A Tailored Self-Management App to Support Older Adults with Cancer and Multi-Morbidities: Development and Usability Testing
Bibliographic record
Abstract
Cancer self-management interventions improve symptom management and confidence, but few interventions target the complex needs of older adults with cancer and multi-morbidities. Despite growing evidence of digital health tools in cancer care, many such tools have not been co-designed with older adults to ensure that they are tailored to their specific needs. The objective of the study was to design a self-and symptom-management app to support older adults with cancer and multi-morbidities. Utilizing a user-centered design thinking framework, we recruited 2 caregivers and 18 older adults with lived experiences of cancer to design a medium-fidelity app prototype. Participants highlighted the importance of tracking functions to make sense of the information about their symptoms, clear displays, and reminders to mitigate concerns related to polypharmacy. This app will create a 'home base' for symprtom management and support for older adults with cancer and multi-morbidities.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".