Evaluasi Digital Adherence Technology Berbasis Kode QR (Q-Monte) untuk Pemantauan Kepatuhan Pengobatan Tuberkulosis

Farid Zulkarnain Nur  Syah; Noor Annisa Susanto; Akhmad Zainuri

doi:10.54445/pharmademica.v5i2.160

Authors

Farid Zulkarnain Nur Syah D-III Farmasi, Politeknik Kesehatan Putra Indonesia Malang, Malang, Indonesia
Noor Annisa Susanto D-III Farmasi, Politeknik Kesehatan Putra Indonesia Malang, Malang, Indonesia
Akhmad Zainuri Teknik Elektro, Universitas Brawijaya, Malang, Indonesia

DOI:

https://doi.org/10.54445/pharmademica.v5i2.160

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Keywords:

Tuberculosis, medication adherence, Q-Monte, QR code, System Usability Scale

Abstract

Tuberculosis (TB) treatment adherence is a critical determinant of therapeutic success; however, monitoring adherence remains challenging in primary healthcare settings. Digital adherence technology (DAT) offers an alternative approach to monitor patient adherence more efficiently. This study aimed to evaluate the initial feasibility of implementing a QR code–based DAT system (Q-Monte) for monitoring TB treatment adherence in primary healthcare centers. This study employed a pilot feasibility design with a prospective observational approach involving 30 outpatient TB patients from two primary healthcare centers in Malang Regency. Participants were recruited using purposive sampling with the following inclusion criteria: TB patients aged ≥12 years, receiving oral anti-tuberculosis therapy, owning an Android-based smartphone and an active WhatsApp number, and being willing to be monitored for 30 days. Patients with HIV co-infection or multidrug-resistant TB (TB-MDR), those who transferred healthcare facilities during the observation period, or those with stroke comorbidity were excluded. Most participants were male (56.7%), aged 45–54 years (30.0%), had completed senior high school education (50.0%), and had no comorbidities (80.0%). Adherence was measured as the percentage of days with QR code scanning, while system usability was assessed using the System Usability Scale (SUS). The results showed an average QR code scanning adherence rate of 90.3% over 30 days, with 46.7% of patients reporting having taken medication without performing the QR code scan. The usability evaluation yielded a mean SUS score of 75.7 (grade B). These findings indicate that Q-Monte is feasible as a tool for monitoring TB treatment adherence in primary healthcare settings, although further optimization of technical support and user education is needed to improve the reliability of digital adherence recording.

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References

AlSahafi, A. J., Shah, H. B. U., AlSayali, M. M., Mandoura, N., Assiri, M., Almohammadi, E. L., Khalawi, A., AlGarni, A., Filemban, M. K., AlOtaibe, A. K., AlFaifi, A. W. A., & AlGarni, F. (2019). High non-compliance rate with anti-tuberculosis treatment: A need to shift facility-based directly observed therapy short course (DOTS) to community mobile outreach team supervision in Saudi Arabia. BMC Public Health, 19(1), 1168. https://doi.org/10.1186/s12889-019-7520-8

Areas, T., Diniz, B. D., Odutola, P., Dantas, C. R., De Freitas, M. C. F. L. C., Hefford, P. M., & Bes, T. M. (2024). Video-observed therapy (VOT) vs directly observed therapy (DOT) for tuberculosis treatment: A systematic review on adherence, cost of treatment observation, time spent observing treatment and patient satisfaction. PLOS Neglected Tropical Diseases, 18(10), e0012565. https://doi.org/10.1371/journal.pntd.0012565

Burzynski, J., Mangan, J. M., Lam, C. K., Macaraig, M., Salerno, M. M., deCastro, B. R., Goswami, N. D., Lin, C. Y., Schluger, N. W., Vernon, A., eDOT Study Team, Bamrah-Morris, S., Bowers, S., Carberry, S., Chuck, C., Dias, M., Gao, G., Garfein, R., Green, V., … Winston, C. (2022). In-Person vs Electronic Directly Observed Therapy for Tuberculosis Treatment Adherence: A Randomized Noninferiority Trial. JAMA Network Open, 5(1), e2144210. https://doi.org/10.1001/jamanetworkopen.2021.44210

Cattamanchi, A., Crowder, R., Kityamuwesi, A., Kiwanuka, N., Lamunu, M., Namale, C., Tinka, L. K., Nakate, A. S., Ggita, J., Turimumahoro, P., Babirye, D., Oyuku, D., Berger, C., Tucker, A., Patel, D., Sammann, A., Turyahabwe, S., Dowdy, D., & Katamba, A. (2021). Digital adherence technology for tuberculosis treatment supervision: A stepped-wedge cluster-randomized trial in Uganda. PLOS Medicine, 18(5), e1003628. https://doi.org/10.1371/journal.pmed.1003628

Charalambous, S., Maraba, N., Jennings, L., Rabothata, I., Cogill, D., Mukora, R., Hippner, P., Naidoo, P., Xaba, N., Mchunu, L., Velen, K., Orrell, C., & Fielding, K. L. (2024). Treatment adherence and clinical outcomes amongst in people with drug-susceptible tuberculosis using medication monitor and differentiated care approach compared with standard of care in South Africa: A cluster randomized trial. eClinicalMedicine, 75, 102745. https://doi.org/10.1016/j.eclinm.2024.102745

De Groot, L. M., Straetemans, M., Maraba, N., Jennings, L., Gler, M. T., Marcelo, D., Mekoro, M., Steenkamp, P., Gavioli, R., Spaulding, A., Prophete, E., Bury, M., Banu, S., Sultana, S., Onjare, B., Efo, E., Alacapa, J., Levy, J., Morales, M. L. L., … Bakker, M. I. (2022). Time Trend Analysis of Tuberculosis Treatment While Using Digital Adherence Technologies—An Individual Patient Data Meta-Analysis of Eleven Projects across Ten High Tuberculosis-Burden Countries. Tropical Medicine and Infectious Disease, 7(5), 65. https://doi.org/10.3390/tropicalmed7050065

Durmuş, A. (2024). The influence of digital literacy on mHealth app usability: The mediating role of patient expertise. DIGITAL HEALTH, 10, 20552076241299061. https://doi.org/10.1177/20552076241299061

Guzman, K., Crowder, R., Leddy, A., Maraba, N., Jennings, L., Ahmed, S., Sultana, S., Onjare, B., Shilugu, L., Alacapa, J., Levy, J., Katamba, A., Kityamuwesi, A., Bogdanov, A., Gamazina, K., Cattamanchi, A., & Khan, A. (2023). Acceptability and feasibility of digital adherence technologies for drug-susceptible tuberculosis treatment supervision: A meta-analysis of implementation feedback. PLOS Digital Health, 2(8), e0000322. https://doi.org/10.1371/journal.pdig.0000322

Hartch, C., Dietrich, M. S., Lancaster, B. J., Mulvaney, S. A., & Stolldorf, D. P. (2025). Satisfaction and Usability of a Commercially Available Medication Adherence App (Medisafe) Among Medically Underserved Patients With Chronic Illnesses: Survey Study. JMIR Human Factors, 12, e63653. https://doi.org/10.2196/63653

Hyzy, M., Bond, R., Mulvenna, M., Bai, L., Dix, A., Leigh, S., & Hunt, S. (2022). System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis. JMIR mHealth and uHealth, 10(8), e37290. https://doi.org/10.2196/37290

Kiwanuka, N., Kityamuwesi, A., Crowder, R., Guzman, K., Berger, C. A., Lamunu, M., Namale, C., Kunihira Tinka, L., Nakate, A. S., Ggita, J., Turimumahoro, P., Babirye, D., Oyuku, D., Patel, D., Sammann, A., Turyahabwe, S., Dowdy, D. W., Katamba, A., & Cattamanchi, A. (2023). Implementation, feasibility, and acceptability of 99DOTS-based supervision of treatment for drug-susceptible TB in Uganda. PLOS Digital Health, 2(6), e0000138. https://doi.org/10.1371/journal.pdig.0000138

Klug, B. (2017). An Overview of the System Usability Scale in Library Website and System Usability Testing. Weave: Journal of Library User Experience, 1(6). https://doi.org/10.3998/weave.12535642.0001.602

Liu, X., Thompson, J., Dong, H., Sweeney, S., Li, X., Yuan, Y., Wang, X., He, W., Thomas, B., Xu, C., Hu, D., Vassall, A., Huan, S., Zhang, H., Jiang, S., Fielding, K., & Zhao, Y. (2023). Digital adherence technologies to improve tuberculosis treatment outcomes in China: A cluster-randomised superiority trial. The Lancet Global Health, 11(5), e693–e703. https://doi.org/10.1016/S2214-109X(23)00068-2

Maraba, N., Orrell, C., Chetty-Makkan, C. M., Velen, K., Mukora, R., Page-Shipp, L., Naidoo, P., Mbatha, M. T., Fielding, K. L., & Charalambous, S. (2021). Evaluation of adherence monitoring system using evriMED with a differentiated response compared to standard of care among drug-sensitive TB patients in three provinces in South Africa: A protocol for a cluster randomised control trial. Trials, 22(1), 389. https://doi.org/10.1186/s13063-021-05337-y

Ravenscroft, L., Kettle, S., Persian, R., Ruda, S., Severin, L., Doltu, S., Schenck, B., & Loewenstein, G. (2020). Video-observed therapy and medication adherence for tuberculosis patients: Randomised controlled trial in Moldova. European Respiratory Journal, 56(2), 2000493. https://doi.org/10.1183/13993003.00493-2020

Shao, Y., Yang, X., Chen, Q., Guo, H., Duan, X., Xu, X., Yue, J., Zhang, Z., Zhao, S., & Zhang, S. (2025). Determinants of digital health literacy among older adult patients with chronic diseases: A qualitative study. Frontiers in Public Health, 13, 1568043. https://doi.org/10.3389/fpubh.2025.1568043

Stoner, M. C. D., Maragh-Bass, A. C., Sukhija-Cohen, A. C., & Saberi, P. (2022). Digital directly observed therapy to monitor adherence to medications: A scoping review. HIV Research & Clinical Practice, 23(1), 47–60. https://doi.org/10.1080/25787489.2022.2103512

Tadesse, A. W., Mganga, A., Dube, T. N., Alacapa, J., Van Kalmthout, K., Letta, T., Mleoh, L., Garfin, A. M. C., Maraba, N., Charalambous, S., Foster, N., Jerene, D., & Fielding, K. L. (2024). Feasibility and acceptability of the smart pillbox and medication label with differentiated care to support person-centered tuberculosis care among ASCENT trial participants – A multicountry study. Frontiers in Public Health, 12, 1327971. https://doi.org/10.3389/fpubh.2024.1327971

Takano, E., Maruyama, H., Takahashi, T., Mori, K., Nishiyori, K., Morita, Y., Fukuda, T., Kondo, I., & Ishibashi, Y. (2023). User Experience of Older People While Using Digital Health Technologies: A Systematic Review. Applied Sciences, 13(23), 12815. https://doi.org/10.3390/app132312815

Thomas, B. E., Kumar, J. V., Chiranjeevi, M., Shah, D., Khandewale, A., Thiruvengadam, K., Haberer, J. E., Mayer, K. H., & Subbaraman, R. (2020). Evaluation of the Accuracy of 99DOTS, a Novel Cellphone-based Strategy for Monitoring Adherence to Tuberculosis Medications: Comparison of DigitalAdherence Data With Urine Isoniazid Testing. Clinical Infectious Diseases, 71(9), e513–e516. https://doi.org/10.1093/cid/ciaa333

Wambi, P., West, N., Nabugoomu, J., Kityamuwesi, A., Crowder, R., Kunihira, L., Wobudeya, E., Adithya, C., Jaganath, D., & Katamba, A. (2024). A mixed methods evaluation of 99DOTS digital adherence technology uptake among adolescents treated for pulmonary tuberculosis in Uganda. Public and Global Health. https://doi.org/10.1101/2024.12.01.24318270

World Health Organization. (2024). Global Tuberculosis Report 2024. https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2024

World Health Organization. (2025). Digital resources for tuberculosis. https://www.who.int/tools/digital-resources-for-tuberculosis