Abstract
Objectives
This study aimed to develop and evaluate the functionality of a smart system-based prototype, “BABAT TB,” a medication box designed to assist tuberculosis (TB) patients in adhering to their treatment schedules.
Methods
The development of the BABAT TB prototype followed the Design Science Research Methodology framework, encompassing the stages of problem identification and motivation, defining the objectives for a solution, and system design and development. Problem identification and motivation were established through semi-structured interviews with TB program officers and document analysis. The prototype integrates two main functional components: a drug quantity monitoring module and a reminder/alarm system for medication schedules, both monitored in real time. Serial communication through a SIM register is used to transmit real-time drug quantity data to the associated application. The system is powered by two 4,000 mAh lithium batteries, providing up to 2 months of use without recharging.
Results
The prototype consists of three core hardware components: the input control circuit, the timer circuit, and the drug amount detection circuit. All modules were successfully assembled and powered. The timer was configured according to medical prescriptions, and the alarm activated at the scheduled times, effectively reminding patients to take their medication.
Conclusions
The BABAT TB prototype effectively measures medication quantities and provides timely alerts, thereby supporting adherence to TB treatment. In addition, it can transmit data related to drug quantities, consultation schedules, and prototype identity cards (IDs) to a database.
Keywords: Tuberculosis, Medication Adherence, Health Information Systems, Equipment Design, Reminder Systems
I. Introduction
Tuberculosis (TB) remains a major global health challenge, with 10.6 million people infected and 1.6 million deaths reported worldwide in 2021 [1]. Southeast Asia accounts for 45% of global TB cases [1], and Indonesia ranks third among countries worldwide, contributing 9.2% of cases between 2020 and 2021 [1]. Despite this high burden, Indonesia’s treatment coverage remains low at 45%, well below the national target of 90%, with a treatment success rate of 85% [1]. The persistently high incidence and mortality rates are strongly correlated with inadequate treatment coverage and poor adherence [2].
Adherence to TB treatment is essential, as the standard regimen requires at least 6 months of continuous medication [3,4]. Non-adherence increases the risk of treatment failure, disease transmission, and drug resistance [5]. Without treatment, up to 70% of smear-positive and 20% of smear-negative TB patients may die within 10 years [6].
Directly observed treatment, short-course (DOTS), is the World Health Organization (WHO)-recommended strategy designed to improve TB treatment adherence through supervised medication intake [7–9]. However, the implementation of DOTS faces multiple challenges, including transportation costs, time constraints, limited healthcare personnel, and privacy concerns [10,11]. In Indonesia, DOTS requires a treatment supervisor, typically a healthcare worker or family member, but its effectiveness may decrease when supervisors are unavailable or experience an increased burden [12].
To address these limitations, WHO has promoted digital interventions to enhance TB treatment adherence [13]. Digital adherence technologies (DATs) have emerged as innovative tools to monitor and support patient compliance [14,15]. These technologies range from basic phone-based systems [16] to smartphone apps [11], digital medicine boxes [17], and ingestible sensors [18].
Several studies have evaluated DATs as alternatives to traditional DOTS strategies [14,19]. For example, video DOT (VDOT), piloted in San Diego, USA, and Tijuana, Mexico, provides a cost-effective option but still depends on patients’ ability to follow scheduled video check-ins [11]. Similarly, 99DOTS, which has been implemented in India for more than 150,000 TB patients, monitors medication intake via mobile phone usage but is limited by mobile access and user proficiency [16,20]. In China, reminder systems using time-based alarms have been effective in reducing treatment dropouts, although communication with healthcare providers remains limited [17]. Another system, CAREcall, piloted in Thailand, combines alarm reminders and communication features but is not accessible to hearing-impaired users and lacks visual confirmation of medication intake [21].
Given the remaining gaps in digital TB adherence technologies—particularly regarding inclusivity, portability, and real-time monitoring—this study aimed to develop and evaluate a novel device called the TB Medication Reminder Box (BABAT TB). This smart system-based prototype features both visual and auditory alerts to accommodate hearing- and visually-impaired users. Moreover, its compact design enhances portability, making it suitable for daily use. As limited research has explored similar devices in Indonesia, this study seeks to provide a practical, inclusive, and effective technological solution for improving TB medication adherence in the local context. Specifically, it aims to design, develop, and assess the functionality of the BABAT TB prototype.
II. Methods
1. Participants
Participants were selected through purposive sampling based on their roles and relevance to the study. Key informants included the TB Program Officer at Sekaran PHC (primary health center) and the Head of the Infectious Disease Prevention and Control Section at the Semarang City Health Office. Sekaran PHC, located on a major university campus, serves a large student population. Some TB patients are students without treatment supervisors, which poses specific challenges to adherence due to geographic separation from family support.
2. Procedures
The development of the BABAT TB smart system-based prototype followed the Design Science Research Methodology, which includes the following stages: (1) problem identification and motivation, (2) definition of objectives for a solution, (3) design and development, (4) demonstration, and (5) evaluation. However, this study focused exclusively on the design and development phase, emphasizing the creation and refinement of the prototype’s functional features.
1) Problem identification & motivation
A phenomenological approach was employed to analyze the needs of TB patients and the TB Program Officer at Sekaran PHC. Data from semi-structured interviews and document reviews were used to identify major barriers to treatment adherence. This stage highlighted the principal obstacles to TB treatment compliance, providing the foundation for designing a responsive and effective intervention.
2) Definition of objectives for a solution
The next step was to define the objectives for a solution. This stage aimed to establish the functional goals of the BABAT TB smart system prototype, based on the needs of TB patients and the TB Program Officer in supporting effective TB treatment.
3) Design and development
The next phase involved refining the system concept based on user requirements, formulating the prototype model, and developing the BABAT TB prototype. The prototype consists of two primary components: a drug quantity reader and a real-time reminder/alarm for medication schedules. BABAT TB operates independently of Wi-Fi, using SIM-based serial communication to transmit real-time medication data to the application. Powered by two 4,000 mAh lithium batteries, the system can function for up to 2 months without recharging. The BABAT TB system is designed to improve treatment adherence by providing timely reminders and minimizing the risk of treatment failure. Figure 1 illustrates the system’s block diagram, with red and blue indicators representing power and input components, respectively.
Figure 1.
Main block diagram of the BABAT TB prototype. “BABAT TB” indicates a smart system-based reminder box for enhancing tuberculosis medication adherence.
Figure 1 presents the system’s block diagram, illustrating its functional architecture and workflow. The system processes two primary inputs: user commands via the Button Selector and weight measurements from the Load Cell for drug quantity detection. These inputs are managed by the ESP32 DevKit, which serves as the central processing unit. Output is delivered through an LCD for visual alerts and a Buzzer for audio notifications.
The BABAT TB prototype was evaluated through laboratory-based simulations to assess functionality under controlled conditions. As the study was limited to the design and development phase, field implementation was not included. The evaluation focused on system architecture and initial functionality, particularly the alarm system and load cell sensors for medication detection. Testing was conducted over a 2-week period, corresponding to the intensive phase of TB treatment. All tests adhered to predefined technical specifications. As a preliminary pilot, the findings are exploratory and do not yet represent performance in real-world settings. These results provide a foundation for future development and large-scale implementation.
3. Measures
During the problem identification and motivation phase, data were collected through semi-structured interviews and document analysis with two key informants: the Head of the Section for Prevention and Control of Infectious Diseases at the Semarang City Health Office and a TB Program Officer at Sekaran PHC. The interview with the Head of Section addressed the number of TB cases and the treatment success rate (TSR) in Semarang City, current TB treatment policies, the implementation of digital programs and adherence technologies, barriers to TB treatment, and estimated needs for improving adherence. The interview with the TB Program Officer at Sekaran PHC focused on the number of TB cases and TSR at the facility, treatment dropout rates, current treatment mechanisms (fixed-dose combination or loose drugs), daily dosages, medication schedules, patient visit routines, the role of treatment supervisors, challenges in managing TB patients, and perceived needs for adherence support.
In the design and development phase, prototype functionality was evaluated by testing the alarm system and the load cell sensor responsible for detecting the remaining number of TB drug doses.
4. Statistical Analysis
A mixed-methods approach was applied in this study. Qualitative data were analyzed using an interactive analysis model, while quantitative evaluation employed descriptive statistics with univariate analysis to assess two key performance metrics:
(1) Alarm system reliability: Measured by the frequency of successful alarm activations over a 14-day simulation.
(2) Medication detection accuracy: Assessed through 43 iterations by calculating the sensor’s error rate in pill detection, along with the mean and standard deviation of weight differences compared with a calibrated digital scale.
5. Ethical Considerations
This study was approved by the Health Research Ethics Commission of Semarang State University (Universitas Negeri Semarang), No. 296/KEPK/EC/2023. All participants provided informed consent prior to participation.
III. Results
Based on interviews and document analysis with the Head of the Prevention and Control of Infectious Diseases Section (P1), several obstacles contributing to TB treatment noncompliance were identified. A key issue is the low TSR in Semarang City, which in 2021 was only 71.72%, well below the WHO-recommended minimum of 90%. The informant highlighted existing TB control programs encompassing promotive, preventive, curative, and capacity-building efforts, including several digital initiatives. However, none of these programs currently incorporate DATs.
“There are several TB control policies in place ..... [but] digital technology to monitor TB treatment adherence is not yet available in Semarang City” (P1).
“If you want to create a digital adherence technology, we support it because, so far, there hasn’t been one.” (P1).
The Head of the Prevention and Control of Infectious Diseases Section expressed agreement that the development of a DAT could improve treatment outcomes, noting that its implementation would likely contribute to increasing TSR among TB patients.
From interviews and document analysis with the TB Program Officer at Sekaran PHC (P2), several barriers to TB treatment adherence were also identified. In 2023 (as of October 26), two patients discontinued treatment during the intensive phase. The officer reported that some TB patients at Sekaran PHC are university students whose demanding academic schedules often cause them to miss doses. Additionally, two student patients lacked drug supervisors due to stigma-related fears that their housemates might learn of their TB diagnosis.
“Two university students with TB do not have a drug supervisor in their rented house because they are afraid that if their housemates know [about TB illness], they would be evicted from their rented house.” (P2)
Therefore, tools that can remind patients and provide a medication storage box are recommended to help them take their TB drugs on time each day, an idea that the TB Program Officer supported.
According to the TB Program Officer at Sekaran PHC, several operational requirements are necessary to improve TB treatment adherence. Patients primarily follow a fixed dose combination consisting of three tablets taken once daily during the intensive phase. The recommended medication intake time is between 7:00 am and 9:00 am, and TB patient check-ups at Sekaran PHC occur once every 2 weeks during this phase.
“TB medication uses fixed-dose combination (FDC) here, the majority of TB patients use three tablets once daily” (P2)
“Regarding the schedule for taking TB medication here, we don’t require them to have a specific time every day, just from 7 am to 9 pm. It’s okay to set the time limit for taking the medicine from 7 am to 9 am” (P2)
“The TB patient check-up schedule here is once a month to take their drug stock” (P2)
Based on the identified challenges and patient needs, the proposed solution was the development of the BABAT TB smart system prototype. Its key features include: (1) medication alarm notifications synchronized with drug intake schedules; (2) a sensor to monitor drug quantity and track stock alongside patient check-up schedules; and (3) user-friendly audio and visual alarms to support both hearing- and visually-impaired patients.
The expected outcome is to achieve 100% treatment compliance among TB patients, ultimately contributing to TB elimination. The system consists of three main hardware components: the input control circuit, the timer circuit, and the drug quantity detection circuit. Figure 1 illustrates the main block diagram of the BABAT TB box. Figure 2 presents the core circuit components, including the ESP32 microcontroller, while Figure 3 depicts the power circuit featuring a security system and a dual 4,000 mAh rechargeable power supply.
Figure 2.
Main circuit of the BABAT TB prototype. “BABAT TB” indicates a smart system-based reminder box for enhancing tuberculosis medication adherence.
Figure 3.
Power circuit of the BABAT TB prototype. “BABAT TB” indicates a smart system-based reminder box for enhancing tuberculosis medication adherence.
Figure 2 shows the schematic of the main electronic circuit, detailing component interconnections. The ESP32 microcontroller is integrated with a real-time clock (DS3132 RTC) module to ensure accurate scheduling. Weight data from the load cell are acquired via the HX711 module, which includes a signal amplifier and an analog-to-digital converter. User interaction is facilitated through input buttons (Button Selector), an LCD for visual output, and a Buzzer for auditory notifications.
Figure 3 illustrates the power supply system designed for portable use. It uses two 1,000 mAh lithium batteries, with charging regulated by a TP4056 module to ensure safety and efficiency. A buck converter regulates voltage for the microcontroller, while a fuse provides overcurrent protection.
1. Hardware Implementation
1) Alarm circuit
The DS3231 real-time clock module was employed to manage alarm settings, maintaining accurate time even when the main battery is depleted, due to its independent 3.3 V backup. The module triggers the buzzer based on scheduled times, with the time displayed on the LCD through the I2C protocol to conserve pins.
Functional testing of the alarm was conducted using a 14-day simulation, where each 5-minute interval represented one day. The alarm consistently activated on schedule and deactivated after the removal of three anti-TB pills, verified by comparing pill counts before and after, as summarized in Table 1.
Table 1.
Alarm functional testing of the BABAT TB prototype
| Number of iterations | Alarm time | Actual medicine (before) | Detected medicine (before) | Alarm ringing active | Actual medicine (after) | Detected medicine (after) | Alarm ringing inactive | ||
|---|---|---|---|---|---|---|---|---|---|
|
|
|
||||||||
| True | False | True | False | ||||||
| 1 | 08:00 | 42 | 42 | √ | 39 | 39 | √ | ||
| 2 | 08:05 | 39 | 39 | √ | 36 | 36 | √ | ||
| 3 | 08:10 | 36 | 36 | √ | 33 | 33 | √ | ||
| 4 | 08:15 | 33 | 33 | √ | 30 | 30 | √ | ||
| 5 | 08:20 | 30 | 30 | √ | 27 | 27 | √ | ||
| 6 | 08:25 | 27 | 27 | √ | 24 | 24 | √ | ||
| 7 | 08:30 | 24 | 24 | √ | 21 | 21 | √ | ||
| 8 | 08:35 | 21 | 21 | √ | 18 | 18 | √ | ||
| 9 | 08:40 | 18 | 18 | √ | 15 | 15 | √ | ||
| 10 | 08:45 | 15 | 15 | √ | 12 | 12 | √ | ||
| 11 | 08:50 | 12 | 12 | √ | 9 | 9 | √ | ||
| 12 | 08:55 | 9 | 9 | √ | 6 | 6 | √ | ||
| 13 | 09:00 | 6 | 6 | √ | 3 | 3 | √ | ||
| 14 | 09:05 | 3 | 3 | √ | 0 | 0 | √ | ||
“BABAT TB” indicates a smart system-based reminder box for enhancing tuberculosis medication adherence.
2) Chassis
The chassis measures 16 cm × 10 cm × 11 cm, providing a total drug storage capacity of 508.9 cm3—sufficient for 1 month of medication. It was produced using 3D printer filament, allowing customizable shaping. Figure 4 shows the chassis design, and Figure 5 displays the physical appearance of the BABAT TB box.
Figure 4.
BABAT TB chassis. “BABAT TB” indicates a smart system-based reminder box for enhancing tuberculosis medication adherence.
Figure 5.
Physical appearance of BABAT TB box. “BABAT TB” indicates a smart system-based reminder box for enhancing tuberculosis medication adherence.
3) Number of drugs read
A load cell sensor integrated within BABAT TB detects the quantity of medication placed at the center of the box (Figure 6).
Figure 6.
Load cell implementation of the BABAT TB—a smart system-based reminder box for enhancing tuberculosis medication adherence.
Table 2 presents the results of load cell sensor testing for monitoring a 2-week drug supply (42 pills). The test compared the actual pill count with sensor readings across 43 removal iterations. Four incorrect readings were recorded, resulting in an error rate of 9%.
Table 2.
Load cell measurement results with a drug capacity of up to a month for 42 drugs
| Number of iterations | Number of drugs | Correct | Incorrect | |
|---|---|---|---|---|
|
| ||||
| Actual | Prototype reading | |||
| 1 | 42 | 42 | √ | |
| 2 | 41 | 41 | √ | |
| 3 | 40 | 40 | √ | |
| 4 | 39 | 39 | √ | |
| 5 | 38 | 38 | √ | |
| 6 | 37 | 37 | √ | |
| 7 | 36 | 36 | √ | |
| 8 | 35 | 35 | √ | |
| 9 | 34 | 33 | √ | |
| 10 | 33 | 33 | √ | |
| 11 | 32 | 32 | √ | |
| 12 | 31 | 30 | √ | |
| 13 | 30 | 30 | √ | |
| 14 | 29 | 29 | √ | |
| 15 | 28 | 28 | √ | |
| 16 | 27 | 27 | √ | |
| 17 | 26 | 26 | √ | |
| 18 | 25 | 25 | √ | |
| 19 | 24 | 24 | √ | |
| 20 | 23 | 23 | √ | |
| 21 | 22 | 21 | √ | |
| 22 | 21 | 21 | √ | |
| 23 | 20 | 20 | √ | |
| 24 | 19 | 19 | √ | |
| 25 | 18 | 18 | √ | |
| 26 | 17 | 17 | √ | |
| 27 | 16 | 17 | √ | |
| 28 | 15 | 15 | √ | |
| 29 | 14 | 14 | √ | |
| 30 | 13 | 13 | √ | |
| 31 | 12 | 12 | √ | |
| 32 | 11 | 11 | √ | |
| 33 | 10 | 10 | √ | |
| 34 | 9 | 9 | √ | |
| 35 | 8 | 8 | √ | |
| 36 | 7 | 7 | √ | |
| 37 | 6 | 6 | √ | |
| 38 | 5 | 5 | √ | |
| 39 | 4 | 4 | √ | |
| 40 | 3 | 3 | √ | |
| 41 | 2 | 2 | √ | |
| 42 | 1 | 1 | √ | |
| 43 | 0 | 0 | √ | |
| Total error | 9% | |||
2. Software Implementation
The system’s circuitry was programmed using an Arduino-based platform, which controlled time settings and user inputs through a Button Selector. The required libraries are shown in Figure 7. Data from the medication box were stored in Firebase real-time database (RTDB), including patient ID, dosage, remaining pills, and estimated days left, as illustrated in Figure 8.
Figure 7.
Arduino integrated development environment library.
Figure 8.
Firebase output.
IV. Discussion
Based on the identified problems and the needs of TB patients and program officers, the proposed solution was the development of the smart system-based prototype BABAT TB, which incorporates the following key features: (1) a medication reminder alarm that alerts patients according to their prescribed TB medication schedule; (2) a drug quantity monitoring system using sensors to detect remaining medication and estimate check-up schedules; and (3) an accessible alarm output with both sound and light indicators designed to accommodate users with hearing or visual impairments.
In 2023, two TB patients at Sekaran PHC discontinued treatment (data as of October 26). Previous studies have reported that patients often stop treatment because they forget to take medication or mistakenly believe that their therapy is complete [22]. At Sekaran PHC, several TB patients are college students, and two of them lack drug supervisors due to fear of stigma from housemates. Busy academic schedules also contribute to missed doses, and non-compliance is influenced by forgetfulness and distance from home [23]. Stigma and discrimination further exacerbate treatment discontinuation [24]. Moreover, the involvement of a drug supervisor has been shown to significantly correlate with treatment adherence [25]. Therefore, tools that provide medication reminders are essential to support TB treatment and reduce non-compliance. A smart system-based solution such as BABAT TB can play a vital role in improving treatment adherence.
During prototype testing, the BABAT TB alarm was set for 8 am, a time that aligns with the recommended daily medication window of 7 am to 9 am. The TB Program Officer at Sekaran PHC agreed that restricting medication intake to this morning window could further enhance adherence.
To meet patient and program officer needs, the BABAT TB sensor continuously monitors the number of tablets taken. During the intensive treatment phase, patients are required to consume three FDC tablets daily. If fewer tablets are taken (e.g., two), the sensor detects the discrepancy and records the corresponding reduction in the medication box. The drug quantity sensor also estimates the number of days remaining until the next check-up based on the current stock in the BABAT TB box. At Sekaran PHC, TB patients in the intensive phase attend check-ups every 2 weeks.
The alarm feature of the BABAT TB prototype includes both auditory and visual signals, ensuring accessibility for users with hearing or visual impairments. All modules were assembled, powered, and the timer configured according to medical prescriptions. The device is portable, compact, and user-friendly. Figure 8 illustrates the BABAT TB user interface.
In a 1-week functional alarm test, the alarm consistently activated at the designated times, effectively supporting daily intake of three anti-TB tablets. The medication reminder schedule corresponded to the prescribed regimen, and the three-tablet dose reflected the standard for most patients.
The load cell sensor in BABAT TB accurately measures the quantity of medication placed in the center of the box. With a total capacity of up to 84 tablets, corresponding to a 28-day supply at three FDC tablets per day, the results indicate that central positioning of the load minimizes measurement error.
Overall, the BABAT TB prototype accurately calculates medication quantities and delivers timely alarms. It also successfully transmits key data, including drug counts, consultation schedules, and device IDs, to the database. However, the current prototype cannot yet verify whether the medication has actually been ingested and remains integrated only with the PHC website, limiting large-scale testing with TB patients.
The existing design requires pills to be placed individually because the sensor system detects single units. This configuration differs from standard clinical practice, where TB medications are typically dispensed in blister packs or sealed packaging, posing a practical limitation. Although the device is portable and suitable for travel, it currently functions solely as a pill container with a monitoring sensor and does not confirm ingestion.
Future development will focus on integrating the BABAT TB system with a mobile application and Internet-of-Things (IoT) technology. IoT integration would enable real-time data transmission, allowing healthcare providers to remotely monitor adherence and intervene when necessary, thereby strengthening treatment supervision and improving clinical outcomes.
Footnotes
Conflict of Interest
No potential conflict of interest relevant to this article was reported.
Acknowledgments
The authors would like to thank all the participants and all experts who participated in this study.
This work was supported by Research Institute and Community Service, Universitas Negeri Semarang (Semarang State University) (Grant No. 235.12.4/UN37/PPK.10/2023).
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