iCanCope PostOp: User-Centered Design of a Smartphone-Based App for Self-Management of Postoperative Pain in Children and Adolescents
Bibliographic record
Abstract
BACKGROUND: Moderate to severe postoperative pain in children is common. Increased pediatric day surgeries have shifted postoperative pain management predominantly to the home setting. Mobile health technology has the potential to overcome barriers to pain care by improving access to self-management resources. However, pain apps generally lack scientific evidence and are highly underutilized due to lack of involvement of end users in their development. Thus, an evidence-based pain self-management smartphone app that incorporates the needs and perspective of children and adolescents (end users) has potential to improve postoperative pain management. OBJECTIVE: This paper aimed to describe how the principles of user-centered design were applied to the development of iCanCope PostOp, a smartphone-based pain self-management app for children and adolescents after surgery. Specifically, it presents 2 completed phases of the user-centered design process (concept generation and ideation) for the iCanCope PostOp app. METHODS: Phase 1 was a multisite needs assessment from the perspective of 19 children and adolescents who had undergone various day surgeries, 19 parents, and 32 multidisciplinary health care providers. Children, adolescents, and parents completed individual semistructured interviews, and health care providers participated in focus groups. Data were summarized using qualitative content analysis. Phase 2 developed a pain care algorithm for the app using Delphi surveys and a 2-day in-person design workshop with 11 multidisciplinary pediatric postoperative pain experts and 2 people with lived experience with postoperative pain. RESULTS: Phase 1 identified self-management challenges to postoperative pain management and recovery; limited available resources and reliance on medications as a predominant postoperative pain management strategy; and shared responsibility of postoperative pain care by children and adolescents, parents, and health care providers. Key app functions of tracking pain, pain self-management strategies, and goal setting were identified as priorities. Phase 2 led to the successful and efficient generation of a complete preliminary pain care algorithm for the iCanCope PostOp app, including clinically relevant inputs for feasible assessment and reassessment of pain and function (rest or sleep, movement or play, and mood or worry), as well as a catalog of pain management advice to be pushed to end users (psychological, physical, pharmacological, and education). CONCLUSIONS: The concept ideation and generation phases of the user-centered design approach were successfully completed for the iCanCope PostOp app. Next steps will include design finalization, app development (iOS or Android), evaluation through a randomized controlled trial, and subsequent implementation of the iCanCope PostOp app in clinical care.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".