Participants’ Perspectives on the iCareBreast Mobile-Based Perioperative Care Program for Women Undergoing Breast Cancer Surgery: Qualitative Process Evaluation
Notice bibliographique
Résumé
BACKGROUND: Breast cancer treatment, particularly during the perioperative period, is often accompanied by significant psychological distress, including anxiety and uncertainty. Mobile health (mHealth) interventions have emerged as promising tools to provide timely psychosocial support through convenient, flexible, and personalized platforms. While research has explored the use of mHealth in breast cancer prevention, care management, and survivorship, few studies have examined patients' experiences with mobile interventions during the perioperative phase of breast cancer treatment. OBJECTIVE: This study aimed to explore the experiences of patients with breast cancer using iCareBreast, a mobile app designed to provide perioperative guidance and psychosocial support. METHODS: A qualitative approach was used to explore participant experiences. A total of 13 English- or Chinese-speaking participants from the intervention group of a clinical study were recruited via purposive sampling between April 2021 and February 2022. Semistructured individual phone interviews were conducted, audio-recorded, and transcribed verbatim. Thematic analysis was performed to identify key patterns of experience, focusing on usability, emotional impact, perceived value, and areas for future improvement. RESULTS: Overall, 4 main themes and 11 subthemes emerged from this study: (1) navigating the app with confidence and comfort, (2) making sense of treatment through relevant and evolving content, (3) finding emotional anchors in a time of uncertainty, and (4) advocating for broader use and continued motivation. Participants found the app user-friendly and appreciated its structure and locally relevant content, which helped reduce anxiety and enhance surgical preparedness. Features such as deep breathing exercises, motivational quotes, survivor stories, mindfulness practices, and peer support links offered emotional comfort and a sense of companionship. Participants strongly advocated for more personalized and adaptive content aligned with their treatment type and recovery progress. They also emphasized the value of interactive elements, such as video demonstrations and accessing messaging functions, to support sustained engagement. Many expressed the need for extended support throughout the adjuvant treatment phases, including chemotherapy and radiotherapy. CONCLUSIONS: The iCareBreast app was perceived as a supportive tool during the perioperative period, helping patients navigate both informational and emotional challenges. However, the findings underscore the importance of extending content across the treatment continuum and enhancing personalization and interactivity. mHealth interventions should be responsive to patients' evolving needs and integrated into clinical care pathways to provide timely, comprehensive, tailored, and ongoing support for women with breast cancer.
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 | 0,026 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,007 | 0,005 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».