Biometric linkage of longitudinally collected electronic case report forms and confirmation of subject identity: An open framework for ODK and related tools

The availability of low-cost biometric hardware sensors and software makes it possible to rapidly, affordably and securely sample and store a unique and invariant biological signature (or biometric “template”) of participants in research and trials. This has applications in consent, linkage of case reporting forms collected at different times, and in confirmation of participant identity for purposes of safety monitoring and adherence to international data laws. The use of mobile electronic data collection software has recently become commonplace in clinical trials and research. A raft of tools based on the open-source ODK project now provide diverse options for data management that work consistently in resource-restricted settings, but none have built-in functionality for capturing biometric templates. In this study, we report the development and testing of a novel open-source app and associated method for capturing and matching biometric fingerprint templates during data collection with the popular data platforms ODK, KoBoToolbox, SurveyCTO, Ona and CommCare. Using data from more than 1000 fingers, we show that fingerprint templates can be used to link data forms with high accuracy and that this accuracy increases with the addition of multiple fingerprints on each data form. By focussing on publishing open-source code and documentation, and by using an affordable (<£50) and mass-produced model of fingerprint sensor, we are able to make this platform freely available to the large global user community that utilises ODK and related data collection systems. ### Competing Interest Statement Callum Stott is employed as a software developer for GetODK Inc. ### Funding Statement This research is funded by the Department of Health and Social Care using UK Aid Funding as part of the UK Vaccine Network, and is managed by the National Institute for Health and Care Research. The views expressed in this publication are those of the author(s) and not necessarily those of the Department of Health and Social Care. (PR-OD-1017-20001) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Observational Research Ethics Committee of The London School of Hygiene & Tropical Medicine gave ethical approval for this work (Ref. 22562). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The latest release of the app and CLI, along with all code relating to this work, are available at the project home page: https://github.com/LSHTM-ORK/ODK_Biometrics. Copies of all code and release version 0.3 are provided as supplementary data. The Keppel App runs on Android Devices. The app works in combination with one of ODK Collect, KoBo Collect and SurveyCTO Collect but may work with other similar apps. The Keppel CLI was programmed in Kotlin and is platform independent. All code is released on the MIT Licence (https://opensource.org/licenses/MIT). At the time of writing, the Mantra MFS100 Biometric C-Type Fingerprint Scanner was widely available from online retailers as well as from the manufacturer www.mantratec.com. Fingerprint data cannot be shared for ethical reasons.