Citation
Cognitive Training with Video Games to Improve Driving Skills and Driving Safety among Older Adults

Material Information

Title:
Cognitive Training with Video Games to Improve Driving Skills and Driving Safety among Older Adults
Creator:
Belchior, Patricia Da Cunha
Place of Publication:
[Gainesville, Fla.]
Florida
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (209 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Rehabilitation Science
Committee Chair:
Mann, William C.
Committee Co-Chair:
Marsiske, Michael
Committee Members:
Richards, Lorie G.
Horgas, Ann L.
Graduation Date:
8/11/2007

Subjects

Subjects / Keywords:
Awards ( jstor )
Dogs ( jstor )
Field of view ( jstor )
Older adults ( jstor )
Selective attention ( jstor )
Simulation training ( jstor )
Simulations ( jstor )
Soldiers ( jstor )
Standard deviation ( jstor )
Video games ( jstor )
Rehabilitation Science -- Dissertations, Academic -- UF
attention, cognitive, elderly, game, simulator, training, ufov, video, visual
Genre:
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Rehabilitation Science thesis, Ph.D.

Notes

Abstract:
Visual attention is one of the most important cognitive skills required for driving an automobile. Although this skill declines with aging, training in older ages has been shown to boost visual attention. Today the most approach for training older adults? visual attention is the UFOV. A practical drawback of using this training, however, is that equipment demands require that the training be done in a laboratory or clinical setting, which can be expensive and inaccessible for the general population. Given the importance of visual attention for driving performance and given the lack of widely available approaches to train this skill, there is a need to explore more inexpensive alternatives for training visual attention. One possible option for training visual attention in older individuals involves video games. Previous research with younger adults has shown positive effects of video game in training on the visual attention of college students, including in useful field of view tasks. The current study investigated the impact of video game training on older adults' visual attention performance; in addition, it was investigated if such improvements would transfer to improved performance in a driving simulator task. Fifty-eight participants. Forty-five participants were assigned to one of the three intervention groups (action video game (Medal of Honor), Useful Field of View (UFOV), placebo control video game (Tetris)) and thirteen participants were assigned to a no contact control group. Before training and immediately after training participants from the intervention groups were evaluated in a UFOV test and in a driving simulator test. The intervention was composed of 6 training sessions, each of 1.5 hour duration. Overall, the results suggest that the UFOV training improved visual attention significantly more than any other group. It was noted, however, that the two video game conditions (Medal of Honor and Tetris) experienced (non-significantly) more visual attention gain than the no contact control group; indeed, on one subtask (Selective attention), the Tetris group experienced significantly more gain than the no-contact control group, even though Tetris had been construed as a no-contact control. Despite general practice-related gain in driving simulator performance for all study groups, the results of the study further indicated that the visual attention gains were not transferred to a simulator driving performance. In contrast, differential effects of the three training conditions were observed in participant Flow, an indicator of participant enjoyment and engagement. Participant?s self-rated flow experience suggested that enjoyment improved over time for the two video game conditions (Medal of Honor, Tetris), but decreased for the more traditional computer-based UFOV training group. In a final study aim, consumer-oriented analyses of participants' opinions about the games they played were conducted. Results of these analyses suggested that video game were acceptable to this older adult population, and that many saw the games as a valid tool for mental exercise. Thus, although more work is needed to establish appropriate dosages, outcome measures, and to identify which games best improve visual attention, the positive evaluations of the games and positive Flow results lend preliminary support that video games can be acceptable and promising intervention tool. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2007.
Local:
Adviser: Mann, William C.
Local:
Co-adviser: Marsiske, Michael.
Statement of Responsibility:
by Patricia Da Cunha Belchior.

Record Information

Source Institution:
UFRGP
Rights Management:
Copyright Belchior, Patricia Da Cunha. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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Table 4-21. Video game ownership and usage
Are you interested in owning a video game?
N (% )
Yes 11 (40.7)
No 16 (59.3)
What had prevented you in the past from
owning a video game? *
N(%)
Lack of interest 12 (44.0)
Cost 9 (33.3)
Knowledge of the game 8 (29.6)
Lack of perceived need 6 (22.2)
Lack of time 4 (14.8)
Training not available 3 (11.1)
Too hard to learn 3 (11.1)
Others 2 (7.4)
What do you foresee preventing you from using
a video game in the future? *
N(%)
Cost 11 (40.7)
Lack of interest 10 (37.0)
Knowledge of the game 4 (14.8)
Lack of perceived need 6 (22.2)
Lack of time 3 (11.1)
Training not available 3 (11.1)
Too hard to learn 3 (11.1)
Others 2 (7.4)
Note: Participants could choose more than one answer.










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Activities and participation The performance of activities of daily living (ADL) and

instrumental activities of daily living (IADL), such as driving, involves the use of fundamental

cognitive skills. Studies show that poor cognitive function leads to increased risk of limitation in

ADL performance (Moritz, Kasl, & Berkman, 1995). In this case, lifestyle would not specifically

affect participation in leisure activities, but lifestyle would increase cognitive function needed to

perform all the array of everyday activities. All activities classified in the ICF could be affected

by enhancing cognitive capacity.

The potential of using ICF to classify cognitive disorders has already being stated

(Arthanat, Nochaj ski & Stone, 2004), however, in the present discussion, the personal level,

defined as positive lifestyle by participation in leisure activities, was added. Thus, the use of

cognitive training to promote cognitive function can be seen as part of the personal component,

and has the potential to influence each level of the model by improving visual attention (body

function and structures level), having an impact on driving performance (activities level) and

promoting independence in other activities by driving (participation level).

Study Purpose

The purpose of this study was to investigate the efficacy of using an action video game

(Medal of Honor; MOH) to improve the visual attention (UFOV) performance of older adults.

The study also investigated the effects of MOH training on a "real-world" outcome of older

adults' simulated driving performance. Another purpose of this study was to investigate

participants' engagement with/enj oyment of videogame- or UFOV-training, and to query

participants' opinions about exiting interface design.










small sample said that the controller should be made easier to use (25.9%). When asked "How

would you change these features?", most participants reported they would change nothing about

the screen (55.5%) or the controller (55.0%).

With regard to perceived benefits, the most common perceived benefit reported was that of

mental exercise (33.3%), but an almost equal percentage (25.0%) felt that gaming could not help

them in any way.

Anecdotal data from participants who played the video games was also collected during

the posttest battery. In general, it seems participants enjoyed the study/gaming experience.

"I enjoyed the training and looked forward to next class each time"

"This has been a very enj oyable experience".

"I enjoyed the whole experience whether or not it stimulated my mind (I hope it did)"

"I enjoyed the computer game. This makes a good competitive game playing with others.

Thanks for introducing me to a game that I have never played before".

"The game playing was fun at times frustrating when I did not perform well".

One pre-experimental concern (that violence, especially in the MOH game, might be off-

putting to some) was affirmed by some respondents. Specifically, a subset of female participants

in the action-game Medal of Honor group did express some concerns about the violence of the

game

"I enjoyed learning the game, as I was curious how the games are played. I could probably

become addicted to them, but I do not intend to buy one. I get no satisfaction out of killing

people..."

"... I was bothered by the violent content...I found the instructors very patient and helpful.

I enjoyed the experience".








APPENDIX B
UFOV TRAINING

Start Point for Customized Training


Speed










Divided
Attention


Selective
Attention









Roenker, Cissell, & Ball, 2002; Ball, Berch, Helmers, Jobe, Leveck, Marsiske, et al., 2002;

Roenker, Cissell, Ball, Wadley, & Edward, 2003).

Useful Field Of View (UFOV) test The UFOV (UFOV" User' s Guide ) is a computer-

administered test. It measures the speed at which individuals can process information within a

300 radius visual field under a variety of cognitively demanding conditions. The test can be

administered in about 15 minutes. The test is assessed binocularly and it involves the detection,

localization, or identification of targets against more complex backgrounds. It consists of four

subtests that assess the speed of processing under increasingly complex task demands (e.g.,

divided attention, selective attention). These were described in detail in the UFOV training

section above. Participants end up with four subtask scores, which can also be summed into a

composite. Each subtask score ranges from 16 ms (fastest) to 500 ms (slowest), and represents

the average presentation time needed for the participant to perform that task with 75% accuracy.

As described elsewhere in detail, the four subtasks are:

(1) Subtask 1, Speed, identify a centrally presented obj ect as car or truck

(2) Subtask 2, Divided attention, identify a centrally presented obj ect as car or truck, while

noting the location of a peripherally presented obj ect

(3) Subtask 3, Selective attention, identify a centrally presented obj ect as car or truck,

while noting the location of a peripheral obj ect that is presented in clutter

(4) Subtask 4, Same-different, note whether two centrally presented obj ects are the same or

different (two cars, two trucks, or a car and a truck), while simultaneously noting the location of

a peripheral obj ect that is presented in clutter

Primary Outcome Measures

The primary, or 'real world', outcome for this study was simulated driving performance.

This primary outcome was selected because it speaks to the practical value, if any, of the visual









Participants were allowed to control their speed, in accordance with posted speed levels.

Thus, Brake Reaction Time was considered not to be a useful indicator of participant response,

since task difficulty and presentation rate of the dog would vary with the speed driven. Brake

reaction distance was found to be less sensitive to individual differences in driving speed, and

was selected as the indicator of choice. The composite score was computed as the average brake

reaction distance, computed for all 18 trials in which the dog appeared. For "error trials" (i.e.,

where the dog was not detected), a "time-out" distance (set to an arbitrary maximum of 400 feet)

was used.

In the mixed between-within ANOVA, the same effects as in all precedent analysis were

used. Group (4, MOH, UFOV, Tetris, and no-contact Control) was the between-persons

variable, and Occasion (2, Pretest, Posttest) was the within-persons variable. The dependent

score was the average brake reaction distance over the 18 dog trials. There was a significant

main effect of Occasion F (1, 51) = 8.5, p = .005, 12 = .14 (indicating that people improved,

requiring shorter reaction distances from pre to posttest), but the main effect of Group was not

significant F (3, 51) = .04, p = .989, 12 = .002. Analyses also revealed a non-significant Group X

Occasion interaction, F (3, 51) = .85, p = .107, r12 = .04. Although there is a significant main

effect for Occasion, the absence of an interaction indicates there were no group differences on

this measure. The mean values and standard deviation for the dependent measure by group are

presented in table 4-12. The relationship between baseline and posttest performance are

illustrated in figure 4-9.

Analysis on the Brake Reaction Distance by Trial Type As with the Useful Field of

View, a concern was that the aggregate, composite mean brake reaction distance across all 18

dog-trials may have obscured any effects that were specific to particular trial types. In particular,



























1.0






a,0.90-



0.60-


MOH

0.30- nt 0



1 2

Pretest TIMIE Posttest



Figure 4-16. Change from pretest to posttest, by group, for the standard deviation of lane
maintenance for block.
Note: UFOV = Useful Field of View; MOH = Medal of Honor





Health condition
(Disorder or disease)
Disorders that can
impact c.*,,,,lo ,


I -
Body function
and structures ~
Visual attention
disorders |


Participation
Activities ~IIndependence;
SEngagement in other
Driving | activities


Figure 1-1. Conceptual Framework for the World Health Organization's International
Classification of Functioning, Disability and Health (ICF)









CHAPTER 3
MATERIALS AND METHODS

Overall Procedure

Participants were recruited by phone calls, by mail and through flyers that were distributed

in the community. Potential participants were pre-screened by telephone and to eligibility criteria

should be met in order for them to be scheduled for a baseline assessment (65 or older, current

driving, willing to participate in 6 training sessions and no previous experience with video

games). The baseline assessment took about I hour and a half to be completed. Participants were

then randomized to one of three intervention groups (MOH-UFOV-Tetris). Training consisted of

6 training sessions of 90 minutes each. After each training session, participants completed a flow

questionnaire to measure their engagement with the training. One week after intervention,

participants returned for posttest. A no-contact control group were added to the study after the

intervention study was completed, this group were not randomized but were recruited from the

same recruitment pool. Participants from the no-contact control group completed pre and posttest

within three weeks interval.

Study Design

The current study employed a pretest-posttest control group design. Analytically, the

study included one within-person condition (i.e., pretest-posttest) and one between-person

condition factor (i.e., four intervention groups). The within-person condition assessed change in

performance on several cognitive and non-cognitive measures over time; for three of four

groups, this was change pre- and post-intervention. The between-persons condition assessed

differences between persons assigned to each of the four treatment conditions. The critical

analytic effect of interest in this study, the occasion X group interaction, assessed whether

change differed by intervention group, such that members of some groups experienced more
























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Noe UO =Ueful Fedof ViewUO TtisCnt









CHAPTER 5
DISCUSSION

Overview

This final chapter is organized into four maj or sections. The chapter first provides a

narrative review of the maj or findings of this study, followed by attempts to interpret these

findings. First, the maj or results of each study's specific aim will be reviewed. Second, a brief

consideration of the limitation of the study will be considered. Finally, directions for future

research on cognitive training with video games among older adults will be suggested.

Summary of Major Findings

Aim 1: Training Effects on Useful Field of View

The first aim of this study was to investigate whether UFOV performance can be improved

by providing training in first person action video games (Medal of Honor (MOH)) and

comparing this training to: (a) "gold- standard" touch screen based UFOV training, b) alternative

video game (Tetris) construed as a placebo control and c) no-contact pre post only group. It was

hypothesized that the UFOV training group would experience the most improvement in UFOV

test scores; moreover, the MOH videogame group was expected to experience more gain in

UJFOV performance than the Tetris group and the no-contact control group (based on the work of

Green and Bavelier, 2003). Results provided partial support for these hypotheses.

First, the UFOV composite score (the sum of four UFOV subtasks) was examined as a

dependent variable. Overall results revealed significant improvement only for the UFOV training

group, which experienced significantly more pre-post gain than the two control conditions

(Tetris, no-contact).

Subsequent analysis was conducted examining the four UFOV subtasks separately as

dependent measures. While there was no significant difference between groups for the easier









moving to the next speed, which was 40 ms faster) was employed until the end of training. No

participants in this study reached "ceiling" on this task before concluding training.

Tetris Training

In this placebo training condition, participants were asked to play the video game Tetris,

with a tutor. In the game, which is a classic 1980s arcade game, seven randomly rendered

tetrominoes or tetrads shapes composed of four blocks each fall down the playing field

(Figure 3-7). The object of the game is to manipulate these tetrominoes with the aim of creating a

horizontal line of blocks without gaps. When such a line is created, it disappears, and the blocks

above (if any) fall. As the game progresses, the tetrominoes fall faster, and the game ends when

the stack of Tetrominoes reaches the top of the playing field. Participants' tutors provided them

with game instructions, and demonstrated/model effective play. Then, the tutor acted as a

"coach", as participants played the game themselves. The goal of this training was to increase

participants' independence, and to increase the number of rows they could clear before ending

the game. This game was selected because, based on the work of Green and Bavelier, playing

of this game for up to ten hours had little or no effect on visual attention performance in

college students. Thus, this condition was construed as a "placebo control" for the Medal

of Honor group. It was thought to control for contact hours with study staff, out-of-house

time for older adults, and any stimulating effects of interacting with video games. The

remainder of this section describes the training procedure in greater detail.

In the first session, the tutors explained to the participants how video games are used and

played. However, before the participants actually started the game, they were given some paper

cuts in the same shapes as the tetraminoes that they would see on the screen (shapes composed of

four blocks each). They were asked to play with them and try to fit them together (create a

horizontal line without gap). Once they successfully completed the task, the tutor introduced the









Future directions

The current study confirmed the value of UFOV training for older adults, at least in terms

of improving UFOV performance. The narrow band of transfer (e.g., no effects observed on a

driving simulator) were disappointing, but consistent with the lack of transfer reported for UFOV

training in much larger trials (see Willis et al., 2006). A promising initial finding was that

participants in the video game groups (especially Tetris) experienced significantly more

Selective Attention gain than no-contact controls, suggesting that there may be some visual

attention benefits of videogaming in older adults. However, many questions were raised by this

pattern of Eindings, and future research must clarify these.

First, future research should attempt to recruit samples of a more diverse population, with a

particular emphasis on attracting participants with lower baseline function. It may also be

sensible, in larger samples, to assess important potential covariates like socioeconomic status,

medication use and health status at baseline.

Secondly, future studies should explore the impacts of larger dosages of training. Recent

studies have suggested the need of at least 30 hours of training sessions to show maj or

improvements in visual attention of undergrad students (Green et al, 2006c). It is reasonable to

think that older adults would benefit from longer dosages of training. Indeed, a linear

interpolation of the pre-post UFOV gain in the Tetris and MOH groups suggests that, with more

exposure, these groups might eventually "catch up" to the UFOV trained group. The reality of

this assumption needs to be tested, and the number of additional sessions needed to achieve

larger UFOV effects must be determined. One reason we sought to explore commercially

available video games was their "scalability"; i.e., these are low cost (under $200) interfaces that

use televisions (already present in most homes). Thus, it would be quite feasible to place these

devices in participants' homes, and to investigate much larger dosages. A secondary advantage












Aim 4: Participants' Opinions About Game Design ................. ................. ....__155
Theoretical Considerations and Study Implications ......__. ..........._. ........._._. ....156
Limitations ............ ..... ._ ...............162...
Future directions ............ ..... ._ ...............165...
Conclusion ............ ..... ._ ...............168...


APPENDIX


A MOH MANUAL GUIDE ............ ..... ._ ...............170..


B UFOV TRAINING ................. ...............196................


LIST OF REFERENCES ................. ...............199................


BIOGRAPHICAL SKETCH .............. ...............209....










Table 4-18. Post hoc analysis for flow engagement for Medal of Honor
Occasion Occasion Mean Difference Std. Error t dSf p. value
1 2 -11.50 4.73 12 12 0.02
3 -9.08 4.18 12 12 0.04
4 -7.00 6.20 12 12 0.27
5 -11.75 5.29 12 12 0.03
6 -10.58 5.42 12 12 0.06
2 1 11.50 4.73 12 12 0.02
3 2.42 3.94 12 12 0.54
4 4.50 6.54 12 12 0.50
5 -0.25 3.97 12 12 0.95
6 0.92 5.49 12 12 0.87
3 1 9.08 4.18 12 12 0.04
2 -2.42 3.94 12 12 0.54
4 2.08 5.68 12 12 0.72
5 -2.67 4.12 12 12 0.52
6 -1.50 4.94 12 12 0.76
4 1 7.00 6.20 12 12 0.27
2 -4.50 6.54 12 12 0.50
3 -2.08 5.68 12 12 0.72
5 -4.75 5.07 12 12 0.35
6 -3.58 6.07 12 12 0.56
5 1 11.75 5.29 12 12 0.03
2 0.25 3.97 12 12 0.95
3 2.67 4.12 12 12 0.52
4 4.75 5.07 12 12 0.35
6 1.17 3.88 12 12 0.77
6 1 10.58 5.42 12 12 0.06
2 -0.92 5.49 12 12 0.87
3 1.50 4.94 12 12 0.76
4 3.58 6.07 12 12 0.56
5 -1.17 3.88 12 12 0.77



















0.00


-50.00-




-!100.00-
O



mJ -150.00-



-200.00-




-250.00-

Medal of Honor UFOV Tetris Control

Intervention group

Figure 4-2. Mean UFOV composite pretest-posttest change scores, by training group
Note: UFOV = Useful Field of View










LIST OF TABLES


Table page

3-1 Measures used in the study. ............. ...............83.....

3-2 Sequence of the position of each dog on the scenario ................ ......... ................84

3-3 Correlation between UFOV and simulator subtasks during pretest. .............. ... .............85

3-4 Correlation between UFOV and simulator subtasks during posttest ................ ...............85

4-1 Sample characteristics both for the total group and by intervention subgroup ................119

4-2 Sample characteristics for completers and non-completers of the study protocol........... 120

4-3 UFOV composite scores before and after testing by group (mean +SD) ........................121

4-4 Post hoc analysis of UFOV composite scores by groups. ............. ....................12

4-5 Mean standardized scores and standard deviations on four UFOV subtasks by
intervention groups for pre and posttest ................. ...............122..............

4-6 Mean standardized scores and standard deviations for processing speed subtask by
intervention groups for pre and posttest ................. ...............122..............

4-7 Mean standardized scores and standard deviations for divided attention subtask by
intervention groups for pre and posttest ................. ...............123..............

4-8 Mean standardized scores and standard deviations for selective attention subtask by
intervention groups for pre and posttest ................. ...............123..............

4-9 Mean standardized scores and standard deviations for the same different trial subtask
by intervention groups for pre and posttest ................. ...............123........... ..

4-10 Post hoc analysis for selective attention subtask by group. .............. ...................12

4-11 Post hoc analysis s for same different trial scores by group ................. ............ .........124

4-12 Mean standardized scores and standard deviations for the overall driving simulator
score by intervention groups for pre and posttest. .............. ...............125....

4-13 Mean standardized scores and standard deviations on four Simulator block by
intervention groups for pre and posttest. ....._.__._ ... ......._. ....__ ..........12

4-14 Mean values and standard deviation for lane maintenance by intervention groups for
pre and posttest. ............. ...............125....











3-17 Example of a dog from block 4 it is placed on a medium eccentricity but with
much clutter and distraction. ........._.._.. ...._... ...............95...

3-18 Example of a rural road scenario used as a filler scenario............... ...............95

3-19 Example of a city scenario used as a filler. ...._. ......_._._ .......__. .........9

3-20 Diagram with illustration on how simulator generated data was collected. ......................97











4-15 Mean standardized scores and standard deviations on lane maintenance on four
Simulator block by intervention groups for pre and posttest ................. ............... ....126

4-16 Mean standardized scores and standard deviation on accuracy by intervention group
for pre and posttest ................. ...............126...............

4-17 Mean standardized scores and standard deviations of Flow scores for the 6 training
sessions, by intervention group ................. ...............127...............

4-18 Post hoc analysis for flow engagement for Medal of Honor ................. ............... .....128

4-19 Post hoc analysis for flow engagement for UFOV ................. ................ ......... .129

4-20 Post hoc analysis for flow engagement for Tetris............... ...............130

4-21 Video game ownership and usage ................. ...............131........... ..

4-22 Video game features .............. ...............132....









hours) due to sensorimotor or cognitive impairments, many continue to drive as long as possible

and do not change their preferred mode of travel (Ball, Owsley, Stalvey, Roenker, Sloane &

Graves, 1998). This behavior puts older drivers at increased risk for crashes. Older drivers are at

a greater risk for crashes and traffic convictions, per capital, than any other adult age group

(Owsley, Ball, Sloane, Roenker & Bruni, 1991; Edward Roybal Center for Research, 2001).

Older drivers with visual and cognitive declines are at a greater risk for crash involvement than

those who do not have similar deficits (Owsley et al., 1991).

Among all the possible cognitive skills related to driving, visual attention has consistently

been found to be one of the most associated factors. It plays a maj or role in driving performance

(Owsley, et al., 1991; Ball, Owsley, Sloane, Roenker, & Bruni, 1993; Richardson & Marottoli,

2003). Visual attention is commonly defined as the focal area from which information can be

acquired within one eye fixation or "glance" (Ball, Beard, Roenker, Miller & Griggs, 1988).

Richardson et al., (2003) found that visual attention is the cognitive skill related to driving

behaviors that is most problematic for older individuals. In their study, driving behavior was

assessed through a road test that included 36 items (e.g., acceleration, braking, lane change,

speed regulation, response to traffic signals). Visual attention was the cognitive skill most

associated with driving behaviors. Out of 36 maneuvers, 25 required the use of visual attention

skills (e.g., yielding right of way, responding to other vehicles or pedestrians, making turns at an

intersection). In a similar study, Ball and Owsley (1991) investigated the types of crashes in

which older adults are most likely to be involved. They found that maj or crashes among older

individuals are more likely to occur at intersections, where the visual attention requirements

(detection, localization and identification of targets) are more likely to be higher.












Garand Clips, and some Grenades.
Save the game
Grab the goods
Head back to the main path, where the fork was. This time go right.



* You'll meet up with two enemies hidden behind a ridge and one who pops up from
the ground.
* Shoot at these enemies.



soldiers, both out in the open initially and hiding in the ground, waiting for you to come towards
them before surprising you>
* Take out all enemies.
* Go up the ridge on the right side of the clearing to find a Medical Kit.



* Proceed forward down the ridge and continue forward to the path in front of you.



* From there, you'll want to continue down the linear path beyond this opening, killing
any more enemies you come across.



< You 'll run into more enemies as you go, but eventually, you 'll come to another small river.
Across the river and to your right, you 'll find Japanese soldiers shooting on some rebel soldiers
and the like fr~om across the river>


* Join in on this battle, eliminating all Japanese soldiers on the left side of the river.



* Lob some Grenades or fire away at the rest of the soldiers in the area across the river
as you go right and run into Martin Clemens, the Allied officer who is with the local
rebel force.









of exploring home-based training is that this better simulates "home based exercise", which has

been shown to be effective (in the physical domain) in boosting the health of older adults. This

is the ultimate goal of this line of research: to incorporate training in the everyday activities of

older adults, and thereby naturally increase the dosage of training incorporated into elders'

everyday lives.

Future research should also explore broader outcome measures. Green and Bavelier, in

their work with younger adults, have been exploring broader outcome measures like enumeration

tasks and multiple obj ect tracking. These measures cannot be automatically used with older

adults (they are quite difficult), and would need to be recalibrated for older users. However, with

more specific tests for critical subcomponents of visual attention, the magnitude and breadth of

videogame training could be better determined. It has been argued (Bavelier, personal

communication, April 2007) that the UAB UFOV test (Ball, et. al., 1988) is a more global test

and might not be able to capture the subtleties of attention changes produced by action

videogame training.

Another area that needs to be investigated in future research concerns driving outcome

measures. There are generally three outcome measures used in driving studies: driving simulator,

behind the wheel assessment and archival crash records. A driving simulator program was

selected for the current study because it constituted a safe way to collect driving data in an

immediate time frame, while controlling visual attention demands in a systematic way. Behind-

the-wheel assessments would have introduced potential safety concerns and difficulty controlling

visual attention demands. Archival crash data would have required a much longer follow up

period than the current study allowed, and who have been plagued by the potential reporting

biases inherent in regulatory driving data. Although simulators seem like an ideal outcome










due to increasing match between the challenges of the game (MOH, Tetris) and participants'

skill level. In addition, the game itself is entertaining and enjoyable. On the other hand, UFOV

is less entertaining and enj oyable, and it also provides participants with less explicit feedback

about their performance improvements. Thus, we did not expect Flow improvements in UFOV

training.

Aim 4 To explore participants' opinion about game design This is a consumer-oriented

aim and this data was intended to provide information that may assist in future improvements of

game design, so that games are more responsive to the needs of older adults.

Participants

Inclusion and Exclusion Criteria

Inclusion criteria for this study included: a) 65 years or older, b) no previous experience

with video games, because previous experience with video games can mask the real effects of

training, c) be a current driver, as driving performance will be tested on a simulator, the

participant must be a current driver d) MMSE of 24 or higher, e) be willing to participate in 6

training sessions, f) visual acuity of 20/70, visual acuity tests were used because eye sensory

function plays an important role in UFOV test performance. In addition, training effects could be

compromised by a lack of visual acuity. Participants with scores of 200 or lower in the UFOV

(accumulated scores) were excluded from the study because they would not have room for

improvement.

Recruitment

Participants were recruited by phone calls, by mail and through flyers that were distributed

in the community. Participants from 3 recruitment pools were contacted. Individuals from the

Rehabilitation Engineering Research Center (RERC) and the National Older Driver Research

and Training Center (NODRTC) were contacted by telephone, and individuals from the Institute









substantial clutter surrounding it and distractions such as other cars driving next to it. There

were a total of 5 dogs in this Block.

Although the pictures can give a good idea of the position of the dogs on the screen, it does

not give a good representation of the dynamics of the simulation run, showing the clutter and

di stracti ons.

The position of each dog (with distance from the center of the road) and the sequence that

they appear on the screen is shown in table 3-2.

"Filler scenarios" -The entire simulation run took about 20 minutes to complete.

Between the onset of each dog there were some "filler" scenarios in which participants had to

keep driving (see figures 3-18 and 3-19 for example of the "filler scenarios").

How data were collected. In this study, data were collected in two ways: 1) by simulator-

generated data and, 2) behavioral data. In the case of the simulator-generated data, although the

participants would drive for approximately 20 minutes, the simulator was programmed to collect

data only where the dogs were located. Data started to be recorded at 400 feet before the dog

could be seen and stop being collected as soon as the participant passed the dog. Between the

onset of each dog, the participant drove into a "filler scenario" (as described above). As soon as

the participant depressed the brake pedal, this was recorded by the simulator as a marking point.

If participant did not see the dog and continued driving, there would be no marking point in that

block, and the block would be recorded as an inaccurate/error trial. The simulator generated three

types of data: brake reaction distance, lane maintenance, and accuracy.

(1) Brake reaction distance was measured as the distance from when the dog first

appeared on the screen (whether the participant could see it or not) to the first instant when the

participant first depressed the brake. The potential scores for brake reaction distance ranged from









Different scenarios The participants started driving in an "acclimation" scenario (Figure

3-9). This scenario was simply a roadway without distractions (e.g., roadside obj ects) and it was

created to get participants familiar with the simulation projection, the steering wheels, brakes and

accelerator. During this scenario, the participants were asked to depress the brakes until they

were comfortable doing so. Throughout the scenarios there were speed limit signs that the

participants had to obey. Participants were asked to control their speed by looking at the

speedometer located in the bottom of the screen, and depressing the accelerometer as required.

Following the acclimation scenario, the participants drove through a rural scenario. In this

village, there was some oncoming traffic and some houses but there was not much clutter (Figure

3-10). The entire scenario had straight roads; the participants did not have to turn right or left.

The task continued with participants driving through a small town. In this town there was

more clutter and distraction as can be seen in figure 3-11.

Then the participants drove through a beach town (Figure 3-12) and then a metropolis,

depicted in the subsequent figure (Figure 3-13).

Position of the dogs through the scenes- There were a total of 18 dogs located throughout

the scenarios. The placement of dogs throughout the scenarios was constructed to approximate

the 4 different UFOV subtasks (processing speed, divided attention, selective attention and

selective attention with a same-different judgment). In this simulator task, the dogs were

positioned from 0 to 1 10 degree eccentricities either on the right or left side of the road. Some

dogs were very easy to find because they were placed either on the center of the road or at a low

eccentricity and there was not much clutter next to them. Other dogs were placed in locations

that were more difficult to find because there was more clutter and distractions on the road, such

as a bicyclist driving in front of the participant' s car.









I can not forget to mention all the students that helped me with my data collection:

Shannon, Jason, Eric, Brian, Matt, Sean, Emily, and Claudia. I hope they all had some fun

teaching older adults to play video games:

I also would like to thank all my Brazilian friends whom I had the opportunity to share my

experience living in a foreign country and that made this journey a little bit more like home. Last

but definitely not least, I want to show my appreciation to a person that I met almost toward the

end of this j ourney but who was able to make it more colorful and who held my hands and that

patiently always cheered me up in moments of despair. "Don't worry paixio, everything will

work out," I think you were right Billy Shields.





















UFOV



..........................g~6 Control
Tetris


MOH


1.50-



1.20-



0.90



0.60



0.30-


Pretest


TIMIE


Posttest


Figure 4-18. Change from pretest to posttest, by group, for the standard deviation of lane


maintenance for block.
Note: UFOV = Useful Field of View; MOH =


Medal of Honor









training. (Ball et al., 2002). In a subsequent long-term follow-up, conducted five years after the

initial training, there were still persist advantages for trained participants on their targets of

training, relative to untrained controls.

The five year net effect of reasoning training was found to be .26 SD; for memory it was

.23 SD, and for Useful Field of View (named "Speed of Processing" in study papers), it was .76

SD (Willis, et al., 2006). In addition, in the five year follow-up, participants in all three training

groups reported less perceived difficulty with IADLs relative to no-contact controls, a difference

which reached significance for the Reasoning group. In addition, in a subgroup of participants

who received extra "booster" training (i.e., yielding a total of eighteen 60-90 minute sessions

from Year 1 to Year 3 of the study), the extra training (compared to the basic ten-session

program) yielded significant improvements on several observed tasks of daily living, as rated by

blind raters. Specifically, persons who received booster Reasoning training experienced

significantly more gain on measures of Everyday Problem Solving (i.e., the ability to read and

understand medication labels, recipes, financial documents, etc.), and persons who received

booster Useful Field of View training experienced significantly more gain on measures of

Everyday Speed (e.g., ability to quickly and accurately read medication labels, find items in a

pantry, look up a phone number in the phone book).

Despite these initially promising results from the ACTIVE trial, built on three decades of

successful training research with older adults (LabouvieVief, & Gonda, 1976; Blieszner, Willis

& Balter, 1981; Scogin, & Bienias, 1988; Willis & Nesselroade, 1990; Verhaeghen, Marcoen &

Goossens, 1992), the more general finding in the literature, across the life span, is that transfer of

training to real-world skills is difficult to achieve (Sternberg & Wagner, 1986; Salomon &

Perkins, 1989). Even in ACTIVE, transfer effects where small and fairly ephemeral. A broad











MISSION 2


* easy play level
* silver bullet on
* unlimited a~nmnoon
* bullet shield on>



* Keep turning the turret and taking aim at the planes as they begin to swoop down from all
directions.
=Don 't bother aiming at the planes flying way high, as they are basically part of the background
and can 't be shot down>










CHAPTER 1
INTTRODUCTION

Driving

Driving is a very important activity for older adults in the United States. It is a means for

achieving independence and social connectedness in American society, especially for those

living in rural areas. In addition, it is linked to other activities of daily living. Isolation and

depression are commonly associated with driving cessation or reduction (Marotolli, Mendes de

Leon, Glass, Williams, Cooney Jr, Berkman, et al., 1997; Fonda, Wallace, & Herzog, 2001).

It is estimated that 89% of American seniors conduct their travel in personal vehicles

(Collia, Sharp, & Giesbrecht, 2003). The total trip miles traveled by people aged 65 and older

increased by 21 percent compared to total trip miles traveled by people aged 25-64 between the

years 1995 and 2001. The average trip miles per person also increased 13 percent for those 65

and older, but stayed almost the same for the younger group (Austin & Faigin, 2003).

With the increase in life expectancy, the number of older adults driving automobiles is also

increasing. It is estimated that by the year 2050 the elderly population will increase to 79 million

persons, more than double its present size (US Census). Older drivers represent 10 percent of the

total driving population in America and, the number of licensed and frail elderly is projected to

increase. In 2002, the elderly population accounted for 150,000 injuries associated with vehicle

crashes or 5% of all people injured in crashes (NHTSA, 2002). This suggests that older drivers'

safety should be considered a public health issue.

Driving is a very complex task that involves several factors, such as mobility, sensory

function, cognition, and the environment per se (e.g., road and vehicle design). Driving can be a

challenge for older individuals due to normally declining factors associated with aging. While

many older adults can modify their driving habits (e.g., drive less, drive only during restricted












* Proceed forward into the sewer.
* After the sewer, press the crouch button again, which will make you stand up again.



* Look right into sewer. Follow the right path when the sewer path splits.



* There is a staircase at the end of the path.
* Proceed up the staircase.



* You will see enemies coming in your direction. Shoot the enemies.
=Hit pause key>


* Look right and proceed forward along the wall to the right. Follow this wall until
some bleachers are reached.



Go up the stadium stairs.



Once on the top of the stairs, look left and you will see a booth with a radio operator.
Enter the booth and shoot the radio operator.



Turn around and exit the radio post.
Look right and proceed forward down the stairs.
Hit pause key>


You will see a brown door at the other side of the stadium.
Once you get close to the door, it will open automatically. If the door does not open,
move around in the open area in front of the door.










MISSION 5


Default settings should be:
easy play level
silver bullet on
unlimited a~nmno on
bullet shield on>


* Go forward a bit until the path widens and follow the brown path ahead until you
meet up with a trio of Japanese soldiers.
* Shoot the enemies here.
=Hit pause key>


* Go forward from there, and the path will swing rightward.
* Here you will see a light brown tree with root coming up from the ground.
=Hit pause key>


* Move towards the left side of the tree. Look right and pick up two Garand clips
positioned around a campfire on the ground.



0 Go pass the campfire and approach a narrow tree trunk. Look left.
o Shoot at the Japanese soldiers surrounding the ridge in front of you.



* Once the path is clear, you can grab some M1 Garand Clips and a Medical Kit on
the ridge they were guarding.
=Hit pause key>


* Work your way rightward you will hear the pounding of a mounted machine gun
here.
* Follow your compass North and Eind the mounted machine gun shooting at you.
* Shoot at the enemy on the machine gun.
=Hit pause key>


* Mount the cleared off-mounted machine gun and use it to clear the Japanese soldiers
ahead.
































Figure 3-19. Example of a city scenario used as a filler.










equipment and software. A drawback of using this training is that equipment demands require

that the training be done in a laboratory or clinical setting, which can be expensive and

inaccessible for the general population. Given the importance of visual attention to driving

performance and given the lack of widely available instruments to train this skill, there is a need

to explore more inexpensive alternatives for training visual attention.

A possible option for training visual attention in older individuals is the use of video

games. Previous research with younger adults has shown positive effects of video games playing

in the training of visual attention in college students (Green & Bavelier, 2003). Although there is

evidence that video game playing enhances motor, perceptual, and cognitive abilities among the

elderly (Drew & Waters, 1986; Clark, Lanphear, & Riddick, 1987), the use of video game

playing to train visual attention among the older population has not been investigated.

Above-and-beyond the practical value of exploring video games as a tool for training

visual attention (i.e., video games use widely available and a more affordable technology), a

secondary reason that video games might be worthwhile exploring concerns enj oyability of or

engagement in the training experience. Video games have been designed, after all, to be "fun".

Thus, video games may provide a motivating and engaging tool to increase visual attention

in elders; such motivational benefits may enhance compliance with and effort in training. Thus,

an important piece of information which needs to be collected in video game training studies

with older adults concerns their engagement and motivation to use this technology. Engagement

in activities often uses "Flow" theory, as described by Csikszentmihalyi (1975), and forms a core

concept in the current study. This is described in further detail in the next chapter.

The use of training strategies to improve cognitive skills in old age and therefore, improve

performance of instrumental activities of daily living (e.g., driving) can be put into perspective










Change score analyses of Selective Attention and Same-Different subtasks

To further explore the significant Occasion by Group interaction for the Selective

Attention subtask and the Same-Different subtask, a follow-up analysis was conducted using

change scores. Specifically, for both subtasks, change scores were computed by subtracting the

pretest score from the posttest scores for each subtask. Negative changes would represent

improvement in performance (faster times). Additionally, Bonferroni corrected follow up t-tests

were used to examine whether there were significant group differences in pre-post change scores

over the two occasions. As noted above for the composite score, we also examined this analysis

on residual change scores (where pretest scores on the dependent measure served as the

covariate, and posttest was the dependent variable) and this did not alter the pattern of findings;

details are omitted here in the service of conciseness.

Selective attention subtask using change scores For the selective attention subtask, a

significant main effect of Group was found, F (3, 54) = 4.4, p = .007, 12 = .20. A post hoc t-test

analysis using Bonferroni correction (Table 4-10) revealed that the UFOV-trained group

improved significantly more (p < .05) than the control group. In addition, the Tetris group

improved significantly more (p < .05) than the control group as well. The UFOV group did not

improve significantly more than either the Tetris or MOH groups, and the two video game

groups did not differ significantly from one another. Figure 4-7 displays the trends in the data.

Same-different change scores For the Same Different subtask, a significant main

effect of Group was also found, F (3, 54) = 7.2 p < .001, r12 = .28. A post hoc analysis using

Bonferroni correction revealed that the UFOV group improved significantly more (p < .05) than

all other groups, and that this was the only significant set of comparisons in these data. Unlike

the Selective Attention task, there was no evidence that either of the video game groups










Table 3-2. Sequence of the position of each dog on the scenario
Distance Condition Left side Center Right side
0 Center of road

65 Low to medium eccentricity, little
clutter X
65 Low to medium eccentricity, little
clutter X
95 High eccentricity, a lot of clutter
110 igheccntriity a ot o clttX

110 High eccentricity, a lot of clutter



800 High eccentricity, a lot of clutter

10 Center of road


50 Low to medium eccentricity, little
clutter X
100 Ho t igh eccentricity, a lot of clte

90 Cen terof roadcetrct, ot

20 Low to meium eccentricity, little
clutter Xn itato
40 Low to meium eccentricity, little
clutter Xn itato
40 Low to heigh eccentricity,a lot of
clutter an itato
90 Low to high eccentricity, a lot of X
clutter and distraction










ex-prisoner facing discrimination and not able to engage in any social relationship). Furthermore,

intervention in one domain has the potential to modify one or more domains. For example, the

use of an assistive device can facilitate execution of a task and in turn facilitate social interaction.

An individual with low vision has a telephone with large keys, being able to perform the task of

dialing activity and making phone call to friends engaging in social interactions -

participation.

ICF classification is used to obtain information of a person functioning instead of a

classification of people with disability. From this perspective, ICF can be used for everybody,

independent of one' s level of health. ICF does not describe disability as a consequence of health

condition; it also involves legislation, attitudes, physical, social environments. The

environmental factor in this model is a key aspect for understanding functioning and disability

because disability must be seen in the societal context (Dahl, 2002).

ICF has an important role in rehabilitation. In contrast to medical interventions that focus

on the disease process, rehabilitation has a broader understanding of the individual, which view

function and health as also associated with personal and environmental factors (Stucki, Ewert &

Cieza, 2002). In this study, the use of cognitive strategies to promote cognitive skills is related to

personal factors, and is associated with each level of the ICF model, as will be described below.

Application of Leisure / Recreation Based Approaches to Maintaining / Enhancing
Cognition and Complex Activity Performance Application at Each Level in the ICF

A set of studies on participation in leisure activities in later life found a relationship

between leisure activities and cognition in later life. A stimulating environment provided by

participation in leisure activities has the potential to improve cognitive capacity and would

reflect in each level in the ICF. From this perspective, participation in leisure activities will be

classified as a lifestyle. Lifestyle seems to represent a number of different constructs, among










Greenfield, P. M., DeWinstanley, P., & Kilpatrick, H., & Kaye, D. (1994). Action video games
and information education: effects on strategies for dividing visual attention. Journal of
Applied Developmental Psychology, 15, 105-123.

Griffith, J. L., Voloschin, P., Gibb, G. D., & Bailey, J, R. (1983). Differences in eye-hand motor
coordination of video-game users and non-users. Perceptuala~nd2otor .\d1/\, 57, 155-
158.

Hoffman, L., McDowd, J., & Atchley, P., & Dubinsky, R. (2005). The role of visual attention in
predicting driving impairment in older adults. Psychology and Aging. 20(4), 610-622.

Hultsch, D., Hertzog, C., Dixon, R., & Small, B. (1998). Memory change in the aged. New York:
Cambridge University Press.

Hultsch, D. F., Hertzog, C., Small, B. J, & Dixon, R. A. (1999). Use it or lose it: Engaged
lifestyle as a buffer of cognitive decline in aging? Psychology and Aging. 14(2), 245-263.

Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure
optimal experience: The flow state scale. Journal of Sport & Exercise Psychology, 18, 17-
35.

Jones, S., Nyberg, L., Sandblom, J., Stigsdotter Neely, A., Ingvar, M., Magnus Petersson, K, et
al. (2006). Cognitive and neural plasticity in aging: General and task-specific limitations.
Neuroscience and Biobehavioral Reviews, 30, 864-871.

Kawashima, R., Okita, K., Yamazaki, R., Tajima, N., Yoshida, H., Taira, M., et al. (2005).
Reading aloud and arithmetic calculation improve frontal function of people with
dementia. The Journal of Gerontology, 60A, 380-384.

Kelly, J. R., Steinkamp, M. W., & Kelly, J.R. (1987). Later-life satisfaction: Does leisure
contribute? Leisure Sciences, 9, 189-200.

Koepp, M. J., Gunn, R. N., Lawrence, A. D., Cunningham, V. J., Dagher, A., Jones, T., et al
(1998). Evidence of striatal dopamine release during a video game. Nature, 393, 266-268.

Kramer, A. F., Bherer, L., Colcombe, S. J., Dong, W., & Greenough, W. T., (2004).
Environmental influences on cognitive and brain plasticity during aging. Journal of
Gerontology, 59A(9), 940-957.

LabouvieVief, G., & Gonda, J. N. (1976). Cognitive strategy training and intellectual
performance in the elderly. Journal ofGerontology, 31, 327-332.

Lee, J. H., Ku, J., Cho, W., Hahn, W. Y., Kim, I. Y., Lee, S. M., et al. (2003). A virtual reality
system for the assessment and rehabilitation of the activities of daily living. Cyber
Psychology and Behavior, 6(4), 383-388.

Lee, H. C., Cameron, D., & Lee, A. H. (2003). Assessing the driving performance of older adult
drivers: on road versus simulated driving. Accident Analysis and Prevention, 35, 797-803.










game in the screen. The trainers played the game for about 10 minutes for the participants to

watch, in order to model game play and the requisite skills. Next, the tutor demonstrated the

controller to the participants. It was the same controller used to play Medal of Honor. However,

in this game, fewer buttons were necessary. The buttons were introduced one at a time and in the

beginning the participants were told not to worry about creating a horizontal line, but rather that

they should attempt to get comfortable with each button. In this condition, there were no step by-

step instructions on a computer screen, because there is no story line in this game. Unlike MOH,

the game scenario did not change over the course of the following sessions. The participants had

to repeat the same task over and over again.

As game play improved, the speed of tetramino dropping changed. In the earlier stages of

game play, the pieces would drop very slowly. Once the participants mastered that level of game

play, which was assessed by the game, the game would automatically move them to the next

level. The task in each level was exactly the same, except that the pieces would fall faster and the

participants had less time to decide where they would place each piece. Unlike the Medal of

Honor game, less active visual scanning required in this game. (It should be noted, however, that

participants needed to note the shape of each new tetramino at the top of the screen, while

simultaneously noting the optimal position for dropping the blocks at the bottom of the screen.

Thus, even in Tetris some continuous visual scanning was required, although Tetris obj ects were

usually presented more in the central and less peripheral fixation region).

Measures

Table 3-1, presents information on each measure used in this test. A detailed description of

each of these measures follows below.











* Your partner will explode the gun, and when that happens, you're able to run out of
the door to the left of the now-destroyed gun, and back outside.



* Once outside, run leftward towards the bridge, killing the Japanese soldiers as you go.



* Cross the bridge to find some of your rebel friends and the rebel Allied officer as
well.
* En route, however, your colleague will trip while he is planting some explosives on
the bridge



* Stop and grab him by facing him and pressing action to complete the second of two
optional objectives on this mission.
* Get over the bridge thereafter.
* After a set amount of time, the bridge will explode.
MISSION COMPLETED!!i










Table 4-22. Video game features
What do you think about these features?
N (%)
Screen (n=26)
Good 26 (100)

Controller
Good 17 (62.9)
Make it easier to use 7 (25.9)
Other 3 (11.1)
How would you change these features?
N (%)
Screening (n = 17)
Nothing 15 (55.5)
Others 2 (7.4)

Controller (n = 20)
Nothing 11 (55.0)
Make it easier 7 (35.0)
Others 2 (7.4)
How do you think the video game can help
you? (n=20)
N(%)
Mental exercise 9 (33.3)
I do not think it can help 5 (25.0)
Eye hand coordination 4 (20.0)
Others 2 (10.0)





















360.00


330.00


300.00

Control
270.00 -..
----- ..Tetris

~J240.00-UO


210.00- O


180.00-


150.00-


120.00

1 2
Pretest TIME Posttest



Figure 4-12. Change between baseline and posttest performance for block 3.
Note: UFOV Useful Field of View; MOH Medal of Honor




































p. value
<0.01
1
1
<0.01
<0.01
<0.01
1
<0.01
1
1
<0.01
1


group
UFOV
Tetri s
Control
MOH
Tetri s
Control
MOH
UJFOV
Control
MOH
UJFOV
Tetri s


UFOV



Tetris



Control



Note: MOH


Medal of Honor; UFOV = Useful Field of View.


Mean
Difference Std. Error
125.03 34.52
-14.39 35.05
5.83 36.33
-125.03 34.52
-139.41 33.90
-119.20 35.22
14.39 35.05
139.41 33.90
20.22 35.74
-5.83 36.33
119.20 35.22
-20.22 35.74


t d~f
3.6 28
-0.4 27
0.1 25
-3.6 28
-4.1 29
3.3 27
0.4 27
4.1 29
0.5 26
-0.1 25
3.3 27
-0.5 26


Intervention Intervention


group
MOH


Table 4-10. Post hoc analysis for selective attention subtask by group.


Intervention
group
MOH



UFOV



Tetris



Control



Note: MOH


Mean
Difference Std. Error t
37.38 27.78
18.60 28.21
-62.15 29.24
-37.38 27.78
-18.78 27.28
-99.53 28.34
-18.60 28.21
18.78 27.28
-80.75 28.76
62.15 29.24
99.53 28.34
80.75 28.76
UJFOV = Useful Field of View.


Intervention
group
UFOV
Tetri s
Control
MOH
Tetri s
Control
MOH
UFOV
Control
MOH
UFOV
Tetri s
Medal of Honor;


lr


value
1
1
0.23
1
1
0.01
1
1
0.04
0.23
0.01
0.04


afd


p.
28
27
25
28
29
27
27
29
26
25
27
26


1.3
0.6
-2.1
-1.3
-0.6
-3.5
-0.6
-0.6
-2.8
2.1
3.5
2.8


Table 4-11. Post hoc analysis for same different trial scores by group.









them is participation in leisure activities (Scarmeas & Stern, 2003). Richards, Hardy and

Wadsworth (2003), studied the effect of participation in leisure activity in a sample of 5362

individuals over a 43-year period, and found a positive association between participation in

leisure activities through the lifespan and cognitive functioning. An active lifestyle might

enhance functioning in later life and be an important approach for successful aging.

Rowe & Kahn (1997), conceptualize successful aging as a hierarchy that consists of three

tasks: 1. Decreasing the risk of disease and disease-related disability, 2. Increasing or

maintaining physical and mental functioning and 3. Being actively engaged with life.

Participation in mental activities in later life has a positive impact on one's life and can influence

each task in the model proposed by Rowe and Kahn.

Several studies found a positive relationship between participation in leisure activity and

cognitive functioning in later life. These studies give us a broad perspective on the importance of

participation in leisure activity for enhancing or maintaining cognition in later life. Cognitive

ability is not fixed, but environmental factors play a role in augmenting cognitive functions in

later life.

Participation in activities that are cognitively demanding is related to cognitive reserve in

later life. One of the studies related to this topic was conducted by Wilson, Barnes and Bennett

(2003). In this study, older individuals were asked to rate their frequency of participation in

common cognitive activities (e.g., reading, playing games like checkers, visiting a library) at five

points in time (age 6, 12, 18, 40 and currently). Results show that lifetime cognitive activity was

related to semantic memory, perceptual speed and viso-spatial abilities.

Scarmeas, Levy, Tang, Manly and Stern. (2001), proposed that leisure activities also

contribute to the cognitive reserve by preserving a set of skills or repertoires necessary for












Need to show how to aim and spray fire extinguisher>
Approach the fire in the immediate left and help him extinguish it
Don't get to close. Fire hurts!
Hit pause key>


* Proceed down the corridor to the first fire-engulfed door.
* Look left, use the Fire Extinguisher to take care of that fire.
=Hit pause key>


* Run into the room, taking out another fire as well.



* Turn around, go back, now, to the previous corridor.
=Hit pause key>


* Look left, continue forward and extinguish the fire in the corridor.



* Move forward, look left at second fire engulfed door, and take care of the Gire.
=Hit pause key>


=Need to show how to hand fire extinguisher>
~Move forward entering kitchen. Approach the chef who is trying to put the fire out
and hand him your Fire Extinguisher, which will allow him to put out the flames
=Hit pause key>



* Work your way forward and then right into the adj acent room.
* En route, as you're in the adj acent room, you'll see a save point (blue radiating light)
that you can use to save your progress if you desire.



* Turn around, move forward, look left (after the second pole) and you will see a door.
* Move towards the door and exit the cafeteria room.










making it 40 ms faster; e.g., going from 240 ms to 200 ms or going from 160 ms to 120 ms), and

returning the peripheral obj ect to the inner eccentricity. Again, the participant progressed at this

speed from inner to outer eccentricity before the task was made 40 ms faster. Training at this

level terminated when the participant achieved a score of 12-out-of-16 or better on at least two

blocks presented at 40 ms and outer eccentricity.

3. If the participant scored below criterion (40 ms) on the Divided Attention Subtask 2,

but above 80 ms on the Selective Attention Subtask 3, training began at this level. (Participants

who "graduated" from the Divided Attention training above also progressed next to this training

level). Participants moving here from Divided Attention training always started at 200 ms

presentation time. As with Divided Attention, participants moving directly to this level of

training had their initial presentation time set to their score in the baseline screening UFOV

score, within 40 ms in multiples of 40 ms. The block-by-block training procedure described

above (i.e., moving to the next level when there were two consecutive trials of 12-out-of-16

correct, and progressing at each speed level from inner-to-outer distracter eccentricity before

moving to the next speed, which was 40 ms faster) was employed until participants achieved

criterion accuracy at an 80 ms presentation speed at the outer eccentricity.

4. If the participant was below criterion (80 ms) on the Selective Attention Subtask 3,

participants automatically progressed to the hardest training level, Same Different Subtask 4.

(Participants who "graduated" from the Selective Attention training also progressed here, starting

at a 200 ms presentation time). As with the other training tracks, the initial level of training set

presentation time set to within 40 ms of baseline UFOV score on this task, in multiples of 40 ms.

Again, the adaptive training method described for Tracks 2 and 3 (after two consecutive trials of

12-out-of-16 correct, participants progressed from inner-to-outer distracter eccentricity before










LIST OF REFERENCES


Arthanat, S., Nochaj ski, S. M., & Stones, J. (2004). The international classification of
functioning disability and health and its application to cognitive disorders. Disability and
Rehabilitation, 26(4), 235-245.

Austin, R. A., & Faigin, B. M. (2003). Effect of vehicle and crash factors on older occupation
injury. In W. C. Mann (Eds). International Conference on Aging Disability and
Independence Proceedings. University of Florida.

Baltes, P. B., Dittmann-Kohli, F., & Kliegl, R. (1986). Reserve capacity of the elderly in aging-
sensitive test of fluid intelligence: Replication and extension, 1(2), 172-177.

Ball, K., Beard, B. L., Roenker, D. L., Miller, R. L, & Griggs, D. S. (1988). Age and visual
search: Expanding the useful field of view. Journal of Optical Society of America, 5(12),
2210-2219.

Ball, K., Berch, D. B., Helmers, K. F., Jobe, J. B., Leveck, M. D., Marsiske, M., et al. (2002).
Effects of cognitive training interventions with older adults. A randomized controlled trial.
Journal of the American M~edical Association, 288( 18), 227 1-228 1 .

Ball, K., & Owsley, C. (1991). Identifying correlates of accident involvement for the older
driver. Human Factors, 33, 583-595.

Ball, K., & Owsley, C. (2000). Increasing mobility and reducing accident of older drivers, in
Schaie KW, Pietrucha M (eds): Mobility and Transportation in the elderly. New York,
Springer: 213-251.

Ball, K., Owsley, C., Sloane, M. E., Roenker, D. L., & Bruni, J. R. (1993). Visual attention
problems as a predictor of vehicle crashes in older drivers. Investigative Ophthalmology &
Visual Science, 34(11), 3110-3120.

Ball, K., Owsley, C., Stalvey, B., Roenker, D. L., Sloane, M. E., & Graves, M. (1998). Driving
avoidance and functional impairment in older drivers. Accident Analysis and Prevention,
30(3), 313-322.

Ball, K., & Owsley, C. (2000). Increasing mobility and reducing accidents of older drivers. In:
Schaie K, Pietrucha M, eds. Mobility and transportation in the elderly. New York:
Springer, 213-250.

Ball, K., Roenker, D. L., & Bruni, J. R. (1990). Developmental changes in attention and visual
search through adulthood. In J. Enns (Ed.), Advances in Psychology, 69, 489-508.

Baltes, P., & Mayer, K. (1999). The Berlin aging study: Aging from 70 to 100. New York:
Cambridge University Press.



























O

UFO

I I



>1 20- 0




Pretest TIME Posttest


Figure 4-4. Change from pretest to posttest, by group, for the Divided Attention UFOV subtask.
Note: UFOV = Useful Field of View; MOH = Medal of Honor























136










weather) (Ball et al., 1991). In addition, accidents are rare events. Owsley et al. (1991),

compared self-report police accidents (accidents where the police were at the scene, as indicated

by the Driving Habit Questionnaire) with state record accidents (number of accidents on the state

record), and found that the number of state record accidents and self-report police accidents did

not match. Both types of reports were expected to be related because in Alabama (where the

study was conducted), police are required to submit written accident reports to the state every

time they go the scene of an accident.

Raedt and Ponj aert-Kristoffersen (2000) used two outcome measures to identify cognitive

factors and driving problems in older adults: accident frequency and road performance. While

cognitive tests accounted for 64 percent of the variance of the scores on the road performance,

cognitive tests accounted for only 19 percent of the variance of the scores on accident frequency.

On-road driving performance On-road driving performance, when used as an outcome

measure for driving research, has the great advantage of measuring one' s ability in a real-life

situation. Although on-road performance seems to be one of the best outcome measures to

measure driving performance, it has some drawbacks. First, there is no control over the

environment and the stimulus presented to the participants, which makes it difficult to generalize

conclusions. Second, when two or more evaluators take part in the study, inter-rater reliability

should be set (Roneker et al, 2003), otherwise data might be compromised. Third, participants

can make mistakes because of the pressure of being tested, and they might also not be familiar

with the car. On-road tests are usually very expensive.

Driving simulator An alternative to on-road measures is the driving simulator. In a

simulator, the environment can be totally controlled, the same scenarios can be used for every

participant and the availability of low cost simulators is also increasing. However, the use of a









was the within-persons independent variable, and Group (4: MOH, UFOV, Tetris, no-contact

Control) was the between-persons independent variable. The dependent measure for each of the

four UFOV subtasks was the average time, in milliseconds (range = 16-500 ms) for participants

to complete the subtask with 75% accuracy.

Speed of Processing Subtask

For the Speed UFOV Subtask, which assessed the speed with which participants could

judge whether they had seen a car or a truck, a significant main effect of Occasion was not found

F (1, 54) = .27, p = .104, r12= .04, also there was no significant main effect of Group F (3, 54) =

.75, p = .526, r12= .04. The Occasion by Group interaction was also not significant, F (3, 54) =

1.4, p = .238, 12 = .07. There was no improvement on this task, because participants were already

at ceiling. The mean values and standard deviation for the dependent measure by group are

presented in table 4-6. Figure 4-3 illustrates the change from pretest to posttest, by group, for the

Speed of processing UFOV subtask.

Divided Attention Subtask

For the Divided Attention UFOV Subtask, which assessed the speed with which

participants could judge whether they had seen a car or a truck while also looking for the

peripheral location of a second car, there was a significant effect of Occasion F (1, 54) = 5.9, p =

.018, rl2= .09 but there was no significant main effect of Group F (3, 54) = 1.7, p = .163, rl2= .09.

The Occasion by Group interaction was also not significant, F (3, 54) = 1.0, p = .379, 12 = .05.

Thus, although there was a significant Occasion main effect (i.e., on average, all participants got

better from the first to the second test), the absence of an interaction indicates that there were no

group differences in change on this measure. In this test, participants were, on average, already

close to ceiling. The mean values and standard deviation for the dependent measure by group are



















12.75-
MOH


12.5

UFOV

S12.25-
,D Tetris





C Control





11.5-1 c/

1 2
Pretest TIME Posttest


Figure 4-19. Change from pretest to posttest, by group for the accuracy (18-trial average).
Note: UFOV = Useful Field of View; MOH = Medal of Honor










on Aging (IOA) registry first received a letter by mail with study information and were asked to

contact study staff if they were interested in more information about the study. Out of 58

participants who completed the study, 7 participants were recruited from the RERC recruitment

pool, 36 were recruited by mail from the loA registry, 10 responded to a press release in the

Senior Times Magazine, 2 were referred by friends and 2 were recruited at a townhouse meeting

at a senior residential community in Gainesville. Participants received $10.00 for each visit. A

participant who attended baseline testing, six training sessions, and the post-training testing

received $80.00. Participants from the no-contact control group received a maximum of $20.00

(pre and posttest).

Sample Characteristics

A total of 70 participants completed the pretest battery for this study. All participants who

completed the pretest battery were then eligible to be randomized to a treatment condition. A

total of 12 (17%) participants withdrew from the study; 9 of these participants withdrew prior to

randomization because the training schedule could not accommodate their personal schedule.

Three participants (one of each training group) withdrew post-randomization, during the training

intervention. Two of those participants were employed and their schedules were in conflict with

the training schedule and one of them withdrew due to perceived boredom of the training task.

All participants from the no-contact control group appeared at both the pre- and post-test

assessments. A total of 58 participants completed the study. In general, participants were young-

old, college educated, they were equally distributed across gender and all participants were

Caucasian, except one Asian participant who withdraw before random assignment.

Testing Design and Procedure

Prior to in-person contact with the study, potential participants were first pre-screened via

telephone. In the phone interview, four inclusion criteria were assessed: a) 65 years or older, b)









visual attention skills they needed to bolster, they were "stuck" at an earlier stage...spending too

much time trying to figure out controllers and what steps to follow in playing the game. This

would suggest that future video game training with older adults would need to (a) spend much

more time on pre-training game orientation; (b) might need to select games with less complex

interface learning; and (c) might need to spend much more time on game practice to achieve

comparable effects to those seen with undergraduates. This latter point is underscored by the

work of Charness Schumann, & Boritz (1992), who has reported on other computer learning

tasks (e.g., word processing) that older adults need much more training time to achieve levels of

performance comparable to that of younger adults.

Larger training dosages may be needed for adults of all ages Above and beyond the

unique challenges of training novice older adults how to play games, Green and Bavelier have

themselves recently indicated that much higher training dosages (30 to 50 hours) may be needed

to achieve optimal visual attention gains (Green and Bavelier, 2006c). The dosage in this study,

nine hours, was based on the report of Green and Bavelier that ten hours of active play would be

sufficient (for younger adults).

Poor selection of visual attention outcomes A third possible explanation for the absence

of MOH training effects may be related to the fact that past studies on video games have reported

improvement in very specific aspects of visual selective attention (Green et al, 2003: & Green et

al, 2006c), and the UFOV subtasks selected for this study may have insufficiently tapped these

aspects of visual attention.

In more recent work, Green and Bavelier have begun to shift to other visual attention

outcomes. While in previous studies they demonstrated that video game players outperformed

non-players on different aspects of visual attention, they recently investigated the effects of video










Speed of processing and Divided attention subtasks, there was a significant group difference in

pre-post change for the Selective attention and Same-different subtasks. In the Selective attention

subtask, results revealed that both the UFOV and the Tetris training groups improved

significantly more than the no-contact controls. Improvement in the two videogame training

conditions was not significantly different between groups, and did not differ significantly from

that seen in the UFOV group. For the Same-Different subtask, the UFOV group improved

significantly more than all other groups, but no other group differences (e.g., in favor of

videogame training) were observed. Our next section below considers several reasons for this

pattern of findings.

Aim 2: Driving Simulator Outcomes

The second aim of this study was to investigate whether video game training might transfer

to simulated driving performance. Given previous research suggesting that UFOV training also

yielded improved driving simulator performance (Roenker et al., 2003), it was hypothesized that

the UFOV-trained participants might show more simulator improvement than all other groups.

In addition, if visual attention had been boosted by MOH training, it was hypothesized that the

MOH group might also show more improvement on driving simulator outcomes than the two

control groups (i.e., Tetris, no-contact). The results did not support this set of hypotheses. None

of the groups, including the UFOV-trained group, improved their scores disproportionately on

any of the three simulator outcomes. There was, however, a general practice-related

improvement on the simulator for all three groups. However, at least one simulator outcome

(brake reaction distance) was significantly correlated with UFOV performance, suggesting that

there was a correlational basis for expecting transfer from improved UFOV scores.









since the UFOV training had the largest effects, in the preceding section, on the more difficult

UFOV subtasks (Selective Attention, Same-Different), a question was whether transfer to

simulator-measured Brake Reaction distance might be greatest for dog trials in which the dog

was more peripherally located and/or in clutter. To further explore this question, the eighteen dog

trials were grouped into four blocks of trials: (a) Block 1: Dog was located in the center of the

road, (b) Block 2: Dog was located at the side of the road, but close to the road, with minimal

clutter (e.g., country road), (c) Block 3: Dog was located a substantial distance from the side of

the road (between 80-110 degrees eccentricity) with more clutter (e.g., the dog was located

between houses or trees), and (d) Block 4: Dog was located a certain distance from the side of

the road (between 25 -90 degrees eccentricity) but with extensive clutter and distraction (e.g.,

traffic, cyclists, pedestrians). Our question of interest was whether evidence of training transfer

(from UFOV or from one of the videogame training conditions) might be greatest on the more

difficult subgroups of simulator trials

A mixed between-within ANOVA was conducted, with two within-persons conditions

and one between-person condition. The first within-person condition was Block (4; centered

dog; dog with minimal eccentricity; dog with substantial eccentricity; dog with substantial

eccentricity and clutter/distraction). The second within-person condition was Occasion (2:

Pretest, Posttest). The between-person condition was Group (4: MOH, UFOV, Tetris, no-contact

Control). The dependent measure was the mean brake reaction distance in each of these

conditions. The mean values and standard deviation for the dependent measure by group are

presented in table 4-13.

Significant effects were revealed for Occasion (pre post), F (1, 51) = 5.7, p = .020, 12=

.10; Block, F (3, 153) = 337.5, p < .001, r12= .86; and Block X Group, F (9, 153) = 3.0, p = .005,











































Figure 3-5. Example of a PowerPoint slide with step-by-step instructions given to participants.


ii~o v I ,,,, Jnv




Move forward/bckf 'u Tueft/righl
Strate lefthght Look upidown

Figure 3-4. Example of the controller used by participants to play the game


General Gamleplay
Ca~ll










significant, gain trends in visual attention were in the right direction. Linear interpolation

suggests that with higher dosages, video game training may be more effective in boosting UFOV

performance. This serves the ultimate goal of this line of research: to investigate the

effectiveness of incorporating game-based mental exercise in the everyday activities of older

adults .










procedures of the first group, except they played a "placebo" game (i.e., reported by Green et al.

NOT to improve visual attention in college students)

Trainers were undergraduate student assistants who had extensive self-reported experience

with action video games. All trainers received instruction in the implementation of the study's

interventions, which were manualized. All undergrad students who worked in this proj ect were

trained by the doctoral student. Prior to start of intervention with participants, they spent time

playing the game using the manuals that were developed. Then, they practiced training with one

another and whenever possible with somebody not familiar with video games. It was important

to make sure that the training was standardized and all students used the same procedures with

all participants.

Medal of Honor Training

The two video game training conditions (MOH and Tetris) differed from UFOV in not

using a computer, but in using a commercially-available video game system instead. The study

employed a Sony PlayStation 2, console model 97060 and dual shock 2 analog controller, model

97026. The game was presented on a 19" TV monitor.

In this training, participants were asked to play the "first person shooter" video game

Medal of Honor Rising Sun, first with a tutor and then more independently. The game is made

up of multiple missions. In each session, participants were first asked to navigate through a

particular on-screen mission (for example, after one's ship is hit by enemy torpedoes while one is

two levels below, one must struggle to reach the ship's deck, extinguishing fires and assisting

crew members along the way; once atop, one must use an artillery gun to discourage combatants'

airplanes, which are dropping bombs on the ship). The missions were broken into step-by-step

maneuvers (Appendix A), and the tutor provided participants with complete assistance to

navigate the scene. When participants successfully made it through the mission, they were asked










improved more than the no-contact control group, and they did not differ from one another.

Mean trends are shown in table 4. 11, and illustrated in Figure 4-8.


Aim 2: Effects of Videogame Training on Driving Simulator Performance

Results in this section focused on our three simulator-based dependent measures: (1)

Brake reaction distance, (2) lane maintenance, and (3) obj ect detection accuracy (i.e., number of

trials in which a dog was correctly detected and responded to). The intent of the analyses in this

section was to examine whether videogame training might transfer to aspects of simulated

driving performance. This conformed with the ultimate goal of this study, which was to

investigate whether visual attention training might be transferred to a real life situation. Despite

the preceding analyses, which found little support for our experimental hypotheses (i.e., that the

Medal of Honor group would experience more visual attention gain than Tetris or no-contact

Controls; instead, the only evidence that video games affected visual attention was for the

placebo control condition, Tetris, which improved more than no-contact controls on the Selective

Attention subtask of the UFOV), this planned analysis was nevertheless examined. The goal of

this set of analyses was to investigate whether there were any preliminary indications of real-

world transfer, even for the traditional (and highly effective) UFOV training.

Driving Simulator Subtask 1: Break Reaction Distance

Analysis on the composite Brake Reaction Distance A mixed between-within ANOVA

was conducted on Brake Reaction Distance. As a reminder, a dog appeared on screen eighteen

(18) times throughout the course of the driving task. The dog always appeared on screen about

400 feet before the car was laterally adjacent to it. The 400 feet before the car was laterally

adj acent to the dog is referred to as a "trial". The position of the dog varied from center-of-road

to varying degrees of eccentricity, with right-or-left positioning counterbalanced.





















360.00-


330.00-


300.00-


270.00-


240.00-


210.00-


180.00-- ** MOH
.. ... ... ... --- --. Tetris

150.00-Control
UFOV


120.00
1 2

Pretest TIME Posttest



Figure 4-11. Change between baseline and posttest performance for block 2.
Note: UFOV = Useful Field of View; MOH = Medal of Honor









Conclusion

Overall, the results of the current study suggested that UFOV training improved visual

attention significantly more than any other group. However, it was noted that (although not

significantly) the two video game groups experienced more visual attention gains than the no-

contact control group. Actually, when the analysis was divided by UFOV subtasks, UFOV and

Tetris (but not MOH) experienced significantly more gain than the no contact control group.

Several factors might explain the reason why MOH gains were not replicated in this study such

as possible insufficient dosage of training and the selection of outcome measures.

The results of this study also indicated that the visual attention gains, even after traditional

UFOV training (which was highly effective), were not transferred to several simulator-based

driving outcome measures. This might suggest that the simulator outcome measures used in the

study were not sensitive enough to detect training-related changes. Future studies will need to

more strongly develop a rationale for the specific outcome measures selected.

However, in contrast to absence of training transfer to simulator based measures,

significant differences between training groups were observed in the session-to-session changes

participants' Flow experiences. That is, analyses of self-rated Flow suggested that engagement

improved over time for the two video game conditions but not for the UFOV training. This

finding is also related to our results regarding participants' opinions about game design, in which

participants affirmed their willingness to use this technology, and many perceived that the

technology was a viable approach for "mental exercise".

Although this study did not provide conclusive evidence regarding the beneficial effects of

video game interventions on older adults' visual attention performance, the preliminary data from

this study "set the stage" for future research by indicating that (a) older adults enjoyed the video

game training, and could be compliant with it, and (b) while video game training effects were not









Table 3-1. Measures used in the study.
Measure Pretest During Posttest
Baseline measures
Mini Mental Status X
Examination
(MMSE)
Snellen vision chart X
Hearing Handicap X
Inventory for Adults
(HHIES)
Geriatric Depression X
Scale (GDS)
Hopkin Verbal X
Learning Test
(HVLT)
Proximal outcome
Useful Field of View X X
Test (UFOV)
Primary outcomes
Driving simulator X X
(Brake reaction
distance, lane
maintenance, and
obstacle detection
accuracy)
Secondary outcomes
Flow X
Game design X
Note: MMSE = Mini Mental State Examination; HHIES = Hearing Handicap Inventory for
Adults; GDS = Geriatric Depression Scale; HVLT = Hopkins Verbal Learning Test; UFOV
Useful Field of View.









functioning. Clark et al., (1987) examined the possibility that the slowdown in performance

among the elderly could be reversed through the use of video games.

Recently researchers have been investigating the use of video game playing in the training

of laparoscopic surgeons. It has been suggested that video game play may improve laparoscopic

skills (Enochsson, Isaksson, Tour, Kjellin, Hedman, Wredmark, et al., 2004; Rosenberg,

Landsittel, & Averch, 2005; Stefanidis, Korndorffer, Dunne, Black, Sierra, Touchard, et al.,

2006). Because laparoscopic surgery uses very small incisions, adequate eye-hand coordination

is required and, studies suggest that video game players increase the efficiency of screening and

decrease the duration of examination. Video game playing has also practical implications in the

military. Cadets trained on video games had a higher flight performance than their untrained

peers (Gopher, Weil, & Bareket, 1994)

The effects of video game playing on visual attention has been the focus of more recent

studies. Although these studies were not conducted with older adults, they have promising

findings that might be further replicated in the older population. Studies found that video game

playing increases efficiency in dividing attention (Greenfield, DeWinstanley, Kilpatrick, &

Kaye, 1994). A similar measure to The Useful Field of View (UFOV) test was used to assess the

efficiency in which participants distributed their attention. Relative to non video game players

(NVGPs), video game players (VGPs) relied on similar types of visual processing strategies but

possessed faster stimulus-response mappings in visual attention tasks (Castel, Pratt, &

Drummond, 2005). Researchers have been also interested in the neurochemical consequences of

video game playing. Using a form of brain imaging (Positron Emission Tomography or PET)

researchers observed a large increase in the amount of dopamine released in the brain, in

particular areas thought to control reward and learning. Dopamine may be important in the










participants did not have much room for improvement. Indeed, several UFOV training studies in

Alabama, which had much larger UFOV training effects, selected participants with low

performance at baseline (Ball et al., 2002; Edwards, et al., 2005). This suggests that the results

of the current study may have shown somewhat weaker training effects than previous research

both due to lower dosages of training (see below) and more advantaged participants. In addition,

many participants reached ceiling performance on subtasks one and two (Speed, Divided

Attention) of the UFOV test.

A third limitation of this study was the number of training sessions in which the

participants were involved. Participant training was divided into 6 training sessions of 90

minutes each. Many times participants needed a break during the training, so that their

cumulative training exposure could well have been less than 9 hours. As discussed earlier, since

some of the training time was also spent learning the game (but not playing the game actively),

the dosage of game play was likely well below optimal, especially for the MOH group.

A fourth limitation of the current study, which is also resource based, concerns the pool of

trainers used in this study. With two exceptions, trainers in this study were volunteer

undergraduate students who had responded to a flyer requesting students to help with this study.

Although these students were committed to the study, they did not have any previous experience

in training older adults. In addition, because of their school schedule, there were occasions in

which a participant was trained by more than one trainer, which could have produced

inconsistency in the training offered. In general, a professional staff of trainers, who consistently

train participants, and who are monitored with regular quality control observations could serve to

improve the effectiveness of training.


















* Tumn around and proceed toward the back of the room.
=Hit pause key>


* Look left and climb the staircase to the top of the steeple.



* At the top of the staircase, follow a path around to the end of the platform.



* Look left at the bells.
* Press ACTION to ring the bells.



* Tumn around and go back down to the bottom of the stairs.
=Hit pause key>


* Look right and proceed forward through the archway and exit the church.



* Look right and proceed toward the truck.
=Hit pause key>


* Make your way towards the back of the truck.
* Face the back of the truck and press ACTION to board the truck.
=Hit pause key>


Look right and find the radiant blue light.
Move toward the light.
Press ACTION button in front of it to save game.
Hit pause key>


The truck will automatically start up.
Tumn around and shoot the soldiers.
Change to Thompson gun









MISSION 3


Default settings should be:
easy play level
silver bullet on
unlimited a~nmno on
bullet shield on>


* Run leftward to the end of the bridge. Follow your soldier companion, while avoiding
running into fire.
=Hit pause key>


* Meet up your brother at the end of the bridge, who is kneeling behind a barricade.

=Hit pause key>


=.hesw~l how to aim>
* Stay behind the barricade and start shooting as many soldiers as you can.



* Walk around and shoot the soldiers coming at you.
SUse your Thompson gun>



SChange to the Garand gun>
* Direct your attention to the porch-like platform on the left side, where you will find a
crouching Japanese soldier taking shots at you. Shot at him.



<.hesw I how to find surgeon pack, and medical kit>
Focus on finding the various goods in the area.










was a deviation from a published version, which included ten 60-90 minutes sessions (Ball et al.,

2000) but which was instituted due to resource constraints (the available time for the researcher

and volunteer trainers to complete the study) and participant schedules. The complexity of the

UFOV subtests is modified by holding the duration of the display constant and by gradually

increasing the complexity of the central task, the peripheral task or both. These modifications

allow individuals to practice the task at customized levels of difficulty until mastery is achieved.

Training sessions for UFOV have been described in previous research (Edwards, Wadley,

Myers, Roenker, Cissell, & Ball, 2002).

This training was adapted from the UFOV training guide from previous studies (Appendix

B). In the initial assessment, participants received a preliminary score on the Useful Field of

View (UFOV) test (see below for how these scores influenced training decisions). In general,

training was started at participants' current skill level, and after they had mastered that level, the

challenge increased, either by reducing the amount of time available to perform the task, or by

increasing the visual complexity of the display to be studied. Based on prior literature, this

training represented the "gold standard" for improving the visual attention of older adults.

It was selected to be used as the reference condition against which the efficacy of video

game training could be compared. The remainder of this section describes the training

procedure in greater detail.

In Session 1, training was customized to participants' baseline UFOV performance. As

noted below, the UFOV consisted of four subtasks. Due to lower bound timing limits and upper

bound time-out programming, possible scores ranged from 16 ms to 500 ms for each subtask.










Analyses on the UFOV Composite

Our initial analysis was conducted, following published work (e.g., Ball et al., 2002;

Willis et al., 2006) on the UFOV composite score. Thus, the dependent variable is the UFOV

composite score (Speed + Divided attention + Selective attention score + Same-Different). The

design was a mixed between-within design. The within-persons independent variable was

Occasion (2: Pretest, Posttest). The between-persons independent variable was Group (4:

MOH, UFOV, Tetris, no-contact Control). The critical effect of interest was the Occasion by

Group interaction, which would inform us about whether there were group differences in the

pattern of pre-post change. A significant main effect was revealed for Occasion (pre post), F (1,

54) = 34.07, p < .001, r12 = .38, suggesting that, across all groups, participants tended to improve

their performance. No significant main effect was found for Group, F (3, 54) = 1.4, p > .005, r12

.07, which suggests that there were no overall group differences, averaging across pre- and

posttest. In general, this reassures that randomization equated groups. With regard to the critical

Occasion by Group interaction, there was a significant interaction effect F (3, 54) = 5.8, p =.002,

12 = .24. Thus, there was a significant differences in change by intervention group. The mean

values and standard deviation for the dependent measure by group are presented in table 4-3.

Pre-post change in UFOV composite performance, and group differences in change, are

illustrated in Figure 4-1.

To further understand this interaction effect (i.e., which groups were changing at a

different rate from others), we computed UFOV change scores (post pre) for each participant.

These were computed by subtracting the composite pretest scores from the composite posttest

scores. A univariate ANOVA using change in the UFOV composite score as the dependent

variable (composite posttest score composite pretest score) was conducted. Treatment group

membership was the independent variable (MOH, UFOV, Tetris, No-contact control). Results









conclusion is that studies on the effects of transfer of skills from laboratory to real life situations

is scarce.

For older adults, the relative absence of training transfer may be due to the fact that many

studies generally exclude persons thought to have incipient dementia, so study participants are

not cognitively impaired in the domains of training. In addition it has been reported that IADL

performance does not start to decline until the 70's or 80's (Willis, 1996). Taken together, this

implies that many older adults may be at "ceiling" in their everyday performance, so measures of

everyday functioning may not have enough "room for improvement" to show training effects.

More generally, there is an absence of careful taxonomic research linking cognitive domains to

everyday functions (Marsiske & Margrett, 2006), and the psychometrics of everyday function

measurement often do not produce scores with substantial variance (Velozo, Magalhaes, Pan &

Leiter, 1995).

Few studies exist in the research literature that investigated the effect of the transfer of

cognitive training to everyday performance. The ACTIVE interventions seem to be a fairly

noteworthy late life exception (both within and outside of the ACTIVE study), with a

particularly positive pattern noted for Useful Field of View training. UFOV training was found

to improve performance of IADL. After participating in 10 1-hour training sessions, participants

performed more quickly and accurately on the Timed Instrumental Activities of Daily Living

(TIADL) tasks than the control group. The TIADL test emulates everyday tasks such as: looking

up phone numbers, counting change and, reading medication bottles (Edwards, Wadley, Myers,

Roenker, Cissel & Ball, 2002; Edwards, Wadley, Vance, Wood, Roenker & Ball, 2005). In

addition to improvement in TIADL scores, speed of processing training has also been found to

transfer to the Road sign test (Roenker, Cissell, Ball, Wadley & Edwards, 2003) and driving









training ("Flow"), Flow improved over time for the two video game conditions, but decreased for

UFOV training group. Finally, when querying participants' opinions about the video games they

had played (in the Tetris and Medal of Honor conditions only), results suggested that video

games have high acceptability for this population, and are perceived as potential "mental

exercise" tools by older respondents.










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field of view and other screening tests to on-road driving performance. Perceptual and
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Older population. (no DOT HS 809 611). Washington D.C.: National Highway and Safety
Admini strati on.

Newman, C. W., Weinstein, B. E., Jacobson, G. P., & Hug, G. A.(1991). Test-retest reliability of
the Hearing Handicap Inventory for Adults. Ear and Hearing. 12, 355-357.

Newson, R. S, & Kemps, E. B. (2005). General Lifestyle activities as a predictor of current
cognition and cognitive change in older adults: A cross-sectional and longitudinal
examination. Journal of Gerontology, 60B(3), P ll 3 -120.

Orosy-Fildes, C., & Allan, R. W. (1989). Psychology of computer use: XII. Video game play:
Human reaction time to visual stimuli. Perceptuala~nd2~otor .\kills\, 69, 243-247.

Owsley, C., Ball, K., Sloane, M. E., Roenker, D. L., & Bruni, J. R. (1991). Visual/cognitive
correlates of vehicles accidents in older drivers. Psychology and Aging, 6(3), 403-415.

Owsley, C., Sloane, M., McGwin, G., & Ball, K. (2002). Timed instrumental activities of daily
living tasks: Relationship to cognitive function and everyday performance assessments in
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Pham, T. M., Winblad, B., Granholm, A., Mohammed, A. H. (2002). Environmental influences
in brain neurotrophins in rats. Pharmacology, Biochemistry and Behavioral, 73, 167-175.

Plemons, J. K., Willis, S. L., & Baltes, P. B. (1978). Modifiability of fluid intelligence in aging:
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Richards, M., Hardy, R., & Wadsowrth, E. J. (2003). Does active leisure protect cognition?
Evidence from a National birth cohort. Social Science and2~edicine, 56, 785-792.

Richardson, E. D., & Marottoli, R. A. (2003). Visual attention and driving behavior among
community-living older persons. Journal of Gerontology, 58(9), 832-836.

Roenker, D. L., Cissell, G. M., Ball, K. K., Wadley, V.G., & Edwards, J, D. (2003). Speed-of-
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Rosenberg, B. H., Landsittel, D., & Averch, T. D. (2005). Can video games be used to predict or
improve laparoscopic skills? Journal ofEndourology, 19(3), 372-376.

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Salomon, G., & Perkins, D. N. (1989). Rock roads to transfer: Rethinking mechanisms of a
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Medicine, 81(1), 64-78.

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Conanunication and Technology Journal, 33, 263-275.

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Useful field of view and other neurocognitive indicators of crash risk in older adults.
Journal of Clinical Psychology in M~edical Settings, 5(4), 425-440.

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flight. Human Factors, 36(3), 387-405.

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International Journal of2\~edicallnfornzatics, 60, 255-261.

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Nature, 423(29), 534-537.

Green, C. S., & Bavelier, D. (2004). The cognitive neuroscience of video games. "Digital Media:
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Green, C. S, & Bavelier, D. (2006a). Enumeration versus multiple object tracking: the case of
action video game players. Cognition, 101, 217-245.

Green, C. S., & Bavelier, D. (2006b). Training visual attention with video games: Not all games
are created equal. hz press.

Green, C. S., & Bavelier, D. (2006c). Effect of action video game on the spatial distribution of
visuo spatial attention. Journal ofExperintental Psychology: Human Perception and
Perfornzance, 32(6), 1465-1478.

Green, C. S., & Bavelier, D. (2007). Action-video-game experience alters the spatial resolution
of vision. Psychological Science, 18(1), 88-94.









revealed a significant main effect of group membership on UFOV change, F (3, 54) = 5.8, p =

.002, 12 = .24. Follow-up Bonferroni post-hoc t-tests were conducted to determine which groups

differed from one another significantly in their UFOV change from pretest to posttest. The

UFOV group improved significantly more than the Tetris group (p = .05) and the no-contact

Control group (p < .01), but the difference in change between MOH and UFOV training groups

did not reach significance (p = .08). Mean differences in change score, by group, are shown in

Table 4-4. While the mean trends in the data suggest that participants in either videogame group

improved more than the control group (see Figure 4-2), this difference did not reach significance.

We also examined this analysis on residual change scores pretestt is entered as a

covariate, and posttest score is the dependent variable; because of the tendency to have high test-

retest correlations between pretest and posttest, this will tend to substantially increase the

statistical power of analyses on change scores), but the pattern of results was similar, and is

therefore not expanded upon here in order to reduce redundancies.

Re-Examining Training Effects on UFOV by Each Subtask

The analyses on the composite score failed to Eind that the MOH intervention improved

UFOV scores significantly more than Tetris or the no-contact Control condition. We thus

decided, as a follow up analysis, to re-examine this main analysis using each of the four UFOV

subtasks as distinct dependent variables. Previous research has suggested that many older adults

are at ceiling on the UFOV Subtask 1 (Speed), and at floor on the UFOV Subtask 4 (Same-

different), suggesting these subtasks may not be very sensitive to change. (Edwards et. a., 2006)

Thus, we were particularly focused on UFOV Subtask 2 (Divided Attention) and Subtask 3

(Selective Attention). A mixed between-within ANOVA was conducted with two within-persons

variables and one between-persons variable. The first within-persons variable was UFOV Task

(4: Speed, Divided Attention, Selective Attention, Same-Different). The second within-persons










* There will be two more Japanese soldiers in an alcove ahead on the right side. You
should shoot them.



Move forward with the tank as it heads left, down the next street.
If you need health, grab the Field Surgeon Pack on your right as the tank turns left.



Suicide soldiers will run at the tank with explosives, shoot them.
=Hit pause key>


Continue up this street towards a decorative fountain on your right.
Shoot at any Japanese soldier as you go, including the suicide bombers.



Go forward, and let the tank turn right again.
=Hit pause key>


Follow the tank down. There won't be too many enemies as you go.
In case you need health, you should shift right into a building as you're going to grab
a Field Surgeon Pack to heal yourself if need be.
=Hit pause key>


=As the road turns left once more, the tank will stop>
There will be one Japanese soldier on your right. Shoot him from the street.
=Hit pause key>


There will be two Japanese soldiers on your left. Get closer to them and shoot.



Go back to the tank's position on the street as it now heads down yet another road.
=Hit pause key>


Continue following the tank until it stops at a forced-dead end made from rubble.











Analysis of Lane Maintenance by Trial Type As with the brake reaction distance a

concern was whether the lane maintenance might differ for particular trial types (i.e., if training

improved dog detection, and this meant that drivers could attend better to lane maintenance at

posttest, would this effect be particularly strong for some types of dog distracters than others?).

To further explore this question, the eighteen dog trials were grouped into four blocks of trials:

(a) Block 1: Dog was located in the center of the road, (b) Block 2: Dog was located at the side

of the road, but close to the road, with minimal clutter (e.g., country road), (c) Block 3: Dog was

located a substantial distance from the side of the road (between 80-1 10 degrees eccentricity)

with more clutter (e.g., the dog was located between houses or trees), and (d) Block 4: Dog was

located a certain distance from the side of the road (between 25 -90 degrees eccentricity) but

with extensive clutter and distraction (e.g., traffic, cyclists, pedestrians). Our question of interest

was whether evidence of training transfer (from UFOV or from one of the video game

conditions) might be greatest on the more difficult subgroup of simulator trials.

A mixed between-within ANOVA was conducted, with two-within-persons conditions and

one between-person condition. The first within-persons condition was Block (4: centered dog;

dog with minimal eccentricity; dog with substantial eccentricity; dog with substantial

eccentricity and clutter/distraction). The second within-person condition was Occasion (2:

Pretest Posttest). The between-person condition was Group (4: MOH, UFOV, Tetris, no-contact

Control). The dependent measure was the standard deviation of lane maintenance in each of the

condition.

Significant effects were revealed for Occasion (pre post) F (1, 51) = 6.7, p = .012, r12 =.

Bloc F (, 13) =3062, p< .01, 2 =.85 and Block x Group F (9, 153) = 306.2, p = .031, r2

.14. No significant interaction was found for Occasion x Group F (3, 51) = .52, p = .667, r12









Preliminary Analysis

Assessment of Baseline Differences Between Treatment Groups

An analysis of variance was conducted to determine whether there were differences

between the four intervention groups in demographic status, age, years of education, gender,

race, two cognitive measures (MMSE and HVLT), depression, and two sensory measures (visual

acuity and hearing). The omnibus test revealed that there were no significant differences between

the four groups, suggesting that randomization had distributed participant characteristics

similarly across the groups. Table 4-1 shows the characteristics of the sample on the variables

assessed, both for the total group and by intervention subgroup. P-values shown reflect the

absence of a significant overall difference between groups.

Attrition

Given the duration and time commitment required for the current study, and the fact that

twelve participants withdrew, it was important to determine whether dropout was selective.

(Selectivity of dropout would suggest that the remaining sample on which the results were based

was positively biased). To examine this, returning and non-returning participants were compared

in baseline characteristics; specifically, analysis of variance was conducted to examine whether

participants who dropped out of the study prior to its completion differed at pretest from the

participants that completed the study (e.g., completed training intervention and posttest) on

demographic and cognitive variables. As shown in Table 4-2, no significant differences emerged

between completers and non-completers of the study protocol, suggesting that dropout did not

bias the generalizability of the study findings. Also, only three participants who dropped out (one

from each intervention group MOH, UFOV, Tetris) knew to which training they were

randomized because they dropped out during the training. The remaining of the participants who



















Q I ~P~ : Tetris

-0Control
O
Q. 600 -'
E~ MOH
o

O'





UFOV

400-

1 2

Pretest TIME Posttest


Figure 4-1. Pre-post change in UFOV composite performance, by training group
Note: MOH = Medal of Honor; UFOV = Useful Field of View.










Another limitation of the study related to the invariant contrast of the presented dog (i.e.,

Dalmatian) throughout the scenarios. We did not assess contrast sensitivity at baseline, and so

could not determine whether individual differences in this visual-perceptual attribute were

responsible for individual differences in responding. Moreover, we did not vary the dog's

perceptual features throughout trials, and so we could not determine whether more-or-less

perceptible dogs would have affected responding.

In our assessment of flow and opinions about game design, we were unable (due to limited

cell sizes) to contrast males and females. It is reasonable to assume, however, that there may

have been gender differences in game acceptability and enjoyment. (For example, the military

style violence of MOH may have been more unfamiliar to female participants than males;

indeed, our participant comments about violence came from the women). Future research should

more systematically sample males and females in order to examine gender differences in game

response. It may be that different kinds of game design would be needed to optimally engage

men versus women.

Another limitation to be mentioned is that, even though the brake reaction distance was

correlated with the UFOV subtasks in this study (providing an empirical basis for expecting

UJFOV-related training gains to transfer), the selection of simulator outcomes in this study was

idiosyncratic, and based on available data from the simulator program. To facilitate

comparability with other research, it may make sense to develop a standardized set of simulator

scenarios and outcomes that can be employed across training studies, and that are selected (in

part) based on a known theoretical or empirical connection to the ability being trained. The

current simulator outcomes had not been used in any previous training study with older adults.









The third subtest is Selective Attention (Figure 2-3). This subtest is identical to subtest 2

except that the target displayed in the periphery (which is always a car) is embedded in a field of

47 triangles or distracters.

The fourth subtest is a modified selective attention subtest, named Same-Different, with

two icons in the center of the screen (inside the white box) (Figure 2-4). In this subtest, the

examinee is presented with two obj ects inside the white box in the center of the screen. The

examinees have to distinguish if the two obj ects are the same (two cars or two trucks) or

different (a car and a truck). There is also a target in the periphery in a clustered scene.

The UFOV measure has been widely used in clinical and rehabilitation settings and

normative UFOV data to adjust performance comparisons across demographically-similar elders

has been developed (Edwards et al, 2006) The UFOV has also been widely used in research

designed to examine driving performance with older adults and it is the primary outcome of the

present study but other outcomes have been investigated and will be described bellow.

Driving Performance Tests

Three other outcome measures are commonly used in studies on driving performance:

accident frequency, driving simulator, and road test. The use of each of these variables has

advantages and disadvantages.

Accident frequency This outcome can be measured either by self-report or state report.

State report of accident frequency has an advantage over self-report because all licensed drivers

have accident records in a standardized format. In addition, state reports include more detailed

information about the accident (e.g., road type, time of a day, location) (Owsley et al., 1991).

The disadvantages of these state reports are: underreporting of accidents are common and can

occur either because of the person or because of the state police. Vehicle accidents can also be

caused by factors that are not intrinsic to the driver (e.g., poor mechanical conditions of the car,




























Figure 2-1. Processing speed subtask


gure L-L. Dvivaea attention suotaSK









Geriatric Depression Scale The Geriatric Depression Scale (GDS) (Yesavage, Brink,

Rose, Lum, Huang, Adey, et al., 1983) is a 30-item self-report assessment designed to identify

depression in the elderly. Each item is constructed to be answered yes or no, to reduce cognitive

complexity of the items. The measure was designed to minimize the influence of somatic

symptoms, which may not be accurate reflections of depression in older adults. One commonly

used cutoff point for mild depression is 10.

HVLT The Hopkins Verbal Learning Test-Rei lsedi' (HVLT-R) (Shapiro, Benedict,

Schretlen, & Brandt, 1999) was used to test individual memory and verbal learning performance.

In this test individuals repeated a list of 12 words read by the instructor. The test consisted of

three learning trials, a delayed/recall trial (20-25 minutes delay) and yes/no delayed recognition

trial. This later trial consisted of a randomized list that included the 12 target words and 12 non

target words, six of which are drawn from the same semantic category as the targets. Raw scores

were derived for total recall, delayed recall, retention (% retained), and a Recognition

discrimination index. No inclusion/exclusion was based on this score, but it was collected as a

possible covariate and to characterize the sample.

Proximal Outcome Variable

A proximal outcome refers to the direct outcome being measured, and is sometimes

construed as the "mechanism" by which training effects are carried to real world outcomes. In

other words, in order for the training effect be transferred to the real world, the training must first

be proven effective on the basic skill or ability that is trained. The proximal outcome in this

study is visual attention, as assessed with the Useful Field of View test (UFOVm; Owsley, Ball,

Sloane, Roenker, & Bruni, 1991; Ball, Owsley, Sloane, Roenker, & Bruni, 1993; Myers, Ball,

Kalina, Roth, & Goode, 2000; Raedt., & Ponjaert-Kristoffersen, 2000; Edwards, Wadley, Myers,










current driver, c) no previous experience with video games and d) be willing to participate in 6

training sessions. If these requirements were met, participants were scheduled for a baseline

assessment.

Baseline Assessment

During the intake session, participants were informed about the research purposes and

procedures, and (if willing) completed an informed consent form. After consenting, the

participants took two more tests to determine their eligibility: 1) Mini Mental State Examination

(MMSE), in which participants were required to score 24 or higher; and 2) a visual acuity test, in

which participants were required to have a score of 20/70 or better. After administration of these

tests, eligible participants were administered the Useful Field of View (UFOV) test. If a

participant had a score of 200 or higher in this test, participants completed the remaining baseline

measures. These included a driving simulator test, a memory test and questionnaires regarding

(a) demographics, (b) depression; and (c) hearing impairment. The duration of the baseline

session was approximately 1.5 hours, and participants could take as many breaks as they needed.

After the baseline assessment was completed, participants were randomized, and scheduled for

their 6 training sessions. Participants from the no-contact control group returned three weeks

after baseline assessment for a posttest.

Random Assignment

After baseline assessment participants who were determined to have met the inclusion

criteria for the study were randomized into one of three gro s (Medal of Honor, UFOV, or

Tetris). Randomization occurred in "triplets" (i.e., after a group of three participants had met

eligibility, one participant was assigned to each of the three groups). After the conclusion of the

interventions for the three groups, and after interim data analyses had been conducted, a fourth

non-equivalent control group was recruited. This separate group of participants was recruited for











LIST OF FIGURES


Figure page

1-1 Conceptual Framework for the World Health Organization's International
Classification of Functioning, Disability and Health (ICF) ................. ............ .........27

2-1 Processing speed subtask ...........__......___ ...............50...

2-2 Divided attention subtask............... ...............50

2-3 Selective attention subtask............... ...............51

2-4 Same and different subtask ...........__......___ ...............51...

3-1 Diagram of study design presenting: Medal of Honor (MOH) group, Useful Field of
View (UFOV) group, Tetris group and the no contact control group. ............. ................86

3-2 Hypothesis 1............... ...............87...

3-3 Hypothesis 2............... ...............87...

3-4 Example of the controller used by participants to play the game ............_.. ..............88

3-5 Example of a PowerPoint slide with step-by-step instructions given to participants. .......88

3-6 PowerPoint slide with instructions on how to use the "jump" button. ............. ................89

3-7 Screen shot of TETRIS game .............. ...............89....

3-8 Example of a driving simulator used in the study............... ...............90.

3-9 Screen shot of the acclimation scenario. ....__. ...._.__... .......___.. ..........9

3-10 Example of the rural scenario. ............. ...............91.....

3-11 Example of a small town scenario .............. ...............92....

3-12 Example of a beach town scenario............... ...............92

3-13 Example of a metropolis scenario............... ...............93

3-14 Example of a dog from block 1 it is positioned in the center of the road.............._._... ...93

3-15 Example of a dog from block 2 it is positioned in a low eccentricity and without
clutter. ............. ...............94.....

3-16 Example of a dog from block 3 it is placed on high eccentricity with some clutter.......94




































O 2007 Patricia da Cunha Belchior









ACKNOWLEDGMENTS

First, I want to show my gratitude to my beloved grandparents. Without their support and

unconditional love through my life, I wouldn't be able to make this far. Any further attempt to

use words to thank them would be futile. They are and will forever be the inspiration for my

work. I also would like to thank my parents and step-parents for their patience, love and support.

I would like to apologize to my family for every time I left them waiting for me to come back

home but now I can proudly say I made it. I want to especially apologize to my brother and my

sisters for not being able to share their life with me.

I also want to convey my gratitude to my committee members. First and foremost, my

mentor and my advisor, William Mann, whom I first met during my first year of college in Brazil

more than 10 years ago, and whose work inspired me to come to the United States for my

graduate studies. Thanks for trusting me in this endeavor. Thanks for your patience in showing

me how to get to this point. And thanks for being there every step of the way. I also would like to

say a special thanks my co-chair Michael Marsiske, who I had the pleasure to meet in the last

two years of my studies and from whom I learned so much. The dedication he has to his students

will never be forgotten. I would not get this dissertation done without his help. I also would like

to thank Lorie Richards, Heather Gibson and Ann Horgas for being part of my committee.

Special thanks to Debora Anderson, the director of the International Center who gave me

tremendous help during my first years at the University of Florida. I also would like to thank all

the staff from the occupational therapy department and thanks to my friends that went through

this journey with me, Rick, Michael, Roxanna, Bhagwant, Dennis, Megan, Jessica, Arlene,

Leigh, Eric,Inga, Jia hwa and specially thanks Cristina, my first friend in this program and with

whom I could share my Latin American background.
































Figure 3-15. Example of a dog from block 2 it is positioned in a low eccentricity and without
clutter.


Figure 3-16. Example of a dog from block 3


it is placed on high eccentricity with some clutter.









based driving simulator in measuring on-road driving performance has been supported by the

literature (Lee, Cameron & Lee, 2003).

Lee and Lee (2005) used a driving simulator to determine which simulated driving tasks

can be used to identify older drivers at risk of traffic violations. The simulator was used to assess

10 driving tasks specifically for testing older adults. The driving tasks consisted of: rule

compliance, traffic sign compliance, driving speed, use of indicator, decision and judgment,

speed compliance, visual attention task, working memory, multi-tasks, and road use obligation.

The simulator computer automatically measured the first five tasks whereas a laboratory

technician collected the remaining data. Recently, researchers from the University of Florida

used a driving simulator to replicate actual road locations. In this study participants drove

through the same scenario on the road and in the replicated scenario in the simulator (Shechtman,

Classen, Stephens, Bendixen, Belchior, Sandhu, et al., 2007). In this study the simulator' s

controls were integrated with an actual vehicle which improved the driving experience.

As has been discussed, the driving simulator has been extensively used in research for

different tasks, however, standardized tests to measure the same constructs are still missing from

the literature. The goal of the present study is to measure visual attention in a simulator. Visual

attention will be measured by brake reaction distance but will use tasks not mentioned in

previous literature. The next chapter has more details about this measure. The next section will

discuss specific interventions that might have the chance to improved scores on either useful

field of view and driving simulator.

UFOV Training With Older Adults

UFOV was one of three abilities trained in the NIA-funded ACTIVE trial (see above). In

ACTIVE, with a total sample of 2,802 at six sites throughout the US, 25% of participants were

randomized to receive UFOV training (other groups received training in reasoning, memory, or










participant became more confident and effective in dog detection due to training, it was

hypothesized that there would be less "weaving" and the standard deviation should be decreased

post-training. Thus, the expectation was that lane maintenance might improve with visual

attention improvements.

(3) Accuracy score The accuracy data was related to the fact that participants depressed

the brakes at that time or not. The accuracy data ranged from 0 to 18. A participant would score

0 if they could not see any dog in that trial and they would score 18 if they saw all the dogs in the

trial. A composite score was calculated by computing a mean score of the 18 trials from each

participant either for brake reaction distance or for accuracy. To give a better representation of

how the simulator generated data was collected, figure 3-20 presents a diagram with detail

description of data collection.

The behavioral data was collected by the instructor. The instructor would make notes about

the participants' driving behavior throughout the run. There were situations in which bicyclists or

pedestrians were close to the road and the participants would brake for the bikes or pedestrian

instead of the dogs. To account for the possibility of errors (e.g., braking for pedestrians instead

of dogs during the trial), the instructor collected the behavioral data. Following each run, the

behavioral data was compared to the simulator-generated data for each block of dogs for each

participant. For example: if in a certain block the simulator recorded an accurate trial (meaning

that they depressed the brake), but according to the behavioral notes the participants stopped for

a bicycle instead of the dog in that particular trial, the data was corrected in the system.

Correlation Between UFOV and Simulator Subtests

A correlation analysis was performed to explore: 1) the correlation among different group

of dogs (center, low to medium eccentricity little clutter, high eccentricity much clutter, and low

to high eccentricity with much clutter and distraction); 2) the correlation among the UFOV










Table 4-20. Post hoc analysis for flow engagement for Tetris


Mean
Occasion Difference Std. Error


Occasion


p. value
14 0.76
14 0.83
14 0.33
14 0.11
14 0.02
14 0.76
14 0.89
14 0.48
14 0.08
14 0.04
14 0.83
14 0.89
14 0.37
14 0.07
14 0.02
14 0.33
14 0.48
14 0.37
14 0.63
14 0.25
14 0.11
14 0.08
14 0.07
14 0.63
14 0.25
14 0.02
14 0.04
14 0.02
14 0.25
14 0.25


-1.36
-0.86
-5.64
-7.93
-12.14
1.36
0.50
-4.29
-6.57
-10.79
0.86
-0.50
-4.79
-7.07
-11.29
5.64
4.29
4.79
-2.29
-6.50
7.93
6.57
7.07
2.29
-4.21
12.14
10.79
11.29
6.50
4.21


4.38
3.87
5.74
4.90
5.01
4.38
3.65
6.05
3.67
5.08
3.87
3.65
5.26
3.81
4.57
5.74
6.05
5.26
4.69
5.62
4.90
3.67
3.81
4.69
3.59
5.01
5.08
4.57
5.62
3.59










game playing on individual differences in the number of simultaneous visual obj ects that could

be readily tracked and attended to(Green et al., 2006a). Two outcome measures have had

particular salience: 1) an enumeration task, in which participants have to report the number of

briefly flashed items in a display as quickly and as accurately as possible and; 2) multiple obj ect

tracking, in which participants are required to allocate their attention to several (moving) items

over time. Video game players showed enhanced performance on both tasks. These results

suggest that future studies may consider broadening the set of outcome measures, which might

be more sensitive to attentional gains produced by video game play than the task used in the

current study.

Why did Tetris appear to have a stronger effect than MOH?

An unexpected finding in this study was that Tetris group participants experienced

significantly more Selective Attention UFOV gain than no-contract control participants. Several

possibilities exist to explain this effect. First, while Green and Bavelier (2003) showed that

Tetris was not a sufficient challenge to boost dynamic visual attention in young adults, it may be

that the whole-screen scanning and monitoring and mental rotation was a higher level of

challenge (and therefore had training effects) for older adults, who were at a lower skill level.

Expressed differently, during the Tetris game, participants had to look at pieces (tetraminoes)

falling from the top of the screen while paying attention on the bottom of the screen, where they

needed to place the pieces. While in studies with college students this stimulus was not enough

to provide visual learning, perhaps this was a more optimal level of challenge for novice older

adults. Second, because Tetris is a much easier game to learn and explain (the core game,

rotating tetraminoes in order to make a flat line, did not become more complex over the course of

training), participants may have "wasted" less time learning the game, and spent more of their









no contact). The content of training was identical to that used in this study. Participants in this

condition received 10 sessions (60-90 minutes each). Posttest assessments were conducted

immediately, and 1, 2, 3 and 5 years post-training. Immediately after training, on the UFOV test,

the pre-post improvement of UFOV-trained participants was 1.45 SD higher than that of

untraining controls (Ball et al). Moreover, five years later, the UFOV performance advantage of

UFOV-trained participants was still .75 SD greater than untrained controls (Willis et al).

Speed of processing training not only appears to improve processing speed, but also

transferred to certain everyday functions, as indicated by improved performance on Timed IADL

(Edwards et al, 2005). In ACTIVE, there was less evidence of transfer to real-world functions

from the basic 10-session training program. However, a subset of 50% of UFOV-trained

participants received up to eight additional booster training sessions (four 12-months after initial

training, and four 36-months after initial training). For boosted participants, Willis et al (2006)

found that, five years post-training, performance on observed tasks of everyday speed (e.g.,

speed and accuracy of reading medication labels, finding items in a pantry, looking up numbers

in a phone book) was significantly higher than that of participants who received only the basic

ten sessions. This seems to strongly argue for extended training and greater training dosages.

Roenker et al (2003) evaluated the effect of UFOV training and simulator training on

UFOV test performance and simulator driving performance. Participants were randomized into a

speed of processing training group, traditional driver training program performed in a driving

simulator, and a low risk control group. The UFOV test consisted of the same test described in

previous section and for simulator measure, participants reaction time was assessed. It was found

that speed of processing training, but not simulator training improved UFOV scores and had

fewer dangerous maneuvers during driving evaluation.





















,4Tetris


125- ,'





O '-


110-- *






1105


11111

1 2 3 4 5 6

Training Session


Figure 4-20. Six training session. Flow experience trend in the data, by intervention group
Note: UFOV = Useful Field of View; MOH = Medal of Honor


















End of trial for assessment
for accurate participant trial ~













FILLER DRIVING






Meas~ureme~nt begns here




Brake

DOG

En I of assess me t for inaccurate
participant trial


Me.'surs_ma;nt.1egins_h~;ere_


Brake


DOG

Ed of asses ment for inaccurate
participant trial


End of trial for assessment
for accurate participant trial


Figure 3-20. Diagram with illustration on how simulator generated data was collected.


















250.00 -


245.00-



S240.00-


E --z control


O. Tetris

E 230.00-
UFOV

MOH
225.00-

1 2
Pretest TIME Posttest


Figure 4-9. Change from pretest to posttest, by group, for the average Brake Reaction Distance
(18-trial average). Distance between the onset of an on-screen dog and the depression
of the brake pedal was recorded, in meters.
Note: MOH = Medal of Honor; UFOV = Useful Field of View.









from the same recruitment pool from the previous participants. This was necessary to assess the

simple effect of taking the pretest-posttest battery twice. Participants took the pre- and posttest

about three weeks apart, with no treatment/stimulation/contact between testing. Group

comparison analyses for Aims 1 and 2 (see next chapter) include this no-contact group.

Posttest

Within one week of completing the training, participants were asked to come back for a

post-testing session. The content of this session was almost identical to that of the initial

assessment. Participants were asked to answer some extra opinion questions regarding the design

of the games and controllers they used during the training. The posttesting required about one

hour and 15 minutes to be completed.

Measure During Intervention

A perceived engagement, or "Flow", questionnaire was administered to the intervention

groups after each training session. The measure required about 5 minutes for completion. All

measures are described in greater detail in the measures section below.

Training

Overview of Training

In this study, for all training interventions, the intervention was provided in small groups,

in six 90 minutes sessions. Participants received two or three training sessions each week. The

first group played a first action video game (Medal of Honor). This game was specifically chosen

because it was found to be the best game to train visual attention (in college students; Green et

al., 2003) in one study. The second group received instruction in Useful Field of View (UFOV).

UFOV was chosen because this is the gold standard training and we wanted to compare training

effect. The third group, Tetris served as a control group, and followed the same training










Table 4-17. Mean standardized scores and standard deviations of Flow scores for the 6 training
sessions, by intervention group.
Total MOH UFOV Tetri s


Training
Session 1
M
SD
Training
Session 2
M
SD
Training
Session 3
M
SD
Training
Session 4
M
SD
Training
Session 5
M
SD
Training
Session 6
M
SD
Note: UFOV


115.78 106.5 123.5 116.0
18.96 23.43 11.13 18.62


118.68 118.0
20.93 23.97


120.57 117.36
17.6 22.63


117.35
22.06


115.58
25.0


119.36
12.62


116.86
27.66


115.18 113.5 110.14 121.64
26.23 24.51 29.92 24.19


120.68 118.25 119.5 123.93
24.11 26.95 17.29 28.49


120.43 117.08 115.57 128.14
27.67 28.28 23.99 30.73


MOH = Medal of Honor; UFOV


Useful Field of View









the sole purpose of protesting and posttesting (without intervention) and was not randomized to

this condition. More detail on the rationale for this group is provided below. Although the no-

contact control group was recruited later in the study and was not randomized, participants came

from the same recruitment sources as the experimental participants.

Rationale for the Inclusion of a No-Contact Control Group

Interim data analyses from the three intervention groups revealed that there were

improvements in the primary outcome measure (UFOV) for all three treatment groups (MOH,

UFOV, Tetris). The improvements for the Tetris group (in particular) were not expected (on the

basis of the earlier Green and Bavelier group, who conceptualized Tetris as a kind of placebo

control condition). Thus, it was difficult (conceptually) to determine whether the improvements

in the two video game groups (MOH and tetris) were due to playing these games, or whether

they reflected simple practice effects due to protesting and posttesting. A prior intervention

study (ACTIVE, Ball et a., 2002) had shown that no-contact control participants experienced

about as much pre-post gain as shown by the videogame groups in this study, which suggested

that the observed improvement might be due to testing alone.

The decision was made, within the context of the current population, to verify that simple

pretest-posttest improvements in participants who received no intervention would, in fact, be as

large as those recruited for the two videogame groups. While the late inclusion of such a group

would raise concerns of non-equivalence, careful comparisons of the no-contact group to the

randomized groups would permit a quantitative characterization of the magnitude of bias, if any.

(As the foregoing participants section revealed, the no-contact control group did not differ on

any assessed variables from the other groups, at baseline).

To summarize: A no-contact control group was added to the study after the data from the

intervention groups was collected. This no-contact group was not randomized but was recruited










* You will see some bushes here that do not let you move forward.
* Use the Machete to clear the path.




* Run down this initial linear path beyond the bushes we just cut, picking up a Medical
Kit and some Garand M1 Clips en route to another bush, which we need to cut out
of the way.




Beyond these second bushes, you'll follow another linear path to the third bush we need
to cut.
Use Machete to cut these bushes as well.





* Run left up the hill there, shooting enemies with your M1911 as you go.



o You'll eventually see a huge gun installation, the Howitzer, on your right
surrounded by Japanese soldiers.
o Shoot at the Japanese soldiers surrounding and on the gun.
o Man the Howitzer.




Shoot at and eliminate all enemies.
<.\hal~l how to use Howitzer>
< The Howitzer packs a powerful punch a ithr the explosions it causes, but it takest~~~~ttttt~~~~tttt a while for it to
reload, so carefully fire it as to not miss and pay the price. Don 't aim directly at enemies, but
rather aim at the ground surrounding a certain amount of enemies to do the ultimate amount of
ddddddddddddddddddamag e you can.

* A tank will come at you, eventually (by your left side), which you'll need to
eliminate. Two or three well-aimed, quick Howitzer shots should do the trick.








Full Text

PAGE 1

1 COGNITIVE TRAINING WITH VIDEO GAME S TO IMPROVE DRIVING SKILLS AND DRIVING SAFETY AMONG OLDER ADULTS By PATRCIA DA CUNHA BELCHIOR A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2 2007 Patrcia da Cunha Belchior

PAGE 3

3 To my grandparents, for their unconditional love

PAGE 4

4 ACKNOWLEDGMENTS First, I want to show my gr atitude to my beloved grandpare nts. Without their support and unconditional love through my life, I wouldnt be able to make this far. Any further attempt to use words to thank them would be futile. They are and will forever be the inspiration for my work. I also would like to thank my parents and step-parents for their patience, love and support. I would like to apologize to my family for every time I left them waiting for me to come back home but now I can proudly say I made it. I want to especially apologize to my brother and my sisters for not being able to share their life with me. I also want to convey my gr atitude to my committee memb ers. First and foremost, my mentor and my advisor, William Mann, whom I first met during my first year of college in Brazil more than 10 years ago, and whose work inspir ed me to come to the United States for my graduate studies. Thanks for trusting me in th is endeavor. Thanks for your patience in showing me how to get to this point. And thanks for bei ng there every step of the way. I also would like to say a special thanks my co-chair Michael Marsiske who I had the pleasure to meet in the last two years of my studies and from whom I learne d so much. The dedication he has to his students will never be forgotten. I would not get this disse rtation done without his help. I also would like to thank Lorie Richards, Heather Gibson and A nn Horgas for being part of my committee. Special thanks to Debora Anderson, the director of the International Center who gave me tremendous help during my first year s at the University of Florida. I also would like to thank all the staff from the occupational therapy department and thanks to my friends that went through this journey with me, Rick, Michael, Roxanna Bhagwant, Dennis, Megan, Jessica, Arlene, Leigh, Eric,Inga, Jia hwa and specially thanks Cris tina, my first friend in this program and with whom I could share my Latin American background.

PAGE 5

5 I can not forget to mention all the students that helped me with my data collection: Shannon, Jason, Eric, Brian, Matt, Sean, Emily, a nd Claudia. I hope they all had some fun teaching older adults to play video games: I also would like to thank all my Brazilian fr iends whom I had the opportunity to share my experience living in a foreign country and that made this journey a little bit more like home. Last but definitely not least, I want to show my appreciation to a pers on that I met almost toward the end of this journey but who was able to make it more colorful and who held my hands and that patiently always cheered me up in moments of despair. Dont worry paixo, everything will work out, I think you were right Billy Shields.

PAGE 6

6 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........9 LIST OF FIGURES................................................................................................................ .......11 ABSTRACT....................................................................................................................... ............13 CHAPTER 1 INTRODUCTION..................................................................................................................15 Driving........................................................................................................................ ............15 The International Classification of F unction Disability and Health (ICF).............................19 ICF Classification............................................................................................................20 Application of Leisure / R ecreation Based Approaches to Maintaining / Enhancing Cognition and Complex Activity Performance Application at Each Level in the ICF............................................................................................................................ ...22 Participation in Leisure Activities Im pacts all Components of ICF Model....................25 Study Purpose.................................................................................................................. .......26 2 LITERATURE REVIEW.......................................................................................................28 Cognitive Training With Older Adults...................................................................................28 Useful Field of View (UFOV) Test.................................................................................32 Driving Performance Tests..............................................................................................34 Studies Using Driving Simulator Outcomes...................................................................36 UFOV Training With Older Adults........................................................................................37 Video Game Training of Cognition........................................................................................39 Video Game Studies Conducted by Green and Bavelier........................................................42 Engagement..................................................................................................................... .......44 Current Thinking About Training With Olde r Adults/Real World/Cognitive Reserve.........46 Importance of the Study........................................................................................................ ..48 3 MATERIALS AND METHODS...........................................................................................52 Overall Procedure.............................................................................................................. .....52 Study Design................................................................................................................... ........52 Study Specific Aims and Hypothesis.....................................................................................53 Participants................................................................................................................... ..........55 Inclusion and Exclusion Criteria.....................................................................................55 Recruitment.................................................................................................................... .55 Sample Characteristics....................................................................................................56 Testing Design and Procedure................................................................................................56

PAGE 7

7 Baseline Assessment.......................................................................................................57 Random Assignment.......................................................................................................57 Rationale for the Inclusion of a No-Contact Control Group...........................................58 Posttest....................................................................................................................... ......59 Measure During Intervention..........................................................................................59 Training....................................................................................................................... ............59 Overview of Training......................................................................................................59 Medal of Honor Training.........................................................................................60 Useful Field of View Training.................................................................................63 Tetris Training..........................................................................................................68 Measures....................................................................................................................... ..........69 Baseline Measures for Part icipant Characterization........................................................70 Proximal Outcome Variable............................................................................................71 Primary Outcome Measures............................................................................................72 Correlation Between UFOV and Simulator Subtests......................................................79 Secondary Outcome.........................................................................................................80 Sample Size.................................................................................................................... ........81 4 RESULTS........................................................................................................................ .......98 Overview....................................................................................................................... ..........98 Preliminary Analysis........................................................................................................... ...99 Assessment of Baseline Differences Between Treatment Groups..................................99 Attrition...................................................................................................................... .....99 Aim 1: Effects of Videogame Trai ning on Visual Attention (UFOV).................................100 Analyses on the UFOV Composite...............................................................................101 Re-Examining Training Effect s on UFOV by Each Subtask........................................102 Speed of Processing Subtask.........................................................................................104 Divided Attention Subtask............................................................................................104 Selective Attention Subtask...........................................................................................105 Same-Different Subtask.................................................................................................105 Change score analyses of Selective A ttention and Same-Different subtasks................106 Aim 2: Effects of Videogame Traini ng on Driving Simulator Performance........................107 Driving Simulator Subtask 1: Break Reaction Distance..............................................107 Driving Simulator Subtask 2: Lane Maintenance........................................................110 Driving Simulator Subtask 3: Dog Detection Accuracy..............................................112 Aim 3: Changes in Engagement/F low over the Course of Training...................................113 Aim 4: Qualitative Participant Inte rviews Regarding Game Design....................................114 Summary........................................................................................................................ .......117 5 DISCUSSION..................................................................................................................... ..153 Overview....................................................................................................................... ........153 Summary of Major Findings.................................................................................................153 Aim 1: Training Effects on Useful Field of View........................................................153 Aim 2: Driving Simulator Outcomes............................................................................154 Aim 3: Changes in Flow Experien ce Through the Course of Training........................155

PAGE 8

8 Aim 4: Participants' Opinions About Game Design.....................................................155 Theoretical Considerations and Study Implications.............................................................156 Limitations.................................................................................................................... ........162 Future directions.............................................................................................................. .....165 Conclusion..................................................................................................................... .......168 APPENDIX A MOH MANUAL GUIDE.....................................................................................................170 B UFOV TRAINING...............................................................................................................196 LIST OF REFERENCES.............................................................................................................199 BIOGRAPHICAL SKETCH.......................................................................................................209

PAGE 9

9 LIST OF TABLES Table page 3-1 Measures used in the study................................................................................................83 3-2 Sequence of the position of each dog on the scenario.......................................................84 3-3 Correlation between UFOV and simu lator subtasks during pretest...................................85 3-4 Correlation between UFOV and simu lator subtasks during posttest.................................85 4-1 Sample characteristics both for the total group and by inte rvention subgroup................119 4-2 Sample characteristics for completers and non-completers of the study protocol...........120 4-3 UFOV composite scores before and after testing by group (mean SD)........................121 4-4 Post hoc analysis of UF OV composite scores by groups................................................121 4-5 Mean standardized scores and standa rd deviations on four UFOV subtasks by intervention groups for pre and posttest...........................................................................122 4-6 Mean standardized scores and standard deviations for processing speed subtask by intervention groups for pre and posttest...........................................................................122 4-7 Mean standardized scores and standard deviations for divided attention subtask by intervention groups for pre and posttest...........................................................................123 4-8 Mean standardized scores and standard deviations for selectiv e attention subtask by intervention groups for pre and posttest...........................................................................123 4-9 Mean standardized scores and standard de viations for the same different trial subtask by intervention groups for pre and posttest......................................................................123 4-10 Post hoc analysis for selec tive attention subtask by group..............................................124 4-11 Post hoc analysis for same different trial scores by group...............................................124 4-12 Mean standardized scores and standard deviations for the overall driving simulator score by intervention groups for pre and posttest............................................................125 4-13 Mean standardized scores and standa rd deviations on four Simulator block by intervention groups for pre and posttest...........................................................................125 4-14 Mean values and standard deviation fo r lane maintenance by intervention groups for pre and posttest............................................................................................................... .125

PAGE 10

10 4-15 Mean standardized scores and standard deviations on lane maintenance on four Simulator block by intervention groups for pre and posttest...........................................126 4-16 Mean standardized scores and standa rd deviation on accuracy by intervention group for pre and posttest...........................................................................................................126 4-17 Mean standardized scores and standard de viations of Flow scores for the 6 training sessions, by intervention group........................................................................................127 4-18 Post hoc analysis for flow engagement for Medal of Honor...........................................128 4-19 Post hoc analysis for flow engagement for UFOV..........................................................129 4-20 Post hoc analysis for flow engagement for Tetris............................................................130 4-21 Video game ownership and usage....................................................................................131 4-22 Video game features....................................................................................................... .132

PAGE 11

11 LIST OF FIGURES Figure page 1-1 Conceptual Framework for the Worl d Health Organizati ons International Classification of Functioning, Di sability and Health (ICF)...............................................27 2-1 Processing speed subtask...................................................................................................50 2-2 Divided attention subtask.................................................................................................. .50 2-3 Selective attention subtask................................................................................................ .51 2-4 Same and different subtask................................................................................................51 3-1 Diagram of study design presenting: Me dal of Honor (MOH) group, Useful Field of View (UFOV) group, Tetris group a nd the no contact control group...............................86 3-2 Hypothesis 1............................................................................................................... ........87 3-3 Hypothesis 2............................................................................................................... ........87 3-4 Example of the controller used by participants to play the game......................................88 3-5 Example of a PowerPoint slide with stepby-step instructions gi ven to participants........88 3-6 PowerPoint slide with instructions on how to use the jump button...............................89 3-7 Screen shot of TETRIS game............................................................................................89 3-8 Example of a driving simu lator used in the study..............................................................90 3-9 Screen shot of the acclimation scenario.............................................................................91 3-10 Example of the rural scenario............................................................................................91 3-11 Example of a small town scenario.....................................................................................92 3-12 Example of a beach town scenario.....................................................................................92 3-13 Example of a metropolis scenario......................................................................................93 3-14 Example of a dog from block 1 it is positioned in the center of the road.......................93 3-15 Example of a dog from block 2 it is positioned in a low eccentricity and without clutter........................................................................................................................ .........94 3-16 Example of a dog from block 3 it is placed on high eccentricity with some clutter.......94

PAGE 12

12 3-17 Example of a dog from block 4 it is placed on a medium eccentricity but with much clutter and distraction...............................................................................................95 3-18 Example of a rural road scenar io used as a filler scenario.................................................95 3-19 Example of a city scen ario used as a filler.........................................................................96 3-20 Diagram with illustration on how simulator generated data was collected.......................97

PAGE 13

13 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy COGNITIVE TRAINING WITH VIDEO GA MES TO IMPROVE DRIVING SKILLS AND DRIVING SAFETY AMONG OLDER ADULTS By PATRCIA DA CUNHA BELCHIOR August 2007 Chair: William Mann Cochair: Michael Marsiske Major: Rehabilitation Science Visual attention is one of the most importa nt cognitive skills required for driving an automobile. Although this skill de clines with aging, training in older ages has been shown to boost visual attention. Today the most approach fo r training older adults vi sual attention is the UFOV. A practical drawback of using this traini ng, however, is that equipment demands require that the training be done in a laboratory or clini cal setting, which can be expensive and inaccessible for the general population. Given the importance of visual attention for driving performance and given the lack of widely available approaches to train this skill, there is a need to explore more inexpensive alternatives for training visual attenti on. One possible option for training visual attention in ol der individuals involves video ga mes. Previous research with younger adults has shown positive effects of video game in training on the visual attention of college students, including in us eful field of view tasks. The current study investigated the impact of video game training on older adults visual attention performance; in additi on, it was investigated if such improvements would transfer to improved performance in a driving simulator task. Fifty-eight par ticipants. Forty-five participants were assigned to one of the three intervention groups (action video game (Medal of

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14 Honor), Useful Field of View (UFOV), placebo control video game (T etris)) and thirteen participants were assigned to a no contact control group. Before training and immediately after training participants from the intervention groups were evaluated in a UFOV test and in a driving simulator test. The intervention was composed of 6 training sessions, each of 1.5 hour duration. Overall, the results suggest that the UFOV tr aining improved visual at tention significantly more than any other group. It was noted, however, that the two video game conditions (Medal of Honor and Tetris) experienced (non -significantly) more visual atte ntion gain than the no contact control group; indeed, on one subtask (Selec tive attention), the Tetris group experienced significantly more gain than th e no-contact control group, even t hough Tetris had been construed as a no-contact control. Despite general practice-related gain in driving simulator performance for all study groups, the results of the study further indicated that th e visual attention gains were not transferred to a simulator driving performance. In contrast, differential effect s of the three training condition s were observed in participant Flow, an indicator of particip ant enjoyment and engagement. Participants self-rated flow experience suggested that enjoyment improved over time for the two video game conditions (Medal of Honor, Tetris), but decreased for th e more traditional computer-based UFOV training group. In a final study aim, consumer-oriented anal yses of participants' opinions about the games they played were conducted. Results of these analyses suggested that video game were acceptable to this older adult population, and that many saw the games as a valid tool for mental exercise. Thus, although more work is needed to establish appropria te dosages, outcome measures, and to identify which games best improve visual attention, the positive evaluations of the games and positive Flow results lend prelimin ary support that video games can be acceptable and promising intervention tool.

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15 CHAPTER 1 INTRODUCTION Driving Driving is a very important activ ity for older adults in the United States. It is a means for achieving independence and social connectedness in American society, especially for those living in rural areas. In additi on, it is linked to other activitie s of daily living. Isolation and depression are commonly associated with drivin g cessation or reduction (Marotolli, Mendes de Leon, Glass, Williams, Cooney Jr, Berkman, et al., 1997; Fonda, Wallace, & Herzog, 2001). It is estimated that 89% of American senior s conduct their travel in personal vehicles (Collia, Sharp, & Giesbrecht, 2003) The total trip miles traveled by people aged 65 and older increased by 21 percent compared to total trip miles traveled by people aged 25-64 between the years 1995 and 2001. The average trip miles per pe rson also increased 13 percent for those 65 and older, but stayed almost the same fo r the younger group (Austin & Faigin, 2003). With the increase in life expectancy, the number of older adults drivi ng automobiles is also increasing. It is estimated that by the year 2050 the elderly population wi ll increase to 79 million persons, more than double its present size (US Cens us). Older drivers represent 10 percent of the total driving population in Ameri ca and, the number of licensed and frail elderly is projected to increase. In 2002, the elderly popu lation accounted for 150,000 injuries associated with vehicle crashes or 5% of all people injured in crashes (NHTSA, 2002). This sugges ts that older drivers safety should be considered a public health issue. Driving is a very complex task that involves several factors, such as mobility, sensory function, cognition, and the environment per se (e .g., road and vehicle design). Driving can be a challenge for older individuals due to normally declining factors associ ated with aging. While many older adults can modify their driving habits (e.g., drive less, drive only during restricted

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16 hours) due to sensorimotor or cognitive impairme nts, many continue to drive as long as possible and do not change their preferred mode of trav el (Ball, Owsley, Stalvey, Roenker, Sloane & Graves, 1998). This behavior puts older drivers at increased risk fo r crashes. Older drivers are at a greater risk for crashes and traffic convict ions, per capita, than a ny other adult age group (Owsley, Ball, Sloane, Roenker & Bruni, 1991; Edward Roybal Center for Research, 2001). Older drivers with visual and c ognitive declines are at a greater risk for crash involvement than those who do not have similar deficits (Owsley et al., 1991). Among all the possible cognitive skills related to driving, visual attention has consistently been found to be one of the most associated factor s. It plays a major role in driving performance (Owsley, et al., 1991; Ball, Owsle y, Sloane, Roenker, & Bruni, 1993; Richardson & Marottoli, 2003). Visual attention is commonly defined as the focal area from which information can be acquired within one eye fixation or glance (Ball, Beard, Roe nker, Miller & Griggs, 1988). Richardson et al., (2003) found that visual attention is the cogn itive skill related to driving behaviors that is most problematic for older individuals. In their st udy, driving behavior was assessed through a road test that included 36 items (e.g., acceleration, braking, lane change, speed regulation, response to traffic signals). Visual attention was th e cognitive skill most associated with driving behaviors. Out of 36 ma neuvers, 25 required the us e of visual attention skills (e.g., yielding right of way, re sponding to other vehicles or pe destrians, making turns at an intersection). In a similar study, Ball and Owsley (1991) investigated the types of crashes in which older adults are most lik ely to be involved. They found that major crashes among older individuals are more likely to occur at intersections, where th e visual attention requirements (detection, localization and iden tification of targets) are more likely to be higher.

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17 Several cognitive tests have been used to investigate the relationship between cognitive skills and car crashes. Scores on the Useful Field of View (UFOV) test, which is a measure of speed of processing and visual at tention, show the highest relations hip to crash involvement in a number of studies (Goode, Ball, Sloane, Roenke r, Roth, Myers, et al., 1998). Thus, several studies on visual attention have used the UFOV to operationalize this construct. UFOV is said to decrease with aging (Sekuler, Bennett & Mamela k, 2000). Thus, the deficit in UFOV associated with aging is a putative cause of increased late-life risk for crashe s and driving errors. This agerelated loss of UFOV skill may not be irreversib le. A growing body of evidence suggests that training of this skill may be effective well into late life. UFOV performance can be improved with traini ng. The ACTIVE clinical trial, a multi-site NIA-funded study of cognitive interventions with older adults, employed a ten-session training program for older adults. Participants received customized training (at their level of ability) and strategy instruction, with extensive pract ice of sample exercises usi ng a touch screen computer. Initial findings from the ACTI VE study found an increase in the size of the UFOV immediately following the training (Ball, Berch, Helmers, Jobe Leveck, Marsiske, et al., 2002), and that these gains were maintained for at least two years. Moreover, a longer-term follow-up with ACTIVE participants further revealed that persons who received UF OV training continued to show performance advantages on UFOV tests, relative to untrained c ontrols, up to five years after training (Willis, Tennstedt, Marsiske, Ba ll, Elias, Koepke, et al., 2006). In accordance with the positive results seen in the ACTIVE trial, today the most common tool to train older adults vi sual attention is the UFOV tr aining program, developed by investigators at Western Kentucky University and the University of Alabama-Birmingham. UFOV training, as in ACTIVE, is practiced in a touch screen environm ent using specialized

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18 equipment and software. A drawback of using th is training is that eq uipment demands require that the training be done in a laboratory or clini cal setting, which can be expensive and inaccessible for the general population. Given the importance of visual attention to driving performance and given the lack of widely available instruments to train this skill, there is a need to explore more inexpensive alterna tives for training visual attention. A possible option for training visual attention in older individuals is the use of video games. Previous research with younger adults ha s shown positive effects of video games playing in the training of visual atte ntion in college students (Green & Bavelier, 2003). Although there is evidence that video game playing enhances moto r, perceptual, and cognitive abilities among the elderly (Drew & Waters, 1986; Clark, Lanphear & Riddick, 1987), the use of video game playing to train visual attention among the older population has not been investigated. Above-and-beyond the practical value of explor ing video games as a tool for training visual attention (i.e., video games use widely available a nd a more affordable technology), a secondary reason that video games might be worthwhile exploring concerns enjoyability of or engagement in the training experience. Video ga mes have been designed, after all, to be fun. Thus, video games may provide a motivating and engaging tool to increase visual attention in elders; such motivatio nal benefits may enhance compliance with and effort in training. Thus, an important piece of information which needs to be collected in video game training studies with older adults concerns th eir engagement and motivation to use this technology. Engagement in activities often uses Flow theory, as described by Csikszentmihalyi (1975), and forms a core concept in the current study. This is descri bed in further detail in the next chapter. The use of training strategies to improve cogniti ve skills in old age and therefore, improve performance of instrumental activ ities of daily living (e.g., driving) can be put into perspective

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19 by using a conceptual model recognized in rehabilitation science. The World Health Organization (WHO), International Classification of Function, Disa bility and Health (ICF), will be used as the framework for this study (F igure 1-1). The ICF provides a good understanding of the use of cognitive training in rehabilitation setti ngs. It also provides the basis for understanding the interrelationship between the person, the environment, health and function. The International Classification of F unction Disability and Health (ICF) The ICF (International Classificat ion of Functioning, Disability and Health) is a member of the World Health Organization Family of Internat ional Classifications. It defines a new model of health. ICF is the result of a revision in the previous model ICIDH (Inte rnational Classification of Impairment Disability and Handicap), created in 1980 to fulfill the need for a model that could capture the multidimensional aspect of disabi lity. Prior health assessment had focused on mortality and cause of death. Within this new pe rspective, the individual level of functioning was also considered as a component of health. Th e ICIDH had many limitations, one of them being that the linear progression of th e model (from impairments to disa bility and handicaps) could not really capture the multidimensional aspect of disa bility. Thirteen years after its approval, ICIDH went through an in depth review process that last ed 10 years. ICF emerged from this process. It uses a neutral terminology that can be applied to ev ery person regardless of their health status. In addition it uses a standardized language whic h facilitates the communication of health professionals around the globe. The ICF model bri ngs together the medical model (which views disability as a problem exclusivel y of the person) and the social m odel (which views disability as a problem exclusively of the environment) to create a biopsychosocial model of health, which takes into account the body, the in dividual and society. In contrast to its earlier version (ICIDH), health is seen in a dynamic process; the enviro nmental component was added to this model, and it is a very important factor in the understand ing of multidimensional as pects of health (stun,

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20 Chatterji, Bickenback, Kostanjsek & Schneider, 2 003). ICF has multiple app lications in the area of rehabilitation. ICF Classification The goal of the ICF is to classify all aspects of health and health related states (World Health Organization [WHO], 2001). ICF information is organized in two parts : Functioning and disability and contextu al factors. These parts are furthe r categorized into components. The components of functioning and disability include body functions and stru ctures, activities and participation. Body function encompasses the ph ysiological function of the body system whereas body structure encompasses the anatomical part of the body. Activity is defined as the execution of a task whereas participation is defined as an involvement in a life situation. Activities and participation also involve two other constructs: ca pacity and performance. Capacity is the ability to execute a task in a standardiz ed environment and performance is the ability to execute a task in the real environment. The components of c ontextual factors include: environmental factors and personal factors. Environmental factors are related to the physical, social and attitudinal environment. Personal factors pertain to the individual background (e.g., lifestyle, age, culture, race). ICF provides its classification through a c oding system for recording functioning and disability status under each component of the mode l. The classification can use from two to four levels of coding. Codes are represented by an alphanumeric system. The letter b denotes components of body functioning; s denotes components of body structure; d denotes components of activities and partic ipation and e denotes environm ental factors. The first level coding involves the letters followed by a numeric c ode that involves the number of a chapter. For example: d9 denotes communit y, social and civic life. The ne xt level coding has two digits, and will further classify the previous level, for example d920 denoted the recreation and

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21 leisure. To indicate the level of health or sever ity of the problem in each domain, qualifiers are placed after a decimal point follo wing the category code. A body function qualifier indicates the presence of impairment (on a five point scale e.g., no impairment, moderate impairment). Body structure qualifiers indicate th e extent of impairment (e.g., no impairment, mild impairment), nature of body structure (e.g., no change in structure, partial absence, additional part) and localization (e.g., right, left, both sides). A capac ity qualifier describes th e ability to execute a task in a standardized environm ent (no support is provided by the environment, e.g., execution of a task without assistive device). A participation qua lifier describes the ability to execute a task in the current environment (with support form the e nvironment, e.g., use of assistive device). An environmental factor qualifier describes the extent to which the environment has enabled (facilitator) or limited (ba rrier) performance. Both of them use a generic qualifier (0-4 scale), but facilitators are specified by replacing the decima l point with +. Persona l factors are not coded in the ICF. A problem in the body function and structures will cause impairment, while a problem in executing activities or engaging into life situ ations will cause an activity limitation or participation restriction respect ively. Disability then is a broad term that encompasses the negative aspects of health (impairment, activity limitation and participation restriction). It is viewed by WHO as an outcome of interactions between health conditions and contextual factors. On the other hand, the successful completion of ev eryday activities across a wide range of life areas is termed functioning. Functioning and disability in the ICF are seen as an interactive and evolutionary processes. Each entity of the model in teracts in a dynamic and non linea r relation. For instance; an individual may have performance problems wit hout impairment or capacity limitation (e.g., an

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22 ex-prisoner facing discrimination a nd not able to engage in any so cial relationship). Furthermore, intervention in one domain has th e potential to modify one or more domains. For example, the use of an assistive device can faci litate execution of a task and in tu rn facilitate social interaction. An individual with low vision ha s a telephone with large keys, bei ng able to perform the task of dialing activity and making phone call to friends engaging in so cial interactions participation. ICF classification is used to obtain inform ation of a person functioning instead of a classification of people with disability. From th is perspective, ICF can be used for everybody, independent of ones level of health. ICF does not describe disability as a consequence of health condition; it also involves le gislation, attitudes, physic al, social environments. The environmental factor in this model is a key aspect for understanding functioning and disability because disability must be seen in the societal context (Dahl, 2002). ICF has an important role in re habilitation. In contrast to me dical interventions that focus on the disease process, rehabilitation has a broa der understanding of the individual, which view function and health as also associated with pers onal and environmental factors (Stucki, Ewert & Cieza, 2002). In this study, the use of cognitive stra tegies to promote cognitive skills is related to personal factors, and is associated with each level of the ICF model, as will be described below. Application of Leisure / Recreation Base d Approaches to Maintaining / Enhancing Cognition and Complex Activity Performanc e Application at Each Level in the ICF A set of studies on participation in leisur e activities in later life found a relationship between leisure activities and cognition in later life. A stim ulating environment provided by participation in leisure activities has the pot ential to improve cognitive capacity and would reflect in each level in the ICF. From this pers pective, participation in leisure activities will be classified as a lifestyle. Life style seems to represent a numbe r of different constructs, among

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23 them is participation in leisure activitie s (Scarmeas & Stern, 2003) Richards, Hardy and Wadsworth (2003), studied the effect of particip ation in leisure activity in a sample of 5362 individuals over a 43-year period, and found a pos itive association betwee n participation in leisure activities thr ough the lifespan and cognitive func tioning. An active lifestyle might enhance functioning in later life and be an important approach for successful aging. Rowe & Kahn (1997), conceptualize successful agi ng as a hierarchy that consists of three tasks: 1. Decreasing the risk of disease and disease-related disabi lity, 2. Increasing or maintaining physical and mental functioning and 3. Being actively engaged with life. Participation in mental activities in later life ha s a positive impact on ones life and can influence each task in the model proposed by Rowe and Kahn. Several studies found a positive relationship betw een participation in leisure activity and cognitive functioning in later life These studies give us a broad perspective on the importance of participation in leisur e activity for enhancing or maintain ing cognition in later life. Cognitive ability is not fixed, but environmental factors pl ay a role in augmenting cognitive functions in later life. Participation in activities that are cognitivel y demanding is related to cognitive reserve in later life. One of the studies related to this topic was conduc ted by Wilson, Barnes and Bennett (2003). In this study, older individua ls were asked to rate their frequency of participation in common cognitive activities (e.g., read ing, playing games like checkers, visiting a library) at five points in time (age 6, 12, 18, 40 a nd currently). Results show that lifetime cognitive activity was related to semantic memory, perceptual speed and viso-spa tial abilities. Scarmeas, Levy, Tang, Manly and Stern. (2001), proposed that leisure activities also contribute to the cognitive reserve by preserving a set of skills or repe rtoires necessary for

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24 cognitive function. Participants of this st udy were 1,772 non-demented individuals who were examined annually, up to 7 years. There is also a relationship between levels of cognitive demands in the activities and levels of cognitive function. Singh-Manoux, Richards and Marmot (2005) conducted a study to examine the relationship of leis ure activities and cogn itive functions. In this study, participants rated their frequency in participati on in cognitive activities. Activiti es were classified as either having high or low cognitive effort. High demandi ng cognitive activities were associated with higher cognitive abilities. In a ddition activities that required so cial interactions had a higher cognitive demand than activities that di d not required social engagement. Research has also supported the notion that the increase in brai n cognitive reserve by participation in leisure activities lowers the risk of dementia, both Alzheimers disease and vascular dementia. Participation in an activity for one day per week, can reduce the risk of dementia by 7 percent. Although participation in cognitively stimulating activities is associated with reduced risk of dementia, participation in physical activity is not (Verghese, Lipton, Katz, Hall, Derby, Kuslansky, Ambrose, Sliwinski & Buschke (2003). According to these studies, lifestyle is related to cognitive functions in later life. In the ICF model, lifestyle is classified under personal fa ctors. In this case, lifestyle would augment cognitive function in later li fe. Although ICF does not use faci litators and barriers as qualifiers for personal factors, it seems that participation in le isure activities functions as a facilitator to the maintenance or enhancement of cognitive functions in later life. Future studies should be done to examine the applicability of these two qua lifiers under personal factors. Furthermore, a relationship is found in the model between personal factors (i n this case lifestyle) and environmental factors. Lifest yle can influence or be influen ced by environmental factors. For

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25 instance, if a person lives in a resource-rich e nvironment, this person will be more willing to have a lifestyle that accounts for improvement in cognitive functioning. The availability of programs that would promote participation in leis ure activities program fo r older adults in the community would act as an environmenta l facilitator for le isure participation. Participation in Leisure Activities Im pacts all Components of ICF Model Body Structure As the results of previous studies indicate, participation in leisure activities contribute s to cognitive reserve. C ognitive reserve is a term commonly used to explain the great variability in the severity of cognitive aging in the face of neurodegenerative changes that are similar in nature and extent. Studies suggest that cognitive reserve probably involves processes that support neuroplastici ty, which is defined as the sel f-organization of the brain to meet environmental demands (Whalley, Deary, A ppleton & Starr, 2004). Scarmeas and Stern, (2003), suggests different ways in which leisure activities may enhance cognitive reserve: First, an engaged lifestyle may increase synaptic de nsity in neocortical association cortex, second, better circuits of synaptic activity may exist in subjects who participate in leisure activities (even though the number of neurons or synapses might be the same) and third, more efficient use of brain networks. ICF gives clas sifications of structure of the brain under body structure component, under code s110. Body Functioning Studies discussed in this section indicated that participation in cognitive stimulating activities, specifically leisure activities, reduce the declin e of memory (Verghese et. al., 2003; Wilson et. al., 2003; Singh-Manoux et. al., 2005) inductive reasoning, verbal meaning, verbal fluency (Singh-Manoux et al., 2005), perceptual speed, visuo-spatial ability (Wilson et. al., 2003). The classificati on of cognitive functioning in found in the ICF under body functioning. Cognitive functioning can be classified under global functioning and specific mental functions.

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26 Activities and participation The performance of activitie s of daily living (ADL) and instrumental activities of daily living (IADL), such as driving, involves the use of fundamental cognitive skills. Studies show that poor cognitive function leads to increased risk of limitation in ADL performance (Moritz, Kasl, & Berkman, 1995). In this case, lifestyle would not specifically affect participation in leisure activities, but lifestyle would increase cognitive function needed to perform all the array of everyday ac tivities. All activities classified in the ICF could be affected by enhancing cognitive capacity. The potential of using ICF to classify c ognitive disorders has already being stated (Arthanat, Nochajski & Stone, 2004), however, in the present di scussion, the personal level, defined as positive lifestyle by participation in leisure activities, was added. Thus, the use of cognitive training to promote c ognitive function can be seen as part of the personal component, and has the potential to influence each level of the model by improving visual attention (body function and structures level), having an impact on driving perf ormance (activities level) and promoting independence in other activi ties by driving (participation level). Study Purpose The purpose of this study was to investigate the efficacy of using an action video game (Medal of Honor; MOH) to improve the visual attention (UFOV) perfor mance of older adults. The study also investigated the effects of M OH training on a real-world outcome of older adults simulated driving performance. Anothe r purpose of this study was to investigate participants engagement with/enjoyment of videogameor UFOV-training, and to query participants' opinions about exiting interface design.

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27 Figure 1-1. Conceptual Framework for the Wo rld Health Organiza tions International Classification of Functioning, Disability and Health (ICF) Health condition (Disorder or disease) Disorders that can impact cognitive Functioning Body function and structures Visual attention disorders Activities Driving Participation Independence; Engagement in other activities Environmental factors Traffic environment; Car design Personal factors Active lifestyle that promotes cog. functioning

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28 CHAPTER 2 LITERATURE REVIEW Cognitive Training With Older Adults There has been a growing body of literature over the last three decades suggesting that agerelated decline in cognition can be reduced or even reversed through cognitive training. The cognitive abilities trained in the majority of the studies have been the ones that typically show early cognitive declines e.g., speed of processing, memory and reasoning. Decline in speed of processing is normativel y experienced in middle age and decline in memory and reasoning generally begins by the 6th decade of life (Schaie, 1996; Baltes, & Mayer, 1999; Christensen, MacKinnon, Korten, Jorm, Henderson, Jacomb, et al., 1999; Colsher, & Wallace, 1991). Age related declines in these cognitive abilities are on the order of one-quarter of a standard deviation over a fourto seven-ye ar interval in late adulthood (Schaie, 1996; Zelinski, & Burnight, 1997; Hultsch, Hertzog, Dixon, & Small, 1998; Zelinski, & Stewart, 1998; Luszcz, 1998; Sliwinski, & Buschke, 1999). The magnitude of decline for reasoning reporte d in the literature has ranged from .22 SD over 7 years to .42 SD over 14 years (Schaie, 1996) Memory changes have been described with estimates including (a) .25 SD over 7 years for se mantic lists (Schaie, 1996), (b) half of a standard deviation over 16 years for immediate wo rd recall and 1.00 SD for immediate text recall (Zelinski, & Burnight, 1997), (c) one quarter of a standard devi ation over 6 years for immediate word recall (Small, Dixon, Hultsch & Hertz og, 1999) and (d) .33 SD over 4 years for word recall (Singer, Verhaeghen, Ghisletta, Lindenbur ger, & Baltes, 2003). Reported speed changes have ranged from .16 SD over 2 years (Ball, & Owsley, 2000) to .25 SD over 4 years (Singer et al, 2002).

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29 Despite the cognitive declines experienced in la ter life, the research literature suggests that cognitive training may have the potential to revers e such declines. However, it is important to note that cognitive training effects have not been broad and general, but really targeted and narrow. Generally, cognitive gains reported in the lit erature have been signif icant and specific to the ability trained (Plemons, Willis, & Baltes, 1978; Willis, Blieszner, & Baltes, 1981; Blieszner, Willis, & Baltes, 1981; Baltes, Dittmann-Kohli, & Kliegl, 1986). ACTIVE is currently the largest clinical trial of cognitive interventions for older adults. It is a six-site trial funded, since 1996, by the National Institute on Aging and the Na tional Institute of Nursing Research. The design of ACTIVE was to use inte rventions that had proven effectiv e in previous research, that focused on enhancing basic cognitive skills in older adults, and that might improve everyday function in older adults who had not yet experienced functional decline but were at risk for such decline. ACTIVE improved on preceding training research by (a) using a larger (i.e., well powered with 2,802 participants) and more he terogeneous sample, including 27% African American elders, and (b) implementing the training protocol simultaneously at multiple site, to guard against laboratory-specific findings. (Ball, Berch, Helmers, Jobe, Leveck, Marsiske, et al., 2002; Willis, Tennstedt, Marsiske, Ball, Elias, Koepke, et al., 2006). In the ACTIVE study, participants were rando mized to one of four conditions: Reasoning training, Memory training, Useful Field of View (Speed) training, and no-contact control. In all three training groups, participants initially receiv ed ten 60-90 minute trai ning sessions, typically administered over five weeks. Initial study resu lts suggested that part icipants in all three intervention aims experienced immediate signifi cant gains on their targ ets of training (for reasoning gains averaged .48 SD, for memory they were .26 SD and for Useful Field of View they averaged 1.45 SD), and training group adva ntages were still observed two years post-

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30 training. (Ball et al., 2002). In a subsequent l ong-term follow-up, conducted five years after the initial training, there were still persist advantages for trained participan ts on their targets of training, relative to untrained controls. The five year net effect of reasoning trai ning was found to be .26 SD; for memory it was .23 SD, and for Useful Field of View (named Sp eed of Processing in st udy papers), it was .76 SD (Willis, et al., 2006). In addition, in the five y ear follow-up, participants in all three training groups reported less perceived di fficulty with IADLs relative to no-contact controls, a difference which reached significance for the Reasoning group. In addition, in a subgroup of participants who received extra booster training (i.e., yi elding a total of eighteen 60-90 minute sessions from Year 1 to Year 3 of the study), the ex tra training (compared to the basic ten-session program) yielded significant improvements on severa l observed tasks of daily living, as rated by blind raters. Specifically, pe rsons who received booster Reasoning training experienced significantly more gain on measur es of Everyday Problem Solving (i.e., the ability to read and understand medication labels, recipes, financia l documents, etc.), and persons who received booster Useful Field of View tr aining experienced significantl y more gain on measures of Everyday Speed (e.g., ability to quickly and accura tely read medication labels, find items in a pantry, look up a phone nu mber in the phone book). Despite these initially promising results from the ACTIVE trial, built on three decades of successful training research with older adults (LabouvieVief, & Gonda, 1976; Blieszner, Willis & Balter, 1981; Scogin, & Bienias, 1988; W illis & Nesselroade, 1990; Verhaeghen, Marcoen & Goossens, 1992), the more general fi nding in the literature, across the life span, is that transfer of training to real-world skills is difficult to achieve (Sternberg & Wagner, 1986; Salomon & Perkins, 1989). Even in ACTIVE, transfer eff ects where small and fairly ephemeral. A broad

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31 conclusion is that studies on the effects of transfer of skills from laboratory to real life situations is scarce. For older adults, the relative ab sence of training transfer may be due to the fact that many studies generally exclude persons thought to have incipient deme ntia, so study participants are not cognitively impaired in the domains of traini ng. In addition it has b een reported that IADL performance does not start to d ecline until the 70s or 80s (Willis, 1996). Taken together, this implies that many older adults may be at ceiling in their everyday performance, so measures of everyday functioning may not have enough room for improvement to show training effects. More generally, there is an absence of careful taxonomic re search linking cognitive domains to everyday functions (Marsiske & Margrett, 2006), and the psychometrics of everyday function measurement often do not produce scores with substantial variance (Velozo, Magalhaes, Pan & Leiter, 1995). Few studies exist in the research literature that investigated the effect of the transfer of cognitive training to everyday performance. The ACTIVE interventions seem to be a fairly noteworthy late life exception (both within and outside of the ACTIVE study), with a particularly positive pattern noted for Useful Field of View training. UFOV training was found to improve performance of IADL. After participat ing in 10 1-hour training sessions, participants performed more quickly and accurately on the Ti med Instrumental Activities of Daily Living (TIADL) tasks than the control group. The TIADL test emulates everyday tasks such as: looking up phone numbers, counting change and, reading me dication bottles (Edwards, Wadley, Myers, Roenker, Cissel & Ball, 2002; Edwards, Wadl ey, Vance, Wood, Roenker & Ball, 2005). In addition to improvement in TIADL scores, speed of processing training has also been found to transfer to the Road sign test (Roenker, Cissell, Ball, Wadley & Edwards, 2003) and driving

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32 performance (Ball, & Owsley, 2000). These studie s support the hypothesis that transfer was only found for the specific cognitive measure being trained. Owsley, Sloane, McGwin and Ball (2002) also investigated which cognitive abilities were independently associated with the time required by older adults to complete IADLs. Results showed that only processing speed was independently associated with TIADL scores: t hose individuals with slower processing speeds were more likely to require longer tim es to complete everyday tasks. These studies support the contention that, among all the cognitive skills usually studied in gerontology research (reasoning, memory and, useful field of view), UFOV training has shown to be the most successful in showing training eff ects that transfer to everyday activities. The Useful Field of View concept and test are described in grea ter detail in the next section. Useful Field of View (UFOV) Test The UFOV is a measure of speed of processing and visual attention. Visual attention is defined as the area in which information can be acquired within one ey e fixation (Ball, Beard, Roenker, Miller, & Griggs, 1988). It is also used as a functional measure of visual processing in older driver performance (Ball, Owsley, Sloane Roenker, & Bruni, 1993). Deterioration of the UFOV begins at age 20 or younger. This deterioration is charact erized by a decrease in the efficiency with which individuals can extract info rmation from a cluttered scene, rather than by a shrinking of the field of view pe r se. Furthermore, the decrease in efficiency is increased when conditions require the division of attention between central and peripheral tasks (Sekuler, Bennett, & Mamelak, 2000). The UFOV test was developed by investigat ors from Alabama and Western Kentucky (UFOV users guide). The UFOV test is divided into four subtests that assess speed of visual processing under increasingly complex task demands. Using both eyes, the examinee must detect, identify and localize briefly presented targets.

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33 The first subtest ("Speed") consists of identif ication of a target pr esented in a centrally located box (figure 2-1). In the beginning of the test, a white box containing an icon of a car is presented on a stationary displa y. After the participant receives the instruction to examine the target, he or she touches the cont inue button and a second screen a ppears. The participant is then asked to select the target that had been presented on the previous screen (in this case, a car). The next screen introduces the truck ic on. As in the previous instructi ons, the participant is asked to select the target presented in the previous scr een (in this case the truck). After these introduction screens, the participant has four trials to practice. The partic ipant can guess, without penalty. There is no feedback with responses throughout th e test. Practice continue s until the participant scores 3 out of 4 correct on a si ngle practice trial. The length of the stimulus presentation in milliseconds is automatically adjusted for each participant. After two correct responses, presentation time is shortened. However, if the response is incorrect, presentation time will increase. The process of tracking the perceptual th resholds is continued until a stable estimate of the presentation time needed for the respondent to achieve 75% of trials correct is calculated. The minimum (best score) for each test is 16ms; the maximum score is 500ms. The second subtest is labeled Divi ded Attention (Figure 2-2). In this case, the participant is asked to identify the centrally presented obj ect and locate a simultaneously presented car displayed in the periphery. A cen tral box, along with a series of 8 boxes attached with radial spokes is numbered from 1 to 8 in a clockwise direction. Presentation time varies according to the accura cy of the participant, and the subtest continues until a stable measure of the threshold is determined. Again, the 75% correct threshold for correct performance is calculated.

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34 The third subtest is Selective Attention (Figure 2-3). This subtest is identical to subtest 2 except that the target displayed in the periphery (w hich is always a car) is embedded in a field of 47 triangles or distracters. The fourth subtest is a modified selective a ttention subtest, named Same-Different, with two icons in the center of the sc reen (inside the white box) (Figur e 2-4). In this subtest, the examinee is presented with two objects inside the white box in the center of the screen. The examinees have to distinguish if the two objects are the same (two cars or two trucks) or different (a car and a truck). Th ere is also a target in the pe riphery in a clustered scene. The UFOV measure has been widely used in clinical and rehabili tation settings and normative UFOV data to adjust performance comp arisons across demogra phically-similar elders has been developed (Edwards et al, 2006) The UF OV has also been widely used in research designed to examine driving performance with olde r adults and it is the primary outcome of the present study but other outcomes have been investigated and will be described bellow. Driving Performance Tests Three other outcome measures are commonly used in studies on driving performance: accident frequency, driving simulator, and road test. The use of each of these variables has advantages and disadvantages. Accident frequency This outcome can be measured either by self-report or state report. State report of accident frequency has an advant age over self-report because all licensed drivers have accident records in a standa rdized format. In addition, state reports include more detailed information about the accident (e.g., road type, time of a day, location) (Owsley et al., 1991). The disadvantages of these state reports are: underreporting of accidents are common and can occur either because of the person or because of the state police. Vehicle accidents can also be caused by factors that are not intrinsic to the driv er (e.g., poor mechanical conditions of the car,

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35 weather) (Ball et al., 1991). In addition, accid ents are rare events. Owsley et al. (1991), compared self-report police accident s (accidents where the police were at the scene, as indicated by the Driving Habit Questionnaire) with state reco rd accidents (number of accidents on the state record), and found that the number of state reco rd accidents and self-report police accidents did not match. Both types of reports were expected to be related because in Alabama (where the study was conducted), police are required to subm it written accident reports to the state every time they go the scene of an accident. Raedt and Ponjaert-Kristoffersen (2000) used two outcome measures to identify cognitive factors and driving problems in older adults: a ccident frequency and road performance. While cognitive tests accounted for 64 percent of the va riance of the scores on the road performance, cognitive tests accounted for only 19 percent of th e variance of the scores on accident frequency. On-road driving performance On-road driving performance, when used as an outcome measure for driving research, has the great advantage of measuri ng ones ability in a real-life situation. Although on-road performance seems to be one of the best outcome measures to measure driving performance, it has some draw backs. First, there is no control over the environment and the stimulus presented to the part icipants, which makes it difficult to generalize conclusions. Second, when two or more evaluators take part in the study, inter-rater reliability should be set (Roneker et al, 2003), otherwise da ta might be compromised. Third, participants can make mistakes because of the pressure of being tested, and they might also not be familiar with the car. On-road tests are usually very expensive. Driving simulator An alternative to on-road measures is the driving simulator. In a simulator, the environment can be totally contro lled, the same scenarios can be used for every participant and the availa bility of low cost simulators is al so increasing. However, the use of a

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36 simulator has some disadvantages including hi gh cost and possible simulator sickness among participants (Mourant & Thattach eny, 2000). Participants do not have the same experience as a real life situation. However, si mulation has a great advantage it provides a more controlled testing situation. For this reason, and because a simulator was availa ble for use in this study, this was the outcome measure used. Simulator driving performance in this study will be measured by brake reaction distance, lane maintenance and accuracy. Studies Using Driving Simulator Outcomes The use of a driving simulator either to e ducate (Fisher, Laurie, Glasser, Connerney, Pollatsek, Duffy et al., 2002) or train (Dorn & Barker, 2005) the general population is found in the literature. Driving simulators have also been useful to investigate the safety of display positions while driving (Wittmann, Kiss, Gugg, Steffen, Fink, Pppel et al., 2006) with study participants providing feedback (Donmez, Boyle & Lee, 2007). Another area in which driving simulators have been gaining attention is to meas ure driving performance of older adults. In this situation, driving performance is generally measured by assessing ones reaction time using different tasks in the simulator. Ronker et. al ( 2003) measured reaction time in a simulator using a light display located on the top panel of the drivers unit. Participants were instructed to brake as quickly as possible when the two red lights were illuminated. In another experiment, the participants at a distance of 5.8 meters viewed a narrated film, th e stimuli were road signs, with and without a red slash through them. Participants were instructed to reac t only to signs without a red slash. In another study, Lee, Lee and Came ron (2003), used a driving simulator to assess visual attention skills by pa rticipants reaction time. Reaction time was measured by displaying two red diamond-shaped images at the top corner s of the monitor screen, which changed to red triangles after every 700 yards of simulated driv ing. Participants were supposed to engage the turn signal whenever the triangles image appear ed on the screen. The validation of a laboratory-

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37 based driving simulator in measuring on-road driving performance has been supported by the literature (Lee, Cameron & Lee, 2003). Lee and Lee (2005) used a driving simulator to determine which simulated driving tasks can be used to identify older driver s at risk of traffic violations. The simulator was used to assess 10 driving tasks specifically for testing older adults. The driv ing tasks consisted of: rule compliance, traffic sign compliance, driving sp eed, use of indicator, decision and judgment, speed compliance, visual atten tion task, working memory, multi-t asks, and road use obligation. The simulator computer automatically measured the first five tasks whereas a laboratory technician collected the remaining data. Recentl y, researchers from the University of Florida used a driving simulator to re plicate actual road locations. In this study participants drove through the same scenario on the ro ad and in the replicated scenar io in the simulator (Shechtman, Classen, Stephens, Bendixen, Belchior, Sandhu, et al., 2007). In this st udy the simulators controls were integrated with an actual ve hicle which improved the driving experience. As has been discussed, the driving simulator has been extensively used in research for different tasks, however, standardized tests to measure the same constructs are still missing from the literature. The goal of the present study is to measure visual attention in a simulator. Visual attention will be measured by brake reaction distance but will use tasks not mentioned in previous literature. The next ch apter has more details about this measure. The next section will discuss specific interventions th at might have the chance to imp roved scores on either useful field of view and driving simulator. UFOV Training With Older Adults UFOV was one of three abilities trained in the NIA-funded ACTIVE trial (see above). In ACTIVE, with a total sample of 2,802 at six site s throughout the US, 25% of participants were randomized to receive UFOV training (other gro ups received training in reasoning, memory, or

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38 no contact). The content of training was identical to that used in th is study. Participants in this condition received 10 sessions (6090 minutes each). Posttest assessments were conducted immediately, and 1, 2, 3 and 5 years post-training Immediately after training, on the UFOV test, the pre-post improvement of UFOV-trained part icipants was 1.45 SD higher than that of untraining controls (Ball et al). Moreover, five years later, the UFOV performance advantage of UFOV-trained participants was still .75 SD greater than untrai ned controls (Willis et al). Speed of processing training not only appears to improve processing speed, but also transferred to certain everyday functions, as indicated by improved performance on Timed IADL (Edwards et al, 2005). In ACTIVE, there was less evidence of tran sfer to real-world functions from the basic 10-session training program. However, a subset of 50% of UFOV-trained participants received up to eigh t additional booster training sessions (four 12-months after initial training, and four 36-months after initial training). For boosted pa rticipants, Willis et al (2006) found that, five years post-training, performa nce on observed tasks of everyday speed (e.g., speed and accuracy of reading medication labe ls, finding items in a pa ntry, looking up numbers in a phone book) was significantly higher than that of participants who received only the basic ten sessions. This seems to strongly argue fo r extended training and greater training dosages. Roenker et al (2003) evaluated the effect of UFOV training and simulator training on UFOV test performance and simulator driving perf ormance. Participants were randomized into a speed of processing training group, traditional driver training pr ogram performed in a driving simulator, and a low risk control group. The UFOV te st consisted of the sa me test described in previous section and for simulator measure, pa rticipants reaction time was assessed. It was found that speed of processing trai ning, but not simulator training improved UFOV scores and had fewer dangerous maneuvers dur ing driving evaluation.

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39 In addition to the cognitive benefits of sp eed of processing trai ning, a recent study found that speed of processing training protects against extensive clinical ly relevant decline in healthrelated quality of life (HRQoL). HRQoL was measured using the eight item Short-Form 36 scale (Wolinsky, Unverzagt, & Smith, 2006). Video Game Training of Cognition Video games have become a large form of entertainment in the American society and the average age of video game players also has been increasing. Therefore, researchers are spending more time investigating the effects of video ga me play on behavior and the brain. Research on video game play goes back to the early 1980s. Th e earliest studies on the effect of video games investigated the effect of video game on vis uo-motor coordination (Griffith, Voloschin, Gibb, & Bailey, 1983), reaction time (Orosy-Fildes, & Alla n, 1989; Yuji, 1996) and spat ial skills (Dorval, & Pepin, 1986; Gagnon, 1985). Studies not only a ddressed theoretical findings but also the practical implications of playing video game. Res earchers were interested in investigating if the decline in cognitive skills in late life could be improved or even reversed by using video game training. One study found that playing a game called Crystal Castle for tw o months (1 hour of training per week) improved both eye-hand coor dination and verbal knowledge in a group of older adults. In addition, partic ipants of this study reporte d being more careful in the performance of activities of daily living (Drew, & Waters, 1986). Another study investigated the effects of playing Pac Man and Donkey Kong. This study explored the performance of older adults on speed ed tasks. Participants played these games for seven weeks (for at least two hours a week). It was found that the experimental group average time dropped 25 milliseconds (Clark, Lanphear, & Riddick, 1987). Drews and Waters (1986) examined the abilities of video game playing to improve perceptual-motor skills and cognitive

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40 functioning. Clark et al., (1987) examined the possibility that the slowdown in performance among the elderly could be reversed through the use of video games. Recently researchers have been investigating th e use of video game playing in the training of laparoscopic surgeons. It has been suggested that video game play may improve laparoscopic skills (Enochsson, Isaksson, Tour, Kjellin, He dman, Wredmark, et al., 2004; Rosenberg, Landsittel, & Averch, 2005; Stefanidis, Korndo rffer, Dunne, Black, Sie rra, Touchard, et al., 2006). Because laparoscopic surgery uses very sm all incisions, adequate eye-hand coordination is required and, studies suggest th at video game players increase the efficiency of screening and decrease the duration of examination. Video game playing has also practical implications in the military. Cadets trained on video games had a higher flight performance than their untrained peers (Gopher, Weil, & Bareket, 1994) The effects of video game playing on visual at tention has been the fo cus of more recent studies. Although these studies we re not conducted with older a dults, they have promising findings that might be further replicated in the older population. Studies found that video game playing increases efficiency in dividing atte ntion (Greenfield, DeWins tanley, Kilpatrick, & Kaye, 1994). A similar measure to The Useful Field of View (UFOV) test was used to assess the efficiency in which participants distributed their attention. Rela tive to non video game players (NVGPs), video game players (VGPs) relied on similar types of vi sual processing strategies but possessed faster stimulus-response mappings in visual attention tasks (Castel, Pratt, & Drummond, 2005). Researchers have b een also interested in the ne urochemical consequences of video game playing. Using a form of brain im aging (Positron Emission Tomography or PET) researchers observed a large increase in the am ount of dopamine released in the brain, in particular areas thought to cont rol reward and learning. Dopami ne may be important in the

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41 modification of the brain followi ng perceptual training, leading to faster and more widespread learning (Koepp, Gunn, Lawrence, Cunningham, Da gher, Jones, et al., 1998). Another study investigated whether 30 minutes of playing a vi deo game would result in differences in brain functioning. It was found that video game playi ng provided more emotional arousal activation (Wang, Mathews, Kalnin, Mosier, Dunn, & Kronenberger, 2006 ). The idea that playing a computer-based game can improve or even reverse the effects of aging related decline has caught the attention of the popular ne ws media. Video games that claim to improve or even reverse the effects of agi ng can be found in the market. Even though these products may be effective, there is little scien tific evidence behind it. On e of these video games is Brain Age, developed by Ninte ndo. It claims to be a way to exercise your brain giving the workout it needs such as arithmetic, memory a nd reading aloud. It was developed by a Japanese neuroscientist (Kawashima, Okita, Yamazaki, Tajim a, Yoshida, Taira, et al., 2005) based, in part, on previous studies in whic h the cognitive functioning of pa tients with Alzheimers disease improved after 6 months of (paper and pencil, tutor-guided) training on arithmetic and reading skills. However, there is no rese arch developed on the effect of this particular game. Another example is a product named Positscience. It consists of a series of comput er-based exercises that have the goal of boosting a variety of brain functi ons. The Positscience program it consists of 40 one-hour sessions. Some positive sc ientific evidence for enhancemen t in cognitive skill has been reported (Mahncke, Connor, Appleman, Ahsa nuddin, Hardy, Wood, et al., 2006), although longterm follow-up and training transfer have not yet been reported in sizable, diverse controlled trials. Although there might be products on the ma rket that can promote cognitive skills in older adults, more research needs to investigate its e ffect. This current study ex amines the effect of

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42 video games, which are widely available, and mi ght have benefits in terms improving cognitive skills in older adults. Video Game Studies Conducted by Green and Bavelier Green and Bavelier (2003) showed that play ing a first person action video game improved a range of visual skills in unde rgraduate students. In their asse ssment battery was a measure that was designed to capture aspects of Useful Field of View; this task differed from the Alabama version, and focused mostly on peri pheral target detection (without the central foveal task). A first substudy compared students with extens ive experience playing action video games to novices, and found that experienced games outperf ormed participants without experience on multiple visual attention tasks. In a second training study, novices were randomized to two conditions (ten hours of Medal of Honor (MOH), an action video game, or ten hours of Tetris, conceptualized as a placebo c ontrol). This substudy found th at non-experienced players who MOH-trained in the study for 10 1-hour sessions showed significantly greater improvement in visual attention skills, including a UFOV-type task, than Tetris controls. The current study was a replication and extension of Green et al.s, visual attention study, extending the prior study by using an older sample, a widely used measure of Useful Field of View as the proximal outcome, and by examining transfer to driving-simulato r based measures of driving performance. Playing a first person action video game impr oves a range of visual skills (Green & Bavelier, 2006a) such as: the ov erall capacity of the attentiona l system (the number of items that can be attended), the ability to effectiv ely deploy attention over space and the temporal resolution of attention (the efficiency with which attention acts over time). Participants in this study underwent training for an hour per day fo r 10 days. The experimental group played the game Medal of Honor: Allied Assault. This game simulates Second World War combat situations, has a relatively simple interface, uses first-person point of vi ew and requires effective

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43 monitoring of the whole visual field. The cont rol group played Tetris, chosen to control for visuo-motor coordination. In additio n to the effects on the spatial a nd temporal aspects of visual attention, playing action video games also e nhances the number of objects that can be apprehended and it is suggested that this enhan cement is mediated by ch anges in visual shortterm memory skills (Green & Bavelier, 2006a). Green and Bavelier (2006b) also investigat ed the characteristics of the games that contributed to enhancement of visual attention. Participants with little or no experience with video games were recruited to participate in the study and they were selected to participate in different games. Participants received training for twelve hours and their performance on two visual attention measures were assessed: 1) the attentional bli nk measure, designed to measure the temporal resolution of visual attention and, 2) the multiple object tracking task, designed to measure the number of moving objects that can be attended simultaneously over a period of several seconds. Several games were used in the study. The experimental group played Unreal Tournment. This is a first person shooter game and requires effective monitoring of the entire visual field. The second group play ed the Americas Army, a free access game created by the US Army. This game is also a first person shooter, but it differs from the experimental game because the participants go through a lot of training exer cises which reduces the time spent in a battle. The game also emphasizes strategy and team work. The third group played Harry Potter: Quidditch World Cup. This game also has the visu al attention component but it also requires the completion of training exercises wh ich took time away from actual playing of the main gain. The fourth group played Tetris, and this game was selected to control for the effect of improved visio-motor coordination. The fifth group underwen t rhythmicity training designed by Interactive Metronome Inc. and this training relies on accurate timing. Results showed that the experimental

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44 group (Unreal Tournament) was the only one with significant changes in sc ores in the outcome measures of attentional blink and multiple object tracking. The authors proposed game characteristics that might improve visual atte ntion, including: the n eed to be fast and unpredictable stimuli. The games must, have unexp ected events that require a fast and accurate response from the player and games that do not encourage the use of passive strategies which might reduce attentional load. In a more recent study, Green and Bavelier (2007) investigated mechanisms underlying improvement in visual attention. They found th at video game playing can alter fundamental characteristics of the visual system such as th e spatial resolution of vi sual processing across the visual field. The spatial resoluti on of visual attention was measur ed by the smallest distance a distracter could be from a targ et without compromising target id entification. Results showed that, compared with a non-video game player, an ac tion video game player could tolerate smaller target-distracter distances. Similar effects we re also observed in non video game players who underwent training in action video games. The authors concluded that there is a causal relationship between video game playi ng and augmented sp atial resolution. Although there are several studies that show improvement in vi sual attention, these studies were conducted with young particip ants. There is no investigation of the effect of video game training to improve visual attenti on in older adults. Given the impor tance of visual attention skill for driving, and given the importance of driving for older adults and the lack of visual attention training, it is important to invest igate possible interventions. It is also necessary to investigate the acceptance of these games by the older populatio n and their engagement in the games. Engagement Engagement in the activity will greatly influe nce the video game experience. In addition, engagement in meaningful activities can increas e quality of life for many individuals (Suto,

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45 1998), contribute to life satisfac tion in later life (Kelly, St einkamp, & Kelly, 1987), and reduce mortality rates (Lennartsson, & Silverstein, 2001). Engagement in leisure activitie s can be conceptualized via Flow theory (Csikszentmihalyi, 1975). Flow can be said to occur when people are able to meet th e challenges of their environment with appropriate skills and accord ingly feel a sense of well-being, a sense of mastery, and a heightened sense of self-est eem (Csikszentmihalyi, 1990). Flow is also characterized by a deep sense of enjoyment. The enjoyment is not simply the result of satisfying a need, but occurs when a person achieves so mething unexpected which has a sense of novelty (Csikszentmihalyi, 1990). Csikszentmihalyi descri bes flow as happening most easily in sports, games, and hobbies because these activities ar e created in a way to facilitate flow. According to Flow theory research, when a person perceives a ch allenge (an intrinsic demand experienced when engaged in an activity) as being greater than his or her perceived skills (the individuals perception of his or her capacity to meet the demands of the activity), the person experiences worry and anxiety. On the other hand, if the person perceives his or her skills as being greater than the challenge at hand, he or she will experience boredom and apathy (Massimini, Csikzentmihalyi & Massimo, 1987). Th e goal is to find a match between challenges and skills in order to have an optimal experience. Farrow and Reid (2004) found that older adults experienced flow when participating in a virtual reality rehabilitation progr am. Participants who participated in this program described a sense of involvement, enjoyment, and control ov er the environment, which contributed to the flow experience. Because video games are thought to represent an innovative approach for training visual attention in older adults, it is also important to investigate participants engagement with this

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46 technology. Such engagement may serve as an indicator of potential compliance with video game training, and speaks more generally to the acceptability and motivati on potential that such training offers. Current Thinking About Training With Old er Adults/Real World/Cognitive Reserve This study also falls within the growing body of literature that suggests that there may be an association between participation in cogni tively stimulating everyday activities and ones cognitive status. Studies suggest that participation in common cognitive activities such as: playing games such as cards, crossword puzzles, checkers and other puzzles is associated with reduced risk for Alzheimers disease (Wilson, Mendes de Leon, Barnes, Schneider, Bienias, Evans, & Bennett, 2002). In additi on, it seems to be have a protective effect against dementia in people who maintain intellectual and social engagement through part icipation in everyday activities (Scarmeas, Levy, & Tang, 2001). Other studies have examined the effect of engagement in mental, physical and social act ivities on non-demented individuals. Researchers often assess activity level using se lf-report measures of the amount of time spent in a range of specific activities (Mackinnon, Christensen, Ho fer, Korten, & Jorm, 2003; Kramer, Bherer, Colcombe, Dong, & Greenough, 2004; Milgram, Si wak-Trapp, Araujo, & Head, 2006). After controlling for sensory functions Newson (2005) found that activity was a significant predictor of current level of speed, picture naming, incide nt recall, and verbal fluency and of cognitive changes in speed, picture naming and inci dent recall (Newson, & Kemps, 2005 ). Ghisletta (2006) found that increased medi a (e.g., listening to the radio, watching television) and leisure activity engagement (e .g., playing games, doing crossword puzzles) may lessen decline in perceptual speed but not in verb al fluency or performan ce (Ghisletta, Bickel, & Lvdn, 2006). Wilson (2003) constructed a measur e of lifelong particip ation in cognitive activities (Fratiglioni, Paillard-Borg, & Winblad, 2004). Partic ipants were rated according to

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47 how often they participated in common cognitive activities at th e age of 6, 12, 18, 40 and current age. This study found that more frequent cogni tive activity was related to better perceptual speed, visuo-spatial ability and semantic memo ry but not to episodic memory or working memory. Scarmeas (2003) discussed the importan ce of a lifestyle change that would supply a reserve that would allow individu als to cope longer before AD is manifested (Scarmeas, & Stern, 2003). Fratiglioni (2004) argues that the three lifestyle component s described above (mental, physical and social), have a common pathwa y .Research from both animal (Winocur, 1988; Pham, Winblad, Granholm, & M ohammed, 2002) and human studies suggest that there is a developmental neuron plasticity with regard to cognition and that cogni tive stimulation in the environment is an important predictor of enhancement and maintenance of cognitive functioning. One such environmental factor that has received a lot of attention by rese archers is an engaged lifestyle. It has been proposed that older adults who are engaged in a more active lifestyle can promote their cognitive functioning. Hence, frequent use of challenging mental tasks would be associated with a relatively hi gher level of cognitive reserve (Hultsch, Hertzog, Small, & Dixon, 1999). Although there is cognitive an d neural plasticity across the adul t life span, there is still the potential for improvement (Jones, Nyberg, Sa ndblom, Stigsdotter N eely, Ingvar, Magnus Petersson, & Backman, 2006). Although most of these studies are correlational in nature, experimental research, as described in the previous secti ons support such findings and gives more evidence for a causal relationship between cognitive training a nd improvement in cognitive skills. Although the body of research on the effects of participatin g in cognitively stimulating activities and the improvement in cognition is broad and leads to op timistic conclusions, to date

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48 there are not many programs designed to provide opportunities for mental stimulation in later life. Therefore, identifying techniqu es to enhance cognitive function in older adults are of utmost importance. Few examples of cognitive training pr ograms are reported in the literature. One such program is the Experience Corps, which is a soci al model for health promotion in older adults (Fried, Carlson, Freedman, Frick, Glass, Hill, et al., 2004; Glass, Freedman, Carlson, Hill, Frick, Ialongo, et al., 2004). This program found some shor t-term improvement in cognitive, social and physical factors. In this program, older volunteer s were place in elementary schools in roles designed to meet school needs. Participan ts spent 15 hours a week in the school for approximately 9 months. Further studies are necessary to investigate more specific cognitive measures. Virtual reality has also been used as a tool for cognitive training (more precisely, cognitive rehabilitation) research. Weiss (2003 ) investigated the effects of vi rtual reality in training stroke patients with unilateral neglect in relearning how to cross a street independently (Weiss, Naveh, & Katz, 2003). Gourlay (2000), used virtual rea lity technology to help cognitively impaired individuals to relearn importa nt daily living skills (Gourla y, Lun, & Lee, 2000). Lee (2003) developed a virtual supermarket to assess and tr ain cognitive abilities in ADL (Lee, Ku, Cho, Hahn, Kim, Lee, Kang, Kim, Yu, Wied erhold, Wiederhold, & Kim, 2003). Importance of the Study Driving is one of the most important activities in late life because it facilitates engagement in several other activities. Driving is a very co mplex task and can represent a challenge to older adults due to the normal declines associated with aging. Visual attention is one of the main cognitive functions related to driving, and rese arch has shown that this cognitive skill can be improved with training. However, there are not many options for visu al attention training

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49 available. This study explored a potentially new fo rm of training of visual attention, which is the video game. The magnitude of video game playing traini ng effects might not co rrespond that of the UFOV training, but video game playing has the adva ntage of being more available and perhaps more enjoyable. In addition, video game training can be done at home, whic h in turn might lead to an increase in training time. The increase in training time might enhance training effect and, video game training can be compared to UFOV training.

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50 Figure 2-1. Processing speed subtask Figure 2-2. Divided attention subtask

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51 Figure 2-3. Selective attention subtask Figure 2-4. Same different subtask

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52 CHAPTER 3 MATERIALS AND METHODS Overall Procedure Participants were recruited by phone calls, by mail and through fl yers that were distributed in the community. Potential participants were prescreened by telephone and to eligibility criteria should be met in order for them to be scheduled for a baseline assessment (65 or older, current driving, willing to participate in 6 training se ssions and no previous experience with video games). The baseline assessment took about 1 hour and a half to be completed. Participants were then randomized to one of thr ee intervention groups (MOH-UFOV-T etris). Training consisted of 6 training sessions of 90 minutes each. After each training session, participants completed a flow questionnaire to measure their engagement w ith the training. One week after intervention, participants returned for postte st. A no-contact control group were added to the study after the intervention study was completed, this group were not randomized but were recruited from the same recruitment pool. Participants from the nocontact control group comp leted pre and posttest within three weeks interval. Study Design The current study employed a pretest-posttes t control group design. Analytically, the study included one within-person condition (i.e ., pretest-posttest) and one betweenperson condition factor (i.e., four intervention groups). The withinperson condition assessed change in performance on several cognitive and non-cognitive measures over time; for three of four groups, this was change preand post-interv ention. The between-persons condition assessed differences between persons assigned to each of the four treatment c onditions. The critical analytic effect of interest in this study, the occasion X gr oup interaction, assessed whether change differed by intervention group, such that members of some groups experienced more

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53 improvement than others. For individuals rando mly assigned to the three intervention groups, between the pretest and posttest they participat ed in 6 training sessi ons of approximately 90 minutes duration. Participants from the no-contac t control group received a pretest, followed by a posttest three weeks later. The four interventions used in the current study were: 1) Medal of Honor (MOH), which is a first person shooter video game; 2) Useful Field of View (UFOV), which is an adaptive, customized visual attention tr aining program using iterative tr aining rules and a touch screen computer and 3) Tetris which is an arcade video game, and which was here construed as a placebo control condition, and 4) a no-contact co ntrol group. This fourth group did not receive any intervention. Detailed information for each training intervention is provided below. A schematic diagram of the study design is shown in figure 3-1. Study Specific Aims and Hypothesis The purpose of this study is to investigate the efficacy of using an action video game (Medal of Honor; MOH) to improve the visual attention (UFOV) perfor mance of older adults. The study will also investigate th e effects of MOH training on a real-world outcome of older adults simulated driving performance. The specific aims of this study are: Aim 1 To investigate whether UFOV perfor mance can be improved by training via a first-person action video game (MOH) and to co mpare such training to: (a) goldstandard touch screen based UFOV training, b) an alternativ e video game (Tetris) construed as a placebo control and c) no-contact pre post only control group Hypothesis: The UFOV training pr otocol has been found to be effective in training the visual attention of older indivi duals. We expect participants receiving the classic UFOV training to experience greater performance improvements th an all other participants; we expect those

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54 receiving MOH training to experience more vi sual attention improvements than either the placebo control (Tetris) or no-cont act control conditions. This hypothesis can be illustrated with the following diagram (Figure 3-2): Aim 2 To investigate if video game training be nefits also transfer to simulated driving performance The ultimate goal of this study is to investigate if visual at tention training can be transferred to a real life situation, which will be measured by driving performance in a simulator. Simulator driving performance will be measured by three dependent variables: (a) Brake reaction distance after part icipants perceive an ob ject (a dog) in or near the roadway; (b) Lane maintenance as participants approach and pass the dogs; and (c) Accuracy of detection of dogs in/near the roadway (i.e., do participants brake for the dog, or do they pass it by without braking). Hypothesis: The UFOV training group will obtai n better scores than the MOH video game groups, MOH group will obtain higher scores than the two control groups (T etris, no-contact). This hypothesis can be illustrated with the following diagram (Figure 3-3): Aim 3 To investigate participants engagement or perceived Flow, through the course of video game or UFOV training Degree of engagement in the tr aining experience may serve as an indicator of potential compliance with and effort /motivation in training. Flow was assessed at the end of each training session in which participants engaged. Thus, it was assessed only for participants in the three traini ng conditions, and it was not assesse d in the pretest-posttest design, but in each of the six training sessions. Hypothesis: Participants who received vi deo game training (MOH or Tetris) will experience a greater increase in Flow scores as compared to participants who received UFOV training. For the video game players, we expect ed that the improvement in Flow would occur

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55 due to increasing match between the challenges of the game (MOH, Tetris) and participants skill level. In addition, the game itself is ente rtaining and enjoyable. On the other hand, UFOV is less entertaining and enjoyable, and it also provides participan ts with less explicit feedback about their performance improvements. Thus, we did not expect Flow improvements in UFOV training. Aim 4 To explore participants opinion about game design This is a consumer-oriented aim and this data was intended to provide informa tion that may assist in future improvements of game design, so that games are more responsive to the needs of older adults. Participants Inclusion and Exclusion Criteria Inclusion criteria for this study included: a) 65 years or older, b) no previous experience with video games, because previous experience w ith video games can mask the real effects of training, c) be a current driver as driving performance will be tested on a simulator, the participant must be a current driv er d) MMSE of 24 or higher, e) be willing to participate in 6 training sessions, f) visual acu ity of 20/70, visual acuity tests were used because eye sensory function plays an important role in UFOV test pe rformance. In addition, training effects could be compromised by a lack of visual acuity. Participan ts with scores of 200 or lower in the UFOV (accumulated scores) were excluded from the st udy because they would not have room for improvement. Recruitment Participants were recruited by phone calls, by mail and through fl yers that were distributed in the community. Participants from 3 recru itment pools were contacted. Individuals from the Rehabilitation Engineering Research Center (R ERC) and the National Older Driver Research and Training Center (NODRTC) were contacted by telephone, and individuals from the Institute

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56 on Aging (IOA) registry first received a letter by mail with study information and were asked to contact study staff if they were interested in more information about the study. Out of 58 participants who completed the study, 7 participants were recruited from the RERC recruitment pool, 36 were recruited by mail from the IoA re gistry, 10 responded to a press release in the Senior Times Magazine, 2 were referred by frie nds and 2 were recruited at a townhouse meeting at a senior residential community in Gainesville. Participan ts received $10.00 for each visit. A participant who attended baseline testing, six training sessions, and the post-training testing received $80.00. Participants from the no-cont act control group received a maximum of $20.00 (pre and posttest). Sample Characteristics A total of 70 participants co mpleted the pretest battery for this study. All pa rticipants who completed the pretest battery were then eligib le to be randomized to a treatment condition. A total of 12 (17%) participants withdrew from the study; 9 of these particip ants withdrew prior to randomization because the training schedule co uld not accommodate their personal schedule. Three participants (one of each training group) withdrew post-r andomization, during the training intervention. Two of those partic ipants were employed and their sc hedules were in conflict with the training schedule and one of them withdrew due to perceived boredom of the training task. All participants from the no-contact control gr oup appeared at both th e preand post-test assessments. A total of 58 participants comple ted the study. In general, participants were youngold, college educated, they were equally distri buted across gender and a ll participants were Caucasian, except one Asian participant who withdraw before random assignment. Testing Design and Procedure Prior to in-person cont act with the study, potentia l participants were fi rst pre-scre ened via telephone. In the phone interview, f our inclusion criteria were asse ssed: a) 65 years or older, b)

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57 current driver, c) no previous experience with vi deo games and d) be willing to participate in 6 training sessions. If these requirements were me t, participants were scheduled for a baseline assessment. Baseline Assessment During the intake session, participants were informed about the research purposes and procedures, and (if willing) completed an informed consent form. After consenting, the participants took two more tests to determine their eligibility: 1) Mini Mental State Examination (MMSE), in which participants were required to score 24 or higher; and 2) a visu al acuity test, in which participants were required to have a score of 20/70 or better. After administration of these tests, eligible participants were administered the Useful Field of View (UFOV) test. If a participant had a score of 200 or hi gher in this test, participants completed the remaining baseline measures. These included a driving simulator test a memory test and questionnaires regarding (a) demographics, (b) depressi on; and (c) hearing impairment The duration of the baseline session was approximately 1.5 hours, and participants could take as many breaks as they needed. After the baseline assessment was completed, par ticipants were randomized, and scheduled for their 6 training sessions. Particip ants from the no-contact cont rol group returned three weeks after baseline assessment for a posttest. Random Assignment After baseline assessment participants who were determined to have met the inclusion criteria for the study, were randomized into one of three groups (Medal of Honor, UFOV, or Tetris). Randomization occurred in triplets (i.e., after a group of thr ee participants had met eligibility, one participant was assigned to each of the three groups). Afte r the conclusion of the interventions for the three groups and after interim data analys es had been conducted, a fourth non-equivalent control group was recr uited. This separate group of participants was recruited for

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58 the sole purpose of pretesting a nd posttesting (without interventi on) and was not randomized to this condition. More detail on the rationale for this group is provided below. Although the nocontact control group was recruited later in th e study and was not randomi zed, participants came from the same recruitment sources as the experimental participants. Rationale for the Inclusion of a No-Contact Control Group Interim data analyses from the three inte rvention groups revealed that there were improvements in the primary outcome measure (UFOV) for all three treatment groups (MOH, UFOV, Tetris). The improvements for the Tetris group (in particul ar) were not expected (on the basis of the earlier Green and Bavelier group, wh o conceptualized Tetris as a kind of placebo control condition). Thus, it was difficult (concep tually) to determine whether the improvements in the two video game groups (MOH and tetris) we re due to playing these games, or whether they reflected simple practice effects due to pretesting and posttesting. A prior intervention study (ACTIVE, Ball et a., 2002) had shown that no-contact control participants experienced about as much pre-post gain as shown by the videogame groups in this study, which suggested that the observed improvement might be due to testing alone. The decision was made, within the context of the current population, to verify that simple pretest-posttest improvements in pa rticipants who received no inte rvention would, in fact, be as large as those recruited for the two videogame gr oups. While the late in clusion of such a group would raise concerns of non-equi valence, careful comparisons of the no-contact group to the randomized groups would permit a quantitative charac terization of the magnitude of bias, if any. (As the foregoing participants section revealed, the no-contac t control group did not differ on any assessed variables from the other groups, at baseline). To summarize: A no-contact c ontrol group was added to the st udy after the data from the intervention groups was collected. This no-cont act group was not randomized but was recruited

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59 from the same recruitment pool from the previous participants. This was necessary to assess the simple effect of taking the pretest-posttest battery twice. Participants took the preand posttest about three weeks apar t, with no treatment/stimulation/ contact between testings. Group comparison analyses for Aims 1 and 2 (see ne xt chapter) include this no-contact group. Posttest Within one week of completing the training, pa rticipants were asked to come back for a post-testing session. The content of this session was almost identical to that of the initial assessment. Participants were asked to answer some extra opinion questions regarding the design of the games and controllers they used during the training. The posttesti ng required about one hour and 15 minutes to be completed. Measure During Intervention A perceived engagement, or "F low", questionnaire was administered to the intervention groups after each training session. The measure required about 5 minutes for completion. All measures are described in greater de tail in the measures section below. Training Overview of Training In this study, for all training interventions, the intervention was provided in small groups, in six 90 minutes sessions. Participants received two or three training sessions each week. The first group played a first action video game (Medal of Honor). This game was specifically chosen because it was found to be the best game to train visual attention (in coll ege students; Green et al., 2003) in one study. The second group received in struction in Useful Field of View (UFOV). UFOV was chosen because this is the gold standard training and we wanted to compare training effect. The third group, Tetris served as a control group, and followed the same training

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60 procedures of the first group, excep t they played a placebo game (i.e., reported by Green et al. NOT to improve visual atte ntion in college students) Trainers were undergraduate st udent assistants who had extensive self-reported experience with action video games. All tr ainers received inst ruction in the implementation of the studys interventions, which were manualized. All undergra d students who worked in this project were trained by the doctoral student. Prior to start of intervention with participants, they spent time playing the game using the manuals that were de veloped. Then, they practiced training with one another and whenever possible with somebody not familiar with video games. It was important to make sure that the training was standardized and all students used the same procedures with all participants. Medal of Honor Training The two video game training conditions (MOH and Tetris) differed from UFOV in not using a computer, but in using a commercially-a vailable video game system instead. The study employed a Sony PlayStation 2, console model 97060 and dual shock 2 analog controller, model 97026. The game was presented on a 19 TV monitor. In this training, participants were asked to play the "first pers on shooter" video game Medal of Honor Rising Sun, firs t with a tutor and then more i ndependently. The game is made up of multiple missions. In each session, particip ants were first asked to navigate through a particular on-screen mission (for example, after on es ship is hit by enemy torpedoes while one is two levels below, one must struggle to reach th e ships deck, extinguishi ng fires and assisting crew members along the way; once atop, one must use an artillery gun to discourage combatants airplanes, which are dropping bom bs on the ship). The missions were broken into step-by-step maneuvers (Appendix A), and the tutor provided pa rticipants with complete assistance to navigate the scene. When participants successf ully made it through the mission, they were asked

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61 to repeat the mission on their ow n, drawing on their tutors help if needed. Once they had mastered a mission on their own, they moved on to the next mission. This was the main intervention under study; Green & Bavelier (2003), have repor ted that playing this game boosts visual attention, as assessed with their measures, in young adults. The remainder of this section describes the traini ng procedure in greater detail. In session 1, the participants had about 15 minutes of tutoring on the video game, as none of them had previous experience with video games before. The tutor explained to the participants that Medal of Honor is a game based on the S econd World War. The tuto rs explained that MOH is an action game; thus, there would be considerab le shooting and some scenes of violence. If at any time participants did not feel comfortable w ith the game, they were allowed to stop playing. No participant withdrew for this reason. Participants were also asked to take a 5-minute break after 45 minutes of playing. The participants also learned that the game wa s divided into missions. It was expected that each participant would be able to complete about 4 missions during the 6 training sessions. In order to finish each mission, several mission objec tives (that were explained in details during each game) would be completed. A guide with st ep-by-step instructions for each mission was developed and the participants were expected to follow instructions provided by the manual. Each mission would be completed twice. The first time, a tutor would give step-by-step instructions. Once the participant completed the mi ssion once, they were expected to repeat the mission without much help from the tutor. We e xpected that by playing each mission twice, the participant would be able to sp end more time concentrating on the parts of the game supposed to promote visual learning rather th en trying to learn the game.

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62 If participants did not have any further que stions about the trai ning, after the tutors instructions the tutor modeled by playing the game for about 10 minutes and the participants watched them playing. Next, the tu tors explained how the controller works and what each button does. This was a brief explanation because a deta iled explanation was provided during the first mission, which served as a tutorial. In the first mission, the participants had a chance to practice each button. Figure 3-4 presents the controller used by participants and the description of some of the buttons used to play this game. Once each button was introduced to the partic ipants during the first mission, they were expected to practice using it unt il they felt comfortable doing so. The tutor moved forward with the instructions only once the participants succes sfully completed the task controlled by that button, e.g., use the button to jump over the wires on the floor. St ep-by-step instructions were presented through PowerPoint slides on a computer that was placed next to the participants. The trainers would control the speed of the presentation of each PowerPoint slide. The instructions were divided step-by-step (an example is pr esented on figure 3-5) and at the end of each instruction (or slide), the participants were aske d to pause the game before moving forward in the game. This was done, during the learning phase, to reduce all time pressure that might have impaired learning. During the first time the partic ipants played the mission, the tutors were very involved in helping them to fi nish the missions, but the seco nd time the participants went through the missions, the tutors only helped if required. Also, each time a new button was introduced to the participants, the instruction was presented in the slides (Figure 3-6). The partic ipants would keep prac ticing the use of each button until they could perform the task successfully.

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63 The difficulty level increased at each ne w mission. Mission 1 Day of Infamy is designed to learn the basic maneuvers used th rough the game, such as turn right/left, look up/down, jump, crouch. The game starts in a bunke r room. The goal is to leave the room, walk upstairs, and put out a fire. After extinguishing the fire you have to keep moving forward and go up another set of stairs. You w ill be given a gun and you have to shoot at some enemy planes. This completes the mission. Mission 2 Pearl Harbor is a very short mission. It starts on a boat and the only job is to shoot down planes fl ying through the area. Mi ssion 3 Fall of the Philippines starts on a bridge with fi res around you. You need to walk out of the bridge, meet some friends and fire on some enemies. Through the mission there are several mission objectives that need to be completed in order to keep moving, such as find the cogwheel of a tank and repair it. Once the tank is fixed you s hould follow it through a village, enemies will be shooting at you and your goal is to search for the enemies in different scenarios, sometimes they are easy to see, sometimes they are hidden. The next mission in the game is the midnight raid on Guadalcanal, but because this scenario happens at night, and the scenes were very dark and hard to see it was skipped. For this study purpose, missi on 4 was the Pistol Pete Shutdown in this mission you have a lot of mission objectives to complete, such as re leasing some of your teammates being held captive. Th rough all the missions, participan ts must continuously visually scan the scenarios because ther e are enemies who try to shoot at you. The game was set up in a way that the participants would have unlimited a mmunition and a have a bulletproof vest. By using these options, the game wa s less challenging and the particip ants could play for a longer time. Useful Field of View Training The Useful Field of View training was ad ministered via a computer. A CPU was connected to a 21" ELO touch screen. The traini ng consisted of six 90-minute sessions. This

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64 was a deviation from a published version, which included ten 60-90 minutes sessions (Ball et al., 2000) but which was instituted due to resource cons traints (the available time for the researcher and volunteer trainers to complete the study) and participant schedules. The complexity of the UFOV subtests is modified by holding the dura tion of the display cons tant and by gradually increasing the complexity of the central task, the peripheral task or both. These modifications allow individuals to practice the task at customi zed levels of difficulty until mastery is achieved. Training sessions for UFOV have been described in previous research (Edwards, Wadley, Myers, Roenker, Cissell, & Ball, 2002). This training was adapted from the UFOV tr aining guide from prev ious studies (Appendix B). In the initial assessment, participants receive d a preliminary score on the Useful Field of View (UFOV) test (see below for how these scores influenced training de cisions). In general, training was started at participants current skill level, and after th ey had mastered that level, the challenge increased, either by reducing the amount of time available to perform the task, or by increasing the visual complexity of the display to be studied. Based on prior literature, this training represented the gold standard for impr oving the visual attention of older adults. It was selected to be used as the reference condition against which the efficacy of video game training could be compared. The remainder of this section describes the training procedure in greater detail. In Session 1, training was customized to pa rticipants' baseline UFOV performance. As noted below, the UFOV consisted of four subt asks. Due to lower bound timing limits and upper bound time-out programming, possible scores ranged from 16 ms to 500 ms for each subtask.

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65 Subtask 1, Speed: Measures the average presenta tion time needed for participants to detect whether a centrally presented two-dimensional wh ite object (against a black background) is a car or a truck with 75% accuracy. Subtask 2, Divided Attention: Measures the average presentation time needed for participants to detect whether a centrally presented two-dimensi onal white object (against a black background) is a car or a truck while simultaneously noting the sc reen location of a peripherally presented car, with 75% accuracy. Subtask 3, Selective Attention: Measures the average presentation time needed for participants to detect whether a centrally presented two-dimensi onal white object (against a black background) is a car or a truck while simultaneously noting the sc reen location of a peripherally located car that is presented in screen clutter, with 75% accuracy. Subtask 4, Same Different: Measures the average presentation time needed for participants to detect whether two centrally presented two-dimensional white objects (ver tically arrayed, against a black background) are (a) two cars--same; (b) two truc ks--same; or (c) a car and a truck--different, while simultaneously noti ng the screen location of a peripherally located car that is presented in screen cl utter, with 75% accuracy. Customization rules were as follows: 1. If the participant scored greater than 30 ms on the Speed task, they were first trained on Speed. In this case, participants were presen ted with the Speed task again, and allowed to practice. They continued to practice until their score moved below 30 ms. If participants remained stuck at a "plateau", a simpler center task (not identifying whether the center object was a car or truck, but simply identifying whet her an object was present) was selected, until participants achieved a score of 30 ms or faster on this simpler (" presence-absence") center task.

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66 Once participants reached 30 ms or faster, they re turned to the "identify (car or truck)" center task until they achieved a score of 30 ms or faster. Finally, before progressing to the next training level, participants moved to the most di fficult center task ("same-different", see above), until they achieved a score of 30 ms or faster. At this point, they progressed to the next Divided Attention training track. 2. If the participant was belo w criterion (30 ms) on the Speed Subtask 1, but above 40 ms on the Divided Attention Subtask 2, training began at this level. (Participants who "graduated" from the Speed training above also progressed next to this training level) Participants moving here from Speed training always started at 200 ms presentation time. Participants moving directly to this level of training had their initial presentation time set to th eir score in the baseline screening UFOV score, within 40 ms in multiples of 40 ms (i.e., if their baseline screening score on Divided Attention Subtask 2 was 162 ms, the part icipant would start at 200 ms; if the baseline Divided Attention score was 251, par ticipants would start at 280 ms). In this training, and in all other training tracks, participants would receive a block of 16 trials at the selected speed, with the peripheral object presented at close eccentricity (i.e., close to the center of the screen). Once the participant achieved two consecutive blocks w ith at least 12-out-of-16 correct responses, the task would be made more difficult by keeping the participant at the same presentation speed, but moving the peripheral object to a moderate level of eccentricity. Once the participant achieved two consecutive blocks with at l east 12-out-of-16 correct responses at this moderate eccentricity, the task would be made more difficult by keeping the participant at the same presentation speed, but moving the peripheral object to a distant/outer level of eccentricity. Once the participant achieved two consecutive blocks with at least 12-out-of-16 correct resp onses at this outer eccentricity, the task would be made more diffi cult by increasing the presentation speed (i.e.,

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67 making it 40 ms faster; e.g., going from 240 ms to 200 ms or going from 1 60 ms to 120 ms), and returning the peripheral object to the inner eccentr icity. Again, the particip ant progressed at this speed from inner to outer eccentricity before the ta sk was made 40 ms faster. Training at this level terminated when the participant achieved a score of 12-out-of-16 or better on at least two blocks presented at 40 ms and outer eccentricity. 3. If the participant scored below criteri on (40 ms) on the Divided Attention Subtask 2, but above 80 ms on the Selective Attention Subtask 3, training began at this level. (Participants who "graduated" from the Divided Attention traini ng above also progressed next to this training level). Participants moving here from Divide d Attention training alwa ys started at 200 ms presentation time. As with Divided Attention, participants moving direc tly to this level of training had their initial presentation time set to their score in the baseline screening UFOV score, within 40 ms in multiples of 40 ms. Th e block-by-block training procedure described above (i.e., moving to the next level when ther e were two consecutive trials of 12-out-of-16 correct, and progressing at each speed level from inner-to-outer distracter eccentricity before moving to the next speed, which was 40 ms fast er) was employed until participants achieved criterion accuracy at an 80 ms presen tation speed at the outer eccentricity. 4. If the participant was be low criterion (80 ms) on the Selective Attention Subtask 3, participants automatically progressed to the ha rdest training level, Same Different Subtask 4. (Participants who "graduated" from the Selective Attention training also progressed here, starting at a 200 ms presentation time). As with the other training tracks, the initia l level of training set presentation time set to within 40 ms of baselin e UFOV score on this task, in multiples of 40 ms. Again, the adaptive training method described for Tr acks 2 and 3 (after two consecutive trials of 12-out-of-16 correct, participants progressed from inner-to-outer distracter eccentricity before

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68 moving to the next speed, which was 40 ms fast er) was employed until the end of training. No participants in this study reached "ceiling" on this task before concluding training. Tetris Training In this placebo training condition, participants were asked to play the video game Tetris, with a tutor. In the game, which is a cla ssic 1980s arcade game, seven randomly rendered tetrominoes or tetrads shapes composed of four blocks each fall down the playing field (Figure 3-7). The object of the game is to manipul ate these tetrominoes with the aim of creating a horizontal line of blocks without gaps. When such a line is created, it disa ppears, and the blocks above (if any) fall. As the game progresses, the tetrominoes fall faster, and the game ends when the stack of Tetrominoes reaches the top of the playing field. Participants tutors provided them with game instructions, and demonstrated/mode l effective play. Then, the tutor acted as a coach, as participants played the game themselv es. The goal of this training was to increase participants independence, and to increase the nu mber of rows they could clear before ending the game. This game was selected because, based on the work of Gr een and Bavelier, playing of this game for up to ten hours had little or no effect on visual at tention performance in college students. Thus, this condition was co nstrued as a placebo control for the Medal of Honor group. It was thought to control fo r contact hours with study staff, out-of-house time for older adults, and any stimulating effects of interacting with video games. The remainder of this section describes th e training procedure in greater detail. In the first session, the tutors explained to th e participants how video games are used and played. However, before the participants actually started the game, they were given some paper cuts in the same shapes as the tetraminoes that they would see on the scre en (shapes composed of four blocks each). They were asked to play with them and try to fit them together (create a horizontal line without gap). Once they successfully completed the task, the tutor introduced the

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69 game in the screen. The trainers played the ga me for about 10 minutes for the participants to watch, in order to model game play and the requ isite skills. Next, the tutor demonstrated the controller to the par ticipants. It was the same controller us ed to play Medal of Honor. However, in this game, fewer buttons were necessary. The buttons were introduced one at a time and in the beginning the participants were to ld not to worry about creating a horizontal line, but rather that they should attempt to get comfortable with each button. In this condition, there were no step bystep instructions on a computer screen, because there is no story line in this game. Unlike MOH, the game scenario did not change over the course of the following sessions. The participants had to repeat the same task over and over again. As game play improved, the speed of tetrami no dropping changed. In the earlier stages of game play, the pieces would drop very slowly. Once the participants mastered that level of game play, which was assessed by the game, the game would automatically move them to the next level. The task in each level was exactly the same, except that the pieces would fall faster and the participants had less time to decide where th ey would place each piece. Unlike the Medal of Honor game, less active visu al scanning required in this game. (It should be noted, however, that participants needed to note th e shape of each new tetramino at the top of the screen, while simultaneously noting the optimal position for droppi ng the blocks at the bottom of the screen. Thus, even in Tetris some continuous visual s canning was required, although Tetris objects were usually presented more in the central and less peripheral fixation region). Measures Table 3-1, presents information on each measure us ed in this test. A detailed description of each of these measures follows below.

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70 Baseline Measures for Partic ipant Characterization MMSE MMSE is scored by calculating the tota l scores of the 7 items. The minimum possible score is 0 and the maximum possible scor e is 30. The usual cut-off point for cognitive impairment is 23/24, with lower scores indicat ing cognitive impairment (Folstein, Folstein, & McHugh, 1975). Those with an MMSE score of 24 or higher were included. All participants that came for baseline assessment met this inclusion criteria. The MMSE score were required because participants might be randomized to a video game training, which i nvolved a learning process; it was thought that persons with dementia might not permit training gains, so low MMSE scores could not profit from the interventions in this study. Visual Acuity A visual acuity eye test was perf ormed using a GoodLite backlit Snellen distance-vision chart, presented at a 10-foot view ing distance. The 10-foot presentation distance was practical to accommodate in the testing office. Participants were positioned 10 feet from the chart and asked to start read ing the letters on the 20/50 line and proceed downward. If the participant got more than three correct scores th ey would move to a more difficult level but if they got fewer than three correct they were move d to an easier level. Participants had to score higher than 20/70 to remain in the tudy. All part icipants that came from baseline met this inclusion criteria. Hearing impairment The Hearing Handicap Inventor y for Adults (HHIES) (Newman, Weinstein, Jacobson & Hug, 1991) was used to ev aluate hearing performance. The HHIES is composed of 10 items, with a mixed pattern of "y es" and "no" indicating less hearing handicap. Inclusion/exclusion of participan ts was not based on this score, but it was collected as a possible covariate and to characterize the sample. In the final tabulated score, th e higher the score, the greater the severity of hearing loss.

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71 Geriatric Depression Scale The Geriatric Depression S cale (GDS) (Yesavage, Brink, Rose, Lum, Huang, Adey, et al., 1983) is a 30-i tem self-report assessment designed to identify depression in the elderly. Each item is constructe d to be answered yes or no, to reduce cognitive complexity of the items. The measure was designed to minimize the influence of somatic symptoms, which may not be accurate reflections of depression in older adults. One commonly used cutoff point for mild depression is 10. HVLT The Hopkins Verbal Learning Test-Revised (HVLT-R) (Shapiro, Benedict, Schretlen, & Brandt, 1999) was us ed to test individual memory and verbal learning performance. In this test individuals repeated a list of 12 words read by the in structor. The test consisted of three learning trials, a delayed/ recall trial (20-25 minutes delay) and yes/no delayed recognition trial. This later trial consiste d of a randomized list that included the 12 target words and 12 non target words, six of which are drawn from the sa me semantic category as the targets. Raw scores were derived for total recall, delayed recall, retention (% retained), and a Recognition discrimination index. No inclusion/exclusion was based on this score, but it was collected as a possible covariate and to characterize the sample. Proximal Outcome Variable A proximal outcome refers to the direct outcome being measured, and is sometimes construed as the "mechanism" by which training e ffects are carried to real world outcomes. In other words, in order for the traini ng effect be transferred to the re al world, the training must first be proven effective on the basic skill or ability that is trained. The proximal outcome in this study is visual attention, as assessed with the Useful Fi eld of View test (UFOV; Owsley, Ball, Sloane, Roenker, & Bruni, 1991; Ball, Owsley, Sl oane, Roenker, & Bruni, 1993; Myers, Ball, Kalina, Roth, & Goode, 2000; Raed t., & Ponjaert-Kristoffersen, 2000; Edwards, Wadley, Myers,

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72 Roenker, Cissell, & Ball, 2002; Ball, Berch, He lmers, Jobe, Leveck, Marsiske, et al., 2002; Roenker, Cissell, Ball, Wadley, & Edward, 2003). Useful Field Of View (UFOV) test The UFOV (UFOV Users Guide) is a computeradministered test. It measures the speed at wh ich individuals can proce ss information within a 30 radius visual field under a variety of c ognitively demanding conditions. The test can be administered in about 15 minutes. The test is assessed binocularly and it involves the detection, localization, or iden tification of targets against more comp lex backgrounds. It consists of four subtests that assess the speed of processing under increasingly complex task demands (e.g., divided attention, selective attention). These we re described in detail in the UFOV training section above. Participants end up with four s ubtask scores, which can also be summed into a composite. Each subtask score ranges from 16 ms (fastest) to 500 ms (slowest), and represents the average presentation time needed for the partic ipant to perform that task with 75% accuracy. As described elsewhere in deta il, the four subtasks are: (1) Subtask 1, Speed, identif y a centrally presented object as car or truck (2) Subtask 2, Divided attention, identify a centrally presented object as car or truck, while noting the location of a pe ripherally presented object (3) Subtask 3, Selective atten tion, identify a central ly presented object as car or truck, while noting the location of a peripheral object that is presented in clutter (4) Subtask 4, Same-different, note whether two centrally presented objects are the same or different (two cars, two trucks, or a car and a truck), while simultaneously noting the location of a peripheral object that is presented in clutter Primary Outcome Measures The primary, or 'real world', outcome for this study was simulated driving performance. This primary outcome was selected because it speaks to the practical value, if any, of the visual

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73 attention training, and exte nds the video game training results to a real world outcome that has not yet been adequately studie d, vis--vis video games, in ol der adults. Moreover, driving performance is greatly influen ced by visual attention (Ball, Owsley, Sloane, Roenker, & Bruni, 1993). In this study, a driving simulator progra m was used to assess the primary outcome measure. It is difficult to measure visual attentio n in situ, on the road, because one cannot control the environment. A high-fidelity driving simula tor was available to the researchers, which permits controlled visual environments, millisecond timing at a high sampling rate (50 Hz), and ensures the safety of participants Of course, as a simulator, ec ological validity concerns are also present. In this task, participants used a st eering wheel and gas-brakepedal controller, which was thought to enhance the realism of the simulation. Type of simulator The simulator used in this st udy was the STISM Driving simulator developed by Systems Technology Incorporated Scenarios were presented using a Dell Optiplex GX270 CPU, 19 inch flat screen mon itor, and Logitech MOMO Force Feedback steering wheel (Figure 3-8). A range of scenes was generated by the simulator. The simulator included driving controls such as accelerator, break pedal and steering wheel. Participants sat about 18 from the monitor. Participants brake reaction distance was assessed in the driving simulator. Simulator task warm up First, the participants received instructions on how to operate the simulator. In this task, participants had to use the steering wheels, brakes and accelerator, as in a real car. They had about 5 minutes to prac tice driving the simulator before the test begun. Simulator task: Roadway obstacle det ection (dog) and responsive braking During the test, the participants were asked to behave as they would behave in a real environment e.g.,

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74 stopping for pedestrians, obeying the speed limit. Participants were informed that as they drove, they would encounter dogs that ha d been placed in different part s of the scenario (i.e, in the center of the roadway or at the side of the road, ei ther close to or far from the road). They were instructed that the main task while driving the s imulator was to visually scan the entire screen and try to detect the location of the target stimulus (i.e., dog). Each time a participant thought they saw the dog, the instant they saw it, they were instructed to perform a "brake reaction" task. Participants were instructed to "jam on the brak e" (i.e., not gentle braking) when they saw a dog, even if the dog was not in or near the roadwa y. This represented one major difference between the simulator and real be hind-the-wheel performance. In the s imulator, participants needed to react as fast as possible by depressing the brak e pedal when they observed the target stimulus that was embedded into the scenarios. Some of the stimuli (e.g., dogs) would be in the middle of the road, some were in the periphery. Ther e were a total of 18 dogs through the scenarios. Participants had to depress th e brake each time a dog was seen. No cars were placed behind the cars the participants were drivi ng so there was no risk of a cr ash by suddenly pressing the brakes. The scenarios would gradually increase in complexity and ar e described further below. Rationale for using the dog Studies using the simulator to measure reaction time mainly use two approaches: Using a light display located on the top panel of the drivers unit in which participants were instructed to brake as quick ly as possible when the lights were illuminated (Roenker, et. al, 2003), and by displaying figure shaped images (e.g., diamond) at the monitor screen, in which participants are supposed to engage the turn signal whenever the shapes appeared on the screen (Lee et al., 2003). Althou gh these approaches would be easier to use, it would not allow placing the stimulus in different parts of the s cenario. Moreover, in this study we tried to replicate the UFOV s ubtasks in a driving simulator.

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75 Different scenarios The participants started driving in an acclimation scenario (Figure 3-9). This scenario was simply a roadway without distractions (e.g., road side objects) and it was created to get participants fam iliar with the simulation projection, the steering wheels, brakes and accelerator. During this scenario, the participants were asked to depress the brakes until they were comfortable doing so. Throughout the scen arios there were speed limit signs that the participants had to obey. Part icipants were asked to contro l their speed by looking at the speedometer located in the bottom of the scree n, and depressing the accel erometer as required. Following the acclimation scenario, the participan ts drove through a rura l scenario. In this village, there was some oncoming traffic and some houses but there was not much clutter (Figure 3-10). The entire scenario had straight roads; the pa rticipants did not have to turn right or left. The task continued with partic ipants driving through a small to wn. In this town there was more clutter and distraction as can be seen in figure 3-11. Then the participants drove through a beach town (Figure 3-12) and then a metropolis, depicted in the subsequent figure (Figure 3-13). Position of the dogs through the scenes There were a total of 18 dogs located throughout the scenarios. The placement of dogs throughout the scenarios wa s constructed to approximate the 4 different UFOV subtasks (processing speed, divided atte ntion, selective attention and selective attention with a same -different judgment). In this simulator task, the dogs were positioned from 0 to 110 degree eccentricities either on the right or left side of the road. Some dogs were very easy to find because they were placed either on the center of the road or at a low eccentricity and there was not much clutter next to them. Other dogs were placed in locations that were more difficult to find because there was more clutter and distractions on the road, such as a bicyclist driving in front of the participants car.

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76 For didactic purposes and to better unde rstand the position of the dogs through the scenario, the different position of the dogs will be referred to by using the term blocks. Thus, the position of dogs was divided in to four blocks that represen ted the easiest (Block 1) to hardest (Block 4) locations (e.g., in the center of the screen versus far from the road). However, block numbers do not reflect the sequence at whic h dog positions of varying difficulty appeared throughout the scenario, which were more or less randomly arrayed, but merely the difficulty of the different positions of the dogs through the scenario. Block one represented the dogs that ar e located in the center of the road and are the easiest to seen (between 0 a nd 10 degrees eccentricity). An example of a dog from Block 1 is presented in figure 3-14, this dog is positioned right in the center of the road and it is very easy to be seen. There were a to tal of 3 dogs in Block1. Block two represented the dogs located at a low to medium eccentricity but there was no clutter or distraction next to them (between 20 and 65 degrees eccentricity). An example of a dog from the second Block is presented in figure 3-15, this dog is located in the periphery, but is close to the road and there is no t clutter next to it. There were a total of 6 dogs in this Block. Block three represented the dogs located at a higher eccentricity and with more clutter around them (between 80-110 degrees eccentricity). An example of a dog from the third Block is presented in figure 3-16, this dog is also located in the periphery, but it is not very close to the road and there is clutter around it. There were a total of 4 dogs in this Block. Block four represented dogs located at a low to high eccentricity but they are surrounded by clutter and distractions (between 25-90 degrees eccentricity). An example of the fourth Block of dog is presented in figure 3-17. This dog is at a medium eccentricity, however there is

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77 substantial clutter surrou nding it and distractions such as othe r cars driving next to it. There were a total of 5 dogs in this Block. Although the pictures can give a good idea of the position of the dogs on the screen, it does not give a good representation of the dynamics of the simulation run, sh owing the clutter and distractions. The position of each dog (with distance from th e center of the road) and the sequence that they appear on the screen is shown in table 3-2. Filler scenarios The entire simulation run took a bout 20 minutes to complete. Between the onset of each dog there were some f iller scenarios in which participants had to keep driving (see figures 3-18 and 3-19 for example of the filler scenarios). How data were collected In this study, data were collected in two ways: 1) by simulatorgenerated data and, 2) behavioral data. In the case of the simulator-gen erated data, although the participants would drive for a pproximately 20 minutes, the simula tor was programmed to collect data only where the dogs were located. Data starte d to be recorded at 400 feet before the dog could be seen and stop being collected as s oon as the participant pa ssed the dog. Between the onset of each dog, the participant dr ove into a filler scenario (a s described above). As soon as the participant depressed the brake pedal, this wa s recorded by the simulator as a marking point. If participant did not see the dog and continued driving, there woul d be no marking point in that block, and the block would be recorded as an inaccu rate/error trial. The si mulator generated three types of data: brake reaction distance, lane maintenance, and accuracy. (1) Brake reaction distance was measured as the distance from when the dog first appeared on the screen (whether the participant could see it or not ) to the first instant when the participant first depressed the brake. The potential scores for brake reaction distance ranged from

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78 1 to 400 meters, where 400 meters represented th e case where the particip ant did not see the dog. The participant would score 1 if he or she pre ssed the brakes on the fi rst moment the dog could be seen in the simulator and they would score 400 if they missed the dog. Because participants might drive at different speeds, there was inter-pa rticipant variability in the amount of time they would have between when the dog was first per ceptible on-screen and when they would be laterally positioned with the dog. Thus, using brake reaction time as a dependent variable would introduce inter-individual differen ces in response time that had not hing do with ac tual reaction time, but with the driving speed. On the ot her hand, regardless of speed, the dog always appeared at 400 feet before the person was laterally adjacent to it; thus, brake reaction distance was selected as a dependent measure because inter-p articipant differences would be solely due to the amount of distance traversed before the dog wa s noticed (i.e., how far the participant had to drive before he/she noticed the dog). We a ssumed that driving distance would be more equivalent and standard between participants, and less contaminat ed by individual differences in driving speed. (2) Lane maintenance score The roadway in which the participant drove always had a width of 12 meters; the car was six meters wide, so a perfectly centered ca r would have a lateral lane position of 6 meters (from the left side of the road). Since the sampling rate of the simulator was 50 Hz, fifty times a second, the lateral positi on of the car-in-lane was assessed. Based on other recent research with the simulator (Cook, 2007), it was determined that the standard deviation of this lateral position was a good indica tor of lane maintenance. The smaller the SD, the less "weaving" the participan t did while driving. This in dicator was assessed under the assumption that driving that was under less attentional control (e.g, because the participant was distracted by effortfully looking for the dog) woul d be characterized by more "weaving". If the

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79 participant became more confid ent and effective in dog dete ction due to training, it was hypothesized that there would be less "weaving" and the standard deviation should be decreased post-training. Thus, the expect ation was that lane maintenan ce might improve with visual attention improvements. (3) Accuracy score The accuracy data was related to the fact that participants depressed the brakes at that time or not. The accuracy data ranged from 0 to 18. A participant would score 0 if they could not see any dog in that trial and they would score 18 if they saw all the dogs in the trial. A composite score was calculated by comp uting a mean score of the 18 trials from each participant either for brake reaction distance or for accuracy. To give a better representation of how the simulator generated data was collecte d, figure 3-20 presents a diagram with detail description of data collection. The behavioral data was collected by the instru ctor. The instructor w ould make notes about the participants driving behavior throughout the run. There were situ ations in which bicyclists or pedestrians were close to the ro ad and the participants would br ake for the bikes or pedestrian instead of the dogs. To account for the possibility of errors (e.g., braking for pedestrians instead of dogs during the trial), the instructor collected the behavioral data. Following each run, the behavioral data was compared to the simulato r-generated data for each block of dogs for each participant. For example: if in a certain block the simulator re corded an accurate trial (meaning that they depressed the brake), but according to the behavioral notes the participants stopped for a bicycle instead of the dog in that particular trial, the data was corre cted in the system. Correlation Between UFOV and Simulator Subtests A correlation analysis was performed to expl ore: 1) the correlation among different group of dogs (center, low to medium eccentricity little clutter, high eccen tricity much clutter, and low to high eccentricity wi th much clutter and distraction) ; 2) the correlation among the UFOV

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80 scores (speed of processing, divi ded attention, selective attention and same different); and 3) the correlation among the four Blocks of dogs with the UFOV scores either at pre and posttest. Table 3-3 presents the correlations at pretest and Table 3-4 shows the correlations at posttest. At both occasions, there was positive manifold among all UFOV measures a nd all brake reaction distance Blocks, suggesting that in general performance was consistent across the subtasks within a measure. These correlations had a qua si-simplex pattern, meaning that adjacent subtasks/Blocks thought to be cl oser in difficulty/complexity were also more associated correlationally. At pretest, Simulator Blocks 2, 3 and 4 (all ha d dogs at the side of the roadway, and were characterized by increasing eccentricity and clutter in the higher blocks) were all associated with UFOV subtasks 3 and 4 (Selective Attention a nd Selective Attention w ith a Same-Different judgment). Thus, the more difficult (and theref ore more variable) task s from UFOV and the Simulator were associated. The results lend so me support to the idea th at simulator and UFOV measures reflect, at least somewhat, overlapping c onstructs. However, given the fairly modest associations between them, and dr awing on the logic of "indirect effects" in regression (Baron & Kenny, 1986), the correlations also imply that very large UFOV effect sizes would be needed before transfer to the simulato r outcome could be expected. At posttest, there was a narrower band of transfer, with only the Block 3 simula tor brake reaction distance related to UFOV tasks 2-4. This may be due partly to the fact that tr aining may have reduced variability in the UFOV and/or simulator outcomes (due to more partic ipants moved to ceiling-level performance), which could have attenuated correlations as well. Secondary Outcome This study has two secondary outcomes measur es: Flow and a game design questionnaire. As discussed in the introduction, a secondary outcome measure, "Flow", explored individuals

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81 personal experience with video game playing. This could only be ad ministered to participants in the video game groups, and was co llected during their six training sessions. This variable was included to determine ones personal experien ce with video game playing and how it would influence individuals compliance with future pr ograms using video games. This variable was operationalized by a Flow questionnaire. The Flow experience was measured using th e Flow State Scale (FSS). This scale was developed by Jackson & Marsh (1996) using the Csikszentmihalyi, (1990) concept of flow. The FSS conceptualizes Flow in nine dimensions : Challenge-skill bala nce, action-awareness merging, clear goals, unambiguous fee dback, concentration on task at hand, sense of control, loss of self-consciousness, transforma tion of time and autotelic experi ence. These dimensions were described previously by Csikszentmihalyi (Csiks zentmihalyi, 1990) and they are also related to use of games and flow. Participants in th e two video game groups answered the Flow questionnaire at the end of each of the six traini ng sessions, after they had played games with a coach for about 90 minutes. Thus, each participant ha d a total of six flow scores. Scores (for each trial) could range from 30-180, higher scores means more flow experience. Game design questionnaire The model of the game de sign questionnaire was adapted from a previous study designed to investigate older adults opinion about technologies. Sample Size Power analysis was conducted with data from the ACTIVE clinical trial. A repeated measure design with 3 experimental groups (act ion video game, UFOV, and placebo control group) and, 2 occasions (pre-test and post-test) wa s used. Using the ACTIVE data, we estimated residual covariance between occas ions provided by SPSS. This an alysis suggested the effect size, Cohens f of occasion (pre/posttest) by group interaction was 0.48, which corresponds to a medium effect size. Using NCSS/PASS program to estimate required sample size using their

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82 effect size, it was found that gr oups of 16 or more participants should have adequate power to detect the critical interaction.

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83 Table 3-1. Measures used in the study. Measure Pretest During Posttest Baseline measures Mini Mental Status Examination (MMSE) X Snellen vision chart X Hearing Handicap Inventory for Adults (HHIES) X Geriatric Depression Scale (GDS) X Hopkin Verbal Learning Test (HVLT) X Proximal outcome Useful Field of View Test (UFOV) X X Primary outcomes Driving simulator (Brake reaction distance, lane maintenance, and obstacle detection accuracy) X X Secondary outcomes Flow X Game design X Note: MMSE = Mini Mental State Examination; HHIES = Hearing Hand icap Inventory for Adults; GDS = Geriatric Depression Scale; HVLT = Hopkins Verbal Learning Test; UFOV = Useful Field of View.

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84 Table 3-2. Sequence of the position of each dog on the scenario Distance Condition Left side Center Right side 0 Center of road X 65 Low to medium eccentricity, little clutter X 65 Low to medium eccentricity, little clutter X 95 High eccentricity, a lot of clutter X 110 High eccentricity, a lot of clutter X 80 High eccentricity, a lot of clutter X 10 Center of road X 50 Low to medium eccentricity, little clutter X 100 High eccentricity, a lot of clutter X 0 Center of road X 20 Low to medium eccentricity, little clutter X 40 Low to medium eccentricity, little clutter X 40 Low to high eccentricity, a lot of clutter and distraction X 90 Low to high eccentricity, a lot of clutter and distraction X 50 Low to high eccentricity, a lot of clutter and distraction X 30 Low to high eccentricity, a lot of clutter and distraction X 20 Low to medium eccentricity, little clutter X 25 Low to high eccentricity, a lot of clutter and distraction X

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85 Table 3-3. Correlation between UFOV and simulator subtasks during pretest. Block_1 Block_2 Block_3 Block_4 UFOV_1UFOV_2UFOV_3 UFOV_4 Block_1 1 Block_2 0.41** 1 Blcok_3 0.27* 0.54** 1 Block_4 0.30* 0.61** 0.56**1 UFOV_1 -0.10 0.26 0.240.211 UFOV_2 -0.08 0.13 0.170.220.48**1 UFOV_3 0.21 0.33* 0.32*0.42**0.42**0.70**1 UFOV_4 0.03 0.27* 0.30*0.26*0.150.32*0.43** 1 Note: ** = p < .001; = p < .05; UFOV_1 = pr ocessing speed subtask; UFOV_2 = divided attention subtask; UFOV_3 = sele ctive attention subtask; UFOV_4 = selective attention with two central icons; Block_1 = block of dogs that are centered to the ro ad; Block_2 = block of dogs on close distance to road without much clutter; Bl ock_3 = block of dogs on far distance to road with a lot of clutter; Block_4 = block of dogs loca ted at low to high eccentricity but they are surrounded by clutter and distractions. Table 3-4. Correlation between UFOV a nd simulator subtasks during posttest. Block_1 Block_2 Block_3 Block_4 UFOV_1UFOV_2UFOV_3 UFOV_4 Block_1 1 Block_2 0.39** 1 Block_3 0.22 0.41** 1 Block_4 0.42** 0.48** 0.28*1 UFOV_1 0.13 0.25 0.220.161 UFOV_2 0.05 0.24 0.31*0.220.221 UFOV_3 -0.01 0.18 0.34*0.210.190.69**1 UFOV_4 0.24 0.17 0.38**0.160.180.41**0.51** 1 Note: ** = p < .001; = p < .05; UFOV_1 = pr ocessing speed subtask; UFOV_2 = divided attention subtask; UFOV_3 = sele ctive attention subtask; UFOV_4 = selective attention with two central icons; Block_1 = block of dogs that are centered to the ro ad; Block_2 = block of dogs on close distance to road without much clutter; Bloc k_3 = block of dogs on far distance to road with a lot of clutter; Block_4 = block of dogs loca ted at low to high eccentricity but they are surrounded by clutter and distractions.

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86 Figure 3-1. Diagram of study desi gn presenting: Medal of Honor (MOH) group, Useful Field of View (UFOV) group, Tetris group a nd the no contact control group. Baseline Baseline Randomization No randomization MOH group UFOV group Tetris group No contact group 6 training sessions No intervention Posttest Posttest

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87 Note: UFOV = Useful Field of View; MOH = Medal of Honor Figure 3-2. Hypothesis 1: Note: UFOV = Useful Field of View; MOH = Medal of Honor Figure 3-3. Hypothesis 2:

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88 Figure 3-4. Example of the controller us ed by participants to play the game Figure 3-5. Example of a PowerPoint slide with step -by-step instructions gi ven to participants.

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89 Figure 3-6. PowerPoint slide wi th instructions on how to use the jump button. Figure 3-7. Screen s hot of TETRIS game

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90 Figure 3-8. Example of a driving simulator used in the study

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91 Figure 3-9. Screen shot of the acclimation scenario. Figure 3-10. Example of the rural scenario.

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92 Figure 3-11. Example of a small town scenario Figure 3-12. Example of a beach town scenario.

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93 Figure 3-13. Example of a metropolis scenario Figure 3-14. Example of a dog from block 1 it is positioned in the center of the road.

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94 Figure 3-15. Example of a dog from block 2 it is positioned in a low eccentricity and without clutter. Figure 3-16. Example of a dog from block 3 it is placed on high eccentricity with some clutter.

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95 Figure 3-17. Example of a dog from block 4 it is placed on a medium eccentricity but with much clutter and distraction. Figure 3-18 Example of a rural road s cenario used as a filler scenario

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96 Figure 3-19. Example of a city scenario used as a filler.

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97 Figure 3-20. Diagram with illustration on how simulator generated data was collected. DOG DOG Brake Brake FILLER DRIVING Measurement begins here Measurement begins here End of assessment for inaccurate participant trial End of assessment for inaccurate participant trial End of trial for assessment for accurate participant trial End of trial for assessment for accurate participant trial 400fee t 400fee t

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98 CHAPTER 4 RESULTS Overview This chapter includes five broad sections. Th e first section corres ponds to a preliminary analysis and the following sections correspond to the primary aims of this study. A preliminary analysis was conducted in order to determine whether there were differences between the training and control group at th e baseline assessments, thereby addressing the effectiveness of the study's randomization. The preliminary analysis also investigated the difference between participants who completed the training and participants who dropped out of the study. This attrition analysis addressed wh ether dropout was selective (there by potentially biasing the study findings). The second section examined whethe r older participants who received videogame training experienced gains in visu al attention performance. More sp ecifically, analyses compared four groups (Medal of Honor [MOH], Useful Fiel d of View [UFOV], Tetr is, no-contact Control) in their pretest-posttest changes in a measure of visual attention, to see whether participants in the MOH group, in particular, experienced gains comparable to traditional UFOV training, and greater than the other two groups. The third se t of analyses examined whether videogame playing might also improve performance on seve ral measures from a driving simulator (e.g., brake reaction distance, accuracy of detecting a peripheral object and lane maintenance). The fourth set of analyses examined whether ch anges in perceived enjoyment and engagement (Flow) in participants in th e three intervention groups (MOH, UFOV, and Tetris), and whether the pattern of changes varied by group. The last set of analyses used descriptive statistics and anecdotal data to explore particip ants opinion about videogame design.

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99 Preliminary Analysis Assessment of Baseline Differences Between Treatment Groups An analysis of variance was conducted to determine whether there were differences between the four intervention gr oups in demographic status, age, years of education, gender, race, two cognitive measures (MMSE and HVLT), depression, and two sensory measures (visual acuity and hearing). The omnibus te st revealed that there were no significant differences between the four groups, suggesting that randomizati on had distributed partic ipant characteristics similarly across the groups. Table 4-1 shows the characteristics of the sample on the variables assessed, both for the total group and by intervention subgroup. P -values shown reflect the absence of a significant overall difference between groups. Attrition Given the duration and time commitment requi red for the current study, and the fact that twelve participants withdrew, it was important to determine wh ether dropout was selective. (Selectivity of dropout would sugge st that the remaining sample on which the results were based was positively biased). To examine this, returnin g and non-returning participants were compared in baseline characteristics; spec ifically, analysis of variance was conducted to examine whether participants who dropped out of the study prior to its completion differed at pretest from the participants that completed the study (e.g., comp leted training intervention and posttest) on demographic and cognitive variables. As shown in Table 4-2, no significa nt differences emerged between completers and non-comp leters of the study protocol, suggesting that dropout did not bias the generalizability of th e study findings. Also, only three pa rticipants who dropped out (one from each intervention group MOH, UFOV, Tetris) knew to which training they were randomized because they dropped out during the tr aining. The remaining of the participants who

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100 dropped out (n = 9) did not know to which trai ning they were randomized because they dropped out before the first training session, when the intervention assignment were revealed. Aim 1: Effects of Videogame Traini ng on Visual Attention (UFOV) The first set of analyses examined the core four-group design that was the basis for this study. The underlying hypothesis, dr awn from Green and Bavelier ( 2003), was that exposure to a first-person shooter action video game (MOH) woul d cause improvements in a measure of visual attention (the UFOV test). We sought to expand prior work in this area, which had been done with undergraduates, by consideri ng an older adult sample. We also wanted to compare the magnitude of expected UFOV gains due to MOH by comparing the MOH intervention to the gold standard for this visual attention out come, the University of Alabama-Birminghams computer-based UFOV training program (Ball et al., 1988). Green and Ba velier had included a Tetris intervention group in their study as a plac ebo control, and we added that to this study as well. Finally, as described in the Methods chapte r, because interim analyses suggested it might be difficult to disentangle simple pretest-to-posttest pr actice effects from the actual effects of videogame playing, we added a fourth non-equiva lent no-contact Control group, which permitted us to compare the size of the MOH training effect s, if any, to the simple effects of taking the UFOV test twice. Prior to conducting these plan ned analyses, we first sought to investigate whether randomization had worked to, in fact, e quate groups at baseline. We assessed whether the four treatment groups were significantly different at pret est on either the UFOV composite score and or any of its four cons tituent subtask scores. For none of the measures were the groups significantly different ( p > .05). Throughout this text 2 is shown to indicate a partial etasquared, which is an indicator of effect size. Values closer to 1.0 indicate a stronger effect size.

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101 Analyses on the UFOV Composite Our initial analysis was conducted, followi ng published work (e.g., Ball et al., 2002; Willis et al., 2006) on the UFOV composite score. Thus, the dependent variable is the UFOV composite score (Speed + Divided attention + Se lective attention score + Same-Different). The design was a mixed between-within design. The within-persons indepe ndent variable was Occasion (2: Pretest, Posttest). The between -persons independent variable was Group (4: MOH, UFOV, Tetris, no-contact Control). The cr itical effect of inte rest was the Occasion by Group interaction, which would inform us about whether there were group differences in the pattern of pre-post change. A significant main eff ect was revealed for Occasion (pre post), F (1, 54) = 34.07, p < .001, 2 = .38, suggesting that, across all gro ups, participants tended to improve their performance. No significant main effect was found for Group, F (3, 54) = 1.4, p > .005, 2 = .07, which suggests that there were no overall group differences, averaging across preand posttest. In general, this reassu res that randomization equated groups With regard to the critical Occasion by Group interaction, there wa s a significant interaction effect F (3, 54) = 5.8, p =.002, 2 = .24. Thus, there was a significant differences in change by intervention group. The mean values and standard deviation for the depende nt measure by group are presented in table 4-3. Pre-post change in UFOV composite performanc e, and group differences in change, are illustrated in Figure 4-1. To further understand this in teraction effect (i.e., whic h groups were changing at a different rate from others), we computed UFOV ch ange scores (post pre) for each participant. These were computed by subtracting the compos ite pretest scores from the composite posttest scores. A univariate ANOVA using change in the UFOV composite score as the dependent variable (composite posttest score composite pretest score) was conducted. Treatment group membership was the independent variable (MOH UFOV, Tetris, No-contact control). Results

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102 revealed a significant main effect of group membership on UFOV change, F (3, 54) = 5.8, p = .002, 2 = .24. Follow-up Bonferroni post-hoc t-tests were conducted to determine which groups differed from one another signif icantly in their UFOV change from pretest to posttest. The UFOV group improved significantly mo re than the Tetris group ( p = .05) and the no-contact Control group ( p < .01), but the difference in change between MOH and UFOV training groups did not reach significance ( p = .08). Mean differences in cha nge score, by group, are shown in Table 4-4. While the mean trends in the data su ggest that participants in either videogame group improved more than the control gr oup (see Figure 4-2), this differe nce did not reach significance. We also examined this analysis on residual change scores (pretest is entered as a covariate, and posttest score is th e dependent variable; because of the tendency to have high testretest correlations between pretest and posttest this will tend to substantially increase the statistical power of analyses on change scores), but the pattern of results was similar, and is therefore not expanded upon here in order to reduce redundancies. Re-Examining Training Effects on UFOV by Each Subtask The analyses on the composite score failed to find that the MOH intervention improved UFOV scores significantly more than Tetris or the no-contact Control condition. We thus decided, as a follow up analysis, to re-examine this main analysis using each of the four UFOV subtasks as distinct dependent va riables. Previous research has suggested that many older adults are at ceiling on the UFOV Subtask 1 (Speed), and at floor on the UFOV Subtask 4 (Samedifferent), suggesting these subtas ks may not be very se nsitive to change. (Edwards et. a., 2006) Thus, we were particularly focused on UFOV Subtask 2 (Divided Attention) and Subtask 3 (Selective Attention). A mixed between-within ANOVA was conducte d with two within-persons variables and one between-person s variable. The first within-persons variable was UFOV Task (4: Speed, Divided Attention, Selective Attenti on, Same-Different). The second within-persons

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103 variable was Occasion (2: Pretest, Posttest). Th e between-persons variable was again Group (4: MOH, UFOV, Tetris, no-contact Control). The m ean values and standard deviation for each UFOV subtest are shown by group and by occasion in Table 4-5. A significant main effect was revealed for Occasion (pre post), F (1, 54) = 34.0, p < .001, 2= .38 and UFOV subtask F (3, 162) = 484.9, p < .001, 2= .90 but no significant main effect was found for Group F (3, 54) = 1.4, p = .232, 2 = .07. For the two way interactions, a significant interaction effect wa s revealed for Occasion x Group, F (3, 54) = 5.8, p = .002, 2= .24 and Occasion x UFOV subtasks, F (3, 162) = 5.3, p < .002 2= .09 but no significant interaction effects were revealed for UFOV subtask x Group F (9, 162) = 1.2, p = .248, 2= .06. A significant three-way interaction was found fo r the Occasion x UFOV subtask x Group effect, F (9, 162) = 4.5, p < .001, 2= .20. The main effect of Occasion reflected a gene ral decrease in the UFOV scores over time and the main effect of UFOV subtask reflected th e finding that in the subt asks 3 and 4 there was more room for improvement. The Occasion x Group inte raction reflected the fact that, in general, the UFOV group had greater improvement at posttest. The Occasion by UFOV subtask interaction reflected the fact th at the amounts of improvement in scores at posttest was seen on particular subtasks; i.e., subtasks 3 and 4 which had more room for improvement. The significance in the three way inte raction (Group x Occasi on x UFOV subtask) revealed that the pattern of group differences in rates of pre-post change varied by UFOV subtask, again reflecting that group difference findings were most concentrated on UFOV subtasks 3 and 4. To decompose the three-way interaction, a follow-up univariate two-way mixed betweenwithin ANOVA was conducted on each of the four UFOV Subtasks (Speed, Divided Attention, Selective Attention, Same-Different). For each of these analyses, Occasion (2: Pretest, Posttest)

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104 was the within-persons indepe ndent variable, and Group (4: MOH, UFOV, Tetris, no-contact Control) was the between-persons independent variable. The dependent measure for each of the four UFOV subtasks was the average time, in milliseconds (range = 16-500 ms) for participants to complete the subtask with 75% accuracy. Speed of Processing Subtask For the Speed UFOV Subtask, which assessed the speed with which participants could judge whether they had seen a car or a truck, a significant main eff ect of Occasion was not found F (1, 54) = .27, p = .104, 2= .04, also there was no significa nt main effect of Group F (3, 54) = .75, p = .526, 2= .04. The Occasion by Group interacti on was also not significant, F (3, 54) = 1.4, p = .238, 2 = .07. There was no improvement on this task because participants were already at ceiling. The mean values and standard devi ation for the dependent measure by group are presented in table 4-6. Figure 4-3 illustrates the change from pret est to posttest, by group, for the Speed of processing UFOV subtask. Divided Attention Subtask For the Divided Attention UFOV Subtas k, which assessed the speed with which participants could judge whether they had seen a car or a truck while also looking for the peripheral location of a second car, ther e was a significant effect of Occasion F (1, 54) = 5.9, p = .018, 2= .09 but there was no significant main effect of Group F (3, 54) = 1.7, p = .163, 2= .09. The Occasion by Group interaction was also not significant, F (3, 54) = 1.0, p = .379, 2 = .05. Thus, although there was a significant Occasion main effect (i.e., on average, all participants got better from the first to the second test), the absenc e of an interaction indi cates that there were no group differences in change on this measure. In th is test, participants were, on average, already close to ceiling. The mean values and standard deviation for the dependent measure by group are

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105 presented in table 4-7. Figure 4-4 illustrates the change from pret est to posttest, by group, for the Divided Attention UFOV subtask. Selective Attention Subtask For the Selective Attention UFOV Subtas k, which assessed the speed with which participants could judge whether they had seen a car or a truck while also looking for the peripheral location of a second car in clutter the main effect of Occasion was significant F (1, 54) = 26.49, p = <. 001, 2 = .32 but the main effect of Group was not significant F (3, 54) = .93, p = .430, 2 = .04. The Occasion by Group interaction was significant, F (3, 54) = 4.4, p = .007, 2 = .20. This interaction suggests that there were group differences in pre-post change on this measure. This effect is further explored with a change score analysis afte r the next section on the Same-Different task. The mean values and standard deviation for the dependent measure by group are presented in table 4-8. Fi gure 4-5 illustrates the change from pret est to posttest, by group, for the Selective Attention UFOV subtask. Same-Different Subtask For the Same-different UFOV Subtask, which assessed the speed with which participants could decide whether two centrally presented objects were the same or different, while also looking for the peripheral location of a second car in clu tter, there was a significant main effect of Occasion F (3, 54) = 9.8, p = .003, 2 .15 but there was no significant main effect of Group F (3, 54) = 1.9, p = .132, 2 .09. The Occasion by Group interaction was again significant, F (3, 54) = 7.2, p < .001, 2 = .28. Thus, the significant interaction says that there were again group differences in pre-post change on this measure, an effect which is further explored with the change score analysis that follows. The mean va lues and standard deviation for the dependent measure by group are presented in table 4-9. Figure 4-6 illustrates the ch ange from pretest to posttest, by group, for the Same-Different UFOV subtask.

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106 Change score analyses of Selective A ttention and Same-Different subtasks To further explore the significant Occasi on by Group interaction for the Selective Attention subtask and the Same -Different subtask, a follow-up analysis was conducted using change scores. Specifically, for both subtasks, ch ange scores were computed by subtracting the pretest score from the posttest scores for each subtask. Negative changes would represent improvement in performance (faster times). Additionally, Bonferroni corrected follow up t-tests were used to examine whether th ere were significant group differen ces in pre-post change scores over the two occasions. As noted above for the com posite score, we also examined this analysis on residual change scores (where pretest scor es on the dependent measure served as the covariate, and posttest was the dependent variable ) and this did not alter the pattern of findings; details are omitted here in the service of conciseness. Selective attention subtask using change scores For the selective attention subtask, a significant main effect of Group was found, F (3, 54) = 4.4, p = .007, 2 = .20. A post hoc t-test analysis using Bonferroni correction (Table 4-10) revealed that the UFOV-trained group improved significantly more (p < .05) than the control group. In addition, the Tetris group improved significantly more (p < .05) than the control group as well. The UFOV group did not improve significantly more than either the Te tris or MOH groups, and the two video game groups did not differ significantly from one another. Figure 4-7 displays the trends in the data. Same-different change scores For the Same Different s ubtask, a significant main effect of Group was also found, F (3, 54) = 7.2 p < .001, 2 = .28. A post hoc analysis using Bonferroni correction revealed that the UFOV group improved signi ficantly more (p < .05) than all other groups, and that this was the only signif icant set of comparisons in these data. Unlike the Selective Attention task, there was no evid ence that either of the video game groups

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107 improved more than the no-contac t control group, and they did not differ from one another. Mean trends are shown in table 4.11, and illustrated in Figure 4-8. Aim 2: Effects of Videogame Training on Driving Simulator Performance Results in this section focu sed on our three simulator-based dependent measures: (1) Brake reaction distance, (2) lane maintenance, an d (3) object detection accu racy (i.e., number of trials in which a dog was correctly detected and re sponded to). The intent of the analyses in this section was to examine whether videogame trai ning might transfer to aspects of simulated driving performance. This conformed with the ultimate goal of this study, which was to investigate whether visual attentio n training might be transferred to a real life situation. Despite the preceding analyses, which f ound little support for our experiment al hypotheses (i.e., that the Medal of Honor group would experience more visual attention gain than Tetris or no-contact Controls; instead, the only evid ence that video games affected visual attention was for the placebo control condition, Tetris, which improved more than no-contact controls on the Selective Attention subtask of th e UFOV), this planned analysis wa s nevertheless examined. The goal of this set of analyses was to i nvestigate whether there were any preliminary indications of realworld transfer, even for the traditiona l (and highly effective) UFOV training. Driving Simulator Subtask 1: Break Reaction Distance Analysis on the composite Brake Reaction Distance A mixed between-within ANOVA was conducted on Brake Reaction Distance. As a reminder, a dog appeared on screen eighteen (18) times throughout the course of the driving task. The dog always appeared on screen about 400 feet before the car was laterally adjacent to it. The 400 feet before the car was laterally adjacent to the dog is referred to as a trial. The position of the dog varied from center-of-road to varying degrees of eccentricity, with right-or-left positioning counterbalanced.

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108 Participants were allowed to control their sp eed, in accordance with posted speed levels. Thus, Brake Reaction Time was cons idered not to be a useful indi cator of partic ipant response, since task difficulty and presenta tion rate of the dog would vary with the speed driven. Brake reaction distance was found to be le ss sensitive to individual di fferences in driving speed, and was selected as the indicator of choice. The composite score was computed as the average brake reaction distance, computed for al l 18 trials in which the dog appeared. For error trials (i.e., where the dog was not detected), a time-out dist ance (set to an arbitr ary maximum of 400 feet) was used. In the mixed between-within ANOVA, the same effects as in all precedent analysis were used. Group (4, MOH, UFOV, Tetris, and nocontact Control) was the between-persons variable, and Occasion (2, Pretest, Posttest) wa s the within-persons variable. The dependent score was the average brake re action distance over the 18 dog tr ials. There was a significant main effect of Occasion F (1, 51) = 8.5, p = .005, 2 = .14 (indicating that people improved, requiring shorter reaction distances from pre to posttest), but the main effect of Group was not significant F (3, 51) = .04, p = .989, 2 = .002. Analyses also reveal ed a non-significant Group X Occasion interaction, F (3, 51) = .85, p = .107, 2 = .04. Although there is a significant main effect for Occasion, the absence of an interact ion indicates there were no group differences on this measure. The mean values and standard deviation for the depende nt measure by group are presented in table 4-12. The relationship betw een baseline and posttest performance are illustrated in figure 4-9. Analysis on the Brake Reaction Distance by Trial Type As with the Useful Field of View, a concern was that the aggregate, com posite mean brake reaction distance across all 18 dog-trials may have obscured any effects that were spec ific to particular trial types. In particular,

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109 since the UFOV training had the largest effects, in the preceding section, on the more difficult UFOV subtasks (Selective Attention, Same-Di fferent), a question was whether transfer to simulator-measured Brake Reacti on distance might be greatest for dog trials in which the dog was more peripherally located and/ or in clutter. To further expl ore this question, the eighteen dog trials were grouped into four bloc ks of trials: (a) Block 1: D og was located in the center of the road, (b) Block 2: Dog was located at the side of the road, but close to the road, with minimal clutter (e.g., country road), (c) Block 3: Dog was located a subs tantial distance from the side of the road (between 80-110 degrees eccentricity) with more clutter (e.g., the dog was located between houses or trees), and (d) Block 4: Dog wa s located a certain distance from the side of the road (between 25 degrees eccentricity) but with extensive clutter and distraction (e.g., traffic, cyclists, pedestrians). Our question of in terest was whether evidence of training transfer (from UFOV or from one of the videogame trai ning conditions) might be greatest on the more difficult subgroups of simulator trials A mixed between-within ANOVA was conducted, with two within-p ersons conditions and one between-person condition. The first wi thin-person condition was Block (4; centered dog; dog with minimal eccentricity; dog with substa ntial eccentricity; do g with substantial eccentricity and clutter/distract ion). The second within-person condition was Occasion (2: Pretest, Posttest). The between-person conditi on was Group (4: MOH, UFOV, Tetris, no-contact Control). The dependent measure was the mean brake reaction distance in each of these conditions. The mean values and standard devi ation for the dependent measure by group are presented in table 4-13. Significant effects were reveal ed for Occasion (pre post), F (1, 51) = 5.7, p = .020, 2= .10; Block, F (3, 153) = 337.5, p < .001, 2= .86; and Block X Group, F (9, 153) = 3.0, p = .005,

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110 2= .15. No significant interaction was found for Occasion X Block, F (3, 51) = .74, p = .107, 2= .04; Occasion X Group, F (3, 153) = 2.1, p = .533, 2= .04. The three-way Occasion X Group X Block interaction wa s also not significant F (9, 153) = 1.3, p = .244, 2 = 0.7. The main effect of Occasion reflected a ge neral reduction in brake distance over time, and the main effect of Block reflected that the conditions hypothesized to have greater difficulty (e.g., Blocks 3 and 4, where the dog was at substa ntial eccentricity and in clutter) had longer brake reaction distances. The Group x Block interaction seems to reflect the fact that, especially on Blocks 1 and 2, the rank order of some gr oups varied across the Blocks of trials. The change between baseline and posttest pe rformance for block 1 (Figure 4-10); block 2 (Figure 4-11); block 3(Figure 4-12) and block 4 (Figure 413) are also presented. Driving Simulator Subtask 2: Lane Maintenance A mixed between-within ANOVA was conducte d on lane maintenance. The withinpersons independent variable was Occasion (2 : Pretest, Posttest). The between-persons independent variable was Group (4: MOH, UFOV Tetris, No-contact Control). The critical effect of interest was the Occasion by Group in teraction, which would inform us about whether there were group differences in the pattern of pre-post change. A significant main effect for Occasion was found F (1, 51) = 6.6, p = .013, 2 = .11 but no significant main effect was found for Group F (3, 51) = .22, p = .091, 2 = .11. With regard to the critical Occasion by Group interaction, there was no significant interaction F (3, 51) = .79, p = .503, 2 = .04. Thus, the groups got better at lane mainte nance at posttest, but there we re no group difference change on this measure. The mean values and standard deviation for the depende nt measure by group are presented in table 4-14. Pre-post accuracy perf ormance and group difference in change, are illustrated in figure 4-14.

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111 Analysis of Lane Maintenance by Trial Type As with the brake reaction distance a concern was whether the lane maintenance might di ffer for particular trial types (i.e., if training improved dog detection, and this meant that driver s could attend better to lane maintenance at posttest, would this effect be pa rticularly strong for some types of dog distracters than others?). To further explore this question, the eighteen dog trials were groupe d into four blocks of trials: (a) Block 1: Dog was located in the center of the road, (b) Block 2: Dog was located at the side of the road, but close to the ro ad, with minimal clutter (e.g., count ry road), (c) Block 3: Dog was located a substantial distance from the side of the road (between 80-110 degrees eccentricity) with more clutter (e.g., the dog was located between houses or trees), and (d) Block 4: Dog was located a certain distance from the side of th e road (between 25 degrees eccentricity) but with extensive clutter and distract ion (e.g., traffic, cyclists, pedest rians). Our question of interest was whether evidence of training transfer (f rom UFOV or from one of the video game conditions) might be greatest on the more difficult subgroup of simulator trials. A mixed between-within ANOVA was conducted, w ith two-within-perso ns conditions and one between-person condition. The first within-persons condition was Block (4: centered dog; dog with minimal eccentricity; dog with substa ntial eccentricity; dog with substantial eccentricity and clutter/distr action). The second within-per son condition was Occasion (2: Pretest Posttest). The between-person conditi on was Group (4: MOH, UFOV, Tetris, no-contact Control). The dependent measure was the standard deviation of lane mainte nance in each of the condition. Significant effects were reveal ed for Occasion (pre post) F (1, 51) = 6.7, p = .012, 2 = .11; Block F (3, 153) = 306.2, p < .001, 2 = .85 and Block x Group F (9, 153) = 306.2, p = .031, 2 = .14. No significant interaction was found for Occasion x Group F (3, 51) = .52, p = .667, 2 =

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112 .03; Occasion x Block F (3, 153) = .92, p = .363, 2 = .01. The three way Occasion x Group x Block interaction was also not significant F (9, 153) = 1.6, p = .171, 2 = .08. The mean values and standard deviation for the dependent measur e by group are presented in table 4-15. It looks like occasion signals general improvement over tim e (less variability while driving), and block reflects the fact that later blocks were more difficult, so SD is great in those blocks. The block by group effect seems to reflect that the rank order of groups varies across the four different types of dog distracter trials, but doesnt seem to be a meaningful effect or one related to the interventions. The change between baseline and posttest performance is presented in the following figures: for block 1 (figure 4-15), bloc k 2 (Figure 4-16), bloc k 3 (Figure 4-17), and block 4 (Figure 4-18). Driving Simulator Subtask 3: Dog Detection Accuracy Another question regarding th e driving simulator performa nce was whether dog detection accuracy (saw the dog or not) differed for each in tervention group (i.e., if participants could accurately detect more dogs at posttest than at pretest). Did the interv entions (especially UFOV and MOH) make participants more accura te detectors of roadside obstacles? Analysis on 18-trial Dog detection accuracy A mixed between-within ANOVA was conducted on Dog detection accuracy. The within -persons independent variable was Occasion (2: Pretest, Posttest). The between-persons in dependent variable was Group (4: MOH, UFOV, Tetris, No-contact Control). The critical effect of interest was the O ccasion by Group interaction, which would inform us about whether there were group differences in the pattern of pre-post change. A significant main effect for Occasion was found F (1, 51) = 4.6, p = .037, 2 = .08 but no significant main effect was found for Group F (3, 51) = .22, p = .876, 2 = .013. With regard to the critical Occasion by Group interaction, th ere was no significant interaction F (3, 51) = .135, p = .939, 2 = .008. Thus, there was an overall increase in accuracy at the posttest but the

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113 increase did not differ by intervention group, sugges ting that it was a gene ralized practice effect. The mean values and standard deviation for th e dependent measure by group are presented in table 4-16. Pre-post accuracy performance and gr oup difference in change, are illustrated in figure 4-19. We did not conduct an analysis by block t ype for this dependent variable, because numbers of trials in each block was very lo w, thus not providing enough variability in the dependent variable for the statistical analysis. Aim 3: Changes in Engagement/Flo w over the Course of Training This set of analyses was confined to the thre e groups of participants who were involved in interventions (MOH, UFOV, Tetris), and constitutes the original fully randomized sample. The question to be asked here was whether th ere were differences in participants task engagement by intervention group, and whether this changed over the course of tr aining. One rationale for using video games to train elders visual attention has been the assumption that games are more interesting and engaging. Thus, Flow scores we re assessed from all intervention groups after each of their six intervention sessions. Th e resulting mixed between-within ANOVA included one within-persons factor, Occasion (6, Trai ning Sessions 1 6), and one between-persons factor, Group (3, MOH, UFOV, Tetris). The de pendent measure was the Flow score obtained at the end of each of the six intervention sessi ons. Flow scores coul d range from 30-180, where higher scores represented more (positive enga gement) flow experience. Results revealed a significant Occasion X Group interaction, F (10, 185) = 2.0, p < .05, 2= .10. Table 4-17 shows the mean and standard deviation for each traini ng group at each session. Figure 4-20 illustrates the six training session Flow experience trend in the data, by intervention group. A post hoc t-test analysis using Bonferroni correction was conducted. However, given the number of comparisons (45), the resulting corrected p-value (.05/45) was so low as to not shed

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114 any light on the reason for the si gnificant interaction. Thus, a Least Squares Difference post-hoc analysis was conducted to provide a preliminar y, exploratory insight into the interaction, acknowledging that family-wise erro r was probably inflated in thes e comparisons. Here, the six training sessions were compared to one another, separately by training group (Tables 4-18; 4-19 and 4-20). The LSD results reveal ed different temporal trends fo r the three intervention groups. For the MOH group, there was a significant increase in Flow after the first sessions (Sessions 2 and 3 were significantly greater th an Session 1) and this persiste d through the end (Session 5 was also significantly higher than Sessi on 1). This suggests that part icipants experienced more Flow right after beginning the training sequence, and maintained th ese gains over time. A congruent pattern was found for the Tetris gr oup. Although the visual pattern of Flow improvement for this group was one of monotonic linear increase ove r the six sessions, concretely Session 6 was significantly higher than Sessions 1 and 2. Thus, as with MOH, participants ended the training with much higher self-rated Flow experience than they began with. A very different pattern was observed for the UFOV training condition. Here, the pattern was more congruent with little change over the six sessions, with the excepti on of one outlier session, Session 4. Session 4 was significantly lower in Flow than Sessions 1 a nd 5. Overall, then, the results suggested that the two videogame conditions experienced growth s in Flow over time, a phenomenon which was not observed in the UFOV training. Aim 4: Qualitative Participant Interviews Regarding Game Design A final consumer-oriented aim of this study was qualitative in nature, in that we wanted participants to relate their phenomenologica l experience with video games, and what recommendations they would make for change. These final analyses examined only the subset of participants who were in one of the two video game conditions (MOH, Tetris). A total of 27 participants, thirteen from the MOH group and fourteen from the Tetris group answered a

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115 qualitative questionnaire. This ques tionnaire was given to the particip ants as part of their posttest battery. Descriptive analysis was used to ex amine this aim, and was supplemented with anecdotal data from participants. The questionnaire was designed to measure part icipants interest in videogame ownership, issues that had prevented them from using videogam es in the past, and possible barriers to use of videogames in the future. A checklist was provided for each of these questions, and participants could choose multiple answers (Table 4-21). The questionnaire also asked for participants opinions about the design of the game they play ed, and how they perceived video game playing might have affected them. This part of the questionnaire was open-ende d, and participants answers were grouped into post-hoc categories via the investigators thematic analysis (Table 422). For the current purposes, only a single rater wa s used to code the themes in participants open-ended results; thus, future congruence am ong multiple coders will need to be assessed. With regard to the quantitative elements of the questionnaire, almost half of the participants (40.7%) said they were interested in owning a video game. The most cited reason for not owning a video game in the past was l ack of interest (44.0%) and cost (33.3%). Correspondingly, when asked What do you foresee preventing you from using a video game in the future? cost was the most cited reason (40.7 %), follo wed by lack of interest (37.0%) and lack of knowledge of the game (14.8%). Table 4-22 displays a summary of responses to our questions about videogame design, and about perceived benefits attri butable to videogames. Not ever y participant answered these questions, so the number of participants who re sponded to each part of the questionnaire is indicated in the table. Participants reported few problems with the screen/screen size (which was a 19 CRT television), and while most of the part icipants reported that the contro ller was good, a

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116 small sample said that the c ontroller should be made easier to use (25.9%). When asked How would you change these features?, most participants reported th ey would change nothing about the screen (55.5%) or th e controller (55.0%). With regard to perceived benefits, the most common perceived benefit reported was that of mental exercise (33.3%), but an almost equal pe rcentage (25.0%) felt that gaming could not help them in any way. Anecdotal data from participants who played the video games was also collected during the posttest battery. In general, it seems participants enjoyed the study/gaming experience. I enjoyed the training and looked forward to next class each time This has been a very enjoyable experience. I enjoyed the whole experience whether or not it stimulated my mind (I hope it did) I enjoyed the computer game. This makes a good competitive game playing with others. Thanks for introducing me to a game that I have never played before. The game playing was fun at times fr ustrating when I did not perform well. One pre-experimental concern (that violence, especially in the MOH game, might be offputting to some) was affirmed by some respondents. Specifically, a subset of female participants in the action-game Medal of Honor group did expr ess some concerns about the violence of the game I enjoyed learning the game, as I was curiou s how the games are played. I could probably become addicted to them, but I do not intend to buy one. I get no satisfaction out of killing people I was bothered by the violen t contentI found the instructor s very patient and helpful. I enjoyed the experience.

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117 The training manual and the step-by-step instru ctions guide developed to be used in this study seems to be generally adequate for the participants. Overall the training was understandable with go als that were easily id entifiable and fun. Found the whole experience very interesti ng. Game was interesting and adequate The training was well conducted, challenging and the level of difficulty was adequate. I enjoyed the challenge of the game very much. I am continuing to play it on line at home Training was a fun experience. Testing my skills against the program. Feeling good about my success Some participants commented about how the vi deo game training stimulated their brain. It stimulated my brain I think the training did help stimulate my hand-eye coordination. It also helped my anticipation in looking ahead to prepare for the next action In general, anecdotal data from participants suggests that video game playing can be an enjoyable experience for th e participants, although the violence in the game is not appealing for some, especially female participants. The de velopment of a manual guide with detailed instructions on game playing seems important in training older adults to play these games. Summary Overall, the results suggest that only UFOV training improved visual attention significantly more than the other video game trai ning. However, although not significant, the two video game conditions experienced more visual attention gain th an the no contact control group, a difference which reached significance for the Te tris group on the Selective Attention UFOV subtask. The results also suggest that the visual attention ga ins were not transferred to a simulator driving performance. In addition, when examining participant s engagement with the

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118 training ("Flow"), Flow improved over time for th e two video game conditi ons, but decreased for UFOV training group. Finally, when querying participants' opinions about the video games they had played (in the Tetris and Medal of Honor conditions only), results suggested that video games have high acceptability for this populatio n, and are perceived as potential "mental exercise" tools by ol der respondents.

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119 Table 4-1. Sample characteristics both for the total group and by intervention subgroup. Total Sample (N = 58) MOH group (N = 14) UFOV group (N = 16) Tetris group (N = 15) Non Contact (N = 13) pvalues Age .81 M 74.7 74.8 73.7 75.8 73.7 (SD) (6.4) (6.3) (6.2) (8.5) (5.3) Education .72 M 16.0 15.8 15.6 16.5 16.3 (SD) (2.4) (2.5) (2.6) (2.6) (1.7) Sex .33 Male N 28 9 5 7 7 (%) (48.3) (64.3) (31.2) (46.6) (53.8) Female N 30 5 11 8 6 (%) (51.7) (35.7) (68.8) (53.4) (46.2) MMSE .42 M 29.8 29.2 29.2 29.4 29.0 (SD) (5.2) (.97) (1.5) (.91) (1.2) HVLT Delayed recall .14 M 9.7 9.0 10.5 10.3 8.7 (SD) (2.4) (2.7) (1.7) (1.3) (3.4) GDS .16 M 4.1 5.5 3.0 5.2 2.8 (SD) (4.1) (4.9) (2.6) (5.3) (2.4) Hearing .14 M 3.7 5.7 2.0 4.3 3.1 (SD) (4.5) (5.5) (2.6) (5.3) (3.8) Vision .26 M 85.2 87.2 86.8 86.3 79.6 (SD) (11.5) (5.9) (3.9) (5.1) (22.3) Snellen equivalent 20/18 20/17 20/17 20/17 20/20 Note: MOH = Medal of Honor; UFOV = Useful Field of View; MMSE = Mini Mental State Examination; HVLT = Hopkins Verbal Learni ng Test-Revised; GDS = Geriatric Depression Scale.

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120 Table 4-2. Sample characteristics for complete rs and non-completers of the study protocol. Total Sample (N = 70) Completed (N = 58) Drop out (N = 12) p -value Age .69 M 74.6 74.5 75.3 (SD) (6.3) (6.6) (4.8) Education .70 M 16.0 15.9 16.3 SD (2.3) (2.4) (1.9) Sex .68 Male N 33 28 5 (%) (47.1) (48.3) (41.7) Female N 37 30 7 (%) (52.9) (51.7) (58.3) MMSE .24 M 29.1 29.2 28.7 (SD) (1.2) (1.2) (1.6) HVLT Delayed recall .43 M 9.6 9.7 9.0 (SD) (2.4) (2.4) (2.2) GDS .21 M 4.4 4.1 6.0 (SD) (4.3) (4.1) (5.2) Hearing .39 M 3.5 3.7 2.4 (SD) (4.6) (4.5) (5.1) Vision .57 M 86.7 86.6 87.5 (SD) (5.0) (5.1) (4.7) Snellen equivalent 20/17 20/17 20/17 Training group and attrition group* .49 MOH N 14 5 (%) (31.1) (45.5) Tetris N 15 4 (%) (33.3) (36.6) UFOV N 16 2 (%) (35.5) (18.2) Note: = 1 participants dropped out before randomization; MOH = Medal of Honor; UFOV = Useful Field of View; MMSE = Mini Mental State Examination; HVLT = Hopkins Verbal Learning Test-Revised; GDS = Geriatric Depression Scale.

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121 Table 4-3. UFOV composite scores before and after testing by group (mean SD) Total MOH UFOV Tetris No-contact pre post pre post pre post pre post pre post UFOV Composite M 704.48 582.59 693.00589.43685.31440.06778.80681.13 654.69636.92 SD 232.70 246.08 254.83209.6794.29 171.25272.76219.00 280.56323.91 Note: MOH = Medal of Honor, UF OV = Useful Field of View Table 4-4. Post hoc analysis of UFOV composite scores by groups. Intervention group Intervention group Mean Difference Std. Error t df p value MOH UFOV 141.6855.252.5 28 0.08 Tetris -5.9056.10-0.1 27 1 Control -85.8058.15-1.4 25 0.88 UFOV MOH -141.6855.25-2.5 28 0.08 Tetris -147.5854.26-2.7 29 0.05 Control -227.4856.37 4.0 27 <0.01 Tetris MOH 5.9056.100.1 27 1 UFOV 147.5854.262.7 29 0.05 Control -79.9057.21-1.4 26 1 Control MOH 85.8058.151.4 25 0.88 UFOV 227.4856.374.0 27 <0.01 Tetris 79.9057.211.4 26 1 Note: MOH = Medal of Honor; UF OV = Useful Field of View

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122 Table 4-5. Mean standardized scores and st andard deviations on four UFOV subtasks by intervention groups for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post UFOV speed M 21.86 17.93 18.29 19.43 19.88 16.94 20.13 18.53 30.15 16.85 SD 19.33 3.26 3.27 4.22 5.48 2.11 8.92 3.62 39.17 2.15 UFOV divided M 70.74 48.83 85.36 43.43 41.69 24.56 95.73 66.67 61.92 63.92 SD 76.72 60.67 86.24 37.96 20.52 11.55 100.61 70.68 72.98 92.01 UFOV selected M 225.53 171.34 225.64 172.64 209.00 118.63 256.93 185.33 209.54 218.69 SD 105.82 116.52 114.76 108.39 54.44 72.71 131.91 119.85 114.22 148.38 UFOV Same different M 386.34 344.48 363.71 353.93 414.75 279.94 406.00 410.60 353.08 337.46 S 89.14 108.82 93.20 95.34 62.39 107.03 90.63 83.82 102.60 112.73 Note: MOH = Medal of Honor, UF OV = Useful Field of View Table 4-6. Mean standardized scores and standa rd deviations for processing speed subtask by intervention groups for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post UFOV speed M 21.86 17.93 18.29 19.43 19.88 16.94 20.13 18.53 30.15 16.85 SD 19.33 3.26 3.27 4.22 5.48 2.11 8.92 3.62 39.17 2.15 Note: MOH = Medal of Honor, UFOV = Useful Field of View.

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123 Table 4-7. Mean standardized scores and standa rd deviations for divided attention subtask by intervention groups for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post UFOV divided M 70.74 48.83 85.36 43.43 41.69 24.56 95.73 66.67 61.92 63.92 SD 76.72 60.67 86.24 37.96 20.52 11.55 100.6170.68 72.98 92.01 Note: MOH = Medal of Honor; UFOV = Useful Field of View. Table 4-8. Mean standardized scores and standard deviations for selec tive attention subtask by intervention groups for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post UFOV selected M 225.53 171.34 225.64172.64209.00118.63256.93185.33 209.54218.69 SD 105.82 116.52 114.76108.3954.44 72.71 131.91119.85 114.22148.38 Note: MOH = Medal of Honor; UFOV = Useful Field of View. Table 4-9. Mean standardized scor es and standard deviations for the same different trial subtask by intervention groups for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post UFOV Same different M 386.34 344.48 363.71353.93414.75279.94406.00410.60 353.08337.46 S 89.14 108.82 93.20 95.34 62.39 107.0390.63 83.82 102.60112.73 Note: MOH = Medal of Honor; UFOV = Useful Field of View.

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124 Table 4-10. Post hoc analysis for se lective attention subtask by group. Intervention group Intervention group Mean Difference Std. Error t df p value MOH UFOV 37.3827.781.3281 Tetris 18.6028.210.6271 Control -62.1529.24-2.1250.23 UFOV MOH -37.3827.78-1.3281 Tetris -18.7827.28-0.6291 Control -99.5328.34-3.5270.01 Tetris MOH -18.6028.21-0.6271 UFOV 18.7827.28-0.6291 Control -80.7528.76-2.8260.04 Control MOH 62.1529.242.1250.23 UFOV 99.5328.343.5270.01 Tetris 80.7528.762.8260.04 Note: MOH = Medal of Honor; UFOV = Useful Field of View. Table 4-11. Post hoc analysis for sa me different trial scores by group. Intervention group Intervention group Mean Difference Std. Error t df p value MOH UFOV 125.0334.523.628<0.01 Tetris -14.3935.05-0.4271 Control 5.8336.330.1251 UFOV MOH -125.0334.52-3.628<0.01 Tetris -139.4133.90-4.129<0.01 Control -119.2035.223.327<0.01 Tetris MOH 14.3935.050.4271 UFOV 139.4133.904.129<0.01 Control 20.2235.740.5261 Control MOH -5.8336.33-0.1251 UFOV 119.2035.223.327<0.01 Tetris -20.2235.74-0.5261 Note: MOH = Medal of Honor; UFOV = Useful Field of View.

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125 Table 4-12. Mean standardized scores and standard deviations for the overall driving simulator score by intervention groups for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post Simulator Composite M 243.24 230.57 244.19226.93248.32227.68237.57231.99 242.59236.27 SD 43.25 38.22 45.42 27.14 44.37 42.94 49.33 39.52 35.41 43.23 Table 4-13. Mean standardized scores and sta ndard deviations on four Simulator block by intervention groups for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post Block1 M 165.66 158.92 172.20173.30176.21161.00170.85144.63 138.55159.63 SD 50.36 53.27 49.43 54.53 40.83 52.72 57.66 53.35 49.55 55.10 Block2 M 190.13 165.14 210.50175.43198.26156.38167.01170.20 187.84160.21 SD 63.21 53.72 67.09 48.13 67.40 61.96 66.70 49.65 44.41 56.47 Block3 M 248.21 245.90 225.85212.92244.21231.62256.46262.31 265.61277.41 SD 68.57 71.78 59.32 66.23 72.43 72.93 76.47 70.56 63.06 66.19 Block4 M 349.53 339.80 342.49332.13354.94350.11347.16334.30 352.32340.62 S 35.36 32.71 38.11 30.05 30.09 24.29 39.39 40.11 36.94 35.18 Note: MOH = Medal of Honor; UF OV = Useful Field of View Table 4-14. Mean values and sta ndard deviation for lane mainte nance by intervention groups for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post Lane maintenance M .679 .616 .624 .513 .738 .698 .626 .610 .720 .616 S .188 .192 .173 .136 .164 .203 .181 .190 .227 .196 Note: MOH = Medal of Honor; UF OV = Useful Field of View

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126 Table 4-15. Mean standardized scores and standa rd deviations on lane maintenance on four Simulator block by intervention groups for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post Block1 M 0.28 0.25 0.33 0.28 0.30 0.24 0.30 0.23 0.21 0.27 SD 0.16 0.15 0.25 0.19 0.08 0.08 0.16 0.08 0.13 0.23 Block2 M 0.31 0.26 0.37 0.28 0.32 0.25 0.27 0.27 0.31 0.24 SD 0.15 0.12 0.21 0.13 0.16 0.12 0.11 0.10 0.14 0.16 Block3 M 0.37 0.34 0.33 0.29 0.42 0.34 0.37 0.35 0.28 0.38 SD 0.21 0.19 0.14 0.12 0.32 0.20 0.11 0.14 0.11 0.28 Block4 M 1.60 1.48 1.35 1.11 1.72 1.80 1.46 1.46 1.86 1.46 S 0.64 0.60 0.56 0.34 0.61 0.68 0.63 0.62 0.60 0.67 Note: MOH = Medal of Honor; UF OV = Useful Field of View Table 4-16. Mean standardized scores and sta ndard deviation on accura cy by intervention group for pre and posttest. Total MOH UFOV Tetris No-contact Measure pre post pre post pre post pre post pre post Accuracy score M 11.7 12.2 12.0 12.6 11.6 12.3 11.7 12.2 11.5 11.8 SD 2.4 1.8 2.5 1.6 2.5 1.5 2.5 2.2 2.3 1.8 Note: MOH = Medal of Honor; UFOV = Useful Field of View.

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127 Table 4-17. Mean standardized scores and standard deviations of Flow scores for the 6 training sessions, by intervention group. Total MOH UFOV Tetris Training Session 1 M 115.78 106.5 123.5116.0 SD 18.96 23.43 11.1318.62 Training Session 2 M 118.68 118.0 120.57117.36 SD 20.93 23.97 17.622.63 Training Session 3 M 117.35 115.58 119.36116.86 SD 22.06 25.0 12.6227.66 Training Session 4 M 115.18 113.5 110.14121.64 SD 26.23 24.51 29.9224.19 Training Session 5 M 120.68 118.25 119.5123.93 SD 24.11 26.95 17.2928.49 Training Session 6 M 120.43 117.08 115.57128.14 SD 27.67 28.28 23.9930.73 Note: UFOV = MOH = Medal of Honor; UFOV = Useful Field of View

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128 Table 4-18. Post hoc analysis for fl ow engagement for Medal of Honor Occasion Occasion Mean Difference Std. Error t df p value 1 2 -11.504.7312120.02 3 -9.084.1812120.04 4 -7.006.2012120.27 5 -11.755.2912120.03 6 -10.585.4212120.06 2 1 11.504.7312120.02 3 2.423.9412120.54 4 4.506.5412120.50 5 -0.253.9712120.95 6 0.925.4912120.87 3 1 9.084.1812120.04 2 -2.423.9412120.54 4 2.085.6812120.72 5 -2.674.1212120.52 6 -1.504.9412120.76 4 1 7.006.2012120.27 2 -4.506.5412120.50 3 -2.085.6812120.72 5 -4.755.0712120.35 6 -3.586.0712120.56 5 1 11.755.2912120.03 2 0.253.9712120.95 3 2.674.1212120.52 4 4.755.0712120.35 6 1.173.8812120.77 6 1 10.585.4212120.06 2 -0.925.4912120.87 3 1.504.9412120.76 4 3.586.0712120.56 5 -1.173.8812120.77

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129 Table 4-19. Post hoc analysis fo r flow engagement for UFOV Occasion Occasion Mean Difference Std. Error t df p value 1 2 2.934.3815150.51 3 4.143.8715150.29 4 13.365.7415150.03 5 4.004.9015150.42 6 7.935.0115150.12 2 1 -2.934.3815150.51 3 1.213.6515150.74 4 10.436.0515150.09 5 1.073.6715150.77 6 5.005.0815150.33 3 1 -4.143.8715150.29 2 -1.213.6515150.74 4 9.215.2615150.09 5 -0.143.8115150.97 6 3.794.5715150.41 4 1 -13.365.7415150.03 2 -10.436.0515150.09 3 -9.215.2615150.09 5 -9.364.6915150.05 6 -5.435.6215150.34 5 1 -4.004.9015150.42 2 -1.073.6715150.77 3 0.143.8115150.97 4 9.364.6915150.05 6 3.933.5915150.28 6 1 -7.935.0115150.12 2 -5.005.0815150.33 3 -3.794.5715150.41 4 5.435.6215150.34 5 -3.933.5915150.28

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130 Table 4-20. Post hoc analysis fo r flow engagement for Tetris Occasion Occasion Mean Difference Std. Error t df p value 1 2 -1.364.3814140.76 3 -0.863.8714140.83 4 -5.645.7414140.33 5 -7.934.9014140.11 6 -12.145.0114140.02 2 1 1.364.3814140.76 3 0.503.6514140.89 4 -4.296.0514140.48 5 -6.573.6714140.08 6 -10.795.0814140.04 3 1 0.863.8714140.83 2 -0.503.6514140.89 4 -4.795.2614140.37 5 -7.073.8114140.07 6 -11.294.5714140.02 4 1 5.645.7414140.33 2 4.296.0514140.48 3 4.795.2614140.37 5 -2.294.6914140.63 6 -6.505.6214140.25 5 1 7.934.9014140.11 2 6.573.6714140.08 3 7.073.8114140.07 4 2.294.6914140.63 6 -4.213.5914140.25 6 1 12.145.0114140.02 2 10.795.0814140.04 3 11.294.5714140.02 4 6.505.6214140.25 5 4.213.5914140.25

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131 Table 4-21. Video game ownership and usage Are you interested in owning a video game? N (% ) Yes 11 (40.7) No 16 (59.3) What had prevented you in the past from owning a video game? N ( % ) Lack of interest 12 (44.0) Cost 9 (33.3) Knowledge of the game 8 (29.6) Lack of perceived need 6 (22.2) Lack of time 4 (14.8) Training not available 3 (11.1) Too hard to learn 3 (11.1) Others 2 (7.4) What do you foresee preventing you from using a video game in the future? N ( % ) Cost 11 (40.7) Lack of interest 10 (37.0) Knowledge of the game 4 (14.8) Lack of perceived need 6 (22.2) Lack of time 3 (11.1) Training not available 3 (11.1) Too hard to learn 3 (11.1) Others 2 (7.4) Note: Participants could choose more than one answer.

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132 Table 4-22. Video game features What do you think about these features? N (%) Screen (n=26) Good 26 (100) Controller Good 17 (62.9) Make it easier to use 7 (25.9) Other 3 (11.1) How would you change these features? N (%) Screening (n = 17) Nothing 15 (55.5) Others 2 (7.4) Controller (n = 20) Nothing 11 (55.0) Make it easier 7 (35.0) Others 2 (7.4) How do you think the video game can help you? (n=20) N ( % ) Mental exercise 9 (33.3) I do not think it can help 5 (25.0) Eye hand coordination 4 (20.0) Others 2 (10.0)

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133 2 1 TIME 800 700 600 500 400 Figure 4-1. Pre-post change in UFOV composite performance, by training group Note: MOH = Medal of Honor; UF OV = Useful Field of View. UFOV Com p osite Scores Pretest Posttest Tetris Control MOH UFOV

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134 Control Tetris UFOV Medal of Honor Intervention group 0.00 -50.00 -100.00 -150.00 -200.00 -250.00 Change scores UFOV COMPOSITE SCORE Figure 4-2. Mean UFOV composite pretestposttest change scores, by training group Note: UFOV = Useful Field of View Chan g e Scores

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135 2 1 TIME 30 25 20 UFOV scores Control Tetris UFOV Medal of HonorIntervention group UFOV SPEED OF PROCESSING Figure 4-3. Change from pretes t to posttest, by group, for the Sp eed of processing UFOV subtask Note: UFOV = Useful Field of View; MOH = Medal of Honor UFOV S p eed of Processin g score Pretest Posttest MOH Tetris Control UFOV

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136 2 1 TIME 100 80 60 40 20 UFOV scores Control Tetris UFOV Medal of HonorIntervention group UFOV DIVIDED ATTENTION Figure 4-4. Change from pretes t to posttest, by group, for the Di vided Attention UFOV subtask. Note: UFOV = Useful Field of View; MOH = Medal of Honor UFOV Divided Attention score Pretest Posttest Tetris Control MOH UFOV

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137 2 1 TIME 250 200 150 100 UFOV score Control Tetris UFOV Medal of HonorIntervention group UFOV SELECTIVE ATTENTION Figure 4-5. Change from pretes t to posttest, by group, for the Se lective Attention UFOV subtask. Note: MOH = Medal of Honor; UF OV = Useful Field of View. UFOV Selective Attention score Pretest Posttest Control Tetris MOH UFOV

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138 2 1 TIME 420 390 360 330 300 270 UFOV scores Control Tetris UFOV Medal of HonorIntervention group UFOV MATCH TRIAL Figure 4-6. Change from pretes t to posttest, by group, for the Sa me Different UFOV subtask. Note: UFOV = Useful Field of View; MOH = Medal of Honor UFOV Same Different score Pretest Posttest Tetris MOH Control UFOV

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139 Control Tetris UFOV Medal of Honor Intervention group 20.00 0.00 -20.00 -40.00 -60.00 -80.00 -100.00 UFOV change scores UFOV SELECTIVE ATTENTION Figure 4-7. Pretest-posttest improvement on the UF OV Selective attention subtask, by treatment group Note: UFOV = Useful Field of View UFOV Chan g e Scores

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140 Control Tetris UFOV Medal of Honor TIME 0.00 -50.00 -100.00 UFOV Change scores UFOV MATCH TRIAL Figure 4-8. Pretest-posttest improvement on the UFOV Same-Different subtask, by treatment group Note: UFOV = Useful Field of View Intervention Group UFOV Change Scores

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141 2 1 TIME 250.00 245.00 240.00 235.00 230.00 225.00 Distance Control Tetris UFOV Medal of HonorGroup SIMULATOR COMPOSITE SCORE Figure 4-9. Change from pretes t to posttest, by group, for the av erage Brake Reaction Distance (18-trial average). Distance between the ons et of an on-screen dog and the depression of the brake pedal was recorded, in meters. Note: MOH = Medal of Honor; UF OV = Useful Field of View. Simulator Com p osite score Control Tetris UFOV MOH Pretest Posttest

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142 2 1 TIME 360.00 330.00 300.00 270.00 240.00 210.00 180.00 150.00 120.00 Distance Figure 4-10. Change between baseline and posttest performance for block 1. Note: UFOV = Useful Field of View; MOH = Medal of Honor Distance Pretest Posttest MOH UFOV Control Tetris

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143 2 1 TIME 360.00 330.00 300.00 270.00 240.00 210.00 180.00 150.00 120.00 Distance Figure 4-11. Change between baseline and posttest performance for block 2. Note: UFOV = Useful Field of View; MOH = Medal of Honor Pretest Posttest MOH Tetris Control UFOV Distance

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144 2 1 TIME 360.00 330.00 300.00 270.00 240.00 210.00 180.00 150.00 120.00 Distance Figure 4-12. Change between baseline and posttest performance for block 3. Note: UFOV = Useful Field of View; MOH = Medal of Honor Pretest Posttest Distance Control Tetris UFOV MOH

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145 2 1 TIME 360.00 330.00 300.00 270.00 240.00 210.00 180.00 150.00 120.00 Distance Figure 4-13. Change between baseline a nd posttest performance for block 4. Note: UFOV = Useful Field of View; MOH = Medal of Honor Distance UFOV Control Tetris MOH Pretest Posttest

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146 2 1 TIME 0.75 0.70 0.65 0.60 0.55 0.50 Estimated Marginal Means Control Tetris UFOV Medal of HonorGroup Figure 4-14. Change between baseline and pos ttest performance for lane maintenance. Note: UFOV = Useful Field of View; MOH = Medal of Honor Lane Maintenance UFOV Control Tetris MOH Pretest Posttest

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147 2 1 TIME 1.80 1.50 1.20 0.90 0.60 0.30 LANE DEVIATION Control Tetris UFOV Medal of HonorIntervention Figure 4-15. Change from pret est to posttest, by group, for the standard deviation of lane maintenance for block1. Note: UFOV = Useful Field of View; MOH = Medal of Honor Lane Maintenance MOH UFOV Tetris Control Pretest Posttest

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148 2 1 TIME 1.80 1.50 1.20 0.90 0.60 0.30 LANE DEVIATION Control Tetris UFOV Medal of HonorIntervention Figure 4-16. Change from pret est to posttest, by group, for the standard deviation of lane maintenance for block2. Note: UFOV = Useful Field of View; MOH = Medal of Honor MOH Tetris UFOV Control Lane Maintenance Pretest Posttest

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149 2 1 TIME 1.80 1.50 1.20 0.90 0.60 0.30 LANE DEVIATION Control Tetris UFOV Medal of HonorIntervention Figure 4-17. Change from pret est to posttest, by group, for the standard deviation of lane maintenance for block3. Note: UFOV = Useful Field of View; MOH = Medal of Honor Lane Maintenance Control Tetris UFOV MOH Pretest Posttest

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150 2 1 TIME 1.80 1.50 1.20 0.90 0.60 0.30 LANE DEVIATION Control Tetris UFOV Medal of HonorIntervention Figure 4-18. Change from pret est to posttest, by group, for the standard deviation of lane maintenance for block4. Note: UFOV = Useful Field of View; MOH = Medal of Honor Lane Maintenance UFOV Control Tetris MOH Pretest Posttest

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151 2 1 TIME 12.75 12.5 12.25 12 11.75 11.5 Estimated Marginal Means Control Tetris UFOV Medal of HonorGroup Figure 4-19. Change from pret est to posttest, by group for the accuracy (18-trial average). Note: UFOV = Useful Field of View; MOH = Medal of Honor Accurac y Pretest Posttest MOH UFOV Tetris Control

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152 6 5 4 3 2 1 Training sessions 130 125 120 115 110 105 Scores Tetris UFOV Medal of HonorIntervention group FLOW Figure 4-20. Six training sessi on. Flow experience trend in the data, by intervention group Note: UFOV = Useful Field of View; MOH = Medal of Honor SCORE Training Session Tetris MOH Control

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153 CHAPTER 5 DISCUSSION Overview This final chapter is organized into four major sections. The chapter first provides a narrative review of the major fi ndings of this study, followed by attempts to interpret these findings. First, the major results of each study s specific aim will be re viewed. Second, a brief consideration of the limitation of the study will be considered. Finally, directions for future research on cognitive training with video games among older adults will be suggested. Summary of Major Findings Aim 1: Training Effects on Useful Field of View The first aim of this study was to investig ate whether UFOV performance can be improved by providing training in firs t person action video games (Medal of Honor (MOH)) and comparing this training to: (a) goldstandard touch screen based UFOV training, b) alternative video game (Tetris) construe d as a placebo control and c) no-contact pre post only group. It was hypothesized that the UFOV traini ng group would experience the most improvement in UFOV test scores; moreover, the MOH videogame grou p was expected to experience more gain in UFOV performance than the Tetris group and the no-contact control group (based on the work of Green and Bavelier, 2003). Results provided partial support for these hypotheses. First, the UFOV composite score (the sum of four UFOV subtasks) was examined as a dependent variable. Overall resu lts revealed significant improve ment only for the UFOV training group, which experienced significantly more prepost gain than the two control conditions (Tetris, no-contact). Subsequent analysis was conducted examining the four UFOV subtasks separately as dependent measures. While there was no signifi cant difference between groups for the easier

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154 Speed of processing and Divided attention subtas ks, there was a significant group difference in pre-post change for the Selective attention and Same -different subtasks. In the Selective attention subtask, results revealed that both the UFOV and the Tetris training groups improved significantly more than the no-c ontact controls. Improvement in the two videogame training conditions was not significantly different between groups, and did not differ significantly from that seen in the UFOV group. For the Same -Different subtask, the UFOV group improved significantly more than all other groups, but no other group differences (e.g., in favor of videogame training) were observe d. Our next section below consid ers several reasons for this pattern of findings. Aim 2: Driving Simulator Outcomes The second aim of this study was to investigat e whether video game training might transfer to simulated driving performance. Given previo us research suggesting that UFOV training also yielded improved driving simulator performan ce (Roenker et al., 2003), it was hypothesized that the UFOV-trained participants might show more simulator improvement than all other groups. In addition, if visual attenti on had been boosted by MOH trai ning, it was hypothesized that the MOH group might also show more improvement on driving simulator outcomes than the two control groups (i.e., Tetris, no-co ntact). The results did not suppor t this set of hypotheses. None of the groups, including the UFOV-trained group improved their scores disproportionately on any of the three simulator outcomes. Ther e was, however, a gene ral practice-related improvement on the simulator for all three group s. However, at least one simulator outcome (brake reaction distance) was si gnificantly correlated with UFOV performance, suggesting that there was a correlational basis for expecti ng transfer from improved UFOV scores.

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155 Aim 3: Changes in Flow Experien ce Through the Course of Training The third aim of this study was to investigate participants engagement with their training activities, and to assess whethe r there were any group differences in the pattern of change in Flow over the six training session. The analyses revealed an inte resting dissocia tion in pattern between the two video game groups and the UFOV trained group. Although the exact pattern differed slightly between the tw o video game groups, in both MOH and Tetris participants ended up with significantly higher Flow ratings than th ey began the training wit h. This suggested that there was growing enjoyment of and engagement w ith the games over the course of training. In contrast, the overall trend in Flow for the more mechanistic UFOV training was flat. There was one outlier session (Session 4), which was significantly lower than all other sessions, but this did not seem to indicate an overall downward trend in Flow. Instead, it appeared that the two game group experienced improvements in Flow (sudden and lasting for MOH, gradually incremental for Tetris), but this was not true for the UFOV group. Aim 4: Participants' Opin ions About Game Design The last aim of this study was to explore participants op inion about game design. This was a consumer-oriented aim, with the goal of sh aping future game design so that it might be more responsive to the needs of the older adult population. Contrary to the stereotype that older adults are unwilling to explore new activities or take advantages of later technologies, the current study showed that th is might not be the case. None of the participants from this study had previous contact with video games. This was a completely new technology for them. Recruitment of participan ts was not a major challenge for this study. The results showed that almost half the pa rticipants, all video game novices, would be willing to acquire a video game system. In fact, past research gives support to the receptivity of new technologies among older adults. Previous res earch has supported the id ea that receptivity of

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156 new technologies is directly infl uenced by ones level of concer n for problems that could be alleviated through the use of technology (Zimmer & Chappel, 1999). Relevant to this study, a substantial group of participants did perceive pos sible benefits from video games. About one third of the participants reported that they believ ed that the video games could serve as a positive form of mental exercise. An encouraging finding from this study was th e participants willingness to learn how to use the video games. Anecdotally, in the early tr aining sessions, some participants frequently expressed feelings of difficulty and challenge and even boredom with the games. But, most participants persisted with the games, and showed marked progress in game play (i.e., progressing to more difficult levels mastering controller operati on). Thus, although the pattern of transfer to UFOV test performance and dr iving simulator outcomes was not as strong as hypothesized, the positive comments of particip ants and the Flow-rela ted findings summarized above continue to support the notion that vi deo games may be a worthwhile and pleasing interface for cognitive interv entions with older adults. Theoretical Considerations and Study Implications Why did we not replicate MOH traini ng effects on visual attention? The results of this study stand in marked counterpoint to those of Green and Bavelier (2003). Their study, with undergraduates, found that as little as ten hours of training with MOH (but not Tetris) could produce si gnificant improvement in UFOV-like visual attention. In this study, after nine hours of MOH expo sure, healthy older adults did not experience significant gain on a different version of the UFOV task. There ar e several possible explana tions for the lack of effect. Ceiling performance on some UFOV subtasks Our participants did not have much room for improvement in subtask one (processing speed) and subtask two (divided attention) of

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157 the UFOV at pre-test. Most participants performe d at ceiling level (highe st score possible) or close to ceiling in these two subt asks, suggesting that there may ha ve been insufficient sensitivity to change on these tasks. Our sample performed substantially better at pr etest than, for example, the ACTIVE sample (Ball et al., 2002), suggesting that the advantaged nature of our cohort may have affected training outcomes. The third (selective attention) a nd fourth subtasks (same-differ ent) offered higher levels of challenge to participants, and co rrespondingly, it was these two task s that varied across training groups. However, even for these tasks, result s suggested no effect of video game training (Same-different task) or a counter-intuitive eff ect (Tetris, not MOH, improved more than nocontact controls). Insufficient training dosage in the video game conditions The second explanation for the absence of expected training findings may be related to the time that participants spent learning how to play each of the video games. It was hypothesized that participants from the MOH group would show more improvement than pa rticipants who receive d Tetris training. The rationale for this hypothesis was taken from previous studies on video games and visual attention. (Green et al, 2003). Gr een et al, 2003 used an experime ntal study design to test the hypothesis that action video game players would have more improvement in visual attention scores as compared to a placebo control game. The video game that they used for control was the Tetris game. Their hypothesis was supported by their findings. In fact, action video game players (undergraduates, in their work) improved in a range of visual attention scores while Tetris players did not. Our current findings were contrary to what they found. In the current study, older adults may have sp ent too much time tryi ng to learn the action video game, specially the game interface. In other words, instead of practicing the dynamic

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158 visual attention skills they need ed to bolster, they were "stuck at an earlier stagespending too much time trying to figure out controllers and what steps to follow in playing the game. This would suggest that future video game training wi th older adults would need to (a) spend much more time on pre-training game orientation; (b) might need to select games with less complex interface learning; and (c) might need to spe nd much more time on game practice to achieve comparable effects to those seen with undergra duates. This latter point is underscored by the work of Charness Schumann, & Boritz (1992), who has reported on other computer learning tasks (e.g., word processing) that ol der adults need much more trai ning time to achieve levels of performance comparable to that of younger adults. Larger training dosages may be needed for adults of all ages Above and beyond the unique challenges of training novice older adults how to play game s, Green and Bavelier have themselves recently indicated that much higher training dosages (30 to 50 hours) may be needed to achieve optimal visual attention gains (Green and Bavelier, 2006c). The dosage in this study, nine hours, was based on the repo rt of Green and Bavelier that te n hours of active play would be sufficient (for younger adults). Poor selection of visual attention outcomes A third possible explanation for the absence of MOH training effects may be related to the fact that past studies on video games have reported improvement in very specific aspects of visual selective attenti on (Green et al, 2003: & Green et al, 2006c), and the UFOV subtasks selected for th is study may have insufficiently tapped these aspects of visu al attention. In more recent work, Green and Bavelier have begun to shift to other visual attention outcomes. While in previous studies they demons trated that video game players outperformed non-players on different asp ects of visual attention, they recently investigat ed the effects of video

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159 game playing on individual differences in the number of simultaneous vi sual objects that could be readily tracked and attende d to(Green et al., 2006a). Tw o outcome measures have had particular salience: 1) an enumer ation task, in which participants have to report the number of briefly flashed items in a display as quickly and as accurately as possible and; 2) multiple object tracking, in which participants ar e required to allocate their atte ntion to several (moving) items over time. Video game players showed enhan ced performance on both tasks. These results suggest that future studies may consider broa dening the set of outcome measures, which might be more sensitive to attentional gains produced by video game play than the task used in the current study. Why did Tetris appear to have a stronger effect than MOH? An unexpected finding in this study was that Tetris group participants experienced significantly more Selective Attention UFOV gain than no-contract control participants. Several possibilities exist to expl ain this effect. First, while Gr een and Bavelier (2003) showed that Tetris was not a sufficient challenge to boost dyn amic visual attention in young adults, it may be that the whole-screen scanni ng and monitoring and mental ro tation was a higher level of challenge (and therefore had traini ng effects) for older adults, who were at a lower skill level. Expressed differently, during the Tetris game, pa rticipants had to look at pieces (tetraminoes) falling from the top of the screen while paying at tention on the bottom of the screen, where they needed to place the pieces. While in studies with college students this stimulus was not enough to provide visual learning, perhaps this was a more optimal level of challenge for novice older adults. Second, because Tetris is a much easier game to learn and explain (the core game, rotating tetraminoes in order to make a flat line did not become more complex over the course of training), participants may have "wasted" less time learning the game, and spent more of their

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160 nine hours actively playing the game. The challenge s of the Tetris game may actually have been more appropriate for elders' skill level. What this speculation strongly suggests is that videogame tr aining findings generated with younger adults may not be automatically generalizable to older adults. Much work must be done to identify the right game, the ri ght level of challenge, the righ t sequence of tr aining activities, the right outcome measure, and the right training dosag e to optimize transfer for older adults. Why was there no training transfer to any driving simulator outcomes? Previous studies in the literat ure had shown that performance on the UFOV test is uniquely correlated with driving performance. Ball et al ., 1993 found that visual attention measure was highly predictive of the self-repo rted history of older adults crash problems. Goode et al., 1998 examined a set of neuropsychological measures thought to be related to cras h risk in older adults and found that, of all cognitive tests administered UFOV was most strongly related to crash involvement. Myers et al., 2000 found that UFOV wa s the best single predictor of an on-road driving test. Given the fact that video game training impr oves visual attention (i.e., a UFOV-like task, according to Green and Bavelier), one might assu me that playing a video game might enhance driving performance. In addition to driving co mpetence, UFOV performance predicts important indices of mobility, including am bulatory ability, life space (the exte nt of travel throughout ones environment) and falls (Ball et al., 1993; Broman, West, Munoz, Bandeen-Roche, Rubin & Turano, 2004; Stalvey, Owsley, Sloa ne & Ball, 1999), suggesting fu rther breadth of transfer possibilities. In the current study, such transfer was not observed. However, there were differences between this study and previous work that help elucidate differences fr om previous studies.

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161 First, in those previous studi es where UFOV was predictive of driving, UFOV composite scores were used. In this study, training effects were observed at the level of specific subtasks (Selective Attention, Same-Differe nt), and this may have mean t that not a broad enough UFOV effect was achieved to obtain transfer, or the cu rrent participants may not have had much room for improvement in this test. Second, our selection of drivi ng simulator outcomes may not have been the right ones. One possible explanation for the lack of tran sfer of UFOV significant score to the driving simulator might be insufficient sensitivity to chan ge in the driving simulator scores (i.e., brake reaction distance, lane maintenance, dog detec tion accuracy). Previous studies have found driving simulator tasks could be good proxies fo r visual attention (Roe nker et al., 2003; Lee et all, 2003), and that UFOV improvement might al so improve driving simulator performance, although they have used different outcomes as previously described. Ho wever, it is also important to note that the ACTIVE study failed to find evidence of UFOV transfer to any selfreported driving outcomes (simulator data were not collected; Ball et al., 2002) Are group differences in Flow changes meaningful? One area in which interesting training group di fferences were observed was self-rated Flow experience. The two game playing groups showed significant gain in Flow from the start to the end of training, while the traditionally trained UFOV group did not. This may have reflected the differences in training experience by group. Specifically, the UFOV training is highly repetitive. While training conditions change incrementally fr om trial to trial, th e root task remains unchanged over the six sessions. Moreover, the in terface is dated (black and white, text and twodimensional icons). The interface is not ve ry "game like", and so is not a source of fun/entertainment, unless participants enjoy the puzzle-like challenge of the task itself. In Tetris

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162 and MOH, the interfaces were clearly games. For MOH, the significant difference between the first session and later sessions (which had si gnificantly higher flow) may have reflected the initial confusion of learning the complex game. For Tetris, Flow gains were more gradual and incremental, and may have reflec ted participants growing sense of mastery of the game. This incremental gain in Flow for the Tetris gr oup is consistent with the conceptions of Csikszentmihalyi's (1990) Flow Theory, which stat es that Flow is said to occur when people are able to meet the challenges of their environment with appropri ate skills and accordingly feel a sense of well-being, a sense of ma stery, and a heightened sense of self-esteem. Tetris appeared to be most likely to prom ote this sense of mastery. Limitations The present study used an innovative cogniti ve training strategy among older adults. The findings might contribute to the advances in the field of cognitiv e training and aging. Nonetheless, this study has several key limitations, owing largely to the co nstraint of resources and time constraints under which data was collect ed. One key limitation of this study is the sample. Specifically, participants who took part in this study were healthy, highly educated and Caucasian, which does not fully represent th e broader American ol der adult population. A second sample selectivity of the study relates to high participant function at baseline. According to UFOV normative data (Edwards et al., 2006), the participants from this study were already below the scores that th eir older peers normally have (i .e., were performing much better than the average older adult). Normative data in dicated that adults with an average age of 74 years and who are highly educated ( > than 12 years of education) have a mean score of 863.85 (UFOV composite score among the four subtasks). In this study, partic ipants with similar demographic characteristics had a mean UFOV score of 704.48, which is much better than the normative data encountered for older adults at this age. This suggests that, on average,

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163 participants did not have much room for improvement. Indeed, se veral UFOV training studies in Alabama, which had much larger UFOV training effects, selected participants with low performance at baseline (Ball et al., 2002; Edwards, et al., 2005). This suggests that the results of the current study may have shown somewhat weak er training effects th an previous research both due to lower dosages of trai ning (see below) and more advant aged participants. In addition, many participants reached ceiling performance on subtasks one and two (Speed, Divided Attention) of the UFOV test. A third limitation of this study was the num ber of training sess ions in which the participants were involved. Participant traini ng was divided into 6 tr aining sessions of 90 minutes each. Many times participants needed a break during the training, so that their cumulative training exposure could well have been less than 9 hours. As discussed earlier, since some of the training time was also spent learning the game (but not playing the game actively), the dosage of game play was likely well be low optimal, especially for the MOH group. A fourth limitation of the current study, which is also resource based, concerns the pool of trainers used in this study. With two exceptions, trainers in this study were volunteer undergraduate students who had re sponded to a flyer requesting stude nts to help with this study. Although these students were committed to the st udy, they did not have any previous experience in training older adults. In addition, because of their school schedule, there were occasions in which a participant was trained by more than one trainer, which could have produced inconsistency in the training offered. In general, a professional staff of tr ainers, who consistently train participants, and who are monitored with re gular quality cont rol observations could serve to improve the effectiveness of training.

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164 Another limitation of the study re lated to the invari ant contrast of the presented dog (i.e., Dalmatian) throughout the scenarios. We did not a ssess contrast sensitivit y at baseline, and so could not determine whether indi vidual differences in this vi sual-perceptual attribute were responsible for individual diffe rences in responding. Moreover we did not vary the dogs perceptual features throughout trials, and so we could not determine whether more-or-less perceptible dogs would have affected responding. In our assessment of flow and opinions about game design, we were unable (due to limited cell sizes) to contrast males and females. It is reasonable to assume, however, that there may have been gender differences in game acceptability and enjoyment. (For example, the military style violence of MOH may have been more unfamiliar to female participants than males; indeed, our participant comments about violence came from the wo men). Future research should more systematically sample males and females in order to examine gender differences in game response. It may be that diffe rent kinds of game design would be needed to optimally engage men versus women. Another limitation to be mentioned is that even though the brake reaction distance was correlated with the UFOV subtasks in this st udy (providing an empirical basis for expecting UFOV-related training gains to transfer), the se lection of simulator outcomes in this study was idiosyncratic, and based on available data fr om the simulator program. To facilitate comparability with other research, it may make se nse to develop a standardized set of simulator scenarios and outcomes that can be employed acro ss training studies, and th at are selected (in part) based on a known theoretical or empirical c onnection to the ability being trained. The current simulator outcomes had not been used in any previous training study with older adults.

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165 Future directions The current study confirmed the value of UFOV tr aining for older adults at least in terms of improving UFOV performance. The narrow ba nd of transfer (e.g., no effects observed on a driving simulator) were disappointing, but consistent with the lack of tran sfer reported for UFOV training in much larger trials (see Willis et al., 2006). A promising initial finding was that participants in the video game groups (espec ially Tetris) experience d significantly more Selective Attention gain than no-contact contro ls, suggesting that there may be some visual attention benefits of videogaming in older adults. However, many questions were raised by this pattern of findings, and future research must clarify these. First, future research should attempt to recr uit samples of a more di verse population, with a particular emphasis on attracting participants wi th lower baseline function. It may also be sensible, in larger samples, to assess important potential covariates like socioeconomic status, medication use and health status at baseline. Secondly, future studies should explore the impa cts of larger dosages of training. Recent studies have suggested the need of at leas t 30 hours of training sessions to show major improvements in visual attention of undergrad stud ents (Green et al, 2006c) It is reasonable to think that older adults woul d benefit from longer dosages of training. Indeed, a linear interpolation of the pre-post UFOV gain in the Tetris and MOH groups suggests that, with more exposure, these groups might eventually "catch up" to the UFOV trained group. The reality of this assumption needs to be tested, and the number of additional sessions needed to achieve larger UFOV effects must be determined. On e reason we sought to explore commercially available video games was their "s calability"; i.e., these are low co st (under $200) interfaces that use televisions (already present in most homes). Thus, it would be quite feasible to place these devices in participants' homes, and to investigate much larger dosages. A secondary advantage

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166 of exploring home-based training is that this better simulates "home based exercise", which has been shown to be effective (in the physical domain) in boosting the health of older adults. This is the ultimate goal of this line of research: to incorporate training in the everyday activities of older adults, and thereby naturally increase the dosage of training incorporated into elders' everyday lives. Future research should also explore broader outcome measures. Gr een and Bavelier, in their work with younger adults, have been explor ing broader outcome measures like enumeration tasks and multiple object tracking. These measures cannot be automatically used with older adults (they are quite difficult), and would need to be recalibrated for older users. However, with more specific tests for critical subcomponents of visual attention, the magnitude and breadth of videogame training could be better determin ed. It has been argued (Bavelier, personal communication, April 2007) that the UAB UFOV test (Ball, et. al., 1988) is a more global test and might not be able to capture the sub tleties of attention ch anges produced by action videogame training. Another area that needs to be investigated in future res earch concerns driving outcome measures. There are generally three outcome measures used in driving studies: driving simulator, behind the wheel assessment and archival cras h records. A driving simulator program was selected for the current study because it constitute d a safe way to collect driving data in an immediate time frame, while cont rolling visual attention demands in a systematic way. Behindthe-wheel assessments would have introduced potential safety c oncerns and difficulty controlling visual attention demands. Arch ival crash data would have re quired a much longer follow up period than the current study allowed, and who have been plagued by the potential reporting biases inherent in regulatory driving data. Although simulato rs seem like an ideal outcome

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167 measure source for visual attent ion intervention studies, future studies using simulator outcome measures should explore devel oping and using standardized simulator scenarios and outcome measures, with pilot work identifying those simu lator outcomes that are most 'saturated' with visual attention variance. Another area for future research developmen t is to better characterize progress in the games themselves. This has two benefits. First, if game playing proficiency can be reliably assessed, this would provide a better indicator of proximal outcome progress on the most direct target of training (i.e., game play). Secondaril y, it would permit a better empirical estimate of how gains in game playing performance translat e into gains in other outcomes. Third, better assessment of game playing performance would better permit adaptive customized training. If the level of game playing difficulty can be conti nuously calibrated to par ticipants' current skill level, this would better permit the tailoring of game scenarios to match each participant's exact level of training need. As has been stated several times in this document, our participants were positively advantaged. All were current drivers, and this may have restricted the amount of room for improvement that our sample had. Future research should also look at more restricted drivers (e.g., those constrained to only daytime driving, or driving in particular neighborhoods) or even drivers who have experienced recent license susp ension for visual attention reasons. For these participants, with more potential room for improve ment, the effect sizes of training like this may actually be higher, and the prac tical consequences of training might be greater: For such individuals, a question to be a ddressed with whether visual atte ntion training might actually be able to help some individuals rehabilitat e and recover their dr iving privileges.

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168 Conclusion Overall, the results of the current study suggested that UFOV training improved visual attention significantly more than any other group. However, it was noted that (although not significantly) the two video game groups experienced more visual attention gains than the nocontact control group. Actually, when the anal ysis was divided by UFOV subtasks, UFOV and Tetris (but not MOH) experienced significantl y more gain than the no contact control group. Several factors might explain th e reason why MOH gains were not replicated in this study such as possible insufficient dosage of training and the selection of outcome measures. The results of this study also in dicated that the visual attenti on gains, even after traditional UFOV training (which was highly effective), were not transferre d to several simulator-based driving outcome measures. This mi ght suggest that the simulator outcome measures used in the study were not sensitive enough to detect training-re lated changes. Future studies will need to more strongly develop a rationale for th e specific outcome me asures selected. However, in contrast to absence of trai ning transfer to simulator based measures, significant differences between tr aining groups were observed in the session-to-session changes participants' Flow experiences. That is, analyses of self-rated Flow suggested that engagement improved over time for the two video game c onditions but not for the UFOV training. This finding is also related to our re sults regarding particip ants opinions about game design, in which participants affirmed their willingness to use this technology, and ma ny perceived that the technology was a viable approach for "mental exercise". Although this study did not provide conclusive ev idence regarding the be neficial effects of video game interventions on older adults' visual attention performance, the preliminary data from this study "set the stage" for future research by indicating that (a) older adults enjoyed the video game training, and could be compliant with it, an d (b) while video game training effects were not

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169 significant, gain trends in visu al attention were in the right direction. Linear interpolation suggests that with higher dosages, video game training may be more effective in boosting UFOV performance. This serves the ultimate goal of this line of research: to investigate the effectiveness of incorporating game-based mental exercise in the everyday activities of older adults.

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170 APPENDIX A MOH MANUAL GUIDE MISSION 1 Go forward out of the bunk room and into the hallway ahead of you, with the flashing red lights and the sirens going on. Go right down the corridor, watching a fello w soldier get electrocuted by some stray wires that have been blown into a door jam. Dont stand too close to the wires or you will be electrocuted as well. From there, look left and proceed forward into the shower room. In the shower room, you'll meet up with another soldier who will tell you to follow him. If you do not meet the soldier, look ri ght and pass into the toilet room. If you saw the soldier, follow him as he goes into the adjacent room, and swing right into a hallway where you'll find a barber s hop pole next to the door in front of you. If you did not find the soldier, look right into a hallway where you'll find a barber shop pole next to the door in front of you.

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171 Ignore the room in front of you, look left and move down through another corridor, where you'll see a man get blasted in the f ace with some extremely hot steam, falling to the ground. Go up the stairs beyond him to the upper part of the USS California. < Need to show how to jump> Go forward, now, down the corridor to a pi pe and some wires sticking out of the floor. The game will instruct you to jump over the pipes and wires, which you should do. < Need to show how to duck> < Need to show how to stand up> Further up the corridor, you'll be instructed to duck under and walk below some pipes coming out of the ceiling. At the end of this corridor, assist the e ngineer shut the door ahead of you by pressing the action button, taking care of an optional objective on this mission. Then, look left and go up the stairs, gett ing closer and closer to the topside. Once here, go left and move forward dow n a corridor, where a man will scream and run leftward down the corridor. Follow him as he approaches a fire down the corridor, grabbing the Fire Extinguisher automatically.

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172 Approach the fire in the immediat e left and help him extinguish it Dont get to close. Fire hurts! Proceed down the corridor to the first fire-engulfed door. Look left, use the Fire Extinguisher to take care of that fire. Run into the room, taking out another fire as well. Turn around, go back, now, to the previous corridor. Look left, continue forward and exti nguish the fire in the corridor. Move forward, look left at second fire e ngulfed door, and take care of the fire. Move forward entering kitchen. Approach the chef who is trying to put the fire out and hand him your Fire Extinguisher, which will allow him to put out the flames Work your way forward and then right into the adjacent room. En route, as you're in the adjacent room, you 'll see a save point (blue radiating light) that you can use to save your progress if you desire. Turn around, move forward, l ook left (after the second pol e) and you will see a door. Move towards the door and exit the cafeteria room.

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173 Look left and move down the corridor to the staircase Follow another staircase up, finally be ing topside on the USS California. The soldier next to you will throw you a Br owning Automatic Rifle LMG. This rifle can do some serious damage, but don't even bother with it. Quickly run up the set of stairs to the large mounted machine gun in front of you, using that as your weapon. Press action button. Using this massive gun (which has unlimited ammo), you'll want to shoot down as many planes as you can, as well as an y torpedoes coming at your ship. Play this part of the mission until you ge t really low on health, at which point you'll be knocked into the ocean water.

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174 MISSION 2 Keep turning the turret and ta king aim at the planes as they begin to swoop down from all directions.

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175 MISSION 3 Run leftward to the end of the bridge. Fo llow your soldier companion, while avoiding running into fire. Meet up your brother at the end of the br idge, who is kneeling behind a barricade. Stay behind the barricade and start s hooting as many soldiers as you can. Walk around and shoot the soldiers coming at you. < Change to the Garand gun> Direct your attention to the porch-like platform on the left side, where you will find a crouching Japanese soldier taki ng shots at you. Shot at him. Focus on finding the various goods in the area.

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176 Turn around and find a tank that needs to be fixed. It is near a staircase. Get close to the tank and it wi ll say Find Tank Coogwheel. Go to the back of the tank. You will see a staircase. Go Pass the staircase and enter the first left entrance. Move towards the end of the room and, turn right. Move forward until you reach the wall, look right again and you will see the cogwheel on the floor. Get close to the cogwheel and press the ACTION button. Turn back, move around the cr ates. Leave the alcove. Turn right and go back to the tank. Get close to the tank (left side) until it sa ys Press ACTION to fix Tank on the top of the screen. Press ACTION button. You should escort the tank by st aying on the right side of it initially and follow it into the street beyond the barricade. You will see a Japanese soldier on the ri ght side of a porch. Shoot at him and continue moving forward.

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177 There will be two more Japanese soldiers in an alcove ahead on the right side. You should shoot them. Move forward with the tank as it heads left, down the next street. If you need health, grab the Field Surgeon Pack on your right as the tank turns left. Suicide soldiers will r un at the tank with e xplosives, shoot them. Continue up this street towards a decorative fountain on your right. Shoot at any Japanese soldier as you go, including the suicide bombers. Go forward, and let the tank turn right again. Follow the tank down. There wont be too many enemies as you go. In case you need health, you shoul d shift right into a buildi ng as youre going to grab a Field Surgeon Pack to heal yourself if need be. There will be one Japanese soldier on your right. Shoot him from the street. There will be two Japanese soldiers on your left. Get closer to them and shoot. Go back to the tanks position on the stre et as it now heads down yet another road. Continue following the tank until it stops at a forced-dead end made from rubble.

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178 Go slightly behind the tank and to the ri ght, you will find a staircase leading up. At the top of this staircase, the door is locked, but you will find a Field Surgeon Pack if you need it. Backtrack all the way to the vacant road before this small complex of houses. You will find soldiers as you come back, you should shoot them. Move toward the tank. From the tank, make a left and move forward all the way to the end. Turn right and move toward the bui lding in your left with smoke. Shoot at the soldiers in front of the building. You will find that the building has a door th ats not closed anymore, but now blown open. Run at the door and ente r into the building. You will see a staircase in the initial room in this building. Behind the staircase is a save point. Get close to the blue radiant light and save the game. Go up the staircase. Move forward on to the pl ank into the new room. Go through the hole in the wall to an outdoor destroyed half-room of this house.

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179 Go down using another plank. You will find yourself outside the build ing and on the street again. Shoot the Japanese machine gunner. Get close the machine gun and mount it, pressing the ACTION button. You will see soldiers coming from your right and left side. Shoot them. Dismount the machine gun by pressing the ACTION button. Turn right onto the street. Look right and follow the street. Turn left at the bend in the street. Proceed forward until you see a huge wooden brown gate on the right hand side. Shoot all enemies. Look directly at the gate. On the right and left hand sides of the gate on the floor, there are sewers. Approach the right sewer gutte r on floor and follow it down. Turn left into the sewer. Press crouch button and turn right.

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180 Proceed forward into the sewer. After the sewer, press the crouch button again, which will make you stand up again. Look right into sewer. Follow the right path when the sewer path splits. There is a staircase at the end of the path. Proceed up the staircase. You will see enemies coming in you r direction. Shoot the enemies. Look right and proceed forward along the wall to the right. Follow this wall until some bleachers are reached. Go up the stadium stairs. Once on the top of the stairs, look left a nd you will see a booth with a radio operator. Enter the booth and shoo t the radio operator. Turn around and exit the radio post. Look right and proceed forward down the stairs. You will see a brown door at the other side of the stadium. Once you get close to the door, it will open automatically. If the door does not open, move around in the open area in front of the door.

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181 Shoot the soldiers in front of you. Move in the direction of the gates that were previously closed. Turn right. Make a left at the bend of the road and look for a hotel-like building ahead of you. Proceed into the building. Look left and enter into the adjacent room. You will see a staircase. Climb staircase. Make a right and follow through doorway. Go left and up the stairs. Make another left and go to rooftop. Once you get to the rooftop, approach the machine gun. Press ACTION button to mount the machine gun. Destroy the tank and crates in the middle of the town square with the machine gun.

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182 Press ACTION button and dismount the machine gun. Turn around and ente r into the doorway. Go down the first set of stairs. Go down the next set of stai rs which will lead you back to the lobby of the hotel. Enter the doorway and look left. Proceed forward through the metal gate and into the courtyard in front of you. Proceed forward towards the fountain. Move towards the truck at the far en d of the courtyar d in your right. Reach the front of the truck. A soldier will automatically greet you. Locate the lost explosives truck objective completed. Turn around. Proceed forward a few steps and look left. You will see a church. Enter the church steeple. Proceed forward.

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183 Look right and find the radiant blue light. Move toward the light. Press ACTION button in front of it to save game. Turn around and proceed toward the back of the room. Look left and climb the staircas e to the top of the steeple. At the top of the staircase, follow a pa th around to the end of the platform. Look left at the bells. Press ACTION to ring the bells. Turn around and go back down to the bottom of the stairs. Look right and proceed forward through the archway and exit the church. Look right and proceed toward the truck. Make your way towards the back of the truck. Face the back of the truck and pr ess ACTION to board the truck. The truck will automatically start up. Turn around and shoot the soldiers.

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184 Using your guns and grenades ta ke out attacking enemies. Destroy the tank by throwing grenades at it or by throwing them on the ground where the tank will pass over. <6 grenades hit will destroy the tank>. Keep firing enemies until the truck comes to a stop. MISSION COMPLETED!!!

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185 MISSION 5 Go forward a bit until the path widens and follow the brown path ahead until you meet up with a trio of Japanese soldiers. Shoot the enemies here. Go forward from there, and the path will swing rightward. Here you will see a light brown tree w ith root coming up from the ground. Move towards the left side of the tree Look right and pick up two Garand clips positioned around a campfire on the ground. o Go pass the campfire and approach a narrow tree trunk. Look left. o Shoot at the Japanese soldiers su rrounding the ridge in front of you. Once the path is clear, you can grab some M1 Garand Clips and a Medical Kit on the ridge they were guarding. Work your way rightward you will hear the pounding of a mounted machine gun here. Follow your compass North and find the mounted machine gun shooting at you. Shoot at the enemy on the machine gun. Mount the cleared off-mounted machine gun an d use it to clear the Japanese soldiers ahead.

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186 Approach the rebels, who are accompanied by an allied officer. Hell talk to you briefly, and then lead you to a small hide out that the rebels have near there. Follow the allied officer to the hide out. Go straight ahead down the cavelike path, where youll find a Field Surgeon Pack some M1 Garand Clips and Grenades Get close to the blue radiant light a nd press action button to save the game. Go back down the path and make a left at an opening in the cave like path. Head up this linear pathway, coming back up outside. Once outside, approach the allied officer again, and hell tell you to look ahead for the lost Allied patrol that we were looking for. Get as close as possible to the Japanese soldiers holding up the Allied prisoners as possible, and begin to fire away. < Be cautious of a sword wielding officer who c an inflict massive damage up close. He will come after you if you get too close to the prisoners. Use the cover in the area (preferably stayi ng in the shrubs to k eep a low profile) and pick away at the soldiers as they come. Eliminate all enemies in the area.

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187 You will need to walk behind the three Alli ed prisoners, now, and untie each of their hands when the on-screen prompt tells you to. Talk to the prisoner in the mi ddle, and hell tell you that he and his crew were sent to take care of Pistol Pete, but were captured. They want to join your group and help you do the job, now. < From there, the rescued prisoners grab their g uns off the porch of th e house behind them.> You can grab two boxes of M1 Garand Clips from that same porch as well. Go behind this house and you will see a small ditch underneath. If you go in a crouch, you will be able to cr awl into this little area and grab the coveted Machete here. Turn around, move forward and stand up. Look left and go around the house until you see break in the fence on the right. Approach the break in the fence. Follow the fence up the grassy hill. Approach the top of the hill on the left side where the fence ends.

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188 You will see some bushes here that do not let you move forward. Use the Machete to clear the path. Run down this initial linear path beyond the bushes we just cut, picking up a Medical Kit and some Garand M1 Clips en route to another bush, which we need to cut out of the way. Beyond these second bushes, youll follow another linear path to the third bush we need to cut. Use Machete to cut these bushes as well. Run left up the hill there, shooting enemies with your M1911 as you go. o Youll eventually see a huge gun inst allation, the Howitzer, on your right surrounded by Japanese soldiers. o Shoot at the Japanese soldie rs surrounding and on the gun. o Man the Howitzer. Shoot at and eliminate all enemies. A tank will come at you, eventually (by your left side), which youll need to eliminate. Two or three well-aimed, qui ck Howitzer shots should do the trick. < Press action to dismount the cannon>

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189 When things calm down around the huge cannon, collect the M1 Garand Clips around the gun, as well as the numerous items the dead enemies undoubtedly left behind as well. Run straight down the hill now (in direct line-of-sight of the Howitzer) and, across the road at the bottom of the hill, youll find two M1 Garand Clips. Run leftward down the path, and go down th e linear direction it leads until you come to a turn in the road that puts you in the direct sight of a Japanese soldier manning a mounted machine gun. Run towards his gun and jump over the ba rricades surrounding the gun to find some M1 Garand Clips. Look across the tiny river to kill two more Japanese so ldiers standing by a second machine gun. Go across the bridge to the other side of th e river, and over to th e location of the two Japanese soldiers we just killed, behind the second mounted machine guns position. Youll find a Medical Kit here. Man the second mounted machin e gun there and wait for the soldiers to run down the path to your right. < Press action button to dismount the machine gun> Go back on the path in front of the bridge and continue up a short ways until the path forks. Take the right fork, which soon thereafter leads to a dead end.

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190 Save the game Grab the goods Head back to the main path, where the fork was. This time go right. Youll meet up with two enemies hidden behind a ridge and one who pops up from the ground. Shoot at these enemies. Take out all enemies. Go up the ridge on the right side of the clearing to find a Medical Kit Proceed forward down the ridge and continue forward to the path in front of you. From there, youll want to continue down the linear path beyond this opening, killing any more enemies you come across. Join in on this battle, eliminating all Japane se soldiers on the left side of the river. Lob some Grenades or fire away at the rest of the soldiers in the area across the river as you go right and run into Martin Clemens, the Allied officer who is with the local rebel force.

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191 Talk to him, and hell tell you to conti nue your quest, automatically giving you first the Thompson M1 SMG to equip yourself with if you so desire. From where you meet up with him, firs t go backwards to a small overlook area, where youll find a Medical Kit Turn around, cross the river and pr oceed down the path beyond it. Eliminate all enemies along the way. < Watch out for kamikaze attacks from the enemies > Kill these enemies, and then bear right and up a small hill to the position of an enemy sniper we killed earlier, when we we re on the other side of the river. Up here, youll find two boxes of M1 Garand Clips, and a Medical Kit as well. Go back down the small hill to a fallen tree trunk. Jump over it and continue up the start of a narrow path, killing Japanese soldiers as you proceed. When the path forks ahead, go left a nd down a small, linear, vacant path. Enter into this cave and follow the linear maze of crates as you go forward. Kill one Japanese soldier on the ground.

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192 Look up the path going up in that room to fire on two or three more soldiers. When all enemies are dead, go up this path and itll lead to another cave, which overlooks the area below with a huge Howitzer. Kill off all five enemies with some quick fire from your Thompson and then collect the various goods around the gun. One of your accompanying soldiers will au tomatically set up an explosive on the large gun and allow you to set if off. Before you do so, look behind the gun to find a little out-of-the-way branch to this cave, where youll find a save point as well as a Medical Kit and some M1 Garand Clips. Save your game, grab the goods Use the detonation box attach ed to destroy the gun. < the detonation box that will be in your left> From here, go towards the gun and look le ft for a door out of this room. Go through this door, and down a pathway, kil ling pistol-wielding Japanese soldiers as you come to an open room in this cav e with a wooden bridge suspended over it

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193 Cross the bridge. At the end of this bridge youll find a Japane se soldier. Japanese soldiers are all over the bottom of this room as well. Kill the soldier(s) on the bridge, then si mply fall down to the ground below, taking out any remaining foes. With all of the Japanese resistance in th e room dead, go right and head back outside. Once out here, go leftward towards the bri dge leading over the gigantic chasm ahead of you. The enemy resistance here will be almost ex clusively on the other side of the bridge, so run across this bridge, taking out the enemies there. On the other side of the bridge to your left, youll find some Grenades and a Thompson Magazine while on the right side of the bridge once you cross it, youll find a Medical Kit and some M1 Garand Clips From here, climb up the large staircase ah ead of you to the ridge above, killing any remaining enemies as you go. Once up the stairs, move right and do a fu ll charge towards the room ahead of you with a mounted machine gun sticking out of it. Make sure to quickly shoot the enemy at the machine gun before he opens up on you, as well as the soldier accompanying him.

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194 When you do, the door leading into that r oom will explode open, granting you access. Grab the M1 Garand Clips near the mounted machine gun. Bear right into the next room with the Howitzer within. Kill the Japanese officer and the two Japane se soldiers before proceeding into that room. As you walk into the room, grab the M1 Garand Clips and the Thompson Ammo as your partner sets explosives on the Howitzer. When your partner runs behind some crat es to watch the Howitzer explode, you should do the same. When the gun explodes, look to the door at your left, leading out of the room, where a Japanese soldier or two will run into the room, gunning at you. Kill these enemies and proceed out of this room, to the corridor beyond the door. Run down this corridor, guns blazing, as you come across the last resistance of Japanese soldiers in this cave system. When you can either go right, or go forward, go right, kill the enemies in this room, and quickly run up the stairs to your right, and up to the final Howitzer in the area.

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195 Your partner will explode the gun, and when th at happens, youre able to run out of the door to the left of the now-destroyed gun, and back outside. Once outside, run leftward towards the bridge killing the Japanese soldiers as you go. Cross the bridge to find some of your rebe l friends and the rebe l Allied officer as well. En route, however, your colleague will trip while he is planting some explosives on the bridge Stop and grab him by facing him and pressi ng action to complete the second of two optional objectives on this mission. Get over the bridge thereafter. After a set amount of time, the bridge will explode. MISSION COMPLETED!!

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196 APPENDIX B UFOV TRAINING Start Point for Customized Training Speed __________ Divided Attention __________ Selective Attention _____________ Is SPEED screening score 30 or lower? Go To YELLOW TRAINING TRACK N O YES Go To PINK TRAINING TRACK Is DIVIDED ATTENTION screening score 40 or lower? Go To BLUE TRAINING TRACK N O YES Is SELECTIVE ATTENTION screening score 80 or lower? Go To GREEN TRAINING TRACK N O YES

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197ID Training Track Color Computer System Number Sess Task Center TaskDuration Ring Bloc k1 Bloc k2 Bloc k3 Bloc k4 Bloc k5 Bloc k6 Notes Easy --Hard Easy------------Hard Hard----------Easy Easy--------Hard 1 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer 2 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer 3 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer 4 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer 5 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer 6 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 Inner Middle Outer

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198400 7 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer 8 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer 9 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer 10 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer 11 Speed Div Sel P/A I SD A 40 80 120 160 200 240 280 320 360 400 Inner Middle Outer

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199 LIST OF REFERENCES Arthanat, S., Nochajski, S. M., & Stones, J. (2004). The internati onal classification of functioning disability and h ealth and its application to cognitive disorders. Disability and Rehabilitation, 26 (4), 235-245. Austin, R. A., & Faigin, B. M. (2003). Effect of vehicle and crash factors on older occupation injury. In W. C. Mann (Eds). International Conference on Aging Disability and Independence Proceedings. University of Florida. Baltes, P. B., Dittmann-Kohli, F., & Kliegl, R. (1986). Reserve capacity of the elderly in agingsensitive test of fluid intelligence: Replication and extension, 1 (2), 172-177. Ball, K., Beard, B. L., Roenker, D. L., Miller, R. L, & Griggs, D. S. (1988). Age and visual search: Expanding the useful field of view. Journal of Optical Society of America, 5 (12), 2210-2219. Ball, K., Berch, D. B., Helmers, K. F., Jobe, J. B., Leveck, M. D., Marsiske, M., et al. (2002). Effects of cognitive training interventions with older adults. A randomized controlled trial. Journal of the American Medical Association, 288 (18), 2271-2281. Ball, K., & Owsley, C. (1991). Identifying correl ates of accident involvement for the older driver Human Factors, 33 583-595. Ball, K., & Owsley, C. (2000). Increasing mobility and reducing accident of older drivers, in Schaie KW, Pietrucha M (eds): Mobility and Transportation in the elderly. New York, Springer: 213-251. Ball, K., Owsley, C., Sloane, M. E., Roenker, D. L., & Bruni, J. R. (1993). Visual attention problems as a predictor of vehicle crashes in older drivers. Investigative Ophthalmology & Visual Science, 34 (11), 3110-3120. Ball, K., Owsley, C., Stalvey, B., Roenker, D. L., Sloane, M. E., & Graves, M. (1998). Driving avoidance and functional impa irment in older drivers. Accident Analysis and Prevention, 30 (3), 313-322. Ball, K., & Owsley, C. (2000). Increasing mobility and reducing accidents of older drivers. In: Schaie K, Pietrucha M, eds. Mobility a nd transportation in the elderly. New York: Springer, 213-250. Ball, K., Roenker, D. L., & Bruni, J. R. (1990). Developmental changes in attention and visual search through adulthoo d. In J. Enns (Ed.), Advances in Psychology, 69 489-508. Baltes, P., & Mayer, K. (1999). The Berlin aging study: Aging from 70 to 100. New York: Cambridge University Press.

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200 Baron, R. M., & Kenny, D. A. (1986). The moderato r-mediator variable di stinction in social psychological research: Con ceptual, strategic, and st atistical considerations. Journal of Personality and Social Psychology, 51 (6), 1173-1182. Bergen, J., & Julesz, B. (1983). Parallel versus se rial processing in rapid patter discriminations. Nature, 303, 696-698. Blieszner, R., Willis, S.L., Baltes, P.B. (1981). Tr aining research in aging on the fluid ability of inductive reasoning. Journal of Applied Developmental Psychology, 2, 247-265. Broman, A. T., West, S. K., Munoz, B., Ba ndeen-Roche, K., Rubin, G.S., & Turano, K.A. (2004). Divided visual attention as a predicto r of bumping while wa lking: The Salisbury eye evaluation. Investigative Ophthalmology an d Visual science, 45(9), 2955-2960. Castel, A. D., Pratt, J., & Dr ummond, E. (2005). The effects of action video game experience on the time course of inhibition of return and the efficiency of visual search, Acta Psychologica 119 217-230. Charness, N., Schumann, C. E., & Boritz, G. A. (1992). Training older adults in word processing: Effects of age, training technique and computer anxiety. International Journal of Aging and Technology, 5, 79-106. Christensen, H., MacKinnon, A., Korten, A., Jorm A. F., Henderson, A. S., Jacomb, P., et al. (1999). An analysis of diversity in the c ognitive performance of elderly community dwellers: Individual differences in ch ange scores as a function of age. Psychology and Aging, 14 365-379. Clark, J. E., Lanphear, A. K., & Riddick, C. C. (1987). The effects of videogame playing on the response selection processing of elderly adults. Journal of Gerontology, 42 (1), 82-85. Collia, D. V., Sharp, J., & Giesbrecht, L. (2003) The 2001 National Household Travel Survey: a look to the travel patterns of older Americans. Journal of Safety Research, 34 (4), 461-470. Colsher, P., & Wallace, R. (1991).Longitudinal a pplication of cognitive f unction measures in a defined population of community dwelling elders. Annals of Epidemiology, 1 215-230. Cook, S. E. (2007). Impact of distraction on simula ted lane navigation in older adults with and without cognitive impairment. Doctoral Diss ertation, Clinical and Health Psychology, University of Florida. Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco: Jossey-Bass. Csikszentmihalyi, M. (1990). Flow: The psychology of the optimal experience. Harper & Row, New York. Dahl, T. H. (2002). International classificati on of functioning, disabi lity and health: An introduction and discussion of its potential imp act on rehabilitation services and research. Journal of Rehabilitation Medicine, 34 201-204.

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201 Donmez, B., Boyle, L., & Lee, J. D. (2007). Safe ty implications of provi ding real-time feedback to distracted drivers. Accident Analysis and Prevention, 39 581-590. Dorval, M., & Pepin, M. (1986). Effect of pl aying a video game on a measure of spatial visualization. Perceptual and Motor Skills, 62 159-162. Dorn, L., & Barker, D. (2005). The effects of dr iver training on simulated driving performance. Accident Analysis and Prevention, 37 (1), 63-69. Drew, B., & Waters, J. (1986). Video games: Utilization of a novel strategy to improve perceptual motor skills and cognitive functioning in the non-institutionalized elderly. Cognitive Rehabilitation, 4 (2), 26-34. Edward, R. (2001). Roybal Centers fo r Research on Applied Gerontology. Issues Brief, 4 University of Alabama at Birmingham. Edwards, J. D., Ross, L. A., Wadley, V. G., Clay, O. J., Crowe, M., Roenker, D. L., et al. (2006). The useful field of view: Normative data for older adults. Archives of Clinical Neuropsychology, 21, 275-286. Edwards, J. D., Wadley, V. G., Myers, R. S., Roenker, D. L., Cissel, G. M., Ball, K. (2002). Transfer of a speed of processing interven tion to near and far cognitive functions. Gerontology,48 329-340. Edwards, J. D., Wadley, V. G., Vance, D. E, Wood, K., Roenker, D. L., & Ball, K. (2005). The impact of speed of processing training on cognitive and everyday performance. Aging and Mental Health, 9 (3), 262-271. Enochsson, L., Isaksson, B., Tour, R., Kjellin, A., Hedman, L., Wredma rk, T., et al. (2004). Visuospatial skills and computer game expe rience influence the performance of virtual endoscopy. Journal of Gastrointestinal Surgery, 8 (7), 876-882. Farrow, S., & Reid, D. (2004). Stroke survivors perception of a leisur e-based virtual reality program. Technology and Disability, 16 69-81. Fisher, D. L., Laurie, N. E., Gl asser, R., Connerney, K. Pollatsek, A. Duffy, S. A., et al (2002) Use of a fixed-base driving simulator to eval uate the effects of experience and PC-Based risk awareness training on drivers decisions. Human Factors, 44 (2), 287-302. Folstein, M. F., Folstein, S. E., & McHugh, P.R. (1975) Mini-mental state. A practical method for grading the cognitive state of patients for the clinicians. Journal of Psychiatric Research, 12, 189-198. Fonda, S. J., Wallace, R. B., & Herzog, A. R. (2001). Changes in driving patterns and worsening depressive symptoms among older adults. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 56, 343-351.

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209 BIOGRAPHICAL SKETCH Patrcia da Cunha Belchior is a doctoral candi date in the Rehabilita tion Science Doctoral Program at the University of Florida. .She ear ned her bachelor's degree in law in 1998 and her bachelor's degree in occupational therapy in 1999 in Brazil. During her doctoral studies she worked as a research assistant for the Rehab ilitation and Engineering Research Center for successful aging and for the National Older Driv ers Research and Trai ning Center. She has shared publications on several journal articles and she has presented national and international conferences about aging and technology. Her cu rrent research focuses on cognitive training using video game to improve driving skill s and driving safety among older adults.