associated with GDM, including variants in the prolactin and melatonin receptor genes.11 12 Lifestyle in early pregnancy, including nutritional factors, has become a key element driving the development of GDM.13 Additionally, prepregnancy overweight or obesity is one of the major risk factors,14 15 along with excessive gestational weight gain, advanced maternal age, a history of GDM, family history of DM, polycystic ovary syndrome, low literacy level and low healthcare knowledge.16–18

Previous research mainly focused on glycaemic control and complications prevention after the diagnosis of GDM. However, domestic and international scholars have also begun to explore early pregnancy interventions aimed at delaying or preventing the onset of GDM in recent years, with lifestyle intervention being the primary method. Lifestyle intervention, encompassing dietary adjustment and physical exercise, have been shown to effectively mitigate the progression of GDM among high-risk pregnant women. The Gestational Diabetes Prevention Study indicated that a combination of moderate physical activity and dietary intervention reduced the incidence of GDM by 39% in high-risk pregnant women.19 A systematic review and meta-analysis involving 30 871 pregnant women demonstrated that dietary patterns such as the Mediterranean diet, dietary approaches to discourage hypertension and the Healthy Eating Index diet decreased the relative risk of GDM by 18%–35%.20 Furthermore, evidence from prospective cohort studies also suggested that a diet rich in vegetables, fruits and rice-based foods reduced the occurrence of GDM in Chinese pregnant women.21 Physical exercise has been recognised as an effective method to manage weight gain during pregnancy and enhance insulin sensitivity.22 A prospective randomised clinical trial conducted in China revealed that engaging in physical activity for at least 30 min three times a week, commencing in early pregnancy, reduced the incidence of GDM in overweight pregnant women by 45.8%.23 24 Additionally, two meta-analyses indicated that the intervention group following an aerobic, low-intensity to moderate-intensity exercise programme had a 28%–31% lower risk of GDM compared with the control group, with a discrepancy in gestational weight gain of approximately 1.1 kg.25 26 Therefore, personalised lifestyle intervention should be implemented in early pregnancy for high-risk women and sustained throughout the duration of pregnancy.

Although lifestyle intervention was commonly used in clinical practice, the prevalence of GDM in China continues rising, indicating a need for more targeted intervention among high-risk women. Traditional maternal and child healthcare heavily relies on face-to-face counselling, which faces challenges such as lack of standardisation and poor patient compliance with behaviour intervention. This makes it difficult to provide effective feedback and support. Therefore, there is an urgent need to shift from a ‘one-size-fits-all’ pattern to individualised intervention strategies that consider individual differences, including behavioural and socioeconomic factors.

With the increasing popularity and advancement of mobile internet, digital products are transforming various aspects of our lives. In the healthcare sector, digital technology is revolutionising the treatment, prevention and management of health conditions. Currently, ‘digital therapeutics’ have emerged as a highly promising field in the healthcare industry. These therapies are evidence-based treatment or intervention delivered to patients through high-quality software programmes. They primarily aim to prevent, manage or treat diseases effectively.

In particular, lifestyle intervention based on digital therapies has emerged as an important research direction for the prevention of GDM. However, despite the widespread interest in this research field, inconsistencies and qualitative differences in the results of existing studies revealed that key questions remain unanswered. These issues include how to develop efficient and feasible digital intervention strategies to minimise the risk of GDM and how to assess the applicability and effectiveness of these intervention in different population. These challenges point to the urgent need to validate and optimise the effectiveness of digital therapeutics-based lifestyle intervention programme for practical application through more rigorous study designs, larger sample size and more nuanced interventions. Based on this background, this study aims to evaluate in depth the effectiveness of an innovative digital therapeutics-based lifestyle intervention programme in the prevention of GDM in high-risk pregnant women through a multicentre, large sample size, non-randomised controlled trial to address the key issues in current research.

Digital therapy is not only complementary to traditional medical treatments but also addresses deficiencies that exist in patients, providers, and the healthcare system by automating the clinical process through artificial intelligence learning to integrate clinical knowledge graphs and expert experience. For patients, digital therapies increase accessibility and adherence to healthcare, reduce the cost of accessing care and enable patients to receive services at their homes and interact one-on-one with healthcare professionals if necessary. For healthcare providers, digital therapies save precious time by automating operations, increasing service capacity, reducing service costs and improving service efficiency. Based on these advantages, we have developed a digital therapeutics-based lifestyle intervention aimed to reducing the risk of GDM through lifestyle changes, thus providing new strategies and perspectives on the prevention of GDM in high-risk pregnant women.

MethodsStudy design

This non-randomised controlled trial based on digital therapeutics is a multicentre, prospective, open-label endpoint trial with a 7-month intervention and follow-up period. The primary focus of this study is to assess the effectiveness and implementation outcomes.

Study population and recruitment

The study population will consist of early pregnant women giving birth in six maternity and child health hospitals in Fujian, China. Eligible participants are pregnant women with risk factors for GDM who own a smartphone. The recruitment will take place in six maternity and child health hospitals located in Fuzhou, Quanzhou, Sanming, Nanping and Longyan cities in Fujian, China. Participants meeting the criteria will be recruited directly by nurses, nutritionists and physicians. The eligibility criteria are outlined in table 1. All participants will be informed about the study objectives, procedures and will choose their desired intervention type, either digital therapeutic support intervention or standard health management. This trial does not involve the collection of biological specimens.

Table 1

The eligibility and exclusion criteria of the trial

Intervention and control conditions

The intelligent medical portal used in our study consists of a smart medical portal, a participant smartphone app and wearable devices (figure 1). The smart medical portal aids in collecting baseline data, conducting intelligent assessments, making diagnoses and managing prescriptions for intervention participants. The participant smartphone app enables self-management, facilitates the collection of home monitoring information, and sends notifications and education materials. Home behaviour monitoring information data are captured using wearable devices. Researchers can extract data from the smart medical portal, participant smartphone app and wearable devices and store it in a central database. The digital therapeutics platform was developed in collaboration with software developers and researchers from Fuzhou Comvee Network & Technology, Fuzhou, China, as well as team members with expertise in nutrition, obstetrics, endocrinologist and physical activity. The development process is continuously updated and iterative to align with the needs and preferences of researchers and users. The participant smartphone app delivers a comprehensive programme comprising recommendations, goals, planing, monitoring, feedback and push notifications related to a healthy diet, physical activity, weight management, blood glucose level and blood pressure. The intervention content provided by the app is evidence based on guidelines and high-quality literature. Two versions of the participant app were developed, with participants having access to only one of the versions. Both versions facilitate the collection of baseline and outcome assessments for all participants through questionnaires.

Figure 1

Overview of the intelligent medical portal consisting of smart medical portal, the participant smartphone app and wearable devices.

Participants expressing interest in the programme will undergo eligibility screening by research team members using the smart medical portal. Written informed consent (online supplemental file 1) including information on the use and sharing of participant data with relevant regulatory authorities will be obtained. Standard procedures will be followed by trained researchers and team members in the nutrition room for measurements such as blood pressure, maternal weight, height and waist circumference. All participants are required to complete self-assessment questionnaires in the app. These questionnaires include the baseline information questionnaire, the The International Federation of Gynecology and Obstetrics (FIGO) nutrition checklist,27 the International Physical Activity Questionnaire-Short Form (IPAQ-SF),28 the Chinese Dietary Guidelines Compliance Index Questionnaire,29 the Pittsburgh Sleep Quality Index (PSQI),30 the Edinburgh Postnatal Depression Scale (EPDS)31 and the Generalised Anxiety Disorder-7 (GAD-7).32 These assessments will be used to evaluate secondary outcomes related to diet, physical activity, sleep quality, depression and anxiety, as well as to gather baseline information.

Blinding strategy

Due to the non-randomised controlled trial, blinding of both participants and researchers was impossible. However, the individuals conducting the trial for participants are different from those responsible for the assessment and data analysis. To prevent contamination between groups, a unique passcode is provided for each intervention participant. During the follow-up visit, participants are instructed not to use other digital health devices.

Intervention group

Participants in the digital therapeutics-based lifestyle intervention group will have access to the interactive application. This application aims to assist pregnant women in managing their lifestyle by promoting adequate weight gain, increased physical activity, optimal glycaemia level and sufficient nutrition for pregnancy. It provides education, self-monitoring and timely feedback to support behaviour modification. Participants can set personal goals for lifestyle changes and receive automated reminders to enter measurements. The application would then provide intelligent feedback. In addition, it can send push notifications, deliver education material and assist with follow-up management based on data calculations. All data are transmitted in real time to the hospital’s smart medical portal, where doctors can view goals and measurements and send management prescriptions to the application to promote sustainable behaviour change. The integration of the smart medical portal and the interactive application form a closed-loop management system within and outside the hospital, enhancing participants’ compliance with recommended behaviour and lifestyle changes. Subsequently, participants are asked to complete additional questionnaires in the second and third trimesters, as detailed in table 2. All measurements conducted during the follow-up visits are repeated in the second and third trimesters.

Table 2

Questionnaires and measured time points

Intervention content

Before enrolment, each participant will receive health education sessions from a nurse, focusing mainly on nutrition and physical activity advice. Following this, a nutritionist will prescribe a personalised dietary intervention, explaining food substitution and encouraging the replacement of high glycaemic index foods with low glycaemic index alternatives. Women without contraindications to exercise will be advised to engage in moderate-intensity exercise lasting 30 min for 5 days per week. Obstetricians will conduct a nutritional evaluation and book the next follow-up appointment within 2–4 weeks. Women will be required to track their weight, blood glucose, blood pressure, diet and physical activity during pregnancy. When these indicators reach critical levels, the app will issue a warning, helping to prevent emergencies. All intervention content used in the app is based on evidence-based guidelines and literature.

Control group

Participants in this group will receive standard medical care offline throughout the trial, including screening, check-ups, counselling and guidance on healthy lifestyles such as a balanced diet, appropriate physical activity and weight management until childbirth. They will be informed of the recommended weight gain range during pregnancy based on different prepregnancy body mass index (BMI) and the risks associated with nutritional metabolic disorders during pregnancy and childbirth and will be advised to attend regular antenatal visits at medical institutions. Depending on the risk of nutritional metabolic disease specific to pregnant women, tailored interventions for nutritional healthcare during pregnancy will be provided. Some pregnant women will be taught how and when to measure their blood glucose levels if abnormalities occur. Healthcare providers will offer standard medical care every 2~4 weeks. Participants will have access to the control app, which only includes recording functions for baseline information and reviews nutrition and physical activity, lacking interactive features and intelligent support.

Primary and secondary outcomes

The primary effectiveness outcomes of the trial include the incidence of GDM and GWG. GDM is typically diagnosed between 24 and 28 weeks gestation using the criteria established by the International Association of the Diabetes and Pregnancy Study Group.33 Participants are required to undergo a 75 g oral glucose tolerance test, with GDM being diagnosed if any of the following values are exceeded: fasting glucose ≥5.10 mmol/L, 1 hour glucose ≥10.0 mmol/L or 2 hour glucose ≥8.5 mmol/L. GWG is assessed through changes in maternal weight (in kilograms) and is categorised as inadequate, appropriate or excessive based on the Chinese gestational weight gain recommendation according to different Chinese prepregnancy BMI.34 35

Maternal and neonatal outcomes

The main secondary outcomes involve assessing improvement in individual lifestyle changes, nutritional diseases, implementation outcomes and other pregnancy-related outcomes. Maternal outcomes encompass changes in individual lifestyle factors such as dietary intake, physical activity, sleep quality, depression and anxiety as well as changes in measurement including blood pressure, body composition, haemoglobin A1c and blood glucose levels. Additional maternal adverse perinatal outcomes consist of gestational hypertension, preeclampsia, preterm delivery, induction of labour and caesarean delivery. Adverse outcomes among infants include macrosomia, being large or small for gestational age, having a full-term birth weight, admission to the intensive care unit, hyperbilirubinaemia, birth trauma and respiratory distress syndrome. Other nutritional diseases to be considered are anaemia, vitamin D deficiency, hyperlipidaemia and so on.

Implementation outcomes

Implementation outcomes specifically refer to the adherence of participants to the intervention. User statistics will be analysed to evaluate adherence to the application.

Self-monitor adherence

The frequency of participant logins and recordings in the app, including food diaries, weight, blood glucose and other records, is used to evaluate self-monitoring adherence to the smartphone app. The recorded days up to 24–28 weeks gestation are categorised as follows: <7 days, 8–30 days, 31–60 days and >60 days or <1 month, 1–2 months, 3–4 months and 5–6 months until delivery. Participants who use the application for more than 60 days at 24–28 weeks gestation or 5–6 months until delivery are considered to have sufficient adherence.

Diet adherence

Diet adherence among participants is assessed using the Chinese Dietary Guidelines Compliance Index questionnaire.36 This questionnaire was developed based on the Chinese Dietary Guidelines (2016) for pregnant women and the Chinese Balanced Dietary Pagoda for pregnant women. The index comprises 13 scoring components, ranging from 0 to 100 points, with scores allocated for each trimester of pregnancy. A higher index score indicates a greater, proportion of pregnant women who have reported food intake in line with recommendations. This straightforward screening tool for dietary status can effectively identify participants in need of dietary guidance and evaluate the impact of interventions on improving diet adherence in the second and third trimesters of pregnancy.37

Physical activity adherence

Physical activity adherence is defined as completing exercise at least 3 days per week.38 For pregnant women who did not have exercise habits before pregnancy, adherence is assessed by achieving physical activity goals, including meeting set goals and changing levels of physical activity. Daily point wins are reported when participants reach their daily goals to meet set goals.39 40 Evaluating changes in physical activity levels involves comparing the duration of exercise performed vs planned, such as average daily step count or physical activity time,41–44 self-reported walking and sitting time,45 and exercise time.46

Other outcome measures

Change in dietary intake measured by the Chinese dietary guidelines compliance index questionnaire from baseline to the second trimester and third trimester.

Change in physical activity measured by IPAQ-SF from baseline to the second trimester and third trimester.

Change in sleep quality measured by the PSQI from baseline to the second trimester and third trimester.

Change in depression measured by the EPDS from baseline to the second trimester and third trimester.

Change in anxiety measured by GAD-7, from baseline to second trimester and third trimester.

Sample size

The incidence of GDM will be used as the primary effectiveness outcome. In previous studies, the incidence of GDM was reduced by 25%–44% as a result of dietary intervention, physical activity intervention and combined lifestyle intervention.19 47–49 The background rate of GDM in Fujian Province is nearly 20%, and we assume a 20% decrease in the incidence of GDM. A total of 622 participants (311 in each group) are required with 80% power to detect the difference at a significance level of 0.05. Considering that approximately 15%–20% of the participants would drop out of the study, we needed to recruit 800 women (400 in each group) to participate in the study.

Data collection and management

All research data are collected through an electronic medical record system and an application designed specifically for the study. The system, including the user-side application and the physician-side diagnostic treatment system, is set up to assess accuracy and completeness of the data and to alert the researcher or participant to complement missing and incomplete data. An electronic data capture system is used for data storage and management, with multiple levels of access and password protection to safeguard data security and confidentiality. Given that participants chose the intervention group based on their personal preference, this study has a special focus on collecting detailed baseline data (eg, age, weight, BMI, medical history, lifestyle habits and mental health status) in order to compare or adjust for potential differences between groups. When analysing the intervention effect, statistical methods will be used to control for differences in baseline characteristics, such as analysis of covariance or multivariate regression modelling, to ensure the accuracy of the assessment of the intervention effect.

Patient and public involvement

Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.

Statistical methods

The independent-sample t-test, χ2 test and/or Wilcoxon rank-sum test will be used to verify the homogeneity between the groups by comparing baseline characteristics, as appropriate. Any significant differences will be regarded as confounding factors in the subsequent analyses. Paired Student’s t-test or McNemar’s test was applied to test the (change in the same group). Binary logistic regression was used to analyse the effectiveness of the digital therapeutic-based lifestyle intervention adjusted for confounding variables, including parity, age, education, income, BMI, family history of DM, previous GDM and other confounders. A linear mixed model with fixed effects will be conducted to examine the intervention on gestational weight gain, diary, physical activity, nutrition status, adherence, physiological outcomes and pregnancy outcomes at different intervention times and trimesters of pregnancy. Subanalysis of the intervention effect on the primary and secondary outcomes stratified by advanced age, parity and educational level will be performed with multivariate analysis. A p<0.05 was considered statistically significant. Analyses will be performed by using IBM SPSS software (V.20.0 or later; IBM), STATA V.15 or later (Stata), SAS software (version 9.3 SAS Institute, Cary, NC, US)) or R software (V.4.1.0 or later), as appropriate for analysis type.

Ethics and dissemination

The trial was approved by the Ethics Committee of Fujian Maternity and Child Health Hospital (approval number: 2023KY046), the Ethics Committee of Jianyang Maternity and Child Health Hospital (approval number: A202401), the Ethics Committee of Fuqing Maternity and Child Health Hospital (approval number: FY2024003), the Ethics Committee of Changting Maternity and Child Health Hospital(approval number: 202401), the Ethics Committee of Datian Maternity and Child Health Hospital (approval number: dtfy202401) and the Ethics Committee of Quanzhou Maternity and Child Health Hospital (approval number:2024(50)). All women will provide written informed consent before entering the study. This trial is registered with Chinese Clinical Trial Registry (www.chictr.org.cn) on 16 May 2023, registration number: ChiCTR2300071496. We will disseminate our findings by publishing articles in leading peer-reviewed journals. In addition, we plan to use social media and online platforms within China, such as Weibo, WeChat and Zhihu, to expand the impact of our research and to engage in dialogue with health policy-makers with the aim of translating the research findings into concrete health policies and prevention strategies.

Discussion

This article presents a detailed description of a non-randomised controlled trial, designed to evaluate the effectiveness of a digital therapeutics-based lifestyle intervention on the incidence of GDM in high-risk pregnant women. The digital therapeutics-based lifestyle intervention aims to change the health behaviour of GDM high-risk pregnant women by providing health information and lifestyle intervention through the development of a smart medical portal and smartphone app. Lifestyle intervention plays an important role in preventing GDM and a smartphone app supports a safe and convenient way for pregnant women to access health information and can be used by anywhere and anyone, especially for those who live in remote areas or have limited access to healthcare resources.

The non-randomised controlled trial will allow participants to choose interventions to their wishes that are more meaningful to them, taking into account individual differences and specific health needs. In addition, the non-randomised controlled trial can be conducted in conditions that are more closely aligned with real medical conditions, which can better reflect the actual effects of the intervention in routine medical practice. Therefore, if the digital therapeutics-based lifestyle intervention significantly helps high-risk pregnant women in preventing GDM, this method is expected to play a very useful role for both pregnant women and medical staff.

However, there are limitations to this study. First, due to the nature of a non-randomised trial, the poor comparability of the baseline characteristics needs to be adjusted through well-designed statistical analyses to assess the effectiveness of the intervention. Second, poor compliance might occur owing to the cumbersomeness of the intervention process in the intervention group. Additionally, while the smart medical technology can provide personalised advice, this personalisation may be limited and may not fully address the unique health conditions and needs of each pregnant woman.