Investigating Challenges in Implementing a Digital Play Intervention in a Complex Organization Across Pediatric Departments: Non-Randomized Controlled Feasibility Trial
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,040 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».