The useful field of view test: Normative data for older adults

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1 Archives of Clinical Neuropsychology 21 (2006) Abstract The useful field of view test: Normative data for older adults Jerri D. Edwards a,, Lesley A. Ross b,d, Virginia G. Wadley b,c,d, Olivio J. Clay b,d, Michael Crowe b,d, Daniel L. Roenker e, Karlene K. Ball b,d a Department of Psychology, The University of Alabama in Huntsville, United States b Departments of Psychology, University of Alabama at Birmingham, United States c Departments of Medicine, University of Alabama at Birmingham, United States d Center for Translational Research on Aging and Mobility, University of Alabama at Birmingham, United States e Department of Psychology, Western Kentucky University, United States Accepted 20 March 2006 The Useful Field of View test (UFOV 1 ) is increasingly used in clinical and rehabilitation settings. To date there have been no normative data for adjusted performance comparisons across demographically-similar, elderly peers. This study examined demographic and cognitive influences on the UFOV in a sample of 2759 participants (65 94 years of age). Performance was found to differ by age and education. Regression analyses examined the relative contributions of age, education, mental status, vision, and health to UFOV performance. All of these factors were found to significantly contribute to UFOV performance, with age accounting for the most variance and education accounting for the least. Normative tables for the UFOV by age and education are provided. These norms will allow researchers and clinicians to compare UFOV performance with similar peers and may help in identifying elderly persons who would benefit from speed of processing training National Academy of Neuropsychology. Published by Elsevier Ltd. All rights reserved. Keywords: UFOV; Useful field of view; Normative data 1. Introduction 1.1. What is the useful field of view? The concept of the useful field of view was introduced by Sanders (1970) who used the term functional visual field to indicate the visual field area over which information can be acquired in a brief glance without eye or head movements. Later, the term occupational visual field was used by Verriest et al., 1985 to describe the same concept. The term useful field of view was first used by Ball and colleagues, and has subsequently come to be most widely associated with a specific computer-based test, the UFOV assessment. Performance on this test is correlated with mental status (Ball, Owsley, Sloane, Roenker, & Bruni, 1993) and many neuropsychological measures of cognitive ability (Goode et al., 1998). Thus, performance on the UFOV test relies not only on the integrity of visual sensory Corresponding author at: UAH Department of Psychology, 301 Sparkman Drive, MH329, Huntsville, AL 35899, United States. Tel.: ; fax: address: edwardjd@uah.edu (J.D. Edwards). 1 UFOV is a registered trademark of Visual Awareness Inc /$ see front matter 2006 National Academy of Neuropsychology. Published by Elsevier Ltd. All rights reserved. doi: /j.acn

2 276 J.D. Edwards et al. / Archives of Clinical Neuropsychology 21 (2006) information but also on an individual s higher-order processing abilities (Ball, Owsley, & Beard, 1990; Owsley, Ball, & Keeton, 1995) Development of the UFOV test Development of the UFOV test was influenced in part by prior studies on visual attention and visual search. For example, researchers demonstrated that visual attention is affected by varying stimulus duration, conspicuity, central task difficulty, addition of secondary tasks, and presence of distractors as well as indicating that the impact of such factors is greater for older individuals (Plude & Hoyer, 1985; Rabbit, 1965; Scialfa, Kline, & Lyman, 1987). Sekuler and Ball collaborated in the early development of the UFOV concept (Sekuler & Ball, 1986). Further research indicated that when older adults were required to localize targets presented at greater distances from a simultaneous, demanding, central task, they performed more poorly than middle-aged or younger participants (Ball, Beard, Roenker, Miller, & Griggs, 1988). Researchers investigating age-related influences on UFOV test performance concluded that the association between poorer UFOV scores and advancing age was attributable to a higher prevalence of age-related speed of processing deficits among older adults Significance of the UFOV test The UFOV test has a number of clinical and practical applications. Ball and Owsley (1993) suggested that the test may be useful for ophthalmologists and optometrists treating older patients who complain of functional visual difficulties in the absence of abnormalities detected by a standard ophthalmologic exam. UFOV fills a void in that it is a better predictor of vision problems in everyday life than standard visual field assessments with perimetry, which detect sensory losses across the visual field. For example, better UFOV performance has been found to be predictive of older adults ability to quickly and accurately perform instrumental activities of daily living (Edwards, Wadley, Vance, Roenker, & Ball, 2005; Owsley, Sloane, McGwin, & Ball, 2002). Similarly, UFOV performance predicts important indices of mobility, including ambulatory ability, life space (the extent of travel throughout one s environment), falls, and driving competence (Ball et al., 1993; Broman et al., 2004; Myers, Ball, Kalina, Roth, & Goode, 2000; Owsley, McGwin, & Ball, 1998; Stalvey, Owsley, Sloane, & Ball, 1999; Vance et al., in press). A main strength of the UFOV test is that it is predictive of vehicle crash involvement among older adults both retrospectively (Ball & Owsley, 1993) and prospectively (Owsley & Ball et al., 1998; Owsley, McGwin, & Ball, 1998). A recent meta-analysis highlighted the UFOV assessment as a valid and reliable index of driving performance and safety (Clay et al., 2005). The measure is sensitive to cognitive declines experienced by older adults with early Alzheimer s disease (Duchek, Hunt, Ball, Buckles, & Morris, 1998; Rizzo, Anderson, Dawson, Myers, & Ball, 2000; Rizzo, Reinach, McGehee, & Dawson, 1997) and serves as a better predictor of crash involvement in this population than conventional neuropsychological measures. The UFOV assessment has recently been used to evaluate the driving capacity of adults with HIV and Multiple Sclerosis who may be experiencing cognitive impairments (Marcotte et al., 2004; Schultheis, Garay, & DeLuca, 2001; Shawaryn, Schultheis, Garay, & DeLuca, 2002). Because of its association with abilities such as driving, with a clear impact on autonomy and quality of life, the UFOV test has been utilized to evaluate driving capacity in stroke patients (Fisk, Owsley, & Mennemeier, 2002; Mazer, Sofer, Korner-Bitensky, & Gelinas, 2001; Mazer et al., 2003), as well as patients with traumatic brain injury (Fisk, Novack, Mennemeier, & Roenker, 2002). The UFOV test is also of particular interest because performance difficulties can be remediated through speed of processing training (Ball et al., 2002; Edwards et al., 2002; Edwards & Wadley et al., 2005; Roenker, Cissell, Ball, Wadley, & Edwards, 2003). Such training has been shown to improve driving safety of older adults for up to 18-month post-training (Roenker et al., 2003), enhance on-road-driving success of stroke patients with right-sided lesions (Mazer et al., 2003), and improve older adults performance of instrumental activities of daily living (Edwards et al., 2002; Edwards & Wadley et al., 2005). Despite the increasing popularity and numerous uses of this assessment, no normative data exist for the older adult population most commonly evaluated with the UFOV test in clinical and field settings. Therefore, secondary analyses of data from a large-scale, multi-site clinical trial were conducted in order to establish normative data for the UFOV test. UFOV performance was explored by age, gender, and levels of education. The impact of vision, mental status, and self-rated health on UFOV performance, as well as the relationship between UFOV and other measures of speed of processing, were also examined.

3 J.D. Edwards et al. / Archives of Clinical Neuropsychology 21 (2006) Method 2.1. Participants The data for this study were obtained from the ACTIVE randomized, controlled trial (Advanced Cognitive Training for Independent and Vital Elderly), an evaluation of the immediate and longitudinal impact of three cognitive training protocols (Ball et al., 2002; Jobe et al., 2001). The normative table for UFOV performance was established with data from participants who completed the UFOV test at baseline (N = 2759). This baseline sample included 670 males and 2089 females who were from 65 to 94 years of age (M = 73.54, S.D. = 5.85). The majority (99.7%) of individuals were of either Caucasian (73%) or African American (26%) descent. Education levels were from fourth grade to Ph.D., with an average educational level of years, corresponding to some vocational school or technical training. Inclusion criteria and recruitment methods for the ACTIVE study are detailed in Jobe et al. (2001) as well as in Ball et al. (2002). Participants were recruited from the areas of Jefferson County, Alabama; Boston, Massachusetts; Indianapolis, Indiana; Baltimore, Maryland; Cumberland, Maryland; metropolitan Detroit, Michigan; Pennsylvania. Individuals who were 65 years and older and did not have any significant physical, cognitive, or functional impairments at the time of enrollment were recruited for participation in the study. Exclusionary criteria for this sample were (a) age younger than 65 years; (b) significant functional impairment; (c) significant cognitive impairment (either self-report of Alzheimer s disease or other dementia, or a Mini-Mental State Examination score less than 24); (d) medical condition with a high probability of functional decline or mortality within 2-year (e.g., cancer); (e) substantial sensory deficits (far visual acuity less than 20/70); (f) inability to attend the various testing and training sessions of the study; or (g) substantial problems in language and communication. Language and communication skills were rated on a three-point scale (corresponding to always, sometimes, or never) by the interviewer for two questions, Was the participant able to make himself/herself understood? and Was the participant able to understand others? A rating of sometimes or never understands on either question resulted in the participant being ineligible Materials Assessments relevant to these analyses were the UFOV test, far visual acuity, the Mini-Mental State Exam (MMSE), and tests tapping the constructs of processing speed and visual attention: the Road Sign Test and Digit Symbol Substitution. The participants self-rated health was evaluated on a five-point scale (1 = excellent to 5 = poor). Several versions of the UFOV have been available, and administration and scoring of the measure have changed over time. These changes, as well as the validity and test retest reliability of the measure, are described in detail elsewhere (Edwards & Vance et al., 2005). This study used the four-subtest (stimulus identification alone; divided attention; selective attention; selective attention in conjunction with same/different discriminations), personal computer, touch screen version (17-in.), which has test retest reliability of 0.74 (Edwards & Vance et al., 2005). In each subtest, targets are presented at varying, brief display durations ( ms) via the double stair-case method. The display duration at which each subtest can be performed accurately 75% of the time is measured. The first subtest entails the identification of a center target presented on the monitor in a 3 cm 3 cm fixation box. The target is the silhouette of a 2 cm by 1.5 cm truck or car. The second subtest, which measures processing speed for a divided attention task, involves identification of this center target and simultaneous localization of a peripheral target, which is a 2 cm 1.5 cm silhouette of a car. This peripheral target is at a fixed eccentricity of 12.5 cm from the center target and is presented at one of eight radial locations. The third subtest includes these two tasks as well as 47 visual distractors that are triangles of the same size and luminance as the peripheral car. The fourth subtest is similar to the third subtest with the exception that the center task is more demanding. In this fourth subtest, two targets are presented in the central fixation box (either two cars, two trucks, or one car and one truck) and the participant must indicate if the targets inside the box are the same or different. As in subtest 3, the simultaneous localization of a peripheral target that is embedded within distractors is also required. Throughout the UFOV test participants indicate their responses (identifying and localizing targets) by merely touching the screen. Scores for each subtest are expressed as the display duration, in ms, at which the participant performed accurately on 75% of trials. Thus, scores for each subtest can range from to 500 ms. The Road Sign Test (RST; Ball & Owsley, 2000) is a computerized measure of everyday cognition that requires participants to manipulate a computer mouse in response to specified road signs. The measure taps both reaction time and appropriate response inhibition. Either three or six road signs are simultaneously presented in different positions

4 278 J.D. Edwards et al. / Archives of Clinical Neuropsychology 21 (2006) on the monitor. At first, all of the signs have slashes through them. Eventually, one of the signs in the display appears without a slash through it, prompting a response from the participant. The participant is instructed to react appropriately to the sign without a slash. Inhibiting any reaction to signs with slashes through them is also required. For example, if a left- or right-turn arrow sign without a slash appears, the participant is to move the mouse in the direction the arrow points. If a bicycle or pedestrian sign without a slash appears on the display, the participant is to click the mouse button. The participant s reaction time from stimulus presentation to correct response is measured in seconds. The RST scores for these analyses were calculated by averaging the participant s reaction time across the three- and six-sign conditions. WAIS-R Digit Symbol Substitution (Wechsler, 1981) involves filling in a grid of empty squares with symbols, by associating the number appearing above each square with the symbol paired with that number in a key at the top of the page. Participants must complete as many substitutions as possible in 90 s. In these analyses the average number of seconds required to substitute one item was calculated for each participant. Far visual acuity was assessed with the ETDRS Chart and Good-Lite model 600A light box. In a darkened room, visual acuity was measured from 10 feet, using the participant s corrective lenses if worn for distance vision. Participants were instructed to read progressively smaller letters on nine rows. According to the ACTIVE scoring system, a total of 10 points was given for each line correctly read. Resulting scores may range from zero, indicating that no letters were read correctly and corresponding to an approximate Snellen score of 20/125, to a score of 90, indicating that all letters were read correctly and corresponding to an approximate Snellen score of 20/16. The MMSE is a brief measure of attention, orientation, memory, construction skills and language (Folstein, Folstein, & McHugh, 1975). It has been widely utilized as a screener for dementia and an indicator of general mental status. Scores can range from 0 to 30, with higher scores reflecting better cognitive functioning. Because individuals with scores lower than 24 were excluded from study participation, the observed range of baseline MMSE scores was truncated, ranging from 24 to Procedure A detailed review of the ACTIVE design and procedure is provided elsewhere (Jobe et al., 2001). Potential participants were first screened via telephone and answered numerous questions about their health, sensory abilities, and availability to participate. A variety of measurements, including demographic information, MMSE, and far visual acuity, were then taken during an in-person screening visit. Those who were eligible and agreed to participate returned for a baseline visit during which numerous cognitive assessments, including the UFOV test, were administered Analyses First, normative data for older adults performance was established by using the screening and baseline descriptive data and calculating correlations between UFOV performance, age, education, far visual acuity, self-rated health, and mental status. UFOV performance by levels of age, education, and mental status was explored through Analysis of Variance (ANOVA). Gender was also explored as a factor to be considered when evaluating UFOV performance through t-tests, ANOVA, and regression analyses. 3. Results Descriptive statistics for UFOV performance on individual subtests, as well as three-subtest and four-subtest totals, are presented in Table 1. A wide range of variability in scores was observed, particularly for subtests 1 and 2. For the more challenging subtests 3 and 4, no participant scored at the ceiling of the possible range (i.e., at the lowest possible score, with lower scores indicating better performance). Nevertheless, there was a good deal of variability in performance on all four subtests, reflecting large individual differences in information processing abilities among older adults. The distributions of total scores are presented in Figs. 1 (four subtests) and 2 (three subtests). Whereas the distribution of the four-subtest total was normal, the distribution of the three-subtest total was positively skewed. Pearson correlations among UFOV test performance, mental status, far visual acuity, self-rated health, education and age were calculated and are presented in Table 2. The relationships between UFOV and other measures that tap processing speed, visual search, and reaction time, were also examined with Pearson correlations and are presented in Table 3. UFOV performance was significantly correlated with far visual acuity, education, self-rated health, mental

5 J.D. Edwards et al. / Archives of Clinical Neuropsychology 21 (2006) Table 1 UFOV subtests and total scores for baseline participants (N = 2759) Minimum Maximum M S.D. Subtest 1 stimulus identification Subtest 2 divided attention Subtest 3 selective attention Total subtests Subtest 4 same/different Total subtests Note: smaller scores reflect better performance. Fig. 1. Distribution of UFOV total scores using four subtests. Fig. 2. Distribution of UFOV total scores using three subtests.

6 280 J.D. Edwards et al. / Archives of Clinical Neuropsychology 21 (2006) Table 2 Pearson correlations among UFOV scores, far visual acuity, mental status, education, and age UFOV subtest 1 score UFOV subtest 2 score UFOV subtest 3 score UFOV subtest 4 score UFOV total 3 subtests UFOV total 4 subtests Far visual acuity Education Age * MMSE Self-rated health * All correlations significant at the 0.01 level (two-tailed), except where denoted by (significant at the 0.05 level). status, and age. However, the magnitude of these correlations was relatively low. Among these variables, the correlations between UFOV and age are of the greatest magnitude, but still somewhat moderate, with the four- and three-subtest total scores correlating with age at Not surprisingly, the three- and four-subtest totals were very highly correlated (r = 0.97) with each other. Considering this strong relationship, as well as the more normal distribution of the four-subtest total, subsequent analyses were primarily conducted using the four-subtest total. Correlations among performance on the UFOV, RST, and Digit Symbol Substitution test are shown in Table 3. The magnitude of these correlations was small to moderate. The better one s performance on UFOV, more correct Digit Symbol Substitutions were made. Also, faster information processing on the UFOV test was associated with faster reactions on the RST. Performance on the RST and Digit Symbol Substitution measures was most strongly related to UFOV subtest 2 performance and to the four-subtest UFOV total, with the magnitude of correlations ranging from 0.41 to Multiple regression analyses were performed to examine the relative contributions of age, education, mental status, self-rated health and visual acuity to UFOV performance. UFOV four-subtest total score was used as the dependent measure. The stepwise method was utilized so that the variables accounting for the most variance were entered first. Using this method age was first entered and accounted for 19% of the variance in UFOV performance. Age was followed by mental status, vision, health and, lastly, education. Each was a significant predictor of UFOV performance and all together accounted for a total of 30% of the variance (p < 0.001). These regression results are depicted in Table 4. In order to compile normative data, UFOV three- and four-subtest total scores were examined across levels of age and education, as well as by gender. Considering the restriction of range among MMSE scores in this sample (due to study design), UFOV performance by MMSE score was not further explored in this sample. Six different age groups were formed using intervals of 5 years (65 69, 70 74, 75 79, 80 84, 85 89, and 90 94). Differences among the age groups in UFOV performance were examined with ANOVA, and Tukey s test was used to make post-hoc between group comparisons. Overall, there were significant age group differences in UFOV performance, F(5, 2758) = , p < Post-hoc tests revealed that all age groups differed significantly from one another in Table 3 Relationship between UFOV and other measures that tap processing speed, visual search, and reaction time UFOV Task 1 score UFOV Task 2 score UFOV Task 3 score UFOV Task 4 score Total UFOV 4 subtests Digit Symbol Substitution Road Sign Test 3-stimuli Road Sign Test 2-stimuli Note: All correlations significant at the 0.01 level (two-tailed).

7 J.D. Edwards et al. / Archives of Clinical Neuropsychology 21 (2006) Table 4 Summary of multiple regression analyses examining the relative contribution of age, years of education, mental status, visual function, and self-rated health to UFOV four-subtest total scores Variable B S.E. t p Step 1 Constant <0.001 Age <0.001 Step 2 Constant <0.001 Age <0.001 Mental status <0.001 Step 3 Constant <0.001 Age <0.001 Mental status <0.001 Visual acuity <0.001 Step 4 Constant <0.001 Age <0.001 Mental status <0.001 Visual acuity <0.001 Self-rated health <0.001 Step 5 Constant <0.001 Age <0.001 Mental status <0.001 Visual acuity <0.001 Self-rated health <0.001 Years of education <0.001 Note: R 2 = for step 1; R 2 = for step 2; R 2 = for step 3; R 2 = for step 4; R 2 = for step 5 (p s < 0.001). UFOV performance (p s < 0.001) with the exception that no significant difference was found comparing those who were years old to those who were 90 years of age or older, or between those 85 and 89-year old and those who were 90 years of age or older (p s > 0.05). Because the two oldest age groups did not differ significantly from one another, these groups were combined for norms purposes. When grouped into five age groups, with the highest group being 85+, statistical analysis with ANOVA and post-hoc tests confirmed that all age groups significantly differed from one another in UFOV performance, F(4, 2758) = , p < UFOV performance by these five age groups is shown in Fig. 3. Education level was used to form three groups: those who did not graduate from high school, those with a high school diploma or equivalent, and those with education beyond high school. UFOV performance among the groups was examined with ANOVA and post-hoc Tukey s test. Significant differences were found for the three educational groups. Those with less education performed more poorly on the UFOV, F(2, 2756) = 43.39, p < Post-hoc tests revealed that all three education level groups were significantly different (p s < 0.005). UFOV performance by education level is presented in Fig. 4. An independent samples t-test was conducted to examine whether significant differences in UFOV scores existed between males and females. No significant difference by gender was found, t(1936) < 1, p = 0.738; therefore, normative data were collapsed across gender. Given the significant differences in UFOV performance by age and education level, normative data stratified by these variables are presented in Table 5 for the four-subtest total. Norms are also presented for the three-subtest total in Table 6 since many clinicians are currently using this version of the UFOV. Recent research has indicated that subtest 2 alone is predictive of future at-fault crash involvement (Ball et al., 2006), and the present results indicate that performance on this subtest correlates well with UFOV total (r = 0.835). Thus, norms for this subtest alone are included in Table 7.

8 282 J.D. Edwards et al. / Archives of Clinical Neuropsychology 21 (2006) Fig. 3. UFOV performance by age group. Note: smaller scores reflect better performance. Fig. 4. UFOV performance by education level. Note: smaller scores reflect better performance. 4. Discussion The primary contribution of the normative data provided by these analyses is that older adults UFOV performance gathered in clinical and field settings can now be compared to stable estimates from the performance of age- and education-similar peers. Using simple z-score calculations (observed score minus expected score/standard deviation), an individual s score can be evaluated relative to the appropriate peer-based mean. Thus, deficits beyond expectation for age can be identified. Furthermore, due to the existence of promising remediation programs, those with identified

9 J.D. Edwards et al. / Archives of Clinical Neuropsychology 21 (2006) Table 5 UFOV norms for four-subtest total by age group and years of education Age group <12 years education M (S.D.) (245.82) (259.72) (297.61) (266.80) (267.97) n =12 years education M (S.D.) (265.99) (257.56) (268.66) (290.42) (293.76) n >12 years education M (S.D.) (241.23) (248.08) (243.43) (260.46) (225.81) n Note: smaller scores reflect better performance. Table 6 UFOV norms for three-subtest total by age group and years of education Age group <12 years education M (S.D.) (205.45) (219.93) (273.82) (255.59) (267.97) n =12 years education M (S.D.) (221.06) (222.49) (244.56) (265.13) (276.44) n >12 years education M (S.D.) (193.88) (207.24) (218.54) (241.47) (217.85) n Note. Smaller scores reflect better performance. UFOV deficits might be candidates for training-related improvements in processing speed and attention, with associated improvements in everyday functions including driving (Ball et al., 2002; Edwards et al., 2002; Edwards & Vance et al., 2005; Edwards & Wadley et al., 2005; Roenker et al., 2003). The norms provided within this paper will be of use to scientists, clinical professionals in the fields of Psychology, Gerontology, Rehabilitation Medicine, Physical and Occupational Therapy, Neurology, Ophthalmology, and Optometry, as well as to automobile insurance companies and state transportation officials. Several features of these analyses warrant discussion. First, the fourth subtest of UFOV was added to the UFOV protocol for the ACTIVE study (Jobe et al., 2001). As is seen in Figs. 1 and 2, the addition of this fourth subtest Table 7 UFOV norms for subtest two by age group and years of education Age group <12 years education M (S.D.) (95.72) (113.36) (158.43) (148.66) (168.46) n =12 years education M (S.D.) (103.93) (109.60) (130.66) (157.72) (156.42) n >12 years education M (S.D.) (86.70) (97.48) (116.33) (132.61) (138.45) n Note. Smaller scores reflect better performance.

10 284 J.D. Edwards et al. / Archives of Clinical Neuropsychology 21 (2006) enhances the psychometric qualities of the UFOV test, resulting in normally distributed total scores among adults 65 years of age and older. Second, the low magnitude of the relationship between far visual acuity and UFOV is consistent with other evidence that although the UFOV measure relies on visual function, the test taps higher-order visual processing and cognitive abilities as well. Accordingly, higher correlations were found between UFOV and measures that tap speed of processing and visual search. The magnitude of the correlations between UFOV and mental status may have been underestimated due to the restricted range among MMSE scores in the ACTIVE study, which was inherent in the study design. An examination of data from a separate investigation (Staying Keen in Later Life SKILL; Edwards & Vance et al., 2005; Edwards & Wadley et al., 2005; Wood et al., 2005), which included adults with MMSE scores less than 23 (range 6 30), indicated that the magnitude of the correlation between UFOV and MMSE increased to 0.39 in this more varied sample. However, the mean MMSE in the SKILL sample was also high (27.99/30), indicating that most individuals in this study, too, were intact with respect to mental status. The relationships between UFOV and a measure of visual search that assesses reaction time and a traditional measure of mental processing speed were also examined. Although these relationships were stronger than that of UFOV to vision, the magnitude of these relationships was small to moderate. Thus, the UFOV appears to capture unique variance in older adults information processing abilities. UFOV performance varied by age and education level, but not by gender. Considering the relatively low magnitude of the correlations among age, education, and UFOV, the impact of these factors is not substantial. Among those aged 65 94, the oldest old and individuals with lower levels of education perform slightly worse on the UFOV. In regression analyses, age accounted for the largest portion of variance in UFOV performance among the variables evaluated, and education accounted for a very small, albeit significant, amount of variance. Overall, there is a great deal of individual variability among older adults in their performance on the UFOV as is evident in the large standard deviations for the subtests and total scores. Thus, the UFOV test will best function as a screening tool for quick identification of individuals clearly intact or impaired. However, more extensive evaluation is necessary for individuals with mid-range performance. For example, in determining driver-fitness, a practitioner may use the test to quickly identify both safe and at-risk drivers and refer those with questionable performance for an on-road evaluation. Recent research has established UFOV subtest two cut-off scores for identifying drivers 75 years of age and older who are at-risk for future motor vehicle crash involvement. A prospective study of 1910 drivers from the state of Maryland found that those who performed at 353 ms or worse on subtest two were 2.02 times more likely to incur an at-fault crash over the subsequent 3-year (Ball et al., 2006). Ongoing research is underway to identify cut-off points for crash risk using both the three and four subtest totals of the pc UFOV version as well as for drivers under the age of 75 years. Future research should further explore the application of this assessment technique to populations other than community-dwelling older adults including patients with Mild Cognitive Impairment, Alzheimer s Disease, Stroke, or possibly younger drivers who are crash-prone. Furthermore, future efforts should investigate whether the UFOV test may be useful in identifying older adults at-risk for functional impairment in areas other than driving. The present results revealed that seventy percent of the variance in UFOV scores is due to factors other than age, education, visual function, self-rated health or mental status. Although performance on the measure is correlated with many different variables, the magnitude of most of these relationships is relatively small. Thus, the UFOV taps unique variance in information processing ability among older adults. It is most likely for this very reason that the UFOV is a significant predictor of many everyday outcomes. Acknowledgements The authors would like to thank the entire ACTIVE team. The ACTIVE study was supported by grants from the National Institute on Aging and the National Institute of Nursing Research to Hebrew Rehabilitation Center for the Aged U01 NR04507, Indiana University School of Medicine U01 NR04508, Johns Hopkins University U01 AG14260, New England Research Institutes U01 AG14282, Pennsylvania State University U01 AG14263, University of Alabama- Birmingham U01 AG14289, and University of Florida U01 AG Drs. Karlene Ball & Daniel Roenker are stockholders in and consultants to the company Visual Awareness Inc., which owns the patent to the Useful Field of View (UFOV ) testing and training software. Dr. Jerri Edwards has also worked as a consultant to Visual Awareness Inc.

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