No abstract

Most studies on older adults' driving practices have relied on self-reported information. With technological advances it is now possible to objectively measure the everyday driving of older adults in their own vehicles over time. Objective. The purpose of this study was to examine the ability of older drivers to accurately estimate their kilometers driven over one year relative to objectively measured driving exposure. Methods. A sub-sample (n=159 of 928; 50.9% male) of Candrive II participants (age ≥ 70 years of age) was used in these analyses based on strict criteria for data collected from questionnaires as well as an OttoView-CD Autonomous Data Logging Device installed in their vehicle, over the first year of the prospective cohort study. Results.Although, there was no significant difference overall between the self-reported and objectively measured distance categories, only moderate agreement was found (weighted kappa = 0.57; 95% CI 0.47 to 0.67). Almost half (45.3%) chose the wrong distance category, and some people mis-estimated their distance driven by up to 20,000 km. Those who misjudged in the low mileage group (≤ 5,000 km) consistently under-estimated, while the reverse was found for those in the high distance categories (≥ 20,000), i.e., they always over-estimated their driving distance. Conclusions. Although self-reported driving distance categories may be adequate for studies entailing broad group comparisons, caution should be used in interpreting results. Use of self-reported estimates for individual assessments should be discouraged.

This initial evaluation of the evolutionary optimization software tool pareto for IMRT treatment planning demonstrates feasibility and provides motivation for continued development. Advantages of this approach over current commercial methods for treatment planning are many, including: (1) fully automated optimization that avoids human controlled iterative optimization and potentially improves overall process efficiency, (2) formulation of the problem as a true multiobjective one, which provides an optimized set of Pareto nondominated solutions refined over hundreds of generations and compiled from thousands of parameter sets explored during the run, and (3) rapid exploration of the final nondominated set accomplished by a graphical interface used to select the best treatment option for the patient.