Volume 12, Issue 1 (2024)
Health Educ Health Promot 2024, 12(1): 9-15 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Damayantie N, Ernawati E, Dewi M, Fahmi I. Nursing Education on Diabetes Through Smartphone Therapeutic Applications. Health Educ Health Promot 2024; 12 (1) :9-15
URL: http://hehp.modares.ac.ir/article-5-72139-en.html
URL: http://hehp.modares.ac.ir/article-5-72139-en.html
1- Department of Nursing, Health Polytecnic of Jambi, Jambi, Indonesia
Abstract: (1521 Views)
Aims: Hypoglycemia is a frequent complication in patients with type 2 diabetes mellitus. By providing education, awareness among diabetic patients could be increased to prevent hypoglycemia. Indonesia has yet to widely implement smartphone-based education programs for hypoglycemia prevention. This study aimed to assess the feasibility of a smartphone application-based diabetes education model, developed using the health belief model and social cognitive theory, to enhance the ability of diabetes mellitus patients to detect hypoglycemia.
Materials & Methods: This design and development research utilized a pre-test and post-test design without a control group and was done on 64 diabetics between May and September 2023. Statistical analyses were performed using the paired t-test.
Findings: The mean patient's score on the ability to prevent hypoglycemia was 45.13 before the intervention. After the intervention, the patient's ability increased by 3.21 to reach 48.34. The paired t-test yielded a p-value of 0.0001, indicating a significant difference in the ability to detect hypoglycemia before and after the intervention.
Conclusion: Nursing Education Diabetic Therapeutic Application (NEDTA) was declared feasible to use in detecting hypoglycemia.
Materials & Methods: This design and development research utilized a pre-test and post-test design without a control group and was done on 64 diabetics between May and September 2023. Statistical analyses were performed using the paired t-test.
Findings: The mean patient's score on the ability to prevent hypoglycemia was 45.13 before the intervention. After the intervention, the patient's ability increased by 3.21 to reach 48.34. The paired t-test yielded a p-value of 0.0001, indicating a significant difference in the ability to detect hypoglycemia before and after the intervention.
Conclusion: Nursing Education Diabetic Therapeutic Application (NEDTA) was declared feasible to use in detecting hypoglycemia.
Keywords: Diabetes Mellitus [Mesh], Health Belief Model [MeSH], Hypoglycemia [MeSH], Smartphone [MeSH]
Article Type: Original Research |
Subject:
Technology of Health Education
Received: 2023/10/26 | Accepted: 2023/11/11 | Published: 2024/01/5
Received: 2023/10/26 | Accepted: 2023/11/11 | Published: 2024/01/5
References
1. Arbiter B, Look H, McComb L, Snider C. Why download data: the benefits and challenges of more diabetes data. Diabetes Spectr. 2019;32(3):221-5. [Link] [DOI:10.2337/ds18-0099]
2. Bellfield EJ, Sharp LK, Xia Y, Gerber BS. Use of a mobile app to facilitate blood glucose monitoring in adolescents with type 1 diabetes: single-subject nonrandomized clinical trial. JMIR Diabetes. 2018;3(1):e3. [Link] [DOI:10.2196/diabetes.8357]
3. Simmons K, Riddley S. Diabetes and technology. Prim Care: Clin Office Pract. 2022;49(2):327-37. [Link] [DOI:10.1016/j.pop.2021.11.005]
4. Kemenkes RI. Hasil utama RISKESDAS 2018 [Internet]. Ministry of Health Health Research and Development Agency. Jakarta: Kemenkes RI; 2018- [cited 2022 June 20]. Available from: https://kesmas.kemkes.go.id/assets/upload/dir_519d41d8cd98f00/files/Hasil-riskesdas-2018_1274.pdf. [Indonesian] [Link]
5. Hariton A, Nita L, Cretu M. A web application for blood glucose monitoring using the iPhone advantages. 2012 International Conference and Exposition on Electrical and Power Engineering; 2012 Oct 25-27; New York :IEEE; 2012. pp. 505-8. [Link] [DOI:10.1109/ICEPE.2012.6463883]
6. Izahar S, Lean QY, Hameed MA, Murugiah MK, Patel RP, Al-Worafi YM, et al. Content analysis of mobile health applications on diabetes mellitus. Front Endocrinol. 2017;8:318. [Link] [DOI:10.3389/fendo.2017.00318]
7. Joaheer R, Nagowah SD. A big data framework for diabetes in Mauritius. 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions; ICTUS); 2017 Dec 18-20; New York; IEEE; 2017. pp. 126-32. [Link] [DOI:10.1109/ICTUS.2017.8285991]
8. Grifantini K. Advances in management technology for diabetes: From personal reminders to digital doctors. IEEE Pulse. 2014;5(3):40-4. [Link] [DOI:10.1109/MPUL.2014.2309579]
9. Eng DS, Lee JM. The promise and peril of mobile health applications for diabetes and endocrinology. Pediatr Diabetes. 2013;14(4):231-8. [Link] [DOI:10.1111/pedi.12034]
10. Morrison C. Digital health and care institutes: Scottish diabetes policy and market report. Glasgow: University of Strathclyde; 2019. [Link]
11. Weinstock RS, Aleppo G, Bailey TS, Bergenstal RM, Fisher WA, Greenwood DA, et al. The role of blood glucose monitoring in diabetes management. Virginia: American Diabetes Association; 2020. [Link] [DOI:10.2337/db2020-31]
12. Yoo JH, Kim JH. Advances in continuous glucose monitoring and integrated devices for management of diabetes with insulin-based therapy: Improvement in glycemic control. Diabetes Metab J. 2023;47(1):27-41. [Link] [DOI:10.4093/dmj.2022.0271]
13. Salber P, Niksch A. Digital health tools for diabetes. J Ambul Care Manag. 2015;38(2):196-9. [Link] [DOI:10.1097/JAC.0000000000000080]
14. Stanger C, Kowatsch T, Xie H, Nahum-Shani I, Lim-Liberty F, Anderson M, et al. A digital health intervention (SweetGoals) for young adults with type 1 diabetes: Protocol for a factorial randomized trial. JMIR Res Protoc. 2021;10(2):e27109. [Link] [DOI:10.2196/27109]
15. Vashist SK, Luong JHT, Vashist SK. Point-of-care technologies enabling next-generation healthcare monitoring and management. Springer: New York; 2019. pp.117-32. [Link] [DOI:10.1007/978-3-030-11416-9_4]
16. Wang W, Seah B, Jiang Y, Lopez V, Tan C, Lim ST, et al. A randomized controlled trial on a nurse‐led smartphone‐based self‐management programme for people with poorly controlled type 2 diabetes: A study protocol. J Adv Nurs. 2018;74(1):190-200. [Link] [DOI:10.1111/jan.13394]
17. Creswell JW. Steps in conducting a scholarly mixed methods study. Lincoln: University of Nebraska; 2013. [Link]
18. Rusdi M. Educational treatment-based research. Jakarta: Rajawali Pers; 2020. [Indonesian] [Link]
19. Dick W, Carey L, Carey JO. The systematic design of instruction. 8th ed. London: Pearson; 2015. [Link]
20. Imas Masturoh SKM, Nauri Anggita T. Health Research Methodology. Jakarta: Rineka Cipta: Kementerian Kesehatan Republik Indonesia; 2018. [Indonesian] [Link]
21. Yudianti I, Nurhayati R. Health Belief Model (HBM) and Delay in Referral of Gynecological Cases. Matern Neonatal Health J. 2018;18-24. [Indonesian] [Link]
22. Crossen SS, Romero CC, Lewis C, Glaser NS. Remote glucose monitoring is feasible for patients and providers using a commercially available population health platform. Front Endocrinol. 2023;14:1063290. [Link] [DOI:10.3389/fendo.2023.1063290]
23. Megawaty I, Syahrul S. Educational interventions in diabetics with the use of health belief models: Literature review. Indonesian Contemporary Nurs J. 2019:4(1);1-10. [Link] [DOI:10.20956/icon.v4i1.5038]
24. Nurhidayati I, Suciana F, Zulcharim I. The relationship between health beliefs and medication adherence in people with Type 2 Diabetes Mellitus. JIlmu Community Nursing. 2019;2(2):27-34. [Indonesian] [Link] [DOI:10.32584/jikk.v2i2.412]
25. Muhlisa M, Amira BSA. Medication Compliance for Diabetes Mellitus Patients Based on the Health Belief Model (HBM) Theory at the Diabetes Center in Ternate City in 2017. Interest: Journal of Health Sciences. 2018;7(2):144-9. [Indonesian] [Link] [DOI:10.37341/interest.v7i2.23]
26. Marsim E, Prisma FA, Prianggi H. Meta analysis: Application of health belief model on the tertiary prevention of type 2 diabetes mellitus. J Health Promot Behav. 2021;6(4):284-97. [Link] [DOI:10.26911/thejhpb.2021.06.04.03]
27. Fitriani Y, Pristianty L, Hermansyah A. Health Belief Model (HBM) Approach to Analyze Compliance of Type 2 Diabetes Mellitus Patients in Using Insulin. J Farmasi Indonesia. 2019;16(2):167-77. [Indonesian] [Link] [DOI:10.30595/pharmacy.v16i2.5427]
28. Sukma FJ, Hidayati IR, Pristianty L. Behavioral factor analysis of the accuracy in using gastritis drugs at the Puskesmas Arjuno in Malang City with the Health Belief Model (HBM) approach. Farmasains: Jurnal Farmasi dan Ilmu Kesehatan. 2021;6(1):15-9. [Link]
29. Puspita RC, Tamtomo D, Indarto D. Health belief model for the analysis of factors affecting hypertension preventive behavior among adolescents in Surakarta. J Health Promot Behav. 2017;2(2):183-96. [Link] [DOI:10.26911/thejhpb.2017.02.02.08]
30. Böhm AK, Jensen ML, Sørensen MR, Stargardt T. Real-world evidence of user engagement with mobile health for diabetes management: Longitudinal observational study. JMIR Mhealth Uhealth. 2020;8(11):e22212. [Link] [DOI:10.2196/22212]
31. Cui L, Schroeder PR, Sack PA. Inpatient and outpatient technologies to assist in the management of insulin dosing. Clin Diabetes. 2020;38(5):462-73. [Link] [DOI:10.2337/cd20-0054]
32. Doyle-Delgado K, Chamberlain JJ. Use of diabetes-related applications and digital health tools by people with diabetes and their health care providers. Clin Diabetes. 2020;38(5):449-61. [Link] [DOI:10.2337/cd20-0046]
33. Clements M, Kaufman N, Mel E. Using digital health technology to prevent and treat disease. Diabetes Technol Ther. 2022;24(S1):S76-95. [Link] [DOI:10.1089/dia.2022.2505]
34. Bello GJ. Mobile Application For Diabetes Monitoring And Management. Murfreesboro: Dean College of Basic and Applied Science Location; 2019. [Link]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |