We will leverage existing technology to develop a mobile health platform that integrates a database, smart card technology, and a mobile phone-based app to make results available at the point of care without the need for Internet connection. Subsequently, we will recruit 300 women who attend church-organized baby shower programs in the four priority local government areas and who consent to participate in the study.

Participants are eligible if they have a positive test result for (1) HIV, (2) hepatitis B surface antigen, and/or (3) sickle cell trait or disease, as defined by heterozygosity (AS) or homozygosity (SS), respectively, for the S variant of hemoglobin β-subunit; and (4) are residing in the local government areas where the four health facilities are situated. Those who decline to have their test results uploaded on the secure Web-based database will be excluded.

Data from the ongoing HBI will be reviewed by study staff to determine eligible participants. Trained research assistants will approach each participant independently to inform them about the study. HBI participants who consent to enroll in the study will have data collected from their community screening session stored in a secure, Web-based database. The stored data will be encrypted into a Quick Response (QR) code embedded on a smart card with a unique identification number printed on the outside of the card. There will be no names or any other identifiers. The smart card will be offered to each participant. The research assistants will administer a questionnaire to each participant to collect information on acceptability of the smart card. Each participant will be asked to present the smart card at prenatal visits and delivery at the selected health care facility. At the health care facility, we will identify dedicated delivery room staff that will be trained to scan the smart card and read its contents using the mobile phone app without the need for Internet connection. Mobile phones will be provided to each facility to use for the duration of the study. When a participant presents at delivery without the card, information can still be obtained from the secure, Web-based database and confirmed using the participant’s name, date of birth, and mobile phone number.

We will assess the proportion of pregnant women who are willing to allow health care workers to retrieve information from their card.

Reach will be calculated as the number of enrolled pregnant women relative to the number of eligible pregnant women in the study catchment area. The effectiveness will be determined by assessing the impact of the mHealth platform on key outcomes, such as screening for HIV, HBV, and sickle cell genotype and health care utilizations for follow-up visits. This will be measured by calculating the percentage of pregnant women that use the smart cards at the point of delivery, thus reducing the need for repeat testing and reducing missed opportunities for reduction of perinatal transmission of HIV and HBV and deaths among children with SCD. The reach and effectiveness of the mHealth platform will be captured through patient records and recruitment assessments.

We will conduct semistructured key informant interviews and focus group discussions with participants to assess perceived social support, obstacles, or barriers with using the cards at the point of delivery. The Perceptions, Enablers, and Nurturers (PEN-3) cultural model [35-37] centralizes culture in the design, implementation, and evaluation of any health intervention. This model will be used as a guide to assess how well informed participants feel with using the tools provided (ie, the smart card), whether its values are clarified, and whether they feel supported in using it as a resource in delivering their personal health information. In particular, we will examine (1) perceptions that may contribute to or hinder use of the smart cards; (2) the enablers or community/health factors, such as the role of health care workers in influencing participants’ use of the cards; and (3) the nurturers, or the role of family, social, or community networks in reinforcing the use of the smart cards. These perceptions, enablers, and nurturers will then be examined to identify whether they are positive (ie, factors that will lead participants to engage with the mHealth platform in general), existential (ie, practices that are unique and have no harmful health consequences), and/or negative (ie, factors that lead participants not to engage with the mHealth platform). The interviews and focus group discussions will be digitally recorded and transcribed verbatim. All data will be analyzed using NVivo version 11 (QSR International) and Krueger’s framework analysis approach, which provides a clear series of steps for qualitative data analysis, including familiarization, identifying a thematic framework, indexing and charting (ie, managing the data, data reduction), mapping, and interpretation [38].

We will assess the proportion and representativeness of health care settings and health care workers who are willing to use the mobile health platform in the study catchment area. To achieve this, we will calculate adoption rates using the proportion of interested health care settings relative to the total number eligible for participation. For implementation, we will capture the degree to which the mHealth platform is used as intended (ie, delivery fidelity) and by trained health care workers (ie, enactment fidelity). For maintenance, we will examine the extent to which the mHealth platform is sustained as part of routine practice in the health care settings (ie, institutionalization) at least 6 months postintervention, leadership support for sustainability or in the health care settings, as well as sustainability climate (ie, providers’ perceptions of the extent to which policies and practices in the health care settings support sustainment of the mobile health platform).

Following introduction and invitation to participate in this study, an expert panel of health care workers (n=8) [39] will assess the usability of the mHealth platform using a diverse think-aloud heuristic protocol. Think-aloud and heuristic evaluations are the most commonly used usability evaluation methods [39]. With think aloud, potential users are asked to complete a set of tasks with the artifact tested (ie, mHealth platform) and verbalize their thoughts as they work on the task [39,40]. It has been found to have high face validity since the data collected reflects actual use of an artifact and not the participant’s judgment about its usability [40]. With heuristic evaluation, the experts inspect a system (ie, mHealth) and evaluate its interface against a list of recognized usability principles known as heuristics [41,42]. These heuristics, which refer to common properties of usable systems, are based on Nielsen’s set of 10 usability principles: (1) use simple and natural dialogue, (2) speak the user’s language, (3) minimize memory load, (4) be consistent, (5) provide feedback, (6) provide clearly marked exits, (7) provide shortcuts, (8) provide good error messages, (9) prevent errors, and (10) provide help and documentation [39,43]. For this study, each expert will individually evaluate the mHealth platform in two steps: navigation and analysis. In the navigation step, the expert will become familiar with the structure and scope of the mHealth platform. In the analysis step, the expert will focus on the design of the platform to determine whether it complies with Nielsen's 10 general usability principles for interaction design. Following the evaluation, a consensus meeting will be held with the experts to identify usability problems and quantify the severity of the problems based on Nielsen’s severity rating scale, which takes into account the frequency (ie, Is the problem common or rare?), impact (ie, Is it difficult or easy for end users to address?), and persistence (ie, Is it a one-time problem or does it trouble end users repeatedly?) of the problem [44]. The problems and notes from the evaluations will be analyzed using standard descriptions that will compare the number of problems among experts and their severity.

We will use concept mapping to gather crucial information from stakeholders regarding sustainability of the overall mHealth platform in the study area. Concept mapping is a structured, participatory, mixed-methods approach to data collection that engages stakeholders in the research and theory generation process [45,46]. It integrates qualitative data with quantitative multivariate statistical analyses to describe ideas on a topic of interest and represent these ideas visually through a series of related two-dimensional maps [47]. It will be used in this study to assess the factors that may facilitate or limit the continued use of the mHealth platform in the study area over time. The recommended minimum number of participants for concept mapping is 40 [47]. We seek to recruit approximately 60 participants in order to enable subgroup analyses and to allow for modest attrition rates at each step of the concept-mapping exercise. Baseline demographics will be collected from all participants.

The concept-mapping sessions will be conducted in the following stages:

1. Preparation Stage: In this stage, stakeholders are identified and the focus question that will guide discussions on the sustainability of the mHealth medical decision model is developed.

2. Generation Stage: In this stage, key stakeholders (ie, midwives, nurses, obstetricians, primary care physicians, study staff, etc) will participate in focus group discussions to generate as many statements as possible in response to a focus question on sustainment of the mHealth platform.

3. Structuring Stage: In this stage, the stakeholders will be asked to sort the statements they generated into similar piles that make sense to them and provide names for the piles created. The stakeholders will then rate each statement based on perceived importance (ie, How important is this factor?) and likelihood (ie, How likely is it that the factor will be sustained over time?).

4. Representation Stage: In this stage, the concept-mapping analyses will be conducted using the Concept System Global Max software program (Concept Systems Inc). Data will be used to conduct multidimensional scaling (MDS) analysis with a two-dimensional solution. The MDS analysis is based on the measurement model that assumes that the relative similarity of objects can be represented in terms of the relative distance between pairs of points [46,47]. From these analyses, coordinate estimates and a two-dimensional point map of distances between the statements will be generated for each set of sorted data. The two-dimensional point map is chosen for its ease and interpretability [46]. To indicate the goodness of fit, a stress value of the point map will be developed to determine how well the MDS solution maps the original data. While it is not possible to conduct power analysis given the nature of the data, with concept mapping, a lower stress value indicates a better fit and reflects a stronger relationship between the optimal and actual configurations [46,47]. Furthermore, hierarchical cluster analysis will be conducted using the two-dimensional x-y coordinate data obtained from the MDS analysis as input and applying Ward’s algorithm as the basis for defining clusters. This approach forces the cluster analyses to partition the MDS configuration into nonoverlapping clusters in two-dimensional space [47]. This technique will also group the outcome statements on each map such that statements placed in the same cluster will be contiguous areas of the map [45]. The resulting output will be a cluster map, which will reveal how the statements generated, as represented by points, are grouped.

5. Interpretation Stage: In this stage, result interpretation is typically a real-time, participatory process where stakeholders interact with the total-group ideas generated. This stage involves gathering stakeholder participants to explain and discuss the results of the concept maps, pattern matching, and go-zones. This includes examining the point map to understand which statements are most related to each other, examining the cluster maps to determine which clusters of statements were rated most important to the focus statement, examining the pattern matching to determine key areas to target based on high ratings, and examining the go-zones to determine the area of most importance for each stakeholder group.

6. Utilization Stage: In this stage, the research team will work with the stakeholders to determine the best ways to use the maps and reports produced as part of the concept-mapping procedures. Possible use of the output from concept mapping includes creating a sustainability plan, which will serve as the basis for tracking the continued use of the mHealth platform in the study catchment area.