Introduction

Managing diabetes during pregnancy requires meticulous glycemic control to reduce risks for both mother and child. Gestational diabetes mellitus (GDM) affects up to 14% of pregnancies globally, while pre-existing type 1 or type 2 diabetes adds further complexity. The traditional approach of self-monitoring blood glucose (SMBG) with fingerstick tests provides only snapshots of glucose levels. Wearable technology has transformed this landscape by enabling continuous, real-time monitoring and data-driven insights. As of 2025, continuous glucose monitors (CGMs), smart insulin pens, and multi-sensor wearables are increasingly adopted in prenatal care. This article examines the current trends in wearable technology for diabetes management during pregnancy, focusing on emerging devices, integration with digital health platforms, personalized feedback systems, challenges, and future directions.

Emerging Technologies and Devices

Continuous Glucose Monitors: Evolving Accuracy and Discretion

Continuous glucose monitors have become smaller, more accurate, and easier to wear. Devices such as the Dexcom G7, Abbott FreeStyle Libre 3, and Medtronic Guardian 4 now offer factory-calibrated sensors that require no fingerstick calibration. These sensors measure interstitial glucose every one to five minutes and transmit data wirelessly. In pregnancy, where glucose targets are tighter and hypoglycemia risks are elevated, CGMs provide essential trend data that can guide insulin dosing and meal planning. Recent studies have demonstrated that CGM use in pregnancies complicated by type 1 diabetes improves glycemic control and reduces neonatal complications such as large-for-gestational-age infants and neonatal hypoglycemia. The latest models are waterproof, have a small profile (e.g., FreeStyle Libre 3 is about the size of two stacked coins), and last up to 14 days, reducing the burden of frequent sensor changes.

Non-Invasive and Multi-Sensor Wearables

Researchers are developing non-invasive glucose monitors that use optical, electromagnetic, or thermal technologies. Devices like the SugarBEAT and GlucoWise measure glucose through the skin without needles, though they are not yet widely approved for pregnancy. Multi-sensor wearables integrate glucose monitoring with other vital signs. For example, the Scanbo patch aims to measure glucose, blood pressure, heart rate, and oxygen saturation. In pregnancy, tracking blood pressure alongside glucose is crucial because gestational diabetes increases the risk of preeclampsia. Some commercial wearables, such as the Apple Watch and Fitbit, now incorporate glucose monitoring through third-party sensors or partner apps, although FDA-cleared sensors remain separate devices.

Smart Insulin Pens and Pumps

Smart insulin pens, such as the NovoPen 6 and InPen, automatically log insulin doses and calculate corrections based on CGM data. They sync with smartphone apps to provide dose reminders and track active insulin. For pregnant women requiring intensive insulin therapy, these devices reduce calculation errors and improve adherence. Insulin pumps with automated insulin delivery (AID), also called hybrid closed-loop systems, are increasingly studied in pregnancy. The MiniMed 780G and Tandem t:slim X2 with Control-IQ have shown promise in maintaining glucose within a tight range during pregnancy, with early studies indicating lower HbA1c and reduced time spent in hypoglycemia. The FDA recently cleared the use of the Tandem Mobi pump for pregnant patients, expanding access to advanced AID technology.

Integration with Mobile and Cloud Platforms

Seamless Data Sharing with Healthcare Teams

Modern wearables sync with cloud-based platforms such as Dexcom Clarity, Abbott LibreView, and Medtronic CareLink. These platforms allow patients to share data with obstetricians, endocrinologists, and diabetes educators in real time. Clinicians can monitor glucose patterns, review trends, and adjust medications remotely. This integration reduces the need for in-person visits, which is especially beneficial for pregnant women who may have mobility limitations or live in rural areas. During the COVID-19 pandemic, telehealth adoption surged, and remote CGM monitoring became standard practice for many high-risk pregnancies. Studies have shown that remote monitoring using wearables improves glycemic outcomes and patient satisfaction, with comparable or better results than traditional in-person care.

Smartphone and Smartwatch Applications

Dedicated smartphone apps (e.g., Glooko, mySugr, One Drop) aggregate data from multiple devices, providing a unified dashboard. Many apps now include food logging, activity tracking, and bolus calculators. Smartwatch compatibility allows women to view glucose readings on their wrist without reaching for a phone. The Apple Watch and Wear OS devices can display CGM data via FDA-cleared companion apps such as Dexcom G7 and LibreLinkUp. Some apps use machine learning to predict glucose excursions based on meals, activity, and sleep, sending proactive alerts. For example, the Dario app uses historical data to warn users about impending hypoglycemia up to 20 minutes in advance.

Interoperability and Open Protocols

The move toward interoperability is a key trend. The Bluetooth-based technology standard called the "Glucose Monitoring Interoperability" (GMI) protocol, supported by the FDA, allows CGMs from different manufacturers to communicate with insulin pumps and data aggregators. Open-source projects like Nightscout and xDrip+ have enabled DIY closed-loop systems, but the commercial availability of interoperable devices is expanding. In 2024, Abbott and Dexcom both released APIs that allow third-party developers to build custom apps, ensuring that pregnant women can use the devices that fit their lifestyle while maintaining data continuity.

Personalized Feedback and AI‑Driven Insights

Real-Time Alerts and Decision Support

Wearables now generate real-time alerts for hyperglycemia, hypoglycemia, and rapid glucose changes. In pregnancy, the risk of nocturnal hypoglycemia is higher due to hormonal changes; devices with customizable thresholds allow women to set alarms that wake them if glucose drops below 60 mg/dL. Advanced algorithms reduce false alarms by analyzing rate of change and contextual data (e.g., recent activity or meal bolus). Some systems, like the DreaMed Advisor Pro, provide insulin dosing recommendations based on CGM data and past insulin usage. These AI-driven tools act as decision support for both patients and clinicians, helping to fine‑tune therapy without increasing hypoglycemia risk.

Predictive Analytics and Lifestyle Coaching

Artificial intelligence models can forecast glucose levels up to 60 minutes ahead using trend data and contextual inputs. For example, the GlucoSense algorithm, integrated into the Dexcom G7 app, predicts hyperglycemic events and suggests corrective actions such as walking or adjusting meal timing. Wearables that track physical activity, sleep, and heart rate variability can incorporate these factors into personalized insights. A pregnant woman who notices postprandial spikes after high‑carb breakfasts may receive suggestions for lower‑glycemic alternatives. Some devices, such as the Lingo by Abbott, are explicitly designed for metabolic health coaching, including GDM prevention. These personalized nudges empower women to make real‑time behavioral changes that improve glucose control.

Closed-Loop and Automated Insulin Delivery

The most advanced application of AI in wearables is the hybrid closed‑loop system. These systems automatically adjust basal insulin delivery based on CGM readings, while the user still manually administers meal boluses. The first randomized trial of the CamAPS FX closed‑loop system in pregnant women with type 1 diabetes showed a 10% increase in time‑in‑range (70–140 mg/dL) and a reduction in hypoglycemia rates compared to standard pump therapy. Newer systems, like the Medtronic 780G with SmartGuard, can adjust basal rates up to every five minutes. While closed‑loop systems are not yet approved for all pregnancies, ongoing studies are expanding indications to include women with type 2 diabetes and gestational diabetes on insulin therapy.

Challenges and Considerations

Data Privacy and Security

Wearables collect highly sensitive health data, including glucose levels, medication doses, and even location (if GPS is enabled). In the United States, these devices are regulated under HIPAA when used in a clinical setting, but many consumer‑grade devices are not. The risk of data breaches is real; in 2023, a major CGM manufacturer suffered a security incident that exposed patient data. Manufacturers are implementing end‑to‑end encryption, HIPAA‑compliant cloud storage, and transparent data‑sharing policies. Pregnant women should be informed about how their data will be used and stored, and clinicians should recommend devices that adhere to high privacy standards.

Cost, Accessibility, and Health Equity

Despite decreasing costs, CGMs and smart pumps remain expensive. A CGM sensor for 14 days costs $70–$100 without insurance, and the full system can exceed $400 per month. Many private insurers cover CGMs for type 1 diabetes during pregnancy, but coverage for gestational diabetes is inconsistent. Medicaid expansion states may offer better access, but gaps persist. Lower‑income and rural women often face barriers: lack of reliable internet for data syncing, limited smartphone access, and reduced support from diabetes educators. Initiatives such as the American Diabetes Association’s CGM Access Initiative aim to improve affordability and education. Device manufacturers are also developing lower‑cost versions for low‑resource settings, such as the FreeStyle Libre 2 with a lower list price in some markets.

Accuracy and Calibration in Pregnancy

Pregnancy induces physiological changes that can affect sensor accuracy. Increased plasma volume, altered tissue perfusion, and hormonal fluctuations may cause discordance between interstitial glucose and capillary blood glucose. Most CGMs are calibrated for non‑pregnant adults; however, dedicated studies have validated the Dexcom G6 and FreeStyle Libre 2 for use in pregnancy, showing acceptable mean absolute relative difference (MARD) of 10–12%. Still, pregnant women occasionally experience unexplained sensor errors, which can lead to incorrect insulin dosing. Manufacturers are updating algorithms to account for pregnancy‑specific factors. The CONCEPTT trial continues to inform best practices for CGM use in pregnancy.

User Burden and Psychological Impact

While wearables reduce the need for frequent fingersticks, they introduce new burdens: sensor insertion discomfort, adhesive allergies, frequent alerts (including at night), and the psychological stress of constant glucose feedback. Some women report alarm fatigue or anxiety about every glucose excursion. Clinicians should counsel patients on managing alert settings and discuss the potential for device‑induced distress. Behavioral interventions, such as Cognitive Behavioral Therapy tailored to diabetes distress, can be integrated into prenatal care. Newer devices offer "silent" modes that only alert for extreme values, and some allow users to temporarily pause alarms for activities like sleep or exercise.

Future Directions

Non‑Invasive and Implantable Sensors

Research into truly non‑invasive glucose monitoring continues. Optical sensors using near‑infrared spectroscopy, Raman spectroscopy, or photoacoustic methods are in late‑stage clinical trials. Two companies, DiaSens and GlucoTrack, are developing non‑invasive ear clips and wristbands that could eliminate the need for any insertion. Implantable sensors, such as the Eversense from Senseonics, are already approved for 90‑ or 180‑day use; their application in pregnancy is being explored because they avoid weekly sensor changes and may provide more consistent readings. However, implantation requires a minor procedure, which is a drawback for use during pregnancy.

Wearable Biosensors for Pregnancy Complications

Beyond glucose, wearables are being developed to detect early signs of complications. Sweat sensors can measure markers like uric acid (linked to preeclampsia) and cortisol (stress hormone). A patch from Epitel monitors maternal heart rate and uterine activity to predict pre‑term labor. Researchers at Northwestern University have created a flexible sensor that tracks fetal heart rate and maternal contractions simultaneously, offering a wearable alternative to traditional fetal monitors. Integrating glucose monitoring with these biosensors could provide a comprehensive picture of maternal‑fetal health, enabling early intervention for conditions that often co‑occur with diabetes.

Machine Learning for Predictive and Preventive Care

The next generation of AI in wearables will move beyond predictive alerts to preventive recommendations. For example, a system might analyze a woman’s meal pattern, insulin sensitivity trend, and exercise history to automatically adjust her insulin‑to‑carb ratio for the next meal. Some startups are working on “digital twins” — software replicas of an individual’s metabolic processes that simulate the effects of diet, insulin, and activity before applying changes in real life. In pregnancy, where hormonal changes are rapid, these models could be updated daily using CGM data. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) has funded several projects exploring AI‑driven tools for gestational diabetes management.

Integration with Electronic Health Records

Currently, most CGM data lives in manufacturer‑specific platforms, not in the patient’s electronic health record (EHR). Leading EHR vendors like Epic are developing integration modules (e.g., “MyChart Glucose” with Dexcom) that allow clinicians to view CGM traces within the familiar EHR interface. This interoperability reduces the need to toggle between systems and enables automated quality improvement. In a pilot program at the University of California, San Francisco, the integration of CGM data into Epic during pregnancy improved the timeliness of insulin dose adjustments by 40%. As more institutions adopt these capabilities, wearable data will become a routine part of prenatal care.

Conclusion

The use of wearable technology in managing diabetes during pregnancy is rapidly evolving from a niche tool to a standard‑of‑care component. Continuous glucose monitors and automated insulin delivery systems have demonstrated clear benefits in improving glycemic control and reducing neonatal complications. Integration with mobile apps, cloud platforms, and electronic health records is making real‑time data accessible to both patients and clinicians, enabling more proactive and personalized management. Despite challenges related to cost, accuracy, data privacy, and user burden, ongoing innovations in non‑invasive sensors, AI‑driven decision support, and multi‑parameter biosensors promise to further transform prenatal care. For pregnant women with diabetes, these technologies offer greater autonomy, reduced anxiety, and the potential for healthier pregnancies and better long‑term outcomes. As research continues and health systems adapt, wearable devices are set to become an integral, empowering part of comprehensive diabetes care during pregnancy.