Investigating Challenges in Implementing a Digital Play Intervention in a Complex Organization Across Pediatric Departments: Non-Randomized Controlled Feasibility Trial
Bibliographic record
Abstract
Background: Patients and health care providers can use playful digital games in a hospital setting to increase motivation and distract patients during painful procedures. Future digital interventions for pediatric hospitalization must do more than distract; they must also encourage socialization and promote physical activity, for example, by exploring novel interactive approaches to boost motivation. Objective: The pilot study investigated the feasibility of a non-randomized controlled trial (non-RCT) assessing a new digital play intervention, Monster Gardener, that aims to motivate and increase physical activity for children and adolescents in a hospital. Methods: This feasibility study was a non-RCT conducted from October to December 2023. We recruited hospitalized children, 7-17 years of age, and health care professionals from 4 pediatric departments at Copenhagen University Hospital - Rigshospitalet, Denmark. The children were allocated to intervention and control groups. Data collection included physical activity data measured with accelerometers, data on app use, and usability questionnaires completed by participants and health care professionals. The control group received usual care and accelerometer measurements, while the intervention group received accelerometer measurements and was invited to play Monster Gardener. We applied the 8 focus areas by Bowen et al to describe and evaluate the app's feasibility. Results: A total of 22 children and adolescents from 3 pediatric departments agreed to participate. Our main findings, based on the framework by Bowen et al, were (1) acceptability: prolonged recruitment due to fewer hospital stays more than 24 hours than expected; (2) demand: software coding error in the app prevented data registration, causing a potentially major risk of data loss; (3) practicality: Monster Gardener was incompatible with certain mobile phones, and discomfort from the adhesive plasters used to attach the accelerometer led to early removal by one-third of participants; (4) implementation: technical problems and perceived complexity hindered successful app deployment; (5) adaptation: the app demonstrated adaptability across different departments; (6) integration: enhanced information sessions with the health care professionals were needed prior to data collection, and participants were too exhausted and overwhelmed by consultations, blood tests, examinations, and pain and nausea from surgical procedures to use the app; (7) expansion: app facilitation requires additional resources, posing a challenge given limited availability of staff; and (8) limited-efficacy testing: participants were inactive 22 hours a day and data loss limited efficacy testing. Conclusions: The digital play intervention showed that Monster Gardener could potentially motivate children to be physically active during pediatric hospitalization; however, when using the framework by Bowen et al, the current version was deemed infeasible for implementation in an RCT. Various organizational, technological, and practical issues must be addressed to improve the intervention prior to effectiveness testing. Future studies should use simpler digital play interventions and invite end users' active involvement in developing the intervention.
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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 | 0.040 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".