Co-Designing Mobile Serious Games to Support Patients With Psoriatic Arthritis and Chronic Pain: Mixed Methods Study
Notice bibliographique
Résumé
Background: Serious games offer promising avenues for clinical care by enhancing patient engagement and delivering therapeutic benefits. In psoriatic arthritis (PsA), chronic pain contributes to emotional distress, functional limitations, and reduced well-being. While symptom-tracking apps exist, few digital interventions directly address chronic pain through engaging, therapeutic experiences tailored to patients' cognitive and physical needs. Objective: This study aimed to co-design mobile serious games-NoPain Games-to support patients with PsA in managing chronic pain. Conducted within the iPROLEPSIS Horizon Europe project, the study involved a multidisciplinary cocreation session with rheumatologists, researchers, and technical experts, followed by a usability feedback session with patients with PsA. The goal was to identify therapeutic priorities, refine game mechanics, and assess usability to inform the development of personalized, accessible digital interventions. Methods: A sequential mixed methods design was used. First, a 90-minute remote cocreation session was held with 14 experts (6 rheumatologists, 4 technical experts, and 4 researchers) from 3 European countries. Participants reviewed game storyboards and discussed therapeutic and design priorities. Thematic analysis of transcribed discussions identified key insights. Next, a 60-minute remote usability feedback session was conducted with 5 patients with PsA (aged 25-64 y), who interacted with 2 high-fidelity game prototypes (Space Oddity and Four Seasons). Usability was assessed using the System Usability Scale (SUS), and qualitative feedback was collected through moderated discussion. Item-level analysis using item characteristic curves provided deeper insight into usability perceptions and item sensitivity. All ethics requirements were met for both study phases. Results: Thematic analysis of the transcribed dialogs of the cocreation session revealed three core themes: (1) therapeutic benefits (pain distraction, memory enhancement, cognitive stimulation, stress reduction, and creative engagement); (2) game difficulty (balancing duration and complexity to sustain engagement without fatigue); and (3) accessibility and interaction (addressing physical limitations, optimizing touchscreen usability, and ensuring inclusive design). These insights informed the development of 2 NoPain Game prototypes, which received a SUS score of 79 (SD 10.4; 95% CI 69.89-88.11), indicating good usability. Item characteristic curve analysis showed strong discrimination for learnability, while ease of use and confidence exhibited ceiling effects. Items like support needs and inconsistency showed minimal variability, and the learning curve demonstrated delayed but meaningful responsiveness at higher usability levels. Qualitative feedback reinforced the relevance of difficulty adjustment, technical refinements, and game mechanics, offering actionable insights for future iterations and broader implementation. Conclusions: This study uniquely contributes to the field by co-designing mobile serious games specifically for patients with PsA, integrating expert and patient input to address chronic pain through accessible, cognitively engaging digital interventions-an approach not previously explored in this population. Future work will refine game mechanics, integrate adaptive difficulty, and conduct clinical trials to evaluate therapeutic efficacy. NoPain Games may serve as complementary tools within digital care ecosystems, offering support tailored to the therapeutic needs and physical limitations of patients with PsA underserved by conventional therapies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,019 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».