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
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".