Feasibility and Acceptability of a Mobile App to Improve Quality of Life of Long-Term Breast Cancer Survivors: Single-Arm Pre-Post Intervention Pilot Study
Notice bibliographique
Résumé
Background: Long-term breast cancer survivors often continue to experience physical and psychological sequelae, despite being cancer-free; these challenges can negatively impact their quality of life and self-efficacy. Mobile health interventions constitute a promising strategy for providing personalized support. However, the feasibility and acceptability of these tools in long-term breast cancer survivors have not yet been sufficiently explored. Objective: This study aimed to evaluate the feasibility and acceptability of the CUMACA-M, a digital health app designed to improve the quality of life and self-efficacy in long-term breast cancer survivors. Methods: A single-arm feasibility pilot study was conducted with pre- and post-intervention evaluations. Participants were recruited from the Navarra Breast Cancer Association (Saray), a nonprofit organization supporting individuals with breast cancer in Navarra, Spain. The inclusion criteria included being female, being aged ≥18 years, having been diagnosed with breast cancer, and being disease-free for at least 5 years after primary treatment. The participants used the CUMACA-M app for 3 months. Feasibility was assessed through recruitment and completion rates, whereas acceptability was measured using the System Usability Scale and open-ended qualitative questions. Changes in quality of life and self-efficacy were analyzed with the Quality of Life-Cancer Survivors (QOL-CS) scale and the Self-Efficacy to Manage Chronic Disease Scale. Paired t tests were performed for pre-post comparisons. Results: A total of 23 women (mean age =52.8, SD 6.1 years) participated, with a 100% retention rate. The System Usability Scale score (mean 80.8, SD 15.2) indicated excellent usability. The health advice module received the highest level of satisfaction, whereas the nutrition and physical activity modules received suggestions for improvement. With respect to the clinical outcomes, no statistically significant differences were found between the pre- and post-intervention scores on the QOL-CS (total score: pre=5.96, SD 1.08; post=5.72, SD 1.20; P=.07) or the Self-Efficacy to Manage Chronic Disease Scale (total score: pre=6.57, SD 1.90; post=6.26, SD 1.82; P=.40). However, a reduction was observed in the QOL-CS spiritual well-being subscale (pre=5.35, SD 1.13; post=4.93, SD 1.22; P=.05). Conclusions: As a pioneering digital intervention for long-term breast cancer survivors, CUMACA-M appears to be a potentially viable and acceptable intervention for this population, as suggested by the high level of usability and absence of dropouts. However, the findings should be interpreted with caution because of the limited sample size and the short follow-up period. The lack of significant changes in quality of life or self-efficacy may be influenced by these constraints. Future studies with larger, more diverse samples and longer follow-up periods are needed to more robustly assess the long-term impact of this intervention.
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| 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 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».