Patient Experiences of Rehabilitation and the Potential for an mHealth System with Biofeedback After Breast Cancer Surgery: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Physiotherapy-led home rehabilitation after breast cancer surgery can protect against the development of upper limb dysfunction and other disabling consequences of surgery. A variety of barriers can limit physical rehabilitation outcomes, and patients may benefit from more support during this time. Mobile health (mHealth) systems can assist patients during rehabilitation by providing exercise support, biofeedback, and information. Before designing mHealth systems for a specific population, developers must first engage with users to understand their experiences and needs. OBJECTIVE: The aims of this study were to explore patients' rehabilitation experiences and unmet needs during home rehabilitation after breast cancer surgery and to understand their experiences of mHealth technology and the requirements they desire from an mHealth system. METHODS: This was the first stage of a user-centered design process for an mHealth system. We interviewed 10 breast cancer survivors under the two main topics of "Rehabilitation" and "Technology" and performed a thematic analysis on the interview data. RESULTS: Discussions regarding rehabilitation focused on the acute and long-term consequences of surgery; unmet needs and lack of support; self-driven rehabilitation; and visions for high-quality rehabilitation. Regarding technology, participants reported a lack of mHealth options for this clinical context and using non-cancer-specific applications and wearables. Participants requested an mHealth tool from a reliable source that provides exercise support. CONCLUSIONS: There are unmet needs surrounding access to physiotherapy, information, and support during home rehabilitation after breast cancer surgery that could be addressed with an mHealth system. Breast cancer survivors are open to using an mHealth system and require that it comes from a reliable source and focuses on supporting exercise performance.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".