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
Bibliographic record
Abstract
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.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".