Development of a mHealth Real-Time Pain Self-Management App for Adolescents With Cancer: An Iterative Usability Testing Study
Bibliographic record
Abstract
PURPOSE: A user-centered design approach was used to refine the mHealth Pain Squad+ real-time pain self-management app for adolescents with cancer for its usability (defined as being easy to use, easy to understand, efficient to complete, and acceptable). METHOD: Three iterative usability testing cycles involving adolescent observation and interview were used to achieve this objective. During each cycle, adolescents used the app while "thinking aloud" about issues encountered. Observed difficulties and errors were recorded and a semistructured interview about the experience was conducted. Using a qualitative conventional content analysis approach, themes related to app usability were identified. RESULTS: Participants required an average of 4.3 minutes to complete the pain assessment component of Pain Squad+. Overall, the app was acceptable. Problematic issues related to software malfunction, interface design flaws, and confusing text. Software revisions were made to address each issue. CONCLUSION: The multifaceted usability approach used provided insight into how a real-time app can be made acceptable to adolescents with cancer and succeeded in developing a Pain Squad+ app that is fit for future effectiveness testing.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".