Involving End Users in the Development and Usability Testing of a Smartphone App Designed for Individuals With Prediabetes: Mixed-Methods Focus Group Study
Bibliographic record
Abstract
Background: Technology is more likely to be used when it is designed to meet the needs of end users. To supplement the Small Steps for Big Changes diabetes prevention program, a smartphone app was developed in partnership with past Small Steps for Big Changes clientele. Usability testing is critical for the ongoing use and adoption of mobile health apps by providing insight on where appropriate adjustments and improvements need to be made to ensure user satisfaction. Objective: A focus group with 7 participants was conducted to examine the app's usability and collect feedback for future iterations. Methods: Past Small Steps for Big Changes clientele participated in a cognitive walkthrough of 8 novel tasks and completed the System Usability Scale survey. Participants were then given the option to use the app for 3 weeks before completing the User-Mobile Application Rating Scale. Results: Analysis of the cognitive walkthrough identified 26 usability problems; each was coded using a heuristic evaluation to describe usability errors. The most frequently coded errors included inappropriate progress feedback, information appearing in an illogical order, counterintuitive design, and issues with app aesthetics. A mean summary score of 66.8% (SD 18.91) was reported for the System Usability Scale, representing a marginal acceptability score and indicating that design issues needed to be resolved. A User-Mobile Application Rating Scale mean score of 3.59 (SD 0.33) was reported, implying an average acceptability rating. Conclusions: These findings identified necessary improvements in the app, ranging from minor aesthetic problems to major functionality problems. Involving end users allows the app to be tailored to the client's preferences and increases the likelihood of usage. This app aligns with Small Steps for Big Changes' program components and behavior change techniques that can improve health outcomes for future clients and allow them to self-monitor their exercise, diet, and goals.
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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 | 0.041 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".