In 2021, it is estimated that 530 million adults between the ages of 20 and 79 worldwide are living with diabetes, a figure projected to increase to 783 million by 2045 [1]. Diabetes is a condition that necessitates self-management to prevent complications. These complications can include blindness, kidney failure, heart attacks, strokes, and lower limb amputations. By maintaining their blood glucose levels within normal ranges through consistent and regular self-management, patients can prevent or delay these complications [2, A1]. Self-Monitoring of Blood Glucose (SMBG) is a commonly used method for tracking and managing blood glucose. However, SMBG has its limitations. It requires patients to prick their skin with a needle, causing discomfort with each use, and necessitates carrying equipment and supplies when away from home. Furthermore, SMBG only provides a snapshot of blood glucose levels at the time of measurement, failing to effectively track continuous fluctuations in blood glucose levels [3].

Continuous Glucose Monitoring (CGM) serves as a blood glucose measurement method that addresses the issues associated with SMBG mentioned earlier. In addition to the convenience of allowing patients to monitor their blood glucose continuously without the need for frequent finger pricking, CGM offers various advantages [4] (Figure 1). CGM consists of a sensor inserted under the skin to measure interstitial fluid glucose concentrations (interstitial glucose levels), a device for transmitting the data collected by the sensor, and a receiving device (e.g., a mobile phone) that displays the measurements. It measures interstitial glucose levels 288 times a day at 5-minute intervals, presenting them in a graph and providing trend arrows to indicate the magnitude and direction of blood glucose fluctuations [A2]. This helps individuals with diabetes analyze their blood glucose variation patterns, facilitating improvements in their lifestyle habits such as diet and physical activity. Additionally, CGM includes an alert function, allowing for the prevention and management of both hypoglycemia and hyperglycemia, and assisting in flexible insulin dosage adjustments [A2]. The blood glucose data obtained through CGM can be shared with family members and healthcare professionals via smartphone applications, which proves useful for personalized medication prescriptions and management consultations [5]. CGM systems have been available for purchase overseas since 2000 [4, 6], and in Korea, the introduction began with Medtronic's Guardian Connect System in May 2018, followed by the formal introduction of Abbott's Libre in September 2020. Libre is a relatively simple intermittent scanning system that does not require blood glucose calibration or a transmitter [A3].

Figure 1
Flow diagram of the research selection process for the narrative review.
CINAHL=cumulative index to nursing and allied health literature; CGM=continuous glucose monitoring; KISS=Korean studies information service system; NAL=national assembly library; RISS=research information sharing service.

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Numerous experimental studies have demonstrated that patients utilizing CGM can manage acute fluctuations in blood glucose, such as hypoglycemia, more effectively than those using SMBG. Moreover, healthcare professionals have found it simpler to adjust treatments for these patients, and a more significant reduction in glycated hemoglobin (HbA1c) levels has been observed [7]. CGM has proven its efficacy in adjusting insulin dosage and facilitating self-management for patients with type 2 diabetes, insulin pump users, and those who administer multiple insulin injections [A4]. It has also shown its capacity to lower HbA1c levels in pregnant women, thereby reducing the risk of macrosomia (large birthweight), neonatal hypoglycemia, and the frequency of intensive care unit admissions [8]. Notably, during periods when diabetes patients may struggle to access hospital care due to pandemics like COVID-19, CGM can serve as a viable alternative [9]. CGM enables real-time monitoring of diabetes patients' blood glucose levels, minimizing contact between healthcare professionals and patients. Healthcare professionals can remotely access patients' glucose data, facilitating diabetes management through wireless calls or video conferencing, eliminating the need for patients to physically visit the hospital [A5].

Diabetes-related organizations in both the United States and Europe recommend using CGM in patients undergoing multiple daily insulin therapy, even during hospital stays, irrespective of age or type of diabetes [10, 11]. The Korean Diabetes Association has also endorsed the use of CGM in its 2021 diabetes treatment guidelines. This recommendation applies to all adults with type 1 diabetes, pregnant women with diabetes, and when necessary, adults with type 2 diabetes to manage blood glucose levels and minimize the risk of hypoglycemia [12]. In countries such as the United States, Japan, Europe, and New Zealand, government assistance is available for CGM supplies, not only for individuals with type 1 diabetes but also for those undergoing insulin therapy for type 2 diabetes [13, 14]. Conversely, Korea only began to subsidize sensor acquisition costs for type 1 diabetes patients in January 2019, and since January 2020, has been covering 70% of CGM costs (approximately 210,000 KRW for 3 months) or 70% of the actual purchase price [15]. When compared to other countries, the extent and government support for CGM use in Korea remain relatively limited.

Although CGM is not yet widespread in Korea, the rising prevalence of diabetes and the rapidly aging population indicate that the number of users is likely to increase. As such, it is crucial to review existing research on CGM and suggest future research directions to lay a scientific foundation for diabetes management and patient care using CGM. Specifically, within the field of nursing, where a significant number of studies target diabetes patients, research employing CGM should be actively promoted. This will support diabetes self-management, encourage healthy lifestyle habits, and decrease diabetesrelated chronic complications, ultimately improving the quality of life for individuals with diabetes.

Therefore, in this study, we aimed to conduct a narrative review 1) to review research related to CGM conducted in Korea, 2) to suggest future research directions based on current trends, and to discuss potential research avenues within the nursing field. A narrative review is an apt method for summarizing and interpreting content in a flexible manner, while also addressing a broad spectrum of questions by synthesizing research published in a specific field [16, 17]. This makes it an ideal tool for presenting new research initiatives. The findings of this study are anticipated to contribute significantly to the effective self-management of diabetes patients and the efficient management of these patients by healthcare professionals. Furthermore, these results could serve as a foundation for policy expansion, such as future government support.

This study's literature analysis incorporated a total of 68 articles (Appendix 1). The selection process is summarized in Figure 1, adhering to the PRISMA flowchart [22]. From the 1,455 articles initially retrieved through database searches, 918 were excluded in the first screening round for not meeting the research selection criteria. Of the remaining 537 articles, 151 duplicates were identified and removed, leaving 386 articles. From these, articles unrelated to CGM (n=259), those concerning CGM development (n=13), studies involving animals (n=10), articles where both the researchers and subjects were not Korean (n=5), conference abstracts (n=25), articles pertaining to artificial pancreases (n=4), and inaccessible articles (n=2) were all excluded. This resulted in the final selection of 68 articles.

The researchers individually reviewed the 68 CGM-related articles and classified them into three major categories and seven subcategories based on research content, objectives, methods, results, and others. The articles were also organized according to their year of publication (Table 1). The three primary categories were identified as "evaluating the performance of CGM", "validating the efficacy of CGM," and "expanding the uses of CGM." Each primary category was further divided into 2-3 subcategories, resulting in a total of seven subcategories. The distribution of articles by publication year was as follows: one article from 2001 to 2005, five articles from 2006 to 2010, 14 articles from 2011 to 2015, 32 articles from 2016 to 2020, and 16 articles from 2021 to 2022. A significant increase in the number of articles in recent years was noted, with a noticeable shift in research content from the first primary category, "evaluating the performance of CGM", to the third, "expanding the uses of CGM." The findings from the analyzed articles, organized by the seven subcategories, are presented below.

Table 1
Themes and Published Years of Studies in the Narrative Review (N=68)

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This study conducted a narrative review to summarize CGM research carried out in Korea, identify research trends, and propose future research directions. We specifically focused on discussing potential research avenues within the field of nursing. Initially, research was primarily centered on evaluating the performance of CGM in comparison to traditional blood glucose monitoring methods [A6,A7,A8,A9], as well as studies that compared CGM metrics to other parameters [A10,A11,A12,A13,A14,A15,A17]. Subsequently, the research scope broadened to validate the effectiveness of CGM in enhancing the health outcomes of diabetic patients [A27,A28,A29,A30,A31,A34]. More recently, studies have incorporated research that utilizes CGM metrics and blood glucose prediction models [A57,A62]. The research themes identified in this review include studies comparing CGM to SMBG, investigations into the potential of CGM to improve patient outcomes, discussions about the expansion of CGM through clinical guidelines and reviews, the creation of blood glucose prediction models using CGM data, and various clinical studies. In this section, we will address relevant issues and international research trends related to these subtopics and suggest future research directions. Finally, we will briefly discuss potential research topics within the field of nursing.

Between 2001 and 2015, numerous studies were conducted to assess the effectiveness of CGM in comparison to SMBG, and to compare CGM data with other parameters. This interest in CGM stemmed from its ability to provide a range of metrics beyond HbA1c, such as the CV, target blood glucose level, and others, through the utilization of the Ambulatory Glucose Profile (AGP) and TIR. While HbA1c has traditionally been the primary physiological outcome variable in intervention studies involving patients with diabetes, CGM offers a more comprehensive and detailed analysis of intervention effects when applied to both intervention and control groups pre and post interventions. The studies included in this review typically analyzed CGM data over a brief period, often around three days. Therefore, to collect long-term big data, it is advisable to conduct retrospective research by correlating CGM results available through hospital Electronic Medical Records (EMRs) and hospital accounts.

Furthermore, CGM has been employed to improve the health outcomes of patients with diabetes, a trend that is anticipated to persist. The effectiveness of CGM for patients with type 1 diabetes has been confirmed, and it is endorsed in various countries, including the United States, Germany, the United Kingdom, Australia, France, and Canada, via national insurance programs [13, 14]. Traditionally, diabetes was managed through SMBG, but the efficacy of CGM has also been proven for gestational diabetes. As a result, the United Kingdom recently revised its guidelines to offer government support for the use of CGM by all pregnant women with type 1 diabetes for up to one year [20]. However, in Korea, there is a lack of sufficient experimental research focusing on gestational diabetes patients using CGM, particularly the need for multi-institutional collaborative studies to evaluate the effects of CGM in patients with gestational diabetes.

In domestic research targeting patients with type 2 diabetes, CGM has shown impressive results in enhancing dietary and activity habits. This has led to significant decreases in HbA1c, fasting blood glucose, and body weight, thereby improving the overall management of diabetes [23]. Consequently, it is crucial to continue research to evaluate the impact of CGM on patients with type 2 diabetes, including those treated with insulin and those who are not. Furthermore, it is worth considering the inclusion of additional interventions, such as personalized education, in conjunction with CGM to boost its efficacy. International research suggests that when CGM is combined with psychoeducation, it is more effective in reducing severe hypoglycemia in patients with type 1 diabetes who have previously experienced severe hypoglycemia, even six months post hypoglycemia education [24].

Since 2016, there have been concerted efforts to develop guidelines for the use of CGM [A3]. This effort has been particularly focused on guidelines for the concurrent use of insulin pumps and CGM, with the initiative primarily spearheaded by field experts [A4]. Moreover, predictive models and deep learning methods, which are gaining popularity across various disciplines, are now being applied to CGM data. This has resulted in a growing trend in research dedicated to blood glucose prediction models [A57,A58,A59,A60,A61,A62]. In addition, numerous studies have been carried out in other clinical research areas, such as long-term transplantation [A63], and metabolic surgery for obesity and gastric bypass surgery [A64,A65]. These areas require precise blood glucose control during the surgical period.

Efforts to expand the use of CGM have primarily involved expert reviews that introduce CGM and discuss its mechanical benefits. In the United States, initiatives to increase CGM usage have included gaining approval for patients aged 2 and older, extending Medicare coverage, and examining the integration of CGM with various insulin pump systems [25]. Other reviews have tackled subjects such as the cost-effectiveness of CGM, issues of discomfort, insertion difficulties, adhesiveness, acceptability, and usability [26], the digital compliance of elderly diabetic patients using CGM in home care [27], and the use of CGM in pregnant diabetic patients from diverse racial backgrounds [20]. In Korea, there is a demand for review studies in areas such as policy evaluations for extending CGM coverage [A56], investigating the use of CGM in home healthcare and the digital medical environment [A52,A54], and the psychological and social nursing aspects for CGM users and their caregivers [A47].

From a predictive modeling standpoint, the real-time collection of CGM data, with 288 measurements taken daily, is quite comprehensive. This makes it a valuable asset for practical applications and research into personalized diabetes self-management based on big data, employing techniques such as machine learning. A search on PubMed using the query (deep learning OR machine learning) and continuous glucose monitoring in titles and abstracts yielded 88 papers as of April 28, 2023, with the earliest dating back to 2016. This suggests that CGM has not yet been widely used in machine learning. Recent international studies have delved into areas like predicting nocturnal hypoglycemia in type 1 diabetic patients undergoing insulin therapy during sleep [28], and forecasting the necessary insulin dosage based on pre-meal blood glucose and carbohydrate content in meals [29]. In addition to constructing models for predicting blood glucose levels, research could also concentrate on predicting complications and other related outcomes. When employing modeling, the use of multivariate models, such as autonomous channel deep learning frameworks, could result in more precise blood glucose predictions. These models take into account the temporal variations of various factors that influence blood glucose levels, learning them independently. This avoids redundancy and incompleteness in input information, ultimately enhancing prediction performance [30].

Recent trends in nursing studies have shown an increase in research utilizing machine learning. The application of CGM to patients with diabetes, in conjunction with the analysis of personal lifestyle data such as diet and exercise, insulin and other medication data, can aid in the development of models that predict blood glucose levels, hypoglycemic events, complications, and more [31]. The data necessary for blood glucose prediction, like calorie intake and carbohydrate quantity, can be gathered using AI-based meal quantity calculation apps. However, in Korea, where communal sharing of side dishes is common, there are ongoing debates about the accuracy of these calculations due to variations in eating habits, cooking methods, and ingredients. Therefore, further research in this area is warranted. Considering that some patients use CGM intermittently, there is room for research focused on enhancing or predicting CGM compliance in patients with diabetes. Our research team is currently considering a project to develop a prediction model that correlates step counts and CGM data to forecast blood glucose fluctuations based on levels of physical activity.

Beyond patients with diabetes, other potential research subjects could benefit from the application of CGM, including those who experience blood glucose fluctuations without a clinical diabetes diagnosis. This group may encompass patients in intensive care units, elderly individuals undergoing major surgeries, and unconscious patients who cannot report symptoms. Research could explore the application of CGM to these groups, assessing its impact on hospitalization duration, complications, and readmission outcomes. Moreover, there is a need for research to develop manuals and efficacy measurements for blood glucose management using CGM. This is particularly important for elderly individuals with low digital device compliance, residents in remote rural areas, and vulnerable populations with limited healthcare access. Addressing and improving the drawbacks of CGM usage, such as cost burden, skin issues, discomfort, pain, and mobile phone-related issues [32], could also be a focus of nursing research. As CGM usage increases in the future, it is crucial for healthcare professionals, including hospital and nursing home nurses, to become familiar with its usage and application. Research is needed on the nursing experience of CGM patients, manual development, and research specific to critical care, primary care, and ward patients. Community nurses, such as home care nurses and public health nurses, also need to become proficient in CGM usage as it becomes more prevalent in local communities. Internationally, manuals for CGM usage are being developed for patients with diabetes with comorbid conditions such as juvenile idiopathic arthritis, polycystic fibrosis, or diabetic retinopathy. In Korea, there is a need for specialized nursing education manuals for diabetes based on comorbid conditions. To achieve this, various preliminary studies are required, including the development of diverse CGM education materials, the establishment of instructor training programs, and research into the experiences of nurses caring for patients using CGM. Furthermore, international literature includes research on blood glucose regulation in patients with diabetes using CGM during the Ramadan fasting period [33]. Similar studies could be conducted in Korea, focusing on unique cultural aspects, such as holidays and traditions. Recently, there has been an emphasis on the active use of CGM, recommended for both type 2 diabetes and gestational diabetes, making it essential to proactively address policy challenges related to reimbursement, education, and counseling fees.

In the field of nursing, research can be designed that merges individualized programs with the efficacy of CGM for diet, physical activity, and stress management. This approach is based on models such as the information-motivation-behavioral skills model, health behavior models, and Bandura's social cognitive theory. For example, research could focus on patients newly diagnosed with diabetes who use CGM for a brief period. This would allow for an understanding of how their blood glucose levels fluctuate in relation to their lifestyle patterns, thereby providing guidance for future diabetes management. Additionally, research could target individuals at high risk in the prediabetes stage, enabling them to use CGM to control their blood glucose levels through modifications in diet and exercise, thereby preventing the onset of diabetes. However, given the significant variation in the duration of CGM use across different studies, a consensus on the minimum usage period is necessary. Ethical considerations must be factored in when selecting control and patient groups for randomized controlled trials. This includes the limitations of applying double or triple blinding methods. There are also issues related to parental involvement and consent acquisition for pediatric patients, as well as challenges associated with patient cooperation. This is particularly relevant in cases like Abbott's Libre, where scanning is required at least every 8 hours. When patients are randomly assigned, their Information and Communication Technology (ICT) skills should be taken into account, particularly in relation to their age. To ensure representativeness, a stratified random sampling approach could be considered. This would maintain the ratio of participants under 60 years old to those above 60 years old at approximately 1:1.5, as previously noted [34].

The big data generated by CGM can be integrated with hospital data, such as blood test indicators, clinical registration data, EMRs, prescription data, and data collected through insulin pumps, pens, various mobile apps, and more. This integrated system is known as the CGM digital ecosystem [35]. Healthcare professionals and researchers predict that research leveraging the CGM digital ecosystem will become progressively more active in the future [35]. CGM is widely used in both domestic and international research to evaluate the effects of drugs [A25,A26,A27,A28,A29,A30,A31] and biological markers [A32,A33], as it can provide a more accurate depiction of changes in blood glucose control status than other tools. Even when a diabetic patient's HbA1c is within the normal range, their blood glucose levels can often fluctuate outside of the normal range, resulting in increased oxidative stress and a higher risk of complications. Consequently, research is required to comprehend the patterns of blood glucose levels exceeding the normal range in patients with diabetes and to discover methods to reduce hypoglycemic episodes. This can be achieved by conducting studies that merge CGM-based big data, hospital data, and mobile app data, thereby moving beyond the traditional focus on fasting blood glucose, postprandial blood glucose, and HbA1c.

Unlike systematic literature reviews or meta-analyses, which follow well-established guidelines, narrative reviews often lack a universally accepted methodology. This can sometimes result in ambiguity in the selection and analysis of papers [36]. Despite this, narrative reviews offer the flexibility to analyze literature from a broad spectrum of fields. In this study, the researchers aimed to mitigate these limitations by adhering to previously published review phases [20] and striving to provide a comprehensive description of the review process. Furthermore, the study implemented a self-assessment using the Narrative Review Paper Quality Assessment Tool [18], a tool designed for editors and reviewers to evaluate narrative review papers.

The prevalence of diabetes, which is the sixth leading cause of death in Korea, is rising [26]. Consequently, CGM is expected to become increasingly important. CGM, which has demonstrated benefits such as improved blood sugar control and fewer hypoglycemic episodes, is anticipated to become a standard management approach for all diabetes patients globally in the near future [35]. In Korea, insurance coverage for CGM began approximately 2~3 years ago, leading to a surge in its usage among patients with diabetes. However, its adoption is not yet widespread, and research on CGM within the Korean nursing field remains limited. Thus, this study aimed to introduce CGM, provide a narrative review of domestic research pertaining to CGM, and discuss future research directions and related issues.

CGM plays a significant role in nursing education, research, and practice, and it presents several challenges in these areas. From an educational standpoint, it's necessary to develop competencies that enable students and nurses to manage patients using devices in the ICT era. This specifically includes the development and application of patient-tailored CGM usage manuals. From a research perspective, experimental studies on CGM's effectiveness, qualitative research on user experiences, and research on prediction model development using machine learning and deep learning can establish evidence for patient-centered care and management. Furthermore, issues related to CGM use barriers, educational needs, and potential problems in various clinical situations for diabetes patients will become important research topics to address. From a practical viewpoint, CGM can be applied in various hospital settings, such as operating rooms and intensive care units, as well as in local communities, especially among elderly individuals and diabetes patients who require continuous blood glucose monitoring and face self-management challenges. Therefore, this narrative review, which aims to provide an overview of domestic CGM-related research and suggest future research directions, is significant as the first step in introducing and expanding the use of CGM in nursing education, research, and practice.