Co-Designing Priority Components of an mHealth Intervention to Enhance Follow-Up Care in Young Adult Survivors of Childhood Cancer and Health Care Providers: Qualitative Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Survivors of childhood cancer are at risk of medical, psychological, and social late effects. To screen for their risks, receipt of consistent, cancer-specific follow-up care is crucial. However, <50% of survivors attend their aftercare, and only 35% of them recognize that they could have a serious health problem. The use of mobile health (mHealth) is a promising form of intervention to educate, connect, and empower survivors of childhood cancer on the importance of follow-up care. OBJECTIVE: This study aimed to use co-design to identify the priority components to include in an mHealth intervention with young adult (aged between 18 and 39 years) survivors of childhood cancer and health care providers. METHODS: This study was conducted between January and November 2022 in Canada and used patient-oriented research methods. Participants were recruited through local or provincial long-term follow-up clinics, using convenience sampling from patient partners who assisted in recruiting survivors across geographical areas in western, central, and eastern Canada, and social media outreach (X, formally known as Twitter; Facebook; and Instagram). Qualitative descriptive data (focus group interviews) from survivors of childhood cancer and health care providers (individual interviews) were gathered. We analyzed the collected data using reflexive thematic analysis and verified it through member checking techniques through an online community engagement event. RESULTS: We conducted with patient partners 5 online (Zoom) focus groups with 22 survivors of childhood cancer (mean age 29.19, SD 4.78 y). We conducted individual telephone interviews with 7 health care providers. Participants identified five priority areas to be included in an mHealth intervention: (1) connections, (2) education and information, (3) engagement, (4) personalization, and (5) resources. Results were shared with and validated by survivors of childhood cancer, their families, health care providers, and academic researchers as part of a community engagement event. Small and large group discussions were facilitated to allow participants to review and discuss the accuracy of the themes derived regarding the core components to be included in mHealth. A graphic recording artist visually captured key ideas from the event. A subset of the participants also completed a web-based satisfaction survey, and responses indicated that the community engagement event was generally well received. CONCLUSIONS: Results from this study have provided the necessary foundation to progress in intervention development. The next step of this multiphased project is to build an innovative and accessible mHealth intervention prototype that is based on the identified core components and is grounded in an established conceptual framework for co-design of mHealth.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".