Smart-Adherence Pillbox (SAP): An Electronic Automatic Pillbox-Based Medication Reminder Device for Tuberculosis Patients at the Purwokerto Pulmonary Clinic
DOI:
https://doi.org/10.23917/pharmacon.v23i1.17888Keywords:
Digital Health Technology, Medication adherence, Smart pillbox, TuberculosisAbstract
Tuberculosis (TB) remains a major global public health challenge, and poor adherence to anti-tuberculosis therapy contributes to treatment failure and the emergence of multidrug-resistant tuberculosis (MDR-TB). This study aimed to develop and evaluate the SMART-ADHERENCE PILLBOX (SAP), an electronic automatic pillbox with real-time monitoring designed to support medication adherence among TB patients. A prospective quantitative feasibility study was conducted, consisting of prototype validation and limited field implementation at the Purwokerto Pulmonary Clinic. Technical validation was performed using 100 operational simulation cycles across four independent replications, while the feasibility study involved eight TB patients in the intensive treatment phase who used SAP for 14 days. Evaluations included technical performance, timing performance, medication adherence, and acceptability based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The results demonstrated that SAP achieved high technical reliability, with success rates exceeding 95% for alarm activation, medication dispensing, connectivity, notification delivery, and electronic logging. Alarm and logging deviations were close to zero, and notification delivery time consistently remained below five seconds, indicating good real-time monitoring capability. Following SAP implementation, adherence rates significantly increased from 83.9% to 97.4%, accompanied by reductions in missed doses and medication-taking delays (p<0.05). In addition, all acceptability domains showed high scores (4.3–4.5), indicating that SAP was well accepted by patients. Overall, SAP has the potential to become an applicable digital adherence technology innovation to support tuberculosis control programs in Indonesia.
Downloads
References
Chai, P., Vaz, C., Goodman, G. R., Albrechta, H., Huang, H.-W., Rosen, R., Boyer, E. W., Mayer, K., & O’Cleirigh, C. (2022). Ingestible electronic sensors to measure instantaneous medication adherence: A narrative review. Digital Health, 8. https://api.semanticscholar.org/CorpusId:247177297 DOI: https://doi.org/10.1177/20552076221083119
Charles, S. C. J., Anusha, K., Mahesh, K., Ramasubramanian, R., Kaliraj, P., Selvaraj, V., & others. (2024). Enhancing Tuberculosis Treatment Adherence: Evaluating the Efficacy of the Support for Treatment Adherence and Medication Protocols (STAMP) Device for Automatic Dispensing and Real-Time Medication Monitoring. Cureus, 16(9).
Crabtree-Ramírez, B., Jenkins, C. A., Shepherd, B. E., Jayathilake, K., Veloso, V., Carriquiry, G., Gotuzzo, E., Cortés, C., Padgett, D., McGowan, C., Sierra-Madero, J., Koenig, S., Pape, J. W., Sterling, T. R., Cahn, P., Cesar, C., Fink, V., Ortiz, Z., Cahn, F., … Ranadive, P. (2022). Tuberculosis treatment intermittency in the continuation phase and mortality in HIV-positive persons receiving antiretroviral therapy. BMC Infectious Diseases, 22. https://api.semanticscholar.org/CorpusId:247956460 DOI: https://doi.org/10.1186/s12879-022-07330-5
Davis, R. A., Leavitt, H. B., Singh, A., Fanouraki, E., Yen, R. W., & Bratches, R. W. (2024). Examining interventions that aim to enhance TB treatment adherence in Southeast Asia: A systematic review and meta-analysis. Indian Journal of Tuberculosis, 71(1), 48–63. DOI: https://doi.org/10.1016/j.ijtb.2023.03.001
Espinosa-Pereiro, J., Sánchez-Montalvá, A., Aznar, M., & Espiau, M. (2022). MDR Tuberculosis Treatment. Medicina, 58. https://pdfs.semanticscholar.org/b00e/1c000abe7356da1ed26bfc3880746bae7e26.pdf
Fenerty, S. D., West, C. E., Davis, S. A., Kaplan, S., & Feldman, S. (2012). The effect of reminder systems on patients’ adherence to treatment. Patient Preference and Adherence, 6, 127–135. DOI: https://doi.org/10.2147/PPA.S26314
Hasan, A., Praveen, S., Tarke, C., & Abdullah, F. (2019). Clinical Aspects and Principles of Management of Tuberculosis. Mycobacterium Tuberculosis: Molecular Infection Biology, Pathogenesis, Diagnostics and New Interventions, 355–374. DOI: https://doi.org/10.1007/978-981-32-9413-4_20
Hussain, A., Ma, Z., Li, M., Jameel, A., Kanwel, S., Ahmad, S., & Ge, B. (2025). The mediating effects of perceived usefulness and perceived ease of use on nurses’ intentions to adopt advanced technology. BMC Nursing, 24. https://doi.org/10.1186/s12912-024-02648-8 DOI: https://doi.org/10.1186/s12912-024-02648-8
Liu, X., Thompson, J., Dong, H., Sweeney, S., Li, X., Yuan, Y., Wang, X., He, W., Thomas, B., Xu, C., & others. (2023). Digital adherence technologies to improve tuberculosis treatment outcomes in China: A cluster-randomised superiority trial. The Lancet Global Health, 11(5), e693–e703. DOI: https://doi.org/10.1016/S2214-109X(23)00068-2
Mahardiananta, I. M. A., Nugraha, I. M. A., Reganata, G. P., & Desnanjaya, I. G. M. N. (2022). Perancangan Alat Bantu Kotak Obat Berbasis Mikrokontroler Dalam Peningkatan Kepatuhan Meminum Obat Pada Pasien TBC. RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer), 5(1), 65–72. DOI: https://doi.org/10.24853/resistor.5.1.65-72
Minaam, D. S. A., & Abd-ELfattah, M. (2018). Smart drugs: Improving healthcare using smart pill box for medicine reminder and monitoring system. Future Computing and Informatics Journal, 3(2), 443–456. DOI: https://doi.org/10.1016/j.fcij.2018.11.008
Mundakir, M., Asri, A., & Winata, S. (2021). Community-based Management and Control of Tuberculosis in Sub-urban Surabaya, Indonesia: A Qualitative Study. Open Access Macedonian Journal of Medical Sciences, 9, 212–217. DOI: https://doi.org/10.3889/oamjms.2021.5801
Musiimenta, A., Tumuhimbise, W., Mugaba, A., Muzoora, C., Armstrong-Hough, M., Bangsberg, D., Davis, J. L., & Haberer, J. (2019). Digital monitoring technologies could enhance tuberculosis medication adherence in Uganda: Mixed methods study. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, 17. https://api.semanticscholar.org/CorpusId:202828763 DOI: https://doi.org/10.1016/j.jctube.2019.100119
Pande, V., Chavan, N., & Dahame, A. (2026). Smart Pill Management System for Elderly Patients. International Journal of Advanced Electrical and Electronics Engineering. https://doi.org/10.65521/ijaeee.v15i1.1847 DOI: https://doi.org/10.65521/ijaeee.v15i1.1847
Ridho, A., Alfian, S., Boven, J. V. van, Levita, J., Yalcin, E. A., Le, L., Alffenaar, J., Hak, E., Abdulah, R., & Pradipta, I. (2021). Digital Health Technologies to Improve Medication Adherence and Treatment Outcomes in Patients With Tuberculosis: Systematic Review of Randomized Controlled Trials. Journal of Medical Internet Research, 24. DOI: https://doi.org/10.2196/preprints.33062
https://pdfs.semanticscholar.org/d76d/e0722ab4dc0faf4bcbd8f72aeeb5e1705b86.pdf
Rosu, L., Madan, J. J., Bronson, G., Nidoi, J., Tefera, M., Malaisamy, M., Squire, B. S., & Worrall, E. (2023). Cost of digital technologies and family-observed DOT for a shorter MDR-TB regimen: A modelling study in Ethiopia, India and Uganda. BMC Health Services Research, 23. https://api.semanticscholar.org/CorpusId:265278892 DOI: https://doi.org/10.1186/s12913-023-10295-z
Santosa, A., Juniarti, N., Pahria, T., & Susanti, R. (2025). Integrating narrative and bibliometric approaches to examine factors and impacts of tuberculosis treatment non-compliance. DOI: https://doi.org/10.5826/mrm.2025.1016
Multidisciplinary Respiratory Medicine, 20. https://api.semanticscholar.org/CorpusId:276672492
Sariem, C. N., Odumosu, P., Dapar, M., Musa, J., Ibrahim, L., & Aguiyi, J. (2019). Tuberculosis treatment outcomes: A fifteen-year retrospective study in Jos-North and Mangu, Plateau State, North—Central Nigeria. BMC Public Health, 20. https://api.semanticscholar.org/CorpusId:221108577 DOI: https://doi.org/10.21203/rs.2.11227/v2
Sazali, M. F., Rahim, S., Mohammad, A. H., Kadir, F., Payus, A., Avoi, R., Jeffree, M. S., Omar, A., Ibrahim, M. Y., Atil, A., Tuah, N. M., Dapari, R., Lansing, M. G., Rahim, A. F. A., & Azhar, Z. (2022). Improving Tuberculosis Medication Adherence: The Potential of Integrating Digital Technology and Health Belief Model. Tuberculosis and Respiratory Diseases, 86, 82–93. DOI: https://doi.org/10.4046/trd.2022.0148
Subbaraman, R., Mondesert, L. de, Musiimenta, A., Pai, M., Mayer, K., Thomas, B., & Haberer, J. (2018). Digital adherence technologies for the management of tuberculosis therapy: Mapping the landscape and research priorities. BMJ Global Health, 3. https://api.semanticscholar.org/CorpusId:53084095 DOI: https://doi.org/10.31219/osf.io/dxu3s
WHO. (2023). Global Tuberculosis Report 2023 (1st ed). World Health Organization.
WHO. (2024). Global Tuberculosis Report 2024 (1st ed). World Health Organization.
Xian, Q., Qiu, Z., Kala, S., Guo, J., Zhu, J., Wong, K., Guo, S., Zhu, T., Hou, X., & Sun, L. (2021). Protocol for the sonogenetic stimulation of mouse brain by non-invasive ultrasound. STAR Protocols, 2. https://api.semanticscholar.org/CorpusId:232479284 DOI: https://doi.org/10.1016/j.xpro.2021.100393
Zhang, L., Hussain, W. M. H. W., & Ali, S. M. M. (2025). Trust transfer in digital healthcare: The role of self-service systems in reducing patient treatment barriers. Digital Health, 11. https://api.semanticscholar.org/CorpusId:283195288 DOI: https://doi.org/10.1177/20552076251396560
