Symptom Management Preference and Persona Development for Mobile Health Design Targeting Chinese Older Adult Patients With Breast Cancer: Descriptive Qualitative Study
Bibliographic record
Abstract
Background: Mobile health (mHealth) for breast cancer care can greatly benefit patients' symptom management. Although research supports the effectiveness of mHealth, older adult patients with breast cancer often face difficulties using it, hindering them from accessing effective symptom management possibilities. Understanding the preference for mHealth among this population is crucial for providing insights into effective mHealth design. Objective: This study aimed to better understand the symptom management preference using mHealth for Chinese older adult patients with breast cancer and use the approach of personas to inform the mHealth design. Methods: This was a descriptive qualitative study. In total, 17 patients with breast cancer aged 60 years and older were recruited from tertiary hospitals in Shanghai, China, using purposive sampling. Data were collected through one-on-one interviews. Content analysis was used to identify the factors that influence participants' symptom management preference using mHealth. The categories of influencing factors of preference informed the persona template and guided the development of the persona. Results: We identified 3 major categories affecting participants' preference for mHealth, including social interaction patterns, mHealth literacy, and symptoms. The following five personas were developed: (1) Positive Manager, (2) Dependent Parent, (3) Management Isolationist, (4) Image Manager, and (5) Clinician Dependent. We provide insights into how these personas can be used when designing and implementing mHealth for symptom management support. Conclusions: Key factors influencing symptom management preference using mHealth among Chinese older adult patients with breast cancer and personas developed based on that can foster a better understanding of this population and initiate future mHealth design and implementation.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".