Tailoring Mobile Data Collection for Intervention Research in a Challenging Context: Development and Implementation in the Malakit Study

Yann Lambert, Muriel Galindo, Martha Suárez-Mutis, Louise Mutricy, Alice Sanna, Laure Garancher, Hedley Cairo, Helene Hiwat, Jane Bordalo Miller, José Hermenegildo Gomes, Paola Marchesini, Antoine Adenis, Mathieu Nacher, Stephen Vreden, Maylis Douine, Yann Lambert, Muriel Galindo, Martha Suárez-Mutis, Louise Mutricy, Alice Sanna, Laure Garancher, Hedley Cairo, Helene Hiwat, Jane Bordalo Miller, José Hermenegildo Gomes, Paola Marchesini, Antoine Adenis, Mathieu Nacher, Stephen Vreden, Maylis Douine

Abstract

Background: An interventional study named Malakit was implemented between April 2018 and March 2020 to address malaria in gold mining areas in French Guiana, in collaboration with Suriname and Brazil. This innovative intervention relied on the distribution of kits for self-diagnosis and self-treatment to gold miners after training by health mediators, referred to in the project as facilitators.

Objective: This paper aims to describe the process by which the information system was designed, developed, and implemented to achieve the monitoring and evaluation of the Malakit intervention.

Methods: The intervention was implemented in challenging conditions at five cross-border distribution sites, which imposed strong logistical constraints for the design of the information system: isolation in the Amazon rainforest, tropical climate, and lack of reliable electricity supply and internet connection. Additional constraints originated from the interaction of the multicultural players involved in the study. The Malakit information system was developed as a patchwork of existing open-source software, commercial services, and tools developed in-house. Facilitators collected data from participants using Android tablets with ODK (Open Data Kit) Collect. A custom R package and a dashboard web app were developed to retrieve, decrypt, aggregate, monitor, and clean data according to feedback from facilitators and supervision visits on the field.

Results: Between April 2018 and March 2020, nine facilitators generated a total of 4863 form records, corresponding to an average of 202 records per month. Facilitators' feedback was essential for adapting and improving mobile data collection and monitoring. Few technical issues were reported. The median duration of data capture was 5 (IQR 3-7) minutes, suggesting that electronic data capture was not taking more time from participants, and it decreased over the course of the study as facilitators become more experienced. The quality of data collected by facilitators was satisfactory, with only 3.03% (147/4849) of form records requiring correction.

Conclusions: The development of the information system for the Malakit project was a source of innovation that mirrored the inventiveness of the intervention itself. Our experience confirms that even in a challenging environment, it is possible to produce good-quality data and evaluate a complex health intervention by carefully adapting tools to field constraints and health mediators' experience.

Trial registration: ClinicalTrials.gov NCT03695770; https://ichgcp.net/clinical-trials-registry/NCT03695770.

Keywords: Guiana Shield; ODK; Open Data Kit; information system; malaria; mobile data collection.

Conflict of interest statement

Conflicts of Interest: None declared.

©Yann Lambert, Muriel Galindo, Martha Suárez-Mutis, Louise Mutricy, Alice Sanna, Laure Garancher, Hedley Cairo, Helene Hiwat, Jane Bordalo Miller, José Hermenegildo Gomes, Paola Marchesini, Antoine Adenis, Mathieu Nacher, Stephen Vreden, Maylis Douine. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.06.2022.

Figures

Figure 1
Figure 1
Distribution sites and staff of the Malakit study, Guiana Shield, April 2018-March 2020.
Figure 2
Figure 2
Constraints influencing the design of the Malakit information system (Malakit study, Guiana Shield, April 2018-March 2020).
Figure 3
Figure 3
Information system of the Malakit study (collection and flow of data), Guiana Shield, April 2018-March 2020. (1) Mobile data collection by facilitators with Android tablets and storage on the Ona server. (2) Data retrieval, decryption, and aggregation with the MalakitR package. (3) Monitoring of visit reports with the Malakit dashboard web app. (4) Data cleaning with the MalakitR package. admin: administrator; EDC: electronic data capture; ICF: informed consent form; MDM: mobile device management; ODK: Open Data Kit; REST API: representational state transfer application programming interface; XForm: form standard used by ODK; XLS: Microsoft Excel spreadsheet.
Figure 4
Figure 4
Screenshot of the Onde app (Malakit study, Guiana Shield, April 2018-March 2020).
Figure 5
Figure 5
Data monitoring with the Malakit dashboard web app (Malakit study, Guiana Shield, April 2018-March 2020) A. Example of data records monitored. B. Flow of data reviewing and validation. Screened visit records are flagged with "No alert" (1) or "Alert" status (2), leading to manual review (3). After review and the facilitator’s feedback, records are validated (4) or flagged for correction (5) and patched (6). Records can be screened against new alert rules (7).
Figure 6
Figure 6
Number and proportion of records for inclusion and follow-up visits according to the 6-month period of study (Malakit study, Guiana Shield, April 2018-March 2020).
Figure 7
Figure 7
Duration of data entry for the inclusion and follow-up questionnaires, with and without malaria episodes reported by participants (Malakit study, Guiana Shield, April 2018-March 2020). The horizontal lines within the boxes represent medians and the whiskers represent IQRs.

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