The Vinyasa Tool for mHealth Solutions: Supporting Human-Centered Design in Nascent Digital Health Ecosystems
Bibliographic record
Abstract
BACKGROUND: mHealth (mobile health) systems have been deployed widely in low- and middle-income countries (LMICs) for health system strengthening, requiring considerable resource allocation. However, most solutions have not achieved scale or sustainability. Poor usability and failure to address perceived needs are among the principal reasons mHealth systems fail to achieve acceptance and adoption by health care workers. A human-centered design approach to improving mHealth system use requires an exploration of users' perceptions of mHealth systems, including the environmental, user-related, and technological aspects of a system. At present, there is a dearth of contextually intelligent tools available to mHealth developers that can guide such exploration before full-scale development and deployment. OBJECTIVE: To develop a tool to aid optimization of mHealth solutions in LMICs to facilitate human-centered design and, consequently, successful adoption. METHODS: We collated findings and themes from key qualitative studies on mHealth deployment in LMICs. We then used the Informatics Stack framework by Lehmann to label, sort, and collate findings and themes into a list of questions that explore the environment, users, artifacts, information governance, and interoperability of mHealth systems deployed in LMICs. RESULTS: We developed the Vinyasa Tool to aid qualitative research about the need and usability of mHealth solutions in LMICs. The tool is a guide for focus group discussions and key informant interviews with community-based health care workers and primary care medical personnel who use or are expected to use proposed mHealth solutions. The tool consists of 71 questions organized in 11 sections that unpack and explore multiple aspects of mHealth systems from the perspectives of their users. These include the wider world and organization in which an mHealth solution is deployed; the roles, functions, workflow, and adoption behavior of a system's users; the security, privacy, and interoperability afforded by a system; and the artifacts of an information system-the data, information, knowledge, algorithms, and technology that constitute the system. The tool can be deployed in whole or in part, depending on the context of the study. CONCLUSIONS: The Vinyasa Tool is the first such comprehensive qualitative research instrument incorporating questions contextualized to the LMIC setting. We expect it to find wide application among mHealth developers, health system administrators, and researchers developing and deploying mHealth tools for use by patients, providers, and administrators. The tool is expected to guide users toward human-centered design with the goal of improving relevance, usability, and, therefore, adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".