The Expanding Role of Continuous Glucose Monitoring in the Management and Prevention of Type 2 Diabetes

Abstract

Continuous glucose monitoring (CGM) has transformed diabetes management by providing continuous, high-resolution insight into glucose dynamics. Initially developed for type 1 diabetes, CGM now demonstrates substantial clinical and behavioral benefits for individuals with type 2 diabetes across diverse therapeutic settings. This narrative review synthesizes current evidence on the expanding role of CGM in optimizing glycemic control and promoting patient-driven lifestyle modification.

Across randomized and real-world studies, CGM consistently improves glycosylated hemoglobin, increases time in range, and reduces glycemic variability, regardless of insulin use. Beyond metabolic outcomes, CGM enhances treatment satisfaction, psychological well-being, and self-efficacy, particularly when combined with structured education and feedback. By enabling individuals to visualize real-time glucose responses to daily behaviors, CGM serves as a powerful catalyst for sustained behavioral change and personalized self-management.

In addition to its therapeutic applications, CGM also provides diagnostic insight by revealing unrecognized glucose excursions that conventional monitoring may miss, facilitating earlier identification of dysglycemia in at-risk individuals. Yet significant barriers persist, including device costs, limited insurance coverage, and the difficulty of translating raw data into actionable insights for patients and clinicians.

In conclusion, CGM has evolved from a glucose-monitoring device to a comprehensive platform that supports both clinical decision-making and behavioral empowerment, bridging the continuum from diabetes prevention to long-term management.

Key Summary Points

Continuous glucose monitoring (CGM) provides real-time insight into glucose dynamics and improves both glycemic control and patient engagement.

CGM demonstrates consistent benefits in individuals with type 2 diabetes, lowering glycosylated hemoglobin (HbA1c) and glycemic variability regardless of insulin use.

Integration of CGM with structured education and personalized feedback enhances treatment satisfaction and self-efficacy.

CGM also aids in the early detection of dysglycemia by revealing glucose excursions not captured by traditional monitoring methods.

Broader adoption of CGM requires solutions to cost, reimbursement, and data interpretation barriers.

Introduction

Type 2 diabetes mellitus (T2DM) represents a major global health burden, accounting for 90% of all diabetes cases and necessitating persistent medical intervention alongside lifestyle modifications [1]. Traditionally, self-monitoring of blood glucose (SMBG) has been a cornerstone of diabetes management; however, it is associated with several inherent limitations. SMBG imposes a substantial physical and psychological burden, particularly for patients with comorbidities such as visual or motor impairments. Furthermore, it provides only fragmented "point-in-time" glucose measurements, offering limited information on critical glycemic patterns. This lack of continuous data may hinder effective therapy intensification and complicate patients’ ability to incorporate glucose monitoring effectively into their daily lives.

In contrast, continuous glucose monitoring (CGM) has emerged as a revolutionary technology that has fundamentally reshaped diabetes care. Initially developed and applied in type 1 diabetes mellitus (T1DM) [2–5], CGM has since demonstrated significant value in the care of patients with T2DM as well [6–9]. In recent years, its use has progressively expanded to a broader range of individuals across the glycemic spectrum, including those with T1DM, insulin-treated T2DM, non-insulin-treated T2DM, and even those with prediabetes. This reflects increasing recognition that continuous glucose data offer clinical utility beyond insulin-treated groups. Evidence supporting CGM is strongest in T1DM, substantial in insulin-treated T2DM, gradually accumulating in non–insulin-treated T2DM, and beginning to emerge in individuals with prediabetes. This evolution, illustrated in Fig. 1, highlights a continuum in which CGM adoption is advancing toward populations with lower immediate clinical necessity but substantially larger overall numbers. CGM devices, including both real-time CGM (rtCGM) and intermittently scanned CGM (isCGM), provide a comprehensive and dynamic depiction of glycemic control by delivering glucose measurements every few minutes without the need for repeated finger pricks. This dense stream of data enables the identification of clinically relevant patterns, including the dawn phenomenon, pronounced postprandial excursions, and short-term glycemic variability. The capacity of CGM to capture and address these fluctuations represents a major advance in personalized diabetes management.

Fig. 1.

Conceptual framework illustrating the expansion of continuous glucose monitoring (CGM) usage across populations with varying clinical necessity and levels of evidence. Populations are plotted according to their relative clinical necessity and level of evidence supporting the CGM benefit. Evidence is strongest for type 1 diabetes (T1DM), moderate for insulin-treated type 2 diabetes (T2DM), emerging for non-insulin-treated T2DM, and limited for prediabetes. Circle size roughly represents the population size, highlighting the widening potential impact as CGM use expands toward lower-risk groups

This comprehensive review aims to examine the multifaceted and evolving role of CGM technology. It will address the documented benefits of CGM in the active management of established T2DM, explore its potential utility in preventing T2DM among individuals with prediabetes, and highlight its use in diagnosis. In addition, this review will critically evaluate the barriers that hinder the widespread and equitable adoption of CGM and outline future directions for research and clinical implementation. This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

CGM in T2DM Management

CGM offers a more holistic and nuanced understanding of an individual’s glycemic profile compared to traditional SMBG. By collecting glucose readings at frequent intervals, CGM uncovers glycemic trends such as the dawn phenomenon, postprandial excursions, and overall glycemic variability. Such patterns, often missed by conventional monitoring, facilitate proactive and timely interventions. Continuous data streams empower patients and healthcare professionals to make informed, real-time decisions regarding diet, physical activity, and medication optimization.

The primary and most consistently demonstrated clinical benefit of CGM in T2DM is improvement in glycosylated hemoglobin (HbA1c). Numerous studies, including randomized controlled trials (RCTs) [10, 11] and real-world observational studies [12–14], have shown that CGM is associated with greater improvements in HbA1c compared to standard care or SMBG. A meta-analysis of 26 RCTs involving 2783 individuals with T2DM demonstrated that, compared with SMBG, rtCGM and isCGM reduced HbA1c by approximately 0.19–0.31 percentage points (2–3 mmol/mol). Time in range (TIR), defined as the percentage of time glucose levels remain within the target range of 70–180 mg/dl (3.9–10 mmol/l), increased significantly among isCGM users and non-significantly among rtCGM users [15]. Additional evidence comes from a systematic review of 15 RCTs (duration 12–36 weeks, n = 2461), which reported that CGM use modestly reduced HbA1c by 0.17% (95% CI − 0.29 to − 0.06) [16]. Importantly, this review also identified significant increases in TIR and reductions in both time above and below range, further supporting the clinical significance of CGM-based glucose data.

Beyond HbA1c, CGM provides actionable metrics that offer a more comprehensive view of glucose management. CGM use consistently increases TIR. For example, the MOBILE RCT [6] demonstrated a 27% increase in TIR among adults with T2DM on basal insulin using rtCGM compared to SMBG, and the IMMEDIATE RCT [17] in non-insulin-treated T2DM showed a 9.9% (2.4 h) increase in TIR with isCGM combined with structured education. Higher TIR levels are strongly associated with lower risks of microvascular complications [18], such as retinopathy and painful diabetic neuropathy, as well as macrovascular complications [19], including cardiovascular disease and all-cause mortality.

In parallel, CGM offers a multidimensional view of glycemic control, moving beyond average glucose or HbA1c toward dynamic metrics like time above range (TAR), time below range (TBR), and glycemic variability. Robust evidence from a recent systematic review [20] supports these benefits: across 12 RCTs (n = 1248 adults with T2DM), CGM use led to a mean reduction in TAR of 5.86% (95% CI − 10.88, − 0.84), a reduction in TBR of 0.66% (95% CI − 1.21, − 0.12), and a decrease in glycemic variability by 1.47% (95% CI − 2.94, − 0.01) compared to SMBG. These improvements were observed regardless of insulin use and were consistent for both real-time and intermittently scanned CGM modalities. These real-time metrics have clear clinical relevance. Reducing TAR translates directly into fewer episodes of hyperglycemia, decreasing the risk of glucose toxicity on vascular tissues and related acute and chronic complications [21]. Lower TBR means patients are less exposed to dangerous hypoglycemia, which is linked to immediate risks such as falls, arrhythmia, and cognitive dysfunction, and greater healthcare costs due to emergency interventions [22, 23]. The reduction in glycemic variability is also important, as higher glycemic variability is independently associated with increased risk for diabetes complications. These include markers of cardiovascular injury (e.g., carotid intima-media thickness and endothelial dysfunction) and adverse changes in retinal structure, such as thinning of the retinal nerve fiber layer [24, 25]. By providing continuous, granular data on all aspects of glucose fluctuations, CGM facilitates more individualized adjustments to therapy, supporting both safety and long-term diabetes management.

Role of CGM in Insulin-Based Treatment

The clinical benefits of CGM are not limited to specific therapeutic approaches but extend across the spectrum of insulin-based and non-insulin diabetes management strategies. In patients receiving intensive insulin therapy, including multiple daily injections (MDI) or continuous subcutaneous insulin infusion, multiple RCTs have demonstrated that CGM use substantially improves glycemic outcomes compared to SMBG [7, 26–29]. In the DIAMOND trial [7], rtCGM use in adults with T2DM on MDI resulted in mean HbA1c levels of 7.7% in the CGM group compared to 8.0% in the control group at 24 weeks (adjusted difference in mean change, − 0.3% [95% CI − 0.5% to 0.0%]; P = 0.022). Similarly, Kim et al. [29] demonstrated the efficacy of isCGM in adults with T2DM on MDI in a 24-week randomized trial. When combined with structured education on interpreting graphical patterns in CGM data, isCGM achieved a significantly greater HbA1c reduction of 1.00% compared to 0.63% with isCGM alone and 0.58% with SMBG (p = 0.0367 and p = 0.0193, respectively).

The benefits of isCGM in intensive insulin therapy extend beyond HbA1c reduction. While some RCTs of isCGM have not demonstrated significant improvements in HbA1c, they consistently reveal substantial benefits in clinically meaningful outcomes related to hypoglycemia reduction and patient experience. The multicenter, open-label trial by Haak et al. [28] demonstrated that isCGM as a replacement for conventional blood glucose monitoring resulted in reductions in hypoglycemia exposure, decreasing TBR < 70 mg/dl (< 3.9 mmol/l) by 1.96% (0.47 h/day) and TBR < 55 mg/dl (< 3.1 mmol/l) by 0.92% (0.22 h/day). Furthermore, isCGM was associated with significant improvements in patient-reported treatment satisfaction and quality of life metrics compared to SMBG [26, 28, 29]. These findings underscore an important paradigm shift in outcomes assessment: while HbA1c remains a valuable marker of long-term glycemic control, reductions in hypoglycemia burden and improvements in patient-reported outcomes represent equally critical therapeutic goals. The demonstrated ability of isCGM to minimize hypoglycemia exposure and enhance patient experience validates its clinical utility even in settings where conventional HbA1c reduction may not be prominent. This multidimensional efficacy profile distinguishes CGM from traditional glucose monitoring approaches and supports its broader integration across diverse insulin therapy regimens in T2DM management.

In patients on basal insulin therapy without prandial insulin, the integration of rtCGM significantly improves glycemic control [30]. The MOBILE study by Martens et al. [6] enrolled 175 adults with T2DM receiving basal insulin and demonstrated that rtCGM led to clinically meaningful improvements in glycemic parameters: HbA1c decreased by an additional 0.4% compared to SMBG, TIR increased by 15% (approximately 3.6 h per day), and both TAR and TBR were substantially reduced. These benefits occurred without significant changes in insulin dosage or body weight, suggesting that CGM enhances glycemic control through behavioral modifications in diet and physical activity as the primary mechanism.

These benefits appear durable over extended follow-up. The Steno2tech study [31] enrolled 76 adults with inadequately controlled T2DM (median HbA1c 8.3%) predominantly on basal insulin and showed sustained improvements with rtCGM over 12 months. Compared with SMBG, rtCGM combined with structured education increased TIR by 15.2% and reduced HbA1c by 0.9%. These improvements were accompanied by reductions in insulin dose (− 10.6 units/day) and body weight (− 3.3 kg), as well as improved patient-reported outcomes, further supporting the role of behavioral change over pharmacological intensification.

Role of CGM in Non-Insulin-Based Treatment

CGM provides clinical benefits even in patients not using insulin, where it has been shown to improve HbA1c and reduce glycemic variability across multiple RCTs [9, 32–34]. These metabolic gains are attributable not only to data density but also to CGM-enabled motivational feedback, which helps patients understand the immediate glycemic consequences of dietary and physical activity choices. This behavioral reinforcement functions as a key mechanism by which CGM supports non-insulin-based treatment.

Furthermore, emerging evidence suggests that even intermittent use of CGM can yield meaningful clinical benefits in non-insulin-treated T2DM populations. While the insulin-treated population in the MOBILE study showed that CGM discontinuation led to rapid deterioration of glycemic control [35], Wada et al. demonstrated in a non-insulin-treated population that persistent HbA1c reduction was maintained at the 6-month mark after discontinuing 3 months of isCGM use [9]. Additionally, Moon et al. validated that brief, intermittent CGM use (one-week rtCGM application with repeat at month 3) in patients with T2DM inadequately controlled on three or more oral antidiabetic drugs resulted in 0.6% HbA1c reduction sustained through 6 months [32]. Similarly, Lee et al. revealed that quarterly one-week rtCGM sessions integrated with digital health applications produced sustained HbA1c improvements over 12 months [36]. These findings suggest that intermittent or periodic CGM use in non-insulin-treated T2DM may offer a cost-effective strategy for glycemic optimization in resource-limited settings.

Retrospective cohort studies provide compelling real-world evidence of substantial clinical benefits extending beyond glycemic metrics, demonstrating significant reductions in hospital admissions for acute diabetes complications following CGM initiation in people with T2DM. The RELIEF study [37] reported a 39.4% reduction in hospital admissions for acute diabetes mellitus events, including diabetic ketoacidosis, severe hypoglycemia, diabetes-related coma, and hyperglycemia, within 12 months of isCGM initiation in a large T2DM cohort, with this protective effect sustained for at least 2 years. This sustained reduction suggests that CGM’s benefits translate into durable improvements in clinical outcomes rather than transient effects. Such reductions have important implications for both patient safety and healthcare economics, reducing the burden of acute diabetes-related emergencies and associated healthcare resource utilization and costs.

Beyond objective metrics, CGM use significantly improves patient-reported outcomes in people with T2DM. Across multiple studies, CGM users consistently report greater treatment satisfaction, reduced diabetes-related distress, and improved psychological well-being compared to those relying on SMBG [9, 17, 28, 29]. These effects are particularly pronounced among older adults on insulin therapy, who often describe enhanced feelings of safety, better sleep quality, and greater confidence in day-to-day glucose management after CGM initiation [38]. These psychosocial benefits appear most pronounced when CGM use is paired with structured education and ongoing professional feedback [29, 39], suggesting that technology and behavioral support are synergistic in optimizing patient experience and adherence.

CGM plays a pivotal role in supporting patient-driven lifestyle modification, particularly when combined with structured education and tailored algorithms. By providing immediate, personalized feedback on how specific food choices and physical activity impact glucose levels, CGM serves as a powerful “silent persuader” for positive behavioral change. Patients can directly observe the relationship between their actions and glycemic responses, fostering deeper understanding and self-awareness essential for effective self-management.

Table 1 summarizes key randomized and real-world studies that have demonstrated the efficacy of CGM in individuals with T2DM.

Table 1.

Studies providing evidence of the efficacy of continuous glucose monitoring in people living with type 2 diabetes mellitus

Study	Population	Treatment	CGM	Study design	Duration	HbA1c	CGM Metrics
DIAMOND study: Beck et al. (2017) [7]	158 adults with T2DM	MDI	rtCGM	RCT	6 months	Adjusted between-group difference: − 0.3%, P = 0.022	No significant difference in CGM-measured hypoglycemia between the groups
Haak et al. (2017) [28]	224 adults with T2DM	MDI	isCGM	RCT	6 months	No significant difference in HbA1c change	Time in hypoglycemia < 70 mg/dl (3.9 mmol/l) reduced for isCGM group compared with controls by 43% (P < 0.001)
Yaron et al. (2019) [26]	101 adults with T2DM	MDI	isCGM	RCT	10 weeks	Adjusted between-group difference: − 0.5%, P < 0.001	N/A
Karter et al. (2021) [27]	36,080 adults with T2DM	MDI	rtCGM	Retrospective cohort study	12 months	Adjusted between-group difference: − 0.6%, P < 0.001	N/A
Kim et al. (2024) [29]	159 adults with T2DM	MDI	isCGM	RCT	24 weeks	Adjusted between-group difference: − 0.4%, P = 0.019	No significant difference in TIR between the groups
MOBILE study: Martens et al. (2021) [6]	175 adults with T2DM	Basal insulin	rtCGM	RCT	8 months	Adjusted between-group difference: − 0.4%, P < 0.001;	Adjusted between-group difference in mean TIR: – 16%, P < 0.001
Carlson et al. (2022) [30]	100 adults with T2DM	Basal insulin	isCGM	Retrospective single-arm	3–6 months	isCGM use reduced HbA1c by 1.4%, P < 0.001	N/A
Steno2tech study: Lind et al. (2024) [31]	76 adults with T2DM	MDI or basal insulin	rtCGM	RCT	12 months	Adjusted between-group difference: − 0.9%, P = 0.002	Adjusted between-group difference in TIR: 15.2%, P = 0.006
Shields et al. (2024) [58]	182 adults with T2DM	Non-insulin or basal insulin	rtCGM	Prospective with matched controls	3 months	Adjusted between-group difference: − 0.62%, P < 0.01	rtCGM increased mean TIR by 22.2%, P < 0.001
Wada et al. (2020) [9]	93 adults with T2DM	OHA	isCGM	RCT	24 weeks	Adjusted between-group difference: − 0.3%, P = 0.022	Adjusted between-group difference in TIR: 9.8%, P < 0.001
Price et al. (2021) [34]	70 adults with T2DM	OHA	rtCGM	RCT	12 weeks	No significant difference in HbA1c change	Mean TIR increased 6.8% in rtCGM group and decreased by 13.3% in the controls
IMMEDIATE study: Aronson et al. (2023) [17]	116 adults with T2DM	OHA	isCGM	RCT	16 weeks	Adjusted between-group difference: − 0.3%, P = 0.048	Mean TIR greater in isCGM group by 9.9%, P < 0.01
Moon et al. (2023) [32]	61 adults with T2DM	OHA	rtCGM	RCT	6 months	Adjusted between-group difference: − 0.7%, P = 0.018; (significant in subgroup using rtCGM for 1 week twice at 3-month intervals)	No significant difference in TIR
Layne et al. (2024) [33]	3840 adults with T2DM	OHA	rtCGM	Retrospective single-arm	12 months	N/A	rtCGM increased mean TIR by 17.3%, P < 0.001

CGM continuous glucose monitoring system, SMBG self-monitoring of blood glucose, T2DM type 2 diabetes mellitus, MDI multiple daily insulin injection, RCT randomized controlled trial, HbA1c glycosylated hemoglobin, OHA oral hypoglycemic agents, TIR time in range

The Patient-Driven Lifestyle Modification Using isCGM

T2DM requires lifelong self-management, yet individualized nutrition therapy remains challenging because postprandial glucose responses vary widely among individuals to identical meals [40]. SMBG provides only intermittent measurements and involves procedural burden, while CGM offers continuous data without frequent finger-pricks; however, patients often struggle to translate this abundance of information into everyday dietary decisions.

The PDF study [41] addressed this by introducing the SEOUL algorithm, a simple patient-centered framework that converts CGM data into immediate dietary decisions. The algorithm uses a 2 × 2 matrix based on perceived dietary quality and postprandial glucose response (Fig. 2). Depending on where a food falls within this matrix, patients are guided to maintain current intake, reduce portion size, or limit/avoid specific foods. The algorithm does not provide a list of healthy/unhealthy foods or glycemic thresholds of postprandial hyperglycemia; instead, patients make these judgments themselves, which minimizes the educational burden of clinicians.

Fig. 2.

The SEOUL algorithm. The SEOUL algorithm provides a simple 2 × 2 decision matrix that helps patients use CGM feedback to make immediate dietary adjustments. The horizontal axis represents the patient’s own assessment of whether the food consumed is generally healthy, while the vertical axis reflects whether it produced an excessive postprandial glycemic excursion. By integrating these two intuitive judgments, the matrix guides the patient toward one of several actions: maintaining their current eating pattern when a generally healthy food elicits minimal postprandial glycemic excursion; reducing the portion when either the food is perceived as unhealthy or the glycemic excursion is high; or avoiding the food when it is both considered unhealthy and associated with a marked postprandial rise. Because the algorithm relies on the patient’s subjective evaluation rather than predefined food categories or numeric thresholds, it supports individualized and flexible real-time decision-making. SEOUL stands for Self-Evaluation Of Unhealthy foods by Looking at postprandial glucose. Adapted from “Effects of Patient-Driven Lifestyle Modification Using Intermittently Scanned Continuous Glucose Monitoring in Patients With Type 2 Diabetes: Results From the Randomized Open-label PDF Study,” Diabetes Care. 2022;45(10):2224–2230, with permission from the American Diabetes Association

In this 12-week multicenter RCT involving 126 adults with T2DM on stable oral agents or basal insulin, the isCGM plus the SEOUL algorithm group achieved significantly greater improvements in glycemic control than standard SMBG group. The intervention group showed greater HbA1c reduction of − 0.50% (95% CI − 0.74 to − 0.26; P < 0.001), reaching a final mean HbA1c of 7.3% compared with 7.8% in the control group. Fasting glucose decreased by − 16.5 mg/dl (− 0.9 mmol/l), and body weight was reduced by − 1.5 kg without increases in insulin dose, indicating behavior-driven benefit. Higher engagement with CGM and consistent use of the algorithm were associated with larger HbA1c reductions.

Patient-reported outcomes also improved. Summary of Diabetes Self-Care Activities Questionnaire scores increased in dietary management, physical activity, and foot care. No severe hypoglycemic or hyperglycemic episodes occurred, supporting the safety and practicality of this self-directed approach.

Collectively, these findings show that pairing isCGM with a simple decision-support tool helps patients translate complex glucose data into practical, dietary decisions. Although the PDF study implemented a paper-based format with diaries and algorithm sheets, the framework is readily adaptable to digital platforms that integrate CGM data, generate individualized feedback, and monitor adherence. Such digital adoption could enhance accessibility, scalability, and long-term engagement while supporting personalized, evidence-informed decision-making among individuals not requiring intensive insulin therapy. These results highlight the potential of simple decision-support tools to advance precision nutrition in real-world diabetes care.

CGM in Prediabetes

CGM facilitates behavioral modification in the prediabetic population by providing real-time, individualized glycemic feedback that directly translates lifestyle choices into observable physiological consequences. This mechanism operates through three synergistic pathways: First, individuals gain direct evidence of how dietary composition (carbohydrate quantity and quality) and physical activity patterns modulate glucose fluctuations, transforming abstract health recommendations into concrete, personalized metabolic insights that serve as potent motivators for behavioral adherence. Second, CGM enables structured self-experimentation, whereby patients modify diet or activity and observe immediate glycemic consequences, deepening metabolic self-awareness and fostering understanding of individualized glucose responses. Third, objective feedback on physical activity’s direct impact on glucose control enhances self-efficacy in exercise goal-setting and reinforces sustained exercise adherence.

Recent trials have demonstrated the efficacy of multimodal, technology-enabled interventions in achieving meaningful metabolic improvements in high-risk populations. A 2025 RCT by Basiri and Rajanala [42] integrated rtCGM feedback into individualized nutrition therapy for 30 individuals with prediabetes and overweight or obesity. Over 30 days, the CGM-guided group showed significant increases in whole-grain and plant-based protein intake, with trends toward higher fruit consumption and lower carbohydrate intake. Sleep efficiency also improved by 5%, suggesting CGM-enhanced nutrition therapy may improve glucose management through multiple pathways.

Extending this approach to structured diabetes prevention programs, Richardson et al. [43] demonstrated the feasibility of integrating CGM into the National Diabetes Prevention Program (DPP), using a single 60-min education session followed by 10 days of CGM wear. Among Arizona Cooperative Extension DPP participants, 77% consented to participate, 89% attended the education session, and 96% believed CGM should be offered regularly within the DPP. Participants reported making specific behavioral changes, including consuming more balanced meals, reducing portions, and timing physical activity around meals to minimize postprandial glucose excursions.

Building on these targeted approaches, Kitazawa et al. [44] combined isCGM with smartphone-based coaching over 12 weeks in individuals at high T2DM risk, showing significant improvements in glycemic variability (TIR increased 31.5 min/day, TBR reduced 8.9 min/day) and body mass index (BMI) (− 0.59 kg/m²), with reduced carbohydrate intake. These benefits were achieved without direct healthcare provider involvement, relying on algorithm-driven feedback. Similarly, Bailey et al. [45] demonstrated that integrated monitoring combining rtCGM and wearable activity trackers substantially enhanced participant awareness of how dietary and exercise behaviors affected glycemic outcomes. This concurrent multimodal feedback prompted data-driven dietary modifications and greater physical activity engagement, including increased exercise adherence and frequent interruption of sedentary behavior.

Ko et al. [46] extended these findings in a workplace setting, conducting a prospective cohort study that integrated CGM with structured education in 73 adults with prediabetes. The 8-week program combining 2 weeks of isCGM with personalized dietary education and mobile health coaching, resulting in significant reductions in body weight (79.7–78.5 kg, p < 0.001) and LDL cholesterol (124.5–113.8 mg/dl [6.9–6.3 mmol/l], p < 0.001), though HbA1c remained unchanged (5.9% to 5.9%, p = 0.108). These improvements were maintained at 6.4-month follow-up. The workplace setting facilitated high engagement through remote education and mobile app-based support, enabling sustained behavioral modification without direct healthcare provider involvement.

Collectively, these findings underscore that synchronized feedback from complementary monitoring modalities amplifies behavioral efficacy more substantially than isolated feedback streams. This effect is particularly pronounced when combined with personalized nutrition counseling or integrated into evidence-based programs like the National DPP, offering a promising approach for preventing progression to T2DM in high-risk populations.

Challenges and Limitations of CGM in Clinical Practice

Despite growing interest in the use of CGM, several challenges limit its adoption across all patient groups, largely due to high device costs and inadequate insurance coverage. These barriers are further magnified in low-income and rural settings, where digital and healthcare infrastructure are often inadequate. Policy analyses indicate that public insurance programs typically restrict CGM reimbursement to patients treated with insulin [47]. Philanthropic initiatives such as the Hong Kong Jockey Club Charities Trust have attempted to reduce disparities by providing free CGM access in certain regions, illustrating how targeted support programs can partially mitigate structural inequities [48].

In addition, the complexity and volume of CGM data can overwhelm patients who lack confidence in interpreting glucose trends or understanding their clinical significance. Uncertainty regarding acceptable glucose ranges represents a leading driver of CGM discontinuation, highlighting the cognitive burden many users experience when processing large datasets. Healthcare professionals face parallel challenges in efficiently analyzing extensive metrics during limited consultation time, often without standardized interpretation frameworks. Emerging AI-driven and software-based solutions—ranging from CGM data analytics platforms to applications analyzing meal photos or delivering automated, pattern-based feedback—show promise in reducing data complexity and enhancing patient self-management capability [49].

Structured education and personalized coaching are critical for maximizing the benefits of CGM. While some individuals intuitively grasp the relationship between behavior and glucose fluctuations, many require targeted training to overcome digital literacy barriers. Studies consistently demonstrate that CGM combined with structured education and coaching yields substantially superior outcomes compared with CGM use alone. For instance, a recent comprehensive telehealth model integrating remote coaching significantly improved patients’ self-management capacity and glycemic control [50]. Users who receive formal education also exhibit markedly improved adherence to dietary and physical-activity recommendations. Major clinical guidelines underscore that structured education and ongoing coaching are indispensable for effective CGM implementation [51]. Moreover, healthcare providers require sufficient training to interpret CGM data efficiently and support patients within the constraints of routine clinical practice.

A further limitation is the scarcity of long-term data in individuals with prediabetes and diet-treated T2DM. Most studies to date have been short in duration, making it unclear whether CGM-driven behavioral changes persist once real-time feedback is withdrawn. Research is needed to identify whether intermittent or periodic CGM use could maintain benefits while reducing cost and user burden. Additionally, future trials should incorporate objective measures such as accelerometers or detailed dietary records, rather than relying solely on self-report, to more accurately assess the behavioral impact of CGM.

Taken together, these limitations highlight the importance of considering not only access and usability but also the clinical relevance of CGM in different patient populations. Current evidence suggests that CGM is most beneficial for individuals who struggle with postprandial excursions, have difficulty interpreting SMBG patterns, or require personalized feedback to support lifestyle modification [26, 28]. However, CGM may offer limited incremental value for individuals who already maintain stable glycemia with structured lifestyle programs or who are unlikely to engage with feedback. Thus, the effectiveness of CGM largely depends on user readiness, educational support, and the capacity to act on glycemic patterns.

Diagnostic Potential of CGM Beyond Glucose Monitoring

Recent evidence suggests that CGM may extend beyond therapeutic use into the diagnostic realm, providing novel biomarkers for early dysglycemia and diabetes risk stratification. Traditional diagnostic tools such as fasting plasma glucose, HbA1c, and the oral glucose tolerance test have well-recognized limitation, including biological variability, ethnic bias, and inability to capture postprandial or intermittent hyperglycemia [52, 53]. These constraints underscore the need for complementary methods capable of reflecting dynamic glucose fluctuations in real-world settings.

Beyond behavioral intervention, CGM demonstrates emerging utility as a diagnostic adjunct, particularly when conventional tests yield incongruent results [54]. By continuously capturing interstitial glucose fluctuations, CGM enables quantification of dynamic indices such as time in tight range (TITR; 70–140 mg/dl [3.9–7.8 mmol/l]) and time above range (TAR; > 140 mg/dl [> 7.8 mmol/l]). These parameters provide more physiologic representation of glycemic behavior, particularly postprandial excursions that traditional markers often miss.

Real-world data support this diagnostic potential. Dunn et al. [55] demonstrated that TITR effectively discriminates between individuals with and without dysglycemia. More compellingly, Marco et al. [56] showed that TAR > 140 mg/dl (> 7.8 mmol/l) from short-term CGM independently predicted 5-year T2DM incidence (adjusted odds ratio 1.06 per 1% increase; area under the curve 0.94), even after adjusting for BMI and HbA1c. These findings indicate that CGM-derived parameters capture early glucose dysregulation preceding overt diabetes, positioning CGM as a valuable tool for early risk identification.

Beyond risk prediction, CGM enables mechanistic phenotyping of glycemic heterogeneity. Advanced analytic frameworks integrating CGM with dietary and microbiome data have begun to delineate interindividual differences in postprandial responses [57]. In one multimodal deep-learning model, continuous glucose traces, meal composition, and gut microbial profiles collectively explained heterogeneous glycemic responses among patients with T2DM, substantially outperforming carbohydrate-based predictors (R = 0.66 vs. 0.35). Such precision analytics could eventually refine diagnostic classification by identifying glycemic phenotypes not captured by current criteria.

Collectively, these studies position CGM as a potential diagnostic adjunct capable of detecting subtle dysglycemia, characterizing early disease trajectories, and personalizing prevention strategies. However, before CGM can be adopted for diagnostic use, standardized thresholds for CGM-derived indices, population-validated reference ranges, and cost-effectiveness analyses must be established.

Conclusions

Accumulating evidence strongly supports integrating CGM into T2DM management. CGM demonstrates substantial improvements in time in range and glycemic variability, reduces acute diabetes-related events, and enhances patient-reported outcomes.

CGM’s value lies in delivering high-resolution glycemic data that reveals individual responses to dietary and activity behaviors. This metabolic phenotyping enables precision-tailored lifestyle interventions across the therapeutic spectrum, from intensive insulin therapy to lifestyle management alone. Application to prediabetes enables early dysglycemia identification and personalized interventions before irreversible β-cell dysfunction.

Critical barriers remain. Cost presents a formidable obstacle, particularly for disadvantaged populations. Data overload necessitates sophisticated decision-support algorithms and intuitive platforms. Educational infrastructure must evolve to equip providers and patients with the necessary competencies. Knowledge gaps persist regarding durability of behavioral modifications, with limited longitudinal research examining translation into objective outcomes including complications and mortality.

CGM technology increasingly intersects with artificial intelligence, transforming it from passive monitoring to active decision support. Integration with digital therapeutics and closed-loop systems positions CGM as a cornerstone of comprehensive diabetes management platforms.

In conclusion, CGM has emerged as transformative technology reshaping T2DM management. However, translating this potential into equitable health improvements requires coordinated efforts: policy reforms ensuring accessibility, technological refinement, educational initiatives, and rigorous long-term research. As the field advances toward precision medicine, CGM stands poised as a catalyst for patient empowerment and personalized diabetes care across the continuum from prevention through chronic disease management.

Author Contributions

Heejun Son drafted the first version of the manuscript. Sun-Joon Moon and Young Min Cho critically reviewed and revised the manuscript for important intellectual content. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.

Funding

No funding or sponsorship was received for this study or publication of this article.

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Declarations

Conflict of interest

Young Min Cho received consultation fees from LG Chemical and Hanmi, research grants from Sanofi, AstraZeneca, and Daewoong Pharmaceuticals. Young Min Cho is an outside director of Daewoong Pharmaceuticals. Heejun Son and Sun-Joon Moon have no conflict of interest to disclose.

Ethical Approval

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors. We thank the investigators and participants of the studies that formed the evidence base for this review.