The Useful Field of View measure does an excellent job of assessing

In human vision, the useful field of view (or UFOV) is the visual area from which information can be extracted without eye or head movements.[1] UFOV size generally decreases with age,[2] most likely due to decreases in visual processing speed, reduced perception, and increased susceptibility to distraction.[1][clarification needed] UFOV performance is correlated with important real-world issues, including risk of an automobile crash. Performance can be improved by computer-based training.[citation needed]

History

UFOV assessment and training programs were primarily developed by Karlene Ball of the University of Alabama at Birmingham, and Daniel Roenker of Western Kentucky University. The first versions of the assessment and training programs were produced at Northwestern University by Robert Sekuler and Ball.[3] These programs were originally made available through Visual Awareness Inc.[citation needed]

Assessment

The traditional UFOV assessment is a computer-based visual test containing three subtests.[citation needed]

• Processing Speed: Determines threshold for discriminating stimuli presented in central vision.
• Divided Attention: Same as 1, but with the addition of a concurrent peripheral target location task.
• Selective Attention: Same as 2 but with the addition of distracters.

The threshold scores are combined to produce an overall performance score.[citation needed]

Impact

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Performance on the UFOV assessment is correlated with several real-world functions:

• Several studies have shown that a reduction in UFOV is correlated with an increased risk of an automobile accident, with poor performers about twice as likely to have an automobile crash as good performers.[4]
• Drivers with poor UFOV performance take longer to cross intersections and initiate crossing later.[5]
• The UFOV assessment is one of the best visual or cognitive predictors of crash rates, surpassing visual acuity tests (used at most Department of Motor Vehicle test sites).[6]
• Poor UFOV performers have more collisions during obstacle navigation while walking.[7]
• People with poor UFOV performance have higher rates of falls causing injuries.[8]

Training

Performance can be improved by computer-based training.[9] Multiple studies have shown that improved UFOV performance generalizes to several real-world functions. UFOV training has been shown to:

• Reduce dangerous driving maneuvers by 36% when measured 18 months following training.[10] The same study showed faster reaction times equating to an additional 22 feet more stopping distance at 55  mph.[citation needed]
• Reduce at-fault automobile crashes by 51% in the five-year period following training.[11]
• Reduce the risk of driving cessation by 40%.[12]
• Help maintain driving distance and driving in difficult situations such as in the dark, in rain, and rush hour traffic.[13]
• Reduce the risk of serious health-related quality of life decline measured at 2 and 5 years.[14]
• Reduce subsequent annual predicted medical care expenditures at one year (by $244) and five years (by $143 p.a.).[15]
• Reduce decline in instrumental activities of daily living measured at 5 yrs post UFOV training.[16]
• Reduce the risk of the onset of clinically significant depression symptoms by 38% measured at 1-year follow-up.[17]
• Improve performance on timed activities of daily living, such as reading medicine instructions, counting change, looking up a phone number, and finding items in a cupboard.[18]
• Increased ability to identify lexical information in older adults.[19]

Note: UFOV is not the same as a visual field or perimetry test that examines the ability of the visual system to process light falling on various regions of the retina. Perimetry tests check for the integrity of the visual system while UFOV tests the ability to pay attention to the information in the visual field particularly when under situations of increased demand for attention.[citation needed]

Notes

1. ^ a b Ball, K., V.G. Wadley, and J.D. Edwards, Advances in technology used to assess and retrain older drivers. Gerontechnology, 2002. 1(4): p. 251-261.
2. ^ Sekuler, A.B., P.J. Bennett, and M. Mamelak, Effects of aging on the useful field of view. Exp Aging Res, 2000. 26(2): p. 103-20.
3. ^ Sekuler, R. and K. Ball, Visual localization: age and practice. J Opt Soc Am A, 1986. 3(6): p. 864-7; Ball, K.K., et al., Age and visual search: expanding the useful field of view. J Opt Soc Am A, 1988. 5(12): p. 2210-9.
4. ^ Ball, K., and C. Owsley Identifying correlate of accident involvement for the older driver. Hum Factors, 1991. 33(5): p. 583-95.; Ball, K., C. Owsley, and M. Sloane, Visual and cognitive predictors of driving problems in older adults. Exp Aging Res, 1991. 17(2): p. 79-80.; Ball, K., et al., Visual attention problems as a predictor of vehicle crashes in older drivers. Invest Ophthalmol Vis Sci, 1993. 34(11): p. 3110-23.; Goode, K.T., et al., Useful Field of View and Other Neurocognitive Indicators of Crash Risk in Older Adults. Journal of Clinical Psychology in Medical Settings, 1998. 05(4): p. 425-440.
5. ^ Pietras, T.A., et al., Traffic-entry behavior and crash risk for older drivers with impairment of selective attention. Percept Mot Skills, 2006. 102(3): p. 632-44.
6. ^ Owsley, C., et al., Visual risk factors for crash involvement in older drivers with cataract. Arch Ophthalmol, 2001. 119(6): p. 881-7.; Owsley, C., et al., Visual processing impairment and risk of motor vehicle crash among older adults. JAMA, 1998. 279(14): p. 1083-8.
7. ^ Broman, A.T., et al., Divided visual attention as a predictor of bumping while walking: the Salisbury Eye Evaluation. Invest Ophthalmol Vis Sci, 2004. 45(9): p. 2955-60.
8. ^ Vance, D.E., et al., Predictors of falling in older Maryland drivers: a structural equation model. J Aging Phys Act, 2006. 14(3): p. 254-69.
9. ^ Ball, K., et al., Effects of cognitive training interventions with older adults: a randomized controlled trial. JAMA, 2002. 288(18): p. 2271-81.
10. ^ Roenker, D.L., et al., Speed-of-processing and driving simulator training result in improved driving performance. Hum Factors, 2003. 45(2): p. 218-33.
11. ^ Ball, K., et al., The Effects of Training on Driving Competence – Crash Risk, in Transportation Research Board Annual Meeting. 2009: Washington DC, USA.
12. ^ Edwards, J.D., P.B. Delahunt, and H.W. Mahncke, Cognitive Speed of Processing Training Delays Driving Cessation. J Gerontol A Biol Sci Med Sci, 2009.
13. ^ Edwards, J.D., et al., The Longitudinal Impact of Cognitive Speed of Processing Training on Driving Mobility. Gerontologist, 2009.
14. ^ Wolinsky, F.D., et al., The ACTIVE cognitive training trial and health-related quality of life: protection that lasts for 5 years. J Gerontol A Biol Sci Med Sci, 2006. 61(12): p. 1324-9.; Wolinsky, F.D., et al., The effects of the ACTIVE cognitive training trial on clinically relevant declines in health-related quality of life. J Gerontol B Psychol Sci Soc Sci, 2006. 61(5): p. S281-7.
15. ^ Wolinsky, F.D., et al., The ACTIVE cognitive training trial and predicted medical expenditures. BMC Health Serv Res, 2009. 9: p. 109.
16. ^ Willis, S.L., et al., Long-term effects of cognitive training on everyday functional outcomes in older adults. JAMA, 2006. 296(23): p. 2805-14.
17. ^ Wolinsky, F.D., et al., The effect of speed-of-processing training on depressive symptoms in ACTIVE. J Gerontol A Biol Sci Med Sci, 2009. 64(4): p. 468-72.
18. ^ Edwards, J.D., et al., The impact of speed of processing training on cognitive and everyday performance. Aging Ment Health, 2005. 9(3): p. 262-71.; Edwards, J.D., et al., Transfer of a speed of processing intervention to near and far cognitive functions. Gerontology, 2002. 48(5): p. 329-40.
19. ^ Grabbe, J. W., & Allen, P.A. (2013). Age-Related Sparing of Parafoveal Lexical Processing. Experimental Aging Research, 39, 419-444.

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