Needs of Patients With Gynecologic Cancer and Their Caregivers for Obtaining mHealth-Supported Self-Management: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Family caregivers of individuals with gynecologic cancer experience high levels of distress. Web-based caregiver support interventions have demonstrated efficacy in improving caregiver outcomes. However, the lack of portability could be a limitation. Mobile health (mHealth) apps could fill this gap and facilitate communication between patient-caregiver dyads. OBJECTIVE: We sought to obtain information on desired usage and features to be used to design an mHealth self-management support app targeting both patients with gynecologic cancer and their caregivers. METHODS: We conducted Zoom focus groups with women who had been treated for gynecologic cancers (ovarian, fallopian, primary peritoneal, uterine, endometrial, cervical, and vulvar); patients were also asked to invite a self-identified "closest support person" (caregiver). A semistructured focus group guide was used to elicit information on patients' and caregivers' perceived gaps in information and support, desired features of an mHealth app, and interest in and preferences for app usage. After transcription, rapid qualitative analysis using a thematic matrix was used to identify common themes across groups. RESULTS: A total of 8 groups were held. The final sample included 41 individuals with gynecologic cancer and 22 support persons or caregivers (total n=63). Patients were aged between 32 and 84 years, and most (38/41, 93%) were White and married. For caregivers (n=22), 15 (68%) identified as male and 7 (32%) as female, with ages ranging between 19 and 81 years. Overall, 59% (n=13) of caregivers were spouses. Questions geared at eliciting 3 a priori topics yielded the following themes: topic 1-gaps in information and support: finding relevant information is time-consuming; patients and caregivers lack confidence in deciding the urgency of problems that arise and from whom to seek information and guidance; topic 2-desired features of the mHealth app: patients and caregivers desire centralized, curated, trustworthy information; they desire timely recommendations tailored to specific personal and cancer-related needs; they desire opportunities to interact with clinical and peer experts through the app; and topic 3-interest and preferences for app usage: need for private space in the app for patients and caregivers to get information and support without the others' knowledge; patients and caregivers desire having control over sharing of information with other family members. CONCLUSIONS: Designing a single mHealth app to be used by patients and caregivers presents unique challenges for intervention designers and app developers. Implications of the study suggest that app developers need to prioritize flexibility in app functionality and provide individuals the ability to control information sharing between patients and caregivers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".