Conceptualizing Usability for the eHealth Context: Content Analysis of Usability Problems of eHealth Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Usability tests can be either formative (where the aim is to detect usability problems) or summative (where the aim is to benchmark usability). There are ample formative methods that consider user characteristics and contexts (ie, cognitive walkthroughs, interviews, and verbal protocols). This is especially valuable for eHealth applications, as health conditions can influence user-system interactions. However, most summative usability tests do not consider eHealth-specific factors that could potentially affect the usability of a system. One of the reasons for this is the lack of fine-grained frameworks or models of usability factors that are unique to the eHealth domain. OBJECTIVE: In this study, we aim to develop an ontology of usability problems, specifically for eHealth applications, with patients as primary end users. METHODS: We analyzed 8 data sets containing the results of 8 formative usability tests for eHealth applications. These data sets contained 400 usability problems that could be used for analysis. Both inductive and deductive coding were used to create an ontology from 6 data sets, and 2 data sets were used to validate the framework by assessing the intercoder agreement. RESULTS: We identified 8 main categories of usability factors, including basic system performance, task-technology fit, accessibility, interface design, navigation and structure, information and terminology, guidance and support, and satisfaction. These 8 categories contained a total of 21 factors: 14 general usability factors and 7 eHealth-specific factors. Cohen κ was calculated for 2 data sets on both the category and factor levels, and all Cohen κ values were between 0.62 and 0.67, which is acceptable. Descriptive analysis revealed that approximately 69.5% (278/400) of the usability problems can be considered as general usability factors and 30.5% (122/400) as eHealth-specific usability factors. CONCLUSIONS: Our ontology provides a detailed overview of the usability factors for eHealth applications. Current usability benchmarking instruments include only a subset of the factors that emerged from our study and are therefore not fully suited for summative evaluations of eHealth applications. Our findings support the development of new usability benchmarking tools for the eHealth domain.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it