End Users' Perspectives on the Quality and Design of mHealth Technologies During the COVID-19 Pandemic in the Philippines: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has expanded the use of mobile health (mHealth) technologies in contact tracing, communicating COVID-19-related information, and monitoring the health conditions of the general population in the Philippines. However, the limited end-user engagement in the features and feedback along the development cycle of mHealth technologies results in risks in adoption. The World Health Organization (WHO) recommends user-centric design and development of mHealth technologies to ensure responsiveness to the needs of the end users. OBJECTIVE: The goal of the study is to understand, using end users' perspectives, the design and quality of mHealth technology implementations in the Philippines during the COVID-19 pandemic, with a focus on the areas identified by stakeholders: (1) utility, (2) technology readiness level, (3) design, (4) information, (5) usability, (6) features, and (7) security and privacy. METHODS: Using a descriptive qualitative design, we conducted 5 interviews and 3 focus group discussions (FGDs) with a total of 16 participants (6, 37.5%, males and 10, 62.5%, females). Questions were based on the Mobile App Rating Scale (MARS). Using the cyclical coding approach, transcripts were analyzed with NVivo 12. Themes were identified. RESULTS: The qualitative analysis identified 18 themes that were organized under the 7 focus areas: (1) utility: use of mHealth technologies and motivations in using mHealth; (2) technology readiness: mobile technology literacy and user segmentation; (3) design: user interface design, language and content accessibility, and technology design; (4) information: accuracy of information and use of information; (5) usability: design factors, dependency on human processes, and technical issues; (6) features: interoperability and data integration, other feature and design recommendations, and technology features and upgrades; and (7) privacy and security: trust that mHealth can secure data, lack of information, and policies. To highlight, accessibility, privacy and security, a simple interface, and integration are some of the design and quality areas that end users find important and consider in using mHealth tools. CONCLUSIONS: Engaging end users in the development and design of mHealth technologies ensures adoption and accessibility, making it a valuable tool in curbing the pandemic. The 6 principles for developers, researchers, and implementers to consider when scaling up or developing a new mHealth solution in a low-resource setting are that it should (1) be driven by value in its implementation, (2) be inclusive, (3) address users' physical and cognitive restrictions, (4) ensure privacy and security, (5) be designed in accordance with digital health systems' standards, and (6) be trusted by end users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".