| Development and Validation of An Ai-Based Spinal De-Loading Exercise Protocol in Individuals With Non-Specific Low Back Pain: A Mixed-Methods Study |
| Paper ID : 1087-SPORTCONGRESS |
| Authors |
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Mostafa Jalili Bafrouei *1, Reza Rajabi2, Hooman Minoonejad3, Seyed Hamed Mousavi4 1PhD Student, Department of Sport Injuries and Biomechanics, Faculty of Sport Sciences and Health, University of Tehran, Tehran, Iran 2PhD, Full professor, Department of Sport Injuries and Biomechanics, Faculty of Sport Sciences and Health, University of Tehran, Tehran, Iran 3PhD, Associate Professor, Department of Sport Injuries and Biomechanics, Faculty of Sport Sciences and Health, University of Tehran, Tehran, Iran 4PhD, Assistant Professor, Department of Sport Injuries and Biomechanics, Faculty of Sport Sciences and Health, University of Tehran, Tehran, Iran. |
| Abstract |
| Introduction: Chronic Non-Specific Low Back Pain (CNSLBP) is a prevalent musculoskeletal disorder associated with significant physical, psychological, and economic burdens. Exercise therapy is central to its management, yet many existing programs lack personalization, limiting their long-term effectiveness. Recent advancements in artificial intelligence (AI) offer opportunities for individualized, data-driven rehabilitation strategies. This study aimed to evaluate the content validity of a newly developed AI-assisted corrective exercise protocol tailored for individuals with CNSLBP. Methods: A hybrid protocol was designed, integrating AI-driven decision-making with expert clinical input to deliver individualized corrective exercise interventions. The protocol consists of 32 items organized across four progressive phases. Content validity was assessed by 79 evaluators, including university professors, therapists, and doctoral students. Lawshe’s Content Validity Ratio (CVR) and the Content Validity Index (CVI) based on Waltz and Bausell’s methodology were used. One-way ANOVA and Bonferroni post-hoc tests were conducted to assess intergroup differences. Results: All 32 items met or exceeded the minimum CVR and I-CVI thresholds, with a scale-level CVI of 0.91 indicating strong overall validity. Statistically significant differences were observed between evaluator groups (F= 575.89, p < .001). Professors rated items significantly lower than both therapists and doctoral students, indicating variation in evaluative criteria across academic and clinical domains. Conclusion: The findings support the content validity and clinical relevance of the AI-based corrective exercise protocol for CNSLBP. The inclusion of diverse expert perspectives reinforces its robustness; however, further empirical testing in clinical populations is warranted to confirm its effectiveness and long-term applicability. |
| Keywords |
| AI-tech, Low back pain, Protocol, Validity, Rehabilitation |
| Status: Abstract Accepted (Oral Presentation) |
