Background

The POINTER study in India aimed to evaluate cognitive and lifestyle interventions for reducing the risk of dementia. A key aspect of the study was to longitudinally track a large cohort of participants across multiple sites over an 18-month period, capturing detailed demographic, clinical, cognitive, and lifestyle data at each visit. Given the study’s scale and complexity, a robust, field-friendly digital platform was required for participant enrollment, follow-up tracking, and multi-source data integration.

The Challenge

A central challenge for POINTER was ensuring accurate and efficient participant identification and tracking over multiple visits. Traditional identifier-based approaches, such as manual ID entry, were prone to human error, especially when collecting data across geographically dispersed study sites. Further, the study protocol required seamless integration of complex workflows for different visit types, clinical assessments, lifestyle evaluations, and cognitive testing—all collected through diverse sources and requiring rigorous data validation.

The Solution – Modified Open Data Kit with Iris Biometrics

To meet these challenges, we developed a customized version of Open Data Kit (ODK), enhanced with Iris biometric integration for participant identification. This modified ODK platform allowed field teams to:

  • Enroll new participants using their demographic details along with Iris biometric capture.
  • Identify participants across follow-up visits using their Iris scan, ensuring error-free and secure identification.
  • Implement tailored workflows for baseline visits, interim visits, and final follow-ups within the same system.
  • Incorporate diverse data collection modules, including structured cognitive assessments, clinical examination records, and lifestyle questionnaires.
  • Integrate data from external sources (e.g., lab reports, imaging data) into the same participant record.

System Architecture and Customization

The base ODK tools (ODK Collect and ODK Central) were extended to support biometric hardware, specifically portable Iris scanners. The ODK form engine was also modified to handle conditional branching and nested workflows tailored to the POINTER study’s protocol. This ensured that data collectors were guided through the correct sequence of activities for each visit type, minimizing protocol deviations and data errors.

The backend system was designed to sync data in real-time (where internet was available) or in batch mode for offline sites. Participant records were automatically linked to their unique biometric identifiers, enabling seamless data reconciliation across visits, even when participants moved between sites.

Implementation and Field Use

The customized ODK platform was deployed across multiple study sites in India, training field teams to use Iris-enabled tablets for both enrollment and follow-up visits. The user interface was designed to be intuitive, even for staff with limited technical expertise. The system’s flexibility allowed adaptations for site-specific variations, such as language customization or varying assessment schedules.

Impact

The introduction of Iris biometrics, combined with customized ODK workflows, significantly improved:

  • Participant identification accuracy: Eliminated risks of duplication or misidentification.
  • Protocol adherence: Embedded workflows ensured field teams consistently followed correct assessment sequences.
  • Data quality: Real-time validation and multi-source data integration reduced data inconsistencies.
  • Operational efficiency: Reduced time spent on manual data reconciliation and tracking.

The customized system handled thousands of participant records, demonstrating scalability and reliability even in low-resource settings with intermittent connectivity.

Key Learnings

  • Open-source platforms like ODK offer a powerful starting point for research data management but require thoughtful customization to meet the needs of complex longitudinal studies.
  • Biometric integration provides a strong safeguard against participant misidentification in multi-visit studies.
  • User-centric design and hands-on training are essential for successful technology adoption in field settings.

Conclusion

The POINTER study in India successfully leveraged a tailored ODK platform with Iris biometric integration to manage a complex, multi-site longitudinal study. This innovative approach ensured data accuracy, improved operational efficiency, and provided a replicable model for future large-scale studies in similar settings.