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OBJECTIVE: To translate, culturally adapt, validate, and investigate the reliability of Arabic version of Bournemouth questionnaire (BQ) for patients with low back pain.

METHOD: Thirty experts (three panels) and 70 low back pain patients (37.5 ± 13.9 years) were involved in this study. Test-retest as well as internal consistency analyses were used to assess reliability. Intraclass correlation coefficient (95% confidence interval) was used to assess test-retest analysis, whereas Cronbach alpha value was calculated to assess the internal consistency. BQ Arabic version validity was evaluated in forms of face, content, internal, and external construct validity. Internal construct validity was evaluated with factor analysis, and external construct validity was tested by the correlation between the Arabic version of BQ and short-form (36) health survey (SF-36) questionnaire.

RESULTS: Factor analysis revealed that BQ had a single factor. BQ has a very good correlation with SF-36 questionnaire (r = .74). Arabic version of BQ has a high internal consistency reliability where Cronbach alpha value was 0.889 at baseline and 0.911 after 1 week. The test-retest analysis was between 0.87 and 0.96 and for the BQ total score was 0.95 (p < .0001) indicating that test-retest results are highly correlated.

CONCLUSION: The Arabic version of BQ is a valid, reliable, and feasible scale for assessment of low back patients. It is short, easy-to-apply, need short time to complete and comprehensive scale. So it may be considered as a preferable scale for clinical assessment of Arabic speaking patients with low back pain.

One of successful approaches for object localization and recognition is sliding window approach where different candidate windows are found and evaluated and the best window is selected to represent the object. To avoid exhaustive search over all windows locations, Efficient Sub-window Search (ESS) [1] algorithm were proposed to efficiently find the best window among all sub-windows using branch and bound technique. In case of multi-class multiple object detection problems, such methods are time consuming. To handle this issue, efficient multi-object detection approach is proposed based on image superpixelization We utilize image superpixels in 2 points. (a) Such preprocessing stage is done once for an image, hence multiple detections could be fast. (b) We observe that image superpixels could help in identifying the promising candidate sub-windows, hence evaluating all sub-windows could be avoided. An efficient brute force sub-window search algorithm is proposed based on these observations. Moreover, for improving its performance, the algorithm is integrated with ESS algorithm. The proposed algorithms are assessed on the PASCAL 2006 and PASCAL 2007 test-set and show that they are faster than the state-of-the art sub-window search algorithms, while achieving a comparable performance comparing with them.