A Mobile Phone App for the Self-Management of Pediatric Concussion: Development and Usability Testing
Bibliographic record
Abstract
BACKGROUND: Concussion is a common injury among Canadian children and adolescents that leads to a range of neurobehavioral deficits. However, noticeable gaps continue to exist in the management of pediatric concussion, with poor health outcomes associated with the inadequate application of best practice guidelines. OBJECTIVE: The aim of this study was to describe the development and assess the usability of a mobile phone app to aid youth in the self-management of concussion. A secondary objective was to assess the usefulness of the app. METHODS: An agile user-centered design approach was used to develop the technology, followed by a formative lab-based usability study for assessment and improvement proposals. Youths aged 10 to 18 years with a history of concussion and health care professionals involved in concussion management were recruited. This study included participants performing 12 tasks with the mobile phone app while using the think aloud protocol and the administration of the System Usability Scale (SUS), posttest questionnaire, and a semistructured interview. RESULTS: A mobile phone app prototype called NeuroCare, an easily accessible pediatric concussion management intervention that provides easy access to expert-informed concussion management strategies and helps guide youth in self-managing and tracking their concussion recovery, was developed. A total of 7 youths aged between 10 and 18 years with a history of concussion and 7 health care professionals were recruited. The mean SUS score was 81.9, mean task success rates were greater than 90% for 92% (11/12) of the tasks, 92% (11/12) of tasks had a total error frequency of less than 11 errors, and mean task completion times were less than 2 min for 100% of the tasks. CONCLUSIONS: Results suggest that participants rated this app as highly usable, acceptable to users, and that it may be useful in helping youth self-manage concussion.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| 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".