Validity and Reliability of the Finnish Diabetes Risk Core Questionnaire Indonesia with the Rasch Model Approach
DOI:
https://doi.org/10.56359/igj.v5i2.1059Keywords:
Type 2 Diabetes Mellitus, FINDRISC, Validity, Reliability, Rasch ModelAbstract
Background and Objective: Type 2 diabetes mellitus is an increasing public health concern in Indonesia, with many individuals remaining undiagnosed due to limited early detection strategies. The Finnish Diabetes Risk Score is a non-invasive screening tool widely used to estimate the risk of developing type 2 diabetes, yet its Indonesian version has not been comprehensively evaluated using advanced measurement methods. This study aimed to assess the validity and reliability of the Indonesian version of the Finnish Diabetes Risk Score using the Rasch Model to ensure its appropriateness for community-based screening. Method: A quantitative, non-experimental study with a cross-sectional design was conducted among adults aged over 20 years who visited a primary health care center in Tasikmalaya. Participants were selected using accidental sampling. Data were collected through the Indonesian version of the Finnish Diabetes Risk Score questionnaire and analyzed using the Rasch Model to evaluate item fit, reliability, separation indices, and item difficulty hierarchy. Result : The findings showed high item reliability and strong item separation, indicating a stable and consistent hierarchy of diabetes risk factors. All items demonstrated acceptable fit to the Rasch Model, supporting the construct validity of the instrument. Conclusion: Although person reliability was relatively low, suggesting limited variability in respondent risk levels. Conclusion: Overall results confirm that the Indonesian version of the Finnish Diabetes Risk Score is valid and reliable at the item level and suitable for identifying individuals at risk of type 2 diabetes mellitus in community settings.
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References
Al-Shudifat, A. E., Johannessen, A., & Stene, L. C. (2021). Validation of diabetes risk scores using item response theory approaches. BMC Endocrine Disorders, 21(1), 1–10. https://doi.org/10.1186/s12902-021-00765-4
American Diabetes Association. (2024). Standards of care in diabetes—2024. Diabetes Care, 47(Suppl. 1), S1–S350. https://doi.org/10.2337/dc24-S001
Bond, T. G., & Fox, C. M. (2020). Applying the Rasch model: Fundamental measurement in the human sciences (4th ed.). Routledge.
Boone, W. J., Staver, J. R., & Yale, M. S. (2020). Rasch analysis in the human sciences. Springer.
Chiolero A, Paradis G, Paccaud F (2015). The pseudo-high-risk prevention strategy. Int J Epidemiol;44(5):1469–73.
Cho, N. H., Shaw, J. E., Karuranga, S., Huang, Y., da Rocha Fernandes, J. D., Ohlrogge, A. W., & Malanda, B. (2022). IDF diabetes atlas: Global estimates of diabetes prevalence. Diabetes Research and Clinical Practice, 183, 109119. https://doi.org/10.1016/j.diabres.2021.109119
Firmansyah, A., Setiawan, H., & Ariyanto, H. (2021). Studi Kasus Implementasi Evidence-Based Nursing: Water Tepid Sponge Bath Untuk Menurunkan Demam Pasien Tifoid. Viva Medika: Jurnal Kesehatan, Kebidanan Dan Keperawatan, 14(2), 174–181.
IDF. International Diabetes Federation. 2021. Available from: https://idf2023.org
International Diabetes Federation. (2021). IDF diabetes atlas (10th ed.). Brussels: IDF.
Kahn, S. E., Hull, R. L., & Utzschneider, K. M. (2020). Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature, 444(7121), 840–846. https://doi.org/10.1038/nature05482
Lee, Y. J., Park, J. E., & Kim, S. H. (2023). Psychometric evaluation of diabetes risk screening tools using Rasch analysis. Health and Quality of Life Outcomes, 21(1), 45. https://doi.org/10.1186/s12955-023-02104-7
Linacre, J. M. (2023). Winsteps® Rasch measurement computer program user’s guide. Winsteps.com.
Makrilakis, K., Liatis, S., Grammatikou, S., & Perrea, D. (2022). Validation of the FINDRISC questionnaire in diverse populations: An IRT-based approach. Diabetes & Metabolic Syndrome, 16(2), 102399. https://doi.org/10.1016/j.dsx.2022.102399
Megasari AL, Noviantari YR (2024). Upaya Peningkatan Deteksi Dini Penyakit Tidak Menular Pada Masyarakat. J Kreat Pengabdi Kpd Masy.;7(5):2155–64.
Prayoga KP, Suryana D, Supriatna M, Budiman N (2024). Penggunaan Rasch Model Untuk Menganalisis Konstruk Instrumen Kontrol Diri Pada Siswa Sekolah Menengah. G-Couns J Bimbing dan Konseling. 9(1):367–81.
Rifqi Rokhman M, Arifin B, Zulkarnain Z, Satibi S, Perwitasari DA, Boersma C, et al (2022). Translation and performance of the Finnish Diabetes Risk Score for detecting undiagnosed diabetes and dysglycaemia in the Indonesian population. PLoS One. 17(7).
Riskesdas (2018). Riset Kesehatan Dasar. Kementrian Kesehat.
Setiawan, H., Ariyanto, H., Firdaus, F. A., & Khaerunnisa, R. N. (2021). pendidikan kesehatan pencegahan skabies di pondok pesantren al-arifin. Martabe, 4(1), 110–117.
Theofanidis, D. (2007). Chronic illness in childhood: psychosocial adaptation and nursing support for the child and family. Health Science Jornal, 1(2), 1–9. Retrieved from http://www.hsj.gr/index.files/VOLUME1_2.htm
World Health Organization. (2022). WHO guideline on waist circumference and waist–hip ratio. WHO Press.
Zhang, L., Wang, Z., & Chen, X. (2021). Rasch analysis of non-invasive diabetes screening instruments in community settings. BMC Public Health, 21, 1874. https://doi.org/10.1186/s12889-021-11834-2
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