Mobile Health App for Adults with Persisting Postconcussion Symptoms: Development and Usability Study
Bibliographic record
Abstract
Background: Diagnostics, treatment, and research of persisting postconcussion symptoms are challenging. Assessing symptoms is essential, but currently implemented methods only allow for retrospective reporting of symptoms. A mobile health (mHealth) symptom mapping app for adults with persisting postconcussion symptoms may be an accessible and cost-efficient alternative. Objective: This study aimed to develop a research-based mobile app for symptom mapping for adults with persisting postconcussion symptoms and investigate its usability, feasibility, and safety. Methods: This was a mixed method development and usability study consisting of three iterative cycles, each including (1) app design and programming, (2) app usability evaluation by the user group, and (3) app review by the clinician group. The outcomes were the mHealth App Usability Questionnaire and Mobile App Rating Scale scores, the number of days with logged symptom data during a home-testing period, and descriptions of adverse events throughout the study period. Semistructured interviews were conducted to explore the user group's experiences further. Results: Twenty-three adults with persisting postconcussion symptoms (median age 52, IQR 34-59 years; 70% female) were included in the user group. Six clinicians (median age 53, IQR 35-60 years), including 3 (50%) females, with a mean of 13 (SD 7) years of experience working with individuals with persisting postconcussion symptoms, were included in the clinician group. The app received a mean score of 5 (SD 1.1) on the mHealth App Usability Questionnaire (7-point Likert scale) from the user group and 4.1 (SD 0.4) on the Mobile App Rating Scale (5-point Likert Scale) from the clinician group. During the 28-day home-testing period, the adherence rate among the participants in the user group was 89% (IQR 78-96), and two adverse events related to increased symptom awareness were registered. Three themes were created through reflexive thematic analysis of the qualitative data: (1) Visualizing the invisible-Enabling reflection and insight; (2) Personalized yet simple-Balancing relevance and usefulness; and (3) More than just a number-The complexity behind the symptom scores. Conclusions: We developed a research-based symptom mapping app for people with persisting postconcussion symptoms. The app received high usability ratings from both the user and clinician groups. The app is a feasible alternative to traditional symptom mapping methods, and it is safe to use for its intended purpose.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".