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. 2025 Jul 21;16(9):1829–1840. doi: 10.1007/s13300-025-01772-1

Real-World Effectiveness of My Dose Coach™-Assisted Basal Insulin Titration in People with Type 2 Diabetes in Saudi Arabia and Kuwait

Mohammed E Al-Sofiani 1,2,✉, Mohamed Almehthel 3,4, Ebaa Al Ozairi 5, Jamaa Sadik 6, Lichen Hao 7, Yasser Akil 8
PMCID: PMC12399482  PMID: 40690110

Abstract

Introduction

My Dose Coach (MDC) is a digital smartphone application approved in multiple countries, including Saudi Arabia and Kuwait, to help people with type 2 diabetes (T2D) titrate their basal insulin as per their clinician-guided, individualized diabetes care plan.

Methods

A retrospective, observational cohort analysis was conducted on MDC user data collected from 1 January 2021 to 1 June 2023 in Saudi Arabia and Kuwait. Primary outcome was change in fasting blood glucose (FBG). Key secondary outcomes included time to achieve FBG and HbA1c targets, and time to first hypoglycemia event. Outcomes were analyzed by FBG target status and frequency of MDC usage (high: > 3 days per week; moderate: > 1– ≤ 3 days per week; low: ≤ 1 day per week).

Results

Among all users (N = 494), mean ± SD FBG decrease was −44.4 ± 72.5 mg/dL. Mean ± SD time to achieve FBG target was 14.8 ± 20.9 days and 12.8 ± 18.8, 29.1 ± 28.0, and 43.5 ± 41.7 days for high-, moderate-, and low-frequency MDC users, respectively. Individualized FBG targets were achieved by 276 (55.9%) users, and high-frequency of MDC use was associated with better target achievement (p < 0.01). Mean ± SD time to achieve HbA1c target was 48.0 ± 40.5 days. Reduction in HbA1c was more in high-frequency MDC users (18.3%) than low-frequency MDC users (6.3%). Mean ± SD time to the first hypoglycemia event was 4.86 ± 4.8 days. Hypoglycemia events were reported in only seven (1.4%) participants and not significantly correlated with MDC use frequency (p = 0.1431).

Conclusions

Current findings show that using MDC is associated with improved glycemic control in people with T2D in Saudi Arabia and Kuwait, with greater benefits observed with higher frequency MDC usage.

Keywords: Fasting blood glucose, Hypoglycemia, Mobile applications, My Dose Coach, Self-monitoring blood glucose, Type 2 diabetes

Key Summary Points

Why carry out this study?
The high prevalence of uncontrolled diabetes in Saudi Arabia and Kuwait underscores the urgent need for more effective diabetes management strategies.
Digital health technologies offer promising solutions to bridge the clinical care gap in personalized diabetes management. When utilized efficiently, these technologies can significantly reduce the burden on healthcare professionals.
The My Dose Coach (MDC) is an approved digital smartphone application that provides dose recommendations for people with type 2 diabetes (T2D) on the basis of their fasting blood glucose (FBG) readings, hypoglycemia data, and physician-directed titration plan. This study assessed the real-world clinical effectiveness of MDC in managing basal insulin titration among people with T2D in Saudi Arabia and Kuwait.
What was learned from this study?
The study results suggest that use of MDC is associated with improved glycemic control and low risk of hypoglycemia in people with T2D from Saudi Arabia and Kuwait.
Furthermore, more frequent (> 3 days/week) use of the MDC app was associated with greater benefits in achieving FBG target compared with moderate (> 1 – ≤3 days/week) and low (≤ 1 day/week) frequency users.
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Introduction

Diabetes continues to be a highly prevalent public health challenge worldwide, with type 2 diabetes (T2D) accounting for 90% of cases [1]. Globally, more than 11.1%, or 1 in 9 of the adult population (20–79 years), are living with diabetes, and it is a significant cause of mortality [1]. In 2019, the prevalence of T2D in the Middle East and North African (MENA) region (except Yemen and Türkiye) was higher than the average global rate [2]. Risk factors implicated in the increasing prevalence of T2D in the MENA region include an aging population, reduced physical activity, and increasing obesity [3]. The prevalence of poorly controlled diabetes in Saudi Arabia and Kuwait is reported to be as high as 60–75%, highlighting the unmet need for more effective approaches to diabetes management in the region [4–7].

The aim of diabetes management is to achieve individualized glycemic targets while avoiding hypoglycemia [8]. Many people with T2D find the addition of basal insulin (BI) effective, but there are several barriers to BI initiation and intensification [4, 9]. These include fear of hypoglycemia, clinical/therapeutic inertia, and delayed insulin titration between clinic visits because of lack of diabetes self-management skills among many people with T2D and shortage of diabetes specialists and specialized centers [4, 9–14]. These barriers generally lead to delayed initiation of BI, inadequate titration, and failure to adhere to prescribed BI treatments, thereby leading to the suboptimal glycemic control that is observed in real-world practice [9, 10, 15]. The VISION study revealed that 50.6% of people with diabetes initiated basal insulin and 46.3% initiated premixed insulin in the MENA region [15].

Accessibility-related challenges can also contribute to the suboptimal management of diabetes in the MENA region. Given there are fewer specialized diabetes centers than primary healthcare settings, dedicated personnel specializing in diabetes management (including educators, specialist nurses, and dieticians) are needed in primary settings to manage the volume of people with diabetes [13, 14, 16]. Digital health technologies have shown promise in addressing this clinical care gap and have the potential to alleviate some of the burden on healthcare professional (HCP) resources if used efficiently [17].

My Dose Coach application (MDC app) is a digital smartphone application designed to help people with T2D titrate BI according to the individualized titration plan defined by their HCP. The app is approved by the US and Saudi FDAs [18, 19], the Ministry of Health in Kuwait, and others [20]. The MDC app provides dose recommendations to people with T2D on the basis of their fasting blood glucose (FBG) readings and hypoglycemia data.

Despite the wide availability of digital heath technologies in the MENA region, there is paucity of clinical studies that address the potential role of mobile health systems in improving diabetes care. In a cross-sectional survey of people with diabetes in Saudi Arabia, more than two-thirds were aware of diabetes management technologies (including mobile health apps, flash glucose monitoring, continuous glucose monitoring, and insulin pumps) but less than 15% had used them [21]. Here, we assess the real-world clinical effectiveness of MDC use in BI titration and FBG control among people with T2D in Saudi Arabia and Kuwait.

Methods

Study Design and Population

This was a retrospective, observational cohort analysis of MDC user data collected from 1 January 2021 to 1 June 2023 (identification period). Data were collected from the MDC Pilot Program in Saudi Arabia and Kuwait; variables included sociodemographic and clinical characteristics (e.g., age, sex, type of insulin, etc.), glycemic variables (FBG, HbA1c), insulin dose, weight, hypoglycemia events, and the number of app users with at least one hypoglycemia event during their titration period.

An individualized care plan for each user was uploaded to the MDC app. Users then recorded FBG in the app as well as receiving individualized BI dose recommendations from the app on the basis of their care plan. HbA1c values were collected by the treating physician from the users’ medical records and recorded in the HCP portal. The titration period was defined by the activation date of the first care plan until the date the participant reached their FBG target, their last reported activation date, or deactivation of their last care plan.

People with T2D newly initiated to BI who had registered with MDC were eligible for inclusion. Users were required to have an activated care plan during the identification period (one titration period could include multiple care plans provided that the activation date of the following care plan was within 14 days after the end of the prior plan), and two or more recorded FBG measurements over a 2-week period (at least one per week), on or after the care plan activation date. The study did not use continuous glucose monitoring (CGM) for glucose monitoring and no other medication data were captured at baseline. Users with T2D with an activated care plan and 0, 1, or 2 FBG measurements occurring less than 2 weeks apart were excluded.

Ethical Approval

MDC users agreed to allow their anonymized data to be used retrospectively as part of the registration process. Institutional Review Board approval from King Saud University (ref. no. 22/0251/IRB), Saudi Arabia and Kuwait, was provided.

Outcomes

The primary outcome was change in FBG from baseline to end of titration window (i.e., 90 days), calculated by subtracting last FBG observation value from the first FBG observation value. Secondary outcomes included: time to achieve FBG target (days from initial care plan activation to the first of three consecutive FBG readings in target), change in HbA1c from baseline, time to achieve HbA1c target, insulin dose change (the change between first and last insulin dose within the titration period, i.e., 90 days), body weight change and time to first hypoglycemia event (number of days from start of care plan until the first recorded hypoglycemia event). Outcomes were also analyzed on the basis of FBG goal status and on MDC usage. FBG goal status was defined as achieved target, still titrating and stopped using MDC. MDC usage was defined as high if the app was used > 3 days per week, as moderate if the app was used > 1 day per week but ≤ 3 days per week, and low if the app was used ≤ 1 day or less per week.

Data Analysis

All MDC users who met the eligibility criteria were included in the analysis. The minimal sample size required to detect a significant difference of 40 mg/dL in mean FBG change from baseline to the end of the titration window was 118 participants. This was based on the following assumptions: 95% significance level, 80% power, standard deviation (SD) of difference of 90, and 64% dropout rate.

Results were analyzed using descriptive statistics, with number and percentage for categorical variables and means with SD/medians with interquartile ranges for continuous variables. Outcomes were assessed by FBG goal status (achieved target; still titrating; stopped using MDC) and by MDC usage (high: > 3 days/week; moderate: > 1– ≤ 3 days/week; low: ≤ 1 day/week). The percentage of MDC users who achieved FBG target was analyzed by chi-squared test for trend, and the association between MDC use and achieving FBG target was assessed using Fisher’s exact test.

Time to FBG target achievement according to MDC use was analyzed by a two-sample t-test. Mean change in FBG between MDC use groups was analyzed by linear regression, controlling for initial FBG value. Hypoglycemia incidence for MDC users who achieved FBG target was assessed by chi-squared test for trend, and the association between frequency of MDC app use and hypoglycemia was assessed using Fisher’s exact test.

Results

Study Participants

Of the 932 people with T2D registered with MDC since 1 January 2021, 881 were registered from Saudi Arabia, and 51 were registered from Kuwait. Among them, 536 participants reported active titration, while a total of 494 (Saudi Arabia, n = 470; Kuwait, n = 24) had at least 90 days’ follow-up and were included in the identification period (1 January 2021 to 1 June 2023) (Table 1). The mean age of active users was 50.4 years, with the majority (84%) aged 26–64 years. Overall, 278 (56.3%) participants were classified as having high MDC usage, 114 (23.1%) had moderate usage, and 102 (20.6%) had low usage.

Table 1.

MDC user characteristics at baseline

Characteristic Overall (N = 494)
Age (years), mean (SD) 50.4 (13.3)
Male/female, n (%) 241 (48.8)/253 (51.2)
Weight (kg), mean (SD) 82.7 (19.4)
HbA1c (%), mean (SD) 9.2 (2.3)
Starting basal insulin dose, mean (SD) 14.1 (17.0)
First FBG reading, mean (SD) mg/dL 170.5 (72.9)
Titration duration (days), mean (SD) 48.0 (40.5)
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FBG fasting blood glucose, HbA1c glycated hemoglobin, MDC My Dose Coach, n number of participants, N total number of participants, SD standard deviation

Glycemic Control

FBG Change and Target Achievement

Among all users, mean (SD) decrease in FBG was −44.4 (72.5) mg/dL. A total of 276 (55.9%) MDC users achieved their individualized FBG target; 69 (14.0%) were still titrating their BI and 149 (30.2%) stopped using MDC. Overall, mean (SD) time to reach FBG target was 14.8 (20.9) days. FBG decrease was greater among those who achieved their FBG target (−57.0 [65.8] mg/dL) than users who were still titrating or stopped using MDC app. Similarly, FBG decrease was greater among those who had high MDC usage (−59.3 [67. 8] mg/dL) compared with moderate or low MDC usage (Fig. 1). Higher frequency MDC use was significantly associated with FBG target achievement, with 87.8% (n = 244) for the high-usage group versus 26.3% (n = 30) for the moderate-usage group, and 2.0% (n = 2) for the low-usage group; p < 0.01. High-frequency app users took the shortest time to reach their FBG target, with a mean (SD) period of 12.8 (18.8) days, versus 29.1 (28.0) days for moderate-frequency users and 43.5 (41.7) days for low-frequency users.

Fig. 1.

Fig. 1

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FBG change in all participants and according to FBG status and MDC usage. Baseline and follow-up data were not available for all the participants. The mean change was calculated for only those participants who had both baseline and follow-up data. N numbers provided on x-axis are for MDC users with first FBG data. Last FBG data were available for N = 425 (total participants): n = 207 (achieved target), n = 69 (titrating), and n = 149 (stopped using MDC); as well as n = 211 (high usage), n = 112 (moderate usage), and n = 102 (low usage). Mean change in FBG (shown within center of bars) is based on users with both a first and last FBG reading. MDC usage was defined as follows: high, > 3 days/week; moderate, > 1– ≤ 3 days/week; and low, ≤ 1 day/week. FBG fasting blood glucose, MDC My Dose Coach, SD, standard deviation

Change in HbA1c Levels

Overall, a total of 136 MDC users had HbA1c data available at baseline and follow-up. Mean HbA1c levels were significantly lower at follow-up (8.7%) than at baseline (9.2%; p < 0.01, Fig. 2). Mean (SD) time to achieve HbA1c target was 48.0 (40.5) days. Among those who met their FBG target, mean HbA1c levels were 8.4% at follow-up versus 8.8% at baseline. For those still titrating, HbA1c levels decreased to 8.5% at follow-up from 9.6% at baseline; for those who had stopped using MDC, mean HbA1c at follow-up was 9.1% versus 9.8% at baseline. A total of 20 (14.7%) participants observed a decrease in their HbA1c levels at follow-up. At follow-up, the mean (SD) value of HbA1c observed in high MDC app users was 8.6% (2.4%), moderate users were 8.4% (2.3%), and low app users was 9.0% (2.7%). A reduction in HbA1c was more common with more frequent users of the app (18.3%) than those with moderate (13.6%) and low usage (6.3%).

Fig. 2.

Fig. 2

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HbA1c change in all participants and according to FBG status and MDC usage. *p < 0.01. Baseline and follow-up data were not available for all the participants. Mean change was calculated for only those participants who had both baseline and follow-up data. N numbers provided on x-axis are for MDC users with HbA1c at baseline. Follow-up data were available for N = 136 (total participants): n = 81 (achieved target), n = 11 (titrating), and n = 44 (stopped using MDC); as well as n = 82 (high usage), n = 22 (moderate usage), and n = 32 (low usage). MDC usage was defined as follows: high, > 3 days/week; moderate, > 1– ≤ 3 days/week; and low, ≤ 1 day/week. FBG fasting blood glucose, MDC My Dose Coach, SD standard deviation

Insulin Dose Change

All users reported at least one insulin dose during the study period. Mean (SD) first dose was 14.1 U (17.0 U) in all 494 users and was 21.0 U (16.6 U) in the 82 users with last-dose data with overall, mean (SD) insulin dose change as 1.52 U (7.0 U) in the 82 users, which is a 11% increase in dosage. Insulin doses were generally lower at first dose than last dose across the subgroups of FBG target achievement and MDC usage (Fig. 3).

Fig. 3.

Fig. 3

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First and last insulin dose in all participants and according to FBG status and MDC usage. N numbers provided on x-axis are for MDC users with first dose data. Last dose data were available for N = 82 (total participants): n = 44 (achieved target), n = 14 (titrating), and n = 24 (stopped using MDC); as well as n = 45 (high usage), n = 24 (moderate usage), and n = 13 (low usage). MDC usage was defined as follows: high, > 3 days/week; moderate, > 1– ≤ 3 days/week; and low, ≤ 1 day/week. FBG fasting blood glucose, MDC My Dose Coach, SD standard deviation

Body Weight Change

The mean (SD) body weight at baseline was 82.7 kg (19.4 kg). After 90 days’ follow-up, the mean (SD) body weight was 83.0 kg (19.2 kg). Mean (SD) change in body weight of −0.58 kg (7.03 kg) was observed from baseline to follow-up period.

Hypoglycemia Events

Among all MDC users, hypoglycemia events were uncommon, with only seven participants (1.4%) experiencing at least one event during titration. The mean (SD) time to the first hypoglycemia event was 4.86 days (4.8 days). Hypoglycemia events were reported by five (1.8%) participants who achieved the FBG target, one (1.4%) participant who was titrating, and another (0.7%) participant who stopped using MDC. Numerically higher number of hypoglycemia events were observed in the high frequent users of MDC app (n = 6, 2.2%) compared with the moderate frequent users (n = 1, 0.9%) and low frequent users (n = 0). High frequent app users were followed up for 14 days, while moderate frequent users were followed up for 4 days. These differences in hypoglycemia event rates by frequency of app use were not statistically significant (p = 0.1431) (Fig. 4).

Fig. 4.

Fig. 4

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Hypoglycemia in all participants and according to FBG status and MDC usage. MDC usage was defined as follows: high, > 3 days/week; moderate, > 1 – ≤ 3 days/week; and low, ≤ 1 day/week. FBG fasting blood glucose, MDC My Dose Coach, SD standard deviation

Discussion

This is the first study that examines the real-world effectiveness of a smartphone-based automated BI titration in the MENA region. Our results suggest that use of MDC is associated with improved glycemic control in people with T2D from Saudi Arabia and Kuwait. In addition, greater benefits in achieving FBG target and reduction in FBG were observed in high-frequency users of MDC compared with moderate- and low-frequency users. The improvement in glycemic control was accompanied by a slight increase in insulin dose and a low risk of hypoglycemia across all groups according to FBG status and MDC usage.

The results of the current study are consistent with the findings of other similar retrospective observational cohort studies in MDC users with T2D in India, Mexico, and Colombia [19]. Previous analysis conducted on 2517 active MDC users showed that 44% of all users achieved their individualized FBG target. Almost 50% of participants were high-frequency app users who had a significantly higher rate of FBG target achievement (75.1%) than the moderate-frequency (26.4%) and low-frequency (3.7%) app users (p < 0.01). Consistent with these previously reported results, the current study showed that 56% of MDC users achieved their individualized FBG target. Furthermore, of the 56% of all users who were high-frequency app users, 88% achieved their FBG target compared with 26% and 2% in the moderate- and low-frequency app user groups, respectively (p < 0.01) [22]. High MDC usage not only reduced the time to reach the FBG target, but also led to greater reductions in FBG compared with other usage groups. These improvements in FBG outcomes may be attributed to changes in insulin dose. Importantly, these positive clinical outcomes were not associated with any significant increase in hypoglycemia incidence, likely because of the small total number of events. Previous studies were also aligned with our studies and reported the association of more frequent MDC usage with better FBG outcomes without an increased risk of hypoglycemia [22].

In this study, the mean HbA1c levels at follow-up were significantly lower than those at baseline and are similar with previously published data. An observational study in China evaluated the use of other mobile applications that also support BI titration in people with T2D [23]. A BI management system was implemented in 297 hospitals across the 6 main regions of China (n = 17,208 people with T2D who were insulin-naive). Participants used the mobile app to self-manage their insulin dosage titrations, with physician support as needed. App use resulted in better glycemic control, with improvements in mean HbA1c levels over 6 months of app usage and significant increases in the proportion of participants achieving their HbA1c targets following 3 and 6 months of app usage. Furthermore, up to two-thirds achieved their HbA1c target within 6 months. Reductions in mean FBG levels were also observed in those using the self-management mobile app [23]. The difference in the outcome may be attributed to the temporal variability in HbA1c measurements, as follow-up values could be recorded at any point after the index date—potentially before or after the achievement of target FBG levels. Such variability can lead to discrepancies between HbA1c and FBG readings. Another plausible explanation for this divergence is the presence of unreported postprandial hyperglycemia, which may not be captured by FBG measurements but can substantially influence HbA1c levels. It is important to recognize that the primary aim of the MDC is to optimize fasting glucose control, rather than PPG regulation. Another study, the TeleDiab-2 study in France, was a randomized controlled trial that evaluated the efficacy and safety of two telemonitoring systems (IVRS and Diabeo-BI) to optimize BI initiation in people with inadequately controlled T2D, compared with standard of care [24]. The study demonstrated that both telemonitoring systems had a positive impact, with improved glycemic control versus the standard of care and no additional hypoglycemia risk [24]; overall, the general evidence supporting the positive impact of digital health apps in the self-management of diabetes for improved glycemic control is in line with the findings of the current study.

Commonly reported barriers to optimal BI titration—and thus diabetes management—include fear of hypoglycemia and weight gain, and concerns about the impact of self-titration of insulin doses on health-related quality of life [9]. Fear of hypoglycemia is reported as a leading cause of delayed or suboptimal insulin treatment in Saudi Arabia [4, 11]. A 2021 cross-sectional study in Saudi Arabia revealed that over one-fifth of people with diabetes had experienced a hypoglycemia event [25]. This may be of particular importance and relevance within a region that observes Ramadan intermittent fasting for 29–30 consecutive days every year. It has been reported that more than 50% of people with diabetes using insulin had at least one hypoglycemia event during Ramadan, and almost one-third experienced more than four events during this period [26]. Together, fears of hypoglycemia and other reported barriers can result in under-titration, insulin omission, and infrequent FBG self-testing, thereby leading to poor glycemic control [9]. In addition, real-world data have also shown that hypoglycemia awareness is a positive predictor of adherence to insulin therapy in people with diabetes [9]. Use of the MDC app may help people to overcome these known challenges to achieving glycemic control, particularly as increased MDC use was not associated with increased risk of hypoglycemia.

Support to facilitate the self-management of diabetes is an essential component for achieving treatment goals [17, 27]. Evidence has consistently shown that providing self-management diabetes education and support resources significantly improves clinical and psychological outcomes and contributes to reductions in all-cause mortality [27]. Further to this, enabling people with diabetes to lead their BI titration can provide greater HbA1c reductions compared with physician-led titration [9, 28]. Use of the MDC app may allow users to feel more comfortable managing their own treatment and encourage greater self-confidence in optimizing their BI titration with the ongoing support of their HCP. Empowering people with T2D to use these technologies may also help to reduce the current burden on HCPs and clinic visits by improving disease awareness and self-management of titration, thereby reducing the need for in-person visits [29].

A cross-sectional survey of people with diabetes in Saudi Arabia found that over one-third of people had reported using digital health apps to monitor blood glucose, body weight, exercise, and calorie intake [30]. A similar proportion of users reported that the health apps were easy to understand and use. In line with these findings, a preliminary study in the Gulf Region, which evaluated the usability of a mobile diabetes management system in people with T2D in Saudi Arabia, reported good general acceptance and high usability ratings [31]. Given the huge potential for increased use of digital health apps to allow more people with diabetes to self-manage their condition [30], it is important to also understand the barriers to app use. Common barriers reported in one study of people with diabetes in Kuwait included unawareness of the digital options available, lack of time and interest, and the perception that app use would be complicated. Language was also a barrier, with the study recommending that future apps should be designed in Arabic [32]. Studies in Saudi Arabia have also highlighted the potential impact of culture in diabetes self-management programs, with cultural attitudes and beliefs recognized by both HCPs and people with diabetes as barriers to disease self-management and education [4, 29].

Some study limitations should be considered when interpreting the results of the current study. The study design was retrospective and therefore causal associations cannot be made and some inferences are limited. Analysis of HbA1c data were limited to participants who had values at both baseline and follow-up. In addition, information on duration of diabetes and adherence to physician recommendations was lacking. Further limitations include that FBG targets were individualized by the treating physician, thus differences in how strict or lenient those FBG targets were set by HCPs may have affected FBG target achievement. Smartphone/internet availability may have impacted MDC usage, although the internet broadband coverage and accessibility to smartphones in Saudi Arabia and Kuwait are among the highest globally. Furthermore, as participants in this study were already registered users of MDC, they may represent a population already motivated to self-manage their diabetes, which may restrict the generalizability of the results.

Conclusions

The findings of the current study show that, among people with T2D in Saudi Arabia and Kuwait, use of the My Dose Coach app is associated with improved glycemic control and better clinical outcomes observed in high-frequency app users. These findings have significant clinical and policy implications for improving the glycemic control among people with T2D who use basal insulin in the MENA region and other parts of the world where the diabetes burden is staggering.

Acknowledgements

The authors express their gratitude to the participants, investigators, and staff involved in the data collection for this study. All listed authors comply with the International Committee of Medical Journal Editors (ICMJE) criteria for authorship, take responsibility for the integrity of the work, and have approved this version for publication.

Medical Writing/Editorial Assistance

Scientific writing assistance for developing the initial draft was provided by Heather St Michael, BSc, and Arthur Holland, PhD, of Fishawack Communications Ltd, part of Avalere Health, and was funded by Sanofi. Medical writing support was provided by Daisy Masih, Ph.D, employee of Sanofi and funded by Sanofi.

Author Contributions

Conceptualization and methodology: Mohammed E. Al-Sofiani, Mohamed Almehthel, Ebaa Al Ozairi, Jamaa Sadik, Lichen Hao, and Yasser Akil. Data collection and interpretation: Lichen Hao and Yasser Akil. Data analysis: Lichen Hao. Writing—review and editing: Mohammed E. Al-Sofiani, Mohamed Almehthel, Ebaa Al Ozairi, Jamaa Sadik, Lichen Hao, and Yasser Akil.

Funding

The study and journal’s Rapid Service Fee was funded by Sanofi, Paris, France. Mohammed E. Al-Sofiani would like to thank the Ongoing Research Funding Program (ORFFT-2025-012-1), King Saud University, Riyadh, Saudi Arabia for financial support.

Data Availability

Qualified researchers may request access to patient-level data and related documents [including, e.g., the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan, and dataset specification]. Patient-level data will be anonymized, and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi’s data sharing criteria, eligible studies, and process for requesting access can be found at https://www.vivli.org.

Declarations

Conflict of Interest

Mohammed E. Al-Sofiani has served on an advisory panel for Abbott, Medtronic, Insulet, VitalAire, and Sanofi and has received honoraria for speaking and consultancy from Abbott, Eli Lilly, Medtronic, Novo Nordisk, Sanofi, and VitalAire; Mohamed Almehthel and Ebaa Al Ozairi have no conflicts of interest to declare; Jamaa Sadik, Lichen Hao, and Yasser Akil are employees of Sanofi and may hold Sanofi shares and/or stock.

Ethical Approval

MDC users agreed to allow their anonymized data to be used retrospectively as part of the registration process. The study was approved by the Institutional Review Board of King Saud University (ref. no. 22/0251 /IRB), Saudi Arabia and Kuwait.

Footnotes

Prior Presentation: Some data included in this manuscript were previously presented at the American Diabetes Association 83rd Scientific Sessions, 23–26 June 2023, San Diego, CA, USA.

References

  • 1.IDF Diabetes Atlas 11th Edition. Brussels, Belgium: International Diabetes Federation; 2025. IDF Diabetes Atlas | Global Diabetes Data & Statistics. Accessed 9 April 2025.
  • 2.Moradinazar M, Babakhani M, Rostami R, Shakiba M, Moradi A, Shakiba E. Epidemiological status of type 2 diabetes mellitus in the Middle East and North Africa, 1990–2019. East Mediterr Health J. 2022;28(7):478–88. [DOI] [PubMed] [Google Scholar]
  • 3.El-Kebbi IM, Bidikian NH, Hneiny L, Nasrallah MP. Epidemiology of type 2 diabetes in the Middle East and North Africa: challenges and call for action. World J Diabetes. 2021;12(9):1401–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Alhagawy AJ, Yafei S, Hummadi A, et al. Barriers and attitudes of primary healthcare physicians to insulin initiation and intensification in Saudi Arabia. Int J Environ Res Public Health. 2022;19(24):16794. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Alsuliman MA, Alotaibi SA, Zhang Q, Durgampudi PK. A systematic review of factors associated with uncontrolled diabetes and meta-analysis of its prevalence in Saudi Arabia since 2006. Diabetes Metab Res Rev. 2021;37(3): e3395. [DOI] [PubMed] [Google Scholar]
  • 6.Badedi M, Solan Y, Darraj H, et al. Factors associated with long-term control of type 2 diabetes mellitus. J Diabetes Res. 2016;2016:2109542. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Abdullah A, Alkandari A, Longenecker JC, et al. Glycemic control in Kuwaiti diabetes patients treated with glucose-lowering medication. Prim Care Diabetes. 2020;14(4):311–6. [DOI] [PubMed] [Google Scholar]
  • 8.American Diabetes Association. Glycemic targets: standards of medical care in diabetes, chapter 6. Diabetes Care. 2022;45(Suppl 1):S83–96. [DOI] [PubMed] [Google Scholar]
  • 9.Russell-Jones D, Pouwer F, Khunti K. Identification of barriers to insulin therapy and approaches to overcoming them. Diabetes Obes Metab. 2018;20(3):488–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Polonsky WH, Arsenault J, Fisher L, et al. Initiating insulin: How to help people with type 2 diabetes start and continue insulin successfully. Int J Clin Pract. 2017;71(8): e12973. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Alshoalah QM, Alfaraj NA, Krishanani MK, Khamis AH. Primary care physicians’ barriers to initiate insulin in Type 2 diabetes mellitus. A survey from the Eastern Province, Saudi Arabia. World Family Med. 2018;16(7):4–13. [Google Scholar]
  • 12.Peyrot M, Barnett AH, Meneghini LF, Schumm-Draeger PM. Insulin adherence behaviours and barriers in the multinational global attitudes of patients and physicians in insulin therapy study. Diabet Med. 2012;29(5):682–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Abdulrhim S, Sankaralingam S, Ibrahim MIM, et al. Collaborative care model for diabetes in primary care settings in Qatar: a qualitative exploration among healthcare professionals and patients who experienced the service. BMC Health Serv Res. 2021;21(1):192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Balla SA, Shaaban KMA, Abdelaziz SI, Abeldafia NMA. Structural quality of diabetes services at primary health care in Khartoum state 2013: health centers versus specialized diabetes centers. Med Sci Healthcare J. 2015;10(8):41–50. [Google Scholar]
  • 15.Jabbar A, Abdallah K, Hassoun A, et al. Patterns and trends in insulin initiation and intensification among patients with Type 2 diabetes mellitus in the Middle East and North Africa region. Diabetes Res Clin Pract. 2019;149:18–26. [DOI] [PubMed] [Google Scholar]
  • 16.Ghafoor E, Masood S, Belkhadir J, et al. Availability and accessibility of diabetes-related technologies in IDF-MENA region. J Diabetol. 2022;13(5):81–4. [Google Scholar]
  • 17.Kerr D, Edelman S, Vespasiani G, Khunti K. New digital health technologies for insulin initiation and optimization for people with type 2 diabetes. Endocr Pract. 2022;28(8):811–21. [DOI] [PubMed] [Google Scholar]
  • 18.Food and Drug Administration. My Dose Coach FDA approval. 2017. https://www.accessdata.fda.gov/cdrh_docs/pdf16/K163099.pdf. Accessed 6 Sep 2023.
  • 19.Kingdom of Saudi Arabia Food and Drug Authority, Medical Device Marketing Authorization, 2023.
  • 20.State of Kuwait Ministry of Health. Pharmaceutical and Herbal Medicines Registration and Control Administration. 2022.
  • 21.Al-Nozha OM, Alshareef EK, Aljawi AF, et al. Diabetic patients’ perspective about new technologies used in managing diabetes mellitus in Saudi Arabia: a cross-sectional study. Cureus. 2022;14(5): e25038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Unnikrishnan AG, Viswanathan V, Zhou FL, et al. Impact of My Dose Coach app frequency of use on clinical outcomes in type 2 diabetes. Diabetes Ther. 2022;13(5):983–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Cai X, Zhang F, Lin C, et al. Achieving effective and efficient basal insulin optimal management by using mobile health application (APP) for type 2 diabetes patients in China. Diabetes Metab Syndr Obes. 2020;13:1327–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Franc S, Joubert M, Daoudi A, et al. Efficacy of two telemonitoring systems to improve glycaemic control during basal insulin initiation in patients with type 2 diabetes: The TeleDiab-2 randomized controlled trial. Diabetes Obes Metab. 2019;21(10):2327–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Elshebiny A, Alali H, Alamer Z, et al. The incidence of hypoglycemia and its risk factors among diabetic patients in the Eastern Province of Saudi Arabia. Int J Med Dev Countries. 2021;5:614–21. [Google Scholar]
  • 26.AlKhaldi YM, AlKhaldi AY, AlQahtani AS, Al-Shahrani BS, Meshawi EA, Albishri BM. Incidence of hypoglycemia and its risk factors among diabetics during Ramadan in Abha city, Aseer Region. KSA J Family Med Prim Care. 2019;8(9):2793–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Davies MJ, Aroda VR, Collins BS, et al. Management of hyperglycaemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2022;65(12):1925–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Russell-Jones D, Dauchy A, Delgado E, et al. Take control: a randomized trial evaluating the efficacy and safety of self-versus physician-managed titration of insulin glargine 300 U/mL in patients with uncontrolled type 2 diabetes. Diabetes Obes Metab. 2019;21(7):1615–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Al Slamah T, Nicholl BI, Alslail FY, Harris L, Melville CA, Kinnear D. Cultural adaptation of self-management of type 2 diabetes in Saudi Arabia (qualitative study). PLoS ONE. 2020;15(7): e0232904. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Rafiullah M, David SK. Health apps usage and preferences among Saudi patients with diabetes: a survey. Int J Clin Pract. 2019;73(5): e13345. [DOI] [PubMed] [Google Scholar]
  • 31.Alanzi T, Istepanian R, Philip N. Design and usability evaluation of social mobile diabetes management system in the Gulf Region. JMIR Res Protoc. 2016;5(3): e93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Abduo H, Curtain C, Othman N. Use of smartphone applications for diabetes management in Kuwait: a pilot study. J Consumer Health Internet. 2020;24(2):111–25. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Qualified researchers may request access to patient-level data and related documents [including, e.g., the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan, and dataset specification]. Patient-level data will be anonymized, and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi’s data sharing criteria, eligible studies, and process for requesting access can be found at https://www.vivli.org.

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