Think-Aloud Testing of a Companion App for Colonoscopy Examinations: Usability Study
Bibliographic record
Abstract
BACKGROUND: Colonoscopies, are vital for initial screening, follow-ups, surveillance of neoplasia, and assessing symptoms like rectal bleeding. Successful colonoscopies require thorough colon preparation, but up to 25% fail due to poor preparation. This can lead to longer procedures, repeat colonoscopies, inconvenience, poorer health outcomes, and higher costs. eHealth tools can enhance bowel preparation and potentially reduce the need for repeat procedures. OBJECTIVE: This usability study aimed to identify strengths and weaknesses in a prototype companion app for colonoscopy exams. The objective was to obtain in-depth insights into the app's usability, ease of use, and content comprehension, with the objective of refining the tool to effectively fulfil its intended purpose, guided by feedback from potential users. METHODS: From February to August 2024, we conducted a qualitative study using the think-aloud (TA) procedure. Each session involved 6 tasks and a semi-structured interview to delve deeper into participants' task experiences. All TA sessions and interviews were recorded. Quantitative usability questions were analysed using Microsoft Excel, while qualitative data underwent coding and analysis based on thematic analysis principles. RESULTS: In total, 17 individuals, all smartphone users, participated in this study. Participants were recruited from one hospital, one private clinic, and one patient organisation in Switzerland. The study found that participants rated the app's usability metrics positively, with an overall mean rating of ease of use at 4.29 (SD 0.59), usefulness at 4.53 (SD 0.72), and comprehensibility at 4.29 (SD 0.92). For the individual features, the mean ratings for ease of use were between 4.00 and 4.65, usefulness ranged from 4.35 to 4.82, and comprehensibility received ratings between 4.29 and 4.53, all measured on a 5-point scale, where 1 represented low agreement and 5 indicated high agreement. Additionally, 100% of participants indicated they will or may use the app if they require a colonoscopy exam. Participants highlighted the need for reminders and alerts in the week leading up to the colonoscopy, along with tailored content, simplified language, and visual aids. CONCLUSIONS: The app prototype demonstrated favourable results with the majority of participants, and the testing process enabled the prompt identification and resolution of usability issues. The next phase will prioritize and assess potential improvements based on urgency and feasibility to guide a focused development plan. Usability testing highlighted features like push notifications and personalised content as top priorities for participants, making them key areas for immediate attention. Moving forward, the app has the potential to function effectively as a companion app for colonoscopy exams. To achieve this, further studies with a larger sample in real-world settings will be crucial. CLINICALTRIAL: Not Applicable.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".