Co-designing a digital health app to manage pain in young children with cancer: report from the generative design phase of intervention development
Bibliographic record
Abstract
ABSTRACT Pain is one of the most prevalent and burdensome pediatric cancer symptoms for young children and their families. A significant proportion of pain episodes are experienced in environments where management options are limited, including at home, and digital innovations such as apps may have positive impacts on pain outcomes for young children in these environments. Our overall aim is to co-design such an app and the objective of this study was to explore the perceptions of children’s parents about app utility, needed system features, and challenges. We recruited parents of young children with cancer and multidisciplinary pediatric oncology clinicians from two pediatric cancer care centers to participate in audio-recorded, semi-structured co-design interviews. We conducted interviews until data saturation was reached. Audio-recordings were then transcribed, coded, and analyzed using thematic analysis. Forty-two participants took part in the process. Participants endorsed the concept of an app as a useful, safe, and convenient way to engage caregivers in managing their young child’s pain. The value of the app related to its capacity to provide real-time, multimodal informational and procedural pain support to parents, while also reducing the emotional burden of pain care. Recommendations for intervention design included accessibility-focused features, comprehensive symptom tracking, and embedded scientific- and clinically-sound symptom assessments and management advice. Predicted challenges associated with digital pain management related to potential burden of use for parents and clinicians. The insights gathered will inform the design principles of our future childhood cancer pain digital research. AUTHOR SUMMARY The lack of meaningful involvement of end-users in intervention development has been a key contributor to difficulties in effectively translating research findings into cancer practice and policy. There is a risk that without the active engagement of children with cancer and their families in designing digital health innovations, researchers and clinicians will fall victim to an unfortunate cycle of producing underutilized evidence—resulting in a limited impact on patient outcomes. Pain is a particular problem for young children with cancer and real-time digital health interventions may be solutions for accessible, effective, and scalable cancer pain management. We are using an established end user-centered co-design process to engage parents and pediatric oncology clinicians in the development of a cancer pain management app. Our work here summarizes the generative co-design phase of this process and the perceptions of parents and clinicians related to app usefulness and needed system features.
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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 | 0.029 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".