Participants’ Perspectives on the iCareBreast Mobile-Based Perioperative Care Program for Women Undergoing Breast Cancer Surgery: Qualitative Process Evaluation
Bibliographic record
Abstract
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.
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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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".