Assessment of remote patient monitoring (RPM) systems for patients with type 2 diabetes: a systematic review and meta-analysis



The objective of this study is to conduct an assessment of Remote Patient Monitoring (RPM) systems compared to usual care for controlling glycosylated hemoglobin in type 2 diabetes.


The study was a systematic review with meta-analysis and meta-regression. A systematic search was performed via the most important electronic databases of medical resources, such as PubMed, Scopus and Cochrane library. The main outcome was HbA1C. The heterogeneity sources were examined using Chi-square (Q) and I2 tests. Meta-analyses were done using Stata version 11 software. Statistical significance was defined as P < 0.05. Random effects model was used in meta-analysis, and the heterogeneity more than 50% was considered as significant.


The results of the systematic review and meta-analysis indicated that the effect size index (Difference of Pre-test/Post-test Control Design-2nd method “using pooled pretest SD” (DPPC2)) among users of RPM for type 2 diabetic patients was −0.32 with a confidence interval of 95% (from −0.45 to −0.19) as compared to the control group. The current study declared a vital role of RPM technology in reduction of hemoglobin glycogen levels. The results of the subgroup analysis showed that RPM is more effective for patients who are residents of cities, having intervention lengths less than 6 months, getting the orders from the physician and using the websites as their intervention type.


The current study indicted the efficacy of RPM in reducing HbA1c among type 2 diabetic patients, which could be a base for policymakers to decide on the introduction of this technology in Iran.

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  1. 1.

    Logan AG, McIsaac WJ, Tisler A, Irvine MJ, Saunders A, Dunai A, et al. Mobile phone–based remote patient monitoring system for management of hypertension in diabetic patients. Am J Hypertens. 2007;20(9):942–8.

    PubMed  Google Scholar 

  2. 2.

    Organization WH. Global report on diabetes: World Health Organization. Geneva; 2016.

  3. 3.

    Control CD. Prevention. National diabetes statistics report, 2017. Centers for Disease Control and Prevention, US Department of Health and Human Services.: Atlanta, GA; 2017.

    Google Scholar 

  4. 4.

    Fathi ahmadsaraei N, Neshat doost HT, Manshaee GR, Nadi MA. The Effectiveness of Acceptance and Commitment Therapy on Quality of Life among Patients with Type 2 Diabetes. Iranian Journal of Health Education and Health Promotion. 2016;4(1):31–9.

    Google Scholar 

  5. 5.

    Whiting DR, Guariguata L, Weil C, Shaw J. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract. 2011;94(3):311–21.

    PubMed  Google Scholar 

  6. 6.

    Organization WH. Use of glycated haemoglobin (HbA1c) in diagnosis of diabetes mellitus: abbreviated report of a WHO consultation. Geneva: World Health Organization; 2011.

    Google Scholar 

  7. 7.

    Holl F, Munteh P, Burk R, Swoboda W. Improving access to care in rural Africa through the use of telemedicine: Using a mHealth system as a case study. Studies in health technology and informatics. 2017;244:105.

    Google Scholar 

  8. 8.

    Vegesna A, Tran M, Angelaccio M, Arcona S. Remote patient monitoring via non-invasive digital technologies: a systematic review. Telemedicine and e-Health. 2017;23(1):3–17.

    PubMed  Google Scholar 

  9. 9.

    Chase HP, Pearson JA, Wightman C, Roberts MD, Oderberg AD, Garg SK. Modem transmission of glucose values reduces the costs and need for clinic visits. Diabetes Care. 2003;26(5):1475–9.

    PubMed  Google Scholar 

  10. 10.

    Bonsignore L, Bloom N, Steinhauser K, Nichols R, Allen T, Twaddle M, et al. Evaluating the Feasibility and Acceptability of a Telehealth Program in a Rural Palliative Care Population: TapCloud for Palliative Care. J Pain Symptom Manag. 2018.

  11. 11.

    Yoo B-K, Kim M, Sasaki T, Hoch JS, Marcin JP. Selected use of telemedicine in intensive care units based on severity of illness improves cost-effectiveness. Telemedicine and e-Health. 2018;24(1):21–36.

    PubMed  Google Scholar 

  12. 12.

    Smith NM, Satyshur RD. Pediatric diabetes telemedicine program improves access to care for rural families: role of APRNs. Pediatr Nurs. 2016;42(6):294.

    PubMed  Google Scholar 

  13. 13.

    Noah B, Keller MS, Mosadeghi S, Stein L, Johl S, Delshad S, et al. Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials. NPJ Digital Medicine. 2018;1(1):2.

    Google Scholar 

  14. 14.

    Wilson LS, Maeder AJ. Recent directions in telemedicine: review of trends in research and practice. Healthcare informatics research. 2015;21(4):213–22.

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Bayliss EA, Steiner JF, Fernald DH, Crane LA, Main DS. Descriptions of barriers to self-care by persons with comorbid chronic diseases. The Annals of Family Medicine. 2003;1(1):15–21.

    PubMed  Google Scholar 

  16. 16.

    Coye MJ, Haselkorn A, DeMello S. Remote patient management: technology-enabled innovation and evolving business models for chronic disease care. Health Aff. 2009;28(1):126–35.

    Google Scholar 

  17. 17.

    community Tgd. Blood Glucose Care. 2018.

  18. 18.

    Riazi H, Larijani B, Langarizadeh M, Shahmoradi L. Managing diabetes mellitus using information technology: a systematic review. Journal of Diabetes & Metabolic Disorders. 2015;14(1):49.

    CAS  Google Scholar 

  19. 19.

    Onkar Sumant PJ. Remote Patient Monitoring Market by Condition (Congestive Heart Failure, Diabetes, Chronic Obstructive Pulmonary Disease, Blood Pressure, and Mental Health), Components (Devices and Software) - Global Opportunity Analysis and Industry Forecast, 2014 - 2022. 2016.

  20. 20.

    Global Remote Patient Monitoring Systems Market. 2017.

  21. 21.

    Baum P, Abadie F, Lupiañez FV, Maghiros I, Mora EV, Talaya MBZ. Market Developments–Remote Patient Monitoring and Treatment, Telecare, Fitness/Wellness and mHealth. Strategic Intelligence Monitor on Personal Health Systems, Phase. 2013;2.

  22. 22.

    Hossain MS, editor. Patient status monitoring for smart home healthcare. Multimedia & Expo Workshops (ICMEW), 2016 IEEE International Conference on; 2016: IEEE.

  23. 23.

    Mack H. Remote patient monitoring market grew by 44 percent in 2016, report says. 2017.

    Google Scholar 

  24. 24.

    Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. 2010.

  25. 25.

    Morris SB. Estimating effect sizes from pretest-posttest-control group designs. Organ Res Methods. 2008;11(2):364–86.

    Google Scholar 

  26. 26.

    Kwon H-S, Cho J-H, Kim H-S, Song B-R, Ko S-H, Lee J-M, et al. Establishment of blood glucose monitoring system using the internet. Diabetes Care. 2004;27(2):478–83.

    PubMed  Google Scholar 

  27. 27.

    Cho J-H, Chang S-A, Kwon H-S, Choi Y-H, Ko S-H, Moon S-D, et al. Long-term effect of the Internet-based glucose monitoring system on HbA1c reduction and glucose stability: a 30-month follow-up study for diabetes management with a ubiquitous medical care system. Diabetes Care. 2006;29(12):2625–31.

    PubMed  Google Scholar 

  28. 28.

    Shea S, Weinstock RS, Starren J, Teresi J, Palmas W, Field L, et al. A randomized trial comparing telemedicine case management with usual care in older, ethnically diverse, medically underserved patients with diabetes mellitus. J Am Med Inform Assoc. 2006;13(1):40–51.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Tildesley HD, Mazanderani AB, Ross SA. Effect of Internet therapeutic intervention on A1C levels in patients with type 2 diabetes treated with insulin. Diabetes Care. 2010;33(8):1738–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control. Diabetes Care. 2011;34(9):1934–42.

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Kleinman NJ, Shah A, Shah S, Phatak S, Viswanathan V. Improved medication adherence and frequency of blood glucose self-testing using an m-Health platform versus usual care in a multisite randomized clinical trial among people with type 2 diabetes in India. Telemedicine and e-Health. 2017;23(9):733–40.

    PubMed  Google Scholar 

  32. 32.

    Warren R, Carlisle K, Mihala G, Scuffham PA. Effects of telemonitoring on glycaemic control and healthcare costs in type 2 diabetes: a randomised controlled trial. J Telemed Telecare. 2018;24(9):586–95.

    PubMed  Google Scholar 

  33. 33.

    Bujnowska-Fedak MM, Puchała E, Steciwko A. The impact of telehome care on health status and quality of life among patients with diabetes in a primary care setting in Poland. Telemedicine and e-Health. 2011;17(3):153–63.

    PubMed  Google Scholar 

  34. 34.

    Tang PC, Overhage JM, Chan AS, Brown NL, Aghighi B, Entwistle MP, et al. Online disease management of diabetes: engaging and motivating patients online with enhanced resources-diabetes (EMPOWER-D), a randomized controlled trial. J Am Med Inform Assoc. 2012;20(3):526–34.

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Istepanian RS, Mousa A, Haddad N, Sungoor A, Hammadan T, Soran H et al., editors. The potential of m-health systems for diabetes management in post conflict regions a case study from Iraq. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2014: IEEE.

  36. 36.

    Steventon A, Bardsley M, Doll H, Tuckey E, Newman SP. Effect of telehealth on glycaemic control: analysis of patients with type 2 diabetes in the Whole Systems Demonstrator cluster randomised trial. BMC Health Serv Res. 2014;14(1):334.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Wakefield BJ, Koopman RJ, Keplinger LE, Bomar M, Bernt B, Johanning JL, et al. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes. Telemedicine and e-Health. 2014;20(3):199–205.

    PubMed  Google Scholar 

  38. 38.

    Fountoulakis S, Papanastasiou L, Gryparis A, Markou A, Piaditis G. Impact and duration effect of telemonitoring on HbA1c, BMI and cost in insulin-treated Diabetes Mellitus patients with inadequate glycemic control: A randomized controlled study. Hormones. 2015;14(4):632–43.

    PubMed  Google Scholar 

  39. 39.

    Zhai Y-k, Zhu W-j, Cai Y-l, Sun D-x, Zhao J. Clinical-and cost-effectiveness of telemedicine in type 2 diabetes mellitus: a systematic review and meta-analysis. Medicine. 2014;93(28).

  40. 40.

    Nicolucci A, Cercone S, Chiriatti A, Muscas F, Gensini G, Group RS. A randomized trial on home telemonitoring for the management of metabolic and cardiovascular risk in patients with type 2 diabetes. Diabetes Technol Ther. 2015;17(8):563–70.

    CAS  PubMed  Google Scholar 

  41. 41.

    Alotaibi MM, Istepanian R, Philip N. A mobile diabetes management and educational system for type-2 diabetics in Saudi Arabia (SAED). mHealth. 2016;2:33.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Anzaldo-Campos MC, Contreras S, Vargas-Ojeda A, Menchaca-Diaz R, Fortmann A, Philis-Tsimikas A. Dulce Wireless Tijuana: a Randomized Control Trial Evaluating the Impact of Project Dulce and Short-Term Mobile Technology on Glycemic Control in a Family Medicine Clinic in Northern Mexico. Diabetes Technol Ther. 2016;18(4):240–51.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Holmen H, Torbjørnsen A, Wahl AK, Jenum AK, Småstuen MC, Årsand E, et al. A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: One-year results from the Norwegian randomized controlled trial RENEWING HEALTH. Diabetes Technology and Therapeutics. 2016;18:S58–S9.

    Google Scholar 

  44. 44.

    Kim HS, Sun C, Yang SJ, Sun L, Li F, Choi IY, et al. Randomized, Open-Label, Parallel Group Study to Evaluate the Effect of Internet-Based Glucose Management System on Subjects with Diabetes in China. Telemedicine and e-Health. 2016;22(8):666–74.

    PubMed  Google Scholar 

  45. 45.

    Cho JH, Kim HS, Yoo SH, Jung CH, Lee WJ, Park CY, et al. An Internet-based health gateway device for interactive communication and automatic data uploading: Clinical efficacy for type 2 diabetes in a multi-centre trial. J Telemed Telecare. 2017;23(6):595–604.

    PubMed  Google Scholar 

  46. 46.

    Dario C, Toffanin R, Calcaterra F, Saccavini C, Stafylas P, Mancin S, et al. Telemonitoring of Type 2 Diabetes Mellitus in Italy. Telemedicine journal and e-health: the official journal of the American Telemedicine Association. 2017;23(2):143–52.

    Google Scholar 

  47. 47.

    Kitsiou S, Pare G, Jaana M, Gerber B. Effectiveness of mHealth interventions for patients with diabetes: an overview of systematic reviews. PLoS One. 2017;12(3):e0173160.

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Saffari M, Ghanizadeh G, Koenig HG. Health education via mobile text messaging for glycemic control in adults with type 2 diabetes: a systematic review and meta-analysis. Primary care diabetes. 2014;8(4):275–85.

    PubMed  Google Scholar 

  49. 49.

    Shabaninejad H, Sarikhani M, Asgharzadeh A. Investigating the effect of providing care thrugh a text message of a cell phone in comparison with the face-to-face approach for controlling blood glucose (HBA1C) in patients with type 2 diabetes: A systematic review and analysis of meta_regression. Iranian Journal of Diabetes and Metabolism. 2018;17(6):272–84.

    Google Scholar 

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This research was part of MSc thesis on Health Technology Assessment, which was approved by school of public health, Tehran University of medical Sciences.

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Correspondence to Mohammadreza Mobinizadeh.

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Salehi, S., Olyaeemanesh, A., Mobinizadeh, M. et al. Assessment of remote patient monitoring (RPM) systems for patients with type 2 diabetes: a systematic review and meta-analysis. J Diabetes Metab Disord 19, 115–127 (2020).

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  • Remote monitoring
  • Glycosylated hemoglobin
  • Type 2 diabetes
  • Systematic review
  • Meta-analysis