Developing an e-Prehabilitation System of Care for Young Adults Diagnosed With Cancer: User-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: A diagnosis of cancer in adolescence or young adulthood can pose many different and unique challenges for individuals, as well as their families and friends. Drawing on the concept of prehabilitation, the provision of high-quality, accessible, timely, reliable, and appropriate information, care, and support for young adults with cancer and their families is critical to ensure that they feel equipped and empowered to make informed decisions relating to their treatment and care. Increasingly, digital health interventions offer opportunities to augment current health care information and support provision. Co-designing these digital health interventions can help to ensure that they are meaningful and relevant to the patient cohort, thereby maximizing their accessibility and acceptability. OBJECTIVE: This study had 4 primary interlinked objectives: understand the support needs of young adults with cancer at the time of diagnosis, understand the potential role of a digital health solution to assist in the delivery of prehabilitation for young adults with cancer, identify appropriate technologies and technological platforms for a digital prehabilitation system of care, and develop a prototype for a digital prehabilitation system of care. METHODS: This was a qualitative study using interviews and surveys. Young adults aged 16 to 26 years diagnosed with cancer within the last 3 years were invited to participate in individual user-requirement interviews or surveys. Health care professionals specializing in the treatment and care of young adults with cancer and digital health professionals working in the industry were also interviewed or completed a survey. Consensus feedback interviews were conducted with 3 young adults and 2 health care professionals after the development of the first generation of the prototype app. RESULTS: In total, 7 individual interviews and 8 surveys were completed with young adults with a range of cancer diagnoses. Moreover, 6 individual interviews and 9 surveys were completed with health care professionals, and 3 digital health professionals participated in one-on-one interviews. A prototype app with the working name of Cancer Helpmate was developed based on these collective participant data. Overall, feedback from participants across the data collection activities suggests that the concept for the app was positive during these developmental stages. Further insightful ideas for the app's future development were also identified. CONCLUSIONS: Young adults with cancer and health care professionals are responsive to the need for more digitally driven services to be developed. Further development of an app such as Cancer Helpmate, which incorporates key features and functionalities directly informed by users, could help to augment the support provided to young adults with cancer.
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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 | 0.000 | 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".