Usability evaluation of a self-management mobile application for individuals with a mild traumatic brain injury
Bibliographic record
Abstract
Objective: Mild traumatic brain injuries (mTBIs) are common and may result in persisting symptoms. Mobile health (mHealth) applications enhance treatment access and rehabilitation. However, there is limited evidence to support mHealth applications for individuals with an mTBI. The primary purpose of this study was to evaluate user experiences and perceptions of the Parkwood Pacing and Planning™ application, an mHealth application developed to help individuals manage their symptoms following an mTBI. The secondary purpose of this study was to identify strategies to improve the application. This study was conducted as part of the development process for this application. Methods: A mixed methods co-design encompassing an interactive focus group and a follow-up survey was conducted with patient and clinician-participants (n = 8, four per group). Each group participated in a focus group consisting of an interactive scenario-based review of the application. Additionally, participants completed the Internet Evaluation and Utility Questionnaire (UQ). Qualitative analysis on the interactive focus group recordings and notes was performed using phenomenological reflection through thematic analyses. Quantitative analysis included descriptive statistics of demographic information and UQ responses. Results: On average, clinician and patient-participants positively rated the application on the UQ (4.0 ± .3, 3.8 ± .2, respectively). User experiences and recommendations for improving the application were categorized into four themes: simplicity, adaptability, conciseness, and familiarity. Conclusion: Preliminary analyses indicates patients and clinicians have a positive experience when using the Parkwood Pacing and Planning™ application. However, modifications that improve simplicity, adaptability, conciseness, and familiarity may further improve the user's experience.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".