Assessing the Quality of Mobile Apps Used by Occupational Therapists: Evaluation Using the User Version of the Mobile Application Rating Scale
Bibliographic record
Abstract
BACKGROUND: The continuous development of mobile apps has led to many health care professionals using them in clinical settings; however, little research is available to guide occupational therapists (OTs) in choosing quality apps for use in their respective clinical settings. OBJECTIVE: The purpose of this study was to use the user version of the Mobile Application Rating Scale (uMARS) to evaluate the quality of the most frequently noted mobile health (mHealth) apps used by OTs and to demonstrate the utility of the uMARS to assess the quality of mHealth apps. METHODS: A previous study surveying OTs' use of apps in therapy compiled a list of apps frequently noted. A total of 25 of these apps were evaluated individually by 2 trained researchers using the uMARS, a simple, multidimensional analysis tool that can be reliably used to evaluate the quality of mHealth apps. RESULTS: The top 10 apps had a total quality score of 4.3, or higher, out of 5 based on the mean scores of engagement, functionality, and aesthetics. Apps scored highest in functionality and lowest in engagement. Apps noted most frequently were not always high-quality apps; apps noted least frequently were not always low-quality apps. CONCLUSIONS: Determining the effectiveness of using apps in clinical settings must be built upon a foundation of the implementation of high-quality apps. Mobile apps should not be incorporated into clinical settings solely based on frequency of use. The uMARS should be considered as a useful tool for OTs, and other professionals, to determine app quality.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".