Using the Kolmogorov-Smirnov test, we observed that the functional field score (corrected), visual attention (arcsine transformation), AFOV mean threshold presentation time, AFOV PDM, age, and years of driving experience were normally distributed.
Finally, AFOV mean threshold presentation time was included in the model, as we wanted to assess the improvement of the predictive value of the model when this variable was added.
Correlation coefficients (Spearman's rho, Table 3) were calculated among age, years of driving experience, visual acuity (logMAR), log contrast sensitivity, AFOV (presentation time), AFOV PDM, visual field (functional field score), visual attention, and the final score (Grade 0 to 3).
An efficient compensatory viewing strategy was defined as a strategy resulting in low threshold presentation times and/or a low PDM on the AFOV test.
Adding AFOV (mean threshold presentation times) to the model significantly improved prediction (Nagelkerke [R.sup.2] = .32).
To detect the target on the AFOV test, participants who failed the driving test needed presentation times that were twice as long as those needed by participants who passed the driving test.
We use three eyepieces: a 40-mm eyepiece with a 68[degrees] AFOV and 14-mm eye relief, a 25-mm eyepiece with a 12[degrees] AFOV and 24-mm eye relief, and a 28-mm Plossl with a 45[degrees] AFOV and 24-mm eye relief.
If you plan to do a lot of public viewings, it is worth upgrading to the 2-inch barrels, but otherwise pick the largest AFOV eyepiece you have on hand.
For example, using eyepiece specifications available on the internet, I found an 8-mm eyepiece with a 50[degrees] AFOV
that has a field stop with a 6.5-mm opening, and another 8-mm eyepiece with a 100[degrees] AFOV
that has a 13.9-mm field stop.