Volume 13, Issue 1 (2025)
Health Educ Health Promot 2025, 13(1): 55-60 |
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:
Nomiko D, Bar A, Monalisa M, Eliezer B. Effectiveness of Risk Detection Application in Breast Cancer Prevention in Women of Childbearing Age. Health Educ Health Promot 2025; 13 (1) :55-60
URL: http://hehp.modares.ac.ir/article-5-78855-en.html
URL: http://hehp.modares.ac.ir/article-5-78855-en.html
Effectiveness of Risk Detection Application in Breast Cancer Prevention in Women of Childbearing Age
1- Department of Nursing, Health Polytechnic of Ministry of Health Jambi, Jambi, Indonesia
Abstract: (412 Views)
Aims: This study aimed to assess the effectiveness of an Android-based breast cancer risk assessment application in enhancing the knowledge of women of reproductive age regarding breast cancer prevention.
Materials & Methods: This study employed a quasi-experimental design with a pre-test-post-test control group approach. Participants were divided into intervention and control groups, each comprising 32 subjects. The intervention group utilized the risk detection application, while the control group received conventional education through a pocketbook. Demographic data and knowledge levels were collected using a validated and reliable questionnaire. Data analysis was conducted using descriptive statistics and inferential tests, including paired t-tests and independent t-tests.
Findings: There was a significant improvement in knowledge levels in both groups following the intervention. In the intervention group, the mean knowledge score increased from 10.940±1.865 at the pre-test to 13.380±1.314 at the post-test (p<0.0001). Similarly, in the control group, the mean score rose from 10.750±1.344 to 11.500±1.368 (p<0.0001). An independent t-test indicated a significant difference in the mean post-test scores between the intervention and control groups (p<0.0001), with the intervention group demonstrating a higher level of knowledge.
Conclusion: The breast cancer risk assessment detection application effectively enhances the knowledge of women of reproductive age regarding breast cancer prevention.
Materials & Methods: This study employed a quasi-experimental design with a pre-test-post-test control group approach. Participants were divided into intervention and control groups, each comprising 32 subjects. The intervention group utilized the risk detection application, while the control group received conventional education through a pocketbook. Demographic data and knowledge levels were collected using a validated and reliable questionnaire. Data analysis was conducted using descriptive statistics and inferential tests, including paired t-tests and independent t-tests.
Findings: There was a significant improvement in knowledge levels in both groups following the intervention. In the intervention group, the mean knowledge score increased from 10.940±1.865 at the pre-test to 13.380±1.314 at the post-test (p<0.0001). Similarly, in the control group, the mean score rose from 10.750±1.344 to 11.500±1.368 (p<0.0001). An independent t-test indicated a significant difference in the mean post-test scores between the intervention and control groups (p<0.0001), with the intervention group demonstrating a higher level of knowledge.
Conclusion: The breast cancer risk assessment detection application effectively enhances the knowledge of women of reproductive age regarding breast cancer prevention.
Article Type: Original Research |
Subject:
Technology of Health Education
Received: 2025/01/1 | Accepted: 2025/01/30 | Published: 2025/02/3
Received: 2025/01/1 | Accepted: 2025/01/30 | Published: 2025/02/3
* Corresponding Author Address: Department of Nursing, Health Polytechnic of Ministry of Health Jambi, dr. Tazar Street, BuluranKenali, Jambi, Indonesia. Postal Code: 36361 (debbiedebbienomiko@gmail.com)
References
1. Sant M, Bernat-Peguera A, Felip E, Margelí M. Role of ctDNA in breast cancer. Cancers. 2022;14(2):310. [Link] [DOI:10.3390/cancers14020310]
2. Harbeck N, Penault-Llorca F, Cortes J, Gnant M, Houssami N, Poortmans P, et al. Breast cancer. Nat Rev Dis Primers. 2019;5(1):66. [Link] [DOI:10.1038/s41572-019-0111-2]
3. Moo TA, Sanford R, Dang C, Morrow M. Overview of breast cancer therapy. PET Clin. 2018;13(3):339-54. [Link] [DOI:10.1016/j.cpet.2018.02.006]
4. Lestari P, Wulansari W. The importance of breast self-examination (be aware) as an effort to detect breast cancer early. Indones J Community Empower. 2019;1(2). [Indonesian] [Link]
5. UICC. GLOBOCAN 2020: New global cancer data [Internet]. Geneva: :union: for International Cancer Control; 2020 [cited 2024, November, 20]. Available from: https://www.uicc.org/news/globocan-2020-global-cancer-data. [Link]
6. RSK DHARMAIS. Is it important to detect breast cancer early? [Internet]. Jakarta: KEMENKES RSK DHARMAIS; 2024 [cited 2024, November, 20]. Available from: https://dharmais.co.id/news/653/Pentingkah-Deteksi-Dini-Kanker-Payudara. [Indonesian] [Link]
7. Akram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. Biol Res. 2017;50(1):33. [Link] [DOI:10.1186/s40659-017-0140-9]
8. Amila A, Sinuraya E, Gulo ARB. Awareness education (breast self-examination) for early detection of breast cancer in Medan high school students. JURNAL ABDIMAS MUTIARA. 2020;1(2):29-40. [Indonesian] [Link]
9. Ginsburg O, Yip C, Brooks A, Cabanes A, Caleffi M, Dunstan Yataco JA, et al. Breast cancer early detection: A phased approach to implementation. Cancer. 2020;126(Suppl 10):2379-93. [Link] [DOI:10.1002/cncr.32887]
10. Milosevic M, Jankovic D, Milenkovic A, Stojanov D. Early diagnosis and detection of breast cancer. Technol Health Care. 2018;26(4):729-59. [Link] [DOI:10.3233/THC-181277]
11. Provencher L, Hogue JC, Desbiens C, Poirier B, Poirier E, Boudreau D, et al. Is clinical breast examination important for breast cancer detection?. Curr Oncol. 2016;23(4):e332-9. [Link] [DOI:10.3747/co.23.2881]
12. Migowski A, Dias MBK, Diz MDPE, Sant'Ana DR, Nadanovsky P. Guidelines for early detection of breast cancer in Brazil. II-New national recommendations, main evidence, and controversies. CADERNOS DE SAUDE PUBLICA. 2018;34(6):e00074817. [Portuguese] [Link] [DOI:10.1590/0102-311x00074817]
13. Manjuri S, Gill SS. Machine learning-based web application for breast cancer prediction. In: Applications of AI for interdisciplinary research. Boca Raton: CRC Press; 2024. [Link] [DOI:10.1201/9781003467199-6]
14. Nugroho A, Fauzi A, Sunarko B, Wibawanto H, Mulwinda A, Iksan N. Web based application system for cancerous object detection in ultrasound images. AIP Conf Proc. 2023;2727(1):040023. [Link] [DOI:10.1063/5.0141519]
15. Ma Z, Shi Y, Yao S, Lu N, Cheng F. Effectiveness of telemedicine-based psychosocial intervention for breast cancer patients: A systematic review and meta-analysis. Support Care Cancer. 2023;31(10):595. [Link] [DOI:10.1007/s00520-023-08052-3]
16. Maas P, Barrdahl M, Joshi AD, Auer PL, Gaudet MM, Milne RL, et al. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2016;2(10):1295-302. [Link] [DOI:10.1001/jamaoncol.2016.1025]
17. Hashemi SM, Rafiemanesh H, Aghamohammadi T, Badakhsh M, Amirshahi M, Sari M, et al. Prevalence of anxiety among breast cancer patients: A systematic review and meta-analysis. Breast Cancer. 2020;27(2):166-78. [Link] [DOI:10.1007/s12282-019-01031-9]
18. Rojas K, Stuckey A. Breast cancer epidemiology and risk factors. Clin Obstet Gynecol. 2016;59(4):651-72. [Link] [DOI:10.1097/GRF.0000000000000239]
19. Niell BL, Freer PE, Weinfurtner RJ, Arleo EK, Drukteinis JS. Screening for breast cancer. Radiol Clin North Am. 2017;55(6):1145-62. [Link] [DOI:10.1016/j.rcl.2017.06.004]
20. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. [Link] [DOI:10.1038/s41586-019-1799-6]
21. Da Costa Vieira RA, Biller G, Uemura G, Ruiz CA, Curado MP. Breast cancer screening in developing countries. Clinics. 2017;72(4):244-53. [Link] [DOI:10.6061/clinics/2017(04)09]
22. Schünemann HJ, Lerda D, Quinn C, Follmann M, Alonso-Coello P, Rossi PG, et al. Breast cancer screening and diagnosis: A synopsis of the European breast guidelines. Ann Intern Med. 2020;172(1):46-56. [Link] [DOI:10.7326/M19-2125]
23. Seely JM, Alhassan T. Screening for breast cancer in 2018-what should we be doing today?. Curr Oncol. 2018;25(Suppl 1):115-24. [Link] [DOI:10.3747/co.25.3770]
24. Waks AG, Winer EP. Breast cancer treatment: A review. JAMA. 2019;321(3):288-300. [Link] [DOI:10.1001/jama.2018.19323]
25. Chetlen A, Mack J, Chan T. Breast cancer screening controversies: Who, when, why, and how?. Clin Imaging. 2016;40(2):279-82. [Link] [DOI:10.1016/j.clinimag.2015.05.017]
26. Al Husaini MAS, Hadi Habaebi M, Gunawan TS, Islam MR. Self-detection of early breast cancer application with infrared camera and deep learning. Electronics. 2021;10(20):2538. [Link] [DOI:10.3390/electronics10202538]
27. Suprapto, Anita KW. Breast cancer screening application based on Android with the certainty factor method. IAIC Int Conf Ser. 2023;4(1):88-96. [Link] [DOI:10.34306/conferenceseries.v4i1.633]
28. Peristiowati Y, Hariyono, Arantrinita. Socialization and early detection of breast cancer using the Android application "MamoApp". J Community Engagem Health. 2024;7(2):131-40. [Link] [DOI:10.30994/jceh.v7i2.617]
29. Prochaska JJ, Coughlin SS, Lyons EJ. Social media and mobile technology for cancer prevention and treatment. Am Soc Clin Oncol Educ Book. 2017;37:128-37. [Link] [DOI:10.1200/EDBK_173841]
30. Gatuha G, Jiang T. Android based naive Bayes probabilistic detection model for breast cancer and mobile cloud computing: Design and implementation. Int J of Eng Res Afr. 2016;21:197-208. [Link] [DOI:10.4028/www.scientific.net/JERA.21.197]
31. Aprianti S, Erika, Kurniawan D. Effect of breast cancer detection application on improving knowledge of early detection of breast cancer (BSE) among adolescents. Int J Nurs Health Serv. 2022;5(5):437-45. [Link]
32. Wu TY, Lee J. Promoting breast cancer awareness and screening practices for early detection in low-resource settings. Eur J Breast Health. 2018;15(1):18-25. [Link] [DOI:10.5152/ejbh.2018.4305]
33. Prabarini LP, Abi Muhlisin SKM. The influence of peer group method breast cancer health education on the level of knowledge, attitudes and conscious behavior of pkk mothers in Karangasem subdistrict [dissertation]. Surakarta: Muhammadiyah University of Surakarta; 2017. [Indonesian] [Link]
34. Yusuf A, Yulita YH, Ab Hadi IS, Nasution A, Lean Keng S. Breast awareness mobile apps for health education and promotion for breast cancer. Front Public Health. 2022;10:951641. [Link] [DOI:10.3389/fpubh.2022.951641]
35. Erbil N, Dundar N, Inan C, Bolukbas N. Breast cancer risk assessment using the Gail model: A Turkish study. Asian Pac J Cancer Prev. 2015;16(1):303-6. [Link] [DOI:10.7314/APJCP.2015.16.1.303]
36. Timothy W, Collins Danielle X, Morales SEG. Neonatal rat myocardial extraction. Physiol Behav. 2016;176(1):139-48. [Chinese] [Link]
37. Alam N, Wirakusumah FF, Soepardan S. Knowledge and application-based awareness behavior for early detection of breast tumors in women of childbearing age. JURNAL ILMIAH KESEHATAN. 2021;13(1):95-103. [Indonesian] [Link] [DOI:10.37012/jik.v13i1.441]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |