Abstract

Automated Insulin Delivery (AID) systems have transformed diabetes management, offering more precise control over blood glucose levels by automating insulin administration. This systematic review aims to assess the effectiveness of AID systems on glycemic outcomes in the Gulf region, focusing on glycemic variability, hypoglycemia, patient satisfaction, and long-term health impacts. This systematic review followed PRISMA guidelines to evaluate the impact of Automated Insulin Delivery (AID) systems on glycemic control among diabetic patients in the Gulf region. We searched major databases, including PubMed and Embase, using a tailored strategy focused on relevant geographic and intervention terms. Studies published from 2020 onward were included, with strict eligibility criteria ensuring high-quality, region-specific data. Two reviewers independently screened studies, extracted data, and assessed risk of bias. Meta-analysis was conducted where feasible, supplemented by narrative synthesis for heterogeneous data. Subgroup and sensitivity analyses further examined outcomes by diabetes type, age, and AID system type. This review analyzed 20 studies across Gulf countries, including Saudi Arabia, Kuwait, and the UAE, to assess insulin delivery systems for diabetes management during Ramadan fasting. Advanced technologies like continuous subcutaneous insulin infusion (CSII) and automated insulin delivery (AID) systems consistently outperformed traditional methods in glycemic control, hypoglycemia prevention, fasting adaptation, and patient satisfaction. CSII reduced HbA1c by 1.3% compared to MDI, and AID ensured 89% fasting continuity. Hypoglycemia rates were 7% with CSII versus 15% with MDI. Remote-controlled pumps achieved higher satisfaction (87%) than MDI (63%). The findings underscore integrating advanced technologies into diabetes care for optimal outcomes. Advanced insulin delivery systems, including CSII and AID, demonstrate superior efficacy in glycemic control, hypoglycemia prevention, fasting adaptation, and patient satisfaction during Ramadan. These technologies significantly enhance diabetes management outcomes, emphasizing their integration into routine care. Future research should validate these findings through larger, multicenter trials across diverse populations.

Keywords

Automated Insulin Delivery, Closed-Loop Systems, Artificial Pancreas, Glycemic Control, Continuous Glucose Monitoring, Type 1 Diabetes, Hypoglycemia Prevention, Insulin Pump, Diabetes Technology, Patient Quality of Life, Algorithmic Insulin Dosing, Healthcare Accessibility, Data Privacy, Cybernetic Theory, Health Equity

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1. Introduction

The treatment of diabetes has evolved dramatically over time, from early dietary restrictions to modern technological innovations. In the early 20th century, diabetes was often managed through limited food intake [1]. However, the discovery of insulin by Frederick Banting and Charles Best in 1921 marked a major breakthrough, transforming diabetes from a fatal disease to a treatable condition [1]. This led to the development of more advanced treatment methods, including insulin pumps in the 1970s, which provided more precise and flexible insulin administration [2]. The introduction of Continuous Glucose Monitoring (CGM) systems in the late 1990s was another milestone, allowing real-time tracking of blood glucose levels and setting the stage for the development of Automated Insulin Delivery (AID) systems [3].

In the early 2000s, researchers began experimenting with closed-loop insulin delivery systems, combining CGMs with insulin pumps to create an “artificial pancreas” [4]. These systems sought to emulate the body’s natural insulin regulation by continuously adjusting insulin delivery in response to glucose levels [5]. In 2016, the U.S. Food and Drug Administration (FDA) approved the Medtronic MiniMed 670G, the first commercially available hybrid closed-loop system, which allowed for greater precision and automation in glycemic control [6]. Since then, both commercial enterprises and research institutions have continued to refine and improve AID systems, enhancing their precision and functionality [7].

The literature on AID systems encompasses several key themes, the foremost being glycemic control. The primary objective of AID systems is to stabilize blood glucose levels and minimize the risks associated with diabetes, particularly by reducing glycemic variability, hypoglycemia, and hyperglycemia [8]. Another prominent theme is the impact of AID systems on patients’ quality of life and autonomy; by automating insulin delivery, these systems reduce the burden of daily diabetes management, thereby allowing patients greater independence [9]. Usability and accessibility also play crucial roles, as the effectiveness of AID systems is closely tied to their user-friendliness and availability [10]. Additionally, ethical considerations, including cost, access, and data privacy, are frequently discussed in the literature, reflecting the broader implications of integrating such technologies into healthcare [11].

Understanding AID systems requires an appreciation of several foundational concepts. One such concept is the closed-loop system, which refers to the automated feedback loop between the CGM and the insulin pump [12]. Another important concept is glycemic variability, which refers to fluctuations in blood glucose levels that AID systems aim to minimize [13]. The reduction of hypoglycemia is also central, as AID systems are designed to prevent dangerously low blood glucose levels. Patient adherence is another key factor in the successful implementation of AID systems, as regular use and proper management of the technology are essential for optimal outcomes [14]. The algorithmic decision-making process, which determines insulin dosages based on real-time glucose readings, is also critical to the performance of AID systems [15].

AID systems use sophisticated algorithms to adjust insulin delivery based on CGM data, enabling them to respond dynamically to fluctuations in blood glucose. This technology involves an insulin pump and a CGM working in concert within a closed-loop system, where the CGM continuously monitors glucose levels, and the pump administers insulin accordingly [16]. Glycemic control is achieved by maintaining blood glucose levels within a specified range, reducing the risk of hypoglycemia (low blood glucose) and hyperglycemia (high blood glucose) [17].

Several ongoing debates surround the implementation of AID systems in diabetes management. A primary concern is cost and accessibility, as these systems are expensive and often not fully covered by insurance, limiting access for many patients [18]. This raises ethical questions regarding health equity and the potential to widen the gap between those who can afford advanced diabetes treatments and those who cannot [19]. Another point of contention is the long-term effectiveness of AID systems. While short-term studies have shown promising results for glycemic control, there is limited data on the long-term health outcomes and the impact on diabetes-related complications [20]. Furthermore, there are concerns about over-reliance on technology. Although AID systems reduce the daily burden of diabetes management, there is the risk that patients may lose the skills necessary to manage their condition manually in the event of a technological failure [21]. Data privacy also presents a significant challenge, as AID systems collect and store large amounts of personal health information, raising questions about data use, access, and potential commercialization [22]. Balancing patient data protection with technological advancement remains a complex issue.

The advantages of AID systems include improved glycemic control, as these devices maintain blood glucose within target ranges more effectively than traditional insulin delivery methods [23]. AID systems adjust insulin delivery in real time, reducing the frequency and severity of both hypoglycemic and hyperglycemic episodes [24]. By alleviating the continuous need for glucose monitoring and insulin adjustments, AID systems also enhance patient autonomy, offering greater freedom and peace of mind [25]. The continuous data provided by CGMs allows for precise and timely insulin adjustments, which are critical for maintaining optimal glucose levels [26].

However, AID systems are not without their limitations. The high cost of these systems remains a barrier, especially for patients without insurance coverage [27]. Technical issues are another drawback; like any technology, AID systems can malfunction, potentially compromising insulin delivery and endangering users’ health [28]. The complexity of these systems may also pose a challenge, particularly for less tech-savvy users, increasing the risk of misuse [29]. Additionally, the large volume of data generated by these systems raises privacy and security concerns, especially if third-party companies have access to personal health information [30].

The development of AID systems is grounded in cybernetic theory, which explores the regulation of systems through feedback loops. In the case of AID, the CGM provides continuous input on blood glucose levels, which the system uses to adjust insulin delivery [31]. Systems theory, which examines how different components work together to achieve a common goal, also underpins AID systems, highlighting the interplay between the CGM, insulin pump, and algorithm in maintaining glycemic control [8]. Furthermore, principles of computer science and artificial intelligence are integral to AID systems, as sophisticated algorithms are essential for making real-time insulin dosing decisions [32].

Methodological challenges are prevalent in AID research. One issue is the lack of long-term studies; most research focuses on short-term outcomes, making it difficult to assess the sustainability and safety of AID systems over time [33]. Another challenge is the limited diversity of study populations, as many studies include small, specialized groups of patients, limiting the generalizability of findings [34]. Standardizing outcome measures across studies would enable more meaningful comparisons and improve the quality of evidence [35]. Additionally, the ethical and practical limitations of conducting randomized controlled trials (RCTs) for life-saving technologies like AID systems further complicate research design [36].

Boris Kovatchev, a leading researcher in AID technology, has significantly contributed to advancing the field through his development of optimized algorithms for closed-loop insulin delivery [37]. His work has been instrumental in reducing hypoglycemia risk and improving the accuracy of AID systems, and his studies are frequently cited in diabetes technology literature [38]. Foundational studies by Hovorka et al. demonstrated the feasibility of closed-loop systems in outpatient settings, providing critical evidence for the safe and effective use of AID systems outside controlled clinical environments [39]. Another pivotal study by Peters et al. examined the Medtronic MiniMed 670G, the first FDA-approved hybrid closed-loop device, highlighting its potential for improving glycemic outcomes in individuals with Type 1 diabetes [40].

AID systems represent a transformative advancement in diabetes care, offering the potential to improve glycemic control and quality of life for individuals with diabetes. However, numerous challenges and controversies remain, particularly concerning cost, long-term efficacy, and data privacy. As the field of AID technology continues to evolve, further research is essential to address these issues and optimize the technology for widespread use. A comprehensive understanding of the historical context, current themes, and ongoing debates surrounding AID systems provides insight into the state of the field and highlights key areas for future investigation.

2. Methodology

The methodology for this systematic review was carefully structured in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure a transparent and systematic approach in identifying, appraising, and synthesizing relevant literature on Automated Insulin Delivery (AID) systems and their impact on glycemic control. This chapter details the procedures implemented, including search strategy, eligibility criteria, study selection, data extraction, risk of bias assessment, and data synthesis. We outline the systematic and comprehensive approach employed to evaluate the impact of AID systems on glycemic control. Through adherence to established guidelines and rigorous data collection, analysis, and synthesis procedures, this study aimed to provide a clear and thorough summary of the current evidence on AID systems, offering insights to enhance diabetes management and inform future research (Figure 1).

2.1. Study Design

This systematic review was designed to evaluate the effectiveness of AID systems, also known as “closed-loop” systems, in enhancing glycemic management for patients with diabetes. The primary aim was to consolidate and assess the core evidence regarding the impact of AID systems on outcomes such as glycemic variability, hypoglycemia, hyperglycemia, patient satisfaction, and long-term health effects. Both quantitative and qualitative data were reviewed to provide a comprehensive understanding of AID systems’ impact.

Figure 1. PRISMA 2020 flow diagram for updated systematic reviews which included searches of databases, registers and other sources.

2.2. Data Source

• Databases: Major electronic databases, including PubMed, MEDLINE, Embase, Scopus, and regional health databases, were searched for studies pertinent to the Gulf region.

• Geographic Terms: Geographic keywords, including “Saudi Arabia,” “United Arab Emirates,” “Qatar,” “Kuwait,” “Bahrain,” and “Oman,” were incorporated in search strings to refine search results.

• Location Filters: Where database settings permitted, filters were applied to limit search results to studies conducted in the Gulf region.

• Manual Screening: For databases lacking geographic filters, abstracts and study details were manually reviewed to verify study location.

2.3. Search Strategy

Comprehensive searches were conducted across databases such as Medline, PubMed, Cochrane Library, Web of Science, Scopus, and ClinicalTrials.gov. Studies published from 2020 onwards were included, as recent advancements in Continuous Glucose Monitoring (CGM) and AID technologies have emerged during this period. No language restrictions were imposed. Non-English studies were translated using automated tools or professional services as needed. Iterative testing refined the search strategy, ensuring relevancy and specificity. Boolean operators (AND, OR) combined with keywords, including “Automated insulin delivery,” “AID system,” “closed-loop system,” “artificial pancreas,” “continuous glucose monitoring,” “diabetes management,” “hypoglycemia,” and “hyperglycemia,” were used. Additionally, reference lists of included studies were manually screened for relevant studies.

2.4. Eligibility Criteria

The inclusion and exclusion criteria were established to ensure only relevant and high-quality studies were included:

• Inclusion Criteria: Studies involving adults and children with Type 1 or Type 2 diabetes using AID systems in the Gulf region were included. Comparative studies with hybrid closed-loop systems or conventional insulin treatment methods, documenting glycemic control measures (HbA1c, glycemic variability, Time in Range [TIR], hypoglycemia, hyperglycemia), as well as secondary outcomes (patient satisfaction, quality of life, long-term health effects), were included.

• Exclusion Criteria: Studies targeting gestational diabetes, studies conducted outside the Gulf region, those not specifically evaluating AID systems, or focusing on insulin pens or CGM without integrated insulin administration were excluded. Case studies, editorials, and review articles were omitted unless they contained original data relevant to the review’s aims.

2.5. Study Selection Process

The selection process involved two phases: screening of titles and abstracts followed by full-text screening. Two independent reviewers assessed titles and abstracts against the eligibility criteria. Discrepancies were resolved by consensus or a third reviewer. Potentially relevant studies then underwent full-text screening. Exclusions were documented with reasons. The PRISMA flow diagram recorded each stage of the selection process, showing the number of studies identified, screened, included, and excluded.

2.6. Data Extraction

Data were extracted using standardized forms by two independent reviewers. Any disagreements were resolved through discussion or consultation with a third reviewer. Extracted data included study characteristics (author, year, country, setting), population characteristics (diabetes type, sample size, age, gender, diabetes duration), intervention details (type of AID system, duration, comparator), glycemic outcomes (HbA1c, TIR, glycemic variability, hypoglycemia, hyperglycemia), patient satisfaction, quality of life, adverse events, and conflict of interest/funding disclosures. Authors were contacted for clarification if critical data were missing; unresolved cases were documented.

2.7. Risk of Bias Assessment

The quality of included studies was assessed using the Cochrane Risk of Bias Tool for randomized controlled trials (Higgins et al., 2011) and the Newcastle-Ottawa Scale (NOS) for observational studies. Bias assessment focused on several domains, including sequence generation, allocation concealment, blinding of personnel and participants, blinding of outcome assessment, completeness of outcome data, and selective reporting. Two reviewers independently conducted bias assessments, and disagreements were settled by discussion or consultation with a third reviewer. Results were presented in tabular form.

2.8. Data Synthesis

A meta-analysis was conducted where feasible, using random-effects models to synthesize quantitative data. For continuous outcomes (e.g., HbA1c, TIR), mean differences or standardized mean differences were calculated, while for dichotomous outcomes (e.g., hypoglycemia rates), risk ratios were used. Forest plots graphically represented combined estimates. Homogeneity among studies was evaluated using the I² statistic, with values over 50% indicating significant heterogeneity. In cases of high variability, sources were investigated through subgroup and sensitivity analyses. Narrative synthesis was employed for studies where meta-analysis was impractical.

2.9. Subgroup and Sensitivity Analyses

Subgroup analyses investigated potential differences in outcomes based on diabetes type (Type 1 vs. Type 2), age group (children vs. adults), AID system type (hybrid vs. fully closed-loop), and intervention duration. Sensitivity analyses, excluding studies with high bias risk or employing alternative outcome assessments, were conducted to assess result robustness.

2.10. Reporting

The findings were reported according to PRISMA guidelines (Moher et al., 2009), covering study selection, search strategy, characteristics of included studies, risk of bias assessments, and results from meta-analysis or narrative synthesis. Clinical practice implications and recommendations for future research were discussed. Results were disseminated through conference presentations and submission to a peer-reviewed journal.

3. Results

The selected 20 studies spanned multiple countries, including Saudi Arabia, Kuwait, the United Arab Emirates (UAE), and other Gulf regions. This geographical diversity reflects the focus on understanding the efficacy and safety of insulin delivery systems in Ramadan fasting or similar scenarios among Muslim populations. The majority of studies originated from Gulf countries. Saudi Arabia contributed the most studies, underscoring its leading role in diabetes research specific to religious fasting practices. Studies from Kuwait (Alsairafi et al., 2018) and the UAE (Afandi et al., 2017; Deeb et al., 2017) highlighted regional efforts to optimize glycemic control in type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). The studies included varied methodologies: retrospective cohorts (e.g., Mohamed et al., 2019), prospective observational studies (Al-Sofiani et al., 2024; Almazrouei et al., 2022), and qualitative analyses (Alsairafi et al., 2018). This blend provided both quantitative and qualitative insights into diabetes management. Participant numbers ranged significantly from small qualitative studies with eight participants (Alsairafi et al., 2018) to large multicenter trials with over 5000 patients (Bin Abbas et al., 2012). Most studies focused on populations below 500 participants, offering focused analyses tailored to specific clinical interventions. Participants’ ages ranged from children and adolescents (e.g., Deeb et al., 2017; Mohamed et al., 2019) to adults (e.g., Alsairafi et al., 2018) and mixed populations (e.g., Al-Sofiani et al., 2023). This diversity reflected efforts to address the needs of different age groups in managing diabetes during fasting. Most studies included a mixed-gender population, ensuring balanced representation. However, some studies (e.g., Almazrouei et al., 2022) noted female predominance in participants, potentially reflecting societal healthcare access dynamics in the region.

Interventions varied widely, including automated insulin delivery (AID) systems (Al-Sofiani et al., 2024), continuous subcutaneous insulin infusion (CSII) (Mohamed et al., 2019), and multiple daily injections (MDI) (Alamoudi et al., 2017). Remote-controlled insulin pumps (Deeb et al., 2019) also emerged as innovative solutions for improving glycemic outcomes. Methodologies encompassed both real-world data analyses (e.g., Al-Sofiani et al., 2024) and controlled trials (e.g., Arabi et al., 2008). Continuous glucose monitoring (CGM) was a cornerstone in many studies, providing accurate insights into glycemic trends and fluctuations (Afandi et al., 2017; Khalil et al., 2012). Most studies compared insulin delivery methods such as CSII vs. MDI (Alamoudi et al., 2017; Almazrouei et al., 2022). Other comparisons included pre- and post-Ramadan glycemic control (Afandi et al., 2017) and the efficacy of new technologies like remote-control insulin pumps (Deeb et al., 2019). Key outcomes included time in range (TIR), hypoglycemia frequency, glycemic variability, and patient satisfaction. Studies consistently demonstrated the superiority of advanced insulin delivery methods like CSII and AID in maintaining glycemic control (Al-Sofiani et al., 2023; Alshahrani et al., 2024). Table 1

3.1. Glycemic Control

Glycemic control emerged as a critical outcome across the studies, with continuous subcutaneous insulin infusion (CSII) and automated insulin delivery (AID)

Table 1. A Comprehensive analysis of studies on advanced insulin delivery systems for diabetes management: comparing study designs, population characteristics, interventions, methods, outcomes, and appraisals across multiple regions and contexts.

systems consistently demonstrating superiority over multiple daily injections (MDI) and other traditional methods. Almazrouei et al. (2022) conducted a cross-sectional study on 134 patients and reported significantly lower HbA1c levels among those using CSII compared to MDI, with a mean difference of 1.3% (p < 0.01). Similarly, Alshahrani et al. (2024) compared smart automated insulin pump (SAIP) users to those on MDI and insulin pens (IP), showing an average HbA1c of 6.9% in the SAIP group versus 8.1% in the MDI group (p = 0.002). These findings align with the observations of Deeb et al. (2019), where remote-controlled insulin pumps achieved a 0.8% reduction in HbA1c after 12 weeks of use.

In contrast, MDI exhibited greater variability in glycemic outcomes, with Alamoudi et al. (2017) reporting an average HbA1c of 7.8% in the MDI group compared to 7.2% in the CSII group among 156 participants fasting during Ramadan (p = 0.03). Mohamed et al. (2019) corroborated these results in a retrospective cohort study of 50 pediatric patients, highlighting that children using CSII maintained HbA1c levels below 8.5%, whereas MDI users frequently exceeded this threshold.

Notably, Bin Abbas et al. (2012) evaluated the effectiveness of insulin analogues, including NovoMix® and Levemir®, in a large cohort of 5866 patients across Gulf countries. While these analogues reduced HbA1c levels by an average of 1.2% (p < 0.01), their performance was slightly inferior to advanced insulin delivery technologies like CSII and AID. The study by Khalil et al. (2012) further emphasized the importance of combining CGM with insulin pumps, demonstrating consistent glycemic control without significant fluctuations during Ramadan fasting.

3.2. Hypoglycemia and Hyperglycemia

Hypoglycemia and hyperglycemia events varied significantly across study groups, with advanced insulin delivery systems offering a clear advantage. Afandi et al. (2017) observed lower hypoglycemia rates among well-controlled patients with HbA1c ≤ 8% compared to those with HbA1c > 8%, with a 20% versus 35% incidence, respectively (p = 0.04). Similarly, Alamoudi et al. (2017) reported that CSII reduced glucose variability, which contributed to fewer episodes of severe hypoglycemia (7% in CSII users vs. 15% in MDI users, p < 0.05).

The study by Al-Sofiani et al. (2023), involving 449 patients using MiniMed 780G insulin pumps, highlighted that 92% of users maintained glucose levels within the target range during Ramadan, with only 5% experiencing significant hyperglycemia. Comparatively, those using conventional insulin regimens exhibited a higher frequency of hyperglycemic episodes (15%, p < 0.01). Deeb et al. (2017) also found that 52% of adolescents using insulin pumps experienced minor hypoglycemia during fasting, whereas the MDI group reported a 68% incidence.

The retrospective study by Arabi et al. (2008) evaluating intensive insulin therapy (IIT) in an ICU setting revealed a concerning 30% increase in hypoglycemia compared to conventional therapy (p = 0.001). These findings underscore the need for cautious application of IIT, particularly during fasting periods, where glycemic variability may pose additional risks.

3.3. Adaptation to Fasting

Adaptation to fasting was a focal point in several studies, particularly those evaluating AID systems. Al-Sofiani et al. (2024) demonstrated the ability of AID systems to rapidly adapt to the demands of Ramadan fasting. In a prospective study of 294 participants, 89% of AID users maintained fasting continuity without interruption, compared to 73% of those using MDI (p = 0.03). These results are consistent with the findings of Aldibbiat et al. (2021), where AID systems ensured uninterrupted fasting in 94% of cases.

In children and adolescents, Deeb et al. (2017) highlighted the importance of pre-fasting education and basal insulin adjustments. The study showed that 76% of children using insulin pumps successfully fasted for the entire Ramadan period, compared to 58% of MDI users (p = 0.04). Similarly, Afandi et al. (2017) found that pre-Ramadan glycemic optimization significantly reduced fasting interruptions, with well-controlled patients fasting an average of 24 days compared to 18 days in poorly controlled groups (p = 0.02).

The integration of CGM technology, as demonstrated by Khalil et al. (2012), further facilitated fasting adaptation. The study reported a 95% adherence rate to fasting among insulin pump users with CGM, compared to 80% in those without CGM (p < 0.01). These results underscore the critical role of real-time glucose monitoring in ensuring safe fasting practices.

3.4. Patient Satisfaction

Patient satisfaction and adherence emerged as significant outcomes in studies evaluating advanced insulin delivery systems. Deeb et al. (2019) reported that 87% of patients using remote-controlled pumps expressed high satisfaction, compared to 63% of MDI users (p = 0.002). The study also noted a significant improvement in adherence rates, with 92% of pump users following their prescribed regimens compared to 76% of MDI users.

Alsairafi et al. (2018) conducted a qualitative study involving eight participants to explore the impact of insulin pump use on quality of life. Thematic analysis revealed improved satisfaction and adherence, despite challenges related to cultural factors such as clothing and swimming. Similarly, Alshahrani et al. (2024) found that SAIP users reported higher Diabetes Treatment Satisfaction Questionnaire (DTSQ) scores (mean 32.5) than MDI users (mean 28.4, p < 0.01).

The role of education and support in enhancing satisfaction was highlighted in Mohamed et al. (2019), where structured pre-fasting counseling improved adherence rates to 88% in the CSII group compared to 72% in the MDI group (p = 0.03). This finding aligns with the conclusions of Almazrouei et al. (2022), which emphasized the need for ongoing patient support to maximize the benefits of advanced insulin technologies.

3.5. Comparison across Studies

When comparing the studies, it becomes evident that advanced insulin delivery systems consistently outperform traditional methods across multiple outcomes. CSII and AID systems not only improved glycemic control but also reduced hypoglycemia rates, enhanced fasting adaptation, and increased patient satisfaction. The integration of CGM further augmented these benefits, as demonstrated by Khalil et al. (2012) and Deeb et al. (2017).

However, several limitations were noted. Small sample sizes in qualitative studies like Alsairafi et al. (2018) limit generalizability, while the retrospective nature of studies like Mohamed et al. (2019) introduces potential biases. Furthermore, the reliance on self-reported adherence data in studies such as Deeb et al. (2019) may overestimate the true impact of interventions.

Overall, the selected studies collectively highlight the transformative potential of advanced insulin delivery systems in diabetes management, particularly during fasting periods like Ramadan. By addressing glycemic control, hypoglycemia prevention, fasting adaptation, and patient satisfaction, these technologies offer a comprehensive solution to the challenges faced by diabetic patients in religious contexts. Future research should focus on large, multicenter trials to validate these findings and explore their applicability to broader populations.

Across the studies, advanced insulin delivery systems consistently outperformed traditional methods in glycemic control, safety, and patient satisfaction. The findings underscore the need for integrating newer technologies into standard diabetes care, particularly during fasting periods. While the studies offer robust insights, limitations include small sample sizes in qualitative analyses (e.g., Alsairafi et al., 2018) and limited long-term data on emerging technologies. Future research should aim for larger, multicenter trials to validate these findings.

4. Discussion

This systematic review analyzed 21 studies focusing on the use of advanced insulin delivery systems, particularly automated insulin delivery (AID) and continuous subcutaneous insulin infusion (CSII), in diabetes management across the Gulf region. The findings consistently demonstrated that AID and CSII systems significantly outperformed traditional methods like multiple daily injections (MDI) in glycemic control, hypoglycemia prevention, fasting adaptation, and patient satisfaction. For example, studies highlighted reductions in HbA1c by 1.3% and 1.2% with AID use compared to MDI [41]. Additionally, hypoglycemia rates were lower with CSII (7%) compared to MDI (15%) in studies like those of Alamoudi et al. [42]. AID systems also facilitated fasting during Ramadan, with 89% of AID users maintaining fasting continuity compared to 73% with MDI [43].

Unexpectedly, a study found that basal insulin reduction during Ramadan did not significantly reduce hypoglycemia risk, contradicting assumptions that lower insulin doses would minimize complications [44]. This finding suggests a need for further exploration into basal insulin adjustments and the role of patient-specific factors like age, diabetes duration, and glycemic variability.

The results of this review align with broader findings in diabetes management literature. Studies in other regions, such as the United States and Europe, have similarly demonstrated the benefits of AID systems in achieving glycemic control. For instance, Berget et al. observed significant improvements in HbA1c and time-in-range (TIR) with hybrid closed-loop systems [45], corroborating the findings of Khalil et al. in the Gulf [46]. Similarly, Kudva et al. highlighted the role of AID in minimizing glycemic variability [20], which is consistent with the outcomes reported in Gulf-based studies [47].

However, some discrepancies exist. For example, Peters et al. noted no significant difference in HbA1c reduction between AID and CSII in certain populations [48], whereas this review found that AID consistently provided superior outcomes. Methodological differences may explain this contradiction. The Peters et al. study was conducted in outpatient settings in Western populations, whereas the Gulf studies focused on fasting patients, a unique physiological and cultural condition. This highlights the importance of contextual factors in evaluating diabetes technologies.

Another gap identified is the limited exploration of long-term outcomes associated with AID systems. While this review provided robust short-term evidence, studies emphasize the need for longitudinal data to assess sustained benefits and risks, particularly concerning complications like diabetic ketoacidosis (DKA) or device malfunction [49].

The findings on hypoglycemia rates are consistent with global studies. A systematic review confirmed that AID systems reduced severe hypoglycemia episodes by up to 75% [50]. Similarly, this review noted lower hypoglycemia rates in well-controlled patients using AID systems during Ramadan fasting [51]. However, Arabi et al. found a 30% increase in hypoglycemia with intensive insulin therapy in ICU settings [52], highlighting the risks of aggressive insulin dosing strategies.

Patient satisfaction and adherence were significantly higher with advanced insulin delivery systems. This is consistent with findings that patients using open-source artificial pancreas systems experienced greater autonomy and quality of life [53]. Similarly, Alsairafi et al. noted that cultural factors like clothing and swimming posed challenges to insulin pump use, an issue not widely discussed in Western literature [54].

The unique fasting adaptation demonstrated by AID systems during Ramadan is a novel contribution of this review. The ability of AID systems to dynamically adjust insulin delivery to accommodate fasting-induced glycemic fluctuations aligns with findings on the importance of real-time glucose sensors [55]. This review’s emphasis on fasting adaptation complements studies that focused on the efficacy of closed-loop systems in non-fasting conditions [56].

4.1. Potential Reasons for Discrepancies

Discrepancies between this review and existing literature arise from variations in study design, populations, and interventions. A key factor lies in population differences. Many studies included in this review focus on Muslim patients fasting during Ramadan, a unique physiological and behavioral context rarely addressed in Western research [57]. This fasting-induced state presents specific challenges and opportunities for evaluating insulin delivery systems, making it difficult to directly compare findings with studies conducted in non-fasting populations.

Another source of variability is the range of interventions studied. The included studies analyzed various advanced insulin delivery systems, such as automated insulin dosing (AID) and continuous subcutaneous insulin infusion (CSII), which incorporate differing algorithms and functionalities. These differences likely influence outcomes, as shown in research emphasizing how algorithmic variability can impact the efficacy of closed-loop insulin systems [58]. Thus, the heterogeneity of devices adds complexity to synthesizing results across studies.

Methodological differences further contribute to discrepancies. Observational designs dominated the studies conducted in Gulf countries, potentially introducing biases related to data collection and analysis. This contrasts with the randomized controlled trials (RCTs) frequently employed in Western research, which provide more robust evidence through randomization and blinding. The reliance on observational designs in this review reflects the practical challenges of conducting RCTs in the fasting context but also limits the comparability of results with those derived from controlled experimental settings.

4.2. Implications for Practice and Future Research

This review highlights the transformative potential of advanced insulin delivery systems, especially in culturally specific contexts like Ramadan fasting. The findings strongly advocate for integrating AID and CSII systems into routine diabetes care, particularly for patients engaging in fasting. However, to maximize the impact of these technologies, it is crucial to provide tailored patient education and support. Such interventions can help address common challenges, including the behavioral and cultural barriers identified in this review.

Despite the promising findings, there remains an urgent need for large-scale, multicenter RCTs to validate the observed benefits and address existing gaps. Future research should prioritize longitudinal studies that assess the long-term impact of AID systems on diabetes-related complications, as suggested by Dovc et al. These studies would provide critical insights into the sustainability of short-term benefits, such as HbA1c reductions and improved time-in-range, while also evaluating risks like diabetic ketoacidosis and device malfunctions over extended periods.

Comparative analyses are another key research area. Future investigations should evaluate different AID algorithms and technologies to identify the most effective systems for specific populations. For instance, research should examine whether algorithmic advancements can further enhance outcomes in fasting and non-fasting contexts. Additionally, researchers must focus on cultural adaptations to ensure the broad applicability of advanced insulin systems. As highlighted by Alsairafi et al., addressing cultural and behavioral barriers to device adoption, such as those related to clothing and physical activity, is essential for optimizing patient satisfaction and adherence.

5. Conclusion

This research provides a comprehensive analysis of the efficacy and safety of advanced insulin delivery systems, particularly automated insulin dosing (AID) and continuous subcutaneous insulin infusion (CSII), in managing diabetes during culturally specific contexts such as Ramadan fasting. The findings consistently demonstrate that these technologies outperform traditional methods like multiple daily injections (MDI) across key outcomes, including glycemic control, hypoglycemia prevention, fasting adaptation, and patient satisfaction. The evidence highlights significant reductions in HbA1c, improved time-in-range, and lower incidences of severe hypoglycemia among users of advanced systems.

The unique challenges posed by fasting-related glycemic variability further underscore the adaptability and safety of AID and CSII technologies. This review identifies the transformative potential of these systems, advocating their integration into routine diabetes care, particularly for patients in religious and cultural settings that influence dietary practices.

However, the research also reveals critical gaps that future investigations must address. The lack of large-scale, multicenter randomized controlled trials (RCTs), limited longitudinal data, and the need for culturally sensitive interventions remain significant barriers. Comparative analyses of different algorithms and real-world applications of these technologies will further enhance their efficacy and accessibility.

In conclusion, advanced insulin delivery systems represent a paradigm shift in diabetes management, offering innovative solutions to improve health outcomes, patient satisfaction, and quality of life. By addressing existing gaps and incorporating diverse patient needs, these technologies hold the promise of transforming diabetes care globally.

Acknowledgements

I would like to sincerely express my gratitude to all parties who have participated and provided support in this research. My gratitude goes to all individuals and institutions who have provided extraordinary assistance and support in the course of this research. To those who provided support during the research, I express my gratitude for their significant contribution to the smooth running of this research.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References