Purpose To compare three different approaches to measuring mobility performance when evaluating the visually impaired.
Methods 488 participants, including 192 glaucoma, 112 age-related macular degeneration, 91 diabetic retinopathy and 93 healthy volunteers, completed the Assessment of Disability Related to Vision (ADREV) mobility course. The performance of participants on the mobility course was evaluated by noting errors made and time required for completion. Errors noted and time taken were compared using multivariate logistic regression to determine which measurement better differentiated patients with visual disease from healthy volunteers. Multivariate logistic regression was also used to evaluate the combined metric of ADREV errors divided by time to determine its ability to discriminate participants with visual disease from healthy volunteers.
Results Errors noted and time taken while ambulating through the standardised mobility course shared a weak but statistically significant association (Pearson's r=0.36, p<0.05). After controlling for demographic and medical comorbidities, logistic regression analysis revealed that errors noted were better at discriminating individuals with visual disease from healthy volunteers (OR 2.8–4.9, 95% CI 1.5 to 10.3) compared with the time taken for mobility course completion (OR 1.1, 95% CI 1.0 to 1.2). These findings were consistent across all comparisons between healthy volunteers and participants with each type of visual impairment. Finally, the combined metric of ADREV errors divided by time was far more predictive of visual disease compared with either time taken or errors noted during mobility testing (OR 11.0–17.7, 95% CI 3.6 to 77.1).
Conclusions A validated scoring system based on errors is more effective when assessing visual disability during mobility testing than recording the time taken for course completion. The combined metric of ADREV errors noted divided by time taken was most predictive of all the methods used to evaluate visual disability during mobility testing.
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