Surveillance of Central Obesity Using the Sehat Indonesiaku (ASIK) App in Denpasar City in 2025: An Evaluation Based on the Logic Model and Surveillance Attributes

Ni Putu Setya Puri Cahyani; Ni Ketut Sutiari

doi:10.24198/mkk.v9i2.70503

Surveillance of Central Obesity Using the Sehat Indonesiaku (ASIK) App in Denpasar City in 2025: An Evaluation Based on the Logic Model and Surveillance Attributes

Ni Putu Setya Puri Cahyani, Ni Ketut Sutiari

Abstract

Central obesity was a major risk factor for noncommunicable diseases (NCDs), with an increasing trend in Denpasar City, highlighting the need for an effective surveillance system. This study evaluated the performance of the ASIK-based central obesity surveillance system using a logic model framework (input, process, and output) and surveillance attributes. A mixed-methods descriptive evaluative study was conducted among NCD program officers at the Denpasar City Health Office and community health centers. The quantitative component used a cross-sectional approach, while qualitative data were collected through in-depth interviews, involving 12 respondents selected through total sampling. Data were collected using structured Likert-scale questionnaires and in-depth interviews and were analyzed descriptively based on CDC surveillance system evaluation guidelines. The results showed that input components were relatively adequate; however, challenges included dual workloads, limited technical training, and insufficient operational support. At the process level, the system operated as a non-integrated manual-digital hybrid, characterized by delayed data entry, duplication of recording, and lack of routine data verification. At the output level, data utilization remained limited to administrative reporting and was not optimally used for program planning or decision-making. In terms of surveillance attributes, simplicity (83.3%), flexibility (100%), and acceptability (91.7%) were categorized as good, while data quality (16.7%), representativeness (25.0%), stability (8.4%), and timeliness (50.0%) were low. In conclusion, the ASIK-based central obesity surveillance system was operational but not yet optimal. Strengthening system integration, improving data quality assurance mechanisms, and enhancing data utilization were essential to improve surveillance effectiveness and support evidence-based decision-making.

Keywords: ASIK application, central obesity, logic model, surveillance system evaluation.

Full Text:

PDF

References

Albali, N., Almudarra, S., & Al-farsi, Y. (2023). Comparative Performance Evaluation of the Public Health Surveillance Systems in 6 Gulf Cooperation Countries : Corresponding Author : 7. https://doi.org/10.2196/41269

Ali, M. P., Visser, E. H., West, R. L., Noord, D. Van, & Woude, C. J. Van Der. (2025). Reporting feedback on healthcare outcomes to improve quality in care : a scoping review. Implementation Science. https://doi.org/10.1186/s13012-025-01424-9

Aritonang, B. (2024). Tantangan Dan Solusi Dalam Implementasi Standar Operasional Prosedur ( Sop ) Kegawatdaruratan Di Puskesmas Challenges And Solutions In The Implementation Of Standard Operating Procedures (SOP) For Emergency Services In Community Health Centers (Puskesmas) dirujuk ke fasilitas kesehatan yang lebih tinggi. 6(2). https://doi.org/10.35451/jkk.v6i2.2543

CDC, H. (2001). Inside : Continuing Education Examination Inside : Continuing Continuing Medical Education for U . S . U . S . Physicians and and Nurses Nurses Inside : Medical Education for for Evaluating Public Health Surveillance Systems Recommendations from the Guide. 50(Cdc).

Ghosh, S., Paul, M., & Bhattacharjee, P. (2023). Sedentary lifestyle with increased risk of obesity in urban adult academic professionals : an epidemiological study in West. Scientific Reports, 1–10. https://doi.org/10.1038/s41598-023-31977-y

Jaya, P., Fkm, Z., Airlangga, U., Korespondensi, A., Epidemiologi, D., Kesehatan, F., Universitas, M., Abstrak, I., Berdarah, D., Jaya, K. P., Jaya, P., Positif, N. P., Jaya, P. P., & Oleh, I. R. D. B. D. (2013). Evaluasi pelaksanaan surveilans kasus demam berdarah dengue di puskesmas putat jaya berdasarkan atribut surveilans. 82–94.

Johannessen, K., Comtet, H., & Fosse, E. (2021). A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory.

Kalil, F. S., & Abdulle, M. S. (2021). Evaluation of Measles Surveillance Systems in Ginnir District , Bale Zone , Southeast Ethiopia : A Concurrent Embedded Mixed Quantitative / Qualitative Study. 997–1008.

Keller, K., Schmitt, V. H., Hahad, O., Espinola-klein, C., & Hobohm, L. (2026). Increasing Obesity Rates Worldwide from 1976 to 2016 : The Obesity Epidemic. 1, 1–12.

Kirk, K., Mcclair, T. L., Dakouo, S. P., Abuya, T., & Kirk, K. (2021). Introduction of digital reporting platform to integrate community-level data into health information systems is feasible and acceptable among various community health stakeholders : A mixed-methods pilot study in Mopti , Mali Correspondence to : 11, 1–12. https://doi.org/10.7189/jogh.11.07003

Machmud, R., Maputra, Y., Andalas, U., Andalas, U., Andalas, U., Andalas, U., & Andalas, U. (2025). Community-Based Risk Factor Surveillance and Its Role in Prevention of Non-Communicable Diseases in Padang: A Qualitative Study. 7(2), 194–209.

Mboera, L. E. G., Rumisha, S. F., Mbata, D., Mremi, I. R., Lyimo, E. P., & Joachim, C. (2021). Data utilisation and factors influencing the performance of the health management information system in Tanzania. 1, 4–11.

Nurhasanah, N., Andini, A., Febriani, A., & Zandra, S. (2025). Gambaran Epidemiologi dan Evaluasi Sistem Surveilans Pneumonia di Puskesmas Cilembang Kota Tasikmalaya Tahun 2024. 3.

Olakotan, O. (2025). Usability Challenges in Electronic Health Records : Impact on Documentation Burden and Clinical Workflow : A Scoping Review. 1–16. https://doi.org/10.1111/jep.70189

Ozonze, O., Scott, P. J., & Hopgood, A. A. (2023). Automating Electronic Health Record Data Quality Assessment. Journal of Medical Systems, 47(1), 1–16. https://doi.org/10.1007/s10916-022-01892-2

Paivaa, C. De, Medeiros, R. A. De, Alexandrino, A., Bezerra, A., Ribeiro, I., & Mirabal, B. (2025). Data quality of the epidemiological surveillance system for congenital syphilis. 35(2), 210–220. https://doi.org/10.36311/jhgd.v35.17295

Rahajeng, E., & Wahidin, M. (2020). Evaluasi Surveilans Faktor Risiko Penyakit Tidak Menular (PTM) Berbasis Data Kegiatan “Posbindu PTM.” Media Penelitian dan Pengembangan Kesehatan, 30(3), 241–256. https://doi.org/10.22435/mpk.v30i3.3569

Richter, A., Sjunnestrand, M., & Strandh, M. R. (2022). Implementing School-Based Mental Health Services : A Scoping Review of the Literature Summarizing the Factors That Affect Implementation.

Sari, N. P., Alamsyah, A., & Saputra, R. (2022). Surveilans Epidemiologi ( Studi Kasus Covid Melalui Test , Isolasi dan Tracing ).

Shalsabilla, Z. (2024). Evaluasi Sistem Surveilans Hepatitis B di Kabupaten Purbalingga , Jawa Tengah 2023 Evaluation of Hepatitis B Surveillance System in Purbalingga Regency , Central Java 2023. 5.

Survey kesehatan indonesia (Ski). (2023). Survei Kesehatan Indonesia 2023 (SKI). Kemenkes, 235.

Suryawan, I. M. A., & Maradona, A. F. (2024). Unveiling the Management of Community Health Centers and Its Impact on Service Quality and Patient Satisfaction in The Tabanan Regency of Bali. 5(6).

Syed, R., Eden, R., Makasi, T., Chukwudi, I., & Mamudu, A. (2023). Digital Health Data Quality Issues : Systematic Review Corresponding Author : 25. https://doi.org/10.2196/42615

World Health Organization, S. D. (2018). Nutrition , overweight and obesity.

Yu, D. (2024). Prevalence and Influencing Factors of Central Obesity among Adults in China : China Nutrition and Health Surveillance.

DOI: https://doi.org/10.24198/mkk.v9i2.70503