Case Study: Ensuring high quality data collection in Ishihuru Study in Remote Tanzania with a Robust Offline Android-Based Data Capture System

1. Introduction:

Public health research in resource-limited settings like remote areas of Tanzania faces significant challenges, including logistical hurdles, unreliable internet connectivity, and the need for accurate participant identification. Traditional paper-based data collection methods are often inefficient, prone to errors, and difficult to manage. This case study explores the development and implementation of a custom-built, offline Android-based electronic data capture (EDC) system, incorporating fingerprint biometrics, to address these challenges in a large-scale public health research study in Tanzania.

2. The Challenge:

A research team aimed to conduct a longitudinal study on maternal and child health in remote Tanzanian villages. These villages lacked consistent internet access, making cloud-based EDC solutions impractical. The study required:

• Offline Data Collection: The system needed to function reliably without internet connectivity.
• Accurate Participant Identification: To minimize duplicate entries and ensure follow-up data was linked to the correct individuals, biometric verification was essential.
• Robust Data Security: Sensitive health data required secure storage and transmission.
• User-Friendly Interface: The system needed to be easily used by local research assistants with varying levels of technical expertise.
• Data Synchronization: A mechanism to securely sync collected data with a central server when internet access was available was crucial.

3. The Solution: Custom-Developed Offline EDC System with Biometrics:

To address these challenges, a custom Android-based EDC system was developed, incorporating the following features:

• Offline Functionality: The system was designed to store data locally on the Android devices, allowing for seamless data collection in areas without internet.
• Fingerprint Biometrics: A fingerprint scanner was integrated with the Android devices for participant registration and verification. This ensured accurate identification and reduced the risk of duplicate entries.
• Customizable Data Entry Forms: The system allowed for the creation of customized data entry forms tailored to the specific needs of the research study. This facilitated efficient and accurate data collection.
• Data Encryption: All data stored on the devices was encrypted to protect participant privacy.
• User-Friendly Interface: The interface was designed to be intuitive and easy to use, with clear instructions and visual aids.
• Secure Data Synchronization: The system included a synchronization module that automatically uploaded encrypted data to a central server at the end of day at the field office where internet connectivity was available. Once synchronized, researchers and administrators could access real-time reports and analytics via a web-based dashboard.
• Data Validation: Built-in data validation rules were implemented to minimize errors during data entry.
• Battery Optimization: The application was optimized to minimize battery consumption, a crucial factor in remote areas with limited access to electricity. Solar power backup system was put in place for redundancy.

4. Implementation:

• Local research assistants were trained on the use of the Android devices and the EDC system.
• The system was piloted in a small number of villages to identify and address any potential issues.
• The research assistants then travelled to the remote villages, conducting the study using the offline EDC system with biometric verification.
• At the end of the day, before storing the data capture devices, a dedicated staff synchronized data with the central server.
• Regular maintenance and troubleshooting support were provided to the research assistants.

5. Results:

• The offline EDC system proved to be highly effective in facilitating data collection in remote areas.
• Fingerprint biometrics significantly improved participant identification accuracy.
• Data entry errors were reduced compared to traditional paper-based methods.
• Data synchronization was successful, ensuring timely access to the collected data.
• The system was well-received by the research assistants, who found it easy to use.
• The project was completed on schedule, and the data collected provided valuable insights into maternal and child health in the region.

6. Lessons Learned:

• Custom-developed offline EDC systems can be highly effective in overcoming the challenges of public health research in resource-limited settings.
• Fingerprint biometrics is a valuable tool for accurate participant identification.
• Thorough training and ongoing support are essential for the successful implementation of such systems.
• Data security and privacy must be prioritized in the design and implementation of EDC systems.
• Battery optimization is critical for prolonged use in areas with limited power.
• The ability to have secure and reliable data synchronization is essential.

7. Conclusion:

The development and implementation of this custom-built, offline Android-based EDC system with fingerprint biometrics significantly improved the efficiency and accuracy of public health research in remote Tanzania. This case study demonstrates the potential of technology to address the challenges of data collection in resource-limited settings and contribute to improved public health outcomes. Future research can build upon this experience to further refine and expand the use of such systems in other challenging environments.