5.5 The usability and effect of an mHealth program to support active rehabilitation for concussion
Bibliographic record
Abstract
<h3>Objective</h3> To determine usability – perceived rating of usefulness, ease of learning, ease of use, effectiveness, satisfaction, and interface quality – and effect of a mobile health (mHealth) program delivering active rehabilitation for concussion. <h3>Design</h3> Case series. <h3>Setting</h3> Multi-center. <h3>Participants</h3> Patients diagnosed with a concussion. <h3>Interventions (or Assessment of Risk Factors)</h3> A mHealth delivered active rehabilitation program was provided to users for two weeks. Outcome Measures mHealth app usability questionnaire (MUAQ) and concussion symptoms. <h3>Main Results</h3> Thirty-one total participants took place in a two-week mHealth program (female, n = 24; male, n = 7). The average age of participants was 33.5 years old. Symptom severity scores decreased by a mean of 12 points in participants (interquartile range [IQR] of the difference = -1.7 – 26) over the two-week program. Of the 31 participants, 23 (74%) responded to the mHealth app usability survey upon completion of the program. On a 4-point Likert scale ranging from somewhat disagree to strongly agree, and a positive response considered either ‘agree’ or ‘strongly’ agreed, 86% of participants were overall satisfied with the app (91% were either ‘satisfied’ or ‘very satisfied’), 87% found it useful for their well-being, 87% found it easy to use, and 91% would use the app again. <h3>Conclusions</h3> A mHealth delivered active rehabilitation program decreased symptom burden in users. Users were overwhelmingly satisfied with the mHealth program, found it easy to use, would recommend it, and would use it again.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".