Acceptability Testing of a Mobile Application Prototype for Tailored Patient Education and Self-Management Along the Transplant Journey
Notice bibliographique
Résumé
The organ transplant journey is challenging and complex for patients. Tailored educational approaches, mobile health solutions, and patient-orientated research may positively impact health and wellbeing for those along the transplant journey. This study explores the prospective acceptability of the prototyped Health Education and Learning Platform (HELP), a mobile app for transplant education and self-management developed via user-centered design. A cross-sectional electronic survey based on the Theoretical Framework of Acceptability (TFA) was distributed via purposive and snowball sampling to transplant patients and care partners. Participants watched a 7-minute video illustrating the prototype prior to completing the survey. Likert-scale questions garnered prospective acceptability within the seven TFA component constructs of affective attitude, burden, ethicality, intervention coherence, opportunity costs, perceived effectiveness, and self-efficacy. Open-ended questions enabled participants to provide qualitative feedback. Data was collected using REDCap and analyzed using descriptive statistics and simple content analysis to categorize free-text responses to TFA component constructs. One hundred seventy-eight responses were received, of which 169 contained demographic information. All provinces in Canada were represented by at least one participant. Approximately half (51.6%) were patients. The majority were aged 31-50 (62.7%) and had completed some level of post-secondary education (76.2%). Overall, more than 70% of participants agreed or strongly agreed with acceptability items related to each of the TFA constructs. The highest affirmed TFA constructs were intervention coherence (83.3%) and self-efficacy (80.3%). Disparity between patients (83.9%) and care partners (53.1%) was observed within the affective attitude construct. Participants expressed overall positive regard for the prototype, including its design and novelty. There is positive interest across Canada in the HELP app as an acceptable tool for delivering transplant education and facilitating self-management. Participants endorse understanding how the app is designed to help, believe it would improve their ability to manage their health, and have confidence they could use it. Confirmation of prospective acceptability suffices to progress the prototype to beta testing.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».