Usability Testing of a Digitized Interventional Prehabilitation Tool for Health Care Professionals and Patients Before Major Surgeries: Formative and Summative Evaluation
Bibliographic record
Abstract
BACKGROUND: The development of a medical device requires strict adherence to regulatory processes. Prehabilitation in this context is a new area in surgery that trains, coaches, and advises patients in mental well-being, nutrition, and physical activity. As staff is permanently drained from clinical care, remote and digital solutions with real-time assessments of data, including patient-related outcome reporting, may simplify preparation before major surgeries. OBJECTIVE: This study aimed to evaluate the usability engineering process for the Prehab App, a newly developed medical device, in order to identify and adapt any design and usability flaws found. METHODS: We hypothesized that formative and summative usability testing would achieve 80% interrater and intrarater reliability and consistency and that the safety-relevant scenarios would uncover undetected risks of the medical device (stand-alone software class IIa). In total, 8 experts and 8 laypersons (patients and potential patients) were asked to evaluate paper-based mockups, followed by an evaluation of the minimal viable product (MVP) of the Prehab App at least more than 8 weeks later after instruction and training. The experts had to face 5 and the laypersons 6 usability scenarios. Their evaluations were measured with the Mobile App Rating Scale (MARS) and trustworthiness checklists (range 0-64, with higher scores indicating trustworthiness), and the usability scenarios were evaluated with the After Scenario Questionnaire (ASQ) and a judgment by an observer. The time taken for the scenarios was also recorded. RESULTS: MARS achieved constant scores of more than 4 out of 5 points for both experts and laypersons. The mean trustworthiness score was 51.3 (SD 2.7) for the experts and 50.8 (SD 2.1) for the laypersons (P=.68) in task I. The interrater correlation, shown by the Fleiss-Kappa value, was 0.87 (range 0.85-0.89) for all raters (N=16), 0.86 (range 0.82-0.91) for the experts (n=8, 50%), and 0.88 (range 0.84-0.93) for the laypersons (n=8, 50%), reflecting almost perfect agreement between the raters. This indicated the high quality of the usability. The usability scenarios were performed with ease, except for the onboarding part, when the wearable was required to be connected; this took a considerable amount of time and was recognized as a challenge to good usability. CONCLUSIONS: The formative and summative evaluation of the Prehab App design resulted in good-to-acceptable results of the design and usability of the critical and safety-relevant areas of the medical device and stand-alone software. Usability testing improves medical devices early in the design and development process, reduces errors, and mitigates risks, and in this study, it delivered a profound ethical and medical justification for a randomized controlled trial (RCT) of the Prehab App in a remote setting as a next step in the development process. TRIAL REGISTRATION: German Registry for Clinical Trials (DRKS00026985); https://drks.de/search/en/trial/DRKS00026985.
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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 | 0.038 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".