Time for Clarity in Exploring the Evidence and Key Concepts of Human-Centered Design in Digital Health Care: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Human-centered design (HCD) methodologies such as design thinking (DT), user-centered design, cocreation, and participatory design (PD) have been adopted to facilitate user and stakeholder involvement in the development of eHealth applications. However, there is frequent confusion around these methodologies, leading to the fragmentation of the discourse and limited integration opportunities. The absence of an empirically grounded framework for HCD limits research and theoretical consensus, particularly in the highly regulated context of eHealth solution development. For this scoping review, the term HCD will be used as an umbrella term, under which the terms user-centered design, patient-centered design, cocreation, co-design, PD, and DT will be used in this protocol. OBJECTIVE: In this paper, we describe a protocol for a scoping review that aims to explore and analyze the scope, definitions, key concepts, and motivations reported in peer-reviewed studies that have applied stakeholder engagement methods such as HCD, PD, or DT in developing eHealth applications. METHODS: A team of 3 reviewers will conduct this scoping review to identify and synthesize key concepts at the intersection of HCD methodologies and their application to the development of eHealth applications. We will follow the Joanna Briggs Institute methodology for scoping reviews and the guidelines for conducting systematic mapping studies in software engineering. The reporting of the results will be guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) extension. This review will include only primary studies reporting on the experience, challenges, and applicability of HCD for the design and development of eHealth applications, identified through the PubMed, IEEE Xplore, Web of Science, Scopus, PsycINFO, CINAHL, and ACM Digital Library databases, and limited to articles from the past 10 years. RESULTS: A preliminary search applying the search strategy resulted in 826 records. The search was initiated in March 2025. Title and abstract screening will conclude by mid-2025, followed by full-text screening, data extraction, and analysis in the second half of 2025. Results are expected to be submitted for publication in the first half of 2026. CONCLUSIONS: This protocol describes a systematic approach for conducting a scoping literature review, aimed at synthesizing definitions, concepts, and methodologies related to HCD in eHealth application development. This review aims to identify research gaps and trends to guide the future of mobile health innovation, with a focus on improving adoption and long-term sustainability, particularly from the perspective of technologies for vulnerable populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/74067.
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 | 0.265 | 0.397 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.070 | 0.029 |
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".