Mobile health applications in the management of hyperglycemia in pregnancy: a mini-review of current tools and future perspectives

Abstract

Hyperglycemia in pregnancy (HIP), encompassing gestational diabetes mellitus (GDM) and pre-gestational diabetes mellitus (PGDM), constitutes a growing clinical challenge, impacting approximately 23 million live births annually worldwide and conferring substantial maternal and fetal risks. This mini-review evaluates mobile health (mHealth) applications for HIP management, focusing on glycemic monitoring, nutritional interventions, physical activity promotion, insulin dose titration, and postpartum surveillance. Reviewed applications facilitate data collection from glucometers and continuous glucose monitoring systems, deliver graphical analytics, tailored recommendations, artificial intelligence-driven coaching, and secure remote data exchange with healthcare professionals, thereby increasing patient adherence, glycemic regulation, and perinatal outcomes, including reductions in HbA1c, neonatal birthweight, and caesarean section rates. Key benefits include enhanced patient empowerment, streamlined telemedicine, and psychosocial support, supported by trials demonstrating superior glycemic indices and reduced hyperglycemic excursions. Nonetheless, challenges persist, including heterogeneous clinical validation, socioeconomic-digital disparities, data security imperatives, and the absence of comprehensive integrated platforms. Future perspectives focus on developing digital systems that combine sensors, artificial intelligence, and online clinics. These systems aim to improve coordinated care for women with HIP, make treatment more effective, enhance user satisfaction, and help healthcare providers use resources efficiently.

1 Introduction

Diabetes in pregnancy is referred to any kind of hyperglycemia in pregnancy (HIP) and it includes gestational diabetes (GDM), in which hyperglycemia is diagnosed in pregnancy, and pre-gestational diabetes (PGDM) in which hyperglycemia is present before conception such as type 1 diabetes (T1DM), type 2 diabetes (T2DM) and other rare forms of diabetes (1, 2). Each of these situations increases the risk of maternal and fetal adverse outcomes and requires a tailored approach, but all require optimizing glucose control, monitoring glucose values, controlling gestational weight gain (GWG), and improving lifestyle. Moreover, intra-uterine exposure to hyperglycemia confers a higher risk of metabolic and cardiovascular diseases to offspring. According to the International Diabetes Federation (IDF), it is estimated that 23 millions of live births in 2024 faced some form of HIP. Of these, the majority (nearly 80%) were complicated by GDM, while 11% were complicated by PGDM and the remaining pregnancies were complicated by diabetes first diagnosed in pregnancy (3, 4).

Pregnant women with diabetes often use mobile devices to obtain information about their health status and to manage the care of their pregnancy. In this context, mobile Health (mHealth) may offer new opportunities for better management of HIP. mHealth is a component of eHealth. According to the Global Observatory for eHealth (GOe) of the World Health Organization (WHO), it is defined as “medical and public health practice supported by mobile devices” (5). The term mHealth most commonly refers to the use of devices, such as mobile phones, tablet computers, personal digital assistants, and wearable devices such as smart watches. It improves health services (disease prevention, health promotion, and management of medical conditions), gives information on clinical practice, and helps data collection. mHealth relies on the use of mobile applications (apps), such as those that use data from medical devices, apps to track wellness, and apps to manage and improve therapy adherence (6, 7). According to the 2020 ADA/EASD consensus report (6, 7), diabetes apps are divided into the following categories: dietary advice/monitoring, physical activity, glucose monitoring, insulin titration, and insulin delivery. When an app acts as a medical therapy, validated through clinical trials (RCTs) and approved by regulatory authorities, it is classified as digital therapeutics (DTx) (7). Not all mHealth software is DTx. Currently, there is not uniformity in the validation pathways of different mHealth apps. Some are fully reviewed and approved by regulatory authorities, while others are based on weak clinical evidence. This is why, according to the 2025 Standards of Care of the American Diabetes Association (8) there are insufficient data to provide recommendations for specific apps for diabetes management, education and support, in the absence of clinical trials and validation by regulatory bodies (9).

In this mini-review, we aim to summarize the main types of available mobile apps for pregnant women with HIP and to highlight benefits, limitations, and future perspectives for the use of mHealth applications in pregnancies complicated by diabetes.

2 Methods

An extensive search was conducted across PubMed/Medline, EMBASE, and ClinicalTrials.gov. In PubMed, the search strategy was developed using the string: [(mHealth) AND (pregnancy)] AND (diabetes), combining core concepts related to both gestational OR pregestational diabetes. A separate additional search was made on keywords such as “smartphone”, “web-based”, “wearable devices”, “telemedicine” AND “apps”. We then extended the search to include the postpartum period. We manually reviewed references to find further studies. We selected studies published from 2012 to 2025, with no language restrictions. Since this is a narrative review, we applied no journal or publication type limits to ensure a broad overview of recent evidence.

3 Potential application area of mHealth applications in pregnancies with diabetes

Diabetes in pregnancy requires a multidisciplinary approach in order to improve metabolic control, to guarantee a correct nutritional therapy, to encourage physical activity, and to monitor obstetrical issues. Mobile technology can help in different areas of HIP management. Table 1 lists the characteristics of the main mobile applications and technologies designed for diabetes in pregnancy. Each mobile application for diabetes in pregnancy has at least one of the following features:

• – Collection of glucose data: from glucometers or continuous glucose sensors, automatically or with manual data entry;

• – Graphs and reports to be shared with the physician in order to facilitate communication between women and healthcare providers (HCPs);

• – Analysis of data: integration of information from glucose data, diet, physical activity and others in order to give customized advice;

• – Decision support system for therapies adjustment;

• – Tailored alarms;

• – Educational function: information about pregnancy management, therapies and diet, diabetes acute complications;

• – Coaching: virtual coaching with chatbot or artificial intelligence (AI) algorithms, sometimes direct connections with HCPs;

3.1 Nutrition and lifestyle applications

Nutritional therapy is fundamental in both women with GDM and with pre-existing diabetes, to balance macronutrients intake and to control glucose values. Moreover, women with PGDM frequently use carbohydrate (CHO) counting to optimize insulin doses at meals. According to the 2020 ADA/EASD consensus report (6), apps for nutritional advice offer databases of CHO, fat, protein, and energy content useful to plan meals or to decide rapid-insulin dosage, with or without CHO counting. There are no specific apps for CHO counting in pregnant women with diabetes, so women are advised to use common apps for carbo-counting, some with implementation with an AI algorithm (i.e., Carbs and Cal, MySugr, MyFitness Pal, Foodily, GoCARB). Nutritional advice for a proper diet is often integrated into specific apps for diabetes in pregnancy [i.e., My Sweet Gestation (10), Pregnant with Diabetes (11), GDm-Health (12), Pregnant + (13)]. For example, use of The Pregnant+ App, which provides nutritional advice, allowed participants to improve their dietary score from baseline to 36 weeks of gestation. Results showed indeed no significant difference between the intervention (Pregnant+ app + usual care) and the control (usual care only) in the healthy dietary score (HDS-P+) (14). Nutrition information can also be obtained from generic apps for pregnant women without diabetes, paying attention to choose the ones that promote a balanced diet and can be set up with personalized goals.

Nutritional therapy and physical activity are fundamental to gaining the right amount of gestational weight, as gaining more weight than suggested by the Institute of Medicine Guidelines is associated with adverse maternal and infant outcomes (15). Physical activity in pregnancy, unless there are obstetrical contraindications, has positive effects. It is recommended that pregnant women with diabetes exercise regularly during pregnancy, focusing on moderate-intensity aerobic activity (1). In women with GDM, regular physical activity is associated with improved glucose control and reduced insulin therapy (1). Smartphone technology has been demonstrated to help improving adherence to physical activity (16). Apps can support women to obtain information on the importance of physical activity, examples of physical exercises, personalized parameters and a physical activity tracker. For example, MyGDMoving (17) is an app designed for pregnant women with diabetes that illustrates the benefits of appropriate physical activity during pregnancy, contains interactive sections that require users to enter data related to their condition and includes questionnaires related to physical activity during pregnancy (IPAQ – International Physical Activity Questionnaire). All information can be shared with the diabetes care team. GDM-Management App (18) allows the woman to enter GDM and lifestyle data, including food intake, physical activity, and weight data, to receive tailored suggestions aligned with clinical recommendations. The StartSmart (19) also generates evidence-based recommendations tailored to each woman’s risk and protective factors. Some apps have a direct connection with activity trackers. For example, Mobiguide (20) includes an accelerometer to detect the level of physical activity. Baby Steps (21), a recent randomized controlled trial (RCT), gives importance to interactive bite-sized information resources in addition to face-to-face sessions, promoting competition among participants, reviewing the daily step count through a direct connection between the app and the activity tracker, thereby letting the women be able to set goals and record information such as body weight. At 48 months, the intervention group showed a non-significant between-group difference of approximately 1000 additional steps/day, but a significant improvement in self-efficacy for exercise was observed at 12 months (22). Other apps were designed for the general population without diabetes, but can be adapted for women with HIP, such as Pears App (16), originally designed for overweight and obese pregnant women. In 2014 a study was published using the e-Mom Roc app (23). It was based on an electronic-intervention, including website and mobile phone components, named e-Moms of Rochester (e-Moms Roc). Pregnant women in the intervention arm used the weight gain tracker and the diet and physical activity goal-setting tools. The majority preferred the weight gain tracker, only 40% the diet and physical activity goal-setting tools. Even if it was conducted among non-diabetic women, the results highlight how diet goals are frequently difficult to achieve. One year later, another intervention involving the development of an mHealth app, the Eating4Two app (24), was published. It aimed to support women through a GWG calculator, providing general dietary information. Pregnant, not diabetic, women especially appreciated having unlimited access to nutritional information.

3.2 Glucose monitoring and insulin therapy applications

During pregnancy with diabetes, it is of paramount importance to follow regular antenatal visits and to make constant adjustments to nutritional therapy or to insulin therapy. Especially after the COVID-19 pandemic, remote sharing of glucose and other clinical data with HCPs has become increasingly common. Telehealth visits, when combined with in-person visits, may improve outcomes, especially in women with GDM (1). In this context, mobile technology can integrate glucose and other clinical data and transmit them to HCPs. Some apps can be used as personal diaries where women can keep information about glucose values, food intake, insulin dosages, weight changes [i.e., MODIAB-web (25), GDM-Management (18), GDm-health (12, 26), Pregnant + (13), myDiabby (27)]. Some glucometers can directly send via Bluetooth capillary blood glucose data to the app (i.e., One Touch Verio, Contour Next, Beurer GLU50evo) and store them in a dedicated cloud. Other apps allow the patient to enter glucose data manually (i.e., MySugr) and share them with HCPs. Apps for glucose data storage often includes the “add note function” that can improve the patient/doctor relationship, because patients could use this function to express emotion and even convey complex communicative intentions (28). Other apps include color range indicators, text messages, or other systems to detect out-of-range patterns between visits, empowering patients to self-manage their disease (29, 30).

Continuous Glucose Monitoring (CGM) should always be offered to women with T1DM in pregnancy and can also be considered in women with GDM or T2DM during pregnancy (1, 31). Glucose values from sensors are displayed in mobile applications, such as dedicated apps provided by sensor producers (i.e. Dexcom Clarity, Carelink) or apps designed for the downloading of different brands of sensors (i.e. Tidepool, GLOOKO). According to the 2020 ADA/EASD consensus report (6, 7), glucose monitoring apps track glucose data and graphically display glucose levels to assist the patient and the healthcare team in managing glucose control. CGM was proven to be the most useful sensor compared to other physical activity sensors (bracelet, hip-worn sensor, and electrocardiography sensor), especially to learn associations between glucose and nutritional intake (32).

International guidelines suggest that insulin should be used to manage T1DM in pregnancy and that it is preferred for the management of T2DM in pregnancy and GDM (1, 31). Insulin therapy requires frequent dose adjustments, especially during pregnancy when glucose needs changes rapidly from week to week. In this context, insulin titration apps can help persons with diabetes calculate basal, prandial, and correction insulin doses. Many insulin titration apps have been developed for T2DM and T1DM patients (i.e., Voluntis Insulia, WellDoc Blue Star, Diabeo, Hygieia D-Nav, Dario Health, Vitadio) and are associated with improvement in HbA1c and time in range (TIR). For example, Diabeo is an app, classified as DTx, provides support in calculating the dose of basal insulin and rapid-acting insulin for people with T2DM and T1DM. The use of the Diabeo system on a smartphone combined with biweekly teleconsultations was effective in reducing HbA1c by 0.91% over six months in a cohort of people with T1DM. Furthermore, when combined with standard care with in-person visits at the usual times, it led to a reduction in HbA1c of 0.67% (33). The app has also been proven effective in supporting patients with T2DM and physicians in the initiation of basal insulin. In fact, the group of patients with T2DM who used Diabeo at the start of basal insulin therapy were able to more easily achieve adequate fasting capillary blood glucose levels and higher insulin doses without severe hypoglycemia (34). In the setting of diabetes in pregnancy, some apps can support women in dosing calculation, even at meals with a bolus calculator. TreC-Diabetes System app (35) is an app designed for T1DM pregnant women from the Italian Trentino Region that guarantees constant connection between women and HCPs, also in rural areas. It includes a decision-making system supported through features such as a carbohydrate-counter and an insulin bolus calculator. GDm-Health mobile app (12, 26) was designed to facilitate transmission of glucose data to HCPs who receive an alert if values were out of range and could consequently send messages to modify insulin doses. Also, other apps such as GlucoseBuddy (36) or myDiabby (27) facilitate communication between women and physicians to receive recommendations on insulin adjustments. A Chinese experiment involved the figure of pharmacists which helped in the interactive platform on CMC app for the communication with patients, suggesting insulin dosage adjustments (with endocrinologists’ contribution) and giving recommendations on the management of hyperglycemia and hypoglycemia events (37). In the group of apps for insulin therapy, it is worth mentioning the insulin delivery apps of insulin pumps or smartMDI that collect and display data and provide a decision support system, and the apps of automated insulin delivery systems (AID) that connect insulin pump and glucose sensors through a specific algorithm (i.e., Medtronic 780G, CamAPS, Control IQ). These apps are often used by women with T1DM in pregnancy. It is important to customize these apps with appropriate glucose targets for pregnancy according to local and international guidelines, as the standard glucose targets are those of the Ambulatory Glucose Profile (AGP) for the non-pregnant population.

3.3 Applications for post-partum follow-up

Women with a history of GDM are at increased risk of developing T2DM and cardiovascular diseases later in life (38), and their offspring are prone to suffer from metabolic disorders (39). The risk of developing T2DM can be reduced by 58% with lifestyle modification (40), but it is usually difficult to give adequate support to women in the post-partum period, when follow-up oral glucose tolerance test should be performed (41, 42). It is also challenging to engage women in lifestyle modification because they need to take care of a newborn, face emotional distress, and have more family and work demands (43). As women after delivery rely on online resources to gather health information (44), mobile apps can be a valuable tool to engage women post-partum. The Health-e Mums app used an evidence-based intervention that provides health coaching materials, diabetes screening reminders, structured goals, personalized automated feedback on body weight, diet, and physical activity progress (45). The app was found to be functional and useful to engage in lifestyle behaviors change and regular diabetes screening. The Smartphone App to Restore Optimum Weight (SPAROW) trial was an RCT designed to investigate the efficacy of a smartphone app in restoring optimal weight following delivery in women with GDM (46). In the intervention group, women used the Nutritionist Buddy (nBuddy) app, recording data about weight, meals, and activity and having web-based interaction with a team of dieticians, physiotherapists, and occupational therapists. While controls received standard care with routine postnatal visits. The women in the intervention group reached an optimal weight more frequently at 4 months control and reported healthier behaviors and lower caloric intake (46).

4 Benefits of mHealth applications in pregnancies with diabetes

The use of mHealth apps in pregnancies with diabetes has been proven effective in improving clinical outcomes. According to the systematic review of Eberle and colleagues (47), mHealth apps contribute to lower HbA1c values, lower fasting and 2-hour after-meal blood glucose levels, and lower mean blood glucose levels. Moreover, they demonstrated a trend towards better patient compliance, lower neonatal birth weight, and lower rate to neonatal intensive care units, and they reported more vaginal deliveries in women who used apps. Better glucose control may also be derived from the diabetes self-management support associated with mHealth apps use (48) and the enhanced women’s compliance with glucose monitoring and treatment (49). The integration of digital nudging strategies and structured self-monitoring may enhance women’s awareness of their metabolic profile, lifestyle choices, and therapeutic management, particularly during the initial weeks post-diagnosis, which represent the most critical period due to the impact of transforming a joyful and intimate event into a highly medicalized pathway. This approach could effectively merge digital health with behavioral medicine.

Diabetes in pregnancy-specific apps can provide time – and cost- efficient tailored interventions for better management (50, 51), especially when apps have a high degree of personalization. Applications can be a valuable part of telemedicine and this system can streamline health interventions enabling better glucose control and lower risk of maternal and fetal complications in women with GDM (52). Women perceive apps as time-saving as they give the possibility to receive medical care from home (48). The lack of in-person interaction is not an issue as women attribute to mHealth apps a function of emotional support (48).

5 Limitations of mHealth applications in pregnancies with diabetes

Mobile phones are now widespread and constantly connected to the internet, allowing the use of complex apps. But it is necessary to consider that women with low income or deprivation may not have free access to the internet. Moreover, the health and digital literacy of women should be taken into account while developing mHealth apps. At present only a few app developers consider health literacy during the development phase or during the outcome evaluation phase (50).

Women complain about the lack of some important features in apps dedicated to the management of HIP. For example, the lack of enough didactic information, direct connections to glucometers, or direct communication with HCPs. In summary, the main limitation perceived by women is the lack of an “all-in-one” technology (48). In the future, AI will maybe contribute to designing more complete and personalized apps (53). In China, doctors have already tried to merge educational materials and clinical data (glucose values, blood pressure and weight) with the WeChat platform, a telemedicine system based on smartphones for women with gestational diabetes mellitus. This all-in-one system succeeded in improving glucose control (54).

Care in virtual modality is not always considered trustworthy as face-to-face care (48). Women need to be sure that they are receiving complete and updated care, also in virtual mode. Social influence, perceived system quality, and perceived information quality are fundamental to building a good trust in the app (55). A user-centered design approach in developing mHealth apps may help elaborate more trusted and friendly apps for the women. Also, involving HCPs in the developing phase may help increase the use and the impact of mHealth apps in clinical outcomes (48).

The safety and privacy of the data used in the app should be guaranteed, not only for the woman but also for the unborn child. In the USA, the Food and Drug Administration (FDA) strictly regulates medical devices with the “Digital Health Innovation Action Plan” (56). While in Europe there is the consortium “Digital Health Europe” to support the digital health transition (57). Regulatory gaps persist as many apps lack validation in accordance with the most recent ADA Standards.

Equity issues should be considered. People with low incomes may lack digital devices or internet connection and face literacy barriers. Moreover, the language of mHealth apps is usually English, other languages may be underrepresented, and this may limit the use in a multicultural context.

Regarding the studies included in this narrative review, many of the included papers are limited by small sample sizes as they report single-center experiences. This may lead to a reduction of the statistical power of the studies and to a lack of reproducibility or also increase the impact of confounding factors. Despite this, the results reported could serve as a basis for more structured research. Similarly, while the results of the few available RCTs differ in statistical significance, the trend towards the utility of implementing app-based tools is confirmed.

6 Future perspectives

A digital diabetes ecosystem is a system in which therapies, devices and digital tools are integrated to support people with diabetes in their daily life, reducing the burden of disease management (58). All these digital solutions are integrated in the digital diabetes virtual clinic (59). Diabetes in pregnancy can be part of the diabetes virtual clinic where pregnant women share data from glucose sensors and wearable devices with HCPs and receive support in pregnancy management (Figure 1). The digital ecosystem is open to integration with different kinds of devices. Glucose sensors and glucometers are necessary to detect glucose data and they can be integrated with other wearable devices such as activity trackers, portable blood pressure cuffs and pulse oximeters. Home digital devices can detect values about weight, body temperature and fetal wellbeing, with portable ultrasound or nonstress test devices. All data must be transmitted securely and in a standardized model (60). mHealth apps can serve as data loggers and diaries or as predictive models (with or without AI implementation), supporting diagnosis and management by HCPs who receive data with appropriate medical interface (60). Personal targets appropriate to pregnancy should be set and alarms can be helpful for women and HCPs to identify critical situations. As health systems are getting more and more digital, the virtual clinic can be implemented with data from electronic medical records and national registries. With this digital support, telemedicine can effectively support in-person visits. This ecosystem may help encounter the increasing number of pregnancies complicated by diabetes, the lack of HCPs and the increasing costs of health systems.

AI is at the moment underutilized in mHealth apps for HIP but may become a new frontier for future research in this field. The integration of AI within mHealth apps can support the predictive analysis of metabolic control and of pregnancy progress. Machine learning algorithms, for example, can serve for glucose forecasting, analyzing past glucose profiles and predicting future glucose levels. With risk stratification based on glucose trends, AI may estimate risk for future obstetrical or neonatal complications. Through machine learning algorithms, AI provides clinical decision support offering tailored therapeutic recommendations and real-time feedback. In the context of PGDM pregnancies, where the rapid increase in insulin requirements necessitates timely and precise management of therapy, AI support may become pivotal for quick insulin titration. Pregnancies, complicated by both GDM and PGDM, are characterized by an increase in insulin resistance as gestational weeks go by. AI can rapidly identify correlations between diet, physical activity, and blood glucose peaks that could otherwise be missed by manual analysis during gestational weeks. AI chatbots using natural language processing may give real-time answers to women’s questions and doubts, augmenting patients’ engagement.

7 Discussion

The mHealth applications are powerful tools in managing diabetes during pregnancy, as they enable better glycemic control, facilitate personalized care and enhance patient empowerment. Existing evidence suggests that applications built upon evidence-based content contribute significantly to improved pregnancy outcomes but efficacy is variable across studies. Systematic reviews like Eberle et al. (47) show consistent HbA1c reduction, but individual RCTs reveal discrepancies. For example, the UK RCT GDm-Health (12) found no significant mean blood glucose change but improved compliance and experimented fewer cesareans, contrasting the Glucose Buddy study of Miremberg et al. (36) which achieved lower off-target glucose and insulin needs. These inconsistencies arise from heterogeneous populations, small single-center populations and variable app integration (i.e., automated vs. manual data entry).

HIP includes both GDM and PGDM, which are different clinical scenarios. They are both characterized by the need to check and control hyperglycemia during gestation, but while GDM usually results in mild hyperglycemia and is limited in time, PGDM is a more complex disease with hyperglycemia lasting before conception and frequently leading to serious adverse obstetrical outcomes. mHealth apps dedicated to PGDM emphasize insulin titration support systems and CGM integration (i.e., TreC-Diabetes bolus calculator for type 1 diabetes, AID apps like CamAPS customized for pregnancy targets), addressing lifelong insulin needs conversely to GDM. GDM tools prioritize lifestyle (i.e., nutrition, physical activity) focusing on GWG and engagement for post -partum diabetes prevention. Consequently, outcomes may differ, studies on mHealth for PDGM highlight improvements in time in range and HbA1c (i.e., Diabeo DTx) while GDM trials focus on compliance and lifestyle and may lack on consistent neonatal or obstetrical benefits.

In this narrative review, we have summarized different global perspectives, illustrating how data-sharing apps between patients and HCPs are used even in small clinical experiences. Such technological communication tools enhance multidisciplinary care coordination and reduce accessibility barriers, ensuring that all pregnant women, regardless of language or socioeconomic status, can benefit from these digital health solutions.

Undoubtedly, there are still many areas of uncertainty that should be addressed by future research: i) there is the need for large multicenter RCTs powered enough for neonatal outcomes, ii) platforms should be combined with the automated integration of different devices and should be enhanced with AI, iii) trials should be addressed to low-literacy and rural settings, iv) cost-effectiveness should be more investigated, v) long-term effect of mHealth should be evaluated, possibly integrating with national registry in order to investigate the effect on T2DM and obesity prevention.

The use of apps and other digital tools in the management of HIP could represent a trend towards more organizational changes as reductions of in-person visits, thanks to timely medical feedback, combined with greater patient empowerment, thus reducing the burden of deferred decision-making. This constitutes a genuine clinical value rather than merely a technological one. While theoretical clinical trials are designed to be replicated globally, the integration with the real-world digital process will drive the large-scale implementation of “all-in-one” technology. It must be supported by active dissemination strategies to promote widespread adoption of the apps among public health professionals.

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