Comparing Continuous Glucose Monitoring and Self-Monitoring of Blood Glucose in Type 2 Diabetes, United Arab Emirates

1. Introduction

Type 2 diabetes mellitus (T2DM) is one of the most significant non-communicable diseases of the twenty-first century and is characterized by chronic hyperglycaemia resulting from insulin resistance and relative insulin deficiency (Khan et al., 2023). The global prevalence of diabetes has increased alarmingly due to population ageing, sedentary lifestyles, dietary transitions, and rising obesity rates (Sun et al., 2022). In 2021, approximately 537 million adults worldwide were living with diabetes, and this number is projected to increase to 783 million by 2045 if effective interventions are not implemented (Sun et al., 2022). More recent estimates from the International Diabetes Federation (IDF) indicate that the global number of adults living with diabetes exceeded 589 million by 2024, underscoring the accelerating nature of this public health crisis (Saeedi et al., 2023).

Diabetes represents a major public health burden in the United Arab Emirates and the wider Gulf Cooperation Council (GCC) region. In the UAE, approximately 20–21% of adults are living with diabetes, placing the country among those with the highest prevalence globally (International Diabetes Federation [IDF], 2024). Similarly, GCC countries report diabetes prevalence rates ranging from 8% to over 22%, reflecting shared lifestyle, genetic, and socioeconomic risk factors across the region (Aljulifi, 2021; IDF, 2024).

This high regional burden, evaluating the real-world effectiveness of technologies such as continuous glucose monitoring (CGM) compared with self-monitoring of blood glucose (SMBG) is particularly important for informing clinical practice and health policy in the UAE and GCC contexts. Despite advances in pharmacological therapies and evidence-based clinical guidelines, a significant proportion of adults with T2DM fail to achieve recommended glycaemic targets, leaving many at increased risk of preventable microvascular and macrovascular complications (American Diabetes Association [ADA], 2024; Fang et al., 2022).

The economic burden of diabetes is likewise substantial. Global healthcare expenditures related to diabetes surpassed USD 966 billion in 2024, reflecting costs associated with medications, glucose monitoring, hospitalizations, and the management of complications (Saeedi et al., 2023). These trends emphasize the urgent need for improved disease-monitoring strategies that support early intervention, sustained self-management, and complication prevention. Consequently, advances in glucose-monitoring technologies have become central to contemporary diabetes management.

The rapid development of diabetes technology and the increasing use of CGM in non-insulin-treated and insulin-treated T2DM populations, this review prioritises contemporary literature, particularly studies published from 2021 onward. Attention is given to the UAE and Northern Emirates context, where region-specific evidence is limited.

Despite substantial advances in diabetes management globally, adults with T2DM in the United Arab Emirates (UAE) continue to experience suboptimal glycaemic control, with mean HbA1c levels frequently exceeding recommended targets (American Diabetes Association [ADA], 2024; Saeedi et al., 2023). In routine clinical practice, reliance on self-monitoring of blood glucose (SMBG) is associated with poor adherence due to discomfort, inconvenience, and the inability to capture glycaemic patterns such as nocturnal hypoglycaemia and postprandial hyperglycaemia (Grady et al., 2021). While continuous glucose monitoring (CGM) has demonstrated superior clinical and quality outcomes compared with self-monitoring of blood glucose (SMBG) in randomized controlled trials, the majority of this evidence is derived from well-resourced healthcare systems in North America and Europe (Uhl et al., 2024). Representation of Middle Eastern populations remains limited, despite the region’s disproportionately high burden of T2DM (Elbarbary and Deeb, 2023; Saeedi et al., 2023). Consequently, real world observational data from the UAE is essential to determine whether benefits observed in controlled trial settings translate into routine clinical practice within this context.

Benefits translate into routine clinical settings and to identify contextual barriers to CGM adoption. Furthermore, although self-care behaviours and self-efficacy are widely theorized as key mechanisms linking glucose-monitoring methods to glycaemic control, few studies concurrently examine behavioural and clinical variables or test these relationships using formal mediation or structural equation modelling approaches (Kim et al., 2023; Silva-Tinoco et al., 2023).Within the Northern Emirates particularly Dibba Fujairah CGM adoption stays low due to cost, limited awareness, and insufficient locally generated evidence to guide clinical decision-making and policy formulations. This study therefore addresses these gaps by comparing CGM and SMBG in terms of glycaemic control and quality of life among adults with T2DM in the UAE, while also examining self-care behaviours and self-efficacy within an integrated theoretical framework.

The findings of this study may support nurses in designing targeted educational interventions, assist clinicians in optimizing treatment plans, and inform policymakers regarding the integration of technology into diabetes care. By linking physiological outcomes with quality well-being, the study also supports a holistic approach to diabetes management aligned with nursing theory and UAE national health priorities (Khowaja et al., 2025).

1.1. Type 2 Diabetes Mellitus: Epidemiology and Clinical Burden

Type 2 diabetes mellitus is a chronic metabolic disorder characterised by insulin resistance and impaired glucose regulation. It represents one of the most prevalent non-communicable diseases worldwide. Recent estimates suggest that hundreds of millions of adults are living with diabetes globally, with further increases expected in the coming decades. More than 90% of diabetes cases are estimated to be T2DM, reflecting the growing influence of ageing populations, urbanisation, sedentary lifestyles, and dietary changes (Sun et al., 2022; Saeedi et al., 2023).

The burden of T2DM is particularly high in the Middle East and North Africa (MENA) region, where diabetes prevalence is among the highest globally. Rapid urbanisation reduced physical activity, high rates of obesity, and changes in dietary patterns have contributed to earlier disease onset and increased complication risk (Khowaja et al., 2025; Saeedi et al., 2023).

In the United Arab Emirates, diabetes represents a major public health challenge. High rates of obesity, sedentary behaviour, and genetic susceptibility contribute to the burden of T2DM. Regional studies indicate variability across the Emirates, with some areas reporting particularly high prevalence and persistent challenges in diabetes management (Domecq et al., 2021; AlKetbi et al., 2025). In the Northern Emirates, including Dibba Fujairah and, diabetes management may be further affected by sociocultural factors, limited health literacy, and access to specialised diabetes care. These contextual factors support the need for region-specific research on glucose-monitoring technologies and their outcomes.

1.2. Glycaemic Control in Adults with T2DM

Effective glycaemic control is central to preventing diabetes-related complications. Poor glycaemic control increases the risk of microvascular complications such as retinopathy, nephropathy, and neuropathy, as well as macrovascular complications such as coronary artery disease and stroke. HbA1c remains the standard clinical indicator of long-term glycaemic control, reflecting average blood glucose levels over approximately three months.

Current diabetes guidelines generally recommend an HbA1c target of below 7% for many non-pregnant adults, while emphasising individualised targets based on age, comorbidities, hypoglycaemia risk, and patient preferences (American Diabetes Association [ADA], 2024). Despite these recommendations, a substantial proportion of adults with T2DM do not consistently achieve recommended HbA1c targets. Studies suggest that adherence difficulties, lifestyle factors, monitoring practices, health literacy, and limited engagement with self-management contribute to suboptimal glycaemic control (Fang et al., 2022; Khan et al., 2023; Grammes et al., 2023).

Glucose monitoring is therefore an essential part of diabetes management. Monitoring allows patients and healthcare providers to show glucose patterns, evaluate treatment response, and guide medication, dietary, and lifestyle adjustments. However, the effectiveness of monitoring depends not only on the availability of glucose readings, but also on the patient’s ability to interpret and act upon glucose information.

1.3. Self-Monitoring of Blood Glucose

Self-monitoring of blood glucose has historically been the cornerstone of outpatient diabetes monitoring. SMBG provides capillary blood glucose readings through finger-prick testing and allows patients to make short-term decisions related to food intake, physical activity, and medication use.

Despite its widespread use, SMBG has several limitations. First, it provides only intermittent glucose readings and therefore may fail to capture post-prandial spikes, nocturnal hypoglycaemia, or daily glycaemic variability. Second, SMBG does not provide trend information or predictive alerts. Third, finger-prick testing may be painful, inconvenient, and burdensome, which can reduce adherence over time. Finally, patients may experience difficulty interpreting isolated glucose values, especially when structured diabetes education is limited (Grady et al., 2021; Elbarbary and Deeb, 2023).

These limitations are particularly relevant for adults with T2DM who require ongoing self-management support. If SMBG readings are infrequent or poorly interpreted, they may not translate into meaningful behavioural or therapeutic adjustments. Therefore, while SMBG remains useful, its capacity to support sustained glycaemic improvement may be limited without adequate education and clinical follow-up.

1.4. Continuous Glucose Monitoring

Continuous glucose monitoring represents an important technological advancement in diabetes care. CGM systems measure interstitial glucose at frequent intervals, often every 1–5 minutes, and provide dynamic glucose profiles, trend arrows, alerts for hypo- and hyperglycaemia, and summary metrics such as time in range (TIR). CGM devices are generally classified as real-time CGM or intermittently scanned CGM, both of which can support more proactive glucose management (Battelino et al., 2023; Rodbard, 2023).

Compared with SMBG, CGM provides more comprehensive information about glucose patterns and variability. This allows patients to observe how meals, activity, medication, illness, and stress affect glucose levels. In this way, CGM is not simply a monitoring device but also a behavioural feedback tool that can support self-care, learning, and timely decision-making.

International literature has documented several potential benefits of CGM, including HbA1c reduction, improved time in range, reduced glycaemic variability, fewer hypoglycaemic episodes, and improved treatment satisfaction (Jancev et al., 2024; Uhl et al., 2024). However, CGM use may also present challenges, including cost, insurance coverage, alarm fatigue, skin irritation, device discomfort, and the need for patient education in interpreting glucose data.

1.5. Comparative Evidence on CGM and SMBG for HbA1c Outcomes

The first objective of this study is to compare HbA1c levels between adults with T2DM using CGM and those using SMBG. Existing international evidence generally suggests that CGM is associated with modest but clinically meaningful improvements in glycaemic control compared with SMBG.

Randomised controlled trials and meta-analyses published in recent years have reported HbA1c reductions favouring CGM, with pooled estimates commonly ranging from approximately 0.3 to 0.5 percentage points. These effects appear stronger among individuals with higher baseline HbA1c, suggesting that patients with poorer initial glycaemic control may benefit most from CGM (Jancev et al., 2024; Uhl et al., 2024).

However, real-world evidence is more mixed. Observational studies often show within-person improvement after CGM initiation, but comparisons between CGM and SMBG users may be affected by channelling bias. In clinical practice, CGM may be prescribed preferentially to patients with poorer glycaemic control, more frequent hypoglycaemia, or more complex diabetes management needs. As a result, CGM users may appear to have worse baseline HbA1c than SMBG users, not because CGM is ineffective, but because CGM is often directed toward higher-risk patients (Bolinder et al., 2022; Elbarbary and Deeb, 2023).

This issue is directly relevant to the present study, which uses real-world medical record data from Dibba Fujairah. Therefore, findings must be interpreted in light of baseline differences between CGM and SMBG users.

1.6. Diabetes-Specific Quality of Life in T2DM

The second objective of this study is to compare diabetes-specific quality-of-life scores between adults with T2DM using CGM and those using SMBG. Quality of life is an important patient-centred outcome because diabetes management affects daily functioning, emotional well-being, treatment burden, social participation, and concerns about complications.

Diabetes-specific quality of life differs from general health-related quality of life because it focuses on the lived experience of diabetes and its management. It may include treatment satisfaction, diabetes distress, fear of hypoglycaemia, perceived burden of monitoring, confidence in self-management, and emotional responses to glucose fluctuations (Speight et al., 2022).

In clinical practice, quality of life is sometimes assessed using single-item global ratings. Although such measures do not capture all dimensions of diabetes-related quality of life, they are feasible in routine care and can provide a pragmatic summary of patients’ overall perceived well-being. In the present study, diabetes-specific quality of life was assessed using a single-item self-reported scale ranging from 1 to 10, extracted from medical records at baseline and follow-up.

1.7. Impact of CGM and SMBG on Quality of Life

Evidence regarding the impact of CGM on quality of life is less consistent than evidence for HbA1c. Some studies report that CGM improves treatment satisfaction, confidence in self-management, and reassurance by reducing uncertainty about glucose levels. CGM may also reduce fear of hypoglycaemia by providing alerts and trend data (Wu et al., 2023; Kwon and Moon, 2025).

However, improvements in overall or global quality of life are not always observed. CGM may introduce new burdens, including alarm fatigue, anxiety related to continuous data, skin irritation, cost concerns, and the need to respond frequently to glucose alerts. Therefore, the quality-of-life impact of CGM may depend on patient readiness, device type, duration of use, education, and the level of healthcare support provided (Elbarbary and Deeb, 2023; Wu et al., 2023).

This mixed evidence supports the importance of examining quality-of-life outcomes in local real-world settings. In the UAE context, cultural expectations, health literacy, technology acceptance, family support, and access to diabetes education may influence whether CGM improves patient-perceived quality of life.

1.8. Barriers and Facilitators to CGM Adoption

Although CGM has demonstrated clinical potential, adoption remains uneven across healthcare settings. Common barriers include high device and sensor costs, limited insurance coverage, limited clinician familiarity with CGM data interpretation, inadequate patient education, and concerns about device accuracy or comfort. These barriers may be particularly relevant in settings where CGM reimbursement is variable or where specialised diabetes services are not equally accessible (Elbarbary and Deeb, 2023).

Facilitators of CGM adoption include structured diabetes education, clinician endorsement, integration of CGM data into routine clinical visits, improved device accuracy, and patient training in responding to glucose trends. In the UAE, health system support, insurance policy, and culturally appropriate education may be important factors influencing CGM uptake among adults with T2DM.

Understanding barriers and facilitators is important because CGM effectiveness depends not only on wearing the device but also on using the information to support self-care behaviours. Therefore, CGM should be considered part of a broader diabetes-management system rather than a standalone intervention.

Recognizing barriers and facilitators is important because CGM effectiveness depends not only on wearing the device but also on using the information to support self-care behaviours. Therefore, CGM should be considered part of a broader diabetes-management system rather than a standalone intervention.

1.9. Theoretical Perspectives Underpinning Glucose Monitoring and Self-Management

The present study is informed by self-care theory, particularly Orem’s Self-Care Deficit Nursing Theory. This theory explains how individuals with chronic illness engage in activities necessary to maintain health and well-being. In T2DM, these activities include glucose monitoring, medication adherence, dietary regulation, physical activity, and recognition of glucose-related symptoms.

Within this framework, SMBG and CGM are both self-care tools, but they differ in the degree of feedback provided. SMBG provides discrete glucose readings, while CGM provides continuous feedback, alerts, and trends. Therefore, CGM may strengthen self-care agency by improving patients’ ability to recognize glucose patterns and make timely behavioural adjustments.

Social Cognitive Theory also supports the role of CGM in diabetes self-management. Continuous visual feedback allows patients to observe the relationship between lifestyle behaviours and glucose outcomes. This feedback may reinforce positive behaviour change, increase self-efficacy, and support sustained glycaemic control.

However, both theories suggest that technology alone is insufficient. Patients require knowledge, confidence, motivation, and supportive education to translate glucose data into effective self-management. This is particularly relevant for nursing practice, where nurses play a central role in patient education, interpretation of glucose data, and reinforcement of self-care behaviours.

1.10. Regional and Local Evidence Gaps

Despite strong international evidence supporting CGM, regional and UAE-specific evidence remains limited. Much of the existing CGM literature originates from North America and Europe, where healthcare systems, reimbursement models, patient education structures, and technology access differ from those in the UAE.

Within the UAE, many diabetes technology studies have focused on type 1 diabetes, paediatric populations, or specialised urban centres. There is limited adult-focused evidence examining CGM among adults with T2DM in routine clinical settings, particularly in the Northern Emirates. Moreover, few studies examine HbA1c and quality of life concurrently, despite the importance of both biomedical and patient-centred outcomes.

This gap is especially relevant to Dibba Fujairah, where local contextual factors may influence diabetes self-management and technology use. Therefore, evidence from this setting is needed to inform clinical practice, nursing education, diabetes service planning, and future policy decisions.

1.11. Summary of Literature Review and Identified Gaps

The literature indicates that CGM has potential advantages over SMBG in improving glycaemic control, particularly among adults with higher baseline HbA1c. CGM provides continuous feedback, trend data, and alerts that may support better self-management compared with intermittent SMBG readings. However, real-world comparisons are complicated by channelling bias, as CGM may be prescribed to patients with poorer baseline control.

Evidence regarding quality of life is less consistent. CGM may improve treatment satisfaction, confidence, and fear of hypoglycaemia, but overall quality-of-life improvements vary across studies and contexts. CGM may also introduce new burdens such as alarm fatigue, cost, and information overload.

1.12. Several gaps remain in literature:

• Limited adult-focused CGM research in the Northern Emirates, UAE.
• Limited real-world comparative evidence between CGM and SMBG among adults with T2DM.
• Limited evidence linking CGM use to diabetes-specific quality of life in the UAE.
• Scarcity of studies examining HbA1c and quality of life together.
• Need for context-specific evidence from Dibba Fujairah to guide clinical and healthcare professional practice.

The present study addresses these gaps by comparing HbA1c and diabetes-specific quality-of-life outcomes between adults with T2DM using CGM and those using SMBG in Dibba Fujairah, UAE.

Prevalence of Diabetes in the United Arab Emirates

The reported prevalence, number of adults affected, and future projections refer specifically to the United Arab Emirates, while the comparison to being more than double the global average is provided to contextualise the UAE’s burden internationally. The United Arab Emirates (UAE) is among the countries with the highest reported prevalence of diabetes worldwide. According to the International Diabetes Federation (IDF), diabetes prevalence among adults aged 20–79 years in the UAE reached 20.7% in 2024, which is more than twice the global average (Saeedi et al., 2023). This prevalence corresponds to approximately 1.27 million adults currently living with diabetes in the UAE, with projections estimating nearly 1.9 million affected individuals by 2050 if current trends continue (Saeedi et al., 2023).

Rapid urbanization, economic growth, physical inactivity, and dietary changes have contributed significantly to this elevated prevalence (Khan et al., 2023). An additional concern is the high proportion of undiagnosed diabetes cases, with approximately 64% of adults in the UAE unaware of their condition, resulting in delayed initiation of treatment and increased risk of complications (Saeedi et al., 2023).

Regional studies indicate substantial variability across the Emirates. Research conducted in Abu Dhabi reports diabetes prevalence rates exceeding 28%, with higher rates observed among males (Domecq et al., 2021; AlKetbi et al., 2025). In the Northern Emirates including Ras Al Khaimah, Fujairah, Umm Al Quwain, and Dibba diabetes management challenges are further exacerbated by sociocultural factors, limited health literacy, and unequal access to specialized care.et al., 2021). These factors highlight the importance of context-specific diabetes-management strategies and regionally grounded research.

Approaches to Glycaemic Monitoring

Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are the primary approaches used for routine glycaemic assessment in adults with T2DM (ADA, 2024). SMBG involves intermittent finger-stick capillary blood sampling and has historically been the cornerstone of outpatient diabetes management. However, SMBG provides only discrete glucose measurements and fails to capture nocturnal hypoglycaemia, postprandial excursions, and glycaemic variability throughout the day (Grady et al., 2021).

Pain, inconvenience, and testing fatigue further limit adherence, reducing the clinical utility of SMBG over time (Grady et al., 2021).The CGM represents a major advancement in glucose-monitoring technology by providing near-continuous interstitial glucose measurements, trend arrows, and custom is able alerts for hypo- and hyperglycaemia (Battelino et al., 2023; Rodbard, 2023). Recent randomized trials and meta-analyses consistently demonstrate that CGM use in adults with T2DM results in modest but clinically meaningful reductions in HbA1c, typically ranging from 0.3% to 0.5% when compared with SMBG (Jancev et al., 2024; Uhl et al., 2024). CGM has also been shown to reduce hypoglycaemic exposure and improve treatment satisfaction (Bolinder et al., 2022; Wu et al., 2023). Beyond glycaemic outcomes, CGM enhances self-efficacy by helping patients better understand the impact of diet, physical activity, stress, and medication adherence on glucose levels (Hermanns et al., 2022; Kim et al., 2023). However, barriers such as device cost, alarm fatigue, skin irritation, limited insurance coverage, and disparities in access remain significant, particularly in low- and middle-income settings and parts of the Middle East (Elbarbary and Deeb, 2023; Ng et al., 2022).

Regional Studies and Research Gaps

Despite extensive prevalence data, few regional studies have examined glucose-monitoring practices and the effectiveness of CGM in real-world clinical settings within the UAE. Much of the available evidence comparing CGM and SMBG originates from randomized clinical trials conducted in North America and Europe, limiting its generalizability to Middle Eastern populations (Elbarbary and Deeb, 2023). Consequently, there is insufficient regional evidence to guide clinicians and policymakers regarding the integration of CGM into routine care. Additional gaps exist in the assessment of quality-of-life outcomes. Many studies priorities treatment satisfaction rather than multidimensional diabetes-specific quality-of-life constructs and often fail to use culturally validated instruments (Alcubierre et al., 2021; Alzughbi et al., 2022). Moreover, although self-care behaviours and self-efficacy are widely theorized as mediators linking glucose-monitoring methods to clinical outcomes, few studies have tested these relationships using formal mediation models that integrate quality and biological data (Kim et al., 2023; Silva-Tinoco et al., 2023). These gaps underscore the need for region-specific, theory-driven research that evaluates CGM effectiveness, quality of life, and behavioural mechanisms within the UAE context.

2. Materials and Methods

2.1. Research Design

A quantitative, comparative observational research design was employed to examine differences in clinical and patient-reported outcomes between adults with type 2 diabetes mellitus (T2DM) using continuous glucose monitoring (CGM) and those using self-monitoring of blood glucose (SMBG). The study utilized routinely collected clinical data obtained from medical records. As participants were not randomly assigned to either glucose-monitoring modality, the design was intended to identify associations and group differences rather than establish causal relationships. Given that CGM may be more frequently prescribed to patients with poorer baseline glycaemic control, the study was potentially susceptible to confounding by indication (channeling bias). To minimize this limitation, baseline characteristics were compared between groups and multivariable regression analyses were conducted, as described in Section 3.10.

2.2. Study Setting

The study was conducted in Dibba Fujairah, United Arab Emirates, within outpatient diabetes clinics and primary healthcare facilities that provide routine follow-up care for adults diagnosed with T2DM. This setting was selected because of the high prevalence of T2DM in the region, the continuing challenges associated with achieving recommended glycaemic targets, the limited availability of adult-focused CGM research in the Northern Emirates, and the presence of structured medical records suitable for retrospective analysis.

2.3. Study Population and Sampling Strategy

2.3.1. Study Population

The study population consisted of adults diagnosed with T2DM who were receiving routine outpatient care at the selected healthcare facilities and who used either CGM or SMBG as part of their diabetes self-management during the study period.

2.3.2. Sampling Strategy

A purposive sampling strategy was adopted to select participants who met predefined clinical and data availability criteria relevant to the study objectives. This approach was considered appropriate because the research relied on retrospective medical record review and required participants to have a confirmed diagnosis of T2DM, documented use of either CGM or SMBG, complete baseline and follow-up HbA1c measurements, and documented diabetes-specific quality-of-life assessments. Only patients whose records contained complete and sufficiently detailed information were included in the study.

2.3.3. Sample Composition

Using this purposive sampling approach, a total of 416 adults with T2DM were included in the study, comprising 204 CGM users and 212 SMBG users. Although this sample does not represent the entire population of individuals with T2DM in Dibba Fujairah, it reflects a clinically relevant subgroup of patients with complete longitudinal documentation suitable for comparative analysis.

2.3.4. Rationale for Purposive Sampling

The sole source of data for this study was patients’ medical records maintained at the participating healthcare facilities, and no primary data collection was undertaken. Purposive sampling was therefore selected to enhance internal validity by ensuring the inclusion of records with complete and reliable data, reducing bias associated with missing baseline or follow-up measurements, capturing real-world clinical prescribing and monitoring practices, and ensuring direct alignment with the study objectives and research questions.

2.4. Inclusion and Exclusion Criteria

Participants were eligible for inclusion if they were aged 30 years or older, had a confirmed diagnosis of T2DM, had used either CGM or SMBG for a minimum of three months, possessed documented baseline and follow-up HbA1c measurements, and had a recorded diabetes-specific quality-of-life score within their medical records. Participants were excluded if they had a diagnosis of type 1 diabetes mellitus or gestational diabetes, used both CGM and SMBG concurrently, or had an acute illness or cognitive impairment that could compromise the reliability of the documented clinical information.

2.5. Data Source and Data Extraction Procedure

2.5.1. Medical Records as Data Source

Data were extracted retrospectively from patients’ medical records using a structured Microsoft Excel data extraction sheet developed specifically for this study. The extraction tool was designed to standardize the collection of information, minimize transcription errors, and ensure consistency across records. To protect confidentiality, each participant was assigned a unique study identification number, and no personally identifiable information was entered into the database.

2.5.2. Excel-Based Data Extraction Sheet

The Excel-based extraction sheet provided a standardized framework for collecting demographic, clinical, and patient-reported outcome data from medical records. Its use ensured consistency in data collection procedures and facilitated efficient preparation of the dataset for statistical analysis.

2.5.3. Variables Extracted from Medical Records

Information collected included demographic characteristics such as age and gender, glucose-monitoring modality (CGM or SMBG), CGM usage status where applicable, baseline and follow-up HbA1c values, frequency of hypoglycaemic and hyperglycaemic episodes, and diabetes-specific quality-of-life scores measured on a 1–10 scale at both baseline and follow-up.

2.6. Definition of Baseline and Follow-Up Measurements

2.6.1. Baseline Measurements

Baseline values for CGM users were defined as the most recent HbA1c measurement recorded before CGM initiation, whereas baseline values for SMBG users were defined as the earliest HbA1c measurement documented during the observation period. A baseline diabetes-specific quality-of-life score, recorded during routine clinical assessment, was also obtained from the medical records.

2.6.2. Follow-Up Measurements

Follow-up HbA1c values were defined as measurements obtained between 12 and 24 weeks after the baseline assessment, consistent with routine clinical monitoring schedules. Follow-up diabetes-specific quality-of-life scores were recorded at the subsequent clinic visit. Participants with incomplete follow-up information were excluded during the data-cleaning process to ensure data completeness.

2.7. Measurement of HbA1c

HbA1c values were obtained from routine laboratory investigations documented in patients’ medical records. All laboratory analyses were conducted using standardized and accredited methods approved by the regional health authority. No additional blood samples were collected specifically for research purposes.

2.8. Measurement of Diabetes-Specific Quality of Life

Diabetes-specific quality of life was assessed using a single-item self-reported scale ranging from 1 to 10 and routinely documented in the medical records during clinical care. Participants were asked to rate their overall quality of life as it related to living with and managing diabetes, with higher scores indicating a better perceived quality of life. This measure was selected because it was routinely available for all participants, was feasible for retrospective analysis, and reflected real-world clinical outcomes relevant to diabetes management.

2.9. Data Analysis Plan and Alignment with Research Questions

Data extracted into Microsoft Excel were imported into IBM SPSS Statistics Version 29 for analysis. For Research Question 1, descriptive statistics were used to summarize participant characteristics, independent-samples analyses were conducted to compare HbA1c outcomes between CGM and SMBG users, and paired analyses evaluated changes from baseline to follow-up within groups. Multivariable regression analyses were also performed to adjust for potential confounding variables. For Research Question 2, group comparisons were used to assess differences in diabetes-specific quality-of-life scores between monitoring modalities, while paired analyses examined changes over time within each group. Statistical significance was established at a p-value of less than .05.

2.10. Ethical Considerations

• ▪ RAK Medical and Health Sciences University (RAKMHSU) Research Ethics Committee (Reference No. RAKMHSU/RES/Post-Graduate/2025-26/6).
• ▪ Ministry of Health and Prevention (MOHAP) Research Ethics Committee (Reference No. MOHAP/REC/2025/85-2025-PG-N).
• ▪ Institutional ethical approval was also granted by the In addition, a supporting letter was obtained MOHAP portal using Bayanati No. 49316 to ensure compliance with UAE healthcare regulations. All data were fully de-identified before analysis, and participant confidentiality was maintained throughout the research process in accordance with national and international ethical standards

3. Results

3.1. Introduction

The statistical findings of the study examining differences in glycaemic control and diabetes-specific quality of life among adults with type 2 diabetes mellitus (T2DM) using continuous glucose monitoring (CGM) compared with those using self-monitoring of blood glucose (SMBG). Data were extracted retrospectively from patient medical records using a structured Microsoft Excel data-extraction sheet.

The results are organized according to the study objectives, research questions, and hypotheses. The first objective was to compare HbA1c levels between adults with T2DM using CGM and those using SMBG. The second objective was to compare diabetes-specific quality-of-life scores between the two monitoring groups.

A total of 416 adults with T2DM were included in the study, consisting of 204 CGM users and 212 SMBG users. Valid sample sizes differed slightly across variables because of incomplete documentation in some medical records. Continuous variables are presented as mean, standard deviation (SD), 95% confidence interval (CI), and p-value, where applicable. A p-value of less than .05 was considered statistically significant.

3.2. Overall Description of the Study Variables

Table 1 presents the overall descriptive statistics for the main study variables. The mean age of participants was 51.66 years (SD = 14.94), and the mean duration of diabetes was 11.79 years (SD = 12.33). The overall mean baseline HbA1c was 8.81% (SD = 3.17), indicating suboptimal glycaemic control in the study population. The mean diabetes-specific quality-of-life score decreased from 7.05 at baseline to 6.53 at follow-up.

3.3. Participant Characteristics by Monitoring Group

Participant characteristics were compared between CGM and SMBG users to describe baseline group differences. As shown in Table 2, CGM users were significantly younger than SMBG users. The mean age was 48.63 years (SD = 16.92) in the CGM group and 54.73 years (SD = 11.89) in the SMBG group, with a statistically significant difference between groups (p < .001).

Duration of diabetes also differed significantly between groups. CGM users had a shorter mean diabetes duration of 9.09 years (SD = 10.24), compared with 14.65 years (SD = 13.69) among SMBG users (p < .001).

3.4. Objective 1: Comparison of HbA1c Levels Between CGM and SMBG Users

The first research question examined whether HbA1c levels differed between adults with T2DM using CGM and those using SMBG.

Research Question 1: Is there a difference in HbA1c levels between adults with T2DM using CGM and those using SMBG?

Hypothesis 1: Adults with T2DM using CGM will demonstrate significantly better glycaemic control compared with those using SMBG.

As shown in Table 3, baseline HbA1c was significantly higher in the CGM group than in the SMBG group. The mean baseline HbA1c was 9.61% (SD = 3.69) among CGM users and 8.01% (SD = 2.30) among SMBG users. The between-group difference was statistically significant (p < .001).

This result should be interpreted cautiously. In real-world clinical settings, CGM may be prescribed preferentially to individuals with poorer glycaemic control or more complex diabetes management needs. Therefore, the higher HbA1c in the CGM group may reflect channelling bias, rather than a negative effect of CGM itself.

3.5. Objective 2: Comparison of Diabetes-Specific Quality of Life Between CGM and SMBG Users

The second research question examined whether diabetes-specific quality-of-life scores differed between CGM and SMBG users.

Research Question 2: Is there a difference in diabetes-specific quality-of-life scores between adults with T2DM using CGM and those using SMBG?

Hypothesis 2: Adults using CGM are expected to report higher diabetes-specific quality of life than those using SMBG.

Diabetes-specific quality of life was measured using a single-item self-reported scale ranging from 1 to 10, with higher scores indicating better perceived quality of life.

As shown in Table 4, baseline QoL scores were slightly higher in the CGM group (M = 7.16, SD = 1.57) than in the SMBG group (M = 6.95, SD = 1.16). However, this difference was not statistically significant (p = .142).

At follow-up, QoL scores remained similar between groups. The mean follow-up QoL score was 6.56 (SD = 1.49) among CGM users and 6.50 (SD = 1.01) among SMBG users. This difference was also not statistically significant (p = .605).

Although CGM users reported slightly higher mean QoL scores at both time points, the differences were small and did not reach statistical significance. Thus, the data do not provide sufficient evidence that CGM users experienced better diabetes-specific quality of life than SMBG users.

3.6. Change in Diabetes-Specific Quality of Life Over Time

Within group changes in diabetes-specific quality of life were examined to describe changes from baseline to follow-up within each monitoring group. Mean change was calculated as: Baseline QoL score − Follow-up QoL score Therefore, a positive mean change indicates a decline in QoL from baseline to follow-up. As shown in Table 5, QoL scores declined significantly over time in both groups. Among CGM users, the mean decline was 0.86 points (SD = 1.10), 95% CI [0.69, 1.03], p < .001. Among SMBG users, the mean decline was 0.53 points (SD = 0.47), 95% CI [0.46, 0.60], p < .001.

3.7. Supplementary Statistical Findings Relevant to Interpretation

Additional analyses were considered to support interpretation of the primary findings. These analyses were not treated as separate hypothesis tests but were used to clarify baseline differences between groups. A regression model predicting baseline HbA1c showed that monitoring group remained significantly associated with baseline HbA1c after adjustment for age, duration of diabetes, gender, and baseline QoL. Specifically, CGM use was associated with higher baseline HbA1c after adjustment (p < .001). Male gender (p = .004) and baseline QoL (p < .001) were also statistically significant predictors, while age and duration of diabetes were not significant predictors after adjustment.

A logistic regression model examining predictors of CGM use showed that baseline HbA1c significantly predicted CGM use. Each 1-percentage-point increase in baseline HbA1c was associated with higher odds of being a CGM user. This finding supports the interpretation that patients with poorer baseline glycaemic control were more likely to be using CGM, consistent with possible channeling bias. These supplementary findings strengthen the conclusion that CGM users entered the study with a more severe glycaemic profile than SMBG users. Therefore, between-group comparisons should be interpreted with caution.

3.8. Summary of Findings by Objective and Hypothesis

The study examined two main outcomes: HbA1c levels and diabetes-specific quality of life (QoL) among adults with T2DM using CGM compared with those using SMBG. For the first objective, the results showed a statistically significant difference in baseline HbA1c between the two groups. However, the direction of the finding was opposite to the hypothesis. CGM users had significantly higher baseline HbA1c than SMBG users (p < .001), showing poorer glycaemic control at baseline rather than better control. Therefore, H1 was rejected.

For the second objective, no statistically significant differences were found in diabetes-specific QoL scores between CGM and SMBG users at baseline (p = .142) or follow-up (p = .605). Although CGM users reported slightly higher mean QoL scores, the differences were not statistically significant. Therefore, H2 was rejected.

Overall, the findings indicate that CGM users had a more severe baseline glycaemic profile, while diabetes-specific QoL outcomes were similar between CGM and SMBG users. These results should be interpreted cautiously because the study used an observational design, and CGM may have been prescribed to patients with poorer glycaemic control.

3.9. Summary

The descriptive analysis showed that CGM users were significantly younger and had a shorter duration of diabetes than SMBG users. These baseline differences indicate that the groups were not fully equivalent at the start of the observation period.

For the first objective, CGM users had significantly higher baseline HbA1c than SMBG users. This finding did not support H1. Instead of demonstrating better glycaemic control, CGM users showed poorer baseline glycaemic control. Supplementary analyses suggested that higher baseline HbA1c was associated with CGM use, supporting the likelihood of channelling bias in the real-world clinical setting.

For the second objective, there were no statistically significant differences in diabetes-specific quality-of-life scores between CGM and SMBG users at baseline or follow-up. Therefore, H2 was not supported. Both groups demonstrated statistically significant declines in QoL from baseline to follow-up; however, the between-group QoL differences remained non-significant. Overall, the findings indicate that CGM users had a more severe baseline glycaemic profile, while diabetes-specific quality-of-life outcomes were similar between CGM and SMBG users. These results should be interpreted cautiously due to the observational design, baseline group differences, and potential channeling bias

4. Discussion

Type 2 diabetes mellitus (T2DM) remains a major public health problem globally and regionally, particularly in the United Arab Emirates (UAE), where suboptimal glycaemic control continues to contribute to preventable complications and increased healthcare utilization. Effective glucose monitoring is central to diabetes self-management because it supports treatment adjustment, lifestyle modification, and patient engagement. Continuous glucose monitoring (CGM) has been increasingly adopted as an alternative to self-monitoring of blood glucose (SMBG), yet evidence from real-world clinical settings in the UAE remains limited.

This study examined whether adults with T2DM using CGM differed from those using SMBG in relation to two main outcomes: HbA1c levels and diabetes-specific quality of life (QoL). The findings showed that CGM users had significantly higher baseline HbA1c than SMBG users, while no statistically significant differences were found in diabetes-specific QoL between groups at baseline or follow-up. Both hypotheses were therefore rejected.

4.1. Discussion of Participant Characteristics

The study included 416 adults with T2DM, comprising 204 CGM users and 212 SMBG users. The findings showed that CGM users were significantly younger than SMBG users and had a shorter duration of diabetes. These baseline differences are important because the study was observational and participants were not randomly assigned to CGM or SMBG.

The younger age profile among CGM users may reflect greater comfort with digital health technologies, higher readiness to adopt newer monitoring systems, or clinician preference for recommending CGM to patients perceived as more capable of using technology-based tools. In contrast, SMBG users were older and had longer diabetes duration, which may reflect established monitoring habits or lower transition to newer technologies.

These differences support the interpretation that monitoring method was not randomly distributed across patients. Instead, CGM use may have been influenced by clinical judgement, patient characteristics, or perceived need. This aligns with concerns raised in real-world CGM literature, where observational comparisons are often affected by channeling bias, meaning that patients selected for CGM may differ systematically from those using SMBG (Bolinder et al., 2022; Elbarbary and Deeb, 2023).

From the perspective of Orem’s Self-Care Deficit Nursing Theory, age, diabetes duration, and technology exposure may function as basic conditioning factors that influence self-care agency. Younger patients may have greater ability or willingness to engage with digital glucose feedback, whereas patients with longer diabetes duration may have established routines that shape monitoring behaviour. Therefore, the participant characteristics provide important context for interpreting the HbA1c and QoL findings.

4.2. Discussion of Research Question 1 and Hypothesis 1

Research Question 1: Is there a difference in HbA1c levels between adults with T2DM using CGM and those using SMBG?

Hypothesis 1: Adults with T2DM using CGM will demonstrate significantly better glycaemic control compared with those using SMBG.

The findings showed a statistically significant difference in baseline HbA1c between groups. However, the direction of the result was opposite to the expected hypothesis. CGM users had a higher mean baseline HbA1c (9.61%) compared with SMBG users (8.01%), indicating poorer glycaemic control among CGM users at baseline.

Therefore, H1 was rejected. This finding appears inconsistent with randomized controlled trials and meta-analyses showing that CGM is associated with modest but clinically meaningful reductions in HbA1c compared with SMBG. For example, recent reviews have reported HbA1c reductions of approximately 0.3–0.5 percentage points favoring CGM, particularly among individuals with higher baseline HbA1c (Jancev et al., 2024). However, the present study differed from randomized trials because it used retrospective medical-record data from routine clinical practice. Patients were not randomly allocated to CGM or SMBG, which limits causal interpretation.

A plausible explanation for the higher HbA1c in the CGM group is channeling bias. In real-world clinical settings, healthcare providers may prescribe CGM preferentially to patients with poorer glycaemic control, frequent glucose fluctuations, or more complex diabetes management needs. Therefore, CGM users may have entered the study with a more severe clinical profile. This explanation is supported by the supplementary analysis indicating that higher baseline HbA1c predicted CGM use.

Thus, the finding should not be interpreted as evidence that CGM worsens glycaemic control. Rather, the result suggests that CGM may have been directed toward patients who were already experiencing poorer glycaemic control. This interpretation is consistent with real-world evidence indicating that between-group comparisons of CGM and SMBG can be affected by baseline differences in patient severity (Bolinder et al., 2022; Elbarbary and Deeb, 2023).

Within Orem’s theoretical framework, CGM is expected to strengthen self-care agency by providing real-time glucose feedback, alerts, and trend data. However, enhanced self-care agency may not immediately translate into lower HbA1c if patients begin CGM with markedly elevated baseline values, limited diabetes knowledge, or insufficient support in interpreting CGM data. Therefore, the lack of support for H1 may reflect the complexity of real-world implementation rather than ineffectiveness of CGM itself.

4.3. Discussion of Research Question 2 and Hypothesis 2

Research Question 2: Is there a difference in diabetes-specific quality-of-life scores between adults with T2DM using CGM and those using SMBG?

Hypothesis 2: Adults using CGM are expected to report higher diabetes-specific quality of life than those using SMBG. The findings showed no statistically significant difference in diabetes-specific QoL between CGM and SMBG users at either baseline or follow-up. At baseline, CGM users had a slightly higher mean QoL score than SMBG users, but the difference was not statistically significant. At follow-up, QoL scores remained similar between the two groups.

Therefore, H2 was rejected. This finding partly differs from studies suggesting that CGM may improve selected QoL domains, particularly treatment satisfaction, reassurance, and reduced hypoglycaemia-related worry (Wu et al., 2023; Speight et al., 2022). However, the literature also indicates that improvements in global QoL are less consistent and may depend on factors such as device type, duration of CGM use, patient education, baseline distress, and ability to interpret glucose data. In the present study, QoL was measured using a single-item self-reported scale ranging from 1 to 10. This approach provided a pragmatic measure available in routine medical records, but it may not have captured specific dimensions of diabetes-related QoL such as treatment satisfaction, diabetes distress, fear of hypoglycaemia, emotional burden, or device-related inconvenience. Therefore, the absence of significant QoL differences may partly reflect the broad nature of the single-item measure.

Another possible explanation is that CGM may introduce both benefits and burdens. While CGM can reduce finger-stick testing and increase glucose awareness, it may also contribute to alarm fatigue, information overload, device discomfort, and anxiety about glucose fluctuations. These competing effects may reduce the likelihood of observing a clear improvement in overall QoL. This interpretation is consistent with literature showing that CGM-related QoL benefits are context-dependent rather than universal (Elbarbary and Deeb, 2023; Wu et al., 2023).

From the perspective of Orem’s Self-Care Deficit Nursing Theory, CGM may support self-care agency only when patients are able to use glucose information effectively. Without sufficient supportive-educative nursing input, patients may not fully benefit from CGM data. Therefore, the lack of significant QoL improvement may suggest that technology alone is insufficient; effective education, interpretation support, and follow-up counselling are required to translate CGM use into improved patient experience.

4.4. Discussion of Change in Quality of Life Over Time

Although no significant between-group differences were found in QoL, both CGM and SMBG groups showed statistically significant declines in QoL from baseline to follow-up. The mean decline was greater in the CGM group than in the SMBG group. However, because follow-up QoL did not differ significantly between groups, this within-group decline should be interpreted cautiously.

The decline in QoL may reflect the ongoing burden of living with T2DM, treatment fatigue, increased monitoring demands, or heightened awareness of disease management responsibilities. In the CGM group, continuous exposure to glucose data may increase awareness of glycaemic instability, which may not always improve perceived well-being. In the SMBG group, continued reliance on finger-stick testing may also contribute to ongoing treatment burden.

This finding highlights the importance of considering QoL as a dynamic outcome influenced by biomedical, behavioural, and psychosocial factors. It also reinforces the need for nurses and diabetes educators to assess patient experience over time rather than if technology adoption automatically improves QoL.

4.5. Interpretation in Relation to the Theoretical Framework

Orem’s Self-Care Deficit Nursing Theory provided the conceptual foundation for this study. The theory proposes that individuals with chronic illness require self-care agency to meet therapeutic self-care demands. In T2DM, these demands include glucose monitoring, medication adherence, dietary regulation, physical activity, and interpretation of glucose patterns.

CGM was conceptualized as a supportive technology that may enhance self-care agency by providing continuous glucose feedback. However, the findings suggest that CGM use alone did not result in better HbA1c or higher QoL in this real-world sample. This does not necessarily contradict Orem’s theory. Instead, the results suggest that self-care agency may depend not only on access to monitoring technology but also on patient education, clinical support, readiness to use data, and baseline disease severity.

The findings therefore support a more nuanced interpretation of the framework: CGM may reduce self-care deficits only when patients are supported to interpret and act upon glucose data. The supportive-educative role of nurses is central in this process. Without structured education and follow-up, CGM may provide information without necessarily producing measurable improvements in glycaemic control or QoL. Strengths of the Study

This study has several strengths. First, it addressed an important evidence gap by examining CGM and SMBG outcomes in a UAE real-world clinical setting, specifically in Dibba Fujairah. This contributes context-specific evidence from a region where adult-focused CGM research remains limited.

Second, the study included a relatively large sample of 416 adults with T2DM, allowing meaningful comparison between CGM and SMBG users. Third, the use of medical-record data reflects routine clinical practice and provides insight into real-world patterns of CGM use and patient outcomes.

Fourth, the study examined both a biomedical outcome, HbA1c, and a patient-reported outcome, diabetes-specific QoL. This dual focus aligns with contemporary diabetes care priorities, which emphasize both glycaemic targets and patient-centred outcomes.

4.6. Limitations of the Study

Several limitations should be acknowledged. First, observational design limits causal inference. Because patients were not randomly assigned to CGM or SMBG, differences between groups may reflect baseline clinical characteristics rather than the effect of monitoring modality.

Second, evidence of possible channelling bias was present. CGM users had significantly higher baseline HbA1c, suggesting that CGM may have been prescribed to patients with poorer glycaemic control. This limits the ability to compare CGM and SMBG as equivalent groups.

Third, QoL was measured using a single-item scale. Although this was feasible and available in medical records, it did not capture multidimensional aspects of diabetes-specific QoL such as emotional distress, treatment satisfaction, diabetes burden, or fear of hypoglycaemia.

Fourth, the study relied on retrospective medical-record data, which may be affected by incomplete documentation, variability in clinical recording, and missing data. Valid sample sizes differed across variables, which may have influenced the precision of estimates.

Finally, the study was conducted in one geographical setting, Dibba Fujairah, which may limit generalizability to other Emirates or healthcare systems. However, the local focus remains valuable because regional CGM evidence is limited.

5. Conclusions

Based on the study findings, the following conclusions were derived:

CGM users had significantly higher baseline HbA1c than SMBG users. This indicates that CGM users had poorer glycaemic control at baseline, and therefore H1 was rejected.

The higher baseline HbA1c among CGM users likely reflects real-world prescribing patterns. CGM may have been preferentially prescribed to patients with more difficult glycaemic control, suggesting possible channeling bias.

There was no statistically significant difference in diabetes-specific QoL between CGM and SMBG users at baseline or follow-up. Therefore, H2 was rejected.

Both CGM and SMBG users experienced significant declines in QoL over time. However, because differences between-group differences were not significant, this decline cannot be attributed to either monitoring method alone.

Technology alone may be insufficient to improve outcomes. CGM may require structured nursing education, patient training, and ongoing support to translate glucose data into meaningful self-care behaviours and improved outcomes.

The findings highlight the need for cautious interpretation of real-world CGM effectiveness. Baseline differences and channeling bias must be considered when evaluating CGM outcomes in non-randomized clinical settings.

5.1. Implications for Nursing Practice

The findings have important implications for nursing practice. Nurses play a central role in diabetes education, glucose monitoring support, and patient follow-up. The results suggest that CGM users may represent a group with poorer baseline glycaemic control and therefore may require more intensive nursing support.

Nurses should assess patients’ readiness to use CGM, ability to interpret glucose trends, and confidence in making behavioural adjustments. CGM education should not focus only on device application but should include interpretation of trend arrows, response to high and low glucose alerts, dietary adjustment, medication timing, and prevention of hypo-and hyperglycaemia.

The lack of significant QoL difference between CGM and SMBG users also suggests that nurses should routinely assess patient burden, frustration, alarm fatigue, and perceived usefulness of monitoring technologies. Patient-centred counselling may help ensure that CGM supports self-care rather than increasing anxiety or treatment burden.

5.2. Implications for Nursing Education

Nursing education programmers should strengthen content related to diabetes technology, including CGM interpretation, patient coaching, and digital health literacy. Nurses require competence not only in explaining device function but also in helping patients translate glucose data into self-care decisions.

Education should also emphasize critical appraisal of real-world evidence. Students and practicing nurses should understand that observational findings may be affected by channeling bias and confounding. This is important when interpreting whether CGM improves outcomes in routine clinical practice. Furthermore, nursing curricula should incorporate patient-centred outcomes such as QoL, diabetes distress, treatment burden, and self-efficacy. These outcomes are essential for holistic diabetes care.

5.3. Implications for Nursing Administration

Nursing administrators should consider developing structured CGM education pathways within diabetes clinics. These pathways may include initial CGM training, follow-up review, documentation standards, and referral processes for patients experiencing difficulty with device use.

Administrators should also support standardized documentation of HbA1c, QoL, hypoglycaemic episodes, and CGM-related education in medical records. Improved documentation would strengthen clinical continuity and support future quality-improvement audits.

The possible channeling bias observed in this study, healthcare organizations should establish clear criteria for CGM initiation and follow-up. This would help ensure that CGM is used appropriately and that high-risk patients receive sufficient support.

5.4. Recommendations for Further Research

• ▪ Based on the study findings and limitations, the following recommendations are proposed: Conduct prospective longitudinal studies to evaluate changes in HbA1c before and after CGM initiation using consistent follow-up intervals.
• ▪ Use matched comparison groups or propensity score methods to reduce channeling bias and improve comparability between CGM and SMBG users.
• ▪ Include multidimensional diabetes-specific QoL instruments in future studies to capture treatment satisfaction, emotional burden, diabetes distress, and fear of hypoglycaemia more comprehensively.
• ▪ Examine the role of nursing education and follow-up support in improving CGM-related outcomes, including self-care behaviour, HbA1c, and QoL.
• ▪ Expand research to other Emirates and GCC settings to improve generalisability and develop regionally relevant evidence for CGM implementation.
• ▪ Investigate patient experiences with CGM qualitatively, including barriers, facilitators, alarm fatigue, technology acceptance, and cultural factors affecting use.
• ▪ Assess longer-term outcomes, including hypoglycaemia, hyperglycaemia, emergency visits, hospitalisation, and cost-effectiveness.
• ▪ Evaluate CGM use in specific subgroups, such as older adults, patients with low health literacy, non-insulin-treated T2DM patients, and individuals with high baseline HbA1c.

Summary

The findings suggest that CGM users in this real-world clinical setting may have had more severe baseline glycaemic profiles, likely reflecting channeling bias. Although CGM has theoretical and evidence-based potential to improve diabetes self-management, this study indicates that technology alone may not be sufficient to improve HbA1c or QoL without structured education and clinical support. This study also highlighted implications for nursing practice, education, and administration, and presented recommendations for future research to strengthen CGM evidence in the UAE and wider regions.