Ease of Use and Usefulness of a Newly Developed Mobile App to Monitor Pain and Adherence Among Individuals With an Achilles Tendinopathy Engaged in a Rehabilitation Program
Bibliographic record
Abstract
OBJECTIVE: Assess the perceived ease of use and perceived usefulness of a newly developed mobile app. DESIGN: Descriptive survey study. SETTING: Home-based rehabilitation program. PARTICIPANTS: A group of 31 adults with a symptomatic Achilles tendinopathy. INTERVENTION: A mobile app was developed to support the deployment of the 12-week active exercise-based rehabilitation program and facilitate the monitoring of exercise adherence twice daily and the assessment of localized Achilles tendon pain using a numeric pain rating scale on a weekly basis. MAIN OUTCOME MEASURES: Results of an online survey encompassing 10 questions, each rated on a 5-point Likert scale (5 = strongly agree; 1 = strongly disagree). RESULTS: Nearly all participants agreed that the mobile app was easy to install (96.4%) and easy to use (100%). Most participants confirmed that no technical issues were encountered (96.4%). The instructional videos were deemed helpful in properly performing the recommended exercises (85.7%), whereas the prompts sent via text message were found to promote adherence (88.9%). The design and appearance of the mobile app were appreciated by a lower percentage of participants (75%). CONCLUSION: Participants confirmed the ease of use and usefulness of the newly developed mobile app and demonstrated a positive attitude toward its use.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".