e-Ouch: Usability Testing of an Electronic Chronic Pain Diary for Adolescents With Arthritis
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the usability of the e-Ouch electronic chronic pain diary in adolescents with juvenile idiopathic arthritis. METHODS: A qualitative usability testing approach with semistructured, audiotaped interviews with two iterative cycles was used. A purposive sample of 10 adolescents per cycle was drawn from a rheumatology clinic in a university-affiliated pediatric tertiary care center. Participants were provided with a brief demonstration of the diary and then asked to use the diary "thinking aloud" to record the pain they experienced: (1) when they woke up that morning, (2) during that afternoon, and (3) from the previous evening. Adolescents were then asked a series of open-ended questions addressing ease of use of the diary. Qualitative thematic analysis was used to generate categories and emerging themes from interview data. RESULTS: All of the adolescents stated the e-Ouch diary was very easy to learn, use, and understand and was satisfying to complete. Participants took less than 9 minutes to complete all three of the diary entries with minimal errors. The usability evaluation revealed aspects of the interface that were suboptimal (eg, VAS slider) and impeded the performance of certain tasks. Adolescents generated ideas on how the diary interface could be improved. CONCLUSIONS: A multifaceted usability approach provided important insight regarding the use of technology by adolescents with arthritis and, more specifically, for understanding how adolescents can more effectively use an electronic chronic pain diary.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".