An mHealth Self-management System for Support Children With Acute Lymphocytic Leukemia and Their Caregivers: Qualitative Co-design Study
Bibliographic record
Abstract
BACKGROUND: The unique features of smartphones have extended their use in different fields, especially in the health care domain. These features offer new opportunities to support patients with chronic conditions by providing them with information, education, and self-management skills. We developed a digital self-management system to support children with cancer and their caregivers in Iran (low- and middle-income country). OBJECTIVE: This study is aimed at the development and preliminary evaluation of a cancer self-management system (CanSelfMan) tailored to the needs of children with cancer and their parents or caregivers. METHODS: This study was conducted in collaboration with a multidisciplinary team between January and February 2020 at MAHAK's Pediatric Cancer Treatment and Research Center. We developed a self-management system in six stages: requirement analysis, conformity assessment, preparation of educational content, app prototyping, preliminary evaluation, and developing the final version. RESULTS: A total of 35 people (n=24, 69% parents and n=11, 31% children) volunteered to participate in the study. However, only 63% (15/24) of parents and 73% (8/11) of children were eligible to participate. By adopting a user-centered design approach, we developed a mobile app, CanSelfMan, that includes five main modules (knowledge base, self-management tips, self-assessment report, ask a question, and reminders) that provide access to reliable information about acute lymphocytic leukemia and the self-management skills required for side effect measurement and reporting. A web-based dashboard was also developed for oncologists and included a dashboard to monitor users' symptoms and answer their questions. CONCLUSIONS: The CanSelfMan app can support these groups by providing access to reliable information about cancer, facilitating communication between children or parents and health care providers, and helping promote medication adherence through a reminder function. The active participation of the target group can help identify their needs. Therefore, through the involvement of stakeholders such as patients, caregivers, and oncologists in the design process, we improved usability and ensured that the final product was useful. This app is now ready to proceed with feasibility studies.
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 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 | 0.015 | 0.014 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".