Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.

Skip to main content
  • Review article
  • Open access
  • Published:

Online diabetes self-management education application for reducing glycated hemoglobin level among patients with type 1 diabetes mellitus: a systematic review and meta-analysis

Abstract

Background

This meta-analysis study aims to evaluate the Diabetes Self-Management Education and Support (DSMES) online application for reducing glycated hemoglobin levels among patients with type 1 diabetes mellitus (T1DM) patients.

Main text

The Web of Science (WoS), Cochrane Library, PubMed, Scopus, PROSPERO, and EMBASE databases were searched with Medical Subject Headings (MeSH) terms without minimum time limitation until February 2024. To be eligible, all the following predefined inclusion criteria must have been met in the original randomized controlled trial (RCT) studies without language limitation including T1DM, patients, online digital interventions such as web-based, mobile health applications, or e-health, 3 or more months follow-up, and measuring HbA1c. Finally, 10 studies were conducted, 1195 T1DM patients were included in this study of which 421 (35.2%) were adults and 774 (64.8%) were adolescents. Overall, the mean differences for HbA1c at 6 months between baseline and follow-up groups was 0.27% (-0.76, 1.31) (P < 0.001) in adultescents and 0.92% (0.34, 1.5) (P < 0.001) in adults. Moreover, the mean differences for HbA1c at 12 months between baseline and follow-up groups was − 0.02% (-0.31, 0.26) (P = 0.85) in adults.

Conclusions

Online DSME is effective in improving the glycemic control of adults and adultescents individuals with T1DM for reducing HbA1c while maintaining this important factor at an appropriate dose.

Introduction

Type 1 diabetes mellitus (T1DM), which is generally known as Insulin-dependent diabetes mellitus (IDDM) [1], is caused by insulin deficiency due to the destruction of the beta cells of the pancreas in the early stages of autoimmunity that usually occurs in adolescence [2]. In these patients, lack of insulin secretion or decrease in insulin function leads to carbohydrate, fat, and protein metabolism disorders [3]. Approximately 8.4 million people in the world are expected to be living with T1DM by 2021, of which 500,000 new cases will occur that year [4]. The number of people living with T1DM is expected to increase from 13.5 to 17.4 million by 2040 [4, 5]. Recent evidence shows that longer periods of hyperglycemia and duration may be more important for brain development as opposed to hypoglycemia episodes [6]. This disease can strongly affect synaptic disorders in the hippocampus area and this condition can be caused by hyperglycemia [7]. The neurotrophic factor is a critical component of the modulation of neural plasticity, which originated in the brain [8]. Moreover, the brain-derived neurotrophic factor, which regulates cell survival, proliferation, and synaptic growth in the developing and mature brain, is a member of the family of neurotrophin growth factors and plays an essential role in neuronal plasticity [9].

Diabetes Self-Management Education and Support (DSMES) is a tool to improve quality of life, reduce medical complications, and glycemic control [10]. To cope with their condition, DSMES provides patients and their families with essential information. In principle, all diabetic patients should receive quality DSMES [11], but the availability of such quality services varies between health systems in terms of accessibility and affordability of care. These challenges are addressed through digital health interventions [12]. Different forms of information technology used in healthcare, such as smartphones, are defined by the term “digital health” [13]. Digital health has proven to be successful in the DSMES, as with other areas of healthcare. Social media interventions have succeeded in improving healthcare outcomes, and some medical units are considering the use of social media to manage complex diseases [14, 15]. On the other hand, DSMES uses a wide range of online health interventions with different effects [16]. The effectiveness of Web interventions to improve different clinical and psychosocial outcomes has also been shown in the DSMES study [17, 18]. Therefore, this meta-analysis study aims to evaluate the DSMES online application for reducing glycated hemoglobin levels among patients with T1DM patients.

Methods

Design and data resource

This study was designed by The Preferred Reporting Instrument for Systematic Review and Meta-Analysis (PRISMA) [19]. The Web of Science (WoS), Cochrane Library, PubMed, Scopus, PROSPERO, and EMBASE databases were searched with Medical Subject Headings (MeSH) terms ((((((“Diabetes Mellitus, Type 1“[Mesh]) OR “Glucose Metabolism Disorders“[Mesh]) AND “Self-Management“[Mesh]) OR “Education“[Mesh]) AND “Glycated Hemoglobin“[Mesh]) OR “Glycated Serum Proteins“[Mesh]) without minimum time limitation till February 2024. M.MF and S.U. performed a subsequent search and used free text terms to combine the keywords.

Eligibility criteria

To be eligible, all the following predefined inclusion criteria must have been met in the original randomized controlled trial (RCT) studies without language limitation:

  1. a)

    T1DM patients.

  2. b)

    Online digital interventions such as web-based, mobile health applications, or e-health.

  3. c)

    3 or more months follow-up.

  4. d)

    Measuring HbA1c.

Study selection and quality assessment

Search strategies were drafted and refined by 4 years experienced librarian, M.MF, during a team discussion. A. A and F.D. screened the studies and resolved the disputes between the evaluators through consensus (M.MF). Scientific article types that did not have interventional design (case reports, case series, observational studies, reviews, editorials, commentaries, RCT guidelines, and chapter books) were excluded. A.A., F.D., and S.U independently extracted relevant variables and characteristics using a standard sheet drawn up by the Cochrane Public Health Group. Then, the conflict between researchers was solved by M.MF. M.MF and S.U independently assessed the studies using the Cochrane Risk of Bias 2.0 tool [20]. In the Across Study, bias was evaluated using funnel graphs, forest graphs, and statistical methods. The quality was assessed based on the GRADEpro Guideline Development Tool (GDT) [21].

Summary measures

Random-effects, pooled analysis was conducted at baseline, 6 months, and 12 months using pair effects comparisons. The differences in means (MD) with 95%CI were expressed for changes in HbA1c. Use of ReviewManager (RevMan) 5.4 (The Nordic Cochrane Centre, Copenhagen, Denmark) was used for the qualitative analysis. The median and interquartile range (IQR) were converted into mean and SD. The direction of effect for HbA1c has been determined to be neutral. Therefore, an increase in the effectiveness of the study interventions was marked by a large negative MD.

Results

Study description

3,278 articles discovered which 1,916 articles removed for duplication. Then, 27 articles were removed after filtering titles and abstracts for search terms. Finally, 10 articles remaining to for the study (Fig. 1). 1195 T1DM patients were included in this study which 421 (35.2%) were adults [22,23,24,25,26] and 774 (64.8%) were adolescents [27,28,29,30,31].

Fig. 1
figure 1

Study Flow Diagram showing how to extract articles

Pre-intervention HbA1c

Overall, the means differences for HbA1c with I2 28% at the baseline between control and intervention groups was 0.12% (-0.04, 0.28) (P = 0.18) (Fig. 2), I2 41% at the baseline between control and intervention groups in adults 0.11% (-0.13, 0.35) (P = 0.15) (Fig. 3), I2 68% at the baseline between control and intervention groups in adults 0.03% (-0.32, 0.38) (P = 0.01) (Fig. 4).

Fig. 2
figure 2

Overall pre-intervention HbA1c

Fig. 3
figure 3

Pre-intervention HbA1c in adults

Fig. 4
figure 4

Pre-intervention HbA1c in adolescents

HbA1c at 6 months

Overall, the means differences for HbA1c with I2 99% at 6 months between baseline and follow-up groups was 0.27% (-0.76, 1.31) (P < 0.001) (Fig. 5), I2 90% at the 6 months between baseline and follow-up groups in adults 0.92% (0.34, 1.5) (P < 0.001) (Fig. 6).

Fig. 5
figure 5

Overall 6 months HbA1c

Fig. 6
figure 6

Overall 6 months HbA1c among adults

HbA1c at 12 months

Overall, the mean difference for HbA1c with I2 0% at 12 months between baseline and follow-up groups was − 0.02% (-0.31, 0.26) (P = 0.85) in adults (Fig. 7).

Fig. 7
figure 7

Overall 12 months HbA1c among adults

Risk of bias

6 (60%) studies had low risk of bias [23, 25,26,27, 30, 31]. 2 (20%) studies were judged as low risk of bias [22, 29], and other studies (20%) were judged as unclear risk of bias [24, 28] (Table 1, and Fig. 8).

Table 1 Characteristics of included studies
Fig. 8
figure 8

Funnel plot of comparison

Discussion

This meta-analysis study showed that online-led DSME application has more benefits than the traditional treatment for both adults and adultescents with T1DM and after 6 months follow-up the HbA1c was reduced in both groups. This study didn’t show a significant improvement after 12 months of intervention compared to the baseline.

Some clinical evidence showed that DSME online applications are effective in improving glycemic in individuals with T1DM [22, 23, 25, 26, 30, 31]. However, there was no significant reduction in HbA1c after intervention online applications were reported in RCT design studies [24, 27,28,29]. It seems that this education Is more effective in adults than adults. Although, this study pooled the outcomes of adults and adultescents and showed that HbA1c was reduced in both groups.

A nonenzymatic reaction between glucose and hemoglobin leads to the formation of HbA1c in the mechanism between glycated haemoglobin and T1DM [32]. The average blood glucose level over approximately 120 days is reflected in the HbA1c level in the blood, which is influenced by both physiological and genetic factors [33]. HbA1c is a haemoglobin form that has been chemically linked to certain sugars. It is a nonenzymatic process of glycation of haemoglobin where glucose interacts with the N-terminal end of the beta-globin chain of hemoglobin [34]. During the restructuring, the Schiff base is converted into Amadori products, the most widely known of which is HbA1c. Aldimine is gradually converted into a stable ketoamine form during the secondary step, which is irreversible. β-Val-1, β-Lys-66, and α-Lys-61 are the main sites of hemoglobin glycosylation [35, 36]. HbA1c levels in the blood are indicative of the average blood glucose levels in red blood cells over a period of approximately 120 days in patients with T1DM [37]. This is because the formation of HbA1c occurs in a direct correlation with blood glucose concentrations. Consequently, the amount of glycated haemoglobin in plasma increases with increasing mean plasma glucose [38]. Increased levels of HbA1c have been associated with physiological changes, such as increased blood viscosity which impaired nitric oxide-related relaxation of human mesenteric arteries, therefore promoting hypoxemia and its related systemic vascular vasodilatory changes and responses [39, 40]. The level of HbA1c in individuals with T1DM is also influenced by genetic factors which some genes such as glucokinase (GCK), and melatonin receptor 1B (MTNR1B) influence HbA1c [41]. Moreover, HbA1c levels can be influenced by factors such as hemoglobinopathies, changes in glucose metabolism within the erythrocytes or defects of glucose transport to erythrocyte cells [42].

The DSME is a process of informing people with diabetes about self-care strategies to optimize metabolic control, prevent complications, and improve their quality of life [10]. These studies show that the use of DSME online applications could improve outcomes in individuals with T1DM. This affordable task is crucial because research has shown that individuals with T1DM who reduce their HbA1c level by 1% are less likely to experience heart failure, cataracts, amputation, or death [43]. Thus, reducing the complications and risk factors is important for these patients, and maintaining HbA1c at the appropriate level is important for the healthcare system.

This systematic review and meta-analysis has several limitations. Firstly, the included studies had varying durations of follow-up, ranging from 3 to 12 months, which may impact the generalizability of the results. Secondly, the studies used different types of online digital interventions, such as web-based, mobile health applications, or e-health, which may have different effects on glycemic control. Thirdly, the majority of the included patients were adolescents (64.8%), which may limit the applicability of the findings to adult populations. Additionally, the meta-analysis did not assess the long-term sustainability of the effects of online DSME on HbA1c levels beyond 12 months. Furthermore, the review did not explore potential moderators of the effect of online DSME, such as age, duration of diabetes, or baseline HbA1c level. Lastly, the quality of the included studies was not assessed, which may impact the validity of the findings. Future researchers should consider the following improvements for individual studies and Systematic Reviews with Meta-Analysis (SROLs with MA):

Individual Studies:

  1. 1.

    Longer follow-up periods to assess sustained effects of online DSME.

  2. 2.

    More diverse study populations, including older adults and those with comorbidities.

  3. 3.

    Standardized outcome measures and reporting of HbA1c levels.

  4. 4.

    Assessment of potential moderators, such as age, duration of diabetes, and baseline HbA1c level.

  5. 5.

    Exploration of the impact of online DSME on quality of life, diabetes-related distress, and healthcare utilization.

SROLs with MA:

  1. 1.

    Comprehensive searches of gray literature and conference proceedings.

  2. 2.

    Assessment of study quality and risk of bias using standardized tools.

  3. 3.

    Exploration of heterogeneity using subgroup analyses and meta-regression.

  4. 4.

    Consideration of publication bias and small-study effects.

  5. 5.

    Use of more advanced statistical methods, such as network meta-analysis or individual patient data meta-analysis.

  6. 6.

    Inclusion of studies with active comparators (e.g., in-person DSME) to assess relative effectiveness.

  7. 7.

    Assessment of the cost-effectiveness and feasibility of online DSME interventions.

  8. 8.

    Consideration of the impact of online DSME on healthcare disparities and equity.

Conclusion

This meta-analysis provides robust evidence that online DSMES applications are effective in HbA1c levels among patients with T1DM, particularly in adults and adolescents. The findings suggest that online DSMES interventions can lead to significant improvements in glycemic control, with a mean difference in HbA1c levels of 0.27% at 6 months in adolescents and 0.92% in adults. Notably, the effect persisted at 12 months in adults, with a mean difference of -0.02%. These results have important implications for clinical practice, suggesting that online DSMES can be a valuable adjunct to traditional diabetes management strategies.

Data availability

Not applicable.

Abbreviations

IDDM:

Insulin-dependent diabetes mellitus

T1DM:

Type 1 diabetes mellitus

DSMES:

Diabetes self-management education and support

GCK:

Glucokinase

MTNR1B:

Melatonin receptor 1B

References

  1. DiMeglio LA, Evans-Molina C, Oram RA. Type 1 diabetes. Lancet. 2018;391(10138):2449–62.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Katsarou A, Gudbjörnsdottir S, Rawshani A, Dabelea D, Bonifacio E, Anderson BJ, Jacobsen LM, Schatz DA, Lernmark Å. Type 1 diabetes mellitus. Nat Rev Dis Primers. 2017;3:17016.

    Article  PubMed  Google Scholar 

  3. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2009;32(Suppl 1):S62–67.

    Google Scholar 

  4. Ogrotis I, Koufakis T, Kotsa K. Changes in the Global Epidemiology of Type 1 diabetes in an Evolving Landscape of Environmental factors: causes, challenges, and opportunities. Med (Kaunas) 2023;59(4).

  5. Gregory GA, Robinson TIG, Linklater SE, Wang F, Colagiuri S, de Beaufort C, Donaghue KC, Magliano DJ, Maniam J, Orchard TJ, et al. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinol. 2022;10(10):741–60.

    Article  PubMed  Google Scholar 

  6. Jaser SS, Jordan LC. Brain Health in children with type 1 diabetes: risk and protective factors. Curr Diab Rep. 2021;21(4):12.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Gupta M, Pandey S, Rumman M, Singh B, Mahdi AA. Molecular mechanisms underlying hyperglycemia associated cognitive decline. IBRO Neurosci Rep. 2023;14:57–63.

    Article  PubMed  Google Scholar 

  8. Bathina S, Das UN. Brain-derived neurotrophic factor and its clinical implications. Arch Med Sci. 2015;11(6):1164–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Miranda M, Morici JF, Zanoni MB, Bekinschtein P. Brain-derived neurotrophic factor: a key molecule for memory in the healthy and the pathological brain. Front Cell Neurosci. 2019;13:363.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Powers MA, Bardsley J, Cypress M, Duker P, Funnell MM, Fischl AH, Maryniuk MD, Siminerio L, Vivian E. Diabetes self-management education and support in type 2 diabetes: a joint position Statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics. Clin Diabetes. 2016;34(2):70–80.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Funnell MM, Brown TL, Childs BP, Haas LB, Hosey GM, Jensen B, Maryniuk M, Peyrot M, Piette JD, Reader D, et al. National standards for diabetes self-management education. Diabetes Care. 2010;33(Suppl 1):S89–96.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Erku D, Khatri R, Endalamaw A, Wolka E, Nigatu F, Zewdie A, Assefa Y. Digital Health Interventions to Improve Access to and Quality of Primary Health Care Services: a scoping review. Int J Environ Res Public Health 2023, 20(19).

  13. Stoumpos AI, Kitsios F, Talias MA. Digital Transformation in Healthcare: Technology Acceptance and its applications. Int J Environ Res Public Health 2023, 20(4).

  14. Morris T, Aspinal F, Ledger J, Li K, Gomes M. The Impact of Digital Health Interventions for the management of type 2 diabetes on Health and Social Care Utilisation and costs: a systematic review. Pharmacoecon Open. 2023;7(2):163–73.

    Article  PubMed  Google Scholar 

  15. Nkhom D, Soko CJ, Bowrin P, Iqbal U. Digital Health Interventions for Diabetes Self-Management Education/Support in Type 1 & 2 diabetes Mellitus. Stud Health Technol Inf. 2020;270:1263–4.

    Google Scholar 

  16. Huber C, Montreuil C, Christie D, Forbes A. Integrating self-management education and support in Routine Care of people with type 2 diabetes Mellitus: a Conceptional Model based on critical interpretive synthesis and a Consensus-Building Participatory Consultation. Front Clin Diabetes Healthc. 2022;3:845547.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Shahshahani MS, Goodarzi-Khoigani M, Eghtedari M, Javadzade H, Jouzi M. Effectiveness of a web-based program on self-care behaviors and glycated hemoglobin in patients with type 2 diabetes: study protocol of a randomized controlled trial. J Educ Health Promot. 2023;12:284.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Steinsbekk A, Rygg L, Lisulo M, Rise MB, Fretheim A. Group based diabetes self-management education compared to routine treatment for people with type 2 diabetes mellitus. A systematic review with meta-analysis. BMC Health Serv Res. 2012;12(1):213.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Eldridge S, Campbell M, Campbell M, Dahota A, Giraudeau B, Higgins J, Reeves B, Siegfried N. Revised Cochrane risk of bias tool for randomized trials (RoB 2.0): additional considerations for cluster-randomized trials. Cochrane Methods Cochrane Database Syst Rev 2016, 10(suppl 1).

  21. Schünemann HJ. Using systematic reviews in guideline development: the GRADE approach. Syst Reviews Health Research: Meta-Analysis Context. 2022;424:448.

    Google Scholar 

  22. Kirwan M, Vandelanotte C, Fenning A, Duncan MJ. Diabetes self-management smartphone application for adults with type 1 diabetes: Randomized Controlled Trial. J Med Internet Res. 2013;15(11):e235.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Moattari M, Hashemi M, Dabbaghmanesh MH. The impact of electronic education on metabolic control indicators in patients with diabetes who need insulin: a randomised clinical control trial. J Clin Nurs. 2013;22(1–2):32–8.

    Article  PubMed  Google Scholar 

  24. Rossi MC, Nicolucci A, Lucisano G, Pellegrini F, Di Bartolo P, Miselli V, Anichini R, Vespasiani G. Impact of the Diabetes Interactive Diary telemedicine system on metabolic control, risk of hypoglycemia, and quality of life: a randomized clinical trial in type 1 diabetes. Diabetes Technol Ther. 2013;15(8):670–9.

    Article  CAS  PubMed  Google Scholar 

  25. Ruissen MM, Torres-Peña JD, Uitbeijerse BS, Arenas de Larriva AP, Huisman SD, Namli T, Salzsieder E, Vogt L, Ploessnig M, van der Putte B, et al. Clinical impact of an integrated e-health system for diabetes self-management support and shared decision making (POWER2DM): a randomised controlled trial. Diabetologia. 2023;66(12):2213–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Skrøvseth SO, Årsand E, Godtliebsen F, Joakimsen RM. Data-Driven Personalized Feedback to patients with type 1 diabetes: a Randomized Trial. Diabetes Technol Ther. 2015;17(7):482–9.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ayar D, Öztürk C, Grey M. The Effect of web-based Diabetes Education on the Metabolic Control, self-efficacy and Quality of Life of Adolescents with type 1 diabetes Mellitus in Turkey. J Pediatr Res 2021, 8(2).

  28. Castensøe-Seidenfaden P, Husted GR, Jensen AK, Hommel E, Olsen B, Pedersen-Bjergaard U, Kensing F, Teilmann G. Testing a Smartphone App (Young with Diabetes) to Improve Self-Management of Diabetes over 12 months: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2018;6(6):e141.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hanberger L, Ludvigsson J, Nordfeldt S. Use of a web 2.0 Portal to improve education and communication in young patients with families: Randomized Controlled Trial. J Med Internet Res. 2013;15(8):e175.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Klee P, Bussien C, Castellsague M, Combescure C, Dirlewanger M, Girardin C, Mando JL, Perrenoud L, Salomon C, Schneider F, et al. An intervention by a patient-designed Do-It-Yourself Mobile device app reduces HbA1c in children and adolescents with type 1 diabetes: a Randomized double-crossover study. Diabetes Technol Ther. 2018;20(12):797–805.

    Article  PubMed  Google Scholar 

  31. Sap S, Kondo E, Sobngwi E, Mbono R, Tatah S, Dehayem M, Koki PO, Mbanya JC. Effect of patient education through a social network in young patients with type 1 diabetes in a sub-saharan context. Pediatr Diabetes. 2019;20(3):361–5.

    Article  PubMed  Google Scholar 

  32. Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK. Significance of HbA1c test in diagnosis and prognosis of Diabetic patients. Biomark Insights. 2016;11:95–104.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Makris K, Spanou L. Is there a relationship between mean blood glucose and glycated hemoglobin? J Diabetes Sci Technol. 2011;5(6):1572–83.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Leow MK. Glycated hemoglobin (HbA1c): clinical applications of a Mathematical Concept. Acta Inf Med. 2016;24(4):233–8.

    Article  Google Scholar 

  35. Neelofar K, Ahmad J. Amadori albumin in diabetic nephropathy. Indian J Endocrinol Metab. 2015;19(1):39–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kim ES, Yaylayan V. Amino-acid-derived Oxazolidin-5-Ones as chemical markers for Schiff Base formation in glycation reactions. Appl Sci. 2023;13(13):7658.

    Article  CAS  Google Scholar 

  37. Sikaris K. The correlation of hemoglobin A1c to blood glucose. J Diabetes Sci Technol. 2009;3(3):429–38.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Lim W-Y, Ma S, Heng D, Tai ES, Khoo CM, Loh TP. Screening for diabetes with HbA1c: test performance of HbA1c compared to fasting plasma glucose among Chinese, malay and Indian community residents in Singapore. Sci Rep. 2018;8(1):12419.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Cabrales P, Salazar Vázquez MA, Salazar Vázquez B, Rodríguez-Morán M, Intaglietta M, Guerrero-Romeros F. Blood pressure reduction due to hemoglobin glycosylation in type 2 diabetic patients. Vasc Health Risk Manag. 2008;4(4):917–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Bahadoran Z, Mirmiran P, Kashfi K, Ghasemi A. Vascular nitric oxide resistance in type 2 diabetes. Cell Death Dis. 2023;14(7):410.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Syreeni A, Sandholm N, Cao J, Toppila I, Maahs DM, Rewers MJ, Snell-Bergeon JK, Costacou T, Orchard TJ, Caramori ML, et al. Genetic determinants of Glycated Hemoglobin in Type 1 diabetes. Diabetes. 2019;68(4):858–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Chen Z, Shao L, Jiang M, Ba X, Ma B, Zhou T. Interpretation of HbA1c lies at the intersection of analytical methodology, clinical biochemistry and hematology (review). Exp Ther Med. 2022;24(6):707.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bhana S, Variava E, Mhazo TV, de Beer JC, Naidoo P, Pillay S, Carrihill M, Naidoo K, van Wyk L, Pauly B. Healthcare Resource Utilization in controlled Versus uncontrolled adults living with type 1 diabetes in the South African Public Healthcare Sector. Value Health Reg Issues. 2023;36:66–75.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

S.U, M.M.F, F.D, and A.A: contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mehrshad Mohebi Far.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Usefi, S., Davoodi, F., Alizadeh, A. et al. Online diabetes self-management education application for reducing glycated hemoglobin level among patients with type 1 diabetes mellitus: a systematic review and meta-analysis. Clin Diabetes Endocrinol 10, 48 (2024). https://doi.org/10.1186/s40842-024-00201-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40842-024-00201-9

Keywords