User Friendliness and Perioperative Guidance Benefits of a Cataract Surgery Education App: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Cataract surgeries are among the most performed surgeries worldwide. A thorough patient education is essential to inform patients about the perioperative process and postoperative target results concerning the intraocular lens and objectives for visual outcomes. However, addressing all relevant aspects and questions is time-consuming. Mobile apps can facilitate this process for both patients and physicians and thus be beneficial. However, the success of such an app depends on its user friendliness and acceptance by patients. OBJECTIVE: This study aimed to evaluate the user friendliness and acceptance of a cataract surgery education app on mobile devices among patients undergoing cataract surgery, the characteristics of patients who benefit the most from app use, and the influence of the app on patient satisfaction with treatment. METHODS: All patients who underwent cataract surgery at an ophthalmological practice from August 2020 to July 2021 were invited to participate in this randomized controlled trial. Out of 493 invited patients, 297 (60.2%) were enrolled in this study. Patients were randomized into 3 different groups. Half of the patients were offered to participate in Group 1 with use of the "Patient Journey" app. However, if they decided not to use the app, they were included in Group 2 (app denial). The other half of the patients were included in Group 3 (control) with no use of the app and with information provided conventionally. The app provided general information on the ophthalmological center, surgeons, cataract, and treatment options. Different questionnaires were used in all 3 groups to evaluate satisfaction with the perioperative process. Group 1 evaluated the app. Demographic characteristics, such as age, gender, and educational degree, were assessed. RESULTS: Group 1 included 77 patients (median age 69 years). Group 2 included 61 patients, and their median age was higher (median age 79 years). Group 3 included 159 patients (median age 74 years). There was no difference in satisfaction with the perioperative process and clinic between the 3 groups. Almost all app users appreciated the digital details provided for the organization and the information on the surgery. Age did not play a major role in appreciation of the app. Female patients tended to appreciate the information provided more than male patients. Patients who did not have a higher university degree experienced more benefits from the informational content of the app and were the most satisfied with the information. However, male patients and academics were in general more aware of technology and handled the app more easily. CONCLUSIONS: The app showed high user friendliness and acceptance, and could particularly benefit specific patient groups. App users demonstrated a noninferior high satisfaction with the treatment in the ophthalmological center in comparison with patients who were informed about the surgery only conventionally.
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 | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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 ».