5.5 The usability and effect of an mHealth program to support active rehabilitation for concussion
Bibliographic record
Abstract
Objective 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. Design Case series. Setting Multi-center. Participants Patients diagnosed with a concussion. Interventions (or Assessment of Risk Factors) A mHealth delivered active rehabilitation program was provided to users for two weeks. Outcome Measures mHealth app usability questionnaire (MUAQ) and concussion symptoms. Main Results 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. Conclusions 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".