Arabic exercise assessment and screening for you tool: development, validation, and cultural adaptation among older adults in Saudi Arabia

Background and aim Regular physical activity is a key determinant of healthy aging; however, physical inactivity remains highly prevalent among older adults. Most existing screening tools emphasize risk assessment rather than providing individualized guidance for safe exercise. The Exercise Assessment and Screening for You (EASY) tool is an algorithm-based instrument designed to guide older adults toward appropriate physical activities based on their health conditions. In this study, we aimed to translate and culturally adapt the EASY tool into Arabic and evaluate its psychometric properties among older adults. Methods We conducted an observational cross-sectional study at King Saud University Medical City, Riyadh (2025–2026), including adults aged ≥50 years. Participants completed an electronic survey that included demographic information, the Arabic EASY (Ar-EASY), the International Physical Activity Questionnaire–Short Form (IPAQ-SF), and the Physical Activity Scale for the Elderly (PASE). Participants were recruited via convenience sampling from family medicine and geriatric clinics, and analytic subsamples varied across psychometric analyses due to data availability. Results A total of 127 older adults were included (median age: 60 years), of whom 63.8% were physically active. The Rasch analysis demonstrated generally acceptable item functioning and scale-targeting abilities. Confirmatory factor analysis supported the five-item model with excellent fit indices (CFI = 1.00, TLI = 1.08, RMSEA = 0.00). EASY scores were negatively correlated with IPAQ-SF scores ( r = −0.31, p = 0.033) but not with PASE scores. The test–retest reliability was moderate (ICC = 0.58). Conclusion The Ar-EASY scale showed acceptable psychometric properties and provided a brief, culturally adapted screening tool to support individualized physical activity guidance in older adults. Future studies should confirm its performance using larger, population-based samples.