Purpose
Continuous Glucose Monitoring (CGM) plays a crucial role in helping patients with diabetes manage their blood sugar levels. This review aimed to understand the context and scope of CGM-related studies in Korea and identified areas for future research, particularly in nursing science.
Methods
The search strategy involved examining eight electronic databases for published studies on CGM, with the search period ending in March 2022.
Four independent reviewers conducted screening, selection, and narrative review of the studies.
Results
Descriptive and substantive analyses were performed for 68 studies on CGM, which covered diverse areas, including: (1) evaluating the CGM performance through comparisons with self-monitoring of blood glucose and correlations with existing indices; (2) validating the efficacy of CGM in improving patient outcomes and assessing various interventions; and (3) expanding the use of CGM, such as clinical guidelines and reviews, developing predictive models, and other clinical studies.
Conclusion
Research on CGM has expanded to include early performance evaluation, efficacy verification, and utilization in various clinical settings. However, there is a lack of nursing-focused studies of CGM. It is recommended to incorporate CGM in nursing research aimed at enhancing self-management for patients with diabetes. Future projects should focus on developing and applying patient-customized CGM user manuals, comparing the effectiveness of CGM among patients with different types of diabetes, exploring qualitative experiences of CGM use, and addressing related issues.
Continuous Glucose Monitoring (CGM) plays a crucial role in helping patients with diabetes manage their blood sugar levels. This review aimed to understand the context and scope of CGM-related studies in Korea and identified areas for future research, particularly in nursing science.
The search strategy involved examining eight electronic databases for published studies on CGM, with the search period ending in March 2022. Four independent reviewers conducted screening, selection, and narrative review of the studies.
Descriptive and substantive analyses were performed for 68 studies on CGM, which covered diverse areas, including: (1) evaluating the CGM performance through comparisons with self-monitoring of blood glucose and correlations with existing indices; (2) validating the efficacy of CGM in improving patient outcomes and assessing various interventions; and (3) expanding the use of CGM, such as clinical guidelines and reviews, developing predictive models, and other clinical studies.
Research on CGM has expanded to include early performance evaluation, efficacy verification, and utilization in various clinical settings. However, there is a lack of nursing-focused studies of CGM. It is recommended to incorporate CGM in nursing research aimed at enhancing self-management for patients with diabetes. Future projects should focus on developing and applying patient-customized CGM user manuals, comparing the effectiveness of CGM among patients with different types of diabetes, exploring qualitative experiences of CGM use, and addressing related issues.
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.
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.
In this study, we utilized the narrative review approach to examine CGM-related research carried out in Korea. This review spans from the earliest studies to the most recent ones, with the aim of analyzing the content and offering guidance for future research.
This narrative review followed the method proposed by Coughlan and Cronin [18], and the specific steps were as follows.
(1) Topic confirmation: Through discussions, researchers have decided to conduct a review of research related to CGM, focusing on Korean subjects.
(2) Initial literature search: Each researcher independently conducted a literature search to evaluate the scope of individual studies related to CGM that involved Korean subjects. After confirming the lack of relevant literature reviews, they decided to proceed with the narrative review.
(3) Formulating review questions and objectives: Following a series of discussions, the researchers defined the objective of this narrative review as follows: "To discern patterns in CGM-related research conducted in Korea, and to suggest potential research topics and directions within the nursing field."
(4) Development of a search strategy: A literature search strategy was meticulously crafted by the researchers. This was done in response to the criticism often directed at narrative reviews for their lack of clarity in the selection criteria and process of paper selection [19, 20]. To ensure a systematic approach, several databases were utilized for the search. These included the National Assembly Library (NAL), Research Information Sharing Service (RISS), Korean studies Information Service System (KISS), and DBpia, as suggested by the National Evidence-based Healthcare Collaborating Agency (NECA) [21]. Furthermore, to identify papers authored by Korean researchers in international journals, we also made use of foreign databases. These included PubMed, Web of Science, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Google Scholar.
In this study, we limited the publication date of the literature to March 2022. The search terms used in the Korean databases included "연속혈당측정" (measurement of continuous glucose monitoring), "연속혈당" (continuous blood glucose), "지속적 혈당모니터링" (continuous glucose monitoring), "continuous glucose monitor (ing)", "실시간 CGM" (realtime CGM), "real-time CGM", "CGM", "당뇨병" (diabetes), "당뇨" (diabetes), "diabetes" in conjunction with "한국" (Korea). The Boolean operators AND/OR and truncation search were applied to both Korean and international databases. For international databases, PubMed was searched using MeSH terms, Embase using EMTREE, and CINAHL using CINAHL headings as additional search components. The main search query was structured as (continuous glucose monitor* OR continuous glucose monitoring* OR CGM*) AND (Diabetes* OR Diabetes Mellitus*) AND (Korea* OR Republic of Korea*), with the AND operator connecting the terms. Additionally, to increase the specificity of the search, the search terms were set to appear in the title and abstract.
The specific selection criteria for the literature included studies that involved either researchers or research subjects of Korean. These studies focused on methods or interventions for managing diabetes patients using CGM, clinical guidelines or reviews related to CGM, or literature in which CGM was prominently mentioned. Studies that were technical in nature and focused on the development of CGM, studies involving animals, and research on artificial pancreases or insulin pumps were excluded.
(5) Literature search: Each researcher conducted literature searches from March 4 to March 31, 2022, utilizing the selected databases and inputting the relevant keywords.
(6) Literature selection: Following the initial literature search, each researcher examined the titles and abstracts of the documents retrieved. Any documents that failed to meet the selection criteria were eliminated during the first round of screening. The remaining documents underwent further processing to remove duplicates, utilizing the literature management software, EndNote X9. Subsequently, the researchers reviewed the content of the literature and made their final selections through collaborative discussions.
(7) Literature evaluation: The chosen literature was evaluated to determine its appropriateness for inclusion in the narrative review.
(8) Analysis and topic development: The selected literature was thoroughly analyzed, and the researchers categorized it into seven distinct groups according to the content of the research. Topics were subsequently developed in line with these categories.
(9) Review composition: The researchers conducted a review and analysis of the chosen literature, organized by topic, and subsequently composed the narrative review.
(10) Implications for practice and research: The researchers examined international papers and diabetes management guidelines for each topic, offering suggestions and implications for future research directions, issues, and practical applications. These recommendations pertain to the research objectives, content, and methodology associated with CGM.
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)
Several studies have compared CGM with SMBG in terms of their ability to detect hypoglycemia and hyperglycemia, adjust insulin prescription doses, and their impact on HbA1c control. Jung et al. [A6] applied CGM for three days to 10 patients with diabetes who had cerebrovascular disease, while also measuring blood glucose levels four times a day using SMBG. The results indicated that CGM was able to detect episodes of hypoglycemia and hyperglycemia that were not identifiable by SMBG, suggesting that CGM is effective for safe blood glucose management in elderly patients with diabetes who had concurrent cerebrovascular diseases. Jeong et al. [A7] conducted a retrospective review of the records of 50 patients with type 2 diabetes undergoing insulin therapy, whose blood glucose levels were not well controlled. They used both SMBG and CGM simultaneously and adjusted insulin prescription doses based on the recorded data. The results revealed frequent episodes of hypoglycemia and hyperglycemia, which were challenging to detect with SMBG alone. The insulin doses were adjusted accordingly, indicating the effectiveness of CGM in blood glucose control. Lee et al. [A8] focused on 12 critically ill patients in the emergency room who were 18 years of age or older and required mechanical ventilation or pressor support. They used both CGM and SMBG and reported a correlation coefficient of 0.87 and an accuracy of 96.8% between the 122 pairs of measurements obtained. Kim et al. [A9] retrospectively compared the records of 172 patients with type 2 diabetes who had used CGM for about a year with the records of 1,500 patients with type 2 diabetes who had received outpatient treatment without using CGM. They reported a significant improvement in HbA1c levels in the CGM user group compared to the control group.
Of the four articles assessing CGM performance, the study by Lee et al. [A8] was specifically aimed at critically ill patients in the emergency room, while the other three studies were centered on patients with diabetes. In a different approach, Kim et al. [A9] evaluated CGM performance through a retrospective review of patient records. Conversely, the remaining three articles [A6,A7,A8] utilized both CGM and SMBG in patients to evaluate CGM performance.
"Evaluating the performance of CGM" involves assessing the accuracy and consistency of the indicators provided by CGM in comparison to established test results. Beyond just blood glucose or HbA1c levels, CGM produces a variety of metrics for assessing blood glucose management levels. These include average blood glucose, blood glucose standard deviation, and the Mean Amplitude of Glycemic Excursions (MAGE). Since 2010, a total of 11 papers have been published that investigate the relationship between these metrics and previously utilized indicators. For instance, Kim [A10] found a correlation between higher MAGE and elevated HbA1c, as well as lower C-peptide levels, when comparing various metrics obtained through CGM with HbA1c and C-peptide levels. Kim et al. [A11] examined the relationship between blood glucose variability and hypoglycemic events. Jin et al. [A12] discovered that CGM data was associated with hypoglycemia, C-peptide, Body Mass Index (BMI), high-density lipoprotein cholesterol, and HbA1c, emphasizing the differences in factors that determine relative and absolute glycemic variability. Suh et al. [A13] studied the relationship between total glucose exposure, postprandial glucose variability, and blood glucose variability, finding a strong correlation between postprandial glucose variability and blood glucose variability in patients with HbA1c levels below 7.5%. Jin et al. [A14] reported independent associations between blood glucose variability and albuminuria, thereby confirming the relationship between blood glucose variability and microvascular diabetic complications. Jun et al. [A15] compared CGM results with five standardized autonomic nervous system tests, discovering that indices of blood glucose variability. And HbA1c variability were independently associated with neuropathy or cardiovascular autonomic neuropathy [A16]. Jung [A17] conducted research on short-term blood glucose variability and microvascular complications. Kim et al. [A18] found a correlation between blood glucose variability and serum bilirubin levels, an endogenous antioxidant. Yoo et al. [A19] identified associations between the Time in Range (TIR) of 70~180 mg/dL and high blood glucose metrics with albuminuria. Yoo and Kim [A20] discussed 10 CGM metrics, including HbA1c, Coefficient of Variance (CV), TIR, Time Above Range (TAR), and Time Below Range (TBR), all of which reflect the degree of blood glucose control. They concluded that TIR provides a more comprehensive assessment of blood glucose control than HbA1c because it reflects daily blood glucose trends.
"Validating the efficacy of CGM " refers to research that confirms the impact of interventions in controlled conditions using CGM, with a focus on enhancing self-management skills and validating various intervention effects. Initially, there were four experimental studies that validated the enhancement of self-management skills in diabetes patients using CGM. Yoo et al. [A21] implemented CGM in elderly individuals aged 65 and above with type 2 diabetes and HbA1c levels ranging from 8.0% to 10.0%. The experimental group, in comparison to the control group using SMBG, demonstrated superior adherence to exercise and dietary control, leading to significant reductions in calorie intake, BMI, body weight, and HbA1c. Lee et al. [A22] carried out a pre-post designed study with an unequal control group to investigate the effects of a tailored diabetes education program using CGM data on patients with type 2 diabetes. The findings revealed that, in comparison to the control group receiving traditional diabetes education, the experimental group showed improved self-care in all areas (diet, medication, exercise, self-management) at 3 and 6 months. In 2020, Park et al. [A23] unveiled a research protocol where 98 patients were divided into three groups (conventional management group, group without feedback from healthcare professionals, group with feedback from healthcare professionals) to monitor them for 48 weeks to evaluate the effects of AI-based dietary management, FoodLens, and CGM use. In 2022, Yoo et al. [A24] created a Dexcom real-time CGM education manual and offered structured and personalized education to 47 adult patients with poorly controlled type 1 diabetes for three months. The results suggested that, in comparison to the control group, the group receiving education in conjunction with rtCGM therapy had significantly improved TIR and HbA1c levels, indicating better maintenance of blood glucose levels within the target range.
A total of 10 studies were conducted to verify the effects of various interventions using indicators generated by CGM. Of these, 7 studies evaluated the effects of medications [A25,A26,A27,A28,A29,A30,A31], 2 studies assessed the effects of biomarkers [A32,A33], and 1 study examined the effects of dietary interventions [A34]. In the studies focused on medication effects, a range of blood glucose variability indices derived from CGM were utilized to gauge the degree of blood glucose regulation in patients with type 2 diabetes following the administration of medications such as mitiglinide, sitagliptin, glimepiride, vildagliptin, evogliptin, linagliptin, dapagliflozin, among others. While HbA1c is traditionally used to evaluate medication effects, it provides a single value that represents blood glucose control over the past three months. In contrast, CGM's indices of blood glucose variability can immediately indicate postmeal hyperglycemia or short-term blood glucose fluctuations, making them more suitable for rapidly assessing medication effects. In the studies assessing biomarker effects, the use of 1,5-anhydroglucitol (1,5-AG) or Fructos Amine (FA) levels in the blood as biomarkers to indicate blood glucose variability in patients with type 2 diabetes was evaluated using CGM [A32,A33]. Choi et al. [A34] conducted a study comparing the effects of consuming a customized diabetes lunchbox, designed by a clinical dietitian, on patients with type 2 diabetes. They used CGM indicators to measure improvements in blood glucose levels.
Recent studies have introduced clinical guidelines pertaining to CGM, and the number of these studies is on the rise [A1,A4,A35,A36,A37,A38,A39]. Both Hur et al. [A1] and Yu et al. [A39] have summarized and presented the contents of the 7th edition of the clinical practice guidelines, which were developed by the Korean Diabetes Association's clinical practice guidelines committee and researchers. This 7th edition, published in May 2021, introduced a new section dedicated to CGM. It advocates for the continuous use of CGM in patients with type 1 diabetes and pregnant patients, and suggests periodic use for patients with type 2 diabetes. The guidelines also underscore the importance of specialized education to analyze and utilize the results derived from CGM. Oh [A4] detailed how to adjust prandial and basal insulin based on CGM data, while also introducing a standard reporting method [19] for the types of indices and metrics to report when using CGM. Jin [A35] proposed a systematic four-stage approach to manage diabetes patients who experience severe hypoglycemia or hypoglycemia unawareness, with the second stage mentioning the use of technical interventions such as CGM or insulin pumps. Gu [A36] offered recommendations for healthcare professionals on how to prepare for CGM users and provide appropriate feedback. Kweon [A37] described how to use CGM data during nutritional education for diabetes patients, while Lee and Kim [A38] provided guidelines specifically for the use of CGM and insulin pumps in pediatric and adolescent patients with type 1 diabetes.
Twenty domestic review articles related to CGM were examined. These reviews offered a multifaceted understanding of the accuracy of sensors, the convenience of use, and the side effects associated with CGM. Initially, many of these reviews introduced CGM, focusing on its various types, functions, benefits, and patient classifications [A40,A41,A42,A43]. Subsequent reviews delved into CGM education and usage strategies [A2,A3,A5,A44,A45,A46,A47,A48,A49,A50,A51,A52,A53,A54,A55,A56]. More specifically, given that the use of CGM in Korea was still in its infancy, there were suggestions to develop and broaden CGM education programs [A3,A44,A45,A46,A54]. The reviews touched on a range of topics, including the role of CGM in the digitalization of medicine, the use of CGM apps in the era of remote interactions due to COVID-19, and the advantages of sharing blood glucose data with healthcare professionals [A2,A5,A54]. The utility of CGM in adjusting diets for patients with post-meal hyperglycemia was also discussed [A55]. Furthermore, some reviews highlighted the need to expand insurance coverage for CGM costs [A49,A50,A51,A52,A53,A56] and to improve the self-problem-solving skills of diabetes patients through the use of CGM [A47].
Six studies developed blood glucose prediction models using algorithms such as machine learning and deep learning, based on data generated by CGM [A57,A58,A59,A60,A61,A62]. Seo et al. [A57], Kim et al. [A58], and Lim et al. [A59] have all developed algorithms to predict postprandial hypoglycemia using machine learning. A personalized blood glucose prediction model, based on deep learning, was proposed for inpatients with type 2 diabetes [A60]. A deep learning-based blood glucose prediction model, designed using CGM data, was reported to have 50% higher accuracy compared to existing models [A61]. Seo et al. [A62] acknowledged that blood glucose fluctuations can greatly vary depending on individual factors such as diet, physical characteristics, stress, and activity. As a result, they developed personalized prediction models for three groups (type 1, type 2, and gestational diabetes) by incorporating these individualized factors into the CGM data.
Six clinical studies employed CGM for various research purposes. These studies have investigated the postoperative conditions of patients who have undergone organ transplantation [A63], metabolic surgery for obesity [A64], and gastrectomy [A65], with a particular emphasis on managing potential post-surgery hypoglycemia. In this context, CGM was used in three studies to monitor surgical patients. Additionally, CGM was used to evaluate the frailty of elderly patients with diabetes [A66]. Kim et al. [A67] explored the correlation between regular autonomic nervous system test results and CGM metrics in patients with diabetes. They found that the ratio of TIR (when blood glucose is maintained between 70 and 180 mg/dL) or to TAR could serve as an indicator for preventing cardiovascular autonomic neuropathy, a diabetes complication. Yoo et al. [A68] carried out a prospective observational study on 106 patients with type 1 diabetes over 24 weeks, using CGM to gather data for the Glucose Management Indicator (GMI), a predictor of HbA1c. Their findings suggested that the international GMI formula tends to overestimate the GMI in Asian populations, prompting them to propose a modified GMI formula.
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.
CONFLICTS OF INTEREST:The authors declared no conflict of interest.
AUTHORSHIP:
Study conception and design acquisition - AJ, KJH, PJ and YY.
Data collection - AJ, KJH, PJ and YY.
Analysis and interpretation of the data - AJ, KJH, PJ and YY.
Drafting and critical revision of the manuscript - AJ, KJH, PJ and YY.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2092656).
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