Usability testing of an mHealth device for swallowing therapy in head and neck cancer survivors
Bibliographic record
Abstract
The objective of this study was to conduct the first patient usability testing of a mobile health (mHealth) system for in-home swallowing therapy. Five participants with a history of head and neck cancer evaluated the mHealth system. After completing an in-application (app) tutorial with the clinician, participants were asked to independently complete five tasks: pair the device to the smartphone, place the device correctly, exercise, interpret progress displays, and close the system. Quantitative and qualitative methods were used to evaluate the effectiveness, efficiency, and satisfaction with the system. Critical changes to the app were found in three of the tasks, resulting in recommendations for the next iteration. These issues were related to ease of Bluetooth pairing, placement of device, and interpretation of statistics. Usability testing with patients identified issues that were essential to address prior to implementing the mHealth system in subsequent clinical trials. Of the usability methods used, video observation (synced screen capture with videoed gestures) revealed the most information.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".