New Checklist for the Heuristic Evaluation of mHealth Apps (HE4EH): Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Diabetes is one of the leading causes of death in developing countries. Existing mobile health (mHealth) app design guidelines lack a description of the support of continuous self-monitoring of health status, behavior change to improve and adopt a healthy lifestyle, and communication with health educators and health care professionals in case of any need. OBJECTIVE: This paper presents the development of a specialized set of heuristics called heuristic evaluation for mHealth apps (HE4EH) as an all-in-one tool and its applicability by performing a heuristic evaluation of an mHealth app. METHODS: An extensive review of heuristics and checklists was used to develop the HE4EH. The HE4EH was evaluated by domain experts for heuristics, checklist items, severity ratings, and overall satisfaction. The OneTouch app, which helps individuals with diabetes manage their blood glucose levels, was evaluated using HE4EH to identify usability problems that need to be fixed in the app. RESULTS: The expert evaluation of HE4EH revealed that the heuristics were important, relevant, and clear. The checklist items across the heuristics were clear, relevant, and acceptably grouped. In terms of evaluating the OneTouch app using the HE4EH, the most frequently violated heuristics included Content, Visibility, Match, and Self-monitoring. Most of the usability problems found were minor. The system usability scale score indicated that the OneTouch app is marginally acceptable. CONCLUSIONS: This heuristic evaluation using the OneTouch app shows that the HE4EH can play a vital role for designers, researchers, and practitioners to use HE4EH heuristics and checklist items as a tool to design a new or evaluate and improve an existing mHealth app.
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 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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".