Identifying the Essential Design Requirements for Usable E-Health Communities in Mobile Devices
Bibliographic record
Abstract
We believe that well-designed and usable user interface is critical, as the adequate use and the effectiveness of any application usually depend on it, especially when a specific system is oriented to play a key role into the process of patients’ care (healthcare applications). Very good examples of healthcare applications are E-Health Communities, which are generally oriented to contribute in the process for improving the quality of life for members of a community suffering from chronic diseases. In order to contribute to previous efforts, we propose a set of 15 basic usability requirements specifically oriented to mobile interfaces for blog-based instant messaging e-health communities. We structured our set of usability requirements on two sources: Firstly we consider the survey-obtained feedback from 72 participants who regularly access social networks from mobile devices. Then we complemented this information with some ideas presented in previous research. We show the effectiveness of the proposal by using an illustrative example (designing an interface-prototype for a mobile e-health community) as a proof-of-concept together with a preliminary usability study. The prototype was entirely created by observing our usability requirements. The results of the study are encouraging and reflect a good correlation between the requirements proposed and the users’ perception. This seems to indicate that although this research-work is focused on providing a starting point to developers with guidance in designing usable interfaces for e-health communities accessed by mobile devices, our findings could be easily adapted and applied to other mobile scenarios for blog-based instant messaging applications.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".