Translation, Cultural Adaptation, and Mixed-Methods User Feedback of the SelfBack App to the Arabic Language for Patients With Low Back Pain: Pilot Mixed-Methods Study
Notice bibliographique
Résumé
Background: Low back pain (LBP) is a pervasive global health concern, significantly impacting the quality of life and burdening health care systems. Effective self-management strategies are essential for mitigating the effects of LBP and empowering individuals in their recovery. Digital health interventions, particularly smartphone apps, offer a promising avenue for delivering accessible and personalized self-management support, potentially improving patient adherence to treatment plans. The SelfBack app exemplifies such digital innovations, demonstrating clinical effectiveness in LBP management and prioritizing personalized user experiences. Objective: This study describes the comprehensive process of translating and culturally adapting the SelfBack app from English to Arabic for the Saudi context. It further evaluates the perceived usability of the adapted Arabic SelfBack app within a cohort of Saudi individuals experiencing LBP, aiming to ensure accessibility and cultural appropriateness for Arabic-speaking users. Methods: A rigorous, 5-stage process was implemented. Stage 1, exchange, focused on adapting the app's core content. English self-assessment scales, used for tailored treatment plans, were replaced with validated, culturally appropriate Arabic versions. Stage 2, translation and cultural adaptation, which translated the content of the patient self-management plan and adapted it for cultural relevance. Stage 3, audio conversion, addressed educational resources. English audio content was professionally translated and rerecorded in Arabic. Stage 4, laboratory usability testing, integrated the Arabic content, verifying interface functionality and right-to-left script compatibility. Stage 5, field usability testing, evaluated the app with 11 Saudi participants experiencing nonspecific LBP using the Arabic System Usability Scale (A-SUS) and semistructured interviews. Results: The translation and adaptation processes are detailed, highlighting the work of an expert panel of linguists, health care professionals, and cultural consultants. The panel identified minimal discrepancies and no significant misunderstandings, demonstrating the accuracy and cultural appropriateness of the adaptation. The mean System Usability Scale (SUS) score was 70%, indicating good usability. Interviews corroborated these results, with participants generally reporting the app as clear, intuitive, and easy to use. However, feedback highlighted areas for improvement, including the perceived number of mandatory questions, a perceived lack of interactivity, repetitive content, and unmet expectations regarding certain functionalities. Conclusions: The Arabic SelfBack app has been successfully developed, demonstrating high user satisfaction, ease of use, and interface efficiency within the target cultural context. The identified criticisms provide actionable insights for future updates. These results suggest that the app is ready for clinical research with Arabic-speaking participants, potentially improving LBP management in Saudi Arabia. Further research will be essential to confirm these initial findings and establish the app's long-term effectiveness.
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 | 0,024 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».