What Patients Want in a Smartphone App That Supports Colonoscopy Preparation: Qualitative Study to Inform a User-Centered Smartphone App
Notice bibliographique
Résumé
BACKGROUND: The preparation for colonoscopy is elaborate and complex. In the context of colorectal cancer screening, up to 11% of patients do not keep their colonoscopy appointments and up to 33% of those attending their appointments have inadequately cleansed bowels that can delay cancer diagnosis and treatment. A smartphone app may be an acceptable and wide-reaching tool to improve patient adherence to colonoscopy. OBJECTIVE: The aim of this qualitative study was to employ a user-centered approach to design the content and features of a smartphone app called colonAPPscopy to support individuals preparing for their colonoscopy appointments. METHODS: We conducted 2 focus group discussions (FGDs) with gastroenterology patients treated at the McGill University Health Centre in Montreal, Canada. Patients were aged 50 to 75 years, were English- or French-speaking, and had undergone outpatient colonoscopy in the previous 3 months; they did not have inflammatory bowel disease or colorectal cancer. FGDs were 75 to 90 min, conducted by a trained facilitator, and audiotaped. Participants discussed the electronic health support tools they might use to help them prepare for the colonoscopy, the content needed for colonoscopy preparation, and the features that would make the smartphone app useful. Recordings of FGDs were transcribed and analyzed using thematic analysis to identify key user-defined content and features to inform the design of colonAPPscopy. RESULTS: A total of 9 patients (7 male and 2 female) participated in one of 2 FGDs. Main content areas focused on bowel preparation instructions, medication restrictions, appointment logistics, communication, and postcolonoscopy expectations. Design features to make the app useful and engaging included minimization of data input, reminders and alerts for up to 7 days precolonoscopy, and visual aids. Participants wanted a smartphone app that comes from a trusted source, sends timely and tailored messages, provides reassurance, provides clear instructions, and is simple to use. CONCLUSIONS: Participants identified the need for postcolonoscopy information as well as reminders and alerts in the week before colonoscopy, novel content, and features that had not been included in previous smartphone-based strategies for colonoscopy preparation. The ability to tailor instructions made the smartphone app preferable to other modes of delivery. Study findings recognize the importance of including potential users in the development phase of building a smartphone app.
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 | 0,014 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,007 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».