Think-Aloud Testing of a Companion App for Colonoscopy Examinations: Usability Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».